B5729 - MARKET DESIGN AND BEHAVIORAL ECONOMICS

Learning outcomes

This course explores the fundamentals of market design, auctions, negotiation strategies, and behavioral economics with a focus on real-world applications and practical experiments. Students will gain insights into how markets function, learn negotiation techniques, and analyze human behavior in economic decision-making through hands-on experiments and class discussions. At the course completion, they are able to confront real-world business challenges with confidence and insight, armed with a robust skill set that combines theory, practice, and the science of human behavior.

Course contents

This course provides an integrated exploration of market design, auctions, negotiation strategies, and behavioral economics, with a focus on environments where both human agents and AI-driven agents interact. It examines how decisions are made and how interactions unfold across different market settings, and how market rules can be designed to promote efficiency, participation, and a fair distribution of the gains from trade, while addressing challenges such as asymmetric information, bounded rationality, behavioral biases, and strategic behavior.

Through case studies, classroom experiments, simulations, and strategic interactions, students will explore how incentives, information, institutional rules, and behavioral factors shape economic and business outcomes.

Auctions are among the principal mechanisms studied in market design. Students will learn about different auction formats, their theoretical foundations, and their practical implementation in settings such as procurement, online platforms, advertising, and the allocation of scarce resources. The course will also examine matching mechanisms and other allocation problems in which prices alone may not be sufficient to organize transactions effectively.

Negotiation and bargaining are another central component of market interaction. Reaching a mutually acceptable agreement requires participants to assess their objectives and alternatives, anticipate the behavior of others, manage information strategically, and choose appropriate bargaining techniques. Students will examine how incentives, framing, anchoring, expectations, and fairness concerns affect both the negotiation process and its outcomes.

Behavioral economics provides the tools needed to understand why actual decisions may differ from the predictions of standard economic models and how firms, policymakers, and institutions can respond to these differences.

In addition to human decision-making and interaction, the course will examine how generative AI systems such as ChatGPT, Claude, and Gemini respond to incentives, negotiate, bid, and interact with humans and with one another. Markets and firms increasingly rely on algorithms and AI systems to set prices, match buyers and sellers, recommend products, allocate advertising, support negotiations, and inform strategic decisions. Understanding the behavior of artificial as well as human agents has therefore become essential. By comparing human and AI behavior, students will learn to use generative AI systems more critically and effectively, while recognizing their limitations.

These topics are particularly relevant for students seeking to understand the contemporary business environment. Firms increasingly operate through digital platforms, data-driven systems, automated decision tools, and complex negotiations. The ability to understand incentives, anticipate the behavior of human and artificial participants, evaluate market rules, and negotiate effectively is valuable across a wide range of managerial and professional settings.

By the end of the course, students will have developed a practical and analytical toolkit for addressing real-world problems involving strategic decision-making, market interaction, negotiation, resource allocation, and AI-assisted business environments.

Topics covered in the lessons will include:

• Behavioral foundations of economic decision-making
• Heuristics, cognitive biases, framing, and bounded rationality
• Strategic interaction and experimental evidence
• Introduction to market design
• Fundamentals and applications of auction theory
• Negotiation and bargaining
• Matching mechanisms and allocation problems
• Behavioral approaches to market and mechanism design
• Selected applications of generative AI to negotiation and market interaction
• Comparison and critical evaluation of AI-generated strategies and decisions
• Real-world applications, experiments, simulations, and case studies

Prerequisite knowledge

To successfully complete the course, students should have a foundational understanding of microeconomics, including supply and demand, market equilibrium, incentives, and basic game theory. Prior coursework in introductory economics, management, or business studies will help students engage with the more advanced topics in market design, auctions, bargaining, and strategic interaction.

No previous technical knowledge of artificial intelligence or programming is required. Students are expected, however, to participate actively in experiments, simulations, class discussions, and selected structured activities involving generative AI systems. An interest in human decision-making, strategic behavior, and the design of economic institutions will enhance the learning experience.

Readings/Bibliography

• Haeringer (2018), Market Design: Auctions and Matching, MIT Press (only the sections specified in class)
• Kahneman, D. (2011), Thinking Fast and Slow, Farrar, Straus, and Giroux, New York (only the sections specified in class).

Further readings will be provided during the course.

Teaching methods

This course employs a variety of teaching methods to provide an engaging learning experience and connect theoretical analysis with practical applications.

Lectures will introduce the theoretical foundations of behavioral economics, market design, auctions, negotiation, and matching mechanisms. They will include interactive elements, such as real-time polling, short exercises, and question-and-answer sessions, to encourage active participation.

