Social agent modeling and simulation : an aid to pre-adapting populations to serious societal disruptions

Serious societal disruptions can be expected in our future, and policy makers need better tools to help populations pre-adapt to them, in particular tools that incorporate internal-subjective behavior drivers and the means by which to model the behaviors they create. The Intelligent Agent paradigm developed in the Computer Science discipline is a powerful technology that enables population modeling at the level of the individual, and could have by now been demonstrably useful in creating tools to support policy makers’ work on this challenge. However, the Rational Agent instantiation of this paradigm, the focus of most Intelligent Agent work to date, is unsuitable for modeling the behavior of real human populations in a major societal disruption, due to avoidance of the internal-subjective bases of human sociality and so-called ‘irrational’ behaviors, exactly those that will dominate decision-making in such disruptions. There is growing understanding of such behavior drivers at the level of detail needed to support the modeling of significant-size populations. We propose a Social Agent instantiation of the Intelligent Agent paradigm for bottom-up modeling that explicitly incorporates these drivers, and we analyze the results of an implementation of this model in ‘EnergyWorld’, an abstract simulation of population behavior when resources needed for well-being are abruptly, significantly and persistently made scarce. Formal validation and verification in this research are limited due to the lack quantitative data on the internal-subjective nature of human decision-making; instead, we argue that the credibility of comparative policy analysis based on differential model parameter sets makes this approach useful for scenario-based policy analysis as a complement to other tools. We believe this to be true even if the disruptions we expect do not arrive in the intensity or form that seem likely today.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat