We study the poor-biased model for money exchange introduced in Cao & Motsch ((2023) Kinet. Relat. Models 16(5), 764–794.): agents are being randomly picked at a rate proportional to their current wealth, and then the selected agent gives a dollar to another agent picked uniformly at random. Simulations of a stochastic system of finitely many agents as well as a rigorous analysis carried out in Cao & Motsch ((2023) Kinet. Relat. Models 16(5), 764–794.), Lanchier ((2017) J. Stat. Phys. 167(1), 160–172.) suggest that, when both the number of agents and time become large enough, the distribution of money among the agents converges to a Poisson distribution. In this manuscript, we establish a uniform-in-time propagation of chaos result as the number of agents goes to infinity, which justifies the validity of the mean-field deterministic infinite system of ordinary differential equations as an approximation of the underlying stochastic agent-based dynamics.

Humans have adapted to an immense array of environments by accumulating culturally transmitted knowledge and skills. Adaptive culture can accumulate either via more distinct cultural traits or via improvements of existing cultural traits. The kind of culture that accumulates depends on, and coevolves with, the social structure of societies. Here, we show that the coevolution of learning networks and cumulative culture results in two distinct pathways to cultural adaptation: highly connected populations with high proficiency but low trait diversity vs. sparsely connected populations with low proficiency but higher trait diversity. Importantly, we show there is a conflict between group-level payoffs, which are maximised in highly connected groups that attain high proficiency, and individual level selection, which favours disconnection. This conflict emerges from the interaction of social learning with population structure and causes populations to cycle between the two cultural and network states. The same conflict creates a paradox where increasing innovation rate lowers group payoffs. Finally, we explore how populations navigate these two pathways in environments where payoffs differ among traits and can change over time, showing that high proficiency is favoured when payoffs are stable and vary strongly between traits, while frequent changes in trait payoffs favour more trait diversity. Our results illustrate the complex interplay between networks, learning and the environment, and so inform our understanding of human social evolution.

Polarization makes it difficult to form positive relationships across existing groups. Decreasing polarization may improve political discourse around the world. Polarization can be modeled on a social network as structural balance, where the network is composed of groups with positive links between all individuals in the group and negative links with all others. Previous work shows that incorporating attributes of individuals usually makes structural balance, and hence polarization, harder to achieve. That work examines only a limited number and types of attributes. We present a generalized model and a simulation framework to analyze the effect of any type of attribute, including analytically as long as an expected value can be written for the type of attribute. As attributes, we consider people’s (approximately) immutable characteristics (e.g., race, wealth) and such opinions that change more slowly than relationships (e.g., political preferences). We detail and analyze five classes of attributes, recapitulating the results of previous work in this framework and extending it. While it is easier to prevent than to destabilize polarization, we find that usually the most effective at both are continuous attributes, followed by ordered attributes and, finally, binary attributes. The effectiveness of unordered attributes varies depending on the magnitude of negative impact of having differing attributes but is smaller than of continuous ones. Testing the framework on network structures containing communities revealed that destroying polarization may require introducing local tensions. This model could be used by policymakers, among others, to prevent and design effective interventions to counteract polarization.

Some epistemic agents will not change their position on a claim. These are dogmatists, common creatures in our epistemic communities. This paper discusses the population-level epistemic effects of increasing numbers of dogmatists. All agents in the model are assigned a degree of belief (using a Likert-type scale) and adopt the beliefs of others in interactions. Subsets of agents are dogmatists. Analysis of model results suggests that even a modest increase in a group's dogmatists can have substantial effects on belief spread. I conclude by arguing that the model (a) helps identify two kinds of dogmatists and (b) suggests another way epistemic bubbles can form.

This paper develops a network economy model to study the propagation of the COVID lockdown shock. Firms are related to each other through buyer–seller relations in the market for intermediate inputs. Firms choose production levels and input combinations using prices that emerge from local interactions. Nothing forbids trade at out-of-equilibrium prices. In such a setting, disequilibrium spills over from one market to another due to the interconnections between markets. These disequilibrium dynamics are capable of generating unemployment when workers released by contracting firms are not frictionlessly absorbed by expanding firms. We calibrate the model to the US economy using a data set with more than 200,000 buyer–seller relations between about 70,000 firms. Computational experiments on the calibrated economy suggest that the COVID lockdown generates a sizeable decline in GDP. The endogenously generated unemployment dynamics is a primary determinant of the cost of the lockdown.

