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Times-Cited = {{745}},
Usage-Count-(Last-180-days) = {{6}},
Usage-Count-Since-2013 = {{28}},
Doc-Delivery-Number = {{BTT80}},
Unique-ID = {{ISI:000288018200016}},
}

@article{ ISI:000251447000003,
Author = {Centola, Damon and Macy, Michael},
Title = {{Complex contagions and the weakness of long ties}},
Journal = {{AMERICAN JOURNAL OF SOCIOLOGY}},
Year = {{2007}},
Volume = {{113}},
Number = {{3}},
Pages = {{702-734}},
Month = {{NOV}},
Abstract = {{The strength of weak ties is that they tend to be long - they connect
   socially distant locations, allowing information to diffuse rapidly. The
   authors test whether this ``strength of weak ties{''} generalizes from
   simple to complex contagions. Complex contagions require social
   affirmation from multiple sources. Examples include the spread of
   high-risk social movements, avant garde fashions, and unproven
   technologies. Results show that as adoption thresholds increase, long
   ties can impede diffusion. Complex contagions depend primarily on the
   width of the bridges across a network, not just their length. Wide
   bridges are a characteristic feature of many spatial networks, which may
   account in part for the widely observed tendency for social movements to
   diffuse spatially.}},
Publisher = {{UNIV CHICAGO PRESS}},
Address = {{1427 E 60TH ST, CHICAGO, IL 60637-2954 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Centola, D (Reprint Author), Harvard Univ, Cambridge, MA 02138 USA.
   Harvard Univ, Cambridge, MA 02138 USA.
   Cornell Univ, Ithaca, NY 14853 USA.}},
DOI = {{10.1086/521848}},
ISSN = {{0002-9602}},
Keywords-Plus = {{SMALL-WORLD; COLLECTIVE ACTION; SOCIAL-MOVEMENTS; RANDOM NETWORKS;
   CRITICAL MASS; DIFFUSION; MODELS; MOBILIZATION; TOPOLOGY; DYNAMICS}},
Research-Areas = {{Sociology}},
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Number-of-Cited-References = {{67}},
Times-Cited = {{304}},
Usage-Count-(Last-180-days) = {{4}},
Usage-Count-Since-2013 = {{61}},
Journal-ISO = {{Am. J. Sociol.}},
Doc-Delivery-Number = {{238KR}},
Unique-ID = {{ISI:000251447000003}},
}

@article{ ISI:000242971600002,
Author = {Chiang, Yen-Sheng},
Title = {{Birds of moderately different feathers: Bandwagon dynamics and the
   threshold heterogeneity of network neighbors}},
Journal = {{JOURNAL OF MATHEMATICAL SOCIOLOGY}},
Year = {{2007}},
Volume = {{31}},
Number = {{1}},
Pages = {{47-69}},
Month = {{JAN-MAR}},
Note = {{99th Annual Meeting of the American-Sociological-Association, San
   Francisco, CA, AUG 14-17, 2004}},
Organization = {{Amer Sociol Assoc}},
Abstract = {{This paper investigates bandwagon dynamics in social networks, using an
   extension of Granovetter's (1978) threshold model. The focus is on the
   pattern of social ties connecting actors with different participation
   thresholds. A benchmark model, in which actors' thresholds are similar
   to their neighbors' thresholds, is built from the principle of
   homophily. Computational experiments show that participation levels
   increase when network structure departs from pure homophily, such that
   actors have new neighbors with discrepant thresholds. Further increasing
   the heterogeneity of network neighbors causes the bandwagon effects to
   wane, however, suggesting that bandwagon dynamics are maximized when
   there is a balance of heterogeneity and homogeneity in social networks.
   This principle is consistent with insights from several empirical
   studies and consistent with the conclusions of other formal models.}},
Publisher = {{TAYLOR \& FRANCIS INC}},
Address = {{325 CHESTNUT ST, SUITE 800, PHILADELPHIA, PA 19106 USA}},
Type = {{Article; Proceedings Paper}},
Language = {{English}},
Affiliation = {{Chiang, YS (Reprint Author), Univ Washington, Dept Sociol, 202 Savery Hall,Box 353340, Seattle, WA 98195 USA.
   Univ Washington, Dept Sociol, Seattle, WA 98195 USA.}},
DOI = {{10.1080/00222500601013536}},
ISSN = {{0022-250X}},
EISSN = {{1545-5874}},
Keywords = {{bandwagon; heterogeneity; interdependence; network; simulation;
   threshold}},
Keywords-Plus = {{COLLECTIVE ACTION; DIFFUSION; INNOVATIONS; POPULATION; CASCADES;
   BEHAVIOR; FADS}},
Research-Areas = {{Mathematics; Mathematical Methods In Social Sciences; Sociology}},
Web-of-Science-Categories  = {{Mathematics, Interdisciplinary Applications; Social Sciences,
   Mathematical Methods; Sociology}},
Author-Email = {{yen506@u.washington.edu}},
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Number-of-Cited-References = {{28}},
Times-Cited = {{14}},
Usage-Count-(Last-180-days) = {{1}},
Usage-Count-Since-2013 = {{6}},
Journal-ISO = {{J. Math. Sociol.}},
Doc-Delivery-Number = {{118VM}},
Unique-ID = {{ISI:000242971600002}},
}

