James Holland Jones

The impact of online social influence on social learning, health behaviour and population health is a new, rapidly developing area of study. Previous research focused specifically on digital influencers suggests they may be able to affect group-level diffusion of health-protective behaviours such that they modify epidemic outcomes. However, formal models have yet to test the intuitive hypothesis that the effect of digital influencers is sufficient to generate tangible real-world effects on epidemics. We develop an agent-based model that incorporates digital influencers into an epidemic scenario to test hypotheses about how competing influence messages affect the diffusion of health-protective behaviours throughout a population and thereby alter the course and outcome of infectious disease epidemics. We find influencers had a persistent independent effect on peak infection intensity and total infection burden of the epidemic, with the greatest effects in highly homophilous scenarios. The presence of health-protective influence effectively flattened the epidemic curve despite equal presence of anti-protective influence.

The impact of social media influence on social learning, health behaviour, and population health is a new, rapidly developing area of study. Previous research focused specifically on digital influencers suggests they may be able to affect group-level diffusion of health-protective behaviours such that they modify epidemic outcomes. However, formal models have yet to test the intuitive hypothesis that the effect of digital social influencers is sufficient to generate tangible, real-world effects on epidemics. We develop an agent-based model that incorporates digital influencers into an epidemic scenario to test hypotheses about how competing influence messages affect the diffusion of health-protective behaviours throughout a population, and thereby alter the course and outcome of infectious disease epidemics. We find digital influencers had a persistent, independent effect on peak infection intensity and total infection burden of the epidemic, with the greatest effects in highly homophilous scenarios. The presence of health-protective influence effectively flattened the epidemic curve despite equal presence of anti-protective influence.

The force of selection on survival changes with age and models of reproductive effort (RE) reflect this finding that increases in infant and adult mortality have different effects on optimal effort. Using an age-structured demographic model, I show that infant mortality constrains selection on the human life cycle. Effort directed at increasing infant survival is strongly favored over efforts to increase adult survival. Complete accounts of human reproductive decisions require age-specificity of effects.

Sierra Leone is among the countries most vulnerable to climate change yet least resourced toconfront its impacts. Mainstream climate governance often casts adaptation as a technicaladjustment to biophysical hazards, but such framings risk naturalizing inequality by obscuringthe longue-durée histories of extraction and dispossession that have structured vulnerability.Drawing on multi-sited ethnographic research in coastal, agrarian, and urban communities inSierra Leone, this paper introduces the concept of counteradaptation to illustrate how peoplenavigate not only environmental volatility but also the antagonistic pressures generated by(neo)colonial systems. Counteradaptation highlights how adaptive moves in one domain—whether foreign extraction, ‘development’ apparatuses, or climate policy regimes—create newpressures that compel situated responses elsewhere. Case studies of fishing communities, timberand gold frontiers, and informal urban settlements show how counteradaptation reframeshabitability as a contested threshold and exposes the limits of technocratic adaptation toolkits. Bysituating climate response within histories of coloniality and predatory accumulation,counteradaptation offers a novel analytic for understanding ‘environmental’ behavior.

Successful climate change adaptation depends on the spread and maintenance of adaptive behaviours. Current theory suggests that the heterogeneity of metapopulation structure can help adaptations diffuse throughout a population. In this paper, we develop an agent-based model of the spread of adaptations in populations with minority–majority metapopulation structure, where subpopulations learn more or less frequently from their own group compared to the other group. In our simulations, minority–majority-structured populations with moderate degrees of in-group preference better spread and maintained an adaptation compared to populations with more equal-sized groups and weak homophily. Minority groups act as incubators for an adaptation, while majority groups act as reservoirs for an adaptation once it has spread widely. This means that adaptations diffuse throughout populations better when minority groups start out knowing an adaptation, as Indigenous populations often do, while cohesion among majority groups further promotes adaptation diffusion. Our work advances the goal of this theme issue by developing new theoretical insights and demonstrating the utility of cultural evolutionary theory and methods as important tools in the nascent science of culture that climate change adaptation needs. This article is part of the theme issue ‘Climate change adaptation needs a science of culture’.

