2026-09-21T11:29:04Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/530972022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Thomas W. Malone.
Chang, Wendy, M. Eng. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2010-03-25T15:00:36Z
2010-03-25T15:00:36Z
2009
2009
http://hdl.handle.net/1721.1/53097
502428301
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.
59 p.
eng
Massachusetts Institute of Technology
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
Electrical Engineering and Computer Science.
Simulating prediction markets that include human and automated agents
Prediction markets with automated trading agent participation
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="3b231936-cc3e-4651-874a-85c30ba78993">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Simulating prediction markets that include human and automated agents</Title>
<Subtitle>Prediction markets with automated trading agent participation</Subtitle>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2009</PublicationDate>
<Authors>
<Author>
<DisplayName>Chang, Wendy, M. Eng. Massachusetts Institute of Technology</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>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.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>