Normative reasoning within the mainstream economic framework has been largely shaped by utilitarian ethics. The growing popularity of effective altruism indicates that the utilitarian spirit has also permeated the sphere of social sentiment, evaluating our pro-social behaviour and charitable giving in terms of efficiency. The present study assesses the appropriateness of judging social outcomes through the prism of allocative efficiency by questioning to what extent the society of effective altruists is robust, sustainable, and resilient. Using computer simulations based on the dictator game, we demonstrate that a society of welfare-maximising effective altruists can achieve an optimal outcome alongside equality under extremely restrictive assumptions, such as the uniformity of giving strategies (i.e. interacting with other effective altruists exclusively) and initial equality of wealth distribution. Yet, in the world of unequal opportunities, utilitarian giving tends to increase wealth disparity. In addition, in polymorphic societies, effective altruists underperform compared to deontological (or unconditional) altruists. Consequently, we demonstrate that striving for allocative efficiency might undermine the equality and resilience objectives and question whether the former should remain the dominant economic normative principle.

Oncomelania hupensis (O. hupensis), the sole intermediate host of Schistosoma japonicum, greatly influence the prevalence and distribution of schistosomiasis japonica. The distribution area of O. hupensis has remained extensive for numerous years. This study aimed to establish a valid agent-based model of snail density and further explore the environmental conditions suitable for snail breeding. A marshland with O. hupensis was selected as a study site in Dongting Lake Region, and snail surveys were monthly conducted from 2007 to 2016. Combined with the data from historical literature, an agent-based model of snail density was constructed in NetLogo 6.2.0 and validated with the collected survey data. BehaviorSpace was used to identify the optimal ranges of soil temperature, pH, soil water content, and vegetation coverage for snail growth, development and reproduction. An agent-based model of snail density was constructed and showed a strong agreement with the monthly average snail density from the field surveys. As soil temperature increased, the snail density initially rose before declining, reaching its peak at around 21°C. There were similar variation patterns for other environmental factors. The findings from the model suggested that the optimum ranges of soil temperature, pH, soil water content and vegetation coverage were 19°C to 23 °C, 6.4 to 7.6, 42% to 75%, and 70% to 93%, respectively. A valid agent-based model of snail density was constructed, providing more objective information about the optimum ranges of environmental factors for snail growth, development and reproduction.

Understanding team diversity has become essential for modern-day organisations. This study explores the impact of knowledge diversity in design teams through computational simulations. By analysing design space characteristics, we study how diverse teams perform compared to less diverse counterparts. Results reveal that highly diverse teams exhibit increased efficiency, quicker convergence, and larger but sparser design spaces. This work contributes to understanding the impact of knowledge diversity in design teams and sets the stage for future systematic studies of diversity.

Technology is a complex adaptive system that is both shaped by, and shapes, institutional arrangements. This critical insight, developed in depth by W. Brian Arthur – the father of complexity economics – is relevant to researchers interested in institutions. Arthur provides a method for capturing the underlying dynamics. He offers conceptual tools centred on the concept of increasing returns to make sense of some crucial challenges. He also suggests technical tools, including agent-based modelling, to tackle ill-defined economic, legal, and institutional problems. This article explores his body of work and derives some institutional insights from it.

Scabies is a parasitic infestation with high global burden. Mass drug administrations (MDAs) are recommended for communities with a scabies prevalence of >10%. Quantitative analyses are needed to demonstrate the likely effectiveness of MDA recommendations. In this study, we developed an agent-based model of scabies transmission calibrated to demographic and epidemiological data from Monrovia. We used this model to compare the effectiveness of MDA scenarios for achieving scabies elimination and reducing scabies burden, as measured by time until recrudescence following delivery of an MDA and disability-adjusted-life-years (DALYs) averted. Our model showed that three rounds of MDA delivered at six-month intervals and reaching 80% of the population could reduce prevalence below 2% for three years following the final round, before recrudescence. When MDAs were followed by increased treatment uptake, prevalence was maintained below 2% indefinitely. Increasing the number of and coverage of MDA rounds increased the probability of achieving elimination and the number of DALYs averted. Our results suggest that acute reduction of scabies prevalence by MDA can support a transition to improved treatment access. This study demonstrates how modelling can be used to estimate the expected impact of MDAs by projecting future epidemiological dynamics and health gains under alternative scenarios.

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.

