The Evolution of Ideas: How Models Illuminate Cultural Change

Author: Denis Avetisyan

A new review synthesizes the diverse modeling approaches used to understand how culture evolves, from individual learning to population-level trends.

Cultural evolution models are distributed across a coordinate system defined by the scale of entities considered and the rigor of agent evaluation, revealing four broad groupings that highlight fundamental differences in how these models approach the complexities of cultural transmission.

This paper presents a comprehensive overview of formal modeling techniques – including agent-based modeling, social network analysis, and evolutionary game theory – used to study cultural evolution and proposes a unifying framework for integrating these approaches.

Despite the inherent complexity of cultural transmission, formal modeling offers a powerful toolkit for dissecting the dynamics of change. This chapter, ‘Modelling cultural evolution’, surveys a diverse landscape of analytical approaches-from individual learning mechanisms to population-level processes-to understand how culture evolves. It proposes a unifying template connecting cognitive processes, behaviors, and macro-level outcomes within dynamic feedback loops, encompassing agents, groups, and their environments. Can a pluralistic yet coherent modeling framework ultimately illuminate the pathways of cultural adaptation and transformation?

The Shifting Sands of Belief: Understanding Cultural Dynamics

Conventional social science methodologies, frequently built upon assumptions of individual rationality and stable preferences, often falter when attempting to account for the swift and sometimes dramatic changes observed in collective beliefs and behaviors. These approaches struggle to explain phenomena like viral trends, sudden ideological shifts, or the rapid adoption of new technologies, as they prioritize logical decision-making over the influence of social context and emotional contagion. Consequently, researchers are increasingly turning to models that incorporate factors beyond individual calculation – such as cultural transmission, network effects, and the power of narratives – to better understand how macro-level changes propagate through populations and reshape individual actions. This necessitates a shift towards frameworks that acknowledge the inherently social and often irrational nature of human belief systems, recognizing that individuals are not isolated decision-makers but rather embedded within complex webs of influence.

Predicting societal trends demands a nuanced understanding of the reciprocal relationship between macro-level phenomena and individual actions. Research increasingly demonstrates that large-scale forces – encompassing economic shifts, political climates, and technological advancements – don’t simply impact individuals, but actively shape their beliefs, values, and behaviors. Simultaneously, the aggregated choices and actions of individuals contribute to, and even alter, these very macro-level forces, creating a dynamic feedback loop. For example, broad economic anxieties can fuel populist movements, while those movements, if successful, reshape economic policies. This interplay suggests that a complete model of social change must move beyond analyzing either individual psychology or large-scale structures in isolation; instead, it requires integrated frameworks capable of capturing how collective behavior emerges from, and subsequently influences, the wider social landscape.

Conventional models in social science frequently posit individuals as rational actors, consistently making decisions to maximize personal benefit. However, this framework often falls short when attempting to explain widespread behavioral shifts or the persistence of beliefs unsupported by evidence. Cultural transmission, the process by which information, values, and behaviors are passed from one generation to the next, introduces complexities beyond individual calculation. Social influence – encompassing conformity, imitation, and the power of norms – demonstrates that people are powerfully shaped by their environments and the actions of others. Consequently, beliefs and behaviors can spread through populations not because of inherent rationality, but because of social pressures, emotional resonance, or simply because “everyone else is doing it.” This highlights the need for models that incorporate the dynamics of social learning and the potent, often subconscious, forces that shape collective behavior.

This adaptation of Coleman’s boat model illustrates how macro-level cultural contexts influence and are shaped by individual actions at the micro-level.

Cultural Evolution: A Framework for Discerning Change

Cultural Evolution Theory provides a framework for analyzing cultural change by treating cultural traits – encompassing beliefs, practices, and technologies – as units of information subject to evolutionary pressures. This approach moves beyond simple diffusion models by acknowledging that the prevalence of a trait within a population isn’t solely determined by its inherent usefulness, but also by factors affecting its spread. The theory investigates how traits vary within a population, how they are transmitted through social learning and other mechanisms, and how selective pressures – including environmental factors, conformity biases, and individual preferences – determine which traits become more or less common over time, resulting in observable cultural shifts.

Cultural evolution, as a theoretical framework, proposes that cultural traits change over time via mechanisms mirroring those of biological evolution. Specifically, variation introduces new ideas, behaviors, or technologies within a population. Transmission occurs through various means, including imitation, teaching, and other forms of social learning, allowing these traits to spread. Finally, selection pressures – which can include environmental factors, individual preferences, and social norms – determine which traits are more likely to be retained and propagated across generations, leading to shifts in cultural characteristics over time. This process doesn’t necessarily imply intentional design; rather, it’s a consequence of differential replication based on factors influencing the likelihood of a trait’s transmission.

