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Humans depart from optimal computational models of interactive decision-making during competition under partial information

Steixner-Kumar, Saurabh and Rusch, Tessa and Doshi, Prashant and Spezio, Michael and Gläscher, Jan (2022) Humans depart from optimal computational models of interactive decision-making during competition under partial information. Scientific Reports, 12 . Art. No. 289. ISSN 2045-2322. PMCID PMC8741801. doi:10.1038/s41598-021-04272-x. https://resolver.caltech.edu/CaltechAUTHORS:20220119-294072700

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Abstract

Decision making under uncertainty in multiagent settings is of increasing interest in decision science. The degree to which human agents depart from computationally optimal solutions in socially interactive settings is generally unknown. Such understanding provides insight into how social contexts affect human interaction and the underlying contributions of Theory of Mind. In this paper, we adapt the well-known ‘Tiger Problem’ from artificial-agent research to human participants in solo and interactive settings. Compared to computationally optimal solutions, participants gathered less information before outcome-related decisions when competing than cooperating with others. These departures from optimality were not haphazard but showed evidence of improved performance through learning. Costly errors emerged under conditions of competition, yielding both lower rates of rewarding actions and accuracy in predicting others. Taken together, this work provides a novel approach and insights into studying human social interaction when shared information is partial.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1038/s41598-021-04272-xDOIArticle
http://www.ncbi.nlm.nih.gov/pmc/articles/pmc8741801/PubMed CentralArticle
ORCID:
AuthorORCID
Steixner-Kumar, Saurabh0000-0002-0603-2922
Rusch, Tessa0000-0002-5445-6885
Spezio, Michael0000-0002-7128-5264
Gläscher, Jan0000-0002-1020-7115
Additional Information:© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Received 26 February 2021; Accepted 14 December 2021; Published 07 January 2022. We thank the contributions of Julia Spilcke-Liss, Julia Majewski, Freya Leggemann, Franziska Sikorski, Jann Martin and Vivien Breckwoldt with the large amount of data collection. We thank Shannon Klotz, Corinne Donnay, and Rena Patel for help with piloting and initial data assessment. SSK, PD, MS and JG were funded by a Collaborative Research in Computational Neuroscience grant awarded jointly by the German Ministry of Education and Research (BMBF, 01GQ1603) and the United States National Science Foundation (NSF, 1608278). JG and TR were supported by the Collaborative Research Center TRR 169 “Crossmodal Learning” funded by the German Research Foundation (DFG) and the National Science Foundation of China (NSFC). MS gratefully acknowledges support from a Scripps College Faculty Research grant. Data availability: The PIs on this project commit to sharing the data publicly on the National Science Foundation CRCNS site once the initial descriptive and IPOMDP modeling papers are accepted for publication. These authors contributed equally: Michael Spezio and Jan Gläscher. Author Contributions: J.G., M.S., and P.D. developed the study concept. All authors contributed to the study design. Testing and data collection were performed by S.S.K. and T.R. S.S.K. performed the data analysis and interpretation under the supervision of J.G. and M.S. S.S.K., J.G. and M.S. drafted the manuscript, and all authors provided critical revisions. All authors approved the final version of the manuscript for submission and declare no conflict of interest. The authors declare no competing interests.
Funders:
Funding AgencyGrant Number
Bundesministerium für Bildung und Forschung (BMBF)01GQ1603
NSFIIS-1608278
Deutsche Forschungsgemeinschaft (DFG)TRR 169
National Natural Science Foundation of ChinaUNSPECIFIED
Scripps CollegeUNSPECIFIED
Subject Keywords:Cooperation; Decision; Human behaviour; Reward; Social behaviour
PubMed Central ID:PMC8741801
DOI:10.1038/s41598-021-04272-x
Record Number:CaltechAUTHORS:20220119-294072700
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220119-294072700
Official Citation:Steixner-Kumar, S., Rusch, T., Doshi, P. et al. Humans depart from optimal computational models of interactive decision-making during competition under partial information. Sci Rep 12, 289 (2022). https://doi.org/10.1038/s41598-021-04272-x
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112971
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:19 Jan 2022 18:21
Last Modified:19 Jan 2022 18:21

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