Dr. Amy Sliva, a scientist at Charles River Analytics, co-authored a SpringerBrief, Data-driven Generation of Policies, with University of Maryland professors Dr. Austin Parker and Dr. V.S. Subrahmanian, and University of Oxford professor Dr. Geraldo Simari. This ebook presents basic and enhanced algorithms that address optimal state change attempts, state change effectiveness, and effect estimators to help researchers analyze tabular data containing states and events whose effects are not well understood.
Dr. Sliva explains, “Essentially, this work is about a data-driven approach that uses human socio-cultural behavioral data to automatically generate possible courses of action or mitigating policies. The basic idea is to understand what aspects of the state of the world can be changed to induce more favorable behavioral responses, while balancing issues such as feasibility, cost, resource constraints, and probability of success.”
Data-driven Generation of Policies is part of the SpringerBrief Computer Science series. SpringerBriefs are approximately 50-125 page ebook summaries of professional and academic content that cover a wide range of cutting-edge research and practical applications.