Agent-based models and hypothesis testing: an example of innovation and organizational networks

Hypothesis testing is uncommon in agent-based modeling and there are many reasons why (see Fagiolo et al. (2007) for a review). This is one of those uncommon studies: a combination of the new and old. First, a traditional neoclassical model of decision making is broadened by introducing agents who interact in an organization. The resulting computational model is analyzed using virtual experiments to consider how different organizational structures (different network topologies) affect the evolutionary path of an organization's corporate culture. These computational experiments establish testable hypotheses concerning structure, culture, and performance, and those hypotheses are tested empirically using data from an international sample of firms. In addition to learning something about organizational structure and innovation, the paper demonstrates how computational models can be used to frame empirical investigations and facilitate the interpretation of results in a traditional fashion.