141 - 150 of 173 results for: ECON

This is the first part of a three course sequence (along with Econ 279 & 280-formerly 277) on behavioral and experimental economics. The sequence has two main objectives: 1) examines theories and evidence related to the psychology of economic decision making, 2) Introduces methods of experimental economics, and explores major subject areas (including those not falling within behavioral economics) that have been addressed through laboratory experiments. Focuses on series of experiments that build on one another in an effort to test between competing theoretical frameworks, with the objects of improving the explanatory and predictive performance of standard models, and of providing a foundation for more reliable normative analyses of policy issues. Prerequisites: 204 and 271, or consent of instructor.

This is part of a three course sequence (along with Econ 278 & 280-formerly 277) on behavioral and experimental economics. The sequence has two main objectives: 1) examines theories and evidence related to the psychology of economic decision making, 2) Introduces methods of experimental economics, and explores major subject areas (including those not falling within behavioral economics) that have been addressed through laboratory experiments. Focuses on series of experiments that build on one another in an effort to test between competing theoretical frameworks, with the objects of improving the explanatory and predictive performance of standard models, and of providing a foundation for more reliable normative analyses of policy issues. Prerequisites: 204 and 271, or consent of instructor.

Economics 280 (formerly ECON 277) is a course primarily directed at graduate students in the Economics department writing dissertations with behavioral or experimental components. Economics 280 is the third part of a three course sequence (along with Econ 278 & 279). The first two quarters, which are taught primarily in lecture format, have two main objectives: 1) examining theories and evidence related to the psychology of economic decision making; 2) introducing methods of experimental economics, and exploring major subject areas (including those not falling within behavioral economics) that have been addressed through laboratory experiments. Focuses on series of experiments that build on one another in an effort to test between competing theoretical frameworks, with the objectives of improving the explanatory and predictive performance of standard models, and of providing a foundation for more reliable normative analyses of policy issues. This third quarter is a practicum, focused on students who have taken (at least one of) the first two quarters and who are now preparing an experimental or behavioral study of their own. Prerequisites: Non-Econ Phd students must complete 204 and 271, or have consent of instructor.

This is a team-based course where students will work on a project to design and carry out an experiment intended to drive social impact in collaboration with a partner organization. The first few weeks will include lectures, hands-on tutorials, and labs designed to guide students through the process of experimental design in the digital context. Special topics include designing and selecting outcome measures that capture the impact of interventions; multi-stage experiments with applications to chatbots; learning how treatment effects vary across subgroups; adaptive experiments using bandits and artificial intelligence; and estimation of policies that target treatments based on subject characteristics. Experiments may be conducted on the customer base of a partner organization through their digital applications or on recruited subjects, such as subjects recruited to interactive chatbots. The teaching team will provide templates and technical assistance for designing and running the experiments. Students from different disciplinary backgrounds will be assigned roles to work in teams on the project. This course is part of the GSB's Action Learning Program, in which you will work on real business challenges under the guidance of faculty. In this intensive project-based course, you will learn research-validated foundations, tools, and practices; apply these tools and learnings to a real project for an external organization; create value for the organization by providing insights and deliverables; and be an ambassador to the organization by exposing them to the talent, values, and expertise of the GSB. You will also have the opportunity to gain practical industry experience and exposure to the organization, its industry, and the space in which it operates; build relationships in the organization and industry; and gain an understanding of related career paths. Prerequisites: Some experience with statistical analysis and the R statistical package. Students with less experience will have an opportunity to catch up through tutorials provided through the course. Non-GSB students are expected to have an advanced understanding of tools and methods from data science and machine learning as well as a strong familiarity with R, Python, SQL, and other similar high-level programming languages. Prerequisite: Econ 102B or equivalent. Students complete applications and enrollment will be with instructors consent. ECON 281 is for non-GSB students.

Basic theories and recent developments in mechanism design and the theory of contracts. Topics include: hidden characteristics and hidden action models with one and many agents, design of mechanisms and markets with limited communication, long-term relationships under commitment and under renegotiation, property rights and theories of the firm.

Technology has enabled the emergence of economic systems of formerly inconceivable complexity. Nevertheless, some technology-related economic problems are so complex that either supercomputers cannot solve them in a reasonable time, or they are too complex for humans to comprehend. Thus, modern economic designs must still be simple enough for humans to understand, and must address computationally complex problems in an efficient fashion. This topics course explores simplicity and complexity in economics, primarily via theoretical models. We will focus on recent advances. Key topics include (but are not limited to) resource allocation in complex environments, communication complexity and information aggregation in markets, robust mechanisms, dynamic matching theory, influence maximization in networks, and the design of simple (user-friendly) mechanisms. Some applications include paired kidney exchange, auctions for electricity and for radio spectrum, ride-sharing platforms, and the diffusion of information. Prerequisites: Econ 203 or equivalent.

This is an introduction to market design, intended mainly for second year PhD students in economics (but also open to other graduate students from around the university and to undergrads who have taken undergrad market design). It will emphasize the combined use of economic theory, experiments, and empirical analysis to analyze and engineer market rules and institutions. In this first quarter we will pay particular attention to matching markets, which are those in which price doesn't do all of the work, and which include some kind of application or selection process. We will also cover some of the basics of auction theory, with a particular emphasis on its connections to matching. In recent years market designers have participated in the design and implementation of a number of marketplaces, and the course will emphasize the relation between theory and practice, for example in the design of labor market clearinghouses for American doctors, school choice programs in a growing number of American cities (including New York and Boston), the allocation of organs for transplantation, online advertising auctions, and the market for transportation. Various forms of market failure will also be discussed. Assignment: One final paper. The objective of the final paper is to study an existing market or an environment with a potential role for a market, describe the relevant market design questions, and evaluate how the current market design works and/or propose improvements on the current design.

Aims to provide a solid basis in game-theoretic tools and concepts, both for theorists and for students focusing in other fields. Technical material will include solution concepts and refinements, potential games, supermodular games, repeated games, reputation, and bargaining models. The class will also address some foundational issues, such as epistemic and evolutionary modeling.Prerequisite: 203 or consent of instructor.

The rapid deployment of LLMs and autonomous agents may reshape how markets are structured and how participants will interact and what frictions will arise. These technologies raise questions about incentives, information, and institutional design. Examples include entry-level labor markets, transportation, healthcare, etc. This PhD-level course surveys recent theoretical and applied research at the intersection of economics, OR, and CS that may be affected by AI. The course further examines how AI-based capabilities challenge and enrich traditional approaches to market design. In what ways does the presence of autonomous agents alter strategic behavior? How should markets be designed when AI systems act as complements or substitutes for human participants? How to achieve alignment in marketplaces with AI agents? How can AI algorithms be incorporated into the design of marketplaces? The course encourages students to begin research projects in this emerging field. The course assumes basic knowledge in game theory and market design.