Bayesian networks of society-environment interactions
Bayesian networks of society-environment interactions are being developed to aid management and decision-making under conditions of environmental variability and uncertainty. By succinctly and effectively translating causal assertions between variables into patterns of probabilistic dependence, Bayesian networks (BNs) facilitate logical and holistic reasoning in complex systems. Such reasoning is necessary for accurate analysis, synthesis, prediction, diagnosis, and decision-making. In society-environment systems, BNs are useful because the predictive link that we want to model is often a complex causal chain, the entirety of which rarely falls within a single, coordinated research project. BNs allow this causal chain to be factored into an articulated sequence of conditional relationships, each of which can then be quantified independently using an approach suitable for the type and scale of information available.
Faculty contact: Mark E. Borsuk