Simulating prediction markets that include human and automated agents

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.

Bibliographic Details
Main Author: Chang, Wendy, M. Eng. Massachusetts Institute of Technology
Other Authors: Thomas W. Malone.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2010
Subjects:
Online Access:http://hdl.handle.net/1721.1/53097
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author Chang, Wendy, M. Eng. Massachusetts Institute of Technology
author2 Thomas W. Malone.
author_facet Thomas W. Malone.
Chang, Wendy, M. Eng. Massachusetts Institute of Technology
author_sort Chang, Wendy, M. Eng. Massachusetts Institute of Technology
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description Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
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spelling mit-1721.1/530972019-04-10T17:13:38Z Simulating prediction markets that include human and automated agents Prediction markets with automated trading agent participation Chang, Wendy, M. Eng. Massachusetts Institute of Technology Thomas W. Malone. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009. Includes bibliographical references (p. 59). In this work I study the interaction of sophisticated trading agents with simpler agents in a prediction market. The goal is to simulate markets with both human and computer agents, and investigate ways to maximize the performance of these markets. I start with the neural net-based agent that is currently used in CCI's collective prediction experiments on football plays. By tuning their training and risk affinity, I configure a "smart" agent to represent the sophisticated computer traders. I implement three types of simple agents to approximate human traders - two are rule based, and one uses aggregate human data from lab experiments. By exploring different combinations of smart versus simple agents, I showed that it is possible for mixes of agents to outperform either types alone. This result is consistent with the larger goal of the collective prediction project, which is to show that humans and computer agents combined in a prediction market can do better than either alone. by Wendy Chang. M.Eng. 2010-03-25T15:00:36Z 2010-03-25T15:00:36Z 2009 2009 Thesis http://hdl.handle.net/1721.1/53097 502428301 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 59 p. application/pdf Massachusetts Institute of Technology
spellingShingle Electrical Engineering and Computer Science.
Chang, Wendy, M. Eng. Massachusetts Institute of Technology
Simulating prediction markets that include human and automated agents
title Simulating prediction markets that include human and automated agents
title_full Simulating prediction markets that include human and automated agents
title_fullStr Simulating prediction markets that include human and automated agents
title_full_unstemmed Simulating prediction markets that include human and automated agents
title_short Simulating prediction markets that include human and automated agents
title_sort simulating prediction markets that include human and automated agents
topic Electrical Engineering and Computer Science.
url http://hdl.handle.net/1721.1/53097
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