Show simple item record

Where does complexity come from in the social sciences? Networks, meaning and collective learning in human action

dc.contributor.authorLazega, Emmanuel
HAL ID: 13781
ORCID: 0000-0001-8844-6426
dc.date.accessioned2013-07-22T13:02:38Z
dc.date.available2013-07-22T13:02:38Z
dc.date.issued2013-06
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/11592
dc.language.isoenen
dc.subjectAnalyse de réseauen
dc.subjectGroupes sociauxen
dc.subjectRelations humainesen
dc.subjectSciences socialesen
dc.subjectIntelligence collectiveen
dc.subjectComportement organisationnelen
dc.subject.ddc302en
dc.titleWhere does complexity come from in the social sciences? Networks, meaning and collective learning in human actionen
dc.typeCommunication / Conférence
dc.description.abstractenThe main argument of this paper is that, in the social sciences, complexity comes from the fact that actors act if they create meanings for their actions –meanings that help them recognize their action as appropriate. In addition they are able, through learning (both individual, relational and collective), to modify these meanings, re-evaluate the appropriateness of their actions and, subsequently, change the course of their actions. Therefore I argue that the trans-disciplinarity coming from the science of complexity should focus on further modelling phenomena such as collective learning based on such premises. Sociologists have long done just that, using small, complex, and rich datasets –usually not big data and models that they perceive as reifying the object too much. This presentation provides an empirical example combining a sociological theory of appropriateness judgments and the analyses of the dynamics of advice networks in order to model collective learning. The example is based on the observation of the work of judges in a courthouse and on the analysis of the dynamics of their advice network. This phenomenon and this example raise the issue of the place of sociology in the current paradigmatic context of complexity sciences. Further modelling of sense-making, appropriateness judgments and collective learning together with other disciplines would also be crucial for complexity sciences if they want to help explain action by human beings who tend to want to make sense of the world, and by extension what happens in their groups and in society at large.en
dc.subject.ddclabelInteraction socialeen
dc.relation.conftitleComplexity in social systems: from data to modelsen
dc.relation.confdate2013-06
dc.relation.confcityPontoiseen
dc.relation.confcountryFranceen
dc.relation.forthcomingnonen


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record