RECONCILING VARIABILITY AND OPTIMAL BEHAVIOR USING MULTIPLE CRITERIA IN OPTIMIZATION MODELS

A major objective in behavioural and evolutionary ecology is to unders tand how animals make decisions in complex environments. Examinations of animal behaviour typically use optimization models to predict the c hoices animals ought to make. The performance of animals under specifi c conditions is then compared against the predicted optimal strategy. This optimization approach has come into question because model predic tions often do not match animal behaviour exactly. This has led to ser ious scepticism about the ability of animals to exhibit optimal behavi our in complex environments. We show that conventional approaches that compare observed animal behaviour with single optimal values may bias the way we view real-world variation in animal performance. Considera ble insight into the abilities of animals to make optimal decisions ca n be gained by interpreting why variability in performance exists. We introduce a new theoretical framework, called `multi-objective optimiz ation', which allows us to examine decision-making in complex environm ents and interpret the meaning of variability in animal performance. A multi-objective approach defines the set of efficient choices animals may make in attempting to reach compromises among multiple conflictin g demands. In a multi-objective framework, we may see variation in ani mal choices, but, unlike single-objective optimizations where there is one `best solution', this variation may represent a range of adaptive compromises to conflicting objectives. An important feature of this a pproach is that, within the set of efficient alternatives, no choice c an be considered to yield higher fitness, a priori, than any other cho ice. Thus, variability and optimal behaviour may be entirely consisten t. We illustrate our point using selected examples from foraging theor y where there is already an optimization program in place.