A method for agent-based models validation

Guerini, Mattia; Moneta, Alessio

doi:10.1016/j.jedc.2017.06.001

Cited by 103 publications

(72 citation statements)

References 45 publications

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“…In many cases, this amounts at replicating the largest possible number of stylized facts characterizing the phenomenon of interest (see Dosi et al, 2010Dosi et al, , 2013Dosi et al, , 2015 for business cycle properties, credit and interbank markets or Pellizzari and Forno, 2006;Jacob Leal et al, 2015 for financial markets). Recent attempts are trying to enrich empirical validation going beyond simple replication of empirical regularities, thereby requesting models to generate series that exhibit the same dynamics (Marks, 2013;Lamperti, 2015), conditional probabilistic structure (Barde, 2015) and causal relations (Guerini and Moneta, 2016) as those observed in the real world data. At least partially, such contributions have been motivated by the unsatisfactory results delivered by calibration.…”

Section: Introductionmentioning

confidence: 99%

Empirical Validation of Simulated Models through the GSL-div: An Illustrative Application

Lamperti

2016

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractA major concern about the use of simulation models regards their relationship with the empirical data. The identification of a suitable indicator quantifying the distance between the model and the data would help and guide model selection and output validation. This paper proposes the use of a new criterion, called GSL-div and developed in Lamperti (2015), to assess the degree of similarity between the dynamics observed in the data and those generated by the numerical simulation of models. As an illustrative application, this approach is used to distinguish between different versions of the well known asset pricing model with heterogeneous beliefs proposed in Brock and Hommes (1998). Once the discrimination ability of the GSL-div is proved, model's dynamics are directly compared with actual data coming from two major stock market indexes (EuroSTOXX 50 for Europe and CSI 300 for China).Results show that the model, once calibrated, is fairly able to track the evolution of both the two indexes, even though a better fit is reported for the Chinese stock market. However, I also find that many different combinations of traders behavioural rules are compatible with the same observed dynamics. Within this heterogeneity, an emerging common trait is found: to be empirically valid, the model has to account for a strong trend following component, which might either come from a unique trend type that heavily extrapolates information from past observations or the combinations of different types with milder, or even opposite, attitudes towards the trend.

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Section: Introductionmentioning

confidence: 99%

Empirical Validation of Simulated Models through the GSL-div: An Illustrative Application

Lamperti

2016

SSRN Journal

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“…Future research could further develop our conceptual model using simulation tools. For example, future studies could construct an agent-based model using existing social complexity [86], social epidemiology [87,88], and LCA [89,90] literature and validate the structure and causality of our model [91] for different case studies.…”

Section: Discussionmentioning

confidence: 99%

From Cascade to Bottom-Up Ecosystem Services Model: How Does Social Cohesion Emerge from Urban Agriculture?

Petit‐Boix

Apul

2018

Sustainability

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Given the expansion of urban agriculture (UA), we need to understand how this system provides ecosystem services, including foundational societal needs such as social cohesion, i.e., people's willingness to cooperate with one another. Although social cohesion in UA has been documented, there is no framework for its emergence and how it can be modeled within a sustainability framework. In this study, we address this literature gap by showing how the popular cascade ecosystem services model can be modified to include social structures. We then transform the cascade model into a bottom-up causal framework for UA. In this bottom-up framework, basic biophysical (e.g., land availability) and social (e.g., leadership) ecosystem structures and processes lead to human activities (e.g., learning) that can foster specific human attitudes and feelings (e.g., trust). These attitudes and feelings, when aggregated (e.g., social network), generate an ecosystem value of social cohesion. These cause-effect relationships can support the development of causality pathways in social life cycle assessment (S-LCA) and further our understanding of the mechanisms behind social impacts and benefits. The framework also supports UA studies by showing the sustainability of UA as an emergent food supplier in cities.

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“…A parallel stream of research is recently focusing on the development of tools to investigate the extent to which agent-based models' outputs is able to approximate reality (see Marks, 2013;Lamperti, 2017Lamperti, , 2016Barde, 2016b,a;Guerini and Moneta, 2016). Some of these contributions also offer new measures that can be used to build objective functions in the place of longitudinal moments within an estimation setting (e.g.…”

Section: Calibration and Validation Of Agent-based Models: The Case Fmentioning

confidence: 99%

Agent-Based Model Calibration Using Machine Learning Surrogates

2017

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Taking agent-based models (ABM) closer to the data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs combining supervised machine-learning and intelligent sampling to build a surrogate meta-model. The proposed approach provides a fast and accurate approximation of model behaviour, dramatically reducing computation time. In that, our machine-learning surrogate facilitates large scale explorations of the parameter-space, while providing a powerful filter to gain insights into the complex functioning of agent-based models. The algorithm introduced in this paper merges model simulation and output analysis into a surrogate meta-model, which substantially ease ABM calibration. We successfully apply our approach to the Brock and Hommes (1998) asset pricing model and to the "Island" endogenous growth model (Fagiolo and Dosi, 2003). Performance is evaluated against a relatively large outof-sample set of parameter combinations, while employing different user-defined statistical tests for output analysis. The results demonstrate the capacity of machine learning surrogates to facilitate fast and precise exploration of agent-based models' behaviour over their often rugged parameter spaces. KEY WORDSAgent based model; calibration; machine learning; surrogate; meta-model. JELC15, C52, C63.

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A method for agent-based models validation

Cited by 103 publications

References 45 publications

Empirical Validation of Simulated Models through the GSL-div: An Illustrative Application

Empirical Validation of Simulated Models through the GSL-div: An Illustrative Application

From Cascade to Bottom-Up Ecosystem Services Model: How Does Social Cohesion Emerge from Urban Agriculture?

Agent-Based Model Calibration Using Machine Learning Surrogates

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