Towards evidence-driven policy design: Complex adaptive systems and computational modeling

Efforts to design public policies for social systems tend to confront highly complex conditions which have a large number of potentially relevant factors to be considered and rapidly changing conditions where continuous adaptation delays or obscures the effect of policies. Given unresolvable uncertainty in policy outcomes, the optimal solution is difficult, if ever possible, to nail down. It is more reasonable to choose a solution that is robust to as many future scenarios that might ensue from the decision. Arriving at such a solution requires policy makers to actively explore and exploit rich information to support their decision making in a cost-efficient, yet rigorous manner. We name this new working style as evidence-driven policy design and outline the characteristics of favorable evidence. We then argue that computational modeling is a potential tool for implementing evidence-driven policy design. It helps the study and design of solutions by simulating various environments, interventions, and the processes in which certain outcomes emerge from the decisions of policy makers. It allows policy makers to observe both the intended and, equally important, unintended consequences of policy alternatives. It also facilitates communication and consensus-building among policy makers and diverse stakeholders.