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Inductive Essays: Tips, Examples, and Topics

Carla johnson.

• June 14, 2023
• How to Guides

Inductive essays are a common type of academic writing. To come to a conclusion, you have to look at the evidence and figure out what it all means. Inductive essays start with a set of observations or evidence and then move toward a conclusion. Deductive essays start with a thesis statement and then give evidence to support it. This type of essay is often used in the social sciences, humanities, and natural sciences.

The goal of an inductive essay is to look at the evidence and draw a conclusion from it. It requires carefully analyzing and interpreting the evidence and being able to draw logical conclusions from it. Instead of starting with a conclusion in mind and trying to prove it, the goal is to use the evidence to build a case for that conclusion.

You can’t say enough about how important it is to look at evidence before coming to a conclusion. In today’s world, where information is easy to find and often contradictory, it is important to be able to sort through the facts to come to a good decision. It is also important to be able to tell when the evidence isn’t complete or doesn’t prove anything, and to be able to admit when there is uncertainty.

In the sections that follow, we’ll talk about some tips for writing good inductive essays, show you some examples of good inductive essays, and give you some ideas for topics for your next inductive essay. By the end of this article, you’ll know more about how to write an inductive essay well.

What You'll Learn

Elements of an Inductive Essay

Most of the time, an inductive essay has three main parts: an intro, body paragraphs, and a conclusion.

The introduction should explain what the topic is about and show the evidence that will be looked at in the essay . It should also have a thesis statement that sums up the conclusion that will be drawn from the evidence.

In the body paragraphs, you should show and explain the evidence. Each paragraph should focus on one piece of evidence and explain how it supports the thesis statement . The analysis should make sense and be well-supported, and there should be a clear link between the evidence and the conclusion.

In the conclusion, you should sum up the evidence and the conclusion you came to based on it. It should also put the conclusion in a bigger picture by explaining why it’s important and what it means for the topic at hand.

How to Choose a Topic for an Inductive Essay

It can be hard to choose a topic for an inductive essay, but there are a few things you can do that will help.

First, it’s important to look at the assignment prompt carefully. What’s the question you’re supposed to answer? What evidence do you have to back up your claim? To choose a topic that is both possible and interesting , you need to understand the prompt and the evidence you have.

Next, brainstorming can be a good way to come up with ideas. Try writing down all the ideas that come to mind when you think about the prompt. At this point, it doesn’t matter if the ideas are good or not. The goal is to come up with as many ideas as possible.

Once you have a list of possible topics , it’s important to pick one that you can handle and that you’re interested in. Think about how big the topic is and if you will have enough time to analyze the evidence in enough depth for the assignment . Also, think about your own passions and interests. If you choose a topic that really interests you, you are more likely to write a good essay .

Some potential topics for an inductive essay include:

– The impact of social media on mental health

– The effectiveness of alternative medicine for treating chronic pain

– The causes of income inequality in the United States

– The relationship between climate change and extreme weather events

– The effects of video game violence on children

By following these tips for choosing a topic and understanding the elements of an inductive essay, you can master the art of this type of academic writing and produce compelling and persuasive essays that draw on evidence to arrive at sound conclusions.

Inductive Essay Outline

An outline can help you to organize your thoughts and ensure that your essay is well-structured. An inductive essay outline typically includes the following sections:

– Introduction: The introduction should provide background information on the topic and present the evidence that will be analyzed in the essay . It should also include a thesis statement that summarizes the conclusion that will be drawn from the evidence.

– Body Paragraphs: The body paragraphs should present the evidence and analyze it in depth. Each paragraph should focus on a specific piece of evidence and explain how it supports the thesis statement . The analysis should be logical and well-supported, with clear connections made between the evidence and the conclusion.

– Conclusion: The conclusion should summarize the evidence and the conclusion that was drawn from it. It should also provide a broader context for the conclusion, explaining why it matters and what implications it has for the topic at hand.

Inductive Essay Structure

The structure of an inductive essay is similar to that of other types of academic essays. It typically includes the following elements:

– Thesis statement: The thesis statement should summarize the conclusion that will be drawn from the evidence and provide a clear focus for the essay .

– Introduction: The introduction should provide background information on the topic and present the evidence that will be analyzed in the essay. It should also include a thesis statement that summarizes the conclusion that will be drawn from the evidence.

– Body Paragraphs: The body paragraphs should present the evidence and analyze it in depth. Each paragraph should focus on a specific piece of evidence and explain how it supports the thesis statement. The analysis should be logical and well-supported, with clear connections made between the evidence and the conclusion.

It is important to note that the body paragraphs can be organized in different ways depending on the nature of the evidence and the argument being made. For example, you may choose to organize the paragraphs by theme or chronologically. Regardless of the organization, each paragraph should be focused and well-supported with evidence.

By following this structure, you can ensure that your inductive essay is well-organized and persuasive, drawing on evidence to arrive at a sound conclusion. Remember to carefully analyze the evidence, and to draw logical connections between the evidence and the conclusion. With practice, you can master the art of inductive essays and become a skilled academic writer.

Inductive Essay Examples

Examples of successful inductive essays can provide a helpful model for your own writing. Here are some examples of inductive essay topics:

– Example 1: The Link Between Smoking and Lung Cancer: This essay could look at the studies and statistics that have been done on the link between smoking and lung cancer and come to a conclusion about how strong it is.

– Example 2: The Effects of Social Media on Mental Health: This essay could look at the studies and personal experiences that have been done on the effects of social media on mental health to come to a conclusion about the effects of social media on mental health.

– Example 3: The Effects of Climate Change on Agriculture: This essay could look at the studies and expert opinions on the effects of climate change on agriculture to come to a conclusion about how it might affect food production..

– Example 4: The Benefits of a Plant-Based Diet: This essay could look at the available evidence about the benefits of a plant-based diet, using studies and dietary guidelines to come to a conclusion about the health benefits of this type of diet.

– Example 5: The Effects of Parenting Styles on Child Development: This essay could look at the studies and personal experiences that have been done on the effects of parenting styles on child development and come to a conclusion about the best way to raise a child.

Tips for Writing an Effective Inductive Essay

Here are some tips for writing acompelling and effective inductive essay:

1. Presenting evidence in a logical and organized way: It is important to present evidence in a clear and organized way that supports the thesis statement and the conclusion. Use topic sentences and transitions to make the connections between the evidence and the conclusion clear for the reader.

2. Considering alternative viewpoints: When analyzing evidence, it is important to consider alternative viewpoints and opinions. Acknowledge counterarguments and address them in your essay, demonstrating why your conclusion is more compelling.

3. Using strong and credible sources: Use credible sources such as peer-reviewed journal articles , statistics, and expert opinions to support your argument. Avoid relying on unreliable sources or anecdotal evidence.

4. Avoiding fallacies and biases: Be aware of logical fallacies and biases that can undermine the credibility of your argument. Avoid making assumptions or jumping to conclusions without sufficient evidence.

By following these tips, you can write an effective inductive essay that draws on evidence to arrive at a sound conclusion. Remember to carefully analyze the evidence, consider alternative viewpoints, and use credible sources to support your argument. With practice and dedication, you can master the art of inductive essays and become a skilled academic writer.

Frequently Asked Questions

1. what is an inductive essay.

An inductive essay is an academic writing that starts with a set of observations or evidence and then works towards a conclusion. The essay requires careful analysis and interpretation of evidence, and the ability to draw logical conclusions based on that evidence.

2. What are the elements of an inductive essay?

An inductive essay typically consists of an introduction, body paragraphs, and a conclusion. The introduction provides background information and presents the thesis statement. The body paragraphs present the evidence and analyze it in depth. The conclusion summarizes the evidence and the conclusion drawn from it.

3. How do I choose a topic for an inductive essay?

To choose a topic for an inductive essay, carefully analyze the assignment prompt, brainstorm ideas, narrow down the topic, and select a topic that interests you.

4. What is the difference between an inductive essay and a deductive essay?

An inductive essay starts with evidence and works towards a conclusion, while a deductive essay starts with a thesis statement and provides arguments to support it.

5. How do I structure an inductive essay?

An inductive essay typically follows a structure that includes a thesis statement, introduction, body paragraphs, and conclusion.

Inductive essays are an important type of academic writing that require careful analysis and interpretation of evidence to come to a conclusion. By using the advice in this article, you can become a good inductive essay writer. Remember to carefully look at the evidence, think about other points of view, use reliable sources, and stay away from logical errors and biases. In conclusion , learning how to write inductive essays is important for developing critical thinking skills and making arguments that are compelling and convincing. You can make a valuable contribution to your field of study and to society as a whole by looking at the facts and coming to logical conclusions. With practice and hard work , you can learn to write good inductive essays that will help you in school and in your career.

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Induction and Its Benefits for Employees Essay

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Purposes of Induction

Benefits of induction to individuals, benefits of induction to your organization, works cited.

• It is necessary to mention that the primary purpose of induction is to make sure that a new employee is introduced to a workplace environment
• Another aspect that should not be overlooked is that individuals are provided with standard information that would help them to start working (Randhawa 108).
• It is also imperative to mention that it can be used to make sure that new employees do not have to deal with difficulties that would affect the process of work or may lead to other significant complications.
• Another purpose of induction that needs to be noted is that it ensures that employees can settle into an environment that is new, and adaptation to the process of work can also be critical in most cases (Sutherland and Canwell 135).
• The fact that it helps to ensure that new employees are aware of the unique traditions and culture of an organization also should not be disregarded, and it allows to avoid possible conflicts or disagreements.
• The first benefit for an individual that should be noted is that it helps new employees to socialize, and it is an essential part of the process of work that should not be overlooked because relationships in the workplace can be incredibly valuable in most cases.
• Also, it is necessary to say that an employee may be provided with a range of benefits at the start of the process, such as gifts and others, and it increases their satisfaction levels dramatically (El-Shamy 12).
• Also, an opportunity to be introduced to new technologies also needs to be regarded as beneficial, and the knowledge that is gained can be vital to the process of work (Frater 99).
• Another benefit for an individual that needs to be mentioned is that any possible hazards are mentioned during the process, and it helps a person to get an understanding of what aspects of a particular job may be dangerous.
• Also, the fact that a new worker may ask numerous questions during the process can also be viewed as an advantage because the information that is gained can be valuable in most cases (Gallagher 236).
• One of the biggest advantages for an organization that needs to be mentioned is that the process of induction helps to ensure that new employees are properly introduced to all the necessary aspects of a particular job and have the knowledge and skills to start working.
• Another aspect that is incredibly beneficial is that performance levels of individuals are also improved significantly as a result of induction in most cases, and this is a factor that needs to be taken into account.
• Also, it is necessary to say that it helps to establish a positive perception of an organization, and the image of the company is of utmost importance.
• It is also paramount to note that it can be beneficial from the financial point of view because it helps to reduce the number of possible risks that would be incredibly costly for an organization most of the time (Thompson 46).
• Another benefit to an organization that should be listed is that the process of induction helps to make sure that employees stay because they understand that they are valued by the company.

The Best Practice

It is imperative to understand that an induction program needs to be well developed to ensure that it is as efficient as possible. Also, such aspects as scheduling and expenses on activities should be paid most attention to because it would help to reduce the possibility of complications.

El-Shamy, Susan. Dynamic Induction: Games, Activities and Ideas to Revitalize Your Employee Induction Process . Farnham, UK: Gower Publishing, 2012. Print.

Frater, Glynis. Business and Communication Systems . Cheltenham, UK: Nelson Thornes, 2003. Print.

Gallagher, Kevin. Skills Development for Business and Management Students: Study and Employability . Oxford, UK: OUP Oxford, 2013. Print.

Randhawa, Gurpreet. Human Resource Management . New Delhi, IN: Atlantic Publishers, 2007. Print.

Sutherland, Jonathan, and Diane Canwell. Key Concepts in Human Resource Management . Basingstoke, UK: Palgrave Macmillan, 2004. Print.

Thompson, Neil. People Management . Basingstoke, UK: Palgrave Macmillan, 2013. Print.

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Induction (Logic and Rhetoric)

• An Introduction to Punctuation
• Ph.D., Rhetoric and English, University of Georgia
• M.A., Modern English and American Literature, University of Leicester
• B.A., English, State University of New York

Induction is a method of reasoning that moves from specific instances to a general conclusion . Also called inductive reasoning .

In an inductive argument , a rhetor (that is, a speaker or writer) collects a number of instances and forms a generalization that is meant to apply to all instances. (Contrast with deduction .)

In rhetoric , the equivalent of induction is the accumulation of examples .

Examples and Observations

• " Induction operates in two ways. It either advances a conjecture by what are called confirming instances, or it falsifies a conjecture by contrary or disconfirming evidence. A common example is the hypothesis that all crows are black. Each time a new crow is observed and found to be black the conjecture is increasingly confirmed. But if a crow is found to be not black the conjecture is falsified." (Martin Gardner, Skeptical Inquirer , Jan.-Feb., 2002
• "If you have trouble remembering the difference between inductive and deductive logic, consider their roots. Induction comes from Latin for 'to induce' or 'to lead.' Inductive logic follows a trail, picking up clues that lead to the end of an argument. Deduction (both in rhetoric and expense accounts) means 'to take away.' Deduction uses a commonplace to pull you away from your current opinion." (Jay Heinrichs, Thank You for Arguing: What Aristotle, Lincoln, and Homer Simpson Can Teach Us About the Art of Persuasion . Three Rivers Press, 2007
• " Inductively valid, or correct, arguments, unlike deductively valid ones, have conclusions that go beyond what is contained in their premises . The idea behind valid induction is that of learning from experience . We often observe patterns, resemblances, and other kinds of regularities in our experiences, some quite simple (sugar sweetening coffee), some very complicated (objects moving according to Newton's laws—well, Newton noticed this, anyway)... "Here is a simple example of an inductively valid argument of the kind sometimes called induction by enumeration : I loaned my friend $50 last November and he failed to pay me back. (Premise) I loaned him another $50 just before Christmas, which he hasn't paid back (Premise), and yet another $25 in January, which is still unpaid. (Premise) I suppose it's time to face facts: He's never going to pay me back. (Conclusion) "We use inductive reasoning so frequently in everyday life that its nature generally goes unnoticed." (H. Kahane and N. Cavender, Logic and Contemporary Rhetoric , 1998)

F.D.R.'s Use of Induction

• "The following passage comes from Franklin D. Roosevelt's speech to Congress on December 8, 1941, the day after Pearl Harbor, declaring a state of war between the United States and Japan. Yesterday the Japanese government also launched an attack against Malaya. Last night, Japanese forces attacked Hong Kong. Last night, Japanese forces attacked Guam. Last night, Japanese forces attacked the Philippine Islands. Last night, the Japanese attacked Wake Island. And this morning, the Japanese attacked Midway Island. Japan has, therefore, undertaken a surprise offensive extending throughout the Pacific area. (Safire 1997, 142; see also Stelzner 1993) Here, Roosevelt has in effect constructed a comparison that involves six items, and his purpose in doing so appears in the final sentence. His 'therefore' signals that he offers a conclusion supported by the preceding list , and these individual instances have been united as examples for the conclusion on the basis of their parallel form . . . . The argument form here, supporting a generalization with examples, is classically known as induction . In the most direct manner, the six examples of Japanese aggression 'add up' to the conclusion. The list strengthens what was already, on the occasion of Roosevelt's speech, an overwhelming case for war." (Jeanne Fahnestock, Rhetorical Style: The Uses of Language in Persuasion . Oxford Univ. Press, 2011)

The Limits of Rhetorical Induction

• "It is important to remember that rhetorical induction does not actually ​ prove anything; it is arguing from the probability that known instances are parallel to and illuminating of those less well known. Whereas full logical induction enumerates all possible instances, the rhetorical argument by example almost always enumerates less than the total. The persuasive  impact of such a method of reasoning is increased, of course, as one increases the number of examples."(Donald E. Bushman, "Example." Encyclopedia of Rhetoric and Composition: Communication From Ancient Times to the Information Age , ed. by Theresa Enos. Taylor & Francis, 1996)

Pronunciation: in-DUK-shun

Etymology:  From the Latin, "to lead in"

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What is Induction

Induction, also known as orientation, is a process of making the new employee familiar with the work environment and the fellow employees.

