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In recent years, various computational models have been developed for studying the dynamics of belief formation in a population of epistemically interacting agents that try to determine the numerical value of a given parameter. Whereas in those models, agents’ belief states consist of single numerical beliefs, the present paper describes a model that equips agents with richer belief states containing many beliefs that, moreover, are logically interconnected. Correspondingly, the truth the agents are after is a theory (a set of sentences of a given language) rather than a numerical value. The agents epistemically interact with each other and also receive evidence in varying degrees of informativeness about the truth. We use computer simulations to study how fast and accurately such populations as wholes are able to approach the truth under differing combinations of settings of the key parameters of the model, such as the degree of informativeness of the evidence and the weight the agents give to the evidence.

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According to the standard definition of anticipatory systems, anticipation is based on a predictive model of the system itself and its environment. The paper abandons this perspective of weak anticipation in favor of what has been called strong anticipation. It is proposed that anticipation is a consequence of canalization caused by the organization of the structural building-blocks of which the system in question consists. Strong anticipation can account for the anticipatory behavior in animals to which we would not impute the ability of creating internal models of themselves.

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