Decision Analysis in Stochastic Sociocultural Systems

Abstract

Decision makers often leverage causal and predictive scientific models, enabling them to better estimate the behavior of a physical or social system and explore several possible outcomes of a policy before actually enacting it. However, these models are only useful insofar as decision makers can effectively interpret the model behaviors, their outputs, and their inherent limitations. In this paper, we describe a methodological approach for uncertainty quantification in stochastic models of sociocultural systems via application of sensitivity analysis techniques, enabling decision makers to explicitly account for uncertainties in model validity or stability when using them as decision support tools. To demonstrate the efficacy of this approach, we present a case study analysis of food security challenges in Gambella, Ethiopia. Using a Bayesian modeling approach, we represent the stochastic interaction of meteorological, agroeconomic, political, and social factors influencing the likelihood of famine in the region and examine the impact of possible mitigating courses of action. Leveraging several sensitivity analysis approaches, we provide evidence of improvements to the decision-making process by making our models more transparent, highlighting the impact that model uncertainty may have when determining the optimal course of action and indicating to the decision maker what questions they need to ask, what data they need to gather, or where to focus their attention to identify and implement the best policy to prevent famine conditions.