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We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for ‘intelligent’ collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives. 

A cognitive network model tested in a longitudinal study shows that belief network dissonance predicts belief change. 

Belief change and spread have been studied in many disciplines—from psychology, sociology, economics and philosophy, to biology, computer science and statistical physics—but we still do not have a firm grasp on why some beliefs change more easily and spread faster than others. To fully capture the complex social-cognitive system that gives rise to belief dynamics, we first review insights about structural components and processes of belief dynamics studied within different disciplines. We then outline a unifying quantitative framework that enables theoretical and empirical comparisons of different belief dynamic models. This framework uses a statistical physics formalism, grounded in cognitive and social theory, as well as empirical observations. We show how this framework can be used to integrate extant knowledge and develop a more comprehensive understanding of belief dynamics. 

Preventive behaviors are crucial to prevent the spread of the coronavirus causing COVID-19. We adopted a complex psychological systems approach to obtain a descriptive account of the network of attitudes and behaviors related to COVID-19. A survey study ( N = 1,022) was conducted with subsamples from the United Kingdom ( n = 502) and the Netherlands ( n = 520). The results highlight the importance of people’s support for, and perceived efficacy of, the measures and preventive behaviors. This also applies to the perceived norm of family and friends adopting these behaviors. The networks in both countries were largely similar but also showed notable differences. The interplay of psychological factors in the networks is also highlighted, resulting in our appeal to policy makers to take complexity and mutual dependence of psychological factors into account. Future research should study the effects of interventions aimed at these factors, including effects on the network, to make causal inferences. 

In this article, we model implicit attitude measures using our network theory of attitudes. The model rests on the assumption that implicit measures limit attitudinal entropy reduction, because implicit measures represent a measurement outcome that is the result of evaluating the attitude object in a quick and effortless manner. Implicit measures therefore assess attitudes in high entropy states (i.e., inconsistent and unstable states). In a simulation, we illustrate the implications of our network theory for implicit measures. The results of this simulation show a paradoxical result: Implicit measures can provide a more accurate assessment of conflicting evaluative reactions to an attitude object (e.g., evaluative reactions not in line with the dominant evaluative reactions) than explicit measures, because they assess these properties in a noisier and less reliable manner. We conclude that our network theory of attitudes increases the connection between substantive theorizing on attitudes and psychometric properties of implicit measures.