A simple artificial life model explains irrational behavior in human decision-making.

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strat...

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Autores principales:	Carolina Feher da Silva, Marcus Vinícius Chrysóstomo Baldo
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3a410a4b37da4992b3d0a1043c0b71dc
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spelling	oai:doaj.org-article:3a410a4b37da4992b3d0a1043c0b71dc2021-11-18T07:20:07ZA simple artificial life model explains irrational behavior in human decision-making.1932-620310.1371/journal.pone.0034371https://doaj.org/article/3a410a4b37da4992b3d0a1043c0b71dc2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22563454/?tool=EBIhttps://doaj.org/toc/1932-6203Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats' neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments.Carolina Feher da SilvaMarcus Vinícius Chrysóstomo BaldoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 5, p e34371 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Carolina Feher da Silva Marcus Vinícius Chrysóstomo Baldo A simple artificial life model explains irrational behavior in human decision-making.
description	Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats' neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments.
format	article
author	Carolina Feher da Silva Marcus Vinícius Chrysóstomo Baldo
author_facet	Carolina Feher da Silva Marcus Vinícius Chrysóstomo Baldo
author_sort	Carolina Feher da Silva
title	A simple artificial life model explains irrational behavior in human decision-making.
title_short	A simple artificial life model explains irrational behavior in human decision-making.
title_full	A simple artificial life model explains irrational behavior in human decision-making.
title_fullStr	A simple artificial life model explains irrational behavior in human decision-making.
title_full_unstemmed	A simple artificial life model explains irrational behavior in human decision-making.
title_sort	simple artificial life model explains irrational behavior in human decision-making.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/3a410a4b37da4992b3d0a1043c0b71dc
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A simple artificial life model explains irrational behavior in human decision-making.

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