We work with a large spatialized array of individuals in an environment of drifting food sources and predators. The behavior of each individual is generated by its simple neural net; individuals are capable of making one of two sounds and are capable of responding to sounds from their immediate neighbors by opening their mouths or hiding. An individual whose mouth is open in the presence of food is “fed” and gains points; an individual who fails to hide when a predator (...) is present is “hurt” by losing points. Opening mouths, hiding, and making sounds each exact an energy cost. There is no direct evolutionary gain for acts of cooperation or “successful communication” per se. In such an environment we start with a spatialized array of neural nets with randomized weights. Using standard learning algorithms, our individuals “train up” on the behavior of successful neighbors at regular intervals. Given that simple setup, will a community of neural nets evolve a simple language for signaling the presence of food and predators? With important qualifications, the answer is “yes.” In a simple spatial environment, pursuing individualistic gains and using partial training on successful neighbors, randomized neural nets can learn to communicate. (shrink)

Is simulation some new kind of science? We argue that instead simulation fits smoothly into existing scientific practice, but does so in several importantly different ways. Simulations in general, and computer simulations in particular, ought to be understood as techniques which, like many scientific techniques, can be employed in the service of various and diverse epistemic goals. We focus our attentions on the way in which simulations can function as (i) explanatory and (ii) predictive tools. We argue that a wide (...) variety of simulations, both computational and physical, are best conceived in terms of a set of common features: initial or input conditions, a mechanism or set of rules, and a set of results or output conditions. Studying simulations in these terms yields a new understanding of their character as well as a body of normative recommendations for the care and feeding of scientific simulations. (shrink)

Environmental variability has been proposed as an important mechanism in behavioral psychology, in ecology and evolution, and in cultural anthropology. Here we demonstrate its importance in simulational studies as well. In earlier work we have shown the emergence of communication in a spatialized environment of wandering food sources and predators, using a variety of mechanisms for strategy change: imitation (Grim, Kokalis, Tafti & Kilb 2000), localized genetic algorithm (Grim, Kokalis, Tafti & Kilb 2001), and partial training of neural nets on (...) the behavior of successful neighbors (Grim, St. Denis & Kokalis 2002). Here we focus on environmental variability, comparing results for all of these mechanisms in a range of different environments: (a) environments with constant resources, (b) environments with random resources around the same mean, and (c) sine-wave variable environments with cycles of ‘boom and bust’. Communication, it turns out, is strongly favored by environmental variability on the pattern of ‘boom and bust.'. (shrink)