A Simple Model of Business Fluctuations with Heterogeneous Interacting Agents and Credit Networks

Author(s):  
Leonardo Bargigli ◽  
Alessandro Caiani ◽  
Luca Riccetti ◽  
Alberto Russo
2005 ◽  
Vol 56 (4) ◽  
pp. 489-512 ◽  
Author(s):  
Domenico Delli Gatti ◽  
Corrado Di Guilmi ◽  
Edoardo Gaffeo ◽  
Gianfranco Giulioni ◽  
Mauro Gallegati ◽  
...  

2003 ◽  
Vol 06 (08) ◽  
pp. 829-837 ◽  
Author(s):  
FRANK WESTERHOFF

The observation that large drawdowns are outliers suggests that a special mechanism may be responsible for large crashes. We develop a simple model with heterogeneous interacting agents which follow technical and fundamental trading rules to determine their orders. Although the chartists are optimistic most of the time, they panic if prices drop sharply. Our main finding is that the selling impact due to a panic attack may be so large that it directly leads to the next panic attack. Such behavior generates temporal correlation in prices, i.e., causes large drawdowns.


2007 ◽  
Vol 11 (S1) ◽  
pp. 1-7
Author(s):  
ALAN KIRMAN

This issue contributes to the discussion of the relation between individual and aggregate behavior. The basic feature of all the models presented is that they allow for direct interaction between the individuals. This, it is shown, leads to a number of interesting phenomena that are difficult to account for in standard models. The basic message is that the inclusion of heterogeneous interacting agents allows us to escape from the pitfalls associated with the reduction to a representative individual and furthermore to show that aggregates will, in general, have behavior that is different from that of the component individuals. Lastly, complex aggregate behavior is not necessarily the result of complicated individual reasoning, it can emerge from very simple rule following by individuals.


2020 ◽  
Author(s):  
Giovanni Dosi ◽  
Mauro Napoletano ◽  
Andrea Roventini ◽  
Joseph Stiglitz ◽  
Tania Treibich

Author(s):  
Frank H. Westerhoff

SummaryModels with heterogeneous interacting agents have proven to be quite successful in the past. For instance, such models are able to mimic the dynamics of financial markets quite well. The goal of our paper is to explore whether this approach may offer new insights into the working of certain regulatory policies such as transaction taxes, central bank interventions and trading halts. Although this strand of research is rather novel, we argue that agent-based models may be used as artificial laboratories to improve our understanding of how regulatory policy tools function.


2017 ◽  
Vol 55 (2) ◽  
pp. 644-647

Christophre Georges of the Department of Economics, Hamilton College reviews “Economics with Heterogeneous Interacting Agents: A Practical Guide to Agent-Based Modeling,” edited by Alessandro Caiani, Alberto Russo, Antonio Palestrini, and Mauro Gallegati. The Econlit abstract for this book begins: “Text for graduate and PhD students, as well as undergraduates with some knowledge of computers and economics comprises four papers emerging from a workshop on agent-based modeling held by the Dipartimento di Scienze Economiche e Sociali at the Università Politecnica delle Marche. Presents a guide to agent-based models (ABM) and the technicalities that need to be solved in order to evaluate the effect of different rules and their switching.”


2020 ◽  
Vol 58 (3) ◽  
pp. 1487-1516 ◽  
Author(s):  
Giovanni Dosi ◽  
Mauro Napoletano ◽  
Andrea Roventini ◽  
Joseph E. Stiglitz ◽  
Tania Treibich

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