Info
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Course 2018-2019 a.y.

30261 - EXPERIMENTAL ECONOMICS AND PSYCHOLOGY

Department of Decision Sciences

Course taught in English

Go to class group/s: 31

CLEAM (6 credits - I sem. - OP  |  3 credits SECS-P/01  |  3 credits SECS-S/06) - CLEF (6 credits - I sem. - OP  |  3 credits SECS-P/01  |  3 credits SECS-S/06) - CLEACC (6 credits - I sem. - OP  |  3 credits SECS-P/01  |  3 credits SECS-S/06) - CLES-BESS (6 credits - I sem. - OP  |  3 credits SECS-P/01  |  3 credits SECS-S/06) - WBB (6 credits - I sem. - OP  |  3 credits SECS-P/01  |  3 credits SECS-S/06) - BIEF (6 credits - I sem. - OP  |  3 credits SECS-P/01  |  3 credits SECS-S/06) - BIEM (6 credits - I sem. - OP  |  3 credits SECS-P/01  |  3 credits SECS-S/06) - BIG (6 credits - I sem. - OP  |  3 credits SECS-P/01  |  3 credits SECS-S/06) - BEMACS (6 credits - I sem. - OP  |  3 credits SECS-P/01  |  3 credits SECS-S/06)
Course Director:
PAOLO PIN

Classes: 31 (I sem.)
Instructors:
Class 31: PAOLO PIN


Prerequisites

The students should be familiar with the basic concepts of microeconomics. They would follow much better the lectures if they know what a utility function is, and how economic agents maximize their expected utility or profits.


Mission & Content Summary
MISSION

The goal is to introduce students to the exciting world of experimental & behavioral economics, including relevant methods (e.g. game theory, experiments, econometrics). Economists often use advanced mathematical methods, but rely on rigid assumptions about human nature. Research in neighboring social sciences, by contrast, typically uses less sophisticated analytical methods while entertaining a richer description of man. Behavioral economics combines the strength of both approaches, incorporating psychological insights into economic analysis with continued use of formal analytical tools. Lab experiments provide a means to gauge the empirical relevance of the resulting models.

CONTENT SUMMARY

Topics:

  • Elementary Rational Choice Theory and its implications for theory testing. Review of the classical models of risk and time preferences.
  • A little bit of of psychology. Relativity, sensitivity to framing, and loss aversion. Heuristics & habits as they relate to ignorance, limited experience and bounded rationality.
  • Applications of insights from psychology to economic choice under certainty and uncertainty.
  • Social preferences and their implications for individual and strategic behavior: fairness, blame.
  • Strategic interaction.
  • Setup of a lab experiment and analysis of experimental data.
  • We cover additional topics, according to class interests, among which: the economics of social networks, online and field experiments, behavioral finance, dual reasonin.

Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Illustrate the theory, with an eye on the actual implementation of it in a lab experiment.
  • Describe precisely how to setup and analyze a lab experiment.
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Organize a critical bibliographic analysis of the literature to motivate an original idea for an experiment.
  • Design an actual experiment, writing precise description and instructions for an experiment.
  • Program an experiment – this requires programming abilities in html/java, python or in the specific software 'oTree' (they are not part of the course but support will be provided to those that will try).
  • Run an experiment, in the lab, in the field, online, etc.
  • Analyze the data of an experiment (even an experiment already run by others in the past), using  data processing software like Stata, Matlab or R (they are not part of the course but support will be provided to those that will try).
  • Comment the outcome of an experiment.

Teaching methods
  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • Individual assignments
  • Group assignments
  • Interactive class activities (role playing, business game, simulation, online forum, instant polls)
DETAILS
  • Reading and commenting of existing research: we read at home and comment in class scientific papers. We also analyze the data from those papers.
  • You are provided datasets and software codes that you can use to analyze the outcome of experiments.
  • Lab experiments testing: student participate to examples of behavioral experiments in class, using apps that the teacher is developing for own research purposes.
  • You are asked to provide a project (group or individual project) that covers part of the research work in behavioral economics.

Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •     x
    ATTENDING AND NOT ATTENDING STUDENTS

    Course Requirements:

    • Individual or group project, ~40% of your grade (12 points).
    • Final written exam of 1h, ~60% of your grade (19 points).

    Note: the same exam rules apply to both attending and non attending students. I expect all the students that register for the written exam to have submitted a project (that can be an individual project, or a group project). Project must be handed in (by email to paolo.pin@unibocconi.it) one week before the written exam (no presentation needed), and the grades are sent to students latest two days before the exam date. The same project remains valid if a student wants to come at any following exam date, in the same academic year (e.g.: a project handed in in December is valid for a written exam in the following June). For group projects, each co-author gets the same grade. The number of co-authors in a project discounts the grades that result from its value. To avoid free riding, I may ask to each co-author to report on the contribution of the other co-authors.


    Teaching materials
    ATTENDING AND NOT ATTENDING STUDENTS
    • There is a textbook that we use in the first part of the course:
      • E. ANGNER, A Course in Behavioral Economic, Palgrave Macmillan, 2012.
    • There is a recent textbook that we partially follow in the final part:
      • P. G. MOFFATT, Experimetrics: Econometrics for experimental economics, Palgrave Macmillan, 2015.
    • The part on game theory follows parts from:
      • S. TADELIS,  Game theory: an introduction, Princeton University Press, 2013.
    • Integrative notes, mock exams, dataset from past experiments, and additional teaching materials on course topics and on extra topics are available in Bboard.
    • Part of the course objective is to train students to read original research articles (that are also available in Bboard).
    Last change 19/06/2018 21:04