A situated agent-based model to reveal irrigators' options behind their actions under institutional arrangements in Southern France

TL;DR

This paper introduces an agent-based model using affordances to simulate irrigators' options under different institutional arrangements, revealing conflicts and potential coordination improvements in water sharing in Southern France.

Contribution

It presents a novel approach integrating affordances into agent-based modeling to analyze how institutional changes affect irrigator behavior and interactions.

Findings

Abandonment of traditional water sharing increases conflicts.

Heterogeneity among irrigators influences collective outcomes.

Sensitivity analysis identifies optimal coordination modalities.

Abstract

There has been little exploration of the explicit simulation of the set of options of actors in agent-based models and its evolution over time. This study proposes to use affordances as intermediate entities between agents' environment and agent actions. We illustrated the approach on a typical gravity-fed network in the South-East of France to explore how the abandonment of traditional sharing of water changes the irrigators' options to irrigate. We simulated a typical dry year irrigation season under two institutional arrangements (i.e. traditional coordination through daily slots and its abandonment). Simulation results are consistent with field surveys, and reveal an increase in the number of internal conflicts among irrigators as the counterpart of the abandonment of traditional sharing of water. They also highlight the consequences of the heterogeneity of the irrigators' interests within the collective institution. The sensitivity analysis of the model allowed identification of optimal modalities of coordination, and a potential compromise between past and current institutional arrangements. The key benefits of using affordances in ABM lie in the study of their population dynamics for characterizing the interaction situations between actors and their environment and for better understanding the model dynamics.