Case studies will bridge theory and practice by examining real-world applications of market design, negotiation strategies, and AI-assisted decision-making. Students will analyze how incentives, information, behavioral factors, and institutional rules influence outcomes in business and market environments.

Hands-on experiments and simulations are a central component of the course. Students will participate in behavioral experiments, simulated auctions, negotiation exercises, and selected activities involving generative AI systems such as ChatGPT, Claude, and Gemini. These activities will allow students to compare interactions among human participants, between humans and AI systems, and among AI agents, and to evaluate how different market rules, instructions, and incentives affect behavior and outcomes.

Class discussions will foster a collaborative learning environment in which students can critically interpret experimental results, case studies, and theoretical predictions. Discussions will address current events, recent research, and emerging developments in behavioral economics, market design, and the use of artificial intelligence in markets and business decision-making.

Assessment methods

Students attending lectures

Students are considered attending students if they participate in at least 80% of the lectures.

Applied Market Design and AI Project: 50% of the final grade

Students will work in groups of two to four members on an applied project concerning a real-world market, auction, matching mechanism, negotiation, or strategic business interaction. Groups of five may be permitted where necessary because of class size. Individual projects are allowed only in exceptional circumstances and with the instructor’s prior approval.

The project must combine the economic analysis of the chosen setting with a small, structured activity involving generative AI. Depending on the research question, students may:

• compare the strategies or decisions generated by different AI systems;
• examine an interaction between a human participant and an AI system;
• compare human–human, human–AI, or AI–AI interactions;
• investigate how changes in incentives, information, instructions, or market rules affect AI-generated decisions;
• analyze a market or business environment in which algorithms or AI systems play an important role.

Students are not required to program or build an autonomous AI system. The purpose of the activity is to use generative AI critically and systematically to investigate a question related to market design, strategic interaction, negotiation, or behavioral economics.

Project proposal and approval

Each group must submit a project proposal of approximately 200–300 words by the end of the fourth lecture. The proposal must identify:

• the topic and market or business setting to be examined;
• the main question to be investigated;
• the relevant theories and concepts from the course;
• the planned use of generative AI;
• the proposed procedure and sources of evidence.

The project may proceed only after approval by the instructor. Proposals will be reviewed to ensure that the project is feasible, appropriately focused, relevant to the course, and sufficiently distinct from those selected by other groups. Different groups may study the same broad market or business setting, provided that their research questions, AI activities, or analytical approaches are clearly differentiated.

The project assessment consists of two components.

1. Group report: 40% of the final grade

The group will submit a report of no more than 3,000 words. The report should:

• define the economic or business problem being investigated;
• describe the relevant market, institution, or strategic interaction;
• identify and apply the relevant theories and concepts studied in the course;
• explain the procedure followed in the AI-based activity;
• present and interpret the main observations or results;
• compare the observed behavior with theoretical predictions or relevant human behavior, where appropriate;
• discuss the reliability and limitations of the analysis;
• provide recommendations for improving the relevant market rules, negotiation strategies, or decision-making processes.

Prompts, relevant AI-generated outputs, interaction records, and any additional data should be included in an appendix and do not count toward the word limit. Students must identify the AI system used and, where available, the model or version, date of access, and relevant settings.

The group report will be evaluated on the basis of:

• the quality and accuracy of the economic analysis;
• the appropriate application of course concepts;
• the clarity and relevance of the research question;
• the design and documentation of the AI-based activity;
• the interpretation of the observations or results;
• the critical evaluation of AI-generated strategies and outputs;
• the feasibility and quality of the recommendations;
• the clarity and organization of the report.

2. Individual analytical reflection: 10% of the final grade

Each student will submit an individual reflection of no more than 600 words. The reflection should:

• describe the student’s contribution to the project;
• identify and discuss the project’s principal finding;
• provide an independent assessment of its methodological limitations;
• explain how the project or AI-based activity could be improved;
• reflect critically on what the comparison between human and artificial behavior reveals, where relevant.

The individual reflection must represent the student’s own analysis and may receive a different grade from the group report.

The group report and individual reflection must be submitted by the date of the first examination session following the end of the course.

Written examination: 50% of the final grade

The written examination is closed-book and lasts 1 hour and 15 minutes. It tests the theoretical and analytical knowledge acquired throughout the course, including the ability to apply course concepts to market-design problems, strategic interactions, behavioral evidence, and selected applications involving artificial agents.

The examination consists of:

• one open question, chosen from two available questions, worth a maximum of 16 points;
• four multiple-choice questions requiring a theoretically grounded justification, worth a maximum of 4 points each.

The examination is worth a maximum of 32 points.