Genital human papillomavirus (HPV) infections are caused by a broad diversity of genotypes. As available vaccines target a subgroup of these genotypes, monitoring transmission dynamics of nonvaccine genotypes is essential. After reviewing the epidemiological literature on study designs aiming to monitor those dynamics, we evaluated their abilities to detect HPV-prevalence changes following vaccine introduction. We developed an agent-based model to simulate HPV transmission in a heterosexual population under various scenarios of vaccine coverage and genotypic interaction, and reproduced two study designs: post-vs.-prevaccine and vaccinated-vs.-unvaccinated comparisons. We calculated the total sample size required to detect statistically significant prevalence differences at the 5% significance level and 80% power. Although a decrease in vaccine-genotype prevalence was detectable as early as 1 year after vaccine introduction, simulations indicated that the indirect impact on nonvaccine-genotype prevalence (a decrease under synergistic interaction or an increase under competitive interaction) would only be measurable after >10 years whatever the vaccine coverage. Sample sizes required for nonvaccine genotypes were >5 times greater than for vaccine genotypes and tended to be smaller in the post-vs.-prevaccine than in the vaccinated-vs.-unvaccinated design. These results highlight that previously published epidemiological studies were not powerful enough to efficiently detect changes in nonvaccine-genotype prevalence.

The effects of repression on dissent are debated widely. We contribute to the debate by developing an agent-based model grounded in ethnographic interviews with dissidents. Building on new psychology research, the model integrates emotions as a dynamic context of dissent. The model moreover differentiates between four repression types: violence, street blockages, curfews and Facebook cuts. The simulations identify short-term dampening effects of each repression type, with a maximum effect related to non-violent forms of repression. The simulations also show long-term spurring effects, which are most strongly associated with state violence. In addition, the simulations identify nonlinear short-term spurring effects of state violence on early stage dissent. Such effects are not observed for the remaining repressive measures. Contrasting with arguments that violence deters dissent, this suggests that violence may fuel dissent, while non-violent repression might suppress it.

This paper presents the results of computational experiments aimed at studying the effect of experience on design teams’ exploration of problem-solution space. An agent-based model of a design team was developed and its capability to match theoretically-based predictions is tested. Hypotheses that (1) experienced teams need less time to find a solution and that (2) in comparison to the inexperienced teams, experienced teams spend more time exploring the solution-space than the problem-space, were tested. The results provided support for both of the hypotheses, demonstrating the impact of learning and experience on the exploration patterns in problem and solution space, and verifying the system's capability to produce the reliable results.

Formal models of past human societies informed by archaeological research have a high potential for shaping some of the most topical current debates. Agent-based models, which emphasize how actions by individuals combine to produce global patterns, provide a convenient framework for developing quantitative models of historical social processes. However, being derived from computer science, the method remains largely specialized in archaeology. In this paper and the associated tutorial, we provide a jargon-free introduction to the technique, its potential and limits as well as its diverse applications in archaeology and beyond. We discuss the epistemological rationale of using computational modeling and simulation, classify types of models, and give an overview of the main concepts behind agent-based modeling.

This article responds directly to Brughmans and Poblome's (2016a) recent application of agent-based modelling to explore the relative integration of the Roman economy. The response will not only be of relevance to debates about the Roman economy, for it also asks critical questions about the use of formal modelling to interpret archaeological data. In posing open-ended questions rather than presenting definitive answers, it seeks to broaden and fuel discussion in a spirit of constructive critique.

Seroprevalence data illustrate that human exposure to Toxocara is frequent. Environmental contamination with Toxocara spp. eggs is assumed to be the best indicator of human exposure, but increased risk of exposure has also been associated with many other factors. Reported associations are inconsistent, however, and there is still ambiguity regarding the factors driving the onset of Toxocara antibody positivity. The objective of this work was to assess the validity of our current conceptual understanding of the key processes driving human exposure to Toxocara. We constructed an agent-based model predicting Toxocara antibody positivity (as a measure of exposure) in children. Exposure was assumed to depend on the joint probability of 3 parameters: (1) environmental contamination with Toxocara spp. eggs, (2) larvation of these eggs and (3) the age-related contact with these eggs. This joint probability was linked to processes of acquired humoral immunity, influencing the rate of antibody seroreversion. The results of the simulation were validated against published data from 5 different geographical settings. Using simple rules and a stochastic approach with parameter estimates derived from the respective contexts, plausible serological patterns emerged from the model in nearly all settings. Our approach leads to novel insights in the transmission dynamics of Toxocara.