Abstract

This paper presents an agent-based modeling framework that captures market price formation and consumer choice by integrating tâtonnement-style price discovery with behavioral demand. Producer agents set prices using a hybrid rule that responds to realized excess demand and inventory, while optionally converging toward a market reference price. Consumer agents select among competing producers via a softmax choice model that balances loyalty and price sensitivity. Consumer loyalty evolves through an exponentially weighted update that accommodates reinforcement from purchases or other satisfaction signals. The resulting system produces emergent dynamics, such as price adjustments under excess supply or demand, lock-in from repeated successful purchases, and producer competition without requiring closed-form supply and demand solutions. The model is implemented in Python, logs tick-level outcomes, and visualizes market trajectories (prices, supply and demand) alongside consumer behavior (choices and loyalty). The framework is modular and extensible, enabling scenario analysis and supporting empirical calibration for applied domains.

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