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Ergodicity-breaking reveals time optimal decision making in humans

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@article{5555e22084f74c4099ce3edff7e835a3,
title = "Ergodicity-breaking reveals time optimal decision making in humans",
abstract = "Ergodicity describes an equivalence between the expectation value and the time average of observables. Applied to human behaviour, ergodic theories of decision-making reveal how individuals should tolerate risk in different environments. To optimize wealth over time, agents should adapt their utility function according to the dynamical setting they face. Linear utility is optimal for additive dynamics, whereas logarithmic utility is optimal for multiplicative dynamics. Whether humans approximate time optimal behavior across different dynamics is unknown. Here we compare the effects of additive versus multiplicative gamble dynamics on risky choice. We show that utility functions are modulated by gamble dynamics in ways not explained by prevailing decision theories. Instead, as predicted by time optimality, risk aversion increases under multiplicative dynamics, distributing close to the values that maximize the time average growth of in-game wealth. We suggest that our findings motivate a need for explicitly grounding theories of decision-making on ergodic considerations.",
author = "David Meder and Finn Rabe and Tobias Morville and Madsen, {Kristoffer H} and Koudahl, {Magnus T} and Dolan, {Ray J} and Siebner, {Hartwig R} and Hulme, {Oliver J}",
year = "2021",
month = sep,
day = "9",
doi = "10.1371/journal.pcbi.1009217",
language = "English",
volume = "17",
pages = "1--25",
journal = "PLOS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "9",

}

RIS

TY - JOUR

T1 - Ergodicity-breaking reveals time optimal decision making in humans

AU - Meder, David

AU - Rabe, Finn

AU - Morville, Tobias

AU - Madsen, Kristoffer H

AU - Koudahl, Magnus T

AU - Dolan, Ray J

AU - Siebner, Hartwig R

AU - Hulme, Oliver J

PY - 2021/9/9

Y1 - 2021/9/9

N2 - Ergodicity describes an equivalence between the expectation value and the time average of observables. Applied to human behaviour, ergodic theories of decision-making reveal how individuals should tolerate risk in different environments. To optimize wealth over time, agents should adapt their utility function according to the dynamical setting they face. Linear utility is optimal for additive dynamics, whereas logarithmic utility is optimal for multiplicative dynamics. Whether humans approximate time optimal behavior across different dynamics is unknown. Here we compare the effects of additive versus multiplicative gamble dynamics on risky choice. We show that utility functions are modulated by gamble dynamics in ways not explained by prevailing decision theories. Instead, as predicted by time optimality, risk aversion increases under multiplicative dynamics, distributing close to the values that maximize the time average growth of in-game wealth. We suggest that our findings motivate a need for explicitly grounding theories of decision-making on ergodic considerations.

AB - Ergodicity describes an equivalence between the expectation value and the time average of observables. Applied to human behaviour, ergodic theories of decision-making reveal how individuals should tolerate risk in different environments. To optimize wealth over time, agents should adapt their utility function according to the dynamical setting they face. Linear utility is optimal for additive dynamics, whereas logarithmic utility is optimal for multiplicative dynamics. Whether humans approximate time optimal behavior across different dynamics is unknown. Here we compare the effects of additive versus multiplicative gamble dynamics on risky choice. We show that utility functions are modulated by gamble dynamics in ways not explained by prevailing decision theories. Instead, as predicted by time optimality, risk aversion increases under multiplicative dynamics, distributing close to the values that maximize the time average growth of in-game wealth. We suggest that our findings motivate a need for explicitly grounding theories of decision-making on ergodic considerations.

UR - http://www.scopus.com/inward/record.url?scp=85114822714&partnerID=8YFLogxK

U2 - 10.1371/journal.pcbi.1009217

DO - 10.1371/journal.pcbi.1009217

M3 - Journal article

C2 - 34499635

VL - 17

SP - 1

EP - 25

JO - PLOS Computational Biology

JF - PLOS Computational Biology

SN - 1553-734X

IS - 9

M1 - e1009217

ER -

ID: 67611086