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.

Abstract: TBA

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.

Abstract: Technological progress is the ultimate driver of economic growth, and forecasting technological progress is one of the pivotal issues for climate mitigation. While there is a rich anecdotal literature for technological change, there is still no overarching theory. Technology evolves under descent with variation and selection, but under very different rules than in biology. The data available to study technology are also very different: On one hand we have historical examples giving the performance of a few specific technologies over spans of centuries; on the other hand, the collection of information is much less systematic than it is for fossils. There is no good taxonomy, so in a sense the study of technological evolution is pre-Linnaean. This may be due to the complexities of horizontal information transfer, which plays an even bigger role for technology than it does for bacteria. There are nonetheless empirical laws for predicting the performance of technologies, such as Moore’s law and Wright’s law, that can be used to make quantitative distributional forecasts and address questions such as “What is the likelihood that solar energy will be cheaper than nuclear power 20 years from now?”. I will discuss the essential role of the network properties of technology, and show how 220 years of US patent data can be used as a "fossil record” to identify technological eras. Finally I will discuss new approaches for understanding technological progress that blend ideas from biology and economics.

Abstract: Where do power laws come from? There exist a handful of famous mechanisms that dynamically leadto scaling laws in complex dynamical systems, including preferential attachment processes and self-organized criticality. One extremely simple and transparent mechanism has so-far been overlooked. We present a mathematical theorem that states that every stochastic process that reduces its number of possible outcomes over time, leads to power laws in the frequency distributions of that so-called sample-space-reducing process (SSR). We show that targeted diffusion on networks is exactly such a SSR process, and we can thus understand the origin of power law visiting distributions that are ubiquitous in nature. We further comment on several examples where SSR processes can explain the origin of observed scaling laws including search processes, language formation and fragmentation processes.