Citing this article

A standard form of citation of this article is:

Groeber, Patrick, Schweitzer, Frank and Press, Kerstin (2009). 'How Groups Can Foster Consensus: The Case of Local Cultures'. Journal of Artificial Societies and Social Simulation 12(2)4 <https://www.jasss.org/12/2/4.html>.

The following can be copied and pasted into a Bibtex bibliography file, for use with the LaTeX text processor:

@article{groeber2009,
   title = {How Groups Can Foster Consensus:   The Case of Local Cultures},
   author = {Groeber, Patrick and Schweitzer, Frank and Press, Kerstin},
   journal = {Journal of Artificial Societies and Social Simulation},
   ISSN = {1460-7425},
   volume = {12},
   number = {2},
   pages = {4},
   year = {2009},
   URL = {https://www.jasss.org/12/2/4.html},
   keywords = {Social Norms, Conventions, Bounded Confidence, Dynamic Networks},
   abstract = {A local culture denotes a set of rules on business behaviour among firms in a cluster. Similar to social norms or conventions, it is an emergent feature of interaction in an economic network. To model its emergence, we consider a distributed agent population, representing cluster firms. Further, we build on a continuous opinion dynamics model with bounded confidence (&\#949;), which assumes that two agents only interact if differences in their behaviour are less than &\#949;. Interaction results in more similarity of behaviour, i.e. convergence towards a common mean. Two aspects extend this framework: (i) The agent's in-group consisting of acquainted interaction partners is explicitly taken into account, leading to an effective agent behaviour as agents try to continue to interact with past partners and thus seek to stay sufficiently close to them. (ii) The in-group network structure changes over time, as agents form new links to other agents with sufficiently close effective behaviour or delete links to agents no longer close in behaviour. Thus, the model introduces a feedback mechanism of agent behaviour and in-group structure. Studying its consequences by means of agent-based computer simulations, we find that for narrow-minded agents (low &\#949;) the feedback mechanism helps find consensus more often, whereas for open-minded agents (high &\#949;) this does not necessarily hold. Overall, the dynamics of agent interaction in clusters as modelled here, are conducive to consensus among all or a majority of agents.},
}

The following can be copied and pasted into a text file, which can then be imported into a reference database that supports imports using the RIS format, such as Reference Manager and EndNote.

TY  - JOUR
TI  - How Groups Can Foster Consensus:   The Case of Local Cultures
AU  - Groeber, Patrick
AU  - Schweitzer, Frank
AU  - Press, Kerstin
Y1  - 2009/03/31
JO  - Journal of Artificial Societies and Social Simulation
SN  - 1460-7425
VL  - 12
IS  - 2
SP  - 4
UR  - https://www.jasss.org/12/2/4.html
KW  - Social Norms
KW  - Conventions
KW  - Bounded Confidence
KW  - Dynamic Networks
N2  - A local culture denotes a set of rules on business behaviour among firms in a cluster. Similar to social norms or conventions, it is an emergent feature of interaction in an economic network. To model its emergence, we consider a distributed agent population, representing cluster firms. Further, we build on a continuous opinion dynamics model with bounded confidence (ε), which assumes that two agents only interact if differences in their behaviour are less than ε. Interaction results in more similarity of behaviour, i.e. convergence towards a common mean. Two aspects extend this framework: (i) The agent's in-group consisting of acquainted interaction partners is explicitly taken into account, leading to an effective agent behaviour as agents try to continue to interact with past partners and thus seek to stay sufficiently close to them. (ii) The in-group network structure changes over time, as agents form new links to other agents with sufficiently close effective behaviour or delete links to agents no longer close in behaviour. Thus, the model introduces a feedback mechanism of agent behaviour and in-group structure. Studying its consequences by means of agent-based computer simulations, we find that for narrow-minded agents (low ε) the feedback mechanism helps find consensus more often, whereas for open-minded agents (high ε) this does not necessarily hold. Overall, the dynamics of agent interaction in clusters as modelled here, are conducive to consensus among all or a majority of agents.
ER  -