The new employee can be inducted into the organisation by introducing his job, fellow workers, supervisors and his subordinates. He should be oriented to the new organisation and its policies, rules and regulations.

Induction is a socialising process by which the organisation tries to make the new employee as its agent for the achievement of its aims and objectives while the new individual employee seeks to make the organisation an agency to achieve his personal goals. Induction makes the new employee feel at home and helps him to adjust with the new environment in the organisation.

According to Michael Armstrong, “Orientation or induction is the process of receiving and welcoming an employee when he first joins a company and giving him the basic information he needs to settle down quickly and happily and start work”.

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Learn about:- 1. Introduction to Induction 2. Meaning of Induction 3. Definitions 4. Concept 5. Objectives 6. Need and Importance 7. Purpose 8. Process 9. Benefits 10. Problem and 11. Program.

What is Induction? — Meaning, Definitions, Objectives, Importance, Process, Purpose, Benefits and Programs

• Introduction to Induction
• Meaning of Induction
• Definitions of Induction
• Concept of Induction
• Objectives of Induction
• Need and Importance of Induction
• Purpose of Induction
• Induction Process
• Benefits of Induction
• Problem of Induction
• Induction Program

What is Induction — Introduction

Introducing the new employee who is designated as a probationer to the job, job location, surroundings, organisation, organisational surroundings, and various employees is the final step of employment process. Some of the companies do not lay emphasis on this function as they view that this function will be automatically performed by the colleagues of the new employees.

This is more so in educational institutions. This process gains more significance as the rate of turnover is high among new employees compared to that among senior employees. This is mainly because of the problem of adjustment and adaptability to the new surroundings and environment. Further absence of information, lack of knowledge about the new environment, cultural gap, behavioural variations, different levels of technology, variations in the requirements of the job and the organisation also disturb the new employee.

Further induction is essential as the newcomer may feel insecure, shy, nervous and disturbed. This situation leads to instability and turnover. Hence, induction plays pivotal role in acquainting the new employee to the new environment, company rules and regulations.

Generally, the newcomer may expect opportunities for advancement, social status and prestige, responsibility, opportunities to use special aptitudes and educational background, challenges and adventure, opportunity to be creative and original and lucrative salary. But jobs with low initial challenge, inadequate feedback, and inadequate performance appraisal result in reality shock. Induction is necessary to reduce reality shock.

“Induction is the process of receiving and welcoming an employee when he first joins a company and giving him the basic information he needs to settle down quickly and happily and start work.”

Lecture, handbook, film, group seminar are used to impart the information to new employees about the environment of the job and the organisation in order to make the new employee acquaint himself with the following heads- (i) About the company’s history, objectives, policies, procedures, rules and regulations, codes, etc.; (ii) About the department; (iii) About the superiors, subordinates, etc.

(i) About the Company:

(a) History, growth, organisation and management, products, market, customers, etc., of the Company.

(b) Basic conditions of employment — hours of work, shift, holidays, retirement benefits.

(c) Pay, allowances, deductions.

(d) Sickness rules, information — pay — sick leave.

(e) Leave rules — casual, special, earned-holidays, vacation.

(f) Work rules, work-load, use of materials, equipment, and machine.

(g) Disciplinary rules and procedure.

(h) Grievance procedure.

(i) Career path, promotion channel.

(j) Unions, negotiating machinery.

(k) Education, training and development facilities.

(I) Health, safety, medical care arrangements.

(m) Canteen and restaurant facilities.

(n) Social benefits and welfare measures.

(o) Telephone calls and correspondence.

(p) Travelling and subsistence expenses.

(q) Uniforms, clothing.

(r) Various employees — their designations — position in the organisation. In addition to using various routine measures, the personnel manager personally explains, clears doubts and queries of the new employee about the company.

(ii) About the Department:

The departmental head concerned introduces the new employee to the important employees and describes briefly about the department and the job. Then the supervisor concerned introduces the employee to all the employees in the section/unit, describes in detail the job or work, material, machine, equipment with which the worker has to work, process of the production, place of the employee’s job and its significance in the process of production, his position in the departmental organisation structure, work distribution, assignment, working hours, shift, quality/standard to be maintained, customers/ users of the product/service, etc.

(iii) About the Superiors, Subordinates, Etc.:

a. Introduce the new employee to the superior to whom he should report.

b. Introduce to other superiors with whom his work is indirectly related.

c. Introduce the new employee to his subordinates with whom he has to work.

d. Introduce the new employee to the subordinates who will report to him.

e. Introduce the new employee to his colleagues.

What is Induction — Meaning

The introduction of new employees into an organisation is important and it demands special consideration. There is good evidence that the subject seldom receives the very careful attention that it truly needs by the employing organisation. Analysis of employee turnover statistics show that a higher employee turnover occurs during the first years of employment.

Undoubtedly a portion of the blame can be given to the faulty recruitment and selection procedures. Equally certainly are the reasons for why so many people leave organisations shortly after joining are connected with the treatment they receive from their employers during the initial phase of employment.

The induction of new employees has to be regarded as comprehensive and systematic programme continuously monitored and evaluated. Too often it has come to mean little more, than a day or two set aside, during which time new employees may have interviews, attend short courses, listen talks about organisation, receive a quantity of literature, be taken on quick guided tours to glimpse the various sections of the organisation and meet a variety of people.

Induction or orientation familiarizes the new employee with the organisation, its mission and goals, culture, systems and procedures and expectations from the employees.

Induction (also known as orientation or indoctrination) is the process of introducing a new employee to the organization, and the organization to the employee by providing him relevant information.

What type of information should be provided to a new employee depends on the organizational practices — whether an organization takes orientation in a formal and comprehensive way or informal and gradual process of learning about the organization over a period of time.

However, a formal orientation is preferable because it tries to bridge the information gap of the new employee. A formal orientation may contain the various types of information.

Many companies prepare booklets which contain information on the organization, its human resource policies and rules, and employee benefits. These booklets are provided to the new employees at the time of orientation.

Orientation or induction is the process of introducing new employees to an organization, to their specific jobs and departments, and in some instances, to their community. It also marks the beginning of the process by which employees are integrated into the organization.

An orientation program principally conveys 3 types of information, namely:

(a) General information about the daily work routine to be followed

(b) A review of the organization’s history, founders, objectives, operations and products or services, as well as how the employee’s job contributes to the organization’s needs.

(c) A detailed presentation of the organization’s policies, work rules and employee benefits.

What is Induction – Definitions Provided by Flippo and Michael Armstrong

Induction, also known as orientation, is a process of making the new employee familiar with the work environment and the fellow employees. The new employee can be inducted into the organisation by introducing his job, fellow workers, supervisors and his subordinates. He should be oriented to the new organisation and its policies, rules and regulations.

Really speaking, induction is a socialising process by which the organisation tries to make the new employee as its agent for the achievement of its aims and objectives while the new individual employee seeks to make the organisation an agency to achieve his personal goals. Induction makes the new employee feel at home and helps him to adjust with the new environment in the organisation.

When a new employee joins an organisation, he is completely a stranger to the people of the organisation, work place and the work environment in the organisation. In the absence of any information and support, there is likely to be anxiety and fear in the mind of that new employee.

He is likely to undergo reality shock caused by a gap between his expectations and the real situation in the organisation. Induction or orientation will help him overcome these problems. Once a candidate is selected and placed on a particular job, the process of familiarising him with the job, with the people who work with him and also with the organisation begins. This process is nothing but induction or orientation.

According to Edwin B. Flippo, “Induction is the welcoming process to make the new employee feel at home and generate in him a feeling of belongingness to the organisation”.

What is Induction — Concept

Induction, also called orientation, is designed to provide a new employee with the information she or he needs to function comfortably and effectively in the organisation. A formal definition of orientation is –

“Orientation is planned induction of the employees to their jobs, their co-workers and the organisation.” [Robert and John]

Typically, orientation conveys three types of information:

1. General information about the daily work routine.

2. A review of organisation’s objectives, operations, products and services as well as job content of new employee.

3. A detailed presentation on organisational policies, work rules, employee benefits, etc.

The idea is to make the employee feel at home in the new environment. It is a well-known fact that employees feel anxious on entering an organisation. They worry about how well they will perform in the new job. They feel inadequate when they compare themselves with more experienced employees. Effective orientation programmes reduce anxiety by providing information on job environment, on supervisors, by introducing them to co-workers and encouraging them to ask questions.

In a study the researchers discovered the following about new employees:

1. The first days on the jobs were anxious and disturbing.

2. New employees’ initiation by persons intensified anxiety.

3. Anxiety interfered with training process.

4. Turnover of new hired employee was caused primarily by anxiety.

5. New workers were reluctant to discuss problems with the supervisors.

What is Induction — Objectives

The objectives of Induction are as follows:

1. To welcome the new employee, relieve his anxieties, and make him feel at home.

2. To develop a rapport between the company and the new employee and make him feel part of the organisation as quickly as possible.

3. To inspire the new employee with a good attitude toward the company and his job.

4. To acquaint new employees with company goals, history, management traditions, policies, departments, divisions, products, and physical layouts.

5. To communicate to new employees what is expected of them, their responsibilities, and how they should handle themselves.

6. To present the basic information the employee wants to know: rules and regulations, benefits, payday, procedures, and general practices.

7. To encourage the new employee to have an inquiring mind, show him how to learn, and assist him toward ‘a discipline effort in developing additional knowledge.

8. To provide basic skills, terms, and knowledge of the business world and help the new employee in human relations.

What is Induction — Need and Importance: Reducing Anxiety of New Employees, Reducing Reality Shock, Reduces Employee Turnover and a Few Others

1. Reducing Anxiety of New Employees – An employee’s first impression is the last impressions. If a new employee is made to feel welcome and comfortable in the new environment, particularly by his immediate superior and co-workers his anxiety would reduce and he would have the positive attitude towards the organisation and his job.

2. Reducing Reality Shock – Every employee has some expectations when he joins his new job and when these expectations do not match with the real situation, the employee experiences a reality shock. An effective orientation programme helps in reducing these reality shocks by providing more real expectations to the new employees.

3. Reduces Employee Turnover – If a new employee gets the impression of being unwanted or ineffective he may react to these feelings by resigning. Turnover is generally high during this initial period and the effective orientation in this phase can reduce this costly reaction.

4. Accommodating Employees – The orientation programme helps the new employees to accommodate with the existing employees by developing the understanding on the various aspects relating to the job with which the new employee is expected to confront.

5. Familiarizing the New Employees – Good orientation saves time because the employee would become familiar with his work, supervisor, and coworkers. Explaining the functioning of the organisation and the department during orientation will save the colleagues’ valuable time later in explaining the job.

6. Developing Realistic Expectations – Effective orientation develops realistic expectations by letting the employee know what is expected from him in terms of values, attitudes, work procedures, norms of behaviour and dress code. All organisations have their own set of values, beliefs, code of conduct which expects-their employee to follow. If the new employee learns and imbibes these during orientation it would be much easier for him to incorporate them in his work values.

7. Increases Enthusiasm – Through effective orientation the newcomer is made aware of his job and how his job fits with the total organisation; how he can contribute to the organisational effectiveness and to whom he may look for in case of any problem. This creates more loyalty and enthusiasm in the mind of the newcomer.

What is Induction – Steps involved in the Process

The induction of a new worker is an important aspect of employment. The acceptance of a job implies entrance into a community in which the worker, as a social being, will seek human satisfaction. This satisfaction depends very much upon his being accepted therein.

This is a two-sided process. On the employer’s part, the applicant has to be turned into a worker who is satisfied with his job and environment. On the other hand, a worker has numerous questions in his mind. His doubts and fears need to be clarified. To achieve this dual aim, a defined practice based on experience, imagination and sympathy is needed. It is a mistake to leave the worker to know and clear his doubts on the working site. The prospective worker should have required induction when a job is under discussion or soon after his selection.

A worker may first come into contact with the receptionist or employment exchange officer when he calls at the employment office. How well he is received will affect his attitude to his engagement at the time and during the years of his employment. He will appreciate courtesy in manner, reasonable clarity and orderly arrangements throughout.

When he has been engaged, the employee must be clear about when he is to begin work and to whom he has to report. A written form of engagement containing these particulars avoids misunderstanding. A printed statement describing the ways of the firm and the rules governing the work and the workers should be given to him or explained by someone who is conversant with these rules. A brochure setting out the most pertinent work rules and explaining any important points of conduct, especially where personal safety is concerned, may be printed for this purpose.

A new employee feels insecure, shy, and nervous while joining a new organization. The reason behind their anxiety could be a lack of adequate information about the organization, organizational policies and practices, job, and work procedures. This creates ambiguity in the minds of new candidates and sometimes leads to frustration. For the same reason, induction and orientation are practiced by every organization either formally or informally. Induction provides relevant information to the new employees about the organization by introducing them with old employees and the organizational culture.

Usually, a new employee joins the organization with very high expectations. Proper induction and orientation can help the employees to understand the reality of the situation. This may develop the confidence in the new employees and they may gradually develop positive thinking about the organization.

In large organizations, the HR department formally commences the induction and orientation program. However, in smaller organizations, the immediate superior of the new employees does it. The different aspects of the induction cannot be understood without exactly knowing what induction is.

An induction process should be:

1. Flexible and interesting;

2. Employee centred; and

3. Meets equal opportunities requirements.

Induction process has three goals :

1. Helps new employees settle in;

2. Helps new employees understand their responsibilities and what is expected of them; and

3. Ensures the employer receives the benefit of the new employee as quickly as possible.

An induction process is not simply for new employees. The same benefits can be received by staff who have been promoted or transferred or those who have returned from a long period of absence. Furthermore you should be careful to include against long term temporary staff, who are entitled to the same training and development as permanent staff members. To not do so could harm your organisation and may be considered discriminatory.

The following steps may be identified as the stages of induction process:

i. Reporting for duty at a certain place to the head of the department concerned.

ii. The head of the department welcomes the new employee.

iii. Introduction to the organisational/branch head by the head of the department.

iv. Organisational/branch head introduces to important employees and describes about the organisation.

v. Departmental head introduces to all the employees of the department, describes the department, total work of the department, etc.

vi. Supervisor concerned introduces to his co-workers in that section/unit to the work/job, material, machine.

vii. Providing information about the duties, responsibilities, rights, facilities, provisions, welfare measures, etc.

viii. Supervisor clarifies the doubts of new employee about the work.

What is Induction — Benefits

In some organizations, a formal employee induction program is almost non-existent or, when done, is performed in a haphazard manner. This is unfortunate since there are a number of very practical and cost-effective implications for conducting a well-run program.

