"What if?" Dealing with uncertainity in Repage\´s mental landscape

This paper is focused on cognitive decision-making about how to solve inconsistencies and incompleteness in social evaluations (e.g. about potential partners in exchange). We propose a development of Repage, a computational system for forming and updating social evaluations. The system draws upon a fundamental difference between reputation and image as a way out from the trade-off between agents´ autonomy and their need to adapt to social environment. A full exploitation of its potentialities includes the activation of a special module, the analyzer, aimed at solving possible inconsistencies, uncertainties and incompleteness in the output of lower level modules by means of inner simulation. In this work, Repage´s analyzer architecture is described; some representative examples of problems posed by the planner to the analyzer is discussed and hypothetical simulations are run within this module to find a solution to uncertainty, avoiding at the same time the exceeding complexities of rule-based reasoning and the costs of reinforcement learning.