Santa Fe Institute Workshop (SFIW) Session 1
Time and Date: 10:00 - 12:30 on 21st Sep 2016
Room: N - Graanbeurszaal
Chair: Stefan Thurner
|46000|| Most Sociable and Most Polite: the Collective Mathematics of Creativity
Abstract: Bayesian models of cognition have been extremely successful at describing human behavior in the laboratory. Yet they can neither predict nor explain our most advanced forms of human communication, from political debate to free markets. Optimal agents will neither trade nor attempt to persuade. Without a rigorous mathematical account of collective reasoning, however, we are unable to imagine new social systems, or to know what is of value in the ones we wish to repair. I show how standard Bayesian models are undermined by the need to explore an indefinitely large problem space. I then present an alternative account of human rationality based on sociability, rather than computation. This framework predicts a central role for reciprocal conversation among equals, bounded conflict, and non-aligned incentives, in discovering new solutions. I conclude with recent empirical evidence for these models, drawn from collaborative research into scientific creativity, parliamentary debate, and play.
|Stuart A Kauffman|
|46002|| Adaptive self-organization of? Bali?s ancient rice terraces
Abstract: Spatial patterning often occurs in ecosystems as a self-organizing process caused by feedback between organisms and the physical environment. Here we show that the spatial patterns observable in centuries-old Balinese rice terraces are also created by feedback between farmer’s decisions and the ecology of the paddies, which triggers a transition from local to global-scale control by groups of farmers. An evolutionary game based on this model predicts spatial patterning that closely matches multispectral image analysis of Balinese rice terraces extending over five orders of magnitude. The model shows for the first time that adaptation in a coupled human-natural system can trigger self-organized criticality (SOC). In standard SOC models, the driver is exogenous, scale invariance of patch distributions occurs across a wide range of parameter values, adaptation plays no role and nothing is optimized. In contrast, adaptive SOC is a self-organizing process of local adaptations that drive parameter settings to a very narrow range at the phase transition, approaching local and global optima.