Economics and Finance


Harvard Q-Guide Predictions Market

Authors: Luis Perez, Haotian Wu

Given the announcement by Harvard College to stop publishing difficulty rating for courses \cite{crimson}, a need has arisen for alternative methods of information gathering among undergraduates. In this paper, we propose different prediction market mechanisms, detailing user input/output, contract definitions, and payment rules for each of the proposed mechanisms. The goal of each mechanism is to obtain accurate predictions that could replace Q-guide data (overall course quality, difficulty rating, and workload rating). We further discuss properties of each prediction market, such as the truthfulness incentives of for individual agents, individual agent's optimal policies, and expected results from each market. We conclude with a discussion and explanation of a simple toy implementation of the market, detailing design consideration that might affect user behaviour in our market, and laying the groundwork for future expansion and testing.

Comments: 14 Pages.

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Submission history

[v1] 2017-12-15 23:44:57

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