Pawel Sobkowicz
Journal of Artificial Societies and Social Simulation 12 (1) 11

Abstract: The growing field of studies of opinion formation using physical formalisms and computer simulation based tools suffers from relative lack of connection to the 'real world' societal behaviour. Such sociophysics research should aim at explaining observations or at proposing new ones. Unfortunately, this is not always the case, as many works concentrate more on the models themselves than on the social phenomena. Moreover, the simplifications proposed in simulations often sacrifice realism on the altar of computability. There are several ways to improve the value of the research, the most important by promoting truly multidisciplinary cooperation between physicists aiming to describe social phenomena and sociologists studying the phenomena in the field. In the specific case of modelling of opinion formation there are a few technical ideas which might bring the computer models much closer to reality, and therefore to improve the predictive value of the sociophysics approach.

Pawel Sobkowicz
Journal of Artificial Societies and Social Simulation 13 (4) 4

Abstract: We describe a computer model of general effectiveness of a hierarchical organization depending on two main aspects: effects of promotion to managerial levels and efforts to self-promote of individual employees, reducing their actual productivity. The combination of judgment by appearance in the promotion to higher levels of hierarchy and the Peter Principle (which states that people are promoted to their level of incompetence) results in fast declines in effectiveness of the organization. The model uses a few synthetic parameters aimed at reproduction of realistic conditions in typical multilayer organizations. It is shown that improving organization resiliency to self-promotion and continuity of individual productiveness after a promotion can greatly improve the overall organization effectiveness.

Pawel Sobkowicz
Journal of Artificial Societies and Social Simulation 20 (2) 5

Abstract: The paper presents an agent-based model of an evolution of research interests in a scientific community. The research epistemic/funding landscape is divided into separate domains, which differ in impact on society and the perceived utility, which may determine the public willingness to fund. Scientific domains also differ in their potential for attention grabbing, crucial discoveries, which make them fashionable and also attract funding. The scientists may `follow' the availability of funds via a stylized grant based scheme. The model includes possible effects of the additional public relation and lobbying efforts, promoting certain disciplines at the cost of others. Results are based on two multi-parameter NetLogo models. The first uses an abstract, square lattice topology, and serves as a tool to understand the effects of the parameters describing the individual preferences. The second model, sharing the internal dynamics with the first one, is based on an actual research topics map and projects statistics, derived from the UK Research Council data for 2007--2016. Despite simplifications, results reproduce characteristics of the British research community surprisingly well.

Pawel Sobkowicz
Journal of Artificial Societies and Social Simulation 21 (4) 8

Abstract: We present an introduction to a novel way of simulating individual and group opinion dynamics, taking into account how various sources of information are filtered due to cognitive biases. The agent-based model presented here falls into the ‘complex agent’ category, in which the agents are described in considerably greater detail than in the simplest ‘spinson’ model. To describe agents’ information processing, we introduced mechanisms of updating individual belief distributions, relying on information processing. The open nature of this proposed model allows us to study the effects of various static and time-dependent biases and information filters. In particular, the paper compares the effects of two important psychological mechanisms: confirmation bias and politically motivated reasoning. This comparison has been prompted by recent experimental psychology work by Dan Kahan. Depending on the effectiveness of information filtering (agent bias), agents confronted with an objective information source can either reach a consensus based on truth, or remain divided despite the evidence. In general, this model might provide understanding into increasingly polarized modern societies, especially as it allows us to mix different types of filters: e.g., psychological, social, and algorithmic.