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Agent-based models (ABMs) provide a methodology to explore systems of interacting, adaptive, diverse, spatially situated actors. Outcomes in ABMs can be equilibrium points, equilibrium distributions, cycles, randomness, or complex patterns; these outcomes are not directly determined by assumptions but instead emerge from the interactions of actors in the model. These behaviors may range from rational and payoff-maximizing strategies to rules that mimic heuristics identified by cognitive science. Agent-based techniques can be applied in isolation to create high-fidelity models and to explore new questions using simple constructions. They can also be used as a complement to deductive techniques. Overall, ABMs offer the potential to advance social sciences and to help us better understand our complex world.

Agent-based models (ABMs) provide a methodology to explore systems of interacting, adaptive, diverse, spatially situated actors. Outcomes in ABMs can be equilibrium points, equilibrium distributions, cycles, randomness, or complex patterns; these outcomes are not directly determined by assumptions but instead emerge from the interactions of actors in the model. These behaviors may range from rational and payoff-maximizing strategies to rules that mimic heuristics identified by cognitive science. Agent-based techniques can be applied in isolation to create high-fidelity models and to explore new questions using simple constructions. They can also be used as a complement to deductive techniques. Overall, ABMs offer the potential to advance social sciences and to help us better understand our complex world. Adapted from the source document.

The aim of this paper is to build a computational model that presents the effects of social dynamics such as evolution on populations applying the theory of Hobbesian Social Contract, social learning and norm diffusion. The phenomenon we are studying is the so-called tragedy of the commons, in which individual agents, having open access to a resource unconstrained by common social structures, act according to their own self-interest, seeking to maximise their own profits. Developing the theoretical framework and agent-based model, we applied to our artificial environment the norm associated with altruism, which modifies agents' behaviour during the simulation, which in turn affects the distribution of wealth. Through the behavioural space, we show that under certain circumstances specified in the parameter, it is possible to obtain a social contract and, as a result, a state of equilibrium. We show that individuals who have obtained the norm are able to enter into a contract, resulting in a rising wealth of the population and a more equilibrium distribution, while if they do not, more inequalities emerge. However, our model is a simplification of Hobbes' theory, admittedly, our agents can spontaneously establish cooperation but there are no complex structures, such as psychological ones, or moral cognition. We believe that this is a skeletal description of the Hobbesian social contract, in which self-interested individuals without obligation to cooperate agree to abide by a norm and its benefits. Depending on how profitable cooperation is (due to the redistribution) and the number of altruistic agents, the community members work for the common good.

Agent-based models have played a prominent role in recent debates about the merits of democracy. In particular, the formal model of Lu Hong and Scott Page and the associated "diversity trumps ability" result has typically been seen to support the epistemic virtues of democracy over epistocracy (i.e., governance by experts). In this paper we first identify the modeling choices embodied in the original formal model and then critique the application of the Hong-Page results to philosophical debates on the relative merits of democracy. In particular we argue that the "best-performing agents" in the Hong-Page model should not be interpreted as experts. We next explore a closely related model in which best-performing agents are more plausibly seen as experts and show that the diversity trumps ability result fails to hold. However, with changes in other parameters (such as the deliberation dynamic) the diversity trumps ability result is restored. The sensitivity of this result to parameter choices illustrates the complexity of the link between formal modeling and more general philosophical claims; we use this debate as a platform for a more general discussion of when and how agent-based models can contribute to philosophical discussions.

PurposeThe purpose of this article is to provide an overview of agent-based modeling as an alternative method for public administration research. It is focused on encouraging public administration scholars to come to better understanding of the method.Design/methodology/approachThis article performed a comprehensive review of methodological issues relative to agent-based modeling.FindingsAfter reviewing the current research themes in public administration and the methodological nature of agent-based modeling, we found that agent-based modeling can help researchers to advance theories by means of sophisticated thought experiment which is not possible by formal modeling and verbal reasoning. We also pointed out that agent-based modeling does not substitute empirical research, but can add much value through being part of a mixed-method and multidisciplinary research.Practical implicationsWe suggested that interested researchers may need to take a strategic approach in developing and describing a pertinent model and reporting its results.Originality/valueAgent-based modeling has rarely been used in public administration research. The article provides an introductory overview for researchers not familiar with ABM and suggests to the academic community future venues that would unfold from agent-based modeling.