AF: Medium: Behavioral design for online environments

Cornell University, Ithaca NY

Investigators

Abstract

Online systems are, to a growing degree, economic systems designed for a large population of users, each with their own motivations. Economic design is more likely to be effective when it is based on accurate models of the agent population toward which it is targeted. A growing literature, however, suggests that people do not quite behave like standard economic agents in a variety of settings, both online and offline. What consequences might such differences in behavior have for the optimal design of these environments? This project will lay the groundwork for a theory of behavioral mechanism design, with the aim of both introducing new theoretical problems and impacting the design of online systems in practice. The diversity of the literature that this research draws upon, and can potentially impact, makes it particularly suitable for bringing awareness of theoretical research to communities that are otherwise largely dissociated from mathematical methods. The PIs hope to achieve this via new courses for graduate students with little or no exposure to mathematics, as well as hands-on learning experiences for middle-school students. This research will address questions based on three families of behavioral phenomena well-documented in experimental work, and will investigate how they impact the design of a range of online systems, such as social computing and crowdsourcing platforms, matching markets, and ranking and recommendation systems. The project will consider mechanism design for "simple" agents who strategize about their participation decision but not about their input (conditional on participating) to a system; pricing and menu design for agents whose decisions may suffer from "choice overload;" and designing learning algorithms whose input comes from behaviorally-biased agents rather than from stationary random sampling processes.