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dc.contributor.authorRubio-Campillo, Xavier
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2016-04-12T10:49:31Z
dc.date.available2016-04-12T10:49:31Z
dc.date.issued2016-01-05
dc.identifier.citationRubio-Campillo, Xavier. Model Selection in Historical Research Using Approximate Bayesian Computation. "PLoS ONE", 05 Gener 2016, vol. 11, núm. 1.
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/2117/85547
dc.description.abstractFormal Models and History Computational models are increasingly being used to study historical dynamics. This new trend, which could be named Model-Based History, makes use of recently published datasets and innovative quantitative methods to improve our understanding of past societies based on their written sources. The extensive use of formal models allows historians to reevaluate hypotheses formulated decades ago and still subject to debate due to the lack of an adequate quantitative framework. The initiative has the potential to transform the discipline if it solves the challenges posed by the study of historical dynamics. These difficulties are based on the complexities of modelling social interaction, and the methodological issues raised by the evaluation of formal models against data with low sample size, high variance and strong fragmentation. This work examines an alternate approach to this evaluation based on a Bayesian-inspired model selection method. The validity of the classical Lanchester's laws of combat is examined against a dataset comprising over a thousand battles spanning 300 years. Four variations of the basic equations are discussed, including the three most common formulations (linear, squared, and logarithmic) and a new variant introducing fatigue. Approximate Bayesian Computation is then used to infer both parameter values and model selection via Bayes Factors. Results indicate decisive evidence favouring the new fatigue model. The interpretation of both parameter estimations and model selection provides new insights into the factors guiding the evolution of warfare. At a methodological level, the case study shows how model selection methods can be used to guide historical research through the comparison between existing hypotheses and empirical evidence.
dc.description.sponsorshipFunding for this work was provided by the SimulPast Consolider Ingenio project (CSD2010-00034) of the former Ministry for Science and Innovation of the Spanish Government and the European Research Council Advanced Grant EPNet (340828).
dc.format.extent18 p.
dc.language.isoeng
dc.publisherPublic Library of Science
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectÀrees temàtiques de la UPC::Enginyeria mecànica
dc.subject.lcshBayesian analysis
dc.subject.lcshHistorical research
dc.subject.otherHistorical dynamics
dc.subject.otherBayesian Computation
dc.subject.otherModel-Based History
dc.subject.otherWarfare
dc.titleModel Selection in Historical Research Using Approximate Bayesian Computation
dc.typeArticle
dc.subject.lemacEstadística bayesiana
dc.subject.lemacHistòria--Metodologia
dc.identifier.doi10.1371/journal.pone.0146491
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://journals.plos.org/plosone/article?id=10.1371/journal.pone.0146491
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/340828/EU/Production and distribution of food during the Roman Empire: Economics and political dynamics./EPNET
local.citation.publicationNamePLoS ONE
local.citation.volume11
local.citation.number1
local.citation.startingPagee0146491
dc.identifier.pmid26730953


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