We build a computational framework to support the planning of development and the evaluation of budgetary strategies toward the 2030 Agenda. The methodology takes into account some of the complexities of the political economy underpinning the policymaking process: the multidimensionality of development, the interlinkages between these dimensions, and the inefficiencies of policy interventions, as well as institutional factors that promote or discourage these inefficiencies. The framework is scalable and usable even with limited publicly available information: development-indicator data. However, it can be further refined as more data becomes available, for example, on public expenditure. We demonstrate its usage through an application for the Mexican federal government. For this, we infer historical policy priorities, that is, the non-observable allocations of transformative resources that generated past changes in development indicators. We also show how to use the tool to assess the feasibility of development goals, to measure policy coherence, and to identify accelerators. Overall, the framework and its computational tools allow policymakers and other stakeholders to embrace a complexity (and a quantitative) view to tackle the challenges of the Sustainable Development Goals.

Some stochastic model of rumours asserts that even an advanced communication network does not guarantee every agent hears certain news because they predict that rumour spreaders convert to stifflers when contacted with an informed agent. In this study, we adapted two rumour spread models to interstellar communication by developing an agent-based model (ABM) for exploring the issue more rigorously. We enhanced the spread models by adding two additional parameters called conversion probability and stop-criterion, which represent the eagerness and persistency of civilizations to establish new contacts. Results of the ABM under several settings suggest that limited SETI searches lead to undiscovered civilizations. Earth may be one of these undiscovered civilizations although an advanced communication network might already be set up. Hence, we speculate that rumour spread models can propose another solution to Fermi's Paradox.

Changes in population and family structures are altering the provision of care for dependent older people. In Southern European countries like Spain, such care is still largely provided by family, typically spouses and adult daughters. However, an increasing proportion of women have entered the labour force, thereby affecting their availability. To study the demand and supply balance of informal care and to quantify the need for formal care when there is a deficit, we have developed a mixed microsimulation–agent-based model (ABM). Based on nuptiality, fertility and mortality levels of cohorts born at ten-year intervals between 1908 and 1968, the model starts with a microsimulation of the lifecycle of individuals and their close relatives until death. The ABM then determines the amount of time available or needed to care for family members, starting from age 50. Estimates are derived from Spanish survey data on employment, disability and time of care received. Surprisingly, results show that the family care deficit was greater in the older cohorts due to higher mortality and thus a greater impact of widowhood. However, for future elderly persons, we foresee that persistent below-replacement fertility and, paradoxically, the prolongation of the lifespan of couples will increase the demand for formal care as there will be more couples with both members incapacitated but without children to take care of them.

The paper presents an agent-based model reproducing a stylized credit network that evolves endogenously through the individual choices of firms and banks. We introduce in this framework a financial stability authority in order to test the effects of different prudential policy measures designed to improve the resilience of the economic system. Simulations show that a combination of micro- and macroprudential policies reduces systemic risk but at the cost of increasing banks’ capital volatility. Moreover, the agent-based methodology allows us to implement an alternative meso regulatory framework that takes into consideration the connections between firms and banks. This policy targets only the more connected banks, increasing their capital requirement in order to reduce the diffusion of local shocks. Our results support the idea that the mesoprudential policy is able to reduce systemic risk without affecting the stability of banks’ capital structure.

Public reason liberalism includes an ideal of political stability where justified institutions reach a kind of self-enforcing equilibrium. Such an order must be stable for the right reasons — where persons comply with the rules of the order for moral reasons, rather than out of fear or self-interest. John Rawls called a society stable in this way well-ordered.

In this essay, I contend that a more sophisticated model of a well-ordered society, specifically an agent-based model, yields a richer and more attractive understanding of political stability. An agent-based model helps us to distinguish between three concepts of political stability — durability, balance, and immunity. A well-ordered society is one that possesses a high degree of social trust and cooperative behavior among its citizens (durability) with low short-run variability (balance). A well-ordered society also resists destabilization caused by noncompliant agents in or entering the system (immunity).

