We have modelled fish biology and ecology since the mid-1980ies, particularly behaviour and life history as function of evolutionary adaptations to the marine environment. It started out with ecology and population dynamics of cod and gobies in a fjord ecosystem but switched quickly to modelling sensing, decision-making, and hehaviour.

The theoretical framin of all our early work, and also much we still do, is to search for evolutionarily optimal solutions to ecological problems. For this, we have used Life History Theory (age-dependent solutions), Dynamic Optimization (age- and state-dependent solutions) and Game Theory (frequency-dependent solutions).

Later, we have focussed on agent-based modelling (ABM, also called IBM) which allows for much richer representation of the fish. Combined with using Genetic Algorithms to evolve adaptive solutions, we can represent a high number of individual agents in an evolving population, where age-, state-, density-, and frequency-dependencies co-occur.

We model appetite, feeding, growth, hormonal dynamics, emotions, cognition, behaviour, stress and wellbeing.

We have also performed field and lab studies to test assumptions in the models.