Explicit trade-off rules in proximate adaptive agents

Authored by Geir Huse, E Strand, M Mangel, J Giske, P Jakobsen, C Wilcox

Date Published: 2003

Sponsors: Norwegian Research Council (NRF) National Marine Fisheries Service

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

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.

Tags

individual-based models Decision-Making Predation risk Atlantic salmon Neural-networks Life-history Antipredator behavior Foraging behavior C-elegans Common currency