Computational Public Policy:

Alice Rivlin mentioned this asepect in 1970, when she published “Systemic Thinking for Social Action.” Argued for more rigor and scientific processes in government decision-making, Rivlin wrote a pithy final line: “Put more simply, to do better, we must have a way of distinguishing better from worse.”

The data science approach is tremendously valuable for public servants and public policy. It pushes people to defy conjecture, consider counterfactuals, reason about complex patterns, and question what an information is missing. It makes people skeptical of tales, which, while often emotionally powerful, are not good sources of information on which to build comprehensive policies. Computational public policy is the application of computer science or mathematics to solving problems in public policy.
The computational public policy includes, but is not limited to, principles and methods for public policy formulation, decision making, analysis, modeling, optimization, forecasting, and simulation.

 

 

Image Source: Alex Engler/The University of Chicago

Publication:

“Optimization and Policy Analysis of Water allocation in a River Basin”, Sustain. Water Resour. Manag. 4, 433–446 (2018). https://doi.org/10.1007/s40899-017-0124-5 (Shivshanker Singh Patel and Parthasarathy Ramachandran)