Friday, March 31, 2017

Sweet and Short Introduction to Complexity Science

There is a key deficit of network theory. It is that it relies on historical data to generate networks; we can see networks that have evolved and analyze them for 2008 financial crises and current products like telematics but what about networks that are yet to emerge?

Agent based modeling effectively addresses this shortcoming.  It is recognized that network theory uncovers a lot of important underlying structures that blind traditional actuarial theory. Moreover, key concepts like robustness, fragility, emergence, the topological geodesic structure into particular network models etc are likely to remain the same even though their manifestations will be different for emerging risks.

Hence, to organize behavior rules to set as base for agent based simulations, Common tools that complexity scientists use are extrapolating network trends from similar risks like extrapolating telematics network for drone insurance, game theory, genetic algorithms, heuristics and cognitive tendencies that we humans apply uncovered by behavioral finance, and neural networks.

Agent based modeling combined individual decision and network rules to model policyholder behavior, allowing us to simulate behavior at an individual level and then analyze the overall, aggregate outcomes. These models simulate the simultaneous operations and interactions of multiple individuals to recreate a system and predict complex phenomena. This process results in emergent behavior at the macro level based on micro-level system interactions.

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