Incorporating animal behavior into ecological models remains a challenge for ecology. At the core of decision-making is the need for trading priority across the organism’s various needs, such as growth, survival and reproduction. However, in naturally complex environments this task incurs intractable computational costs. Therefore, most models in behavioral ecology ignore the animal’s proximate complexities and use simple decision rules specific to the current contexts. Thus, these models account for what the organism should do but do not provide mechanistic explanation of how it may be decided. We posit that this does not solve the complexity challenge, and the underlying cognitive machinery should not be ignored. From the point of view of the animal, the fundamental problem is what are the best contexts to choose and what stimuli to respond to for achieving a specific goal such as homeostasis, survival and reproduction. This requires a cognitive machinery enabling the organism to make predictions about the future and behave autonomously, and this machinery can also be utilized in modeling decisions and behavior.
Results/Conclusions
We provide a framework including three essential aspects: (a) the focus on the autonomous individual, (b) the need to limit, as much as integrate, information from the environment, and (c) the importance of goal-directed rather than purely stimulus-driven cognitive and behavioral control. The resulting simulation models integrate cognition, decision-making and behavior in the whole integrated phenotype including the genome, physiology, hormonal systems, perception, emotions, motivation and cognition. We then find that the fundamental state to consider for decision-making is not stomach contents, fat reserves or body mass per se, but the global organismic state that includes the animal’s subjective “mind”. This approach also provides an avenue for evolutionary understanding of subjective phenomena and self-awareness as evolved mechanisms for adaptive decision-making in natural environments. It also gives a basis for understanding animal well-being, as well-being factors are evolved to help the animal find its near-future priorities and simplify the calculation of its next decisions.