A systems approach to unravel complex water management institutions

A systems approach to unravel complex water management institutions

This paper was not what I was looking for at all. The methods were interesting as I'd never seen an implementation of Bayesian network analysis before. This paper is a good reminder that 'systems' approaches and 'complexity' approaches are different in some subtle but important ways. The goal is to understand the way water management rights are decided by analyzing how much surveyed individuals felt one thing led to a feeling about something else. The idea was that there were 'rules' governing the behavior of agents in an institutional water management environment in a hamlet in the Indian Himalayas and that decisions made did not account for the adaptive behavior of agent beliefs in their arena.

There were a number of rules the author defined in the system. The first, and which I thought most important, were the boundary rules. Control of boundary rules were critical since they defined who had some control and who did not. For instance, India's Land Reform Act defined who had the right to act in the water management based on land ownership. On top of that court rulings had an effect on the definition of land ownership as well, so two different acts defined actors in the system.

There were a number of things I didn't understand. For instance this sentence: "About 67% of the households perceived the probability of inadequate leadership affecting water distribution." What does that mean? They perceived it to be inadequate? They perceived the probability to be what? Did they just perceive it to be probable? There were something things I understood, for instance the periphery of the belief network were the boundary rules, which fed into position rules. In essence we could map out how boundary rules (defined who the actors were) interacted with position rules (the political position the actors take) with lead to aggregation rules (perception of the outcomes).

The goal the author had was to do away of the linear approach to outcomes and consider a nonlinear approach where relationships between actors and rules were important. I think the author accomplished this. But I don't feel any more enlightened by the approach. I think there was better information in the narrative than there was in the Bayesian network - the Bayesian network seems completely superfluous. I think this is a clear case where the methodology has outstripped the ability to fruitfully apply it.