Modular Bayesian Inference and Learning of Decision Networks as Stand-Alone Mechanisms of the MABEL Model: Implications for Visualization, Comprehension, and Policy-Making

Abstract

This paper describes a modular component of the MABEL model agents’ cognitive inference mechanism. The probabilistic and probabilogic representation of the agents’ environment and state space is coupled with a Bayesian belief and decision network functionality, which in fact holds Markovian semiparametric properties. Different approaches to modeling multi-agent systems are described and analyzed; problem-, model-, and knowledge-driven approaches to agent inference and learning are emphasized. The notion of modularity in agent-based modeling components is conceptualized. The modular architecture of the decision inference mechanism allows for a flexible architectural design that can be either endogenous or exogenous to the agent-based simulation model. A suite of decision support tools for modular network inference in the MABEL model is showcased; the emphasis is on the component object model versus interoperability development interfaces. These tools provide the complex functionality of developing “models within models,” thus simplifying the need for extensive research support and for a high-end level of knowledge acquisition from the end-users’ perspective. Finally, the paper assesses the validity of visual modeling interfaces for data- and knowledge-acquisition mechanisms that can provide an essential link between an in vitro research model, and the complex realities that are observed and processed by decision-makers, policy-makers, communities, and stakeholders.