  A local culture denotes a commonly shared behaviour within a cluster of
firms. Similar to social norms or conventions, it is an emergent feature
resulting from the firms' interaction in an economic network. To model these
dynamics, we consider a distributed agent population, representing e.g. firms
or individuals. Further, we build on a continuous opinion dynamics model with
bounded confidence ($\epsilon$), which assumes that two agents only interact if
differences in their behaviour are less than $\epsilon$. Interaction results in
more similarity of behaviour, i.e. convergence towards a common mean. This
framework is extended by two major concepts: (i) The agent's in-group
consisting of acquainted interaction partners is explicitly taken into account.
This leads to an effective agent behaviour reflecting that agents try to
continue to interact with past partners and thus to keep sufficiently close to
them. (ii) The in-group network structure changes over time, as agents can form
new links to other agents with sufficiently close effective behaviour or delete
links to agents no longer close in behaviour. Thus, our model provides a
feedback mechanism between the agents' behaviour and their in-group structure.
Studying its consequences by means of agent-based computer simulations, we find
that for narrow-minded agents (low $\epsilon$) the additional feedback helps to
find consensus more often, whereas for open-minded agents (high $\epsilon$)
this does not hold. This counterintuitive result is explained by simulations of
the network evolution.
