Modelling Social Evolutionary Processes and Peer Effects in Agricultural Trade Networks: the Rubber Value Chain in Indonesia

Abstract

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.