American Anthropology is engaged in significant self-reckonings that call for big changes to how anthropology is practiced. These include (1) recognizing and taking seriously the demands to decolonize the ways research is done; (2) addressing precarious employment in academic anthropology; and (3) creating a discipline better positioned to respond to urgent societal needs. A central role for ethnographic methods training is a thread that runs through each of these four reckonings. This paper, written by a team of cultural, biocultural, and linguistic anthropologists, outlines key connections between ethnographic methods training and the challenges facing anthropology. We draw on insights from a large-scale survey of American Anthropological Association members to examine current ethnographic methods capabilities and training practices. Study findings suggest that there is a strong desire among anthropologists for deeper training in foundational methods such as participant observation, entering the field, and ethnographic fieldwork writ-large, combined with training in critical and collaborative methods, as well as in quantitative methods. In addition, respondents report having competencies in ethnographic data collection methods, but a lack of training in methods for systematically analyzing the data they collect. We explore these and other findings to answer three guiding questions: To what extent do our current anthropological practices in ethnographic methods training serve to advance or undermine current calls for disciplinary change? To what extent do instructors themselves identify disconnects between their own practices and the need for innovation? And, finally, what can be done, and at what scale, to leverage ethnographic methods training to meet calls for disciplinary change?

Social learning is a critical adaptation for dealing with different forms of variability. Uncertainty is a severe form of variability where the space of possible decisions or probabilities of associated outcomes are unknown. We identified four theoretically important sources of uncertainty: temporal environmental variability; payoff ambiguity; selection-set size; and effective lifespan. When these combine, it is nearly impossible to fully learn about the environment. We develop an evolutionary agent-based model to test how each form of uncertainty affects the evolution of social learning. Agents perform one of several behaviours, modelled as a multi-armed bandit, to acquire payoffs. All agents learn about behavioural payoffs individually through an adaptive behaviour-choice model that uses a softmax decision rule. Use of vertical and oblique payoff-biased social learning evolved to serve as a scaffold for adaptive individual learning – they are not opposite strategies. Different types of uncertainty had varying effects. Temporal environmental variability suppressed social learning, whereas larger selection-set size promoted social learning, even when the environment changed frequently. Payoff ambiguity and lifespan interacted with other uncertainty parameters. This study begins to explain how social learning can predominate despite highly variable real-world environments when effective individual learning helps individuals recover from learning outdated social information.

By structuring information in a systematic relational framework, narratives are cultural attractors that are particularly well-suited for transmission. The relational structure of narrative is partly what communicates causality, but this structure also complicates both transmission and selection on cultural elements by introducing correlations among narrative elements and between different narratives. These correlations have implications for adaptation, complexity, and robustness.

Successful climate change adaptation depends on the spread and maintenance of adaptive behaviors. Current theory suggests that the heterogeneity of metapopulation structure can help adaptations diffuse throughout a population. In this paper, we develop an agent-based model of the spread of adaptations in populations with minority-majority metapopulation structure, where subpopulations learn more or less frequently from their own group compared to the other group. In our simulations, minority-majority-structured populations with moderate degrees of in-group preference better spread and maintained an adaptation compared to populations with more equal-sized groups and weak homophily. Minority groups act as incubators for an adaptation, while majority groups act as reservoirs for an adaptation once it has spread widely. This means that adaptations diffuse throughout populations better when minority groups start out knowing an adaptation, as Indigenous populations often do, while cohesion among majority groups further promotes adaptation diffusion. Our work advances the goal of this theme issue by developing new theoretical insights and demonstrating the utility of cultural evolutionary theory and methods as important tools in the nascent science of culture that climate change adaptation needs.

Climate change adaptation involves the management of climate-related risks, and the IPCC says we must prioritize adaptation immediately. However, researchers and policymakers have little systematic understanding of which adaptations are effective at reducing risks, including under different climate conditions. Drawing on data from human communities past and present, we review how features of climate variability—temporal autocorrelation, frequency, and severity—may predict which candidate climate change adaptations communities innovate or adopt. Using a case study of climate and remittances in Africa, we outline how researchers can characterize features of climate data relevant to adaptation—autocorrelation, frequency, and severity—and then qualitatively compare these data to candidate adaptations. We include suggestions for how to involve communities in these explorations, from setting climate thresholds to identifying impactful hazards. By better understanding the relationship between climate variability and common solutions used by communities, researchers and policymakers can better support communities as they adapt to contemporary climate change.