This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Learning to Trust: Uncovering Unobserved Multi-Period Behavioral Strategies from Observed Stage Game Actions Using Finite Automata

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Jim Warnick () (University of Pittsburgh)
Robert L. Slonim () (Case Western Reserve University)
Abstract

We introduce a methodology to infer players' unobserved multi-period strategies from their observed stage game actions in economic decision-making experiments. We use finite-state automata to model multi-period strategies by employing an algorithm that synthesizes a minimal state automaton from a sequence of inputs and outputs. The inputs and outputs are the players' and their opponents' behavior, and the automaton is the decision rule. We use this methodology to examine new experimental data from finitely and infinitely repeated trust games. We synthesize an automaton for every behavioral observation in the experiment. Although we are able to infer that over 70 unique finite-state automata strategies are used in he infinite horizon game, over 90 percent of the data may be explained by a very small number of behaviorally interpretable strategies. We find that subjects who are in a position to initiate trust use a harsh punishment strategy infrequently (when trust is not reciprocated) in early play, but learn predominantly to use this strategy over time. By contrast, players who are in a position to reciprocate trust do not learn to reciprocate (perhaps because the harsh punishment strategy does not emerge until near the end of the session). In fact, these players appear to be exhibiting "gambler's fallacy" behavior by forming incorrect subjective probabilities that the infinite game will end. Our inference methodology is able to capture this behavior by generating automata that count the number of periods of play. We conclude that our strategy inference technique enables us to better our understanding of the nature of strategic behavior in trust games.

Download Info
To our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.

Publisher Info
Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 1999 with number 121.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: 01 Mar 1999
Date of revision:
Handle: RePEc:sce:scecf9:121

Contact details of provider:
Postal: CEF99, Boston College, Department of Economics, Chestnut Hill MA 02467 USA
Fax: +1-617-552-2308
Web page: http://fmwww.bc.edu/CEF99/
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).

Related research
Keywords:

This paper has been announced in the following NEP Reports:

Statistics
Access and download statistics

Did you know? You may want to explore EconPapers, which displays the same data as IDEAS in a different way.

This page was last updated on 2009-11-13.


This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.