Polarisation is a widespread societal issue, dividing populations into opposing groups whose beliefs may span multiple, seemingly unrelated, topics. This phenomenon creates distinct cultural groups with internally consistent yet mutually opposing beliefs, strengthening group identities and deepening societal divides. We develop a mathematical model to investigate how polarisation and emergent associations between unrelated beliefs occur. Unlike prior models assuming irrationality, informational segregation, or similarity biases, our model employs an epistemic distinction between two filtering mechanisms: content and source filtering. In content filtering, agents evaluate beliefs based on intrinsic coherence, rejecting information opposing their current beliefs. Source filtering, in contrast, involves adopting beliefs based on the perceived credibility of information sources, where credibility corresponds to mutual consistency between individuals’ belief systems. We further incorporate external signals through varying innovation and loss rates to reflect empirical biases from real-world data. Simulations show that source filtering rapidly clusters beliefs into two dominant, highly polarised groups. Within each group, beliefs become tightly correlated, creating strong opposition between the groups across all cultural traits. Thus, new associations emerge spontaneously between previously unrelated beliefs. Arbitrary and initially independent beliefs transform into ideological bundles serving as clear group signals. External signals further reinforce these associations, allowing advantages of particular beliefs (e.g. empirical evidence) to transfer to associated beliefs. Content filtering can also produce polarised groups, but beliefs remain aligned with preexisting logical or factual structures. Source filtering mirrors algorithmic biases such as collaborative filtering prevalent on digital platforms, where content exposure depends heavily on shared user preferences rather than intrinsic coherence. Our findings illustrate how these biases amplify polarisation by reinforcing homogeneous belief packages, intensifying ideological divides within cultural systems.

An empirical result in Moral Foundations Theory is that liberals and progressives endorse the individualizing factors of care and fairness, while conservatives claim that the binding factors of authority, loyalty, and purity are equally relevant when determining what is moral. Does this translate into persuasiveness of arguments and opinion change? We here test the hypothesis that conservatives can be swayed by binding moral arguments, while everyone is susceptible to individualizing moral arguments. Using a classic experimental design (N = 375) where respondents are given moral arguments for a position in nine moral issues, we find support for this hypothesis. In line with motivational matching, the moral foundation support of respondents predicts the type of arguments to which they are susceptible. Along with previous studies on which type of moral argument supports which moral position in the public debate, these findings provide a mechanistic explanation for public opinion change, and in particular for the observation that moral values are becoming more liberal and progressive across the board. Although people tend to be resistant to belief revision, their opinions on politically polarized issues can change when arguments match their beliefs, reflected in their ideology.

We introduce a mathematical model of cultural evolution to study cultural traits that shape how individuals exchange information. Current theory focuses on traits that influence the reception of information (receiver traits), such as evaluating whether information represents the majority or stems from a trusted source. Our model shifts the focus from the receiver to the sender of cultural information and emphasizes the role of sender traits, such as communicability or persuasiveness. Here, we show that sender traits are probably a stronger driving force in cultural evolution than receiver traits. While receiver traits evolve to curb cultural transmission, sender traits can amplify it and fuel the self-organization of systems of mutually supporting cultural traits, including traits that cannot be maintained on their own. Such systems can reach arbitrary complexity, potentially explaining uniquely human practical and mental skills, goals, knowledge and creativity, independent of innate factors. Our model incorporates social and individual learning throughout the lifespan, thus connecting cultural evolutionary theory with developmental psychology. This approach provides fresh insights into the trait-individual duality, that is, how cultural transmission of single traits is influenced by individuals, who are each represented as an acquired system of cultural traits.

A specific goal of the field of cultural evolution is to understand how processes of transmission and selection at the individual level lead to population-wide patterns of cultural diversity and change. Models of cultural evolution have typically assumed that traits are independent of one another and essentially exchangeable. But culture has a structure: traits bear relationships to one another that affect the transmission and selection process itself. Here, we introduce a modelling framework to explore the effect of interdependencies on the process of learning. Through simulations, we find that introducing a simple structure changes the cultural dynamics. Based on a basic filtering mechanism for parsing trait relationships, more elaborate cultural filters emerge. In a mostly incompatible cultural domain of traits, these filters organize culture into mostly (but not fully) consistent and stable systems. Incompatible domains produce small homogeneous cultures, while more compatibility increases size, diversity and group divergence. When individuals copy based on a trait's features (here, its compatibility relationships), they produce more homogeneous cultures than when they copy based on the agent carrying the cultural trait. We discuss the implications of considering cultural systems and filters in the dynamics of cultural change. This article is part of the theme issue 'Foundations of cultural evolution'.