Some of the recognized benefits include:

i. Lower turnover, especially new recruit turnover

ii. Increased productivity

iii. Improved employee morale

iv. Lower recruiting and training costs

v. Facilitation of learning

vi. Reduction of the new employee’s anxiety

vii. Induction helps to build up a two-way channel of communication between management and workers.

viii. Proper induction facilitates informal relation and team work among employee.

ix. Effective induction helps to integrate the new employee into the organization and to develop a sense of belonging.

x. Induction helps to develop good relation.

xi. A formal induction program proves that the company is taking interest in getting him off to good start.

xii. Proper induction reduces employee grievances, absenteeism and labour turnover. Induction is helpful in supplying information concerning the organization, the job and employee welfare facilities.

What is Induction – 7 Major Problems : Lack of Training, Large Information, Administrative Work, Initial Job and Other Problems

Following are the problems in the way of effective induction:

1. Lack of Training – The supervisor or immediate boss who provides the orientation to the new comer may not be trained about the methods of orientation or sometime the supervisor may not have the enough time to orient the new comer.

2. Large Information – The new comer is at once provided with the lot of information about the mission, vision, goals, objectives, organisational structure, departments, duties, responsibilities etc. that it becomes difficult for him to understand all such at once.

3. Administrative Work – When an employee joins the firm he has to fulfil many administrative formalities and at the same time he is provided the orientation which increases the pressure in the new comer.

4. Initial Jobs – At the initial stage employee is only provided with the manual jobs that discourages job interest and company loyalty. So, the initial jobs to the new comer influence to a great extent the interest of the employee in the organisation.

5. Trial and Error Induction – Employee is provided with the sketchy induction under the mistaken belief that trial and error method is the best method of induction. This leads to the increase in confusions and complexities in the mind of the new comer.

6. Balance in the Different Levels of Orientation – The new comer is forced to balance between the broad orientation by the HR department and narrow orientation at the department level.

7. Other Problems –

Some of the other problems in the way of effective orientation programme are:

(i) Employee is put to job so soon that his mistake can put the company to loss also.

(ii) Sometimes the newcomers are assigned with the challenging jobs, and their failure can discourage them to perform further.

(iii) Employee can develop wrong perception when he is asked to perform number of small jobs because of the short time span spent on the each job.

What is Induction — Induction Program: Contents, Principles, Merits and Evaluation

The approach to induction and orientation program (or simply induction program) varies depending on the type of industry, organization, and the job position. In small organizations, usually informal induction program is performed. However, in case of large organizations, a formal induction program is conducted that may last for a duration of 2-4 weeks.

The main activities performed during an induction program prior to the placement of new employees to their specific jobs. On completion of the induction program, the HR department may conduct special anxiety reduction seminars to overcome the doubts of new employees and ensure that they have been oriented properly. This is known as the evaluation of the induction program. After this, the new employees are ready to be placed on their jobs.

The generalized induction program starts with the welcoming of the new employees to the organization, which is followed by explaining the new employee about the organization. New recruits are provided with the organization’s manual and shown their new work locations. The next step is to acquaint them with the details of salary, benefits, holidays, and leaves. The information regarding future training opportunities and career prospects is also provided to the new employees.

In addition, the induction program clarifies the doubts of new employees on various issues. Specifically, most of the induction programs consist of three stages. The first stage starts with the general introduction of the employees by the HR department of the organization. It is followed by the introduction to the department and the job, which is presented by the employees’ reporting manager. The last step in this induction program is to conduct the follow up meeting to verify that the purpose of the induction program has been fulfilled successfully.

Contents of Orientation Programme :

Following should be included in the effective orientation programme:

1. Company’s history, philosophy, mission, vision, goals, and operations.

2. Products and services in which the company deals.

3. Organisation structure of the company with details on the authority responsibility relationships.

4. Conditions of employment, salary, pension arrangements, holidays, and sickness rules.

5. Working arrangements in particular, software packages used, reporting relationship and any key facts about the job not yet covered.

6. The system of HR management and especially the arrangements and opportunities for staff development.

7. The whole range of facilities provided for the benefit, welfare, and the recreation of the employees.

8. Catering arrangements, health and safety rules, and what to do if there are any problems.

9. Location of different departments and the employees’ services.

10. Personnel Policies and Strategies

11. Grievance handling procedure

12. Suggestions system

13. Safety Measures

14. Rules and regulations

15. Opportunities for training, promotions, career development, transfer etc.

16. Terms and conditions of service

17. Benefits and services for the employees.

Principles for Effective Orientation Programme :

Orientation programme is beneficial to both i.e., employees as well as organisation. Many organisations do not give due importance to this programme, due to which the wrong image travels in the minds of new comers. This also reduces the effectiveness of the organisation.

Following are the principles for effective orientation programme:

1. Involvement of Top Management:

For each and every human resource management function, involvement and support of top management is necessary. But in case of orientation the role of top management becomes all the more important because the involvement and support of top management in orientation process sends the signal to the new comer that top management values its human resources.

New comers provide more importance to the shaking hands with the executives than to receive folders carrying information about the organization. So top management support is a pre requisite for the successful orientation programme.

2. Preparation for New Employees:

The existing employees should be prepared for the new employees. They should give the warm welcome to the new comers and also their soft behaviour and attitude towards the new comers is necessary for making the new comers feel comfortable in the new environment. There must be someone to receive the newcomers and make them comfortable before the orientation programme.

3. Determination of the Information Need of the New Employees:

The new employee should be provided with which type of information should be determined before the orientation programme. While providing the information at the time of orientation it should be kept in mind that there should not be information overload as the employees go on learning throughout their organisational life.

4. Planning the Presentation of Information:

It should be planned before the orientation programme that who will provide what information to the new comer. It should be taken care that there is no duplication of information and all the relevant information should be provided to the new comer in the sequenced manner.

5. Concluding Session:

Concluding session should be organized at the end of the orientation programme, many companies also call it special anxiety reduction session. In this session the new comers are asked to raise their queries on the different aspects in relation to their job. The main objective of this session is to identify whether the new comers have understood all that was intended.

Merits of Induction Programme :

The induction programme demands time and serious thinking.

It is done in order to enjoy the below mentioned benefits:

i. It enables the new employees to learn their jobs more quickly.

ii. Creates a sense of belongingness and satisfaction in newcomers.

iii. It reduces employee anxiety, fear, nervousness, absenteeism and grievances.

iv. It reduces attrition rate.

v. It facilitates informal communication and public relations.

vi. It enables team building and two-way communication.

vii. It enhances productivity quickly.

viii. To protect the less aware and illiterate employees from mischievous people.

ix. It leads to employee confidence, motivation and morale.

x. It helps the new entrants to overcome reality shock and to get along with others.

Evaluating Inductio n:

Primary Evaluation – The reaction, learning and behaviour of the inductee and how well these match up to the objectives of the induction.

Secondary Evaluation – The effects of the induction on the organisation in terms of staff retention, attendance, flexibility, equal opportunities, health and safety and customer care.

Induction programmes, as with any training, should be modified according to the results of the evaluation.

Related Articles:

• Objectives of Induction in HRM
• Placement: Meaning, Definition, Importance, Principles, Benefits, Problems
• Economies and Diseconomies of Scale
• Types of Training in HRM

A look at the induction process, and the purpose of induction for employer and employee

An employee’s first impressions of an organisation have a significant impact on their integration within the team and job satisfaction. Induction is an opportunity for an organisation to welcome their new recruit, help them settle in and ensure they have the knowledge and support they need to perform their role. For an employer, effective induction may also affect employee turnover, absenteeism and employer brand.

This factsheet covers the purpose of induction. It looks at the induction process, including who should attend, who should be involved, what to include (as well as what to avoid), and the role of HR and L&D teams. There’s also an induction checklist to help organisations plan or refine their own process.

• What is induction
• The purpose of induction
• The benefits of an effective induction programme
• HR and L&D teams' role in induction
• The induction process
• Induction essentials checklist
• Useful contacts and further reading

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Essay on the induction of employees in an organization.

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Read this essay to learn about the Induction of Employees in an Organisation. After reading this essay you will learn about: 1. Definition of Induction 2. Objectives of Induction 3. Procedure 4. Contents of Induction Programme 5. Elements of Good Induction Programme 6. Problems 7. Practices 8. Induction Training In India.

• Essay on the Induction Training In India

Essay # Definition of Induction :

Inductions may be viewed as the socialising process by which the organisation seeks to make an individual its agent for the achievement of its objectives and the individual seeks to make an agency of the organisation for the achievement of his personal goals.

A few definitions of induction are as follows:

According to Edwin B. Flippo, “Induction is the welcoming process to make the new employee feel at home and generate in him a feeling of belongingness to the organisation.”

According to Michael Armstrong, “Orientation or induction is the process of receiving and welcoming an employee when he first joins a company and giving him the basic information he needs to settle down quickly and happily and start work.”

After selecting compatible personnel the organisation must communicate to the new employees its philosophy, policies, customs and practices. Planned induction helps the new employee creates a good attitude, reduces labour turnover and the employee feels at home right from the very beginning.

“Orientation or induction is thus the process of indoctrination, welcoming, acclimatisation, acculturation and socialisation.”

Essay # Objectives of Induction :

An organisation especially a large one should have a systematic induction process to achieve the following aims:

1. To promote a feeling of belonging and loyalty to the organisation among new comers so that they may not form false impression regarding the company because first impression is the last impression.

2. To build up the new employee’s confidence in the organisation and in himself so that he may become an efficient employee.

3. To bring an agreement between the organisation goals and the personal goals of the organisation.

4. To give the new employee information regarding company (its structure, product, policies, rules and regulations) and facilities provided by the company such as cafeterias, locker room, break time, leave rules etc.

5. To introduce the new worker to the supervisor and the fellow workers with whom he has to work.

6. To create a sense of security for the worker in his job by impressing upon the idea that fairness to the worker is the inherent policy of the organisation.

7. To lessen or reduce the cost of replacing the worker in the early impressionable period because of lack of information or incorrect business impressions.

Essay # Procedure for Induction :

There is no model induction procedure. Each industry develops its own induction procedure as per its needs.

The procedure should basically follow the following steps:

1. The new person should be given a definite time and place to report.

2. A very important step is that the supervisor or the immediate boss should meet and welcome the new employee to the organisation.

3. Administrative work should be completed as early as possible. Such items as vacations, probationary period, medical leave, suggestion systems etc. should be conveyed to the employee.

4. Departmental orientation should be conducted. This should include a get acquainted talk, introduction to the department, explanation of the functions of the department, job instructions and to whom he should look for help and guidance when he has any problem.

5. Verbal explanations are, usually, supplemented by a wide variety of printed material, employee hand book, employee manuals, house journals, picture stories, pamphlets etc., along with short guided four around the plant.

Orientation programme usually covers things like employee compensation benefits, personnel policies, employee’s daily routine, company organisation and operations, safety measures and regulations. The supervisor should ensure that he covers all the necessary orientation steps.

Essay # Contents of Induction Programme :

Every organisation has an obligation to make integration of the individual into it as smooth and comfortable as possible. Small organisations may do it through informal orientation by the employee’s immediate supervisor whereas large organisations usually develop formal orientation programmes.

The range of information that may be covered under orientation training is as follows:

(i) Company’s history, philosophy and operations

(ii) Products and services of the company

(iii) Company’s organisation structure

(iv) Location of departments and employee services

(v) Personnel policies and practices

(vi) Employee’s activities

(vii) Rules and regulations

(viii) Grievance procedure

(ix) Safety measures

(x) Standing orders

(xi) Terms and conditions of service

(xii) Benefits and services for employees

(xiii) Opportunities for training, promotions, transfers etc.

Essay # Elements of Good Induction Programme :

A good induction programme has three main elements:

(I) Introductory Information :

Introductory information regarding the history of the company and company’s products, its organisational structure, policies, rules and regulations etc. should be given informally or in group session in the personnel department. It will help the candidate to understand the company and the organisational policies and standards well.

(II) On the Job Information :

Further information should be given to the new employee by the department supervisor in the department concerned where he is placed on the job about departmental facilities and requirements such as nature of the job, the extent of his liability and employee’s activities such as recreational facilities, safety measures, job routine etc.

(III) Follow up Interview :

A follow up interview should be arranged several weeks after the employee has been on the job by the supervisor or a representative of the personnel department to answer the problems that a new employee may have on the job.

Essay # Problems in Induction :

An orientation programme can go wrong for a number of reasons.

The HR department should try to avoid following errors:

1. The supervisor who has to induct the employee may not be trained or may be too bossy.

2. Employee is overwhelmed with too much information in a short time

3. Employee is confused with a wide variety of forms to be filled

4. In the initial stages, employee is given only manual jobs that discourage job interest and company loyalty

5. Employee is asked to perform challenging jobs where there are high chances of failure that could needlessly discourage the employee.

6. Employee is given only a sketchy induction under the mistaken belief that “trial and error” method is the best induction.

7. Employee is forced to balance between a broad orientation by the HR department and a narrow orientation at the departmental level.

8. Employee is thrown into action too soon. His mistakes can damage the company.

9. Employee may be asked to work on a number of jobs and he may develop wrong perceptions because of short periods spent on each job.

Essay # Practices of Induction :

Different induction practices which are generally used in an industry are:

(I) Induction Guide:

Such guide books are prepared by the personnel department with information on what induction steps have been taken and what are still to be covered various steps to be taken and by whom and when the instructions are to be given are listed in the guide book.

In some large concerns guide books containing the information regarding the company and its various personnel policies are distributed among the new comers.

(II) Counselling :

The supervisor may induct the new employees working under him by introducing and counselling them by reassuring and reinforcing the confidence and guarding against false impression.

On the basis of this interview, personnel department can take action to know the employee’s feelings and to remove the difficulties faced by him through personal talks, guidance and counselling. It may be coordinated by the joint efforts of job supervisor and the personnel department. Periodic following is required to ensure that the employee is properly placed and feels at home.

The best method of induction training is talk plus pictures followed by printed materials. A tour of the plant and the department should be arranged to acquaint the new employee with the overall operations of the company.

Essay # Induction Training In India :

Examples of the induction training programmes followed by a few companies in India are as follows:

1. Maruti Udyog:

Maruti Udyog has different types of induction programmes for different fields.

For engineers the programme is offered in four parts:

i. Familiarise with various functions and meet division heads

ii. Work on shop floor

iii. Work at various other departments

iv. Work finally in those departments for about 2 months, where they eventually have to work.

The company takes its new employees through a one day structured induction training programme.

The programme includes:

i. Briefing on the company’s market position

ii. Business of the company

iii. Functioning style

iv. Organisational structure

v. HR policies

In additional to this, six month behavioural training is offered in team building, self development, customer sensitivity etc. Finally the new employees are put through an appraisal process to judge the progress.

3. Standard Chartered Bank:

The Bank picks up management trainees from premier B-Schools and gives them induction training for about six months. During this period, the trainees spend time in various divisions of the bank to get a brief view of the bank’s operations and get a chance to meet each of the business heads.

Afterwards, a two day session on team building is also conducted. After taking charge of the jobs, the new employees have to attend a review session about the job itself.

4. Sony India:

This company does not follow any uniform policy for acclimatisation and there is no specific time frame given to the newcomers. The company, however, gives enough opportunities to them to understand the process, the culture and the systems of the organisation. In the case of junior managers, new recruits are given less time as compared to the entrants who need to supervise, chalk out strategies and delegate work.

Overall, Sony tries to bring out the best in a person, thus, allowing the individuals to develop their abilities.

Related Articles:

• Induction: Meaning and Importance of Induction
• How To Make An Induction Programme More Effective?

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Methodology

Inductive Reasoning | Types, Examples, Explanation

Published on January 12, 2022 by Pritha Bhandari . Revised on June 22, 2023.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning , where you go from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

Note Inductive reasoning is often confused with deductive reasoning. However, in deductive reasoning, you make inferences by going from general premises to specific conclusions.

