The Agent‐Based Approach to Post Keynesian Macro‐Modeling

Guilmi, Corrado Di

doi:10.1002/9781119483328.ch3

Cited by 6 publications

(9 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Examples with much more complex structured aggregate properties can easily be found, e.g. in ABMs with social network elements (Di Guilmi, 2018;Namatame & Chen, 2016). Explanations of these sorts focus on the relation between social properties, something which (4) does not allow and hence it follows, once more, that ABM-based explanations are not necessarily individualist.…”

Section: Are Abm-based Explanations Necessarily Individualist In the ...mentioning

confidence: 99%

Are ABM explanations in the social sciences inevitably individualist?

Kincaid

Zahle

2022

Synthese

View full text Add to dashboard Cite

Agent-based models (ABMs) are increasingly important in social science research. They have two obvious apparent virtues: they can model complex macrosociological phenomena without strong assumptions about agents and without analytic solutions for models, and they seem to instantiate the methodological individualist program in a concrete way. We argue that the latter claim is false. After providing schematic accounts of ABM models and a first introduction to ways in which to characterize individualist explanations, we work through six conceptions of individualist explanations that are decreasingly "less individualist" and argue that ABM-based explanations in the social sciences are not inevitably individualist in any of these senses. ABMs allow for explanatory relations among social entities and properties in the model environment and they are silent on what basic agents are, allowing social entities and properties to play basic explanatory roles.

show abstract

Section: Are Abm-based Explanations Necessarily Individualist In the ...mentioning

confidence: 99%

Are ABM explanations in the social sciences inevitably individualist?

Kincaid

Zahle

2022

Synthese

View full text Add to dashboard Cite

show abstract

“…Their strength lies in the micro-interactions between individual decision-making agents (Tesfatsion 2006) each with their own characteristics; for example, businesses, households, or consumers, as well as spatial characteristics that geographically localize these interactions (Barthelemy 2016;Crosato et al 2018). These interactions can, in turn, be affected by banks' policies (Teglio et al 2012;Dosi et al 2013;Banwo et al 2019) and macroeconomics conditions (Cardaci 2018;Guilmi 2017;Mérő 2019). These agent-to-agent interactions produce complex (Jensen 2010), nonlinear effects such as tipping-points (Brock and Durlauf 2001;Harré et al 2019), boom-bust cycles (Geanakoplos et al 2012), and chaos Hommes 1998, Xin andHuang 2017), as well as the equilibrium dynamics predicted by classical models.…”

Section: Related Workmentioning

confidence: 99%

Explaining herding and volatility in the cyclical price dynamics of urban housing markets using a large-scale agent-based model

et al. 2021

View full text Add to dashboard Cite

Urban housing markets, along with markets of other assets, universally exhibit periods of strong price increases followed by sharp corrections. The mechanisms generating such non-linearities are not yet well understood. We develop an agentbased model populated by a large number of heterogeneous households. The agents' behavioral rules are consistent with the concept of bounded rationality. The model is calibrated using several large and distributed datasets of the Greater Sydney region (demographic, economic and financial) across three specific and diverse periods since 2006. The model is not only capable of explaining price dynamics during these periods, but also reproduces the novel behavior actually observed immediately prior to the market peak in 2017, namely a sharp increase in the variability of prices. This novel behavior is related to a combination of trend-following aptitude of the household agents (herding) and their collective propensity to borrow. Trend-following behavior is found to be essential in replicating market dynamics.

show abstract

“…Fagiolo e Roventini (2016, pp. 27-35) também resumem muito do que tem sido pesquisado na busca de respostas para a validade de certas políticas públicas, a partir dessa metodologia, enquanto Guilmi (2017) apresenta as principais áreas em que os ABMs tem sido usados por macroeconomistas Pós-Keynesianos, e Riccetti et al (2015, pp. 306-309) é uma boa primeira referência para o leitor interessado em um histórico do uso de ABMs em economia.…”

Section: Consistentunclassified

Complexidade aplicada ao estudo da dinâmica do investimento: um modelo baseado em agentes (ABM) de inspiração Kaleckiana

Oliveira¹

View full text Add to dashboard Cite

Conclusão 213 Bibliografia 219 Anexo: Tabelas 231 10 Para uma análise da frequência de distribuição empírica do "Q" de Tobin entre firmas americanas, ver Scharfenaker e dos Santos (2015). 11 Considerações empíricas sobre a validade do "Q" de Tobin podem ser encontrada na descrição de nosso modelo, na Parte V desse capítulo. 12 Para maiores detalhes sobre a derivação desse modelo, recomendamos Magnabosco (2015, cap. 1).

show abstract

The Agent‐Based Approach to Post Keynesian Macro‐Modeling

Cited by 6 publications

References 46 publications

Are ABM explanations in the social sciences inevitably individualist?

Are ABM explanations in the social sciences inevitably individualist?

Explaining herding and volatility in the cyclical price dynamics of urban housing markets using a large-scale agent-based model

Complexidade aplicada ao estudo da dinâmica do investimento: um modelo baseado em agentes (ABM) de inspiração Kaleckiana

Contact Info

Product

Resources

About