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Evolution and the Mechanisms of Decision Making$
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Peter Hammerstein and Jeffrey R. Stevens

Print publication date: 2012

Print ISBN-13: 9780262018081

Published to MIT Press Scholarship Online: May 2016

DOI: 10.7551/mitpress/9780262018081.001.0001

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Putting Mechanisms into Behavioral Ecology

Putting Mechanisms into Behavioral Ecology

Chapter:
(p.21) 2 Putting Mechanisms into Behavioral Ecology
Source:
Evolution and the Mechanisms of Decision Making
Author(s):

Alex Kacelnik

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262018081.003.0002

This chapter contrasts two approaches to the study of mechanism and function in decision making: rules of thumbs (or heuristics) and the contributions of experimental psychology and psychophysics. The first approach is most frequently used by behavioral ecologists. It implements a behavioral gambit by which researchers address hypothetical decision problems without reference to independently known cognitive processes. The second approach shares interest in the functional consequences of behavior, but shows greater subordination to empirical research on behavioral and cognitive mechanisms. Here natural selection is seen to act on processes that tune behavior to the environment across broad domains. Associative learning and Weber’s Law are two putative evolutionary responses to such challenges. In the second approach these independently known traits, rather than ad-hoc rules or heuristics, are considered as candidates for effecting decisions, and this can often lead to asking for the functional problem a posteriori, querying what selective pressures might have led to the presence of the trait. Based on examples from foraging research, it is argued that for a majority of decision problems investigated across vertebrates, the second approach is preferable. It is also recognized that dedicated rules are preferable when the relevant information acts across generations and involves little learning.

Keywords:   Strüngmann Forum Reports, decision making, choice, learning, heuristics, marginal value theorem, optimal strategies, perception, reinforcement learning

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