{"title":"Cooperation and the Globalization-Localization Dilemmas","authors":"Jayati Deshmukh, S. Srinivasa","doi":"10.25088/complexsystems.31.1.59","DOIUrl":null,"url":null,"abstract":"Evolution of cooperation among self-interested agents is revisited in this paper in the context of globalization and localization. A globalized society is characterized by disentrenchment—or routine interactions between strangers across subcultures. Such interactions are rich in novelty, but also have high levels of distrust and insecurity. A localized society is comprised of clusters of subcultures where most social interactions happen. Each tightly knit subculture is rich in mutual familiarity and trust, but not conducive to the spread of novel ideas. A second dimension is that of utilitarian knowledge. Historically, social acquaintances were the primary (if not the only) source of utilitarian knowledge. With technologies like the internet, diffusion of utilitarian knowledge in a society is no longer modulated by acquaintance networks. This leads us to two different forms of (dis)entrenchment:\u2028(dis)entrenchment of knowledge and (dis)entrenchment of acquaintance, leading to four societal configurations. This paper asks how each of the configurations fares with respect to the evolution of cooperation. Entrenchment is represented using well-known network models from the literature, and evolution of cooperation is modeled by the evolutionary version of the iterated prisoners’ dilemma game. Based on simulation runs, we note that acquaintance and knowledge are characteristically different aspects. We find that disentrenched knowledge is more conducive for evolution of cooperation in networks rather than disentrenched acquaintances.","PeriodicalId":50871,"journal":{"name":"Advances in Complex Systems","volume":"20 1","pages":"59-85"},"PeriodicalIF":0.7000,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Complex Systems","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.25088/complexsystems.31.1.59","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Evolution of cooperation among self-interested agents is revisited in this paper in the context of globalization and localization. A globalized society is characterized by disentrenchment—or routine interactions between strangers across subcultures. Such interactions are rich in novelty, but also have high levels of distrust and insecurity. A localized society is comprised of clusters of subcultures where most social interactions happen. Each tightly knit subculture is rich in mutual familiarity and trust, but not conducive to the spread of novel ideas. A second dimension is that of utilitarian knowledge. Historically, social acquaintances were the primary (if not the only) source of utilitarian knowledge. With technologies like the internet, diffusion of utilitarian knowledge in a society is no longer modulated by acquaintance networks. This leads us to two different forms of (dis)entrenchment: (dis)entrenchment of knowledge and (dis)entrenchment of acquaintance, leading to four societal configurations. This paper asks how each of the configurations fares with respect to the evolution of cooperation. Entrenchment is represented using well-known network models from the literature, and evolution of cooperation is modeled by the evolutionary version of the iterated prisoners’ dilemma game. Based on simulation runs, we note that acquaintance and knowledge are characteristically different aspects. We find that disentrenched knowledge is more conducive for evolution of cooperation in networks rather than disentrenched acquaintances.
期刊介绍:
Advances in Complex Systems aims to provide a unique medium of communication for multidisciplinary approaches, either empirical or theoretical, to the study of complex systems. The latter are seen as systems comprised of multiple interacting components, or agents. Nonlinear feedback processes, stochastic influences, specific conditions for the supply of energy, matter, or information may lead to the emergence of new system qualities on the macroscopic scale that cannot be reduced to the dynamics of the agents. Quantitative approaches to the dynamics of complex systems have to consider a broad range of concepts, from analytical tools, statistical methods and computer simulations to distributed problem solving, learning and adaptation. This is an interdisciplinary enterprise.