Distinguishing between these three concepts complicates the necessary reformulation of the idea of a well-ordered society. Going forward, public reason theorists must now distinguish between types of assurance, specify heretofore unknown aspects of reasonable behavior, and reconceive of the nonideal preconditions for forming a stable, ideal social order.

How closely integrated were the commercial centres of the Roman world? Were traders aware of supply and demand for goods in other cities, or were communities of traders in cities protectionist and working opportunistically? Widely traded commodities such as terra sigillata tablewares in the Eastern Mediterranean provide an ideal opportunity to explore the economic processes that underlie the archaeological evidence. Agent-based computational modelling allows various such processes to be explored, and also identifies areas for further investigation.

Choosing appropriate management strategies and effective conservation actions requires information about the future consequences of current conservation actions; however, this crucial information is rarely available to conservation planners. This study applies scenario planning and agent-based modelling (ABM) to assess the potential impact of alternative management strategies on future suitability and functional connectivity of Cross River gorilla (CRG) habitat in the Nigeria–Cameroon border region. The CRG population is small and fragmented, with many subpopulations and migration corridors located outside protected areas. This study used ABM to simulate human land use in the study area over a period of 15 years under different management scenarios and assessed the impact on future suitability and functional connectivity of CRG habitat. The simulations showed that a landscape approach with greater focus on interventions to change human behaviour towards conserving gorillas and sustainable forest use would result in greater improvement in habitat suitability and functional connectivity compared to focusing on improving law enforcement within existing protected areas. However, the best scenarios were when both law enforcement and behaviour change increased. The results highlight the importance of human behaviour change to conservation in human-dominated landscapes and can inform conservation planning and management of other species and in similar landscapes.

Objective: To develop and apply a novel modeling approach to support medical and public health disaster planning and response using a sarin release scenario in a metropolitan environment.

Methods: An agent-based disaster simulation model was developed incorporating the principles of dose response, surge response, and psychosocial characteristics superimposed on topographically accurate geographic information system architecture. The modeling scenarios involved passive and active releases of sarin in multiple transportation hubs in a metropolitan city. Parameters evaluated included emergency medical services, hospital surge capacity (including implementation of disaster plan), and behavioral and psychosocial characteristics of the victims.

Results: In passive sarin release scenarios of 5 to 15 L, mortality increased nonlinearly from 0.13% to 8.69%, reaching 55.4% with active dispersion, reflecting higher initial doses. Cumulative mortality rates from releases in 1 to 3 major transportation hubs similarly increased nonlinearly as a function of dose and systemic stress. The increase in mortality rate was most pronounced in the 80% to 100% emergency department occupancy range, analogous to the previously observed queuing phenomenon. Effective implementation of hospital disaster plans decreased mortality and injury severity. Decreasing ambulance response time and increasing available responding units reduced mortality among potentially salvageable patients. Adverse psychosocial characteristics (excess worry and low compliance) increased demands on health care resources. Transfer to alternative urban sites was possible.

Conclusions: An agent-based modeling approach provides a mechanism to assess complex individual and systemwide effects in rare events. (Disaster Med Public Health Preparedness. 2009;3:75–87)

We consider a hybrid model, created by coupling a continuum and an agent-based model of infectious disease. The framework of the hybrid model provides a mechanism to study the spread of infection at both the individual and population levels. This approach captures the stochastic spatial heterogeneity at the individual level, which is directly related to deterministic population level properties. This facilitates the study of spatial aspects of the epidemic process. A spatial analysis, involving counting the number of infectious agents in equally sized bins, reveals when the spatial domain is nonhomogeneous.

In this paper, we explore the consequence of learning to forecast in a very simple environment. Agents have bounded memory and incorrectly believe that there is nonlinear structure underlying the aggregate time series dynamics. Under social learning with finite memory, agents may be unable to learn the true structure of the economy and rather may chase spurious trends, destabilizing the actual aggregate dynamics. We explore the degree to which agents' forecasts are drawn toward a minimal state variable learning equilibrium as well as a weaker long-run consistency condition.