Application of Agent Based Modeling to Insurance Cycles

Doctoral thesis English OPEN
Zhou, F.
  • Subject: HB

Traditional models of analyzing the general insurance market often focus on the behavior of a single insurer in a competitive market. They assume that the major players in this market are homogeneous and have a common goal to achieve a same long-term business objective, such as solving profit (or utility) maximization. Therefore these individual players in the traditional models can be implemented as a single representative economic agent with full rationality to solve the utility optimization. To investigate insurance pricing (or underwriting) cycles, the existing literature attempts to model various isolated aspects of the market, keeping other factors exogenous. We and that a multi-agent system describing an insurance market affords a helpful understanding of the dynamic interactions of individual agents that is a complementary to the traditional models. Such agent-based models (ABM) try to capture the complexity of the real world. Thus, economic agents are heterogeneous and follow divergent behavioral rules depending on their current unique competitive situations or comparative advantages relating to, for example, their existing market shares, distribution channels, information processes and product differentiations. The real-world continually-evolving environment leads agents to follow common rules of thumb to implement their business strategies, rather than completely be utility-maximizer with perfect foresight in an idealized world. The agents are adaptively learning from their local competition over time. In fact, the insurance cycles are the results of these dynamic interactions of agents in such complex system.
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    6.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 6.2 Directions for future research . . . . . . . . . . . . . . . . 198 205 4.1 Basic statistics of UK motor sector (Year: 1983-2011) . . . 90 4.2 Initial values of base case parameters . . . . . . . . . . . . 92 4.3 Model veri cation: claim parameter illustration 1 . . . . . 108 4.4 Model veri cation: actual market rm size distribution . . 111 4.5 Model validation: testing stationarity of data series . . . . 115 4.6 UK motor correlogram: ACF and PACF . . . . . . . . . . 116 4.7 Model validation: AIC results of AR process . . . . . . . . 117 4.8 UK motor residual testing: uncorrelatedness check . . . . . 117 4.9 Testing residuals (uncorrelatedness): Durbin-Watson statistics118 4.10 Chow test: UK motor insurance data . . . . . . . . . . . . 121 4.11 UK Motor AR2 regression . . . . . . . . . . . . . . . . . . 123 4.12 Simulated 900 years AR2 regression . . . . . . . . . . . . . 124 4.13 ARMA model selection: UK motor insurance data . . . . . 125 3.1 An illustration of a complex adaptive system . . . . . . . .

    3.2 An illustration of agent based models . . . . . . . . . . . .

    3.3 Forest re model . . . . . . . . . . . . . . . . . . . . . . .

    3.4 Sugarscape model . . . . . . . . . . . . . . . . . . . . . . .

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