@article{ ISI:000232342000002,
Author = {Gibson, DR},
Title = {{Concurrency and commitment: Network scheduling and its consequences for
   diffusion}},
Journal = {{JOURNAL OF MATHEMATICAL SOCIOLOGY}},
Year = {{2005}},
Volume = {{29}},
Number = {{4}},
Pages = {{295-323}},
Month = {{OCT-DEC}},
Abstract = {{Network ties are thought to be concurrent-one can ``have{''} many
   friends at once, for instance-but their concrete enactment is largely
   serial and episodic, guided by priorities that steer a person from one
   encounter to the next. Further, dyadic encounters require that two
   people be simultaneously available to interact, creating the need for
   coordinated scheduling. Here I study the consequences of scheduling for
   network diffusion, using a computer simulation that interposes a
   scheduling process between a pre-existing network and instances of
   contagion. The pace and extent of diffusion are shown to depend upon the
   interaction of network topology, contagion rule (on first-contact versus
   at some threshold), and whether actors try to remedy past scheduling
   imperfections. Scheduling turns central actors into diffusion
   bottlenecks, but can also trigger early adoption by giving actors false
   readings on the status of their network alters. The implications of
   scheduling extend beyond diffusion, to other outcomes such as
   decision-making, as well as to network evolution.}},
Publisher = {{TAYLOR \& FRANCIS LTD}},
Address = {{4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Gibson, DR (Reprint Author), Univ Penn, Dept Sociol, 3718 Locust Walk, Philadelphia, PA 19104 USA.
   Univ Penn, Dept Sociol, Philadelphia, PA 19104 USA.}},
DOI = {{10.1080/00222500590957491}},
ISSN = {{0022-250X}},
Keywords = {{networks; scheduling; simulation; diffusion}},
Keywords-Plus = {{BIASED NET THEORY; SOCIAL NETWORKS; COMPLEX NETWORKS; EVOLUTION;
   DYNAMICS; APPROXIMATIONS; PARTNERSHIPS; SIMULATIONS; CONTAGION; MODELS}},
Research-Areas = {{Mathematics; Mathematical Methods In Social Sciences; Sociology}},
Web-of-Science-Categories  = {{Mathematics, Interdisciplinary Applications; Social Sciences,
   Mathematical Methods; Sociology}},
Author-Email = {{gibsond@sas.upenn.edu}},
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Number-of-Cited-References = {{42}},
Times-Cited = {{8}},
Usage-Count-(Last-180-days) = {{0}},
Usage-Count-Since-2013 = {{5}},
Journal-ISO = {{J. Math. Sociol.}},
Doc-Delivery-Number = {{970WB}},
Unique-ID = {{ISI:000232342000002}},
}

