A Software Architecture for Mechanism-Based Social Systems Modelling in Agent-Based Simulation Models

Vu, Tuong Manh; Probst, Charlotte; Nielsen, Alexandra; Bai, Hao; Meier, Petra; Buckley, Charlotte; Strong, Mark; Brennan, Alan; Purshouse, Robin C.

doi:10.18564/jasss.4282

Cited by 23 publications

(36 citation statements)

References 10 publications

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“…We argue that it is of ultimate importance for the field of Business and Economics to gain a deep(er) and substantial understanding of the dynamics emerging from the current infodemic. We propose to employ powerful techniques like agent-based modelling which could be a valuable approach for simulating the effects of actions taken in a crisis situation such as the COVID-19 pandemic in conjunction with the human behavior within the crisis (Adam 2020 ; Squazzoni et al 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Understanding the dynamics emerging from infodemics: a call to action for interdisciplinary research

et al. 2021

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Research on infodemics, i.e., the rapid spread of (mis)information related to a hazardous event, such as the COVID-19 pandemic, requires integrating a multiplicity of scientific disciplines. The dynamics emerging from infodemics have the potential to generate complex behavioral patterns. To react appropriately, it is of ultimate importance for the fields of Business and Economics to understand these dynamics. In the short run, they might lead to an adaptation in household spending or to a shift in buying behavior towards online providers. In the long run, changes in investments, consumer behavior, and markets are to be expected. We argue that the dynamics emerge from complex interactions among multiple factors, such as information and misinformation accessible to individuals and the formation and revision of beliefs. (Mis)information accessible to individuals is, amongst others, affected by algorithms specifically designed to provide personalized information, while automated fact-checking algorithms can help reduce the amount of circulating misinformation. The formation and revision of individual (and probably false) beliefs and individual fact-checking and interpretation of information are heavily affected by linguistic patterns inherent to information during pandemics and infodemics and further factors, such as affect, intuition, and motives. We argue that, to get a deep(er) understanding of the dynamics emerging from infodemics, the fields of Business and Economics should integrate the perspectives of Computer Science and Information Systems, (Computational) Linguistics, and Cognitive Science into the wider context of economic systems (e.g., organizations, markets or industries) and propose a way to do so. As research on infodemics is a strongly interdisciplinary field and the integration of the above-mentioned disciplines is a first step towards a holistic approach, we conclude with a call to action which should encourage researchers to collaborate across scientific disciplines and unfold collective creativity, which will substantially advance research on infodemics.

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Section: Discussionmentioning

confidence: 99%

Understanding the dynamics emerging from infodemics: a call to action for interdisciplinary research

et al. 2021

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“…Step 0 represents a pre-condition for the model integration process. We assume that a library of theory building blocks, implemented as model components according to a common meta-theoretical software architecture is available [19]. In…”

Section: Fig 1 Model Discovery Processmentioning

confidence: 99%

“…Specifically, prevalence of alcohol use in the past 12 months, average quantity of alcohol consumed per day (grams of ethanol) and average frequency of alcohol use (drinking days per month). Models are built according to a mechanism-based social systems modeling (MBSSM) software architecture, following a general micro-macro scheme which describes the dynamic interplay between individuals and social structures, leading to emergent population-level patterns in alcohol use [19]. Individuals in the models are from a representative population-level US microsimulation model 1980-2015 that accounts for births, deaths and migration over time [2].…”

Section: Alcohol Use Modelingmentioning

confidence: 99%

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Using Multi-objective Grammar-Based Genetic Programming to Integrate Multiple Social Theories in Agent-Based Modeling

Davies

Buckley

et al. 2021

Lecture Notes in Computer Science

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“…ere are also many discussions and proposals about different aspects of the ABS development process. A recently proposed software architecture [50], namely MBSSM (Mechanism-Based Social System Modelling), is designed based on a middle-range theory approach to express individual social theory mechanisms in a unified way. Following this line of thought, our model discovery process can be addressed as a protocol concerned with structural uncertainty.…”

Section: Implications For Complex Systems Modeling Practicementioning

confidence: 99%

Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social Systems

Buckley

Bai

et al. 2020

Complexity

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The generative approach to social science, in which agent-based simulations (or other complex systems models) are executed to reproduce a known social phenomenon, is an important tool for realist explanation. However, a generative model, when suitably calibrated and validated using empirical data, represents just one viable candidate set of entities and mechanisms. The model only partially addresses the needs of an abductive reasoning process—specifically it does not provide insight into other viable sets of entities or mechanisms nor suggests which of these are fundamentally constitutive for the phenomenon to exist. In this paper, we propose a new model discovery framework that more fully captures the needs of realist explanation. The framework exploits the implicit ontology of an existing human-built generative model to propose and test a plurality of new candidate model structures. Genetic programming is used to automate this search process. A multiobjective approach is used, which enables multiple perspectives on the value of any particular generative model—such as goodness of fit, parsimony, and interpretability—to be represented simultaneously. We demonstrate this new framework using a complex systems modeling case study of change and stasis in societal alcohol use patterns in the US over the period 1980–2010. The framework is successful in identifying three competing explanations of these alcohol use patterns, using novel integrations of social role theory not previously considered by the human modeler. Practitioners in complex systems modeling should use model discovery to improve the explanatory utility of the generative approach to realist social science.

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A Software Architecture for Mechanism-Based Social Systems Modelling in Agent-Based Simulation Models

Cited by 23 publications

References 10 publications

Understanding the dynamics emerging from infodemics: a call to action for interdisciplinary research

Understanding the dynamics emerging from infodemics: a call to action for interdisciplinary research

Using Multi-objective Grammar-Based Genetic Programming to Integrate Multiple Social Theories in Agent-Based Modeling

Multiobjective Genetic Programming Can Improve the Explanatory Capabilities of Mechanism-Based Models of Social Systems

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