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INSEAD Course/Program Name
Application closes on
National :15 Dec
International :15 Dec

PhD Decision Sciences

 Course Level
Full Time

5 Years
 Start month

 Tuition fee

60000 EUR
60000 EUR

Application fee

International 50 EUR
National 50 EUR
Decision Sciences
Scores accepted
IELTS (min)7.5
TOEFL-IBT (min)105
TOEFL-PBT (min)620
GRE (avg)325
GMAT (avg)702

World B-School Ranking

About this course

The area of Decision Sciences includes risk management, decision making under uncertainty, statistics and forecasting, operations research, negotiation and auction analysis, and behavioural decision theory.


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Eligibility Criteria

Selection Criteria

  • Proven academic record - irrespective of subject background.
  • Creativity and Independent thought - necessary to become a great researcher.
  • Motivation, Determination and Drive - to follow through with goals.
  • Ability to work in English - as English is the sole medium of instruction at INSEAD

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Course Modules

Core Courses

Probability and Statistics (A) and (B)

Regardless of the setting, management decisions are necessarily made under conditions of uncertainty. This course introduces a framework for thinking about problems involving uncertainty and, building on this framework, develops some tools for interpreting data. The goal is to provide an appropriate foundation in probability and classical statistics for subsequent courses and for a research and teaching career.
The course is self-contained. Students will be given four assignments during the course, to be done individually. There will be a take-home exam at the end of the course. Final grades will be based on the assignments and the exam.

Advanced Courses

Bayesian Analysis

This course covers methods of Bayesian inference and prediction, with emphasis on the general approach of modeling real-world problems of interest to data analysts and decision makers. Topics include subjective probability, evaluation of probabilities, revision of probabilities, Bayesian modeling, inference and prediction for various processes, Bayesian estimation and hypothesis testing, and comparisons with classical/frequentist methods.
The course is self-contained. Students will be given assignments during the term and a take-home exam at the end, to be done individually. Final grades will be based on the assignments and the exam.

Behavioural Decision Theory

On one level, this course is about the psychology of decision making. The class will cover issues in choice, judgmental forecasting, memory, belief representation, and much more. The class intends to persuade the participants on the premise that, “The world is not what it seems.”

  • What you take to be real is only a representation of the real – it’s an approximation that is largely a product of your own expectations;
  • What seems certain to you is in fact not certain – likely you are wrong;
  • Whatever you remember is a reconstruction – it’s an approximation of the past, a judgment of what has happened;
  • Whenever you perceive order in the world, you are being fooled by your mind, which abhors unpredictability and disorder;
  • Whatever you think causes you to act the way you do – to make the choices you make, for instance – is likely wrong: these are often justifications that our minds generate ex post;
  • And more

The world is not what it seems. This, not how people choose between different lotteries, is what the class aims to show.

Choice Over Time

Although it never makes the front pages, it a nevertheless a major concern, both in government and in business: what price do we assign to the future? Or, more precisely, how do we discount, that is, how do we determine what value to attribute now to the socio-economic impacts that an investment made today will bring in the long term? This raises a fundamental question: are we too selfish, selecting large discount rates and therefore investing insufficiently for the well-being of future generations, or are we, on the contrary, too virtuous? An obvious application can be found in the problem of climate change. The key objective in this course is to answer these questions by using different approaches. In most lectures, it will be assumed that the representative agent maximizes the discounted expected utility of her consumption flow. The course is designed for participants interested in the treatment of risk in dynamic frameworks that are classical in finance, macroeconomics and environmental economics. There is no pre-requisite for participating in this course.

Dynamic Programming Applications

The main objective of the course is to introduce students to quantitative decision making under uncertainty through Dynamic Programming. Along the way, it presents basic mathematical formulations and solution concepts for important managerial problems such as inventory management, dynamic pricing, portfolio selection, and asset selling. The course also highlights applications and extensions of the general methodology that are relevant to Data Scientists, e.g., problems with imperfect state information and approximate DP techniques.

Experimental Economics

The class will treat experimental economics as an approach to doing experimental research, not so much as a body of findings. The class takes into account that most likely students enrolled in this course will not go off to economics departments to be experimentalists. However, most of the participants will go on to become business school faculty and some will do experiments.
As proposed, the general approach of experimental economists to experiments – if there is a general approach – is quite useful and in many ways is superior to the one employed by, say, social psychologist types. That said, in other ways, the experimental econ mindset is quite limiting, as believed, and is inferior to the social psychologists’.  Throughout the course we will contrast the experimental econ and the social psychological approach to going about one’s business. Doing so will give the class a basis for coherent, grounded discussions, and may give you something to think about: What kind of (experimental) researcher do I want to be?

