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Someone at UC Riverside - and alas, I don't remember his name! - pointed me toward these items.
It seems 'dynamic games' are different than 'evolutionary games', in that the strategies don't evolve over time; we just have players optimally responding to changing circumstances, finding a Nash equilibrium at every moment. The person telling me about this was criticizing dynamic game theory for being unrealistic, but saying evolutionary game theory looked more interesting. Still, I should learn about it.
Here are his interests:
New approach to macroeconomic modeling by means of jump Markov processes by specifying transition rates appropriately in the backward Chapman-Kolmogorov (master equation); solutions of master equations to obtain aggregate dynamic equations, and fluctuations by solving the associated Fokker-Planck equations. Modeling and analysis of multi-agent models to investigate such things as herding behavior and return dynamics, i.e., power-laws in share or stock markets; Modeling and analysis of multiple country models by state space time series technique; aggregation of economy with heterogeneous agents by neural network methods; adaptive learning algorithms.
I read a bunch of this book a long time ago, but now I'm more interested so I should reread it. Summary:
Stuart Kauffman here presents a brilliant new paradigm for evolutionary biology, one that extends the basic concepts of Darwinian evolution to accommodate recent findings and perspectives from the fields of biology, physics, chemistry and mathematics. The book drives to the heart of the exciting debate on the origins of life and maintenance of order in complex biological systems. It focuses on the concept of self-organization: the spontaneous emergence of order that is widely observed throughout nature Kauffman argues that self-organization plays an important role in the Darwinian process of natural selection. Yet until now no systematic effort has been made to incorporate the concept of self-organization into evolutionary theory. The construction requirements which permit complex systems to adapt are poorly understood, as is the extent to which selection itself can yield systems able to adapt more successfully. This book explores these themes. It shows how complex systems, contrary to expectations, can spontaneously exhibit stunning degrees of order, and how this order, in turn, is essential for understanding the emergence and development of life on Earth. Topics include the new biotechnology of applied molecular evolution, with its important implications for developing new drugs and vaccines; the balance between order and chaos observed in many naturally occurring systems; new insights concerning the predictive power of statistical mechanics in biology; and other major issues. Indeed, the approaches investigated here may prove to be the new center around which biological science itself will evolve. The work is written for all those interested in the cutting edge of research in the life sciences.