Table of contents

What is inductive reasoning, inductive reasoning in research, types of inductive reasoning, inductive generalization, statistical generalization, causal reasoning, sign reasoning, analogical reasoning, inductive vs. deductive reasoning, other interesting articles, frequently asked questions about inductive reasoning.

Inductive reasoning is a logical approach to making inferences, or conclusions. People often use inductive reasoning informally in everyday situations.

You may have come across inductive logic examples that come in a set of three statements. These start with one specific observation, add a general pattern, and end with a conclusion.

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In inductive research, you start by making observations or gathering data. Then , you take a broad view of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

You distribute a survey to pet owners. You ask about the type of animal they have and any behavioral changes they’ve noticed in their pets since they started working from home. These data make up your observations.

To analyze your data, you create a procedure to categorize the survey responses so you can pick up on repeated themes. You notice a pattern : most pets became more needy and clingy or agitated and aggressive.

Inductive reasoning is commonly linked to qualitative research , but both quantitative and qualitative research use a mix of different types of reasoning.

There are many different types of inductive reasoning that people use formally or informally, so we’ll cover just a few in this article:

Inductive reasoning generalizations can vary from weak to strong, depending on the number and quality of observations and arguments used.

Inductive generalizations use observations about a sample to come to a conclusion about the population it came from.

Inductive generalizations are also called induction by enumeration.

• The flamingos here are all pink.
• All flamingos I’ve ever seen are pink.
• All flamingos must be pink.

Inductive generalizations are evaluated using several criteria:

• Large sample: Your sample should be large for a solid set of observations.
• Random sampling: Probability sampling methods let you generalize your findings.
• Variety: Your observations should be externally valid .
• Counterevidence: Any observations that refute yours falsify your generalization.

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Statistical generalizations use specific numbers to make statements about populations, while non-statistical generalizations aren’t as specific.

These generalizations are a subtype of inductive generalizations, and they’re also called statistical syllogisms.

Here’s an example of a statistical generalization contrasted with a non-statistical generalization.

Causal reasoning means making cause-and-effect links between different things.

A causal reasoning statement often follows a standard setup:

• You start with a premise about a correlation (two events that co-occur).
• You put forward the specific direction of causality or refute any other direction.
• You conclude with a causal statement about the relationship between two things.
• All of my white clothes turn pink when I put a red cloth in the washing machine with them.
• My white clothes don’t turn pink when I wash them on their own.
• Putting colorful clothes with light colors causes the colors to run and stain the light-colored clothes.

Good causal inferences meet a couple of criteria:

• Direction: The direction of causality should be clear and unambiguous based on your observations.
• Strength: There’s ideally a strong relationship between the cause and the effect.

Sign reasoning involves making correlational connections between different things.

Using inductive reasoning, you infer a purely correlational relationship where nothing causes the other thing to occur. Instead, one event may act as a “sign” that another event will occur or is currently occurring.

• Every time Punxsutawney Phil casts a shadow on Groundhog Day, winter lasts six more weeks.
• Punxsutawney Phil doesn’t cause winter to be extended six more weeks.
• His shadow is a sign that we’ll have six more weeks of wintery weather.

It’s best to be careful when making correlational links between variables . Build your argument on strong evidence, and eliminate any confounding variables , or you may be on shaky ground.

Analogical reasoning means drawing conclusions about something based on its similarities to another thing. You first link two things together and then conclude that some attribute of one thing must also hold true for the other thing.

Analogical reasoning can be literal (closely similar) or figurative (abstract), but you’ll have a much stronger case when you use a literal comparison.

Analogical reasoning is also called comparison reasoning.

• Humans and laboratory rats are extremely similar biologically, sharing over 90% of their DNA.
• Lab rats show promising results when treated with a new drug for managing Parkinson’s disease.
• Therefore, humans will also show promising results when treated with the drug.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

In deductive reasoning, you make inferences by going from general premises to specific conclusions. You start with a theory, and you might develop a hypothesis that you test empirically. You collect data from many observations and use a statistical test to come to a conclusion about your hypothesis.

Inductive research is usually exploratory in nature, because your generalizations help you develop theories. In contrast, deductive research is generally confirmatory.

Sometimes, both inductive and deductive approaches are combined within a single research study.

Inductive reasoning approach

You begin by using qualitative methods to explore the research topic, taking an inductive reasoning approach. You collect observations by interviewing workers on the subject and analyze the data to spot any patterns. Then, you develop a theory to test in a follow-up study.

Deductive reasoning approach

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

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Induction: Progress in Philosophy of Science

• February 2022

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The Problem of Induction

The original problem of induction can be simply put. It concerns the support or justification of inductive methods; methods that predict or infer, in Hume's words, that “instances of which we have had no experience resemble those of which we have had experience” (THN, 89). Such methods are clearly essential in scientific reasoning as well as in the conduct of our everyday affairs. The problem is how to support or justify them and it leads to a dilemma: the principle cannot be proved deductively, for it is contingent, and only necessary truths can be proved deductively. Nor can it be supported inductively—by arguing that it has always or usually been reliable in the past—for that would beg the question by assuming just what is to be proved.

A century after Hume first put the problem, and argued that it is insoluble, J. S. Mill gave a more specific formulation of an important class of inductive problems: “Why,” he wrote, “is a single instance, in some cases, sufficient for a complete induction, while in others myriads of concurring instances, without a single exception known or presumed, go such a little way towards establishing an universal proposition?” (Mill 1843, Bk III, Ch. III). (Compare: (i) Everyone seated on the bus is moving northward. (ii) Everyone seated on the bus was born on a prime numbered day of the month.)

In recent times inductive methods have fissioned and multiplied, to an extent that attempting to define induction would be more difficult than rewarding. It is however instructive to contrast induction with deduction: Deductive logic, at least as concerns first-order logic, is demonstrably complete. The premises of an argument constructed according to the rules of this logic imply the argument's conclusion. Not so for induction: There is no comprehensive theory of sound induction, no set of agreed upon rules that license good or sound inductive inference, nor is there a serious prospect of such a theory. Further, induction differs from deductive proof or demonstration (in first-order logic, at least) not only in induction's failure to preserve truth (true premises may lead inductively to false conclusions) but also in failing of monotonicity: adding true premises to a sound induction may make it unsound.

The characterization of good or sound inductions might be called the characterization problem: What distinguishes good from bad inductions? The question seems to have no rewarding general answer, but there are nevertheless interesting partial characterizations, some of which are explored in this entry.

1. The contemporary notion of induction

2.1 the justification of induction, 2.2 karl popper's views on induction, 3.1 elementary probability, 3.2 carnap's inductive logic, 3.3 reichenbach's frequentism, 4.1 induction and deduction, 4.2 a demonstrative argument to show the soundness of induction, 4.3 rationalistic criticism of hume, 5.1 the paradox of the ravens, 5.2 the grue paradox and the new riddle of induction, 5.3 return of the ravens, 6.1 pragmatism: induction as practical reason, 6.2 on the value of evidence, other internet resources, related entries.

The Oxford English Dictionary (OED Online, accessed October 20, 2012) defines “induction,” in the sense relevant here, as

7. The process of inferring a general law or principle from the observation of particular instances (opposed to deduction n., q.v.)

That induction is opposed to deduction is not quite right, and the rest of the definition is outdated and too narrow: much of what contemporary epistemology, logic, and the philosophy of science count as induction infers neither from observation nor particulars and does not lead to general laws or principles. This is not to denigrate the leading authority on English vocabulary—until the middle of the previous century induction was understood to be what we now know as enumerative induction or universal inference ; inference from particular inferences:

a 1 , a 2 , …, a n are all F s that are also G ,

to a general law or principle

All F s are G

The problem of induction was, until recently, taken to be to justify this form of inference; to show that the truth of the premise supported, if it did not entail, the truth of the conclusion. The evolution and generalization of this question—the traditional problem has become a special case—is discussed in some detail below.

A few simple counterexamples to the OED definition may suggest the increased breadth of the contemporary notion:

All observed emeralds have been green. Therefore, the next emerald to be observed will be green.

New York is east of the Mississippi. Delaware is east of the Mississippi. Therefore, everything that is either New York or Delaware is east of the Mississippi.

Further, on at least one serious view, due in differing variations to Mill and Carnap, induction has not to do with generality at all; its primary form is the singular predictive inference—the second form of enumerative induction mentioned above—which leads from particular premises to a particular conclusion. The inference to generality is a dispensable middle step.

Although inductive inference is not easily characterized, we do have a clear mark of induction. Inductive inferences are contingent, deductive inferences are necessary. Deductive inference can never support contingent judgments such as meteorological forecasts, nor can deduction alone explain the breakdown of one's car, discover the genotype of a new virus, or reconstruct fourteenth century trade routes. Inductive inference can do these things more or less successfully because, in Peirce's phrase, inductions are ampliative. Induction can amplify and generalize our experience, broaden and deepen our empirical knowledge. Deduction on the other hand is explicative. Deduction orders and rearranges our knowledge without adding to its content.

Of course, the contingent power of induction brings with it the risk of error. Even the best inductive methods applied to all available evidence may get it wrong; good inductions may lead from true premises to false conclusions. (A competent but erroneous diagnosis of a rare disease, a sound but false forecast of summer sunshine in the desert.) An appreciation of this principle is a signal feature of the shift from the traditional to the contemporary problem of induction.

How to tell good inductions from bad inductions? That question is a simple formulation of the problem of induction. In its general form it clearly has no substantive answer, but its instances can yield modest and useful questions. Some of these questions, and proposed answers to them, are surveyed in what follows.

Some authorities, Carnap in the opening paragraph of The Continuum of Inductive Methods (1952) is an example, take inductive inference to include all non-deductive inference. That may be a bit too inclusive; perception and memory are clearly ampliative but their exercise seems not to be congruent with what we know of induction, and the present article is not concerned with them. The scope of the contemporary concept is charted in the taxonomy in section 3.2 below.

Testimony is another matter. Although testimony is not a form of induction, induction would be all but paralyzed were it not nourished by testimony. Scientific inductions depend upon data transmitted and supported by testimony and even our everyday inductive inferences typically rest upon premises that come to us indirectly.

2. Hume on induction

The source for the problem of induction as we know it is Hume's brief argument in Book I, Part III, section VI of the Treatise (THN). The great historical importance of this argument, not to speak of its intrinsic power, recommends that reflection on the problem begin with a rehearsal of it.

First a note on vocabulary. The term ‘induction’ does not appear in Hume's argument, nor anywhere in the Treatise or the first Inquiry , for that matter. Hume's concern is with inferences concerning causal connections, which, on his account are the only connections “which can lead us beyond the immediate impressions of our memory and senses” (THN, 89). But the difference between such inferences and what we know today as induction, allowing for the increased complexity of the contemporary notion, is largely a matter of terminology.

Secondly, Hume divides all reasoning into demonstrative, by which he means deductive, and probabilistic, by which he means the generalization of causal reasoning. The deductive system that Hume had at hand was just the weak and complex theory of ideas in force at the time, augmented by syllogistic logic (THN, Book I, Part III, Section I for example). His ‘demonstrations’ rather than structured deductions are often founded on the principle that conceivable connections are possible, inconceivable connections impossible, and necessary connections those the denials of which are impossible or inconceivable. That said, and though we should today allow contingent connections that are neither probabilistic nor causal, there are few points at which the distinction is not clear.

It should also be remarked that Hume's argument applies just to what is known today as enumerative induction , based on instances, and primarily to singular predictive inference (including ‘predictions’ about the present or past; see section 3.2 below for a taxonomy of inductive inference) but, again, its generalization to other forms of inductive reasoning is straightforward. In what follows we paraphrase and interpolate freely so as to ease the application of the argument in contemporary contexts.

The argument should be seen against the background of Hume's project as he announces it in the introduction to the Treatise : This project is the development of the empirical science of human nature. The epistemological sector of this science involves describing the operations of the mind, the interactions of impressions and ideas and the function of the liveliness that constitutes belief. But this cannot be a merely descriptive endeavor; accurate description of these operations entails also a considerable normative component, for, as Hume puts it,

[o]ur reason [to be taken here quite generally, to include the imagination] must be consider'd as a kind of cause, of which truth is the natural effect; but such-a-one as by the irruption of other causes, and by the inconstancy of our mental powers, may frequently be prevented. (Hume THN, 180)

The account must thus not merely describe what goes on in the mind, it must also do this in such a way as to show that and how these mental activities lead naturally, if with frequent exceptions, to true belief (see Loeb 2006 for further discussion of these questions).

Now as concerns the argument, its conclusion is that in induction (causal inference) experience does not produce the idea of an effect from an impression of its cause by means of the understanding or reason, but by the imagination, by “a certain association and relation of perceptions.” The center of the argument is a dilemma: If inductive conclusions were produced by the understanding, inductive reasoning would be based upon the premise that nature is uniform;

that instances of which we have had no experience, must resemble those of which we have had experience, and that the course of nature continues always uniformly the same. (THN, 89)

And were this premise to be established by reasoning, that reasoning would be either deductive or probabilistic (i.e., causal). The principle can't be proved deductively, for whatever can be proved deductively is a necessary truth, and the principle is not necessary; its antecedent is consistent with the denial of its consequent. Nor can the principle be proved by causal reasoning, for it is presupposed by all such reasoning and any such proof would be a petitio principii .

The normative component of Hume's project is striking here: That the principle of uniformity of nature cannot be proved deductively or inductively shows that it is not the principle that drives our causal reasoning only if our causal reasoning is sound and leads to true conclusions as a “natural effect” of belief in true premises. This is what licenses the capsule description of the argument as showing that induction cannot be justified or licensed either deductively or inductively; not deductively because (non-trivial) inductions do not express logically necessary connections, not inductively because that would be circular. If, however, causal reasoning were fallacious, the principle of the uniformity of nature might well be among its principles.

The negative argument is an essential first step in Hume's general account of induction. It rules out accounts of induction that view it as the work of reason. Hume's positive account begins from another dilemma, a constructive dilemma this time: Inductive inference must be the work either of reason or of imagination. Since the negative argument shows that it cannot be a species of reasoning, it must be imaginative.

Hume's positive account of causal inference can be simply described: It amounts to embedding the singular form of enumerative induction in the nature of human, and at least some bestial, thought. The several definitions offered in Enquiries concerning Human Understanding and concerning the Principles of Morals (EHU, 60) make this explicit:

[W]e may define a cause to be an object, followed by another, and where all objects similar to the first are followed by objects similar to the second . Or, in other words, where, if the first object had not been, the second never had existed .

Another definition defines a cause to be:

an object followed by another, and whose appearance always conveys the thought to that other.

If we have observed many F s to be followed by G s, and no contrary instances, then observing a new F will lead us to anticipate that it will also be a G . That is causal inference.

It is clear, says Hume, that we do make inductive, or, in his terms, causal, inferences; that having observed many F s to be G s, observation of a new instance of an F leads us to believe that the newly observed F is also a G . It is equally clear that the epistemic force of this inference, what Hume calls the necessary connection between the premises and the conclusion, does not reside in the premises alone:

All observed F s have also been G s,

a is an F ,

do not imply

It is false that “instances of which we have had no experience must resemble those of which we have had experience” (EHU, 89).

