Dynamic decision-making (DDM) research grew out of a perceived need for understanding how people control dynamic, complex, real-world systems. DDM has describable characteristics and, with some unavoidable sacrifice of realism, is suitable for study in a laboratory setting through the use of complex computer simulations commonly called 'microworlds'. This paper presents a taxonomic definition of DDM, an updated review of existing microworlds and their characteristics, and a set of cognitive demands imposed by DDM tasks. Although the study of DDM has garnered little attention to date, we believe that both technological advancement and the relationships between DDM and naturalistic decision making, complex problem solving, and general systems theory have made DDM a viable process by which to study how people make decisions in dynamic, real-world environments.

A fundamental aspect of human beings is that they learn. The process of learning and what is learned are impacted by a number of factors, both cognitive and social; that is, humans are boundedly rational. Cognitive and social limitations interact, making it difficult to reason about how to provide information to impact what humans know, believe, and do. Herein, we use a multi-agent dynamicnetwork simulation system, Construct, to conduct such reasoning. In particular, we ask, What media should be used to provide information to most impact what people know, believe, and do, given diverse social structures? All simulated agents are boundedly rational both at the cognitive and social level, and so are subject to factors such as literacy, education, and the breadth of their social network. We find that there is no one most effective intervention; rather, to be effective, messages and the media used to spread the message need to be selected for the population being addressed. Typically, a multimedia campaign is critical.

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