Literature DB >> 17467743

State-dependent choice and ecological rationality.

Andrew L Nevai1, Thomas A Waite, Kevin M Passino.   

Abstract

Decision makers who minimize costly errors should flexibly adjust the way they trade off competing demands, depending on their current state. We explore how state (amount of hoarded food) affects willingness to take extra predation risk to obtain larger food rewards, particularly in animals that may overemphasize safety. Assuming a sigmoid fitness function, we explore how a supplement in state influences this willingness trade danger for food energy. Above a threshold, the model predicts the supplement will weaken this willingness. Incremental increases in state in the deceleratory phase yield smaller fitness gains, so it pays to increase emphasis on safety after receiving a supplement. Below this threshold, the model makes the opposite prediction because incremental increases in state yield bigger fitness gains and so it pays to decrease emphasis on safety. We use the model to explain why hoarding gray jays (Perisoreus canadensis) were induced by an experimental subsidy to accept greater danger. This formerly puzzling finding makes sense if the jays' effective hoard was relatively small, due to theft and decomposition. We discuss adaptive state-dependent choice as a general explanation for apparently irrational behavior.

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Year:  2007        PMID: 17467743     DOI: 10.1016/j.jtbi.2007.03.029

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  3 in total

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Authors:  Soyoun Kim; Irina Bobeica; Nao J Gamo; Amy F T Arnsten; Daeyeol Lee
Journal:  Psychopharmacology (Berl)       Date:  2011-10-07       Impact factor: 4.530

2.  Starlings uphold principles of economic rationality for delay and probability of reward.

Authors:  Tiago Monteiro; Marco Vasconcelos; Alex Kacelnik
Journal:  Proc Biol Sci       Date:  2013-02-06       Impact factor: 5.349

3.  Trust your gut: using physiological states as a source of information is almost as effective as optimal Bayesian learning.

Authors:  Andrew D Higginson; Tim W Fawcett; Alasdair I Houston; John M McNamara
Journal:  Proc Biol Sci       Date:  2018-01-31       Impact factor: 5.349

  3 in total

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