Introducing a decision making framework to help users detect, evaluate, assess, and recommend (DEAR) action within complex sociotechnical environments

Abstract

As causality becomes non-linear, data-driven decision making becomes challenging because traditional Decision Support Systems (DSSs) do not support these environments. Existing analytical methods struggle in complex environments where variables that interact with each other as well as themselves. DEAR is a new framework that supports complex decision making. This framework performs change detection, evaluates causal relationships and interactions, assesses the risk of known interventions, and recommends action using a guided exploration strategy that blends historically successful solutions with possible alternatives. Fear of viral outbreaks drove the need for public policy to mitigate mosquito populations in Columbus, OH. Through monitoring environmental variables, this approach detected changes in precipitation and temperature. Using CCM to assess causality, this approach determined that temperature was the primary driver of the complex mosquito population ecosystem. It determined the probability that mosquito populations would also rise. Through forming a risk assessment based upon the historic success of mosquito control systems, this approach is able to offer real-time guidance as to which vector-based mitigation strategy to pursue.