Abstract
Investigating the evolution of animal behavior is difficult. The fossil record leaves few clues that would allow us to recapitulate the path that evolution took to build a complex behavior, and the large population sizes and long time scales required prevent us from re-evolving such behaviors in a laboratory setting. We present results of a study in which digital organisms–self-replicating computer programs that are subject to mutations and selection–evolved in different environments that required information about past experience for fitness-enhancing behavioral decisions. One population evolved a mechanism for step-counting, a surprisingly complex odometric behavior that was only indirectly related to enhancing fitness. We examine in detail the operation of the evolved mechanism and the evolutionary transitions that produced this striking example of a complex behavior.
Highlights
Like structural and physiological traits, behavioral traits are an essential aspect of the biology of animals, and elicit similar evolutionary questions
Investigating the evolution of animal behavior is slowed by the fact that behavior leaves little evidence in the fossil record, and can generally be observed only in living specimens under restricted conditions of the sort they would experience in nature
The discussion is organized in three parts: (1) Evolved Behavior describes the observed behavior of the case study organism in test environments, (2) Evolved Odometry provides a detailed instruction-by-instruction analysis of how the evolved step-counting mechanism operates, and (3) Evolving the Step Counter examines the evolutionary history of case study organism from the initial ancestor
Summary
Like structural and physiological traits, behavioral traits are an essential aspect of the biology of animals, and elicit similar evolutionary questions. Investigating the evolution of animal behavior is slowed by the fact that behavior leaves little evidence in the fossil record, and can generally be observed only in living specimens under restricted conditions of the sort they would experience in nature. A promising new approach avoids these limitations using digital evolution models, which instantiate evolutionary causal processes in a virtual environment so that hypotheses about the evolution of behavior functions can be tested experimentally. Studying the evolution of complex behavior in a virtual world offers the opportunity to observe behavioral evolution in action, from simple capabilities to complex behavioral repertoires. We can examine the details of underlying genetic structure to discern its relationship to behavior, an option that is available only rarely and to a highly limited degree when studying behavior in living specimens
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