Three Illustrations of Artiicial Life's Working Hypothesis

Artiicial life uses computer models to study the essential nature of the characteristic processes of complex adaptive systems| proceses such as self-organization, adaptation, and evolution. Work in the eld is guided by the working hypothesis that simple computer models can capture the essential nature of these processes. This hypothesis is illustrated by recent results with a simple population of computational agents whose sensorimotor functionality undergo open-ended adaptive evolution. These might illuminate three aspects of complex adaptive systems in general: punctuated equilibrium dynamics of diversity, a transition separating genetic order and disorder, and a law of adaptive evolutionary activity. Artiicial life studies computer models of the processes characteristic of complex adaptive systems|processes like self-organization, self-reproduction, adaptation , and evolution. Complex adaptive systems take many forms, each of which diiers from the others in myriad ways. By abstracting away from the diverse details , artiicial life hopes to reveal fundamental principles governing broad classes of complex adaptive systems. This hope rests on artiicial life's working hypothesis that simple computer models can capture the essential nature of complex adaptive systems 1]. I propose to pursue artiicial life's working hypothesis by applying a \ther-modynamic" methodology 5, 6, 3, 4, 2, 7]. Recently it has been suggested that there is a close, intrinsic connection between the content of evolution and ther-modynamics (e.g., Brooks and Wiley 8]). By contrast, I envisage the two elds as sharing the methodology of developing and investigating statistical macrovari-ables. Thermodynamics investigates macrovariables like temperature, pressure, and speciic heat, and the fruits of this method include simple, basic laws and classiications (like the ideal gas law and the phase transition separating the solids and liquids). By analogy, the \thermodynamic" approach in artiicial life seeks to identify statistical macrovariables that capture the distinctive features of complex adaptive systems. The most straightforward sign that this methodology is bearing fruit would be the demonstration that appropriate macrovariables can be used to frame simple, basic laws and classiications that apply to broad classes of complex adaptive systems.