Cultural evolution theory has long been inspired by evolutionary biology. Conceptual analogies between biological and cultural evolution have led to the adoption of a range of formal theoretical approaches from population dynamics and genetics. However, this has resulted in a research programme with a strong focus on cultural transmission. Here, we contrast biological with cultural evolution, and highlight aspects of cultural evolution that have not received sufficient attention previously. We outline possible implications for evolutionary dynamics and argue that not taking them into account will limit our understanding of cultural systems. We propose 12 key questions for future research, among which are calls to improve our understanding of the combinatorial properties of cultural innovation, and the role of development and life history in cultural dynamics. Finally, we discuss how this vibrant research field can make progress by embracing its multidisciplinary nature. This article is part of the theme issue 'Foundations of cultural evolution'.

We suggest a new method for detecting patterns of social clustering based on choice data. The method compares similar subjects within and between cohorts and thereby allows us to isolate the effect of peer influence from that of exogenous factors. Using this method on Norwegian register data, we address the question of whether students tend to cluster socially based on similar background. We find that common background correlates with making the same choices of curricular tracks, and that both exogenous preferences and peer influence matter. This applies to immigrant students from the same country, and, to some extent, to descendants of immigrants, but not to students from culturally similar countries. There are also small effects related to parents' education and income.

A widely accepted view in the cultural evolutionary literature is that culture forms a dynamic system of elements (or 'traits') linked together by a variety of relationships. Despite this, large families of models within the cultural evolutionary literature tend to represent only a small number of traits, or traits without interrelationships. As such, these models may be unable to capture complex dynamics resulting from multiple interrelated traits. Here we put forward a systems approach to cultural evolutionary research-one that explicitly represents numerous cultural traits and their relationships to one another. Basing our discussion on simple graph-based models, we examine the implications of the systems approach in four domains: (i) the cultural evolution of decision rules ('filters') and their influence on the distribution of cultural traits in a population; (ii) the contingency and stochasticity of system trajectories through a structured state space; (iii) how trait interrelationships can modulate rates of cultural change; and (iv) how trait interrelationships can contribute to understandings of inter-group differences in realised traits. We suggest that the preliminary results presented here should inspire greater attention to the role of multiple interrelated traits on cultural evolution, and should motivate attempts to formalise the rich body of analyses and hypotheses within the humanities and social science literatures.

Liberals and conservatives often take opposing positions on moral issues. But what makes a moral position liberal or conservative? Why does public opinion tend to become more liberal over time? And why does public opinion change especially fast on certain issues, such as gay rights? We offer an explanation based on how different positions connect with different kinds of moral arguments. Based on a formal model of opinion dynamics, we predicted that positions better connected to harm and fairness arguments will be more popular among liberals and will become more popular over time among liberals and conservatives. Finally, the speed of this trend will be faster the better the position connects to harm and fairness arguments. These predictions all held with high accuracy in 44years of polling on moral opinions. The model explains the connection between ideology and moral opinions, and generates precise predictions for future opinion change.

Ethno-racial workplace segregation increases already existing ethno-racial inequality. While previous research has identified discriminatory employers as drivers of workplace segregation, this study addresses the role of the employees. Sociological and social psychological theory suggest that people prefer to surround themselves with people who positively confirm their social identity or who contribute with higher group status. Through web-based surveys, we measure employee attitudes and preferences concerning ethno-racial workplace diversity, to what extent they differ by ethnicity/race, and if they contain intersectional patterns. Thereafter, we use simulation models to analyze the consequences for workplace segregation that these preferences would have, if realized. The main survey results showed that all ethno-racial groups favored their own in-group as colleagues, especially European Americans. As a secondary choice, the respondents preferred the out-group with the highest labor market status. Intersectional patterns were identified, as minority women were preferred as colleagues over minority men. Our simulation model, based on the results of two surveys on stated vs. indirectly revealed preferences, showed that employee preferences were at best not diverse enough to desegregate workplaces. When based on the most common preferences (i.e. excluding a few outliers), the simulations even suggested that these preferences can cause segregation. We relate these findings to Schelling's model of segregation.

This paper presents a new approach for studying temporal sequences across ordinal variables. It involves three complementary approaches (frequency tables, transitional graphs, and dependency tables), as well as an established adaptation based on Bayesian dynamical systems, inferring a general system of change. The frequency tables count pairs of values in two variables and transitional graphs depict changes, showing which variable tends to attain high values first. The dependency tables investigate which values of one variable are prerequisites for values in another, as a more direct test of causal hypotheses. We illustrate the proposed approaches by analyzing the V-Dem dataset, and show that changes in electoral democracy are preceded by changes in freedom of expression and access to alternative information.