American Zoologist · 1996 · 584 citations · 91 references
Life SciencesAnimal SciencesZoologyAnimal BehaviorBehavioral EcologyDecision TheoryAnimal Decision-makingBehavioral Decision-makingForaging EcologyRisk AversionRisk PreferencesRisk-sensitive Decision MakingFood Choice Under UncertaintyForaging Decision ModelsInformation Use In ForagingMechanistic ExplanationMechanistic InterpretationRisky BehaviorTrade-offs Between Foraging
This paper concerns the response of foraging animals to variability in rate of gain, or risk. Both the empirical and theoretical literatures relevant to this issue are reviewed. The methodology and results from fifty-nine studies in which animals are required to choose between foraging options differing in the variances in the rate of gain available are tabulated. We found that when risk is generated by variability in the amount of reward, animals are most frequently risk-averse and sometimes indifferent to risk, although in some studies preference depends on energy budget. In contrast, when variability is in delay to reward, animals are universally risk-prone. A range of functional, descriptive and mechanistic accounts for these findings is described, none of which alone is capable of accommodating all aspects of the data. Risk-sensitive foraging theory provides the only currently available explanation for why energy budget should affect preference. An information-processing model that incorporates Weber's law provides the only general explanation for why animals should be risk-averse with variability in amount and risk-prone with delay. A theory based on the mechanisms of associative learning explains quantitative aspects of risk-proneness for delay; specifically why the delay between choice and reward should have a stronger impact on preference than delays between the reward and subsequent choice. It also explains why animals should appear to commit the “fallacy of the average,” maximising the expected ratio of amount of reward over delay to reward when computing rates rather than the ratio of expected amount over expected delay. We conclude that only a fusion of functional and mechanistic thinking will lead to progress in the understanding of animal decision making.
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