Knowledge transfer in agent-based computational social science

Anzola, David

doi:10.1016/j.shpsa.2018.05.001

Cited by 11 publications

(8 citation statements)

References 33 publications

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“…Researchers could feel deterred from implementing more complex models that capture intricate insights from the qualitative tradition, if there is not consensus about the impact of basic representational choices. In addition, theory-building in the field, given the contextual nature of modelling and the expressiveness of agent-based models, is relatively localised (Anzola 2018;Anzola and Rodríguez-Cárdenas 2018). There are not many instances of models that are progressively made more robust so to account for more intricate theoretical or methodological insights.…”

Section: Grounding the Parsing Process On Methodological Concernsmentioning

confidence: 99%

“…Agent-based computational social science, as an emergent disciplinary field, 1 is the result of interdisciplinary collaboration that brings together major insights from complexity science, computer science and social science (Anzola 2018). Social science delimits the object of study, computer science provides the method and complexity science contributes most of the philosophical underpinnings.…”

Section: The Disciplinarisation Process In Agent-based Computational Social Sciencementioning

confidence: 99%

“…The incorporation of insights from the qualitative tradition are precluded by the relatively widespread consensus, put together during the emergence of the field, around the belief that agent-based models, first, are not accurate representations, second, imply some sort of simplification and, third, are to be validated against external theory or data (Axelrod 1997;Epstein and Axtell 1996;Gilbert and Troitzsch 2005). The question about the role of the observer as a source of error is usually subordinate to a question about correspondence, since modelling is an indirect approach to knowledge (Anzola 2018).…”

Section: Converging Disciplinary Traditionsmentioning

confidence: 99%

“…The lack of specificity and potentially conflicting character of these commitments do not lead to the formation of disagreements or interfere with everyday practices because a disciplinary identity needs not be entirely supported by what practitioners actually do. Verification and validation were inherited in agent-based computational social science from computer science and software engineering (Anzola 2018), but practitioners have always acknowledged the methodological distinctiveness of the method. Potential conflicts and disagreements are avoided simply by acknowledging, among other things, that, in agent-based social simulation, verification and validation can easily overlap (e.g., David 2013;Gilbert and Troitzsch 2005).…”

Section: Implicit Effects On Parsing Processes and Shared Commitmentsmentioning

confidence: 99%

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Disagreement in discipline-building processes

Anzola

2019

Synthese

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Successful instances of interdisciplinary collaboration can eventually enter a process of disciplinarisation. This article analyses one of those instances: agent-based computational social science, an emerging disciplinary field articulated around the use of computational models to study social phenomena. The discussion centres on how, in knowledge transfer dynamics from traditional disciplinary areas, practitioners parsed several epistemic resources to produce new foundational disciplinary shared commitments, and how disagreements operated as a mechanism of differentiation in their production. Two parsing processes are examined to illustrate this claim. The first one is the parsing of the qualitative-quantitative dualism, arguably the most important methodological disagreement in social science. The second one is the parsing of prediction, a key value in contemporary science. The analysis evidences that disagreements have fostered both external and internal dynamics of differentiation in agent-based computational social science. The former have permitted a more efficient use of epistemic resources, whereas the latter have forced practitioners to modify the foundational narrative and the agenda of the field.

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Section: Grounding the Parsing Process On Methodological Concernsmentioning

confidence: 99%

Section: The Disciplinarisation Process In Agent-based Computational Social Sciencementioning

confidence: 99%

Section: Converging Disciplinary Traditionsmentioning

confidence: 99%

Section: Implicit Effects On Parsing Processes and Shared Commitmentsmentioning

confidence: 99%

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Disagreement in discipline-building processes

Anzola

2019

Synthese

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“…We usually do not notice the flaws and shortcomings until we put these components together in practice. Today, with the increase of computational power and the development of different methods such as ABM (Railsback, 2019), computational social science (Anzola, 2019;Edelmann, Wolff, Montagne, & Bail, 2020;Keuschnigg, Lovsjo, & Hedstrom, 2018) has opened a window for researchers to study these laws and their strengths and weaknesses by building artificial communities inside the computer (Bruch & Atwell, 2015;Macy & Willer, 2002), albeit simple and ad hoc.…”

Section: Introductionmentioning

confidence: 99%

A Computational Approach to Homans Social Exchange Theory

Enayat¹,

Ardebili²,

Kivi³

et al. 2020

Preprint

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How does society work? How do groups emerge within society? What are the effects of emotions and memory on our everyday actions? George Homans, like us, had a perspective on what society is, except that he was a sociologist. Homans theory, which is an exchange theory, is based on a few propositions about the fundamental actions of individuals, and how values, memory, and expectations affect their behavior. In this paper, our main interest and purpose are to find out whether these propositions can satisfy our conception of society and generate essential properties of it computationally. To do so, Based on Homans' prepositions, we provide the opportunity for each agent to exchange with other agents. That is, each agent transacts with familiar agents based on his previous history with them and transacts with newly found agents through exploration. One novelty of our work is the investigation of implications of the base theory while covering its flaws with minimal intervention; flaws which are inevitable in a non-mathematical theory. The importance of our work is that we have scrutinized the consequences of an actual sociological theory. At the end of our investigation, we propose another proposition to Homans theory, which makes the theory more appealing, and we discuss other possible directions for further research.

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Capturing the representational and the experimental in the modelling of artificial societies

Anzola

2021

Euro Jnl Phil Sci

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Knowledge transfer in agent-based computational social science

Cited by 11 publications

References 33 publications

Disagreement in discipline-building processes

Disagreement in discipline-building processes

A Computational Approach to Homans Social Exchange Theory

Capturing the representational and the experimental in the modelling of artificial societies

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