The Formation of Production Networks: How Supply Chains Arise from Simple Learning with Minimal Information
This addresses the challenge of understanding supply chain dynamics for economists and policymakers, offering a novel approach without relying on equilibrium assumptions.
The paper tackles the problem of how production networks form endogenously by modeling firms that use reinforcement learning to set prices, production volumes, and input purchases under uncertainty, resulting in a steady-state network that adapts to shocks like demand shifts and productivity changes.
We develop a model where firms determine the price at which they sell their differentiable goods, the volume that they produce, and the inputs (types and amounts) that they purchase from other firms. A steady-state production network emerges endogenously without resorting to assumptions such as equilibrium or perfect knowledge about production technologies. Through a simple version of reinforcement learning, firms with heterogeneous technologies cope with uncertainty and maximize profits. Due to this learning process, firms can adapt to shocks such as demand shifts, suppliers/clients closure, productivity changes, and production technology modifications; effectively reshaping the production network. To demonstrate the potential of this model, we analyze the upstream and downstream impact of demand and productivity shocks.