Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm.
Different people may use different strategies, or decision rules, when solving complex decision problems. We provide a new Bayesian procedure for drawing inferences about the nature and number of decision rules present in a population, and use it to analyze the behaviors of laboratory subjects confr...
| Publicado en: | Econometrica Vol. 72; no. 3; pp. 781 - 823 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Wiley-Blackwell
May 2004
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=513175104&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 513175104 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00129682 ECN jtl: Econometrica issn: 00129682 maglogo: N pubinfo: dt: May 2004 vid: 72 iid: 3 pid: 480 pub: Wiley-Blackwell artinfo: ui: 513175104 10.1111/j.1468-0262.2004.00512.x ppf: 781 ppct: 42 formats: tig: atl: Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm. aug: au: Houser, Daniel Keane, Michael McCabe, Kevin su: Experimental economics Behavioral economics Bayesian analysis Mathematical models of decision making sug: subj: Experimental economics Behavioral economics Bayesian analysis Mathematical models of decision making ab: Different people may use different strategies, or decision rules, when solving complex decision problems. We provide a new Bayesian procedure for drawing inferences about the nature and number of decision rules present in a population, and use it to analyze the behaviors of laboratory subjects confronted with a difficult dynamic stochastic decision problem. Subjects practiced before playing for money. Based on money round decisions, our procedure classifies subjects into three types, which we label “Near Rational,” “Fatalist,” and “Confused.” There is clear evidence of continuity in subjects' behaviors between the practice and money rounds: types who performed best in practice also tended to perform best when playing for money. However, the agreement between practice and money play is far from perfect. The divergences appear to be well explained by a combination of type switching (due to learning and/or increased effort in money play) and errors in our probabilistic type assignments. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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