@article{ ISI:000230327600004,
Author = {Biggs, M},
Title = {{Strikes as forest fires: Chicago and Paris in the late nineteenth
   century}},
Journal = {{AMERICAN JOURNAL OF SOCIOLOGY}},
Year = {{2005}},
Volume = {{110}},
Number = {{6}},
Pages = {{1684-1714}},
Month = {{MAY}},
Abstract = {{Historians have persistently likened strike waves to wildfires,
   avalanches, and epidemics. These phenomena are characterized by a
   power-law distribution of event sizes. This kind of analysis is applied
   to outbreaks of class conflict in Chicago from 1881 to 1886. Events are
   defined as individual strikes or miniature strike waves; size is
   measured by the number of establishments or workers involved. In each
   case, events follow a power law spanning two or three orders of
   magnitude. A similar pattern is found for strikes in Paris from 1890 to
   1899. The ``forest fire{''} model serves to illustrate the kind of
   process that can generate this distribution.}},
Publisher = {{UNIV CHICAGO PRESS}},
Address = {{1427 E 60TH ST, CHICAGO, IL 60637-2954 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Biggs, M (Reprint Author), Univ Illinois, Urbana, IL 61801 USA.
   Univ Illinois, Urbana, IL 61801 USA.}},
DOI = {{10.1086/427675}},
ISSN = {{0002-9602}},
Keywords-Plus = {{SELF-ORGANIZED CRITICALITY; UNITED-STATES; COLLECTIVE BEHAVIOR;
   THRESHOLD MODELS; SOCIAL-MOVEMENTS; NATURAL HAZARDS; CLASS ALLIANCE;
   DIFFUSION; LABOR; NETWORKS}},
Research-Areas = {{Sociology}},
Web-of-Science-Categories  = {{Sociology}},
Author-Email = {{biggsm@uiuc.edu}},
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Number-of-Cited-References = {{98}},
Times-Cited = {{28}},
Usage-Count-(Last-180-days) = {{2}},
Usage-Count-Since-2013 = {{14}},
Journal-ISO = {{Am. J. Sociol.}},
Doc-Delivery-Number = {{943BU}},
Unique-ID = {{ISI:000230327600004}},
}

@article{ ISI:000229155200002,
Author = {Neill, DB},
Title = {{Cascade effects in heterogeneous populations}},
Journal = {{RATIONALITY AND SOCIETY}},
Year = {{2005}},
Volume = {{17}},
Number = {{2}},
Pages = {{191-241}},
Month = {{MAY}},
Abstract = {{We present a model of sequential choice which explains the emergence and
   persistence of unpopular, inefficient behavioral norms in society. We
   model individuals as naive Bayesian norm followers, rational agents
   whose subjective expected utility is increased by adherence to an
   established norm. Agents use Bayesian reasoning to combine their private
   preferences and prior beliefs with empirical observations of others'
   decisions. When agents must infer the preferences of others from
   observation, this can result in negative cascades, causing the majority
   of agents to choose a dispreferred action (because they believe,
   incorrectly, that they are following the majority preference). We
   demonstrate that negative cascades can result even when the degree of
   conformity is relatively low, and under a wide range of conditions
   (including heterogeneity in preferences, priors, and impact of public
   opinion). This allows us to present a general model of how rational
   norm-following behavior can occur, and how unpopular norms might emerge,
   in real populations with heterogeneous preferences and beliefs.}},
Publisher = {{SAGE PUBLICATIONS LTD}},
Address = {{1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Neill, DB (Reprint Author), Carnegie Mellon Univ, Dept Comp Sci, 5000 Forbes Ave, Pittsburgh, PA 15213 USA.
   Carnegie Mellon Univ, Dept Comp Sci, Pittsburgh, PA 15213 USA.}},
DOI = {{10.1177/1043463105051633}},
ISSN = {{1043-4631}},
Keywords = {{cascades; herding; naive Bayes; norm followers}},
Keywords-Plus = {{PLURALISTIC IGNORANCE; THRESHOLD MODELS; INFORMATIONAL CASCADES;
   COLLECTIVE BEHAVIOR; STRATEGIC DELAY; MARKET CRASHES; HERD BEHAVIOR;
   INVESTMENT; DYNAMICS}},
Research-Areas = {{Sociology}},
Web-of-Science-Categories  = {{Sociology}},
Author-Email = {{neill@cs.cmu.edu}},
ORCID-Numbers = {{Neill, Daniel/0000-0001-6282-1240}},
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Number-of-Cited-References = {{35}},
Times-Cited = {{5}},
Usage-Count-(Last-180-days) = {{1}},
Usage-Count-Since-2013 = {{6}},
Journal-ISO = {{Ration. Soc.}},
Doc-Delivery-Number = {{926WU}},
Unique-ID = {{ISI:000229155200002}},
}

