Many human cultural and technological traits are built from multiple closely interacting parts that can form modules, changing through innovation, collaboration, and social learning. Understanding how such traits evolve requires a model that captures the epistatic interactions among these parts as well as the uniquely cultural processes acting on them. A new theoretical study by Laurel Fogarty and Rebecca B. Hoyle generalizes the concept of adaptive landscapes to do exactly that.

In classical adaptive landscape models, a population searches a landscape of possible trait combinations for peaks that represent optimal solutions. The new framework extends this idea to cultural systems, incorporating modularization, communication, and collaboration—processes that are not features of genetic systems but may be quite common in cultural evolution. The researchers show that modularization combined with the collaborative communication of search results is more successful at finding optima than searching a full adaptive landscape, but it can also be more costly.

However, the models highlight an important limitation. Under many real-world conditions in which technology evolves, a full search of many thousands of possible modules may not be feasible. Time, financial, energetic, and environmental constraints are likely to be strong, restricting how much of the landscape cultural populations can explore.

Under these constrained conditions, the models reveal a critical threshold for the number of modules searched. Below this value, the global optimum is more likely to be reached by searching the full landscape rather than breaking the problem into modules. Crucially, the position of this threshold depends on the complexity of the technology involved.

The work offers a theoretical bridge between the genetics-inspired mathematics of fitness landscapes and the study of human learning and interaction that drives cultural change. Because cultural systems can communicate, collaborate, and modularize in ways genes cannot, their evolutionary dynamics may differ in fundamental ways from biological ones. The authors suggest that analyzing cultural fitness landscapes is likely to be a fruitful avenue for research into technological change, especially when combined with elements of human learning and interaction known to be important features of cultural evolution.

As a model-based theoretical study, the findings describe general conditions under which modular versus full-landscape search succeeds, rather than measurements of any particular technology, but they provide a concrete framework for asking why some innovations emerge quickly while others remain out of reach.

Subject of Research: Adaptive landscape models of modular search and collaboration in cultural and technological evolution

Article Title: The evolutionary accessibility of cultural fitness landscapes

Article References: Fogarty, L., & Hoyle, R. B. (2026). The evolutionary accessibility of cultural fitness landscapes. PLOS Computational Biology, 22(10), e1014830. https://doi.org/10.1371/journal.pcbi.1014830

Image Credits: AI Generated

DOI: 10.1371/journal.pcbi.1014830

Keywords: cultural evolution, adaptive landscapes, modularization, technological change, social learning, epistasis, collaboration, communication, fitness landscapes, innovation, evolutionary, accessibility