AF: Medium: Behavioral design for online environments

在线系统越来越多地成为为大量用户设计的经济系统，每个用户都有自己的动机。经济设计是更有可能是有效的，当它是基于准确的模型的代理人口的目标。然而，越来越多的文献表明，无论是在线还是离线，人们在各种环境中的行为都不像标准的经济行为人。这种行为差异对这些环境的最佳设计可能产生什么后果？该项目将为行为机制设计理论奠定基础，目的是引入新的理论问题，并影响在线系统的设计实践。本研究所借鉴的文献的多样性，并可能产生潜在的影响，使其特别适合于将理论研究的意识带到那些在很大程度上与数学方法无关的社区。PI希望通过为很少或根本没有接触数学的研究生开设新课程，以及为中学生提供实践学习经验来实现这一目标。这项研究将解决基于实验工作中记录良好的三种行为现象的问题，并将调查它们如何影响一系列在线系统的设计，例如社交计算和众包平台，匹配市场，以及排名和推荐系统。该项目将考虑机制设计的“简单”的代理人制定策略，他们的参与决策，但不是他们的输入（有条件的参与）到一个系统;定价和菜单设计的代理人的决定可能会遭受“选择超载”;和设计学习算法的输入来自行为偏差的代理人，而不是从静态随机抽样过程。

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.