@article{ ISI:000229472000006,
Author = {Centola, D and Willer, R and Macy, M},
Title = {{The emperor's dilemma: A computational model of self-enforcing norms}},
Journal = {{AMERICAN JOURNAL OF SOCIOLOGY}},
Year = {{2005}},
Volume = {{110}},
Number = {{4}},
Pages = {{1009-1040}},
Month = {{JAN}},
Abstract = {{The authors demonstrate the uses of agent-based computational models in
   an application to a social enigma they call the ``emperor's dilemma,{''}
   based on the Hans Christian Andersen fable. In this model, agents must
   decide whether to comply with and enforce a norm that is supported by a
   few fanatics and opposed by the vast majority. They find that cascades
   of self-reinforcing support for a highly unpopular norm cannot occur in
   a fully connected social network. However, if agents' horizons are
   limited to immediate neighbors, highly unpopular norms can emerge
   locally and then spread. One might expect these cascades to be more
   likely as the number of `` true believers{''} increases, and bridge ties
   are created between otherwise distant actors. Surprisingly, the authors
   observed quite the opposite effects.}},
Publisher = {{UNIV CHICAGO PRESS}},
Address = {{1427 E 60TH ST, CHICAGO, IL 60637-2954 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Centola, D (Reprint Author), Cornell Univ, Dept Sociol, Ithaca, NY 14853 USA.
   Cornell Univ, Dept Sociol, Ithaca, NY 14853 USA.}},
DOI = {{10.1086/427321}},
ISSN = {{0002-9602}},
Keywords-Plus = {{PLURALISTIC IGNORANCE; COLLEGE DRINKING; SMALL-WORLD; ALCOHOL-USE;
   NETWORKS; DYNAMICS; CASCADES; REVENGE; IMPACT}},
Research-Areas = {{Sociology}},
Web-of-Science-Categories  = {{Sociology}},
Author-Email = {{dc288@cornell.edu}},
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Doc-Delivery-Number = {{931FU}},
Unique-ID = {{ISI:000229472000006}},
}

@article{ ISI:000223503200012,
Author = {Watts, DJ},
Title = {{The ``new{''} science of networks}},
Journal = {{ANNUAL REVIEW OF SOCIOLOGY}},
Year = {{2004}},
Volume = {{30}},
Pages = {{243-270}},
Abstract = {{In recent years, the analysis and modeling of networks, and also
   networked-dynamical systems, have been the subject of considerable
   interdisciplinary interest, yielding several hundred papers in physics,
   mathematics, computer science, biology, economics, and sociology
   journals (Newman:2003c), as well as a number of books (Barabasi 2002,
   Buchanan 2002, Watts 2003). Here I review the major findings of this
   emerging field and discuss briefly their relationship with previous work
   in the social and mathematical sciences.}},
Publisher = {{ANNUAL REVIEWS}},
Address = {{4139 EL CAMINO WAY, PO BOX 10139, PALO ALTO, CA 94303-0139 USA}},
Type = {{Review}},
Language = {{English}},
Affiliation = {{Columbia Univ, Dept Sociol, New York, NY 10027 USA.
   Columbia Univ, Dept Sociol, New York, NY 10027 USA.
   Santa Fe Inst, Santa Fe, NM USA.}},
DOI = {{10.1146/annurev.soc.30.020404.104342}},
ISSN = {{0360-0572}},
Keywords = {{graph theory; mathematical models; network data; dynamical systems}},
Keywords-Plus = {{SMALL-WORLD NETWORKS; GROWING RANDOM NETWORKS; SCALE-FREE NETWORKS;
   SOCIAL NETWORKS; LOGIT-MODELS; LOGISTIC REGRESSIONS; INFECTIOUS-DISEASE;
   METABOLIC NETWORKS; COMPLEX NETWORKS; WIDE-WEB}},
Research-Areas = {{Sociology}},
Web-of-Science-Categories  = {{Sociology}},
Author-Email = {{djw24@columbia.edu}},
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Number-of-Cited-References = {{177}},
Times-Cited = {{311}},
Usage-Count-(Last-180-days) = {{5}},
Usage-Count-Since-2013 = {{96}},
Journal-ISO = {{Annu. Rev. Sociol.}},
Doc-Delivery-Number = {{848YD}},
Unique-ID = {{ISI:000223503200012}},
}

extension: bib


text:
require bash.inc

PR = "r7"

SRC_URI += "\
           file://builtins.patch;patch=1 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-001;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-002;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-003;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-004;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-005;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-006;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-007;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-008;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-009;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-010;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-011;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-012;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-013;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-014;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-015;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-016;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-017;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-018;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-019;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-020;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-021;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-022;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-023;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-024;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-025;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-026;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-027;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-028;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-029;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-030;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-031;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-032;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-033;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-034;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-035;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-036;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-037;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-038;patch=1;pnum=0 \
           http://ftp.gnu.org/gnu/bash/bash-3.2-patches/bash32-039;patch=1;pnum=0 \
           file://default_path.patch;patch=1 \
           "

license: MIT

license_type: permissive

branch_name: refs/heads/master

extension: bb