This course aims to introduce you to behavioral economics and its applications. At the end of the course, students will be expected to have learned the fundamentals of the behavioral approach to judgment and decision making and, most importantly, to have learned the tools and acquired the skills necessary to successfully apply behavioral insights in different contexts for different scopes.

At the completion of this course, students will be able to: Knowledge and understanding The course will offer key concepts and ideas from behavioral economics and psychology, both in the context of decisions and in the context of judgment. Students will be able to appreciate behavioral descriptive theories of choice and judgment against the backdrop of traditional normative theories of rationality such as expected utility theory and Bayesian updating. Emphasis will be given to the application of behavioral insights for ethically influencing consumer behavior (nudging).  Define behavioral descriptive theories of choice and judgement; Apply behavioral insights for ethically influencing consumer behavior (nudging). Applying knowledge and understanding An integral part of this course is the project work that students will build throughout the semester with the help of the instructors' and peers' feedback. They will learn how to take insights from behavioral economics and apply them to a behavioral issue to try and bring about behavior change. While students will not be expected to be carrying out actual experiments, they are supposed to provide both a behavioral diagnosis to identify the levers of behavior change, and an application of behavioral insights to operate those levers. Apply behavioral economics and insights to an issue to bring about behavior change; Provide behavioral diagnoses of an issue; Identify the levers of change on a behavioral issue. Making Judgements Students will be able to understand the bounds of traditional normative models, which will improve their analytical and decision-making skills. In particular, students will appreciate the importance of taking into account data and the pitfalls of relying on partial data, as it is often intuitively done. Thus, this course not only will expand the conceptual toolbox of students, enabling them to make judgment on a wider area, but will also give students the conceptual tools to avoid common mistakes and ameliorate their judgment skills. Define the bounds of traditional normative models; Discuss the importance of having complete data; Make precise and confident judgements of behavioral interventions. Communication Skills Part of the lectures will be devoted to class discussion, helping students articulate their knowledge and sharpen their communication skills. Part of student assessment will revolve around presentations, discussions and providing feedback. Engage in collegial discussions and feedback. Learning Skills This course stresses the importance of autonomous learning and will empower students by offer them innovative analytical tools to reason about choices in society and in the economic models on which behavioral interventions are based. Evaluate economic models for decision makings.

Expected utility Theory and Violations Prospect Theory Heuristics and Bias Intertemporal Choice Nudging and Behavioral interventions Behavioral insights Experiments in Nudging Social Norms Norm-Nudging Behavioral Strategy

The key textbooks for the course are: Dilip Soman (2015). The Last Mile. Guidance Centre Univ of Toronto. Cristina Bicchieri (2016). Norms in the Wild. OUP USA. Erik Angner (2020). A Course in Behaviorla Economics (3rd ed.). Red Globe Press.

Individual assessments through Infographic; Paper Reading; Video Development; Exercises and Quizzes; Forum Discussion; Project Work; The first half of the course will be based mostly on theory with an individual focus. The second half of the course will be based on applications of the theory with a team-based approach.

Students will be evaluated on the following activities (% of total grade): Individual Weekly assignments: 50% Weekly individual and group work: 10% Research- based project: 30% Mandatory in person assessment: 10%

Active attendance at the course; Quality of the proposed issue for the thesis.