Optimal foraging theory revisited (original) (raw)

Unorthodox Optimal Foraging Theory

From Animals to Animats 6, 2000

As the Simulation of Adaptive Behaviour (SAB) field continues to mature, it is essential that general methodological positions become elaborated into practical programmes of research. This paper describes how a particular flavour of SAB modelling-the use of genetic algorithms to design situated agent (animat) architectures-can effectively complement 'optimal foraging theory', as it is understood in theoretical biology. This allows several fundamental problems that arise directly out of the framework of orthodox optimal foraging theory to be addressed, but, as with any trade-off, is not without disadvantages of its own.

Generalizing foraging theory for analysis and design

, 2011

Foraging theory has been the inspiration for several decision-making algorithms for task-processing agents facing random environments. As nature selects for foraging behaviors that maximize lifetime calorie gain or minimize starvation probability, engineering designs are favored that maximize returned value (e.g. profit) or minimize the probability of not reaching performance targets. Prior foraging-inspired designs are direct applications of classical optimal foraging theory (OFT). Here, we describe a generalized optimization framework that encompasses the classical OFT model, a popular competitor, and several new models introduced here that are better suited for some task-processing applications in engineering. These new models merge features of rate maximization, efficiency maximization, and risk-sensitive foraging while not sacrificing the intuitive character of classical OFT. However, the central contributions of this paper are analytical and graphical methods for designing decision-making algorithms guaranteed to be optimal within the framework. Thus, we provide a general modeling framework for solitary agent behavior, several new and classic examples that apply to it, and generic methods for design and analysis of optimal task-processing behaviors that fit within the framework. Our results extend the key mathematical features of optimal foraging theory to a wide range of other optimization objectives in biological, anthropological, and technological contexts.

Natural selection and rational decision: two concepts of optimization

Journal of Evolutionary Economics, 2009

Human and biological scientists have been using two different kinds of optimization: "Selection optimization" characterizes competition in human and nonhuman societies sharing same market or niche. "Rationality optimization" characterizes human and nonhuman decision making processes. The two kinds of optimization generate the same result: how agents end up behaving efficiently. Both kinds cut across the disciplines of economics and biology: Biologists use rationality optimization such as the "inclusive fitness" hypothesis; economists use selection optimization such as market competition. Nonetheless, the paper argues that the two kinds of optimization are not redundant.

Optimality and Natural Selection in Markets

Journal of Economic Theory, 2002

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Optimal search behavior and classic foraging theory

Journal of Physics A-mathematical and Theoretical, 2009

Random walk methods and diffusion theory pervaded ecological sciences as methods to analyze and describe animal movement. Consequently, statistical physics was mostly seen as a toolbox rather than as a conceptual framework that could contribute to theory on evolutionary biology and ecology. However, the existence of mechanistic relationships and feedbacks between behavioral processes and statistical patterns of movement suggests that, beyond movement quantification, statistical physics may prove to be an adequate framework to understand animal behavior across scales from an ecological and evolutionary perspective. Recently developed random search theory has served to critically re-evaluate classic ecological questions on animal foraging. For instance, during the last few years, there has been a growing debate on whether search behavior can include traits that improve success by optimizing random (stochastic) searches. Here, we stress the need to bring together the general encounter problem within foraging theory, as a mean for making progress in the biological understanding of random searching. By sketching the assumptions of optimal foraging theory (OFT) and by summarizing recent results on random search strategies, we pinpoint ways to extend classic OFT, and integrate the study of search strategies and its main results into the more general theory of optimal foraging.

Complex Evolutionary Systems in Behavioral Finance

CHAPTER 4 Complex Evolutionary Systems in Behavioral Finance Cars Hommes and Florian Wagener CeNDEF, School of Economics, University of Amsterdam 4.1. Introduction 218 4.2. An Asset-Pricing Model with Heterogeneous Beliefs 221 4.2. 1. The Fundamental ...

Natural Evolution and Collective Optimum-Seeking

1992

On the one hand many people admire the often strikingly efficient results of organicevolution, on the other hand, however, they presuppose mutation and selection to be arather prodigal and unefficient trial-and-error strategy like Monte-Carlo sampling. Takinginto account the parallel processing of a heterogeneous population and sexual propagationwith recombination as well as the endogenous adaptation of strategy characteristics,simulated evolution reveals a