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University of Birmingham > Talks@bham > Analysis Seminar > Evolutionary game-theoretic modelling of cyber-security, crime prevention, inspection and corruption
Evolutionary game-theoretic modelling of cyber-security, crime prevention, inspection and corruptionAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact José Cañizo. This talk is part of the Mathematics Colloquium A large class of stochastic conflict interactions is analysed by modelling the pressure executed by the major player (or principal) on the large group of small players that can either resist this pressure or collaborate with the major player. In order to be able to cope with a large number of players, it is suggested to look for a limiting behaviour of various Markov decision models of interacting small agents, as a number of players tend to infinity, namely interacting pairwise, in groups, by coalition formations and supplemented by evolutionary growth. As it turns out, for a large class of models, the limit can be described by a deterministic evolution on the distributions of the state spaces of small players. Mathematically the most challenging task concerns the situations with an infinite state space of small players arising, in particular, in the models of evolutionary growth. These models have been extensively studied recently, due mainly to their ability to predict the power-tail distributions, well observed experimentally in the abundance of real world processes (Zipf’s law). Our analysis makes it possible to strategically enhance the performance of these laws. Possible applications include processes of inspection, corruption, cyber-security, counter-terrorism, banks and firms merging and many others. In particular, our approach unifies results obtained by experts in these different areas, which turned out to deal with mathematically identical models. This talk is part of the Analysis Seminar series. This talk is included in these lists:Note that ex-directory lists are not shown. |
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