Simone Ferrari-Toniolo, Leo Chi U Seak, Wolfram Schultz
{"title":"Risky choice: Probability weighting explains independence axiom violations in monkeys.","authors":"Simone Ferrari-Toniolo, Leo Chi U Seak, Wolfram Schultz","doi":"10.1007/s11166-022-09388-7","DOIUrl":null,"url":null,"abstract":"<p><p>Expected Utility Theory (EUT) provides axioms for maximizing utility in risky choice. The Independence Axiom (IA) is its most demanding axiom: preferences between two options should not change when altering both options equally by mixing them with a common gamble. We tested common consequence (CC) and common ratio (CR) violations of the IA over several months in thousands of stochastic choices using a large variety of binary option sets. Three monkeys showed consistently few outright <i>Preference Reversals</i> (8%) but substantial graded <i>Preference Changes</i> (46%) between the initial preferred gamble and the corresponding altered gamble. Linear Discriminant Analysis (LDA) indicated that gamble probabilities predicted most <i>Preference Changes</i> in CC (72%) and CR (88%) tests. The Akaike Information Criterion indicated that probability weighting within Cumulative Prospect Theory (CPT) explained choices better than models using Expected Value (EV) or EUT. Fitting by utility and probability weighting functions of CPT resulted in nonlinear and non-parallel indifference curves (IC) in the Marschak-Machina triangle and suggested IA non-compliance of models using EV or EUT. Indeed, CPT models predicted <i>Preference Changes</i> better than EV and EUT models. Indifference points in out-of-sample tests were closer to CPT-estimated ICs than EV and EUT ICs. Finally, while the few outright <i>Preference Reversals</i> may reflect the long experience of our monkeys, their more graded <i>Preference Changes</i> corresponded to those reported for humans. In benefitting from the wide testing possibilities in monkeys, our stringent axiomatic tests contribute critical information about risky decision-making and serves as basis for investigating neuronal decision mechanisms.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11166-022-09388-7.</p>","PeriodicalId":48066,"journal":{"name":"Journal of Risk and Uncertainty","volume":"65 3","pages":"319-351"},"PeriodicalIF":1.3000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9840594/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Risk and Uncertainty","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1007/s11166-022-09388-7","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/7/22 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0
Abstract
Expected Utility Theory (EUT) provides axioms for maximizing utility in risky choice. The Independence Axiom (IA) is its most demanding axiom: preferences between two options should not change when altering both options equally by mixing them with a common gamble. We tested common consequence (CC) and common ratio (CR) violations of the IA over several months in thousands of stochastic choices using a large variety of binary option sets. Three monkeys showed consistently few outright Preference Reversals (8%) but substantial graded Preference Changes (46%) between the initial preferred gamble and the corresponding altered gamble. Linear Discriminant Analysis (LDA) indicated that gamble probabilities predicted most Preference Changes in CC (72%) and CR (88%) tests. The Akaike Information Criterion indicated that probability weighting within Cumulative Prospect Theory (CPT) explained choices better than models using Expected Value (EV) or EUT. Fitting by utility and probability weighting functions of CPT resulted in nonlinear and non-parallel indifference curves (IC) in the Marschak-Machina triangle and suggested IA non-compliance of models using EV or EUT. Indeed, CPT models predicted Preference Changes better than EV and EUT models. Indifference points in out-of-sample tests were closer to CPT-estimated ICs than EV and EUT ICs. Finally, while the few outright Preference Reversals may reflect the long experience of our monkeys, their more graded Preference Changes corresponded to those reported for humans. In benefitting from the wide testing possibilities in monkeys, our stringent axiomatic tests contribute critical information about risky decision-making and serves as basis for investigating neuronal decision mechanisms.
Supplementary information: The online version contains supplementary material available at 10.1007/s11166-022-09388-7.
期刊介绍:
The Journal of Risk and Uncertainty (JRU) welcomes original empirical, experimental, and theoretical manuscripts dealing with the analysis of risk-bearing behavior and decision making under uncertainty. The topics covered in the journal include, but are not limited to, decision theory and the economics of uncertainty, experimental investigations of behavior under uncertainty, empirical studies of real world risk-taking behavior, behavioral models of choice under uncertainty, and risk and public policy. Review papers are welcome.
The JRU does not publish finance or behavioral finance research, game theory, note length work, or papers that treat Likert-type scales as having cardinal significance.
An important aim of the JRU is to encourage interdisciplinary communication and interaction between researchers in the area of risk and uncertainty. Authors are expected to provide introductory discussions which set forth the nature of their research and the interpretation and implications of their findings in a manner accessible to knowledgeable researchers in other disciplines.
Officially cited as: J Risk Uncertain