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Risky choice: probability weighting explains Independence Axiom violations in monkeys

Ferrari-Toniolo, Simone and Seak, Leo Chi U. and Schultz, Wolfram (2021) Risky choice: probability weighting explains Independence Axiom violations in monkeys. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20220506-250480800

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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 serve as basis for investigating neuronal decision mechanisms.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/2021.11.11.468261DOIDiscussion Paper
ORCID:
AuthorORCID
Ferrari-Toniolo, Simone0000-0001-9009-0764
Seak, Leo Chi U.0000-0001-8188-6891
Schultz, Wolfram0000-0002-8530-4518
Alternate Title:Probability weighting explains Independence Axiom violations of Expected Utility Theory in monkeys, Axiomatic consistency of risky monkey choices
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. Version 1 - November 13, 2021; Version 2 - May 4, 2022. We thank Ms. Christina Thompson, Mr. Aled David and Dr. Henri Bertrand for animal and technical support. The Wellcome Trust (WT 095495, WT 204811), the European Research Council (ERC; 293549) and the US National Institutes of Mental Health Conte Center at Caltech (NIMH; P50MH094258) supported this work. Author Contributions: SF-T and WS designed the experiment, SF-T and LCUS conducted the experiments, SF-T and LCUS analyzed the data, SF-T, LCUS and WS wrote the paper. The authors declare no competing interests. Data Sharing: The data from this study will be made available upon reasonable request.
Funders:
Funding AgencyGrant Number
Wellcome Trust095495
Wellcome Trust204811
European Research Council (ERC)293549
NIHP50MH094258
Subject Keywords:Choice, probability, gamble, preference reversal
DOI:10.1101/2021.11.11.468261
Record Number:CaltechAUTHORS:20220506-250480800
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220506-250480800
Official Citation:Risky choice: probability weighting explains Independence Axiom violations in monkeys. Simone Ferrari-Toniolo, Leo Chi U Seak, Wolfram Schultz. bioRxiv 2021.11.11.468261; doi: https://doi.org/10.1101/2021.11.11.468261
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:114642
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:06 May 2022 17:52
Last Modified:06 May 2022 17:52

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