Is It Better to Forget? Stimulus-Response, Prediction, and the Weight of Past Experience in a Fast-Paced Bargaining Task
dc.contributor.author | Gibson, Faison P. | en_US |
dc.date.accessioned | 2006-09-11T15:11:38Z | |
dc.date.available | 2006-09-11T15:11:38Z | |
dc.date.issued | 2002-05 | en_US |
dc.identifier.citation | Gibson, Faison P.; (2002). "Is It Better to Forget? Stimulus-Response, Prediction, and the Weight of Past Experience in a Fast-Paced Bargaining Task." Computational & Mathematical Organization Theory 8(1): 31-47. <http://hdl.handle.net/2027.42/44725> | en_US |
dc.identifier.issn | 1381-298X | en_US |
dc.identifier.issn | 1572-9346 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/44725 | |
dc.description.abstract | Decision makers in dynamic environments such as air traffic control, firefighting, and call center operations adapt in real-time using outcome feedback. Understanding this adaptation is important for influencing and improving the decisions made. Recently, stimulus-response (S-R) learning models have been proposed as explanations for decision makers' adaptation. S-R models hypothesize that decision makers choose an action option based on their anticipation of its success. Decision makers learn by accumulating evidence over action options and combining that evidence with prior expectations. This study examines a standard S-R model and a simple variation of this model, in which past experience may receive an extremely low weight, as explanations for decision makers' adaptation in an evolving Internet-based bargaining environment. In Experiment 1, decision makers are taught to predict behavior in a bargaining task that follows rules that may be the opposite of, congruent to, or unrelated to a second task in which they must choose the deal terms they will offer. Both models provide a good account of the prediction task. However, only the second model, in which decision makers heavily discount all but the most recent past experience, provides a good account of subsequent behavior in the second task. To test whether Experiment 1 artificially related choice behavior and prediction, a second experiment examines both models' predictions concerning the effects of bargaining experience on subsequent prediction. In this study, decision models where long-term experience plays a dominating role do not appear to provide adequate explanations of decision makers' adaptation to their opponent's changing response behavior. | en_US |
dc.format.extent | 177945 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Kluwer Academic Publishers; Springer Science+Business Media | en_US |
dc.subject.other | Economics / Management Science | en_US |
dc.subject.other | Artificial Intelligence (Incl. Robotics) | en_US |
dc.subject.other | Management | en_US |
dc.subject.other | Operation Research/Decision Theory | en_US |
dc.subject.other | Methodology of the Social Sciences | en_US |
dc.subject.other | Sociology | en_US |
dc.subject.other | Dynamic Decision Making | en_US |
dc.subject.other | Game Theory | en_US |
dc.subject.other | Stimuls-response | en_US |
dc.subject.other | Reinforcement Learning | en_US |
dc.title | Is It Better to Forget? Stimulus-Response, Prediction, and the Weight of Past Experience in a Fast-Paced Bargaining Task | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Industrial and Operations Engineering | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | University of Michigan Business School, 701 Tappan Street, Ann Arbor, MI, 48109-1234, USA | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/44725/1/10588_2004_Article_405185.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1023/A:1015128203878 | en_US |
dc.identifier.source | Computational & Mathematical Organization Theory | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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