We present a general framework for analyzing the contribution to reproductive success of a behavioural action. An action may make a direct contribution to reproductive success, but even in the absence of a direct contribution it may make an indirect contribution by changing the animal's state. We consider actions over a period of time, and define a reward function that characterizes the relationship between the animal's state at the end of the period and its future reproductive success. Working back from (...) the end of the period using dynamic programming, the optimal action as a function of state and time can be found. The procedure also yields a measure of the cost, in terms of future reproductive success, of a suboptimal action. These costs provide us with a common currency for comparing activities such as eating and drinking, or eating and hiding from predators. The costs also give an indication of the robustness of the conclusions that can be drawn from a model. We review how our framework can be used to analyze optimal foraging decisions in a stochastic environment. We also discuss the modelling of optimal daily routines and provide an illustration based on singing to attract a mate. We use the model to investigate the features that can produce a dawn song burst in birds. State is defined very broadly so that it includes the information an animal has about its environment. Thus, exploration and learning can be included within the framework. (shrink)

Optimization models have often been useful in attempting to understand the adaptive significance of behavioral traits. Originally such models were applied to isolated aspects of behavior, such as foraging, mating, or parental behavior. In reality, organisms live in complex, ever-changing environments, and are simultaneously concerned with many behavioral choices and their consequences. This target article describes a dynamic modeling technique that can be used to analyze behavior in a unified way. The technique has been widely used in behavioral studies of (...) insects, fish, birds, mammals, and other organisms. The models use biologically meaningful parameters and variables, and lead to testable predictions. Limitations arise because nature's complexity always exceeds our modeling capacity. (shrink)

A general framework for analysing the effects of variability and the effects of interruptions on foraging is presented. The animal is characterised by its level of energetic reserves, x. We consider behaviour over a period of time [0,T]. A terminal reward function R(x) determines the expected future reproductive success of an animal with reserves x at time T. For any state x at a time in the period, we give the animal a choice between various options and then constrain it (...) to follow a background strategy. The best option is the one that maximizes expected future reproductive success. Using this framework, we show that sensitivity to variability in amount of energy gained is logically distinct from sensitivity to variability in the time at which food is obtained. We also show that incorporating interruptions results in both a preference for variability in time and a preference for a reward followed by a delay as opposed to the same delay before the reward. (shrink)

Responses are strengthened by consequences having to do with the survival of individuals and species. With respect to the provenance of behavior, we know more about ontogenic than phylogenic contingencies. The contingencies responsible for unlearned behavior acted long ago. This remoteness affects our scientific methods, both experimental and conceptual. Until we have identified he variables responsible for an event, we tend to invent causes. Explanatory entities such as “instincts,” “drives,” and “traits” still survive. Unable to show how organisms can behave (...) effectively under complex circumstances, we endow them with special abilities permitting them to do so.Behavior exhibited by most members of a species is often accepted as inherited if all members were not likely to have been exposed to relevant ontogenic contingencies. When contingencies are not obvious, it is perhaps unwise to call any behavior either inherited or acquired, as the examples of churring in honey guides and following in imprinted ducklings show. Nor can the relative importance of phylogenic and ontogenic contingencies be argued from instances in which unlearned or learned behavior intrudes or dominates. Intrusions occur in both directions.Behavior influenced by its consequences seems directed toward the future, but only past effects are relevant. The mere fact that behavior is adaptive does not indicate whether phylogenic or ontogenic processes have been responsible for it. Examples include the several possible provenances of imitation, aggression, and communication. The generality of such concepts limits their usefulness. A more specific analysis is needed if we are to deal effectively with the two kinds of contingencies and their products. (shrink)

Behavioral ecologists often assume that natural selection will produce organisms that make optimal decisions. In the context of information processing, this means that the behavior of animals will be consistent with models from fields such as signal detection theory and Bayesian decision theory. We discuss work that applies such models to animal behavior and use the case of Bayesian updating to make the distinction between a description of behavior at the level of optimal decisions and a mechanistic account of how (...) decisions are made. The idea of ecological rationality is that natural selection shapes an animal's decision mechanisms to suit its environment. As a result, decision-making mechanisms may not perform well outside the context in which they evolved. Although the assumption of ecological rationality is plausible, we argue that the exact nature of the relationship between ecology and cognitive mechanism may not be obvious. (shrink)