Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups

Moody, James; White, Dougľas R.

doi:10.2307/3088904

Cited by 860 publications

(713 citation statements)

References 118 publications

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“…The theory is consistent with network-based approaches to modeling artifact lineages, trade, and the social exchange of knowledge and beliefs [2,90]. It is also more consistent than a 'survival of the fittest' perspective on culture with findings that human society is more cooperative than is predicted by either expected utility or natural selection on genetic variation [9,63] and with evidence that cultural evolution, construed in selectionist terms, actually reduces cooperation, at least in some situations 11 [77].…”

Section: Evoc: a Computational Model Of Cultural Evolutionsupporting

confidence: 70%

“…Building on the concept of a self-replicating automaton, Holland [68] developed the genetic algorithm, a computer program that embodies the algorithmic structure of natural selection, and setting in motion the thriving field of natural computing (see [90] for a review). Holland proposed that structures, natural or artificial, that evolve through a selectionist process share three fundamental principles.…”

Section: The Algorithmic Structure Of Natural Selectionmentioning

confidence: 99%

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An evolutionary framework for cultural change: Selectionism versus communal exchange

Gabora

2013

Physics of Life Reviews

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Dawkins' replicator-based conception of evolution has led to widespread misapplication of selectionism across the social sciences because it does not address the paradox that inspired the theory of natural selection in the first place: how do organisms accumulate change when traits acquired over their lifetime are obliterated? This is addressed by von Neumann's concept of a self-replicating automaton (SRA). A SRA consists of a self-assembly code that is used in two distinct ways: (1) actively deciphered during development to construct a self-similar replicant, and (2) passively copied to the replicant to ensure that it can reproduce. Information that is acquired over a lifetime is not transmitted to offspring, whereas information that is inherited during copying is transmitted. In cultural evolution there is no mechanism for discarding acquired change. Acquired change can accumulate orders of magnitude faster than, and quickly overwhelm, inherited change due to differential replication of variants in response to selection. This prohibits a selectionist but not an evolutionary framework for culture and the creative processes that fuel it. The importance non-Darwinian processes in biological evolution is increasingly recognized. Recent work on the origin of life suggests that early life evolved through a non-Darwinian process referred to as communal exchange that does not involve a self-assembly code, and that natural selection emerged from this more haphazard, ancestral evolutionary process. It is proposed that communal exchange provides an evolutionary framework for culture that enables specification of cognitive features necessary for a (real or artificial) society to evolve culture. This is supported by a computational model of cultural evolution and a conceptual network based program for documenting material cultural history, and it is consistent with high levels of human cooperation. Evolutionary Framework Culture 3Keywords: creativity, cultural evolution, meme, natural selection, replicator, selfreplicating automaton Evolutionary Framework Culture 4 IntroductionLike organisms, elements of culture exhibit descent with modification; new ideas and artifacts build adaptively on previous ones. If it were possible to root the social and behavioral sciences in an evolutionary framework, they might achieve a unification comparable with Darwin's unification of the life sciences. Thus it is unsurprising that, dating back to Herbert Spencer's introduction of the notion of social Darwinism a few years after Darwin's Origin of Species, Darwinian thinking has been applied to a range of phenomena outside of biology, including creativity [14,111], neural copying and pruning [11,12,13,23,26], law [55], cosmology [118], computer-mediated communication [74], and perhaps most extensively, cultural and economic change [7,8,15,71,86,87,107,108]. Elements of culture build on one another cumulatively, as demonstrated even in laboratory settings [10]. Not only does culture accumulate over time, but it adapts, diversifies,...

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Section: Evoc: a Computational Model Of Cultural Evolutionsupporting

confidence: 70%

Section: The Algorithmic Structure Of Natural Selectionmentioning

confidence: 99%

An evolutionary framework for cultural change: Selectionism versus communal exchange

Gabora

2013

Physics of Life Reviews

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“…For ease of reference, the former will in the remainder of this article be referred to as 'connected memberships', while the latter are designated as 'isolated memberships'. Using this terminology, connected memberships are argued to expand individuals' networks beyond one single association (Paxton, 2007; see also Moody & White, 2003;Cornwell & Harrison, 2004), thus allowing members to 'transfer trust gained within their association to individuals outside the association' (Paxton, 2007, p. 51). Isolated memberships, however, 'are inherently bounded, and should therefore be less likely to transfer trust' (Paxton, 2007, pp.…”

Section: Introductionmentioning

confidence: 99%

Association Membership and Generalized Trust: Are Connections between Associations Losing their Value?

Geys

2012

Journal of Civil Society

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“…Additionally, groups can also be algorithmically determined, as the output of the specific clustering algorithm, i.e. without a precise a priori definition [5]. In such case, group definition is determined by the algorithm and its parameters.…”

Section: Social Communitymentioning

confidence: 99%

An Introduction to Community Detection in Multi-layered Social Network

Bródka¹,

Filipowski²,

Kazienko³

2013

Information Systems, E-Learning, and Knowledge Management Research

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Abstract. Social communities extraction and their dynamics are one of the most important problems in today's social network analysis. During last few years, many researchers have proposed their own methods for group discovery in social networks. However, almost none of them have noticed that modern social networks are much more complex than few years ago. Due to vast amount of different data about various user activities available in IT systems, it is possible to distinguish the new class of social networks called multi-layered social network. For that reason, the new approach to community detection in the multi-layered social network, which utilizes multi-layered edge clustering coefficient is proposed in the paper.

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Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups

Cited by 860 publications

References 118 publications

An evolutionary framework for cultural change: Selectionism versus communal exchange

An evolutionary framework for cultural change: Selectionism versus communal exchange

Association Membership and Generalized Trust: Are Connections between Associations Losing their Value?

An Introduction to Community Detection in Multi-layered Social Network

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