Excellence: 16 points
The answer is exhaustive, correct, and complete, demonstrates excellent analytical ability, and uses technical terminology accurately and effectively.

Very good performance: 14–15 points
The answer is correct, comprehensive, and complete, and demonstrates very good critical and analytical ability.

Good performance: 11–13 points
The answer is correct and substantially complete, and demonstrates a good level of critical analysis and an appropriate use of technical terminology.

Sufficiency: 9–10 points
The answer is broadly correct and demonstrates a satisfactory ability to analyze the topic. Technical terminology is mostly used correctly.

Insufficiency: 6–8 points
The answer is only partially correct, lacks completeness or depth, and demonstrates limited critical analysis. Explanations may be superficial, connections between concepts may be weak, and technical terminology may be incorrect or inconsistent.

Failure: 0–5 points
The answer is largely incorrect, incomplete, lacking essential elements, or off-topic.

In the multiple-choice questions, students must select an answer and justify their choice on the basis of the relevant theory. The evaluation considers the correctness of the selected answer, the quality of the justification, and the use of appropriate technical terminology.

Excellence: 4 points
The selected answer is correct, and the justification demonstrates an excellent understanding of the relevant theory and a precise use of technical terminology.

Good performance: 3 points
The selected answer is correct, and the justification demonstrates a very good understanding of the relevant theory, with only minor omissions or errors. Technical terminology is mostly correct.

Sufficiency: 2 points
The selected answer is correct, and the justification demonstrates an adequate understanding of the relevant theory, although it may not be exhaustive. Technical terminology is generally appropriate.

Insufficiency: 1 point
The selected answer is incorrect, but the justification demonstrates some understanding of the relevant theory.

Failure: 0 points
The selected answer is incorrect, and the justification demonstrates minimal or no understanding of the relevant theory.

Students obtaining more than 30 points in the overall weighted grade will receive a final grade of 30 cum laude.

Students not attending lectures

Written examination: 100% of the final grade

The written examination is closed-book and lasts two hours. It assesses the theoretical and analytical knowledge acquired through the study of the course materials, including the ability to apply course concepts to market-design problems, auctions, negotiation and bargaining, matching mechanisms, behavioral evidence, and selected applications involving artificial agents.

The examination consists of:

• two open questions, worth a maximum of 8 points each;
• eight multiple-choice questions requiring a theoretically grounded justification, worth a maximum of 2 points each.

The examination is worth a maximum of 32 points.

Open questions

In their answers to the open questions, students must demonstrate mastery of the relevant topics, an ability to perform critical analysis, and an appropriate use of technical terminology.

Excellence: 8 points

The answer is exhaustive, correct, and complete, demonstrates excellent analytical ability, and uses technical terminology accurately and effectively.

Very good performance: 7–7.5 points

The answer is correct, comprehensive, and complete, and demonstrates very good critical and analytical ability.

Good performance: 5.5–6.5 points

The answer is correct and substantially complete, and demonstrates a good level of critical analysis and an appropriate use of technical terminology.

Sufficiency: 4.5–5 points

The answer is broadly correct and demonstrates a satisfactory ability to analyze the topic. Technical terminology is mostly used correctly.

Insufficiency: 3–4 points

The answer is only partially correct, lacks completeness or depth, and demonstrates limited critical analysis. Explanations may be superficial, connections between concepts may be weak, and technical terminology may be incorrect or inconsistent.

Failure: 0–2.5 points

The answer is largely incorrect, incomplete, lacking essential elements, or off-topic.

Multiple-choice questions

For each multiple-choice question, students must select an answer and justify their choice on the basis of the relevant theory. The evaluation considers the correctness of the selected answer, the quality of the justification, and the use of appropriate technical terminology.

Excellence: 2 points

The selected answer is correct, and the justification demonstrates an excellent understanding of the relevant theory and a precise use of technical terminology.

Good performance: 1.5 points

The selected answer is correct, and the justification demonstrates a very good understanding of the relevant theory, with only minor omissions or errors. Technical terminology is mostly correct.

Sufficiency: 1 point

The selected answer is correct, and the justification demonstrates an adequate understanding of the relevant theory, although it may not be exhaustive. Technical terminology is generally appropriate.

Insufficiency: 0.5 points

The selected answer is incorrect, but the justification demonstrates some understanding of the relevant theory.

Failure: 0 points

The selected answer is incorrect, and the justification demonstrates minimal or no understanding of the relevant theory.

Students obtaining more than 30 points in the examination will receive a final grade of 30 cum laude.

Teaching tools

https://virtuale.unibo.it/

Links to further information

https://virtuale.unibo.it/

Office hours

See the website of Emanuela Carbonara