Cultural Evolution Theory acknowledges that human behavior is not exclusively driven by rational decision-making. Individuals frequently adopt beliefs and behaviors through social learning, observing and imitating the actions of others within their social groups. This process is significantly impacted by conformity bias, where individuals tend to align their behaviors and beliefs with those of the majority, even if those beliefs contradict personal information or judgment. Consequently, cultural traits can spread not because of their inherent utility, but due to these cognitive biases and the pressures of social cohesion, leading to the propagation of behaviors that may not be optimal from a purely rational perspective.

Mapping the Currents: Modeling Cultural Spread

The Susceptible-Infected-Recovered (SIR) model, originating in epidemiological studies, offers a foundational framework for analyzing cultural diffusion. In this application, ‘Susceptible’ individuals represent those not yet adopting a cultural trait, ‘Infected’ individuals have adopted the trait and may transmit it to others, and ‘Recovered’ individuals represent those who have abandoned the trait or are no longer actively transmitting it. The model utilizes parameters such as transmission rate (β) – the probability of adoption upon contact – and recovery/abandonment rate (γ) to simulate the spread and potential stabilization of a cultural trait within a population. While originally designed for disease modeling, the core principles of compartmentalizing a population and tracking transitions between states are directly applicable to understanding the dynamics of cultural change, providing a quantifiable and predictive basis for further research.

Social Network Analysis (SNA) investigates the relationships between individuals or entities, representing these connections as nodes and edges to visualize network structure. Key to understanding cultural diffusion within these networks are concepts like the “small-world” phenomenon, characterized by short average path lengths between nodes despite potentially large network size, and “scale-free” networks, where a few nodes (hubs) possess a disproportionately large number of connections. Analysis focuses on metrics such as degree centrality (number of connections), betweenness centrality (frequency a node lies on shortest paths between others), and clustering coefficient (measure of interconnectedness within a node’s immediate network) to identify influential actors and transmission pathways. These methods allow researchers to model how information, innovations, or behaviors propagate through a population, accounting for the impact of network topology on diffusion rates and patterns.

Agent-Based Modeling (ABM) and Population Dynamics utilize computational power to simulate the behaviors of individual agents-representing people or groups-and their interactions within a defined system. ABM allows researchers to specify rules governing agent actions and observe macro-level patterns that emerge from these micro-level behaviors, such as the adoption or abandonment of a cultural trait. Population Dynamics, often employing differential equations, models changes in population size and composition over time, incorporating factors like birth rates, death rates, migration, and adoption/rejection rates of innovations. These methods move beyond aggregate data to explore how individual decisions and network structures contribute to observable cultural shifts, enabling the testing of hypotheses about cultural transmission and the prediction of future trends. $\frac{dN}{dt} = bN - dN$ is a basic example of a population dynamics equation, where N is population size, b is the birth rate, and d is the death rate.

This diagram illustrates the iterative modeling process, depicting how observations inform model refinement and prediction.

The Dance of Influence: Refining the Model with Learning and Selection

Cultural evolution isn’t simply about the ‘best’ ideas winning out; it’s a complex interplay of strategic interactions where traits compete for adoption, much like players in a game. Game Theory provides the analytical tools to model these interactions, revealing how the success of a cultural trait depends not only on its inherent qualities but also on the strategies of those who adopt – or reject – it. Its extension, Evolutionary Game Theory, further illuminates this dynamic by considering how strategies change over time, with more successful traits becoming more prevalent. This framework demonstrates that even seemingly suboptimal traits can persist if they are part of a stable equilibrium, meaning that no individual benefits from changing their behavior. Consequently, understanding cultural transmission requires analyzing the payoffs – the benefits or costs – associated with adopting different traits within a given social context, and how these payoffs shape the long-term trajectory of cultural change.

Individuals don’t simply accept information passively; rather, they continuously refine their understanding of the world through a process remarkably well-described by Bayesian Inference. This statistical framework posits that beliefs are not fixed, but are instead probabilities updated in light of new evidence. When encountering a novel behavior or piece of information, an individual assesses its plausibility based on prior beliefs and the reliability of the source – a trusted elder versus a casual acquaintance, for example. This assessment generates a posterior belief, a weighted combination of the prior and the new evidence. The strength of this update is governed by $P(H|E) = \frac{P(E|H)P(H)}{P(E)}$ , where $P(H|E)$ represents the posterior probability of a hypothesis (H) given evidence (E). Crucially, social cues act as powerful forms of evidence; observing the success of a behavior in others strengthens the belief in its efficacy, driving cultural transmission and adaptation. This iterative process of belief updating, guided by both personal experience and social learning, allows individuals to navigate a complex world and effectively adopt beneficial cultural traits.

