A Behaviorally Informed Survey-Powered Market Agent

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Published: Sep 12, 2014
DOI: https://doi.org/10.5750/jpm.v8i2.867
Keywords:
Automated Agents Market Algorithm Trading Agents

Main Article Content

Jessica Inchauspe
University of Pennsylvania
Pavel Atanasov
University of Pennsylvania
Barbara Mellers
University of Pennsylvania
Philip Tetlock
University of Pennsylvania
Lyle Ungar
University of Pennsylvania

Abstract

We introduce a new method for converting individual probability estimates (obtained through surveys) into market orders for use in a Continuous Double Auction prediction market. Our Survey-Powered Market Agent (SPMA) algorithm is based on actual forecaster behavior, and offers notable advantages over existing market agent algorithms such as Zero Intelligence Plus (ZIP) agents: SPMAs only require probability estimates (and not bid direction nor quantity), are more behaviorally realistic, and work well when probabilities change over time. We validate SPMA using prediction market data and probability estimates elicited through surveys from a large set of forecasters on 88 individual forecasting problems over the course of a year. SPMA outperforms simple averages of the same probability forecasts and is competitive with sophisticated opinion poll aggregation methods and prediction markets. We use a rich set of market and poll data to empirically test the assumptions behind SPMA’s operation. In addition to aggregation efficiency, SPMA provides a framework for studying how forecasters convert probability estimates into trading orders, and offers a foundation for building hybrid markets which mix market traders and individuals producing independent probability estimates.

Article Details

Issue
Vol. 8 No. 2 (2014)
Section
Articles
Author Biography

Jessica Inchauspe, University of Pennsylvania

Psychology department, Researcher

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