TY - CHAP
T1 - Causation in Agent-Based Computational Social Science
AU - Anzola, David
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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
AB - 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
UR - http://www.scopus.com/inward/record.url?scp=85087908240&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85087908240&partnerID=8YFLogxK
UR - https://repository.urosario.edu.co/handle/10336/28717
U2 - 10.1007/978-3-030-34127-5_5
DO - 10.1007/978-3-030-34127-5_5
M3 - Capítulo (revisado por pares)
AN - SCOPUS:85087908240
SN - 9783030341268
VL - 1
T3 - Springer Proceedings in Complexity
SP - 47
EP - 62
BT - Advances in Social Simulation - Looking in the Mirror, 2018
A2 - Verhagen, Harko
A2 - Borit, Melania
A2 - Bravo, Giangiacomo
A2 - Wijermans, Nanda
PB - Springer
CY - Suiza
T2 - 14th Social Simulation Conference, 2018
Y2 - 20 August 2018 through 24 August 2018
ER -