Identifying the processes by which human cultures spread across different populations is one of the most topical objectives shared among different fields of study. Seminal works have analyzed a variety of data and attempted to determine whether empirically observed patterns are the result of demic and/or cultural diffusion. This special issue collects articles exploring several themes (from modes of cultural transmission to drivers of dispersal mechanisms) and contexts (from the Neolithic in Europe to the spread of computer programming languages), which offer new insights that will augment the theoretical and empirical basis for the study of demic and cultural diffusion. In this introduction we outline the state of art in the modeling of these processes, briefly discuss the pros and cons of two of the most commonly used frameworks (equation-based models and agent-based models), and summarize the significance of each article in this special issue.

Several forms of social learning rely on the direct or indirect evaluation of the fitness of cultural traits. Here we argue, via a simple agent-based model, that payoff uncertainty, that is, the correlation between a trait and the signal used to evaluate its fitness, plays a pivotal role in the spread of beneficial innovation. More specifically, we examine how this correlation affects the evolutionary dynamics of different forms of social learning and how each form can generate divergent historical trajectories depending on the size of the sample pool. In particular, we demonstrate that social learning by copying the best model is particularly susceptible to a sampling effect caused by the interaction of payoff uncertainty, the number of models sampled (the sample pool), and the frequency with which a trait is present in the population. As a result, we identify circumstances in which smaller sample pools can act as “cultural incubators” that promote the spread of innovations, while more widespread sampling of the population actually retards the rate of cultural evolution.

With the current surge of simulation studies in archaeology, there is a growing concern for the lack of engagement and feedback between modelers and domain specialists. To facilitate this dialogue, I present a compact guide to the simulation modeling process applied to a common research topic and the focus of this special issue of Human Biology—human dispersals. The process of developing a simulation is divided into nine steps grouped in three phases. The conceptual phase consists of identifying research questions (step 1), finding the most suitable method (step 2), designing the general framework and the resolution of the simulation (step 3), and filling in that framework with the modeled entities and the rules of interactions (step 4). This is followed by the technical phase of coding and testing (step 5), parameterizing the simulation (step 6), and running it (step 7), and the results of the simulation are analyzed and recontextualized (step 8). In the dissemination phase, the findings of the model are disseminated in publications and code repositories (step 9). Each step is defined and characterized and then illustrated with examples of published human dispersal simulation studies. While not aiming to be a comprehensive textbook-style guide to simulation, this overview of the process of modeling human dispersals should arm any nonmodeler with enough understanding to evaluate the quality, strengths, and weaknesses of any particular archaeological simulation and provide a starting point for further exploration of this common scientific tool.

In this article we explore the theoretical limits of the inference of cultural transmission modes based on sparse population-level data. We approach this problem by investigating whether different transmission modes produce different temporal dynamics of cultural change. In particular, we explore whether different transmission modes result in sufficiently different distributions of the average time a variant stays the most common variant in the population, t_max, so that their inference can be guaranteed on the basis of an estimate of t_max. We assume time series data detailing the frequencies of different variants of a cultural trait in a population at different points in time and investigate the temporal resolution (i.e., the length of the time series and the distance between consecutive time points) that is needed to ensure distinguishability between transmission modes. We find that under complete information most transmission modes can be distinguished on the basis of the statistic t_max; however, we should not expect the same results if only infrequent information about the most common cultural variant in the population is available.

We investigated the relationship between the dispersal potential of a hominin population, its local-scale foraging strategies, and the characteristics of the resource environment using an agent-based modeling approach. In previous work we demonstrated that natural selection can favor a relatively low capacity for assessing and predicting the quality of the resource environment, especially when the distribution of resources is highly clustered. That work also suggested that the more knowledge foraging populations had about their environment, the less likely they were to abandon the landscape they know and disperse into novel territory. The present study gives agents new individual and social strategies for learning about their environment. For both individual and social learning, natural selection favors decreased levels of environmental knowledge, particularly in low-heterogeneity environments. Social acquisition of detailed environmental knowledge results in crowding of agents, which reduces available reproductive space and relative fitness. Agents with less environmental knowledge move away from resource clusters and into areas with more space available for reproduction. These results suggest that, rather than being a requirement for successful dispersal, environmental knowledge strengthens the ties to particular locations and significantly reduces the dispersal potential as a result. The evolved level of environmental knowledge in a population depends on the characteristics of the resource environment and affects the dispersal capacity of the population.

Our interaction with complex computing machines is mediated by programming languages (PLs), which constitute one of the major innovations in the evolution of technology. PLs allow flexible, scalable, and fast use of hardware and are largely responsible for shaping the history of information technology since the rise of computers in the 1950s. The rapid growth and impact of computers were followed closely by the development of PLs. As occurs with natural, human languages, PLs have emerged and gone extinct. There has been always a diversity of coexisting PLs that compete somewhat while occupying special niches. Here we show that the statistical patterns of language adoption, rise, and fall can be accounted for by a simple model in which a set of programmers can use several PLs, decide to use existing PLs used by other programmers, or decide not to use them. Our results highlight the influence of strong communities of practice in the diffusion of PL innovations.

We present paleoeconomy reconstructions for premodern agriculture, selecting, wherever required, features and parameter values specific for the Cucuteni—Trypillia cultural unity (CTU; 5,400–2,700 BC, mostly the territory of modern Ukraine, Moldova, and Romania). We verify the self-consistency and viability of the archaeological evidence related to all major elements of the agricultural production cycle within the constraints provided by environmental and technological considerations. The starting point of our analysis is the paleodiet structure suggested by archaeological data, stable isotope analyses of human remains, and palynology studies in the CTU area. We allow for the archeologically attested contributions of domesticated and wild animal products to the diet, develop plausible estimates of the yield of ancient cereal varieties cultivated with ancient techniques, and quantify the yield dependence on the time after initial planting and on rainfall (as a climate proxy). Our conclusions involve analysis of the labor costs of various seasonal parts of the agricultural cycle of both an individual and a family with a majority of members that do not engage in productive activities that require physical fitness, such as tillage. Finally, we put our results into the context of the exploitation territory and catchment analysis, to project various subsistence strategies into the exploitation territory of a farming settlement. The simplest economic complex based on cereals and domestic and wild animal products, with fallow cropping, appears to be capable of supporting an isolated, relatively small farming settlement of 50–300 people (2–10 ha in area) even without recourse to technological improvements such as the use of manure fertilizer. Our results strongly suggest that dairy products played a significant role in the dietary and labor balance. The smaller settlements are typical of the earliest Trypillia A stage but remain predominant at the later stages. A larger settlement of several hundred people could function in isolation, perhaps with a larger fraction of cereals in the diet, only with technological innovations, such as manure fertilizer and, most important, ard tillage. The ard radically relieves the extreme time pressure associated with soil preparation for sowing. It appears that very large settlements of a few hundred hectares in area, found in the CTU region, could function only if supported by satellite farming villages and stable exchange networks. In turn, this implies social division of labor and occupation, sufficiently complex social relations, stable exchange channels, and so on: altogether, a proto-urban character of such settlements. A model is proposed for the lifetime of a farming settlement, assuming that it is limited by the soil fertility (the depleted resources model), that provides a lifetime estimate consistent with the archaeological evidence available (100–150 years). The model shows that the lifetime strongly depends on the fraction of the arable land area kept fallow. We also discuss, quantify, and assess some strategies to mitigate the risks of arable agriculture associated with strong temporal fluctuations in the cereal yield, such as manure fertilization, and increased fraction of cereals in the diet combined with producing grain surplus for emergency storage.