AF: Medium: Behavioral design for online environments

在线系统在越来越大的程度上是为大量用户设计的经济系统，每个用户都有自己的动机。当经济设计基于目标主体群体的精确模型时，它更有可能是有效的。然而，越来越多的文献表明，人们在各种环境下的行为并不完全像标准的经济主体，无论是线上还是线下。这些行为上的差异会对这些环境的最佳设计产生什么影响呢？本项目将为行为机制设计理论奠定基础，旨在引入新的理论问题，并影响实践中的在线系统设计。本研究所借鉴的文献的多样性，以及可能产生的潜在影响，使得它特别适合将理论研究的意识带到那些在很大程度上与数学方法分离的社区。pi希望通过为很少或没有接触过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。本研究将解决基于实验工作中记录良好的三种行为现象家族的问题，并将调查它们如何影响一系列在线系统的设计，例如社会计算和众包平台，匹配市场以及排名和推荐系统。该项目将考虑为“简单”代理设计机制，这些代理为其参与决策制定策略，而不是为其对系统的输入（以参与为条件）制定策略；为决策可能遭受“选择过载”的代理设计定价和菜单；设计学习算法，其输入来自行为有偏见的代理，而不是来自平稳的随机抽样过程。

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.