Co-evolutionary analysis: a policy exploration method for system dynamics models

Abstract

In system dynamics (SD), complex nonlinear systems can generate a wide range of possible behaviours that frequently require search and optimization algorithms in order to explore optimal policies. Within the SD literature, the conventional approach to optimization is the formulation of a single objective function, with a targeted parameter list, and the entire model is simulated repeatedly in order to arrive at optimum values. However, many sector-based SD models contain heuristics of intended rationality, and a desired outcome for modellers to be able to explore the policy implications of locally rational behaviours. This can now be achieved through a method known as coevolution, which allows modellers to divide an unsolved problem into constituent parts, where each part can be solved with respect to its own fitness function. In this paper, we specify a solution for evolving locally rational strategies across a multi-sector SD structure. Using the beer distribution game (BDG) as an illustration, we demonstrate the utility of this approach in terms of the impact of two different order management strategies on the policy space of the BDG.