Modeling Realistic Farmer Behavior in Heuristic-Based Irrigation Decisions with Fuzzy Logic

Abstract This study addresses the challenge of integrating complex human behavior into hydrological models by developing a behavioral simulation model (BSM) within a multiagent framework. Focusing on farmers’ response to water scarcity in Iran’s Lake Urmia basin, the BSM utilizes fuzzy logic to explicitly model heuristic irrigation decisions driven by subjective perception (leaf appearance) under bounded rationality. This descriptive approach contrasts with conventional optimization models by prioritizing the interpretability of farmer decision-making. The model was validated against historical evapotranspiration data [Nash–Sutcliffe efficiency (NSE) > 0.75] and observed irrigation frequencies. The simulations yielded two key insights. First, farmer behavioral bias significantly influences the physical environment: more risk-averse agents accelerated the long-term decline of the groundwater table (simulated 1.7-m divergence over 7 years) compared with risk-seeking agents. Second, the sensitivity analysis demonstrated that policy flexibility is crucial. When the surface water delivery schedule was rigid (30-day intervals), the influence of farmer behavioral factors was minimal. However, a more flexible schedule (15-day intervals) enabled agents to exercise their adaptive heuristics, resulting in a significantly higher sensitivity to individual decision thresholds. These findings underscore that policies focused solely on volumetric reduction are insufficient. To promote sustainable water management and groundwater recovery, policymakers must design flexible delivery schedules that accommodate and leverage farmers’ adaptive behavioral heuristics, thereby aligning individual actions with long-term ecological stability.