Abstract
This paper investigates the influence of the preferences of individuals on the process of collective perception in the collective decision-making systems. To do this, the Ising model from the context of Social Impact Theory is studied on a dynamic network of agents within an environment. This model additionally considers the mechanisms of the direct modulation of positive feedback. We propose learning rules for updating the preferences. Such rules depend on the undertaken decisions of the individuals. The experiments are evaluated on the best-of-2 collective perception problem and compared with the state-of-the-art voting mechanisms such as majority and voter models. The results show that assigning preferences to the agents allows a designer to take control over the outcome of the collective decision-making process. In addition, the agents with a right conjecture can faster reach the correct conclusion even if only \(20\%\) of the initial population holds the target opinion.
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