Evol Ecol Res 5: 835-865 (2003)     Full PDF if your library subscribes.

Explicit trade-off rules in proximate adaptive agents

Jarl Giske,1* Marc Mangel,3 Per Jakobsen,2 Geir Huse,1‡ Chris Wilcox4§ and Espen Strand1

1Department of Fisheries and Marine Biology,  2Department of Zoology, University of Bergen, Postboks 7800, 5020 Bergen, Norway,  3Department of Applied Mathematics and Statistics, Jack Baskin School of Engineering and  4Department of Environmental Studies, University of California, Santa Cruz, CA 95064, USA

Author to whom all correspondence should be addressed.
e-mail: jarl.giske@ifm.uib.no


Organisms in nature are both proximate (operating by rules of thumb) and adapted (the rules influenced by natural selection). We introduce new methods that can be used to study in silico versions of organisms behaving according to proximate adapted rules. Our approach goes beyond neural networks and offers an alternative to optimization methods. It is based on the idea that organisms receive signals from the environment, that the signals are modified by internal (state-dependent) factors to create feelings (which we refer to as hedonic tones), and that behavioural processes (decisions) are a response to the hedonic tones. We illustrate these ideas through a model of a fish moving in a vertically structured environment, subject to predation and competition from conspecifics. The fish in our model responds to food, light, temperature and conspecifics, without any reference to current or future fitness. We use a combination of hedonic modelling to process the response, and genetic algorithms to modify the response via natural selection, according to internal needs and evolutionary history. We show that many different combinations of genes can lead to similar fitnesses, so that this approach generates genetic diversity. We compare our results with those of a variety of empirical studies and show that our approach can lead to new links between empirical and simulation studies.

Keywords: adaptation, affect, behaviour, individual-based modelling, perception, proximate, sensing, trade-off.

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