In novel environments, strategic decision-making is often premised on analogy, and recognition lies at its heart. Recognition refers to a class of cognitive processes through which a problem is interpreted associatively in terms of something that has been experienced in the past. Despite recognition's centrality to strategic choice, we have limited knowledge of its nature and its influence on strategic decision-making in individuals, much less in the multi-agent settings in which these decisions typically occur. In this paper, we develop a model that extends neural nets techniques to capture recognition processes in groups of decision-makers. We use the model to derive some fundamental properties of collective recognition.

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