Hume's positive view is that the experience of constant conjunction fosters a “habit of the mind” that leads us to anticipate the conclusion on the occasion of a new instance of the second premise. The force of induction, the force that drives the inference, is thus not an objective feature of the world, but a subjective power; the mind's capacity to form inductive habits. The objectivity of causality, the objective support of inductive inference, is thus an illusion, an instance of what Hume calls the mind's “great propensity to spread itself on external objects” (THN, 167).

Hume's account of causal inference raises the problem of induction in an acute form: One would like to say that good and reliable inductions are those that follow the lines of causal necessity; that when

is the manifestation in experience of a causal connection between F and G , then the inference

All observed F s have also been G s, a is an F , Therefore, a , not yet observed, is also a G ,

is a good induction. But if causality is not an objective feature of the world this is not an option. The Humean problem of induction is then the problem of distinguishing good from bad inductive habits in the absence of any corresponding objective distinction.

Two sides or facets of the problem of induction should be distinguished: The epistemological problem is to find a method for distinguishing good or reliable inductive habits from bad or unreliable habits. The second and deeper problem is metaphysical . This is the problem of distinguishing reliable from unreliable inductions. This is the problem that Whitehead called “the despair of philosophy” (1925, 35). The distinction can be illustrated in the parallel case of arithmetic. The by now classic incompleteness results of the last century show that the epistemological problem for first-order arithmetic is insoluble; that there can be no method, in a quite clear sense of that term, for distinguishing the truths from the falsehoods of first-order arithmetic. But the metaphysical problem for arithmetic has a clear and correct solution: the truths of first-order arithmetic are precisely the sentences that are true in all arithmetic models. Our understanding of the distinction between arithmetic truths and falsehoods is just as clear as our understanding of the simple recursive definition of truth in arithmetic, though any method for applying the distinction must remain forever out of our reach.

Now as concerns inductive inference, it is hardly surprising to be told that the epistemological problem is insoluble; that there can be no formula or recipe, however complex, for ruling out unreliable inductions. But Hume's arguments, if they are correct, have apparently a much more radical consequence than this: They seem to show that the metaphysical problem for induction is insoluble; that there is no objective difference between reliable and unreliable inductions. This is counter intuitive. Good inductions are supported by causal connections and we think of causality as an objective matter: The laws of nature express objective causal connections. Ramsey writes in his Humean account of the matter:

Causal laws form the system with which the speaker meets the future; they are not, therefore, subjective in the sense that if you and I enunciate different ones we are each saying something about ourselves which pass by one another like “I went to Grantchester,” “I didn't”. (Ramsey 1931a, 137)

A satisfactory resolution of the problem of induction would account for this objectivity in the distinction between good and bad inductions.

It might seem that Hume's argument succeeds only because he has made the criteria for a solution to the problem too strict. Enumerative induction does not realistically lead from premises

All observed F s have also been G s a is an F ,

to the simple assertion

Therefore, a , not yet observed, is also a G .

Induction is contingent inference and as such can yield a conclusion only with a certain probability. The appropriate conclusion is

It is therefore probable that, a , not yet observed, is also a G

Hume's response to this (THN, 89) is to insist that probabilistic connections, no less than simple causal connections, depend upon habits of the mind and are not to be found in our experience of the world. Weakening the inferential force between premises and conclusion may divide and complicate inductive habits; it does not eliminate them. The laws of probability alone have no more empirical content than does deductive logic. If I infer from observing clouds followed by rain that today's clouds will probably be followed by rain this can only be in virtue of an imperfect habit of associating rain with clouds.

Hume's argument is often credited with raising the problem of induction in its modern form. For Hume himself the conclusion of the argument is not so much a problem as a principle of his account of induction: Inductive inference is not and could not be reasoning, either deductive or probabilistic, from premises to conclusion, so we must look elsewhere to understand it. Hume's positive account does much to alleviate the epistemological problem—how to distinguish good inductions from bad ones—without treating the metaphysical problem. His account is based on the principle that inductive inference is the work of association which forms a “habit of the mind” to anticipate the consequence, or effect, upon witnessing the premise, or cause. He provides illuminating examples of such inferential habits in sections I.III.XI and I.III.XII of the Treatise (THN). The latter accounts for frequency-to-probability inferences in a comprehensive way. It shows that and how inductive inference is “a kind of cause, of which truth is the natural effect.”

Although Hume is the progenitor of modern work on induction, induction presents a problem, indeed a multitude of problems, quite in its own right. The by now traditional problem is the matter of justification: How is induction to be justified? There are in fact several questions here, corresponding to different modes of justification. One very simple mode is to take Hume's dilemma as a challenge, to justify (enumerative) induction one should show that it leads to true or probable conclusions from true premises. It is safe to say that in the absence of further assumptions this problem is and should be insoluble. The realization of this dead end and the proliferation of other forms of induction have led to more specialized projects involving various strengthened premises and assumptions. The several approaches treated below exemplify this.

One of the most influential and controversial views on the problem of induction has been that of Karl Popper, announced and argued in The Logic of Scientific Discovery (LSD). Popper held that induction has no place in the logic of science. Science in his view is a deductive process in which scientists formulate hypotheses and theories that they test by deriving particular observable consequences. Theories are not confirmed or verified. They may be falsified and rejected or tentatively accepted if corroborated in the absence of falsification by the proper kinds of tests:

[A] theory of induction is superfluous. It has no function in a logic of science. The best we can say of a hypothesis is that up to now it has been able to show its worth, and that it has been more successful than other hypotheses although, in principle, it can never be justified, verified, or even shown to be probable. This appraisal of the hypothesis relies solely upon deductive consequences (predictions) which may be drawn from the hypothesis: There is no need even to mention “induction” . (LSD, 315)

Popper gave two formulations of the problem of induction; the first is the establishment of the truth of a theory by empirical evidence; the second, slightly weaker, is the justification of a preference for one theory over another as better supported by empirical evidence. Both of these he declared insoluble, on the grounds, roughly put, that scientific theories have infinite scope and no finite evidence can ever adjudicate among them (LSD, 253–254; Grattan-Guiness 2004). He did however hold that theories could be falsified, and that falsifiability, or the liability of a theory to counterexample, was a virtue. Falsifiability corresponds roughly to to the proportion of models in which a (consistent) theory is false. Highly falsifiable theories thus make stronger assertions and are in general more informative. Though theories cannot in Popper's view be supported, they can be corroborated : a better corroborated theory is one that has been subjected to more and more rigorous tests without having been falsified. Falsifiable and corroborated theories are thus to be preferred, though, as the impossibility of the second problem of induction makes evident, these are not to be confused with support by evidence.

Popper's epistemology is almost exclusively the epistemology of scientific knowledge. This is not because he thinks that there is a sharp division between ordinary knowledge and scientific knowledge, but rather because he thinks that to study the growth of knowledge one must study scientific knowledge:

[M]ost problems connected with the growth of our knowledge must necessarily transcend any study which is confined to common-sense knowledge as opposed to scientific knowledge. For the most important way in which common-sense knowledge grows is, precisely, by turning into scientific knowledge. (Popper LSD, 18)

3. Probability and induction

A probability on a first-order language is a function that assigns a number between zero and one inclusive to each sentence in the language. The laws of probability require that if A is any sentence of the language then

The probability P is said to be regular iff the condition of P3 is also necessary, i.e., iff no contingent sentence has probability one.

Given a probability P on a language L the conditional probability P ( B  |  A ) is defined for pairs A , B of sentences when P ( A ) is positive:

If P ( A ) > 0 then P ( B | A ) = P ( A ∧ B ) / P ( A )

Conditional probability may also be taken as fundamental and simple probability defined in terms of it as, for example, probability conditioned on a tautology (see, for example, Hajek 2003).

Sentences A , B are said to be independent in P if

P ( A ∧ B ) = P ( A ) P ( B ).

The set { A 1 , …, A k } is thoroughly independent in P iff for each non-null subset { B 1 , …, B n } of { A 1 , …, A k }

P ( B 1 ∧ … ∧ B n ) = P ( B 1 ) P ( B 2 ) … P ( B n )

From the above laws and definitions it follows that:

(So every consistent sentence has positive probability.)

If A and B are logically equivalent then

[( A ∧ B ) ∨ ¬ A ]

[( A ∧ B ) ∨ ¬ B ]

are both logically necessary. Hence by P3 and P2, if A and B are logically equivalent

P ( A ) = P ( A ∧ B ) = P ( B )

If A and B are independent in P , then

P ( A ∧ ¬ B ) = P ( A ) − P ( A ∧ B ) = P ( A ) − P ( A ) P ( B ) = P ( A ) (1 − P ( B ) = P ( A ) P (¬ B )

One simple and important special case concerns a language L ( k ), the vocabulary of which includes just one monadic predicate R and k individual constants a 1 , …, a k . A k-sequence in L ( k ) is a conjunction that includes for each constant a i either R ( a i ) or ¬ R ( a i ) (not both). In a standard interpretation k -sequences represent samples from a larger population of individuals; then R and ¬ R represent presence and absence of a trait of interest.

We state without proof the generalization of C5:

P is symmetrical on a language L ( k ) iff it is invariant for the permutation of individual constants, i.e., iff

P [ A ( a 1 , …, a n )] = P[ A ( b 1 , …, b n )]

for each formula A and any individual constants { a i }, { b i }

Independence is sufficient for symmetry in the following precise sense:

The condition of C7 is not necessary; symmetry does not imply independence, i.e., there are languages L ( k ), k -sequences A in L ( k ) and symmetrical probabilities P on L ( k ) such that A is not thoroughly independent in P . (A simple example in section 3.2 below illustrates this.)

If X = { x 1 , …, x n } is a finite set of individuals and Y ⊆ X , then the relative frequency of Y in X is the proportion of members of X that are also members of Y :

R ( Y | X ) = (1/ n ) C { X ∩ Y }

One relation between probability and relative frequency is easily expressed in terms of symmetry. We state this without proof (see Carnap LFP, 495 for a proof):

C8 can be understood from a Kantian-Critical point of view to express that relative frequency is the schema of (symmetrical) probability; the manifestation of probability in experience.

Bayes' Theorem (to be distinguished from Bayes' Postulate , to be treated in section 4 ) is a truth of probability useful in evaluating support for probabilistic hypotheses. It is a direct consequence of the definition of conditional probability.

A second important principle, often used in conjunction with C9 is:

The simple probabilities defined in section 3.1 above can serve to illustrate and compare approaches to probabilistic induction; Carnap's logicism, Reichenbach's frequentism, and Bayesian subjectivism. These sponsor different formulations and proposed solutions of the problem of induction.

Perhaps the most evident difference among the three theories is just that of the bearers or objects of probability. Probability applies to sentences in Carnap' logicism, to event-types for Reichenbach and to beliefs in subjectivism.

Carnap's taxonomy of the varieties of inductive inference (LFP 207f) may help to appreciate the complexity of the contemporary concept.

• Direct inference typically infers the relative frequency of a trait in a sample from its relative frequency in the population from which the sample is drawn.
• Predictive inference is inference from one sample to another sample not overlapping the first. This, according to Carnap, is “the most important and fundamental kind of inductive inference” (LFP, 207). It includes the special case, known as singular predictive inference , in which the second sample consists of just one individual.
• Inference by analogy is inference from the traits of one individual to those of another on the basis of traits that they share.
• Inverse inference infers something about a population on the basis of premises about a sample from that population.
• Universal inference , mentioned in the opening sentence of this article, is inference from a sample to a hypothesis of universal form.

Probability in Carnap's theory is a metalinguistic operator, as it is in the exposition of section 3.1 above. In this context the problem of induction is to choose or to design a language appropriate to a given situation and to define a probability on this language that properly codifies inductive inference.

Carnap writes m ( s ) for the probability of the sentence s and

c ( h , e ) = m ( h ∧ e ) / m ( e )

when m ( e ) > 0, for the degree of confirmation of the hypothesis h on evidence e . Degree of confirmation satisfies the laws of probability and in addition symmetry. In standard cases c and m are also regular.

K -sequences ( state descriptions in Carnap's terminology) are the most specific sentences in a language L ( k ): every consistent sentence is logically equivalent to a disjunction of these pairwise incompatible conjunctions, so fixing the probabilities of all state descriptions, which must always sum to one, fixes the probability of every consistent sentence in the language. (The principle C1 of section 3.1 fixes the probability of inconsistent sentences at zero.) State descriptions are isomorphic if they include the same number of negations. A structure description is a maximal disjunction of isomorphic state descriptions; all and only the state descriptions with the same number of negations. Symmetry entails that state descriptions in the same structure description are equiprobable.

To fix ideas we consider L (3) which we take to represent three draws with replacement from an urn including an indefinite number of balls, each either Red ( R ) or Black (¬ R ) . There are then eight state descriptions (eight possible sequences of draws) and four structure descriptions: a state description says which balls drawn have which color. A structure description says just how many balls there are of each color in a sequence of draws without respect to order.

From a deductive-logical point of view, the set of logical consequences of a given state description is a maximal consistent set of sentences of L (3): The set is consistent (consisting as it does of the logical consequences of a consistent sentence) and maximal; no sentence of L (3) not implied by the set is consistent with it. The state descriptions correspond to models, to points in a logical space. A (symmetrical) probability on L (3) thus induces a normal measure on sets of models: Any assignment of non-negative numbers summing to one to the state descriptions or models fixes probabilities. In this finite case, the extent to which evidence e supports a hypothesis h is the proportion of models for e in which h is true. Deductively, e logically implies h if h is true in every model for e . Degree of confirmation is thus a metrical generalization of first-order logical implication.

There are two probabilities that support contrasting logical-probable relations among the sentences of L (3). The simpler of these, m † and c †, is uniform or equiprobable over state descriptions; each state description has probability 1/8. From the point of view of induction it is significant that every 3-sequence (every sequence of three draws) is thoroughly independent in m †. This means that drawing and replacing a Red ball provides no evidence about the constitution of the urn or the color of the next ball to be drawn. Carnap took this to be a strong argument against the use of m † in induction, since it seemed to prohibit learning from experience.

Although m † may not serve well inductively, it is one of a class of very important probabilities. These are probabilities that are equiprobable for R , and in which for each k , every k -sequence is thoroughly independent. Such measures are known as Bernoullian probabilities , they satisfy the weak law of large numbers, first proved by Jacob Bernoulli in 1713. This law states that in the Bernoullian case of thorough independence and equiprobability, as the number of trials increases without bound, the difference between the probability of S and its relative frequency becomes arbitrarily small.

The second probability in question is m * and c *. m * is uniform (equiprobable) not on state descriptions but on structure descriptions. This can be thought of as enforcing a division of labor between cause and chance: The domain of cause includes the structures of the urn and the balls, the number and colors of the balls, the way the balls are shuffled between draws and so on. Given these causal factors, the order in which the balls are drawn is a matter of chance; this order is not determined by the mechanics of the physical set up just described. Of course the mechanics of the draw are also causally determined, but not by the mechanics of the physical set up.

In the present example a simple calculation shows that:

m *( R (1)) = m *( R (2)) = m *( R (3)) = 1/2 c *( R (2), R (1) = 2/3 c *( R (3), R (1) ∧ R (2)) = 3/4 c *( R (3), R (1) ∧ ¬ R (2)) = 3/8 c *( R (2), ¬ R (1)) = 1/3 c *( R (3), ¬ R (1) ∧ ¬ R (2)) = 1/4

m * ( c *) is thus affected by evidence, positively and negatively, as m † is not. R (1), R (2) and R (3) are not independent in m *. This establishes, as promised above in section 3.2 , a symmetrical probability in which k -sequences are not thoroughly independent. Symmetry is a demonstrably weaker constraint on probability than independence.

