Causation in Agent-Based Computational Social Science

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Abstract

Even though causation is often considered a constitutive aspect of scientific explanation, agent-based computational social science, as an emergent disciplinary field, has systematically neglected the question of whether explanation using agent-based models is causal. Rather than discussing the reasons for this neglect, the article builds on the assumption that, since explanation in the field is already heavily permeated by causal reasoning and language, the articulation of a causal theory of explanation would help standardisation. With this goal in mind, the text briefly explores four candidate accounts of causation on which a causal theory of explanation in agent-based computational social science could be grounded: agent causation, algorithmic causation, interventionist causation and causal mechanisms. It suggests that, while the first two accounts are intuitively appealing, for they seem to stress the most important methodological aspects of agent-based modelling, a more robust theory of causal explanation could be developed if the field focuses, instead, on causal mechanisms and interventions.
Original languageEnglish (US)
Title of host publicationAdvances in Social Simulation
Subtitle of host publicationLooking in the Mirror
Place of PublicationCham
PublisherSpringer
Chapter5
Pages47-62
Number of pages16
ISBN (Electronic)2213-8692, 978-3-030-34127-5
ISBN (Print)2213-8684, 978-3-030-34126-8
DOIs
StatePublished - 2020

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  • Cite this

    Anzola Pinzon, D. E. (2020). Causation in Agent-Based Computational Social Science. In Advances in Social Simulation: Looking in the Mirror (pp. 47-62). Springer. https://doi.org/10.1007/978-3-030-34127-5_5