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Agent-based computational economics (ACE) is the computational study of economic processes modeled as dynamic systems of interacting agents. Here agentrefers broadly to a bundle of data and behavioral methods representing an entity constituting part of a computationally constructed world. Examples of possible agents include individuals (e.g. consumers, producers), social groupings (e.g. families, firms, communities, government agencies), institutions (e.g. markets, regulatory systems), biological entities (e.g. crops, livestock, forests), and physical entities (e.g. infrastructure, weather, and geographical regions). Thus, agents can range from active data-gathering decision makers with sophisticated embodied cognitive capabilities to passive world features with no cognitive function.

Agent-based modelling in economics Lynne Hamill and Nigel Gilbert, Centre for Research in Social Simulation (CRESS), University of Surrey, UK New methods of economic modelling have been sought as a result of the global economic downturn in 2008. This unique book highlights the benefits of an agent-based modelling (ABM) approach. It demonstrates how ABM can easily handle complexity: heterogeneous people, households and firms interacting dynamically. Unlike traditional methods, ABM dus not require people or firms to optimise or economic systems to reach equilibrium. ABM offers a way to link micro foundations directly to the macro situation. Key features: -Introduces the concept of agent-based modelling and shows how it differs from existing approaches.-Provides a theoretical and methodological rationale for using ABM in economics, along with practical advice on how to design and create the models.-Each chapter starts with a short summary of the relevant economic theory and then shows how to apply ABM.-Explores both topics covered in basic economics textbooks and current important policy themes; unemployment, exchange rates, banking and environmental issues.-Describes the models in pseudocode, enabling the reader to develop programs in their chosen language.-Supported by a website featuring the NetLogo models described in the book. Agent-based Modelling in Economics provides students and researchers with the skills to design, implement, and analyze agent-based models. Third year undergraduate, master and doctoral students, faculty and professional economists will find this book an invaluable resource.

Aims to give a view of the scientific production in the fields of Agent-based Computational Economics, mainly in Market Finance and Game Theory. Based on communications given at AE'2005 (Lille, USTL, France), this book offers a panorama of advances in ACE, both theoretical and methodological that is of interest academics as well as practitioners

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.

Methods like Event History Analysis can show the existence of diffusion and part of its nature, but do not study the process itself. Nowadays, thanks to the increasing performance of computers, processes can be studied using computational modeling. This thesis presents an agent-based model of policy diffusion mainly inspired from the model developed by Braun and Gilardi (2006). I first start by developing a theoretical framework of policy diffusion that presents the main internal drivers of policy diffusion - such as the preference for the policy, the effectiveness of the policy, the institutional constraints, and the ideology - and its main mechanisms, namely learning, competition, emulation, and coercion. Therefore diffusion, expressed by these interdependencies, is a complex process that needs to be studied with computational agent-based modeling. In a second step, computational agent-based modeling is defined along with its most significant concepts: complexity and emergence. Using computational agent-based modeling implies the development of an algorithm and its programming. When this latter has been developed, we let the different agents interact. Consequently, a phenomenon of diffusion, derived from learning, emerges, meaning that the choice made by an agent is conditional to that made by its neighbors. As a result, learning follows an inverted S-curve, which leads to partial convergence - global divergence and local convergence - that triggers the emergence of political clusters; i.e. the creation of regions with the same policy. Furthermore, the average effectiveness in this computational world tends to follow a J-shaped curve, meaning that not only time is needed for a policy to deploy its effects, but that it also takes time for a country to find the best-suited policy. To conclude, diffusion is an emergent phenomenon from complex interactions and its outcomes as ensued from my model are in line with the theoretical expectations and the empirical evidence.Les méthodes d'analyse de biographie (event ...

Most of the intriguing social phenomena of our time, such as international terrorism, social inequality, and urban ethnic segregation, are consequences of complex forms of agent interaction that are difficult to observe methodically and experimentally. This book looks at a new research stream that makes use of advanced computer simulation modelling techniques to spotlight agent interaction that allows us to explain the emergence of social patterns. It presents a method to pursue analytical sociology investigations that look at relevant social mechanisms in various empirical situations, such as markets, urban cities, and organisations

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.