In later work Carnap introduced systems (the λ systems) in which different predicates could be more or less sensitive to evidence.

Carnap's logical probability generalized the metalinguistic relation of logical implication to a numerical function, c ( h , e ), that expresses the extent to which an evidence sentence e confirms a hypothesis h . Reichenbach's probability implication is also a generalization of a deductive concept, but the concept generalized belongs first to an object language of events and their properties.

This generalization extends classical first-order logic to include probability implications. These are formulas (Reichenbach TOP, 45)

x ∈ A ⊃ p x ∈ B

where p is some quantity between zero and one inclusive.

In a more conventional notation this probability implication between properties or classes may be written

P ( B | A ) = p

(Reichenbach writes P ( A , B ) rather than P ( B | A ). The latter is written here to maintain consistency with the notations of other sections.)

Reichenbach's probability logic is a conservative extension of classical first-order logic to include rules for probability implications. The individual variables ( x , y ) are taken to range over events (“The gun was fired,” “The shot hit the target”) and, as the notation makes evident, the variables A and B range over classes of events (“the class of firings by an expert marksman,” “the class of hits within a given range of the bullseye”) (Reichenbach TOP, 47). The formal rules of probability logic assure that probability implications conform to the laws of conditional probability and allow inferences integrating probability implications into deductive logic, including higher-order quantifiers over the subscripted variables.

Reichenbach's rules of interpretation of probability implications require, first, that the classes A and B be infinite and in one-one correspondence so that their order is established. It is also required that the limiting relative frequency

lim n →∞ N ( A n ∩ B n ) / n

where A n , B n are the first n members of A , B respectively, and N gives the cardinality of its argument, exists. When this limit does exist it defines the probability of B given A (Reichenbach 1971, 68):

P ( B | A ) = df lim n →∞ N ( A n ∩ B n ) / n , when the limit exists.

The complete system also includes higher-order or, as Reichenbach calls them, concatenated probabilities. First-level probabilities involve infinite sequences; the ordered sets referred to by the predicates of probability implications. Second-order probabilities are determined by lattices, or sequences of sequences. (Reichenbach 1971, chapter 8 and ¶41).

3.3.1 Reichenbachian induction.

On Reichenbach's view, the problem of induction is just the problem of ascertaining probability on the basis of evidence (TOP, 429). The conclusions of inductions are not asserted, they are posited . “A posit is a statement with which we deal as true, though the truth value is unknown” (TOP, 373).

B in A = N ( A n ∩ B n ) / n

lim n →∞ [ N ( A n ∩ B n ) / n ]

N ( A n ∩ B n ) / n .

(This corresponds to the Carnapian λ-function c 0 (λ(κ) = 0) which gives total weight to the empirical factor and no weight to the logical factor.)

It is significant that finite relative frequencies are symmetrical, independent of order, but limiting relative frequencies are not; whether a limit exists, and if it exists its value, depend upon the order of the sequence. The definition of probability as limiting relative frequency thus entails that probability, and hence inductive inference, so defined is not symmetrical.

Reichenbach's justification of induction by enumeration is known as a pragmatic justification (see also Salmon 1967, 52–54). It is first important to keep in mind that the conclusion of inductive inference is not an assertion, it is a posit. Reichenbach does not argue that induction is a sound method; his account is rather what Wesley Salmon (1963) and others have referred to as vindication : that if any rule will lead to positing the correct probability, the inductive rule will do this, and it is, furthermore, the simplest rule that is successful in this sense.

What is now the standard difficulty with Reichenbach's rule of induction was noticed by Reichenbach himself and later strengthened by Salmon (1963). It is that for any observed relative frequency in an initial segment of any finite length, and for any arbitrarily selected quantity between zero and one inclusive, there exists a rule that leads to that quantity as the limit on the basis of that observed frequency. Salmon goes on to announce additional conditions on adequate rules that uniquely determine the rule of induction. More recently Cory Juhl (1994) has examined the rule with respect to the speed with which it approaches a limit.

4. Bayesianism and subjectivism

Bayesian induction incorporates a subjectivistic view of probability, according to which probability is identified with strength of belief. Objective Bayesianism incorporates also normative epistemic constraints. (“Logical Foundations of Evidential Support,” (Fitelson 2006a) is a good example of the genre.) Contemporary Bayesianism is not only a doctrine, or family of positions, about probability. It applies generally in epistemology and the philosophy of science as well. “Bayesian statistical inference for psychological research” (Edwards et al. 1963) gave a general Bayesian account of statistical inference. Savage (1954), Jeffrey (1983) and Skyrms (1980) give extensive Bayesian accounts of decision making in situations of uncertainty. More recently objective Bayesianism has taken on the traditional problem of the justification of universal inference. The matter is briefly discussed in section 5 below .

The Bayesian approach to induction can be illustrated in the languages L ( k ) of section 3.1 : Recall that an urn contains three balls, each either Red or Black (= not Red). It is not known how many balls of each color there are. Balls are to be drawn, their colors recorded, and replaced. On the basis of this evidence, the outcomes of the successive draws, we are to support beliefs about the constitution of the urn. There are four possible constitutions, determined by the numbers of Red (and Black) balls in the urn. We can list these as alternative hypotheses stating the number of Reds:

• H 0 : 0 Reds, 3 Blacks
• H 1 : 1 Red, 2 Blacks
• H 2 : 2 Reds, 1 Black
• H 3 : 3 Reds, 0 Blacks

It is useful to consider what our beliefs would be if we knew which hypothesis was true. If the probability P on the language L ( k ) gives our beliefs about this setup, then P is, as remarked in section 3.1 , symmetric. Further, if, for example, we knew that there were two Reds and one Black ball in the urn the sequences of draws would be symmetric and (thoroughly) independent with constant probability (= 2/3) of Red on each draw. To what extent a given sequence of draws supports the different hypotheses is, on the other hand, not at all clear. If σ( k ) is a k -sequence we want to find the probabilities P ( H i | σ( k )), for i = 0, 1, 2 and 3. We do know that after the first draw we shall reject either H 0 or H 3 , but little else is evident. Notice however that if the probabilities P ( H i | σ( k )), the extent to which given sequences support the different hypotheses, are not readily available, we just saw that their converses, P (σ( k ) | H i ) (these are the likelihoods of the hypotheses given the evidence σ( k )) are easily and directly calculated: If, for example, the k -sequence σ( k ) includes n Reds and ( k − n ) Blacks then

P (σ( k )| H 2 ) = (2/3) n (1/3) ( k − n )

Each k -sequence is thus thoroughly independent in each conditional probability, P (__| H i ), with constant probability of R j . These conditional probabilities are thus Bernoullian.

Bayes' theorem ( C9 of section 3.1 ) expresses the probability P ( H | E ) in terms of the likelihood P ( E | H ).

P ( H | E ) = P ( E | H ) P ( H ) / P ( E )

Bayes' postulate says in this case that if we have no reason to believe that any of the four hypotheses is more likely than the others, then we may consider them to be equiprobable. Since the hypotheses are pairwise incompatible, on the basis of this assumption it follows from C9.1 of section 3.1 that

P ( E ) = ∑ i P ( E | H i ) P ( H i )

And hence that for each hypothesis H j ,

P ( H j | E ) = P ( E | H j ) P ( H j ) / ∑ i P ( E | H i ) P ( H i )

Thus, for example, we have that

P ( H 1 | R 1 ) = (1/3) / ∑ i P ( E | H i ) P ( H i ) = (1/3) / 2 = 1/6

Similarly, P ( H 2 | R 1 ) = 1/3, P ( H 3 | R 1 ) = 1/2.

The simple, and obvious, criticism of the Bayesian method is that the prior (before knowledge of any evidence) probabilities fixed by Bayes' postulate are arbitrary. The Bayesian response is that the Bayesian method of updating probabilities with successive outcomes progressively diminishes the effect of the initial priors. This updating uses the posterior probabilities resulting from the first draw as the “prior” probabilities for the second draw. Further, as the number of trials increases without bound, the updated probability is virtually certain to approach one of the conditional probabilities P (_ | H i ) (de Finetti 1937). (See Zabell 2005 for a precise formulation and exposition of the de Finetti theorem and Jeffrey 1983, Section 12.6, for a more brief and accessible account.)

Our deep and extensive understanding of deductive logic, in particular of the first-order logic of quantifiers and truth functions, is predicated on two metatheorems; semantical completeness of this logic and the decidability of proofs and deductions . The decidability result provides an algorithm which when applied to a (finite) sequence of sentences decides in finitely many steps whether the sequence is a valid proof of its last member or is a valid deduction of a given conclusion from given premises. Semantical completeness enables the easy and enlightening movement between syntactical, proof theoretic, operations and reasoning in terms of models. In combination these metatheorems resolve both the metaphysical and epistemological problems for proofs and demonstrations in first-order logic: Namely, what distinguishes valid from invalid logical demonstration? and what are reliable methods for deductive inference? (It should however be kept in mind that neither logical validity nor logical implication is decidable.) Neither of these metatheorems is possible for induction. Indeed, if Hume's arguments are conclusive then the metaphysical problem, to distinguish good from bad inductions, is insoluble.

But this is not to say that no advance can be made on the epistemological problem, the task of finding or designing good inductive methods; methods that will lead to true conclusions or predictions if not inevitably then at least in an important proportion of cases in which they are applied. Hume himself, in fact, made significant advances in this direction: first in the section of the Treatise (I.III.XIII) on inductive fallacies in which he gives an account of how it is that “we learn to distinguish the accidental circumstances from the efficacious causes,” (THN 149) and later (THN I.III.XV, “Rules by which to judge of causes and effects,”) which rules are clear predecessors of Mill's Four Methods (Mill 1843, Bk III, Ch. VIII).

As concerns differences between induction and deduction, one of these is dramatically illustrated in the problems with Williams' thesis discussed in section 4.2 below: This is that inductive conditional probability is not monotonic with respect to conditions: Adding conditions may increase or decrease the value of a conditional probability. The same holds for non-probabilistic induction: Adding premises to a good induction may weaken its strength: That the patient presents flu-like symptoms supports the hypothesis that he has the flu. When to this evidence is added that he has been immunized against flu, that support is undermined. A second difference concerns relativity to context, to which induction but not deduction is susceptible. We return to this question in section 5 below.

Among those not convinced by Hume's arguments stated in section 2.1 above are D.C. Williams, supported and corrected by D.C. Stove, and David Armstrong. Williams argued in The Ground of Induction (1947) that it is logically true that one form of probabilistic inductive inference is sound and that this is logically demonstrable in the theory of probability. Stove reiterated the argument with a few reformulations and corrections four decades later. Williams held that induction is a reasonable method. By this he intended not only that it is characterized by ordinary sagacity. Indeed, he says that that an aptitude for induction is just what we mean by ‘ordinary sagacity’. He claims that induction, or one important species of it, is reasonable in the (not quite standard sense) of being “ logical or according to logic ”.

Hume, on the other hand, according to Williams held that:

[A]lthough our nervous tissue is so composed that when we have encountered a succession on M 's which are P we naturally expect the rest of M 's to be P , and although this expectation has been borne out by the event in the past, the series of observations never provided a jot of logical reason for the expectation, and the fact that the inductive habit succeeded in the past is itself only a gigantic coincidence, giving no reason for supposing it will succeed in the future. (Williams 1947, 15)

Williams and Stove maintain that while there may be, in Hume's phrase, no “ demonstrative arguments to prove” the uniformity of nature, there are good deductive arguments that prove that certain inductive methods yield their conclusions with high probability.

The specific form of induction favored by Williams and Stove is now known as inverse inference ; inference to a characteristic of a population based on premises about that population (see the taxonomy in section 3.2 above ). Williams and Stove focus on inverse inferences about relative frequency. In particular on inferences of the form:

(Williams 1947, 12; Stove 1986, 71–75) (This includes, of course, the special case in which r = 1)

Williams, followed by Stove, sets out to show that it is necessarily true that the inference from (i) to (ii) has high probability:

Given a fair sized sample, then, from any [large, finite] population, with no further material information, we know logically that it very probably is one of those which [approximately] match the population, and hence that very probably the population has a composition similar to that which we discern in the sample. This is the logical justification of induction. (Williams 1947, 97)

Williams and Stove recognize that induction may depend upon context and also upon the nature of the traits and properties to which it is applied. And Stove, at least, does not propose to justify all inductions: “That all inductive inferences are justified is false in any case” (Stove 1986, 77).

Williams' initial argument was simple and persuasive. It turns out, however, to have subtle and revealing difficulties. In response to these difficulties, Stove modified and weakened the argument, but this response may not be sufficient. There is in addition the further problem that the sense of necessity that founds the inferences is not made precise and becomes increasingly stressed as the argument plays out.

There are two principles on which the (i) to (ii) inference depends: First is the proportional syllogism ( C8 of section 3.1 ). Second is a rule relating the frequency of a trait in a population to its frequency in samples from that population:

• Frequency Principle : It is necessarily true that the relative frequency of a trait in a large finite population is close to that of most large samples from that population. (We say that such samples resemble the population.)

The proof of the Frequency Principle depends upon the fact that the relative frequencies of a trait in samples from such a population are normally distributed with mean equal to the frequency of the trait in the population and dispersion (standard deviation) that diminishes with the sizes of the population and the samples.

Williams' ingenious argument from (i) to (ii) begins with an induction on a ‘hyperpopulation’ (Williams 1947, 94–96) of all samples of size k (‘ k -samples’—like the k -sequences of the previous section) drawn from a large finite population X of individuals. The ‘individuals’ of the hyperpopulation are themselves k -samples from the population X .

Now let P be a symmetrical probability. (Williams assumes symmetry without explicit notice.) Given a large population X and a k -sample S of appropriate size from X , in which the relative frequency of the trait R is r , the content of the premise (i) above can be expressed in two premises:

Premise A. S is a k -sample from X . Premise B. The relative frequency of R in S is r , i.e., f(R | S ) = r

To show that (i) implies (ii), Williams argued from A and B as follows: It follows from the Frequency Principle that

It follows from Premise A, (1), and the Proportional Syllogism (C8) that

By Premise B and the definition of resemblance

It follows from (2) and (3) by the laws of probability that

Hence, goes the argument, (i) above implies (ii).

We might like to reason in this way, and Williams did reason in this way, but as Stove pointed out in The Rationality of Induction (1986, 65) it ignores the failure of monotonicity. Inductive inference in general and inductive conditional probabilities in particular are not monotonic : adding premises may change a good induction to a bad one and adding conditions to a conditional probability may change, and sometimes reduce, its value. Here, (3) depends on Premise B but suppresses mention of it, thus failing to respect the requirement to take account of all available and relevant evidence. In stating (3) Williams neglected the critical distinction between the probability of f ( R | X ) = r conditioned on resemblance:

and the result of adding Premise B, which states the relative frequency of R in S 0 , to the condition of (3a)

When, however, the conditions of (3) are expanded to take account of premise B,

the result does not follow from the premises; (3*) is true for some values of r and not for others.

As Maher describes this effect (and as Williams himself (1947, 89) had pointed out):

Sample proportions near 0 or 1 increase the probability that the population is nearly homogeneous which, ceteris paribus , increases the probability that the sample matches the population; conversely, sample proportions around 1/2 will ceteris paribus , decrease the probability of matching. (Maher 1996, 426)

Thus the addition of premise B to the condition of (3) might decrease the probability that S 0 resembles the population X : (3b) might be low while (3a) is high.

