Analysis of Multi-Actor Policy Contexts Using Perception Graphs

• Main Author: Bots, Pieter W. G.
• Format: Conference Proceeding
• Language: English
• Subjects: Algebra; Applied computing > Operations research > Decision analysis; Bayesian methods; Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Intelligent agents; Computing methodologies > Artificial intelligence > Knowledge representation and reasoning; Computing methodologies > Machine learning > Machine learning approaches > Rule learning; Computing methodologies > Modeling and simulation > Model development and analysis > Modeling methodologies; Context modeling; Employment; Heuristic algorithms; Inference mechanisms; Information systems > Information systems applications > Decision support systems; Intelligent agent; Mathematics of computing > Discrete mathematics > Graph theory > Graph algorithms; Matrices; Public healthcare; Uncertainty
• DOI: 10.1109/IAT.2007.31

Policy making is a multi-actor process: it involves a variety of actors, each trying to further their own interests. How these actors decide and act largely depends on the way they perceive the policy problem. This paper describes Dynamic Actor Network Analysis (DANA), a graph-based method/tool to analyze a policy context by modeling how actors view a policy issue. Each actor view is modeled as a perception graph, a type of causal map that represents the (probabilistic) relations between goals, policy actions and external influences. Cross-comparison of these perception graphs reveals properties of the multi-actor policy network, such as factor relevance, resource dependency, conflict, and possible tradeoffs. Although DANA models technically have the potential for simulating policy scenarios, some interesting methodological problems remain.