Evolutionary learning in complex social networks

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Complex systems and agent-based modelling are attracting a growing interest from experts from various disciplines to simulate and study real world applications. The implications are of significance because they broaden our understanding of little known complex social phenomena, including reasoning about the causes of observed system behaviour and predicting the outcomes of tested case scenarios or interventions. Exciting systems that this field encompasses include: terrorist networks, prehistoric civilizations, and vehicle safety. An agent for instance can represent an individual, a household, or a driver. System designers often build a state based configuration for agents to react in a dynamic environment. Hence, the model provides a sandbox approach for the researchers to test various hypotheses. For example, an ancient civilization can be modelled using real field data where an archaeologist can then examine the effects on the emerging population size and movement under drought conditions compared to normal conditions in order to test a hypothesis suggesting that a drought caused the population to leave the study area. The research program proposes applying new techniques in agent learning when developing agent behaviour in a complex system. Beyond the basic finite state machine that an agent can use to react to its dynamic environment, a more realistic approach for agent intelligence proposes reactive agents that can accumulate knowledge at the individual and population levels, learn new plans and evolve new strategies on their own to fulfill their goals. Human behaviour however, is distinguished from other animal behaviours and consequently the conventional evolutionary frameworks need to be extended in order to build more realistic artificial agents. Through the proposed methods, the outcome of this research program will enhance the learning theory in artificial models and researchers working with agent-based simulations would be able to create more accurate and intelligent models using the algorithms from this work.