Conditional probability contrasts with the deductive logic of the material conditional in this respect:

( A → B ) implies [( A ∧ C ) → B ]

P ( B | A ) = p does not imply P ( B | A ∧ C ) = p

Stove's response to this difficulty was to point out that neither he nor Williams had ever claimed that every inductive inference, nor even every instance of the (i) to (ii) inference, was necessarily highly probable. All that was needed on Stove's view to establish Williams's thesis was to provide one case of values for r, X, S and R for which the inference holds necessarily. This would, Stove claimed, show that at least one inductive inference was necessarily rational.

Stove (1986, chapter VII) provided examples of specific values for the above parameters that he argued do license the inference. Maher pointed out that the argument depends upon tacit assumptions about the prior probabilities of different populations and their constitutions, and that when this is taken account of the conclusions no longer follow deductively. Scott Campbell (2001) continued the discussion.

Williams' original argument when expressed in general terms is simple and seductive: It is a combinatorial fact that the relative frequency of a trait in a large population is close to its relative frequency in most large samples from that population. The proportional syllogism is a truth of probability theory: In the symmetrical case relative frequency equals probability. From these it looks to be a necessary truth that the probability of a trait in a large population is close to its relative frequency in that population. We have seen that and why the consequence does not follow. Various efforts at weakening the original Williams thesis have been more or less successful. It is in any event plausible that there are at least some examples of inductions for which some form of the Williams thesis is true, but the thesis emerges from this dialectic considerably weakened.

D.M. Armstrong, like Williams and Stove, is a rationalist about induction.

About one-third of What is a Law of Nature (Armstrong 1983) is devoted to stating and supporting three rationalistic criticisms of what Armstrong calls the regularity theory of law. Put very generally, the various forms of the regularity theory all count laws, if they count them at all, as contingent generalizations or mere descriptions of the events to which they apply: “All there is in the world is a vast mosaic of local matters of particular fact, just one little thing and then another,” as David Lewis put this view (1986, ix). Armstrong argues against all forms of the regularity theory; laws, on his view, are necessary connections of universals that neither depend nor supervene on the course of worldly events but determine, restrict, and govern those events. The law statement, a linguistic assertion, must in his view be distinguished from the law itself. The law itself is not linguistic; it is a state of affairs, “that state of affairs in the world which makes the law statement true” (Armstrong 1991, 505). A law of nature is represented as ‘ N ( F , G )’ where F and G are universals and N indicates necessitation; Necessitation is inexplicable, it is “a primitive, which we are forced to postulate” (Armstrong 1983, 92). “That each F is a G , however, does not entail that F -ness [the universal F ] has N to G -ness” (Armstrong 1983, 85). That is to say that the extensional inclusion ‘all F 's are G 's’ may be an accidental generalization and does not imply a lawlike connection between F 's and G 's. In a “first formulation” of the theory of laws of nature (Armstrong 1983, 85), if N ( F , G ) is a law, it “does not obtain of logical necessity, if it does obtain then it entails the corresponding Humean or cosmic uniformity: ( x )( Fx ⊃ Gx ).” In later reconsideration (Armstrong 1983, 149), however, this claim is withdrawn: N ( F , G ) does not entail that all F 's are G 's; for some F 's may be “interfered with,” preventing the law's power from doing its work.

Armstrong's rationalism does not lead him, as it did Williams and Stove, to see the resolution of the problem of induction as a matter of demonstrating that induction is necessarily a rational procedure:

[O]rdinary inductive inference, ordinary inference from the observed to the unobserved, is, although invalid , nevertheless a rational form of inference. I add that not merely is it the case that induction is rational, but it is a necessary truth that it is so. (Armstrong 1983, 52)

Armstrong does not argue for this principle; it is a premise of an argument to the conclusion that regularity views imply the inevitability of inductive skepticism; the view, attributed to Hume, that inferences from the observed to the unobserved are not rational (Armstrong 1983, 52). Armstrong seems to understand ‘rational’ not in Williams' stronger sense of entailing deductive proofs, but in the more standard sense of (as the OED defines it) “Exercising (or able to exercise) one's reason in a proper manner; having sound judgement; sensible, sane” (Williams' “ordinary sagacity,” near enough)

The problem of induction for Armstrong is to explain why the rationality of induction is a necessary truth (Armstrong 1983, 52). Or, in a later formulation, to “lay out a structure of reasoning which will more fully reconcile us (the philosophers) to the rationality of induction” (Armstrong 1991, 505). His resolution of this problem has two “pillars” or fundamental principles. One of these is that laws of nature are objective natural necessities and, in particular, that they are necessary connections of universals. The second principle is that induction is a species of inference to the best explanation (IBE), what Peirce called ‘abduction’.

[T]he core idea is very simple: observed regularities are best explained by hypotheses of strong laws of nature [i.e., objective natural necessities], hypotheses which in turn entail conclusions about the unobserved. (Armstrong 2001, 503)

IBE, as its name suggests, is an informal and non-metric form of likelihood methods. Gilbert Harman coined the term in “The Inference to the Best Explanation,” (Harman 1965, see also Harman 1968) where he argued that enumerative induction was best viewed as a form of IBE: The explanandum is a collection of statements asserting that a number of F 's are G 's and the absence of contrary instances, and the explanans , the best explanation, is the universal generalization, all F's are G's . IBE is clearly more general than simple enumerative induction, can compare and evaluate competing inductions, and can fill in supportive hypotheses not themselves instances of enumerative induction. (Armstrong's affinity for IBE should not lead one to think that he shares other parts of Harman's views on induction.)

An instantiation of a law is of the form

N ( F , G ) a 's being F , a 's being G

where a is an individual. Such instantiations are states of affairs in their own right.

As concerns the problem of induction, the need to explain why inductive inferences are necessarily rational, one part of Armstrong's resolution of the problem can be seen as a response to the challenge put sharply by Goodman: Which universal generalizations are supported by their instances? Armstrong holds that necessary connections of universals, like N ( F, G ), are lawlike, supported by their instances, and, if true, laws of nature. It remains to show how and why we come to believe these laws. Armstrong's proposal is that having observed many F 's that are G , and no contrary instances, IBE should lead us to accept the law N ( F, G ).

[T]he argument goes from the observed constant conjunction of characteristics to the existence of a strong law, and thence to a testable prediction that the conjunction will extend to all cases. (Armstrong 1991, 507)

5. Paradoxes, the new riddle of induction and objectivity

The traditional problem of induction as Hume formulated it concerned what we now know as universal inference (see the taxonomy in section 3.2 above ). The premise

One or several A 's have been observed to be B 's, and no A 's are known to be not B 's.

Inductively supports

All A 's are B 's.

And singular predictive inference :

One or several A 's have been observed to be B 's, and no A 's are known to be not- B . a , heretofore unobserved, is known to be A , and not known to be not- B

The first of these forms, universal inference, can be codified or schematized by means of two definitions and two principles:

∀ x ( Ax → Bx ),

( Aa ∧ Ba ).

( Aa ∧ ¬ Ba ).

• Nicod's principle: Universal generalizations are supported or confirmed by their positive instances and falsified by their negative instances (Nicod 1930, 219).
• Equivalence principle : Whatever confirms a generalization confirms as well all its logical equivalents.

A simple example, due to C. G. Hempel, shows that all is not as simple as it might at first appear: By Nicod's principle

( Aa ∧ Ba ) supports ∀ x ( Ax → Bx ).

This last is logically equivalent to

∀ x [( Ax ∧ ¬ Bx ) → ( Ax ∧ ¬ Ax )].

But nothing can be a positive instance of the latter, and hence ( Aa ∧ Ba ) cannot support it. Thus if the equivalence principle is to obtain, Nicod's principle cannot be a necessary condition of inductive support, though it may be sufficient. The difficulty is endemic; the structure of logical equivalents may differ, but that of instances cannot.

The paradox of the ravens shows that even when suitably restricted, the Nicod principle is not without problems: By instance confirmation ‘a is not black and not a raven’ confirms ‘all non-black things are non-ravens.’ Since this is logically equivalent to ‘all ravens are black,’ by the equivalence principle:

• Paradoxical conclusion. ‘a is non-black and not a raven’ confirms ‘all ravens are black.’

And this is, or at least seems, paradoxical; that a non-raven lends support to a hypothesis about the color of ravens is highly implausible.

The paradox resides in the conflict of this counterintuitive result with our strong intuitive attachment to enumerative induction, both in everyday life and in the methodology of science.

The initial resolution of this dilemma was proposed by C. G. Hempel (1945) who credits discussion with Nelson Goodman. Assume first that we ignore all the background knowledge we bring to the question, such as that there are very many things that are either ravens or are not black, and that we look strictly at the truth conditions of the premise (this is neither a raven nor black) and the supported hypothesis (all ravens are black). The hypothesis says (is equivalent to)

Everything is either a black raven or is not a raven.

This hypothesis partitions the world into three exclusive and exhaustive classes of things: non-black ravens, black ravens, and non-ravens. Any member of the first class falsifies the hypothesis. Each member of the other two classes confirms it. A non-black non-raven is a member of the third class and is thus a confirming instance.

If this seems implausible it is because we in fact do not, as assumed, ignore the context in which the question is raised. We know before considering the inference that there are some black ravens and that there are many more non-ravens, many of which are not black. Observing, for example, a white shoe thus tells us nothing about the colors of ravens that we don't already know, and since (sound) induction is ampliative, good inductions should increase our knowledge. If we didn't know that many non-ravens are not black, the observation of a non-black, non-raven would increase our knowledge.

On the other hand, we don't know whether any of the unobserved ravens are not black, i.e., whether the first and falsifying class of things has any members, Observing a raven that is black tells us that this object at least is not a falsifying instance of the hypothesis, and this we did not know before the observation.

As Goodman puts it, the paradox depends upon “tacit and illicit evidence” not stated in its formulation:

Taken by itself, the statement that the given object is neither black nor a raven confirms the hypothesis that everything that is not a raven is not black as well as the hypothesis that everything that is not black is not a raven. We tend to ignore the former hypothesis because we know it to be false from abundant other evidence—from all the familiar things that are not ravens but are black. (Goodman 1955, 72)

The important lesson of the paradox of the ravens and the Hempel-Goodman resolution of it is that inductive inference is sensitive to background information and context. What looks to be a good induction when considered out of context and in isolation turns out not to be so when the context, including background knowledge, is taken into account. The inductive inference from

a is a white shoe

all ravens are black

is not so much unsound as it is uninteresting and uninformative.

Recent discussion of the paradox continues and improves on the Hempel-Goodman account by making explicit, and thus licit, the suppressed evidence. Further development, along generally Bayesian lines, generalizes the earlier approach by defining comparative and quantitative concepts of support capable of differentiating support for the two hypotheses in question. We return to the matter in discussing objective Bayesian approaches to induction below.

Suppose that at time t we have observed many emeralds to be green and no emeralds to be any other color. We thus have evidence statements

Emerald a is green, emerald b is green, etc.

and these statements support the generalization:

All emeralds are green.

Now define the predicate “grue” to apply to all things observed before time t just in case they are green and to other things just in case they are blue. Then we have also the evidence statements

Emerald a is grue, emerald b is grue, etc.

Hence the same observations support incompatible hypotheses about emeralds to be observed after t ; that they are green and that they are blue.

A few cautionary remarks about this frequently misunderstood paradox:

• The grue hypothesis is not well supported by its instances. The paradox makes it clear that there is something wrong with instance confirmation and enumerative induction as initially characterized.
• Neither the grue evidence statements nor the grue hypothesis entails that any emeralds change color. This is a common confusion. (See, for example, Armstrong 1983, 58; and Nix & Paris 2007, 36.)
• The grue paradox cannot be resolved, as was the ravens paradox, by looking to background knowledge (as would be the case if it entailed color changes). Of course we know that it is extremely unlikely that unobserved emeralds are grue. That just restates the point of the paradox and does nothing to resolve it.

That the definition of grue includes a time parameter is sometimes advanced as a criticism of the definition. But, as Goodman points out, were we to take “grue” and its obverse “bleen” (“blue up to t , green thereafter”) instead of “green” and “blue” as primitive terms, definitions of the latter, standard English, terms would include time parameters. The question here is whether inductive inference should be relative to the language in which it is formulated. Deductive inference is relative in this way as is Carnapian inductive logic.

The grue paradox raises and illustrates problems of a different nature from those raised by the paradox of the ravens: “All ravens are black” whether true or false is a clear example of a solid generalization. The questions that are raised by the ravens paradox concern the nature of this support and the role of context; not that the nature of the hypothesis itself can be called into question. The grue paradox, on the other hand, presents us with a quite different question; here is a generalization of appropriate form that is clearly not, indeed apparently cannot, be supported by its instances. What is the difference between healthy generalizations, which, like “All ravens are black” are supported by their instances, and grue-type generalizations that cannot be so supported? That is Goodman's new riddle of induction . (But see Mill's remark cited at the beginning of this article where the riddle is anticipated.)

The old, or traditional problem of induction was to justify induction; to show that induction, typically universal and singular predictive predictive inference, leads always, or in an important proportion of cases, from true premises to true conclusions. This problem, says Goodman, is, as Hume demonstrated, insoluble, and efforts to solve it are at best a waste of time. We have been looking at the wrong problem; it is only a careless reading of Hume that prevented us from seeing this. Once we see the difficulty, more homely examples than the grue hypothesis are easy to come by:

Albert is in this room and safe from freezing.

Everyone in this room is safe from freezing.

For the same reasons

supports the counterfactual

If Nanook of the north were in this room he would be safe from freezing.

Albert is in this room and is a third son.

does not support

Everyone in this room is a third son.

support the counterfactual

If my only son were in this room he would be a third son.

It is not the least of Goodman's accomplishments to have shown that three questions all issue from the same new riddle:

• What is the difference between those generalizations that are supported by their instances and those that are not?
• Which generalizations support counterfactual conditionals?
• How are lawlike generalizations to be distinguished from accidental generalizations?

Goodman's own response to the new riddle was that those generalizations that are supported by their instances involve predicates that have a history of use in prediction. Such predicates Goodman called projectible .

The project of the Carnapian logic of confirmation was to put inductive reasoning on the sure path of a science; to give a unified and objective account of inductive inference including clear rules of procedure, in close analogy to deductive inference (see Carnap LFP, section 43). The Hempel-Goodman resolution of the ravens paradox, according to which reference to context may be essential to induction, threatens to undermine this enterprise before it properly begins: If Hempel and Goodman have it right, a is a black raven may confirm all ravens are black in one context and not in another.

This relativity produces yet another problem of induction: How can the objectivity of inductive inference be assured given that it depends upon context? Context dependence must in this regard be distinguished from non-monotonicity: Monotonicity and its contrary concern relations among inductive arguments or inferences; a sound inductive argument can be converted into an unsound argument by adding premises. Context dependence, on the other hand, means that one and the same argument may be sound in one context and not in another. Context dependence, but not non-monotonicity, entails relativity and loss of objectivity.

It is useful to distinguish discursive context, such as that there are many more black things than ravens, from non-discursive context. Hume gives us a famous and striking example of the latter:

[A] man, who being hung out from a high tower in a cage of iron cannot forbear trembling, when he surveys the precipice below him, tho he knows himself to be perfectly safe from falling. (THN 149)

Hume's view is that such contextual factors can be neutralized by general rules, which rules will ascribe “one inference to our judgment, and the other to our imagination” (THN 149). Something like this must be the right account.

