Humans depart from optimal computational models of interactive decision-making during competition under partial information
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Humans depart from optimal computational models of interactive decision-making during competition under partial information. / Steixner-Kumar, Saurabh; Rusch, Tessa; Doshi, Prashant; Spezio, Michael; Gläscher, Jan.
In: SCI REP-UK, Vol. 12, No. 1, 289, 07.01.2022, p. 289.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Humans depart from optimal computational models of interactive decision-making during competition under partial information
AU - Steixner-Kumar, Saurabh
AU - Rusch, Tessa
AU - Doshi, Prashant
AU - Spezio, Michael
AU - Gläscher, Jan
N1 - Publisher Copyright: © 2022, The Author(s).
PY - 2022/1/7
Y1 - 2022/1/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85122655229&partnerID=8YFLogxK
U2 - 10.1038/s41598-021-04272-x
DO - 10.1038/s41598-021-04272-x
M3 - SCORING: Journal article
AN - SCOPUS:85122655229
VL - 12
SP - 289
JO - SCI REP-UK
JF - SCI REP-UK
SN - 2045-2322
IS - 1
M1 - 289
ER -
