RT info:eu-repo/semantics/article
T1 Social Simulation Models as Refuting Machines
A1 Mauhe, Nicolas
A1 Izquierdo, Luis R.
A1 Izquierdo, Segismundo S.
K1 Social Simulation, Computer Simulation, Refutation, Modelling, Counter-Example, Markov Chain
AB This paper discusses a prominent way in which social simulations can contribute (and have contributed) to the advance of science; namely, by refuting some of our incorrect beliefs about how the real world works. More precisely, social simulations can produce counter-examples that reveal something is wrong in a prevailing scientific assumption. Indeed, here we argue that this is a role that many well-known social simulation models have played, and it may be one of the main reasons why such well-known models have become so popular. To test this hypothesis, here we examine several popular models in the social simulation literature and we find that all these models are most naturally interpreted as providers of compelling and reproducible (computer-generated) evidence that refuted some assumption or belief in a prevailing theory. By refuting prevailing theories, these models have greatly advanced science and, in some cases, have even opened a new field of research.
PB European Social Simulation Association
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/81330
UL https://uvadoc.uva.es/handle/10324/81330
LA eng
NO Journal of Artificial Societies and Social Simulation 26 (2) 8
NO Producción Científica
DS UVaDOC
RD 03-feb-2026