As concerns discursive context, objective Bayesianism seeks to eliminate or ameliorate the relativity illustrated and exemplified in the Ravens paradox first by supplementing the Nicod principle with a definition or necessary and sufficient condition of inductive support. This is done in terms of one of several appropriate objective probabilities, governed by normative principles. (Carnap's measures discussed in section 4.2 are examples of these.) Given such a probability, P , there are a number of alternative definitions of support. One widely accepted definition states that evidence E supports a hypothesis H if and only if E raises the probability of H (Carnap LFP, 464; Maher 1996; Fitelson and Hawthorne 2006, 10).

Support principle. Evidence E supports hypothesis H if and only if P ( H | E ) > P ( H )

We look briefly at two objective Bayesian approaches to the problem of relativity in induction.

5.3.1 Logical Bayesianism

Patrick Maher, in “Inductive Logic and the Ravens Paradox” (1999), argues that the conclusion of the Ravens paradox is in fact not paradoxical, and that its appearance as such is deceptive. Maher claims that for certain objective logical probabilities (defined by Carnap in the λ system of 1980) a is neither black nor a raven raises the probability of all ravens are black , thus supports the latter, and that there is hence no paradox. The probabilities in question, like the probability P* described in section 3.2 above , weight homogeneous structure descriptions more heavily.

Maher also accounts for our initial (and mistaken) rejection of the paradoxical conclusion;

By citing a false principle that is easily confused with PC. This is

(See also Hempel 1945, 19.)

As Maher puts it, “the observation of non-ravens tells us nothing about the color of [unobserved] ravens.”

5.3.2 Moving context into content

Branden Fitelson and James Hawthorne (2006) undermine the relativity entailed by context dependence by making of conditional probability a three-place function, in which probability is conditioned on evidence and context, the latter to include also background knowledge.

P K ( H, E )

to indicate relativity to background knowledge, including perhaps knowledge of (discursive) context. This permits the distinction of support relative to (i) some background knowledge, (ii) our actual, present background knowledge, (iii) tautological or necessary background knowledge and (iv) any background knowledge whatever.

In the signal example of the ravens paradox, background knowledge including a few remarkably weak modal assumptions (such as “Finding a to be a black raven neither absolutely proves nor absolutely falsifies ‘All ravens are black.’”) entail that a non-black non-raven supports that all ravens are black.

6. Knowledge, values and evaluation

In 1953 Richard Rudner published “The Scientist qua Scientist Makes Value Judgments,” in which he argued for the thesis expressed in its title. Rudner's argument was simple:

[S]ince no hypothesis is ever completely verified, in accepting a hypothesis the scientist must make the decision that the evidence is sufficiently strong or that the probability is sufficiently high to warrant the acceptance of the hypothesis. (Rudner 1953, 2)

Sufficiency in such a decision will and should depend upon the importance of getting it right or wrong.

The argument has a precise point in the case of tests or experiments with known error probability (the probability of rejecting a true hypothesis or of accepting a false hypothesis) but it applies quite generally: Tests of hypotheses about drug toxicity may and should have less chance of going wrong than those about the quality of a “lot of machine-stamped belt buckles”. The argument is not restricted to scientific inductions; it shows as well that our everyday inferences depend inevitably upon value judgments; how much evidence one collects depends upon the importance of the consequences of the decision.

Isaac Levi, in responding to Rudner's claim, and to later formulations of it, distinguished cognitive values from other sorts of values; moral, aesthetic, and so on. (Levi 1986, 43–46) Of course the scientist qua scientist, that is to say in his scientific activity, makes judgments and commitments of cognitive value, but he need not, and in many instances should not, allow other sorts of values (fame, riches) to weigh upon his scientific inductions. (See also Jeffrey, 1956 for a related response to Rudner's argument.)

What is in question is the separation of practical reason from theoretical reason. Rudner denies the distinction; Levi does too, but distinguishes practical reason with cognitive ends from other sorts. Recent pragmatic accounts of inductive reasoning are even more radical. Following Ramsey (1931a) and Savage (1954), they subsume inductive reasoning under practical reason; reason that aims at and ends in action. These and their successors, such as Jeffrey (1983), define partial belief on the basis of preferences; preferences among possible worlds for Ramsey, among acts for Savage, and among propositions for Jeffrey. Preferences are in each case highly structured. In all cases beliefs as such are theoretical entities, implicitly defined by more elaborate versions of the pragmatic principle that agents (or reasonable agents) act (or should act) in ways they believe will satisfy their desires: If we observe the actions and know the desires (preferences) we can then interpolate the beliefs. In any given case the actions and desires will fit distinct, even radically distinct, beliefs, but knowing more desires and observing more actions should, by clever design, let us narrow the candidates.

In all these theories the problem of induction is a problem of decision, in which the question is which action to take, or which wager to accept. The pragmatic principle is given a precise formulation in the injunction to act so as to maximize expected utility, to perform that action, A i , among the possible alternatives, that maximizes

U ( A i ) = ∑ j P ( S j | A i ) U ( S j ∧ A i )

where the S j are the possible consequences of the acts A i , and U gives the utility of its argument.

One significant advantage of treating induction as a matter of utility maximization is that the cost of gathering more information, of adding to the evidence for an inductive inference, can be factored into the decision. Put very roughly, the leading idea is to look at gathering evidence as an action on its own. Suppose that you are facing a decision among acts A i , and that you are concerned only about the occurrence or non-occurrence of a consequence S . The principle of utility maximization directs you to choose that act A i that maximizes

U ( A i ) = P ( S | A i ) U ( S ∧ A i )

Suppose further that you have the possibility of investigating to see if evidence E , for or against S , obtains. Assume further that this investigation is cost-free. Then should you investigate and find E to be true, utility maximization would direct you to choose that act A i that maximizes utility when your beliefs are conditioned on E :

U E ( A i ) = P ( S | E ∧ A i ) U ( S ∧ E ∧ A i ) + P (¬S | E∧A i ) U ( ¬S∧E∧A i )

And if you investigate and find E to be false, the same principle directs you to choose A i to maximize utility when your beliefs are conditioned on ¬ E :

U ¬ E ( A i ) = P ( S | ¬ E ∧ A i ) U ( S ∧¬ E ∧ A i )         + P (¬ S | ¬ E ∧ A i ) U (¬ S ∧¬ E ∧ A i )

Hence if your prior strength of belief in the evidence E is P ( E ), you should choose to maximize the weighted average

P ( E )( U E ( A i ) + P (¬ E )( U ¬ E ( A i )

and if the maximum of this weighted average exceeds the maximum of U ( A i ) then you should investigate. About this, several brief remarks:

Notice that the utility of investigation depends upon your beliefs about your future beliefs and desires, namely that you believe now that following the investigation you will maximize utility and update your beliefs.

Investigation in the actual world is normally not cost-free. It may take time, trouble and money, and is sometimes dangerous. A general theory of epistemic utility should consider these factors.

I. J. Good (1967) proved that in the cost-free case U ( A i ) can never exceed U E ( A i ) and that when the utilities of outcomes are distinct the latter always exceeds the former (Skyrms 1990, chapter 4).

The question of bad evidence is critical. The evidence gathered might take you further from the truth. (Think of drawing a succession of red balls from an urn containing predominantly blacks.)

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• Teaching Theory of Knowledge: Probability and Induction , organization of topics and bibliography by Brad Armendt (Arizona State University) and Martin Curd (Purdue).
• Forecasting Principles , A brief survey of prediction markets.

actualism | Bayes' Theorem | -->Carnap, Rudolf --> | conditionals | confirmation | epistemology: Bayesian | epistemology: evolutionary | epistemology: naturalism in | epistemology: social | fictionalism: modal | Frege, Gottlob: theorem and foundations for arithmetic | Goodman, Nelson | Hempel, Carl | Hume, David | logic: inductive | logic: non-monotonic | memory | Mill, John Stuart | perception: epistemological problems of | Popper, Karl | probability, interpretations of | -->Ramsey, Frank --> | Reichenbach, Hans | testimony: epistemological problems of | Vienna Circle

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The author would like to thanks to Patrick Maher for helpful comments, and the editors would like to thank Wolfgang Swarz for his suggestions for improvement.

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Discuss the Value and Importance of Employee Induction in the Modern Workplace

Introduction.

In the modern workplace, employee induction programs are very important.  The main reason why induction is important for an organization is that it helps to integrate new employees into the business and show them the procedures, systems, culture, and values of an organization. It also familiarizes them with the new environment. A well-performed induction communicates to employees that the business values and cares about them (Bornman, 2014). A proper induction reduces the number of accidents and mistakes at work and improves the quality of work and ensures that the customers are satisfied. However, various benefits arise from  employee inductions to the company.

Importance of Employee Induction in the Modern Workplace

Reduces Costs and Turnover

The well-structured induction training program is an important and natural development of a recruitment process. The programs are important to ensure the success of a worker, ensure that they adapt easily to their new obligations. Every business intends to get its return on investment. Therefore, someone leaving in a half a year is not a good business result and not proper for an organization’s culture, productivity and morale. While a business is keen to get through the backlog within the shortest time possible, the new member of staff requires some time to arrive at an organization’s specific culture. They should also know their obligation in the business and become conversant with ‘why’ and ‘how’ to do such things, and see their queries regarding the company being addressed (Patwardhan, 2020).

Reduces Risks and Ensures Efficiency

New workers need to be across any legal and compliance needs related to a business, and the procedures and processes of “how” to do business. They should understand the culture, mission, vision, and goals of an organization. For them to operate efficiently and be involved in their job, they should be educated on the organizational policies. This includes understanding their duties and responsibilities as employees. It is important if they sign off on such policies to ensure that they understand, which is a good practice for risk management for possible confusion down the track. It cannot be assumed that because they know where the organization’s manual is on the internet, they understood it (Patwardhan, 2020).

Leads to a Smooth Changeover

With the correct form of induction, employees can clearly understand an organization’s corporate prospects and ensure that the new hires do not pick up a second-hand partisan view. A good induction program does not have to be wide or huge, but it is organized and simply rolled out to every new worker. Induction increases a worker’s initial experience with the business. It reveals that they are maintained, cared for and that the business is devoted to their success. From a practical viewpoint, it ensures that the transition into a business is measured, smooth, and relaxed (Patwardhan, 2020).

Gives the New Members of a Team Confidence in their Business Practices 

Normally, new workers should feel anxious or insecure about the new responsibility and how they fit in the organization’s business practices and culture. The process of onboarding is a good business opportunity to make a perfect first impression. Induction enables new workers to clearly understand how an organization works, where it is, where it foresees itself in the future, and how they, as new workers can contribute to making such vision a reality. Inductees will have an understanding of the business’s background, values and culture, policies, learning and development, benefits, and health and safety guidelines, among others (Powers, 2019). Ultimately, the process of induction is a good opportunity to make the new workers proud of their new business. Inductees will feel included and valued because they will understand that their contributions to the business are valued.

Sets the Scene for New Role of Inductees

The induction process familiarizes the new workers with the organization and their main roles and responsibilities. After the induction process, the new staff should clearly understand their role in the business and have the information needed to prepare for the new role (Powers, 2019).

Shows the Professionalism of a Business 

Induction is a good opportunity for a business to build a good impression. The process needs careful preparation and is a formal way of welcoming new employees into the business. This shows an organization’s commitment to observing the professional values as far as performing its business and handling its existing workers and new employees (Powers, 2019).

Gives the New Members a Structure to Settle

Inducting new workers gives a way for the business to give a structure and help them settle in their role and make the process of integration seamless. Typically, it includes a road map for new employees, which includes relevant training and their long-term goals (Powers, 2019).

Makes Sure that the Vital Elements of Workers and Practices are Well-Defined 

Induction allows the new workers to meet and become familiar with other staff and the managerial team and socialize and begin to build relationships (Powers, 2019). Also, it ensures that inductees understand the rules and regulations, code of conduct, and employee expectations. A well-organized process of orientation is a witness to the business’s commitment to making its workers feel valued. Induction enables new employees to feel welcome and get rid of their worries and confusion. In the end, the business gains from a well-thought-out process of induction. This includes improved job satisfaction, performance, and improved employee retention.

Establishes Good Communication

Induction training helps new workers establish good communication with an organization. As part of the training program, the new workers are introduced to their direct supervisor, other workers, leads, and managers of an organization. This makes them calmer when communicating with them later (Antonacopoulou & Güttel, 2010).

Employer Brand Building Opportunity

Each organization wants to employ the best talent in the market. Brand building rotates on packaging a company to stimulate job applicants or potential workers to an organization. Each organization is looking for the best talent in the market and must prove that as an organization they have the best processes, systems, benefits/perks, employee development opportunities that might be employed in the organization. Induction makes a company excite the new worker about its products, systems, services, structures, growth opportunities, and line management support (Antonacopoulou & Güttel, 2010). Once new workers are excited, they would recommend such an organization to their friends, family, school mates, among other people, and endorse the brand of that specific organization. More people may apply to such an organization and then the business can recruit the very best talents.

Research by Brandon Hall Group recommends that firms with strong onboarding processes boost their new hire rate of retention by 82 percent and their worker productivity by over 70 percent. The need for employee induction cannot be overstated. Robert Half found out that 59 percent of hiring managers in Australia have had workers resign during their probation time because of poor processes for onboarding and 43 percent of managers lost new workers within one month of hiring them (Bryson, 2018). Despite such alarming statistics, it appears that managers are missing a beat regarding the efficiency of their induction efforts. The Robert Half survey above shows that 28 percent of employment managers believe their present onboarding process is ‘excellent’, 51percent consider their process as ‘good’, and 16 percent believe what they do in the induction space is ‘sufficient’ (Bryson, 2018). Such disparity in manager insight and retaining figures proposes there is an important detach between how managers believe they perform from an induction viewpoint and what is happening when a new worker joins an organization. The paradigm is just simple, to ensure the success and retention of new employees, an organization must ensure their induction experience is consistent, structured, and positive.

Organizations that do not value induction training often risk personnel having the feeling of being drowned. They may also feel that they are wasting their time learning about the systems, protocols, and processes that everybody else appears to know about, and are scared to ask others (everybody appears so busy, leaving them to feel unskilled and misused). New workers should instead focus on tasks with a stronger financial outcome (Edwards, 2005). Therefore, rather than consider inductions a wastage of time, it is important to view a good induction program as a process of helping people become more productive. A good program should comprise system training and if the system used by an organization is complex, then there should be a schedule for formal training. A good induction program should not necessarily be a long process but if the work is complex, possibly consider running it for some time to prevent information burden for the workers. Inductions are important for organizational employees and the health of an organization.

Each new worker must go through induction to get the right impression on the business. Induction can be an advantage for each organization to reduce turnover of employees, boost efficiency and help the organization to be an employer of choice for extremely skilled employees. Also, new workers will adjust and mix quickly into the company and perform to their best. A well-performed induction communicates to employees that the business values and cares about them. How a new worker fails or succeeds at work may depend on the process of induction.

Reference List

Antonacopoulou, E. P., & Güttel, W. H. (2010). Staff induction practices and organizational socialization: A review and extension of the debate.  Society and business review .

Bornman, L. (2014).  Exploring Realistic Job Previews in the modern workplace: an employee perspective  (Doctoral dissertation, University of Pretoria).

Bryson, A. (2018). Mutual gains? The role of employee engagement in the modern workplace. In  Rethinking Entrepreneurial Human Capital  (pp. 43-62). Springer, Cham.

Edwards, M. R. (2005). Employer and employee branding: HR or PR.  Managing human resources: personnel management in transition ,  4 , 266-286.

Patwardhan, S. (2020). Employee volunteering programs: an emerging dimension of modern workplaces.  Mandated Corporate Social Responsibility , 215-243.

Powers, K. (2019). Stress and the Modern Workplace.  Workplace Psychology .

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