84563 - Behavioral Economics

Learning outcomes

At the end of the class, the student has acquired knowledge of the most active and influential areas of research in behavioral economics. In particular, the student understands the empirical methodologies adopted, the theoretical implications of the findings, and their possible applications in economic environment with and without strategic interaction.

Course contents

The core theory used in economics rests on basic assumptions on individual selfishness, rationality and utility maximization. During the last three decades, these assumptions have been put into question by a growing stream of literature, broadly defined as “behavioral economics,” which aims at improving the descriptive accuracy of these assumptions, while maintaining the formal discipline of economic modeling. Based on field and experimental evidence on how individual behavior departs from the conventional theoretical predictions, behavioral economists have posited explanations for these departures, proposed extensions to the existing models and alternative theoretical approaches aimed at capturing them, and considered what are the possible practical implications of these findings.

This course aims at providing a critical overview of the most active and influential areas of research in this field, with a focus on the empirical methodologies adopted, on the theoretical implications of the findings, and on their possible applications (e.g., in the fields of health and labor economics, finance, marketing). It is divided into two main sections, the first dealing with individual choices, the second with strategic interaction. In the first part of the course we will discuss topics related to preferences over risky and uncertain outcomes, intertemporal choices, reference dependence and loss-aversion. In the second part of the course, we will deal with the literature concerning behavioral game theory, which uses the tools of standard game theory, but takes into consideration emotions, mistakes, limited foresight, and doubts about how smart others are.

Prerequisite knowledge:

Microeconomics - Master level [https://www.unibo.it/en/study/course-units-transferable-skills-moocs/course-unit-catalogue/course-unit/2025/521604] and Game Theory [https://www.unibo.it/en/study/course-units-transferable-skills-moocs/course-unit-catalogue/course-unit/2025/521605]

 

Topics
Part 1

• Anomalies in decision-making
• Decision-making under risk
• Reference dependence, loss aversion and endowment effect
• Intertemporal preferences

Part 2

• Behavioral game theory
• social preferences
• Motivated beliefs

Readings/Bibliography

Required readings
The required readings consist mostly of journal articles and book chapters. The full list of required readings, organized by topic, will be made available on the Virtuale page of the course. These readings are necessary to prepare for the final exam, both for attending and non-attending students.

Recommended reference
Dhami, Sanjit. The Foundations of Behavioral Economic Analysis. Oxford University Press, 2016.
This book can be used as a general reference, but it is not required.

Additional material
Slides, problem sets, mock-experiment instructions, and any additional readings will be made available on Virtuale.

Teaching methods

The course combines lectures, guided discussion, short classroom activities, and the analysis of empirical evidence and theoretical models.

Classes typically start from a behavioral regularity, anomaly, or empirical finding, which is then discussed in relation to the standard economic benchmark.

When appropriate, students may be invited to take part in short classroom or online activities designed to illustrate the mechanisms discussed in class. The results of these activities may be used as a starting point for comparison with findings from the literature.

The course then develops the main theoretical models proposed to account for the evidence and discusses their implications for economic behavior, policy design, and practical applications.

Assessment methods

Students will be assessed based on four weekly problem sets, a final in-class exam, and an optional presentation.

The examination aims to evaluate the achievement of the following goals:

• knowledge of the most influential models of behavioral economics, in the realms of decision making under uncertainty, intertemporal decision making, behavioral game theory, and social preferences;
• understanding of the empirical methodologies adopted in the articles surveyed during the course;
• understanding of the theoretical implications of the empirical findings, and of their possible practical implications.

The problem sets will be published on the course’s e-learning platform, Virtuale, at the end of each week, and must be completed within 7 days.

In the problem sets, which account for 5% of the final grade each, students will have to comment on or interpret graphs, figures, and equations from the studies discussed in class, or briefly summarize their results. Problem sets are used to help students consolidate the analytical tools discussed in class and to develop their ability to interpret models, figures, experimental results, and empirical evidence. Teamwork is allowed, but each student must submit their own answer. “Carbon-copied” work will be penalized. Failing to submit a problem set by the deadline implies the loss of the corresponding 5% of the final grade.

The final exam, which accounts for 80% of the final grade, will last 2 hours and will include a set of 6 questions of increasing complexity, covering both the mathematical and analytical aspects of the models discussed in class and their interpretation.

The maximum possible grade is 30 cum laude. The final grade is computed as follows. Let S be the weighted score obtained from the four problem sets and the final exam, where the problem sets account for up to 20% of the grade and the final exam accounts for up to 80%. The corresponding grade is given by 31 × S. Grades are rounded to the nearest integer. Scores above 30 correspond to 30 cum laude.

The grade is graduated as follows:

• below 18: failed;
• 18–23: sufficient;
• 24–27: good;
• 28–30: very good;
• 30 cum laude: excellent.

The exam will be held in the computer lab. Questions will be randomly extracted from a predefined question bank, so the exam content may differ across students.

During the final exam, students may use standalone calculators, but they may not use any other electronic devices apart from the computer assigned to them for the exam. They may not communicate with others, consult notes, books, or other written material, or access resources other than those explicitly made available for the exam. Any attempt to violate these rules will result in the student’s exclusion from the exam.

Students with learning disorders and/or temporary or permanent disabilities are invited to contact the responsible University office as soon as possible: https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students . The office will propose possible adjustments. Requests for adaptation must be submitted to the lecturer at least 15 days before the exam date; the lecturer will assess the appropriateness of the proposed adjustments, taking into account the teaching objectives of the course.

At the end of the course, students have the opportunity to choose one paper at the frontier of the literature and briefly discuss it in a short presentation. The presentation is optional. For students who choose to give a presentation, it may lead to an increase of up to 2 points or a decrease of up to 1 point in the final grade, depending on the quality of the presentation and discussion. In all cases, the maximum final grade remains 30 cum laude.

For assessment purposes, the use of generative AI is not allowed during the final exam. For problem sets and the optional presentation, the use of generative AI is allowed only as a limited, declared, and non-substantial support tool, for example for language editing or preliminary summarizing. Substantial use of generative AI to produce answers, solve exercises, interpret results, or generate the content of the assignment is not allowed. Students remain fully responsible for the accuracy, originality, and integrity of the work they submit.

Teaching tools

Teaching materials, slides, problem sets, instructions for mock experiments, and additional information will be made available through Virtuale.

During the course, students will be involved in mock experiments, which should provide them with a more vivid idea of the issues to be examined later during the lecture, and active participation to the in-class discussion will be encouraged.

Participation in the mock experiments and the completion of the four problem sets is required. Attending classes is instead not compulsory, even though it is recommended.

The mock experiments will be computer-based and will require the use of a pc, tablet or smartphone connected to the internet.

Generative AI tools may be used as a support for individual study, for example to summarize readings, revise notes, or generate self-assessment questions.

Office hours

See the website of Maria Bigoni