Opinion Polarization in Human Communities Can Emerge as a Natural Consequence of Beliefs Being Interrelated.

The emergence of opinion polarization within human communities-the phenomenon that individuals within a society tend to develop conflicting attitudes related to the greatest diversity of topics-has been a focus of interest for decades, both from theoretical and modelling points of view. Regarding modelling attempts, an entire scientific field-opinion dynamics-has emerged in order to study this and related phenomena. Within this framework, agents' opinions are usually represented by a scalar value which undergoes modification due to interaction with other agents. Under certain conditions, these models are able to reproduce polarization-a state increasingly familiar to our everyday experience. In the present paper, an alternative explanation is suggested along with its corresponding model. More specifically, we demonstrate that by incorporating the following two well-known human characteristics into the representation of agents: (1) in the human brain beliefs are interconnected, and (2) people strive to maintain a coherent belief system; polarization immediately occurs under exposure to news and information. Furthermore, the model accounts for the proliferation of fake news, and shows how opinion polarization is related to various cognitive biases.

Optimal collective decision making: consensus, accuracy and the effects of limited access to information.

"Knowledge is power"-holds the popular proverb, because knowledge and information is indeed one of the cornerstones of effective decision making, a requisite all living beings face continually. In fact, effective decision making is a matter of life and death, for individuals and groups alike. Furthermore, in case of group decisions, consensus is also often desirable. This latter one has been studied extensively by means of formal (mathematical) tools (in the field of opinion dynamics), while the first requirement, the process of yielding accurate information has been largely neglected, at least so far. In the present paper we study the optimal structure of groups which are embedded into an external, observable environment for (i) reaching consensus (ii) having well-informed members, and (iii) for those cases when both aspects are equally important. The groups are characterised by their communication networks and individual properties. We find that the group structures fundamentally differ from each other since having well-informed members requires highly specialised individuals embedded into a structured communication network, while consensus is promoted by non-hierarchical networks in which individuals participate equally. We also find that-contrary to intuition-high access to information calls forth hierarchy, and that suggestibility promotes accuracy, not consensus.

Group performance is maximized by hierarchical competence distribution.

Groups of people or even robots often face problems they need to solve together. Examples include collectively searching for resources, choosing when and where to invest time and effort, and many more. Although a hierarchical ordering of the relevance of the group members' inputs during collective decision making is abundant, a quantitative demonstration of its origin and advantages using a generic approach has not been described yet. Here we introduce a family of models based on the most general features of group decision making, and show that the optimal distribution of competences is a highly skewed function with a structured fat tail. Our results are obtained by optimizing the groups' compositions through identifying the best-performing distributions for both the competences and for the members' flexibilities/pliancies. Potential applications include choosing the best composition for a group intended to solve a given task.