Archive for the ‘Networks’ Category
The generality of network properties allows the utilization of the ‘wisdom’ of biological systems surviving crisis events for many millions of years. Yeast protein-protein interaction network shows a decrease in community-overlap (an increase in community cohesion) in stress. Community rearrangement seems to be a cost-efficient, general crisis- management response of complex systems. Inter-community bridges, such as the highly dynamic ‘creative nodes’ emerge as crucial determinants helping crisis survival.
Crisis periods can be slowed down, postponed, or prevented by agents of independent and unpredictable behavior, such as stem cells (in organisms), omnivores or top predators (in ecosystems), or market gurus (in markets). Since biological networks are blueprints of organisms, which survived billions of crisis situations, this above examples highlight their great potential as role models of social behavior.
Networks of interconnected nodes have long played a key role in Cognitive Science, from artificial neural networks to spreading activation models of semantic memory. Recently, however, a new Network Science has been developed, providing insights into the emergence of global, system-scale properties in contexts as diverse as the Internet, metabolic reactions, and collaborations among scientists. Today, the inclusion of network theory into Cognitive Sciences, and the expansion of complex systems science, promises to significantly change the way in which the organization and dynamics of cognitive and behavioral processes are understood. In this paper, we review recent contributions of network theory at different levels and domains within the Cognitive Sciences.
During the last three or four decades labour, union, research and advocacy networks have interacted with networks of anti-capitalist or alter-globalist social justice activists. These interactions have enabled the (self-)organisation of working people across production networks linking the Global North and South. Especially after the crisis erupted in 2007-8, the process has expanded to include networks of self-employed, unemployed, marginalized and increasingly radicalised knowledge and service workers. Online social networks such as Facebook, Twitter, and Google Plus, but also free, ‘libre’ and open source software (FLOSS), offer new experiences complementary to traditional forms of organization. This Appendix presents a concise overview of networks constituted around the quest for ‘associated social relations of production’, or ‘peer production communities’. Whether labelled hackers, makers, diggers, guerrilla translators and so forth, and involving FLOSS and hardware production, collaborative digital, creative, artistic, media, graphic, and architecture projects, or Do-it-Yourself (DIY) or Do-it-with-Others (DIWO) practices, these networks connect highly educated individual knowledge, information, education and service workers.
Over the past decade there has been a growing public fascination with the complex connectedness of modern society. This connectedness is found in many incarnations: in the rapid growth of the Internet, in the ease with which global communication takes place, and in the ability of news and information as well as epidemics and financial crises to spread with surprising speed and intensity. These are phenomena that involve networks, incentives, and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which our decisions can have subtle consequences for others. This introductory undergraduate textbook takes an interdisciplinary look at economics, sociology, computing and information science, and applied mathematics to understand networks and behavior. It describes the emerging field of study that is growing at the interface of these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.
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This article contributes to an ongoing theoretical effort to extend the insights of relational and network sociology into adjacent domains. We integrate Simmel’s late theory of the relational self into the formal analysis of social relations, generating a framework for theorizing forms of association among self-relating individuals. On this model, every “node” in an interaction has relations not only to others but also to itself, specifically between its ideality and its actuality. We go on to integrate this self-relation into a formal model of social relations. This model provides a way to describe configurations of social interactions defined by the forms according to which social relations realize participants’ ideal selves. We examine four formal dimensions along which these self-relational relationships can vary: distance, symmetry, scope, and actualization.
“The ‘arborescenť model of thought designates the epistemplogy that informs all of Western thought, from botany to information sciences to theology”. Arbolic thought is a model to describe a system that is hierarchical, centered around a core belief, reductivistic, increasingly specialized, non-cyclical, linear, and ripe with segmentation and striation. Similar to a tree-like description of biological evolution or genealogy, arborescent systems start from a central origin and continue to evolve by branching into successively specialized generations. Vertical in nature, the arbolic is ordered, structured and “scientific”: it has a distinct train of thought, a clear inheritance, an order.
In contrast, the rhizome is brought forward as a matted web of interlinked concepts. Inspired by the wandering, non-centered root systems of grasses and plants, the rhizome appears non-linear, horizontal, nomadic, deterritorialized and heterogeneous. The rhizome cuts across and between the order of vertical space, connecting multiple points simultaneously in a network of nodes. Connected to each other at arbitrary points, the rhizomatic system is more concerned with the multiplicitous interlinking of concept, action and being.
Although it lacks a central dogma of a trunk/brain, it is a horizontal, bottom-up system that produces an emergent system of metabehavior that is strong, robust, and intelligent… in the non-standard sense of the word. Within nature, rhizomatic systems like ants or grassy weeds eventually win: “True, the weed produced no lilies, no battleships, no Sermons on the Mount… Eventually the weed gets the upper hand… The lily is beautiful, the cabbage is provender, the poppy is maddening – but the weed is rank growth… it points a moral.”
If intelligence could exist without a central brain, the rhizome would be it.