  Understanding market participants' channel choices is important to policy
makers because it yields information on which channels are effective in
transmitting information. These channel choices are the result of a recursive
process of social interactions and determine the observable trading networks.
They are characterized by feedback mechanisms due to peer interaction and
therefore need to be understood as complex adaptive systems (CAS). When
modelling CAS, conventional approaches like regression analyses face severe
drawbacks since endogeneity is omnipresent. As an alternative, process-based
analyses allow researchers to capture these endogenous processes and multiple
feedback loops. This paper applies an agent-based modelling approach (ABM) to
the empirical example of the Indonesian rubber trade. The feedback mechanisms
are modelled via an innovative approach of a social matrix, which allows
decisions made in a specific period to feed back into the decision processes in
subsequent periods, and allows agents to systematically assign different
weights to the decision parameters based on their individual characteristics.
In the validation against the observed network, uncertainty in the found
estimates, as well as under determination of the model, are dealt with via an
approach of evolutionary calibration: a genetic algorithm finds the combination
of parameters that maximizes the similarity between the simulated and the
observed network. Results indicate that the sellers' channel choice decisions
are mostly driven by physical distance and debt obligations, as well as
peer-interaction. Within the social matrix, the most influential individuals
are sellers that live close by to other traders, are active in social groups
and belong to the ethnic majority in their village.

General Economics;Economics