Emergent Consequences: Unexpected Behaviors in a Simple Model to Support Innovation Adoption, Planning, and Evaluation
Many proven clinical interventions that have been tested in carefully controlled field settings have not been widely adopted. We study an agent-based model of innovation adoption. Traditional statistical models average out individual variation in a population. In contrast, agent-based models focus on individual behavior. Because of this difference in perspective, an agent based model can yield insight into emergent system behavior that would not otherwise be visible. We begin with a traditional logic of innovation, and cast it in an agent-based form. The model shows behavior that is relevant to successful implementation, but that is not predictable using the traditional perspective. In particular, users move continuously in a space defined by degree of adoption and confidence. High adopters bifurcate between high and low confidence in the innovation, and move between these groups over time without converging. Based on these observations, we suggest a research agenda to integrate this approach into traditional evaluation methods.
KeywordsAgent-based models emergent behavior innovation adoption clinical evaluation
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