By Professor Akira Namatame
Self-contained and unified in presentation, this necessary booklet presents a wide advent to the interesting topic of many-body collective platforms with adapting and evolving brokers. The assurance contains online game theoretic platforms, multi-agent structures, and large-scale socio-economic platforms of person optimizing brokers. the range and scope of such structures were gradually turning out to be in machine technological know-how, economics, social sciences, physics, and biology.
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Extra resources for Adaptation And Evolution in Collective Systems (Advances in Natural Computation)
In Chapter 8, we deal with social congestion problems. Dispersion games provide a simple model for understanding the mechanisms behind many paradigms for all types of congestion that may arise when we need to utilize limited resources. We introduce a new adaptive model based on the give-and-take strategy, in which agents yield to others if they gain, and otherwise randomize their actions. In Chapter 9, we explore an alternative learning model, coupled learning, and focus on coupling dynamics that may change in time according to coupled behavioral rules.
Therefore, it is important to consider with whom an agent should interact. 44 Adaptation and Evolution in Collective Systems In order to describe the ways of interaction, the random matching model is frequently used. There are also a variety of interaction models, depending on how agents meet, and what information is revealed before interaction. (1) Global interaction model When all agents are modeled to interact with all other agents in the same population, the model is referred to as a global interaction model.
Of course agents with learning capabilities can be particularly useful in dynamic environments, but when agents are allowed to learn or evolve into other agents, it is not guaranteed that the outcome will be desirable. Agents are both heterogeneous and versatile. As a result of both behavioral heterogeneity and versatility, small differences in agents can make large differences in collective outcomes. Heterogeneity turns up repeatedly as a crucial factor in collective systems. But the situation is not always as simple as saying that heterogeneity is desirable and homogeneity is not good.
Adaptation And Evolution in Collective Systems (Advances in Natural Computation) by Professor Akira Namatame