Reinforcement learning provides a powerful computational lens through which to examine the dynamics of cultural transmission. This approach models cultural learning as a process where individuals, acting as ‘agents’, adjust their behaviors based on the consequences they experience – rewards encouraging repetition of beneficial traits, and punishments discouraging less adaptive ones. By framing culture as a series of trial-and-error interactions, researchers can simulate how behaviors spread through a population, identifying which traits are most likely to persist and evolve. The framework allows for the investigation of nuanced scenarios, such as the impact of varying reward structures, the role of imitation, and the emergence of complex cultural norms, effectively demonstrating how cultural evolution can be understood as a form of adaptive learning driven by environmental feedback and social consequences.

Emergent Patterns and Future Directions in Cultural Evolution

Cultural shifts, seemingly complex and sweeping, often originate from the accumulated effect of numerous individual interactions and decisions. Researchers find that large-scale patterns of cultural change – what are known as emergent phenomena – aren’t centrally planned or directed, but instead ‘bubble up’ from the bottom through the repeated engagement of people with one another and their environment. These interactions, governed by principles of social learning and transmission, create a dynamic system where simple behaviors, when multiplied across populations, can lead to unexpected and substantial societal transformations. This process mirrors complex systems observed in nature, where intricate structures and behaviors emerge from the interplay of basic components, highlighting the power of decentralized dynamics in shaping cultural evolution.

Cultural selection operates as a fundamental force shaping societal evolution, mirroring natural selection but acting on ideas, beliefs, and practices instead of genes. This process favors cultural traits that enhance the survival or reproductive success of those who adopt them, leading to their increased prevalence within a population. Traits offering practical advantages, emotional resonance, or social cohesion are more likely to be transmitted across generations – or even within a single generation through social learning – creating a cascade effect. Consequently, seemingly complex cultural patterns aren’t necessarily the result of centralized planning, but rather the emergent outcome of this decentralized, competitive process where successful traits propagate and less adaptive ones fade, continually reshaping the cultural landscape.

Continued investigation necessitates a deepening of current modeling techniques to more fully represent the intricate processes of cultural evolution. The research presented here underscores the importance of refining these analytical tools, not simply to enhance predictive capabilities regarding societal shifts, but also to achieve a more nuanced comprehension of the underlying cultural dynamics at play. Greater accuracy in forecasting trends hinges on incorporating more variables and feedback loops within these models, acknowledging the non-linear nature of cultural change and the interplay between individual agency and collective behavior. Ultimately, a robust grasp of these techniques promises to unlock deeper insights into the forces shaping human societies and the long-term trajectory of cultural development.

The pursuit of understanding cultural evolution, as detailed in the modeling approaches presented, inherently acknowledges the limitations of any single predictive framework. The article highlights the need to move beyond simplistic assumptions and embrace the complexity of emergent phenomena arising from agent interactions. This resonates with the sentiment expressed by Wilhelm Röntgen: “I have made a discovery which will revolutionize medical science.” Röntgen’s statement, though pertaining to a different field, speaks to the profound impact that rigorous investigation-and the willingness to challenge established norms-can have on our understanding of the world. Just as Röntgen’s work revealed hidden structures, the modeling paradigms discussed seek to uncover the underlying mechanisms driving cultural change, accepting that sensitivity to outliers and initial conditions will invariably shape outcomes. The proposed template for integration, therefore, isn’t about achieving a definitive answer, but rather establishing a robust process for iteratively refining our understanding in the face of uncertainty.

What’s Next?

The proliferation of models, as this work illustrates, isn’t necessarily progress. Each parameter tuned, each assumption solidified, represents a narrowing of possibility, a premature commitment to a single explanation. The field risks mistaking the map for the territory, the elegance of an equation for actual understanding. A hypothesis isn’t belief-it’s structured doubt, and anything confirming expectations needs a second look. The real challenge isn’t building more models, but building models that actively seek their own refutation.

Future work will likely focus on integrating these disparate approaches, yet true integration demands acknowledging their inherent limitations. Agent-based modeling captures micro-level interactions, but struggles with scaling. Replicator dynamics offer population-level insights, but often at the cost of individual agency. The proposed template is a useful starting point, but it’s merely scaffolding. The crucial step is developing metrics that assess not just predictive power, but also the model’s conservatism-its resistance to incorporating genuinely novel data.

Ultimately, the study of cultural evolution isn’t about finding the ‘correct’ model. It’s about embracing the inherent messiness of complex systems and accepting that uncertainty is not a barrier to knowledge, but its very foundation. Rationality isn’t emotionless-it’s the discipline of uncertainty, and a willingness to abandon cherished assumptions in the face of inconvenient truths.

Original article: https://arxiv.org/pdf/2601.00433.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/