EURACE: A massively parallel agent-based model of the European economy

Deissenberg, Christophe; Hoog, Sander van der; Dawid, Herbert

doi:10.1016/j.amc.2008.05.116

Cited by 222 publications

(133 citation statements)

References 15 publications

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“…It enables researchers to study emergent system-level patterns and outcomes from generalized interactions at the individual agent level [4][5][6]. ABMs have been widely applied in many fields, including ecology, biology, social science, economic science, computer science, and the geospatial sciences [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Model developers, therefore, are inevitably faced with a series of computational issues, e.g., long execution time and high memory consumption [2,14,15]. To address these computational issues in large-scale ABMs, a collection of parallel algorithms and systems have been created to enhance model capabilities and improve simulation performance [2,13,[15][16][17]. However, most of these solutions target specific research problems and are restricted to a few theoretical or predetermined scenarios; thus, they cannot provide generic support for dynamic modeling of geospatial phenomena.…”

Section: Introductionmentioning

confidence: 99%

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4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Guan

et al. 2016

IJGI

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Significant computation challenges are emerging as agent-based modeling becomes more complicated and dynamically data-driven. In this context, parallel simulation is an attractive solution when dealing with massive data and computation requirements. Nearly all the available distributed simulation systems, however, do not support geospatial phenomena modeling, dynamic data injection, and real-time visualization. To tackle these problems, we propose a distributed dynamic-data driven simulation and analysis system (4D-SAS) specifically for massive spatial agent-based modeling to support real-time representation and analysis of geospatial phenomena. To accomplish large-scale geospatial problem-solving, the 4D-SAS system was spatially enabled to support geospatial model development and employs high-performance computing to improve simulation performance. It can automatically decompose simulation tasks and distribute them among computing nodes following two common schemes: order division or spatial decomposition. Moreover, it provides streaming channels and a storage database to incorporate dynamic data into simulation models; updating agent context in real-time. A new online visualization module was developed based on a GIS mapping library, SharpMap, for an animated display of model execution to help clients understand the model outputs efficiently. To evaluate the system's efficiency and scalability, two different spatially explicitly agent-based models, an en-route choice model, and a forest fire propagation model, were created on 4D-SAS. Simulation results illustrate that 4D-SAS provides an efficient platform for dynamic data-driven geospatial modeling, e.g., both discrete multi-agent simulation and grid-based cellular automata, demonstrating efficient support for massive parallel simulation. The parallel efficiency of the two models is above 0.7 and remains nearly stable in our experiments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Guan

et al. 2016

IJGI

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“…The agents in these markets are heterogeneous and consist of seven major types: households, firms, banks, central banks, government, Investment Goods firms, and Eurostat (Figure 3). Agents can participate in more than one market, and each of the markets (sub-models) is developed first before being integrated into a larger framework (Cincotti, Raberto, and Teglio 2012;Deissenberg, van der Hoog, and Dawid 2008). Nonetheless, like all models, it does contain simplifications.…”

Section: Eurace As An Ecological Machinementioning

confidence: 99%

Building Better Ecological Machines: Complexity Theory and Alternative Economic Models

Bier

Schinkel

2016

Engaging STS

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Computer models of the economy are regularly used to predict economic phenomena and set financial policy. However, the conventional macroeconomic models are currently being reimagined after they failed to foresee the current economic crisis, the outlines of which began to be understood only in [2007][2008]. In this article we analyze the most prominent of this reimagining: Agent-Based models (ABMs). ABMs are an influential alternative to standard economic models, and they are one focus of complexity theory, a discipline that is a more open successor to the conventional chaos and fractal modeling of the 1990s. The modelers who create ABMs claim that their models depict markets as ecologies, and that they are more responsive than conventional models that depict markets as machines. We challenge this presentation, arguing instead that recent modeling efforts amount to the creation of models as ecological machines. Our paper aims to contribute to an understanding of the organizing metaphors of macroeconomic models, which we argue is relevant conceptually and politically, e.g., when models are used for regulatory purposes.

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“…Deissenberg et al 2008). The agenda of this project was to develop an agent-based closed macroeconomic model with strong empirical grounding and micro-foundations that provides a uniform platform to address issues in different areas of economic policy.…”

Section: Abstract: Agent-based Simulation Models Are Used By An Incrementioning

confidence: 99%

Agent-based Models for Economic Policy Design

Dawid

Neugart

2010

Eastern Econ J

Self Cite

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Agent-based simulation models are used by an increasing number of scholars as a tool for providing evaluations of economic policy measures and policy recommendations in complex environments. Based on recent work in this area we discuss the advantages of agent-based modelling for economic policy design and identify further needs to be addressed for strengthening this methodological approach as a basis for sound policy advice.Economic policy advice requires a thorough understanding of the relevant economic mechanisms that are responsible for the (overall) effects of policy measures in the economy. Policy advice based on models where aspects, that crucially affect real world effects of the considered policy measures, are missing might be flawed and result in misleading recommendations. Theoretical work based on certain model structures accompanied by empirical evidence aims at giving us guidance on the causal relationship of key economic variables. Methods for the analysis of such relationship within established classes of (typically equilibrium) models, by means of analytical, numerical or econometric approaches are well developed and continuously improving. However, a key challenge, when providing policy advice, is that we have to select among the various explanations for certain economic phenomena. It is hardly the case -maybe never -that only a single school of thought or an exclusive methodological approach is able to explain a set of observable economic relationships. There are different explanations for business cycle fluctuations, the distribution of income or the role of human capital investments for economic growth just to name a few topics that policymakers care about. However, the choice of the modeling approach (which aspects are in, which are out) and the tools of analysis might crucially influence the predictions of effects of policy measures and even the set of questions about policy effects that can be sensibly addressed in the framework of the model. Obviously, the assertion which aspects of the economic environment have to be captured in order to provide meaningful policy advice on a certain issue is highly subjective -a prime example of differing subjective viewpoints in this respect is the controversial debate about the appropriate modeling strategy in macroeconomics and financial economics in the aftermath of the 2008/09 economic crisis (see Colander et al. 2009, or Schneider andKirchgässner 2009) -and we will not attempt to resolve the issue of how to select among different modeling approaches. Rather we would like to make a few points how a fairly new protagonist in the area of model-based economic policy advice -agent-based modelscan enrich the possibilities of a modeler to capture economic phenomena that seem relevant to policy makers and extend the set of questions that can be asked about policy effects. Also, we briefly point to 1 Department of Business Administration and Economics and Institute of Mathematical Economics, Bielefeld University, Germany.2 School of Economics and ...

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EURACE: A massively parallel agent-based model of the European economy

Cited by 222 publications

References 15 publications

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Building Better Ecological Machines: Complexity Theory and Alternative Economic Models

Agent-based Models for Economic Policy Design

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