Empiricism and Agent-Based Modelling

Smajgl, Alex; Barreteau, Olivier

doi:10.1007/978-1-4614-6134-0_1

Cited by 11 publications

(10 citation statements)

References 21 publications

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“…We see social simulation as a complementary method to classical psychological research methods. In recent years, important progress has been made in particular with the use of behavioral theory in agent based models, and the parameterization of agents using data have contributed to the maturation of the methodology (Smajgl & Barreteau, 2014). This grounding in empirical research/ data results in specific research questions for field or lab experiments.…”

Section: Methodological Aspectsmentioning

confidence: 99%

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Introduction of the special issue “Social simulation in environmental psychology”

Jager

Ernst

2017

Journal of Environmental Psychology

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Section: Methodological Aspectsmentioning

confidence: 99%

“…The modeler is thus confronted with questions such as "What theory(ies) to use in a targeted simulation model", "How to translate conceptual theoretical descriptions into computational causal models" and "How to parameterize a simulated population using empirical data?" (Smajgl & Barreteau, 2014).…”

Section: Methodological Aspectsmentioning

confidence: 99%

Introduction of the special issue “Social simulation in environmental psychology”

Jager

Ernst

2017

Journal of Environmental Psychology

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“…The physicist John Platt (1964) famously observed that "Many-perhaps most-of the great issues of science are qualitative, not quantitative, even in physics and chemistry." Nevertheless, qualitative data is often described as though it is inferior to quantitative data for modeling purposes, something to be settled for when it is impossible or impractical to acquire quantitative data (Smajgl and Barreteau, 2014). Such reluctance can arise from the perception that models derived from qualitative data are "vague and therefore difficult […] to validate or falsify" (Di Baldassarre et al, 2015).…”

Section: Nature Of the Challengementioning

confidence: 99%

Eight grand challenges in socio-environmental systems modeling

Elsawah

Filatova

Jakeman

et al. 2020

SESMO

131

120

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Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.

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“…To this end, ABM is increasingly using empirical data to obtain more realistic model representations [10], [11]. However, the majority of present ABM efforts involve (1) manual development of an agent model, (2) ad-hoc tuning of a large number of parameters [12], and (3) validation limited to a qualitative expert assessment (e.g., surveys or census) or overall fit of aggregate behavior to ground truth using the original data on which the model was calibrated [13].…”

Section: Introductionmentioning

confidence: 99%

Discovering Agent Models using Process Mining: Initial Approach and a Case Study

Bemthuis

Lazarova-Molnar

2022

2022 IEEE Intl Conf on Parallel &Amp; Distributed Processing With Applications, Big Data &Amp; Cloud Computing, Sustainable Com

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Agent-based modeling is widely used for modeling and simulation of self-organizing sociotechnical systems that are composed of distributed autonomous agents. In these systems, macro level behaviors emerge from local micro level behaviors of agents that follow rules and interact with each other and the environment. Although the individual agents' behaviors are typically described by sets of simple rules, the many interactions, heterogeneous populations, and complex topologies can make it challenging, or even impossible, to predict or steer the emergent behaviors beyond micro levels. Hence, the actual behaviors of such systems are generally hard to know beforehand, and they need to be observed to extract realistic models. In this paper, we propose a proof-ofconcept approach to discover agents' underlying models from log data generated from their behaviors, utilizing process mining. To conceptualize and demonstrate our initial approach, we use an illustrative example of the popular Schelling's model of segregation. Our findings provide encouraging initial evidence on how agent models can be extracted utilizing process mining techniques.

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Empiricism and Agent-Based Modelling

Cited by 11 publications

References 21 publications

Introduction of the special issue “Social simulation in environmental psychology”

Introduction of the special issue “Social simulation in environmental psychology”

Eight grand challenges in socio-environmental systems modeling

Discovering Agent Models using Process Mining: Initial Approach and a Case Study

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