Foundations of Utility, Risk and Decisions

Whether we like it or not we all feel that the world is uncertain. From choosing a new technology to selecting a job, we rarely know in advance what outcome will result from our decisions. Most decisions under uncertainty require not only a determination of the attractiveness of their possible consequences (preferences over outcomes), but also some appraisal of their likelihoods (beliefs). Expected utility theory (initially axiomatized by von Neumann and Morgenstern, 1944, and Savage, 1954) provides the most standard and tractable combination of preferences and beliefs that allows rational choice modeling under uncertainty. The course will focus on the formal and normative aspects of expected utility through three popular frameworks: von Neumann and Morgenstern, Savage, and Anscombe Aumann. Then we will study how to measure utility and subjective probabilities under expected utility. The last session will be devoted to a multi-attribute extension of expected utility.

Nonexpected Utility

The descriptive power of expected utility has been challenged by behavioral evidence showing that people deviate systematically from the expected utility paradigm. Since the end of the 70's several alternatives to the expected utility paradigm have been proposed in order to accommodate these deviations.
Aim of the course is to (quickly) present the classical paradigm of decision under risk and uncertainty, to analyze deviations from the paradigm, and to illustrate the most popular alternatives to expected utility. These alternatives are named “nonexpected utility.”
The class will focus on the rank-dependent models, prospect theory, regret and disappointment theories, multiple priors and other models depending on the interest. The emphasis will be both normative and descriptive. In addition, recent and ongoing empirical evidence will be presented.
The course is designed for students and faculty willing to familiarize with the research in nonexpected utility and looking to apply alternative models of behavior to finance, marketing, management, consumer behavior and other areas of interest. There is no pre-requisite for participating in this course.

Selected Topics in Decision Sciences

This course will discuss both classic and current models of rational choice theory (subjective probability and expected utility, non-expected utility, game theory, asset pricing, social choice) from a mathematical and behavioral perspective that differs from conventional presentations. The single rationality concept that links all these models is shown to be the principle of no-arbitrage, namely that agents acting alone or in groups should not behave in such a way as to expose themselves to a sure loss at the hands of a clever observer. The key mathematical principle that yields numerical representations of what goes in in the minds of rational agents, according to the no-arbitrage standard, is the separating hyperplane theorem of convex analysis. This modeling approach will be shown to lead to simple and geometrically appealing generalizations of the standard models which address issues such as incomplete preferences, state-dependent utility, non-expected utility, incomplete markets, and generalizations of Nash equilibrium.

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How to Apply

Supporting Documents

All supporting documents should be in electronic format and uploaded through our online application platform. Only selected candidates for admission will be asked to provide original copies to the PhD Office to secure their admission in the programme. Thus, no originals or hard copies of transcripts should be sent to the PhD Office prior to admission to the programme.

1. Photograph (passport sized) in JPEG or GIF format, uploaded to your profile.
2. Application fee (non-refundable) of 50 €, which is paid online by credit card. Your application will not be considered until the correct fee is received. 
3. Electronic copy of CV/Resume
4. Electronic copy of Official Transcripts of Grades and Diploma / Degree Certificates with certified translation in English, where applicable
5. Electronic copy of official GMAT or GRE score report
6. Electronic copy of original TOEFL score report. (Note: We do not accept IELTS score in lieu of the TOEFL; if any of your degree's medium of instruction is in English, the TOEFL requirement is automatically waived. Your transcripts from this degree will suffice as proof of waiver)
7. Supplementary information survey form that is accomplished and submitted online (part of your online application form, found under the Supporting Documents section).

Recommendation Letters

A maximum 3 Letters of Recommendation are required coming from faculty, scholars or individuals who can evaluate the applicant’s academic ability and potential for research and teaching. When requesting recommendations, candidates are asked to use the appropriate online forms. Only 3 electronic recommendation letters are required to complete the application.

On the section of Letters of Recommendation, applicants are requested to enter the details of referees, including the 'official' email address (official email addresses do not include gmail, yahoo, or hotmail email clients). Once done, applicants need to click on the link 'Send an email to your recommenders now'.

Important: Refrain from clicking more than once the 'Send an email to your recommenders now' link, to avoid unnecessary email requests sent to your referees.

Applying Online

All applications to INSEAD are fully electronic and must be submitted online. To successfully access the application, you must download or upgrade your browsers to the following minimum requirements: Internet Explorer 9 and above, Firefox 8 and above, Chrome 10, Safari 5 and above, and Opera 10 and above.

A complete application includes the following: duly accomplished online application form, e-copies (e.g. PDFs) of supporting documents and application fee of 50€. Incomplete applications (missing documents or pages of documents, information or payment) will not be considered by the PhD Admissions Committee.

The application form is comprised of:

1. Your Profile: personal information, test results, academic background, professional background, proficieny in the English language and other languages, international exposure and activities and interests.
2. Statement of Purpose or letter of motivation (1500 essay), stating current goals, career plans, reasons for being interested in the PhD Programme. In as much depth as possible, discuss study plans and intended area of specialisation. 
3. Statement of Integrity (by agreeing to the terms and conditions when submitting the application form)

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