Introduction to the Special Issue on Agent-Based Models for Economic Policy Advice

Author(s):  
Blake LeBaron ◽  
Peter Winker
Author(s):  
Blake LeBaron ◽  
Peter Winker

SummaryThis special issue of the Journal of Economics and Statistics is devoted to the use of agent-based models for economic policy advice. It presents a collection of research papers in different fields of applications. Special emphasis is laid on discussing the potential and possible limitations of agent-based models for economic policy advice. The editorial provides an overview on the role of agent-based modeling in economic policy referring also to the papers presented. Furthermore, it highlights the strength of the approach, i.e., the explicit microfoundation and the modeling of heterogenous agents. Finally, we also report on current limitations of the method with regard to economic policy advice and point at some areas deserving further research.


2007 ◽  
Vol 30 (3) ◽  
pp. 189-194 ◽  
Author(s):  
G. Fagiolo ◽  
C. Birchenhall ◽  
P. Windrum

2020 ◽  
Vol 1 (1) ◽  
pp. 29
Author(s):  
Elanjati Worldailmi ◽  
Ismianti Ismianti

Bank Indonesia (BI) as the central bank in Indonesia has launched a movement to use non-cash instruments in conducting transactions on economic activities. The majority of Indonesian people are increasingly ready to trade without cash or cashless society. The country's economic policy factors, the availability of various non-cash payments, and online sales and purchases, encourage the tendency to use non-cash transactions (e-payment). One way to find out these trends is to use a model. Models can help understand and explain real phenomena more easily and efficiently than directly observing. One model that can be used is Agent Based Modeling and Simulation (ABMS). By using ABMS, the development of models with complex behaviors, dependencies, and interactions can be developed more easily. ABMS is able to describe processes, phenomena, and situations. In this study, the factors that influence the tendency to use e-payment are obtained from various references. From these factors, then created a scenario as a sub-purpose of this model. In simulations using ABMS, detailed descriptions explained based on ODD Protocol elements can be more easily understood and complete. ODD systematically evaluates a model. The advantage is that ODD can improve the accuracy of model formulas and make the theoretical basis more visible.


2017 ◽  
Vol 23 (1/2) ◽  
pp. 2-12 ◽  
Author(s):  
Davide Secchi

Purpose The purpose of this editorial is to introduce the Special Issue “Agent-Based Models of Bounded Rationality” and to provide an overview of its rationale and main objectives. Design/methodology/approach After outlining the overall framework to justify the choice of agent-based modeling in relation to bounded rationality, an overview of the six papers published in the Special Issue is presented. Findings The paper argues that simulation of complex adaptive social systems is a way to set the ground for updating the concept of bounded rationality and prepare for it to still play a significant role in the years to come. Originality/value After its introduction, bounded rationality remained mostly used but seldom discussed in both its assumptions and its meaning. The originality of this introduction is to unveil some of the points that keep rationality still at the core of organization and team research.


2010 ◽  
Vol 37 (1) ◽  
pp. 44-50 ◽  
Author(s):  
Herbert Dawid ◽  
Michael Neugart

Author(s):  
Frank Westerhoff ◽  
Reiner Franke

With the help of two examples, this chapter illustrates the usefulness of agent-based models as tools for economic policy design. The first example applies a financial market model in which the order flow of speculators, relying on technical and fundamental analysis, generates intricate price dynamics. The second example applies a Keynesian-type goods market model in which the investment behavior of firms, relying on extrapolative and regressive predictors, generates complex business cycles. It adds a central authority to these two setups and explores the impact of simple intervention strategies on the model dynamics. On the basis of these experiments, the chapter concludes that agent-based models may help us understand how markets function and evaluate the effectiveness of various stabilization policies.


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