Deriving the Classical and New-Keynesian Phillips Curves using Machine Learning Simulations - Albert Molnár; János Varga; Ágnes Csiszárik-Kocsir (Decision Making: Applications in Management and Engineering, 2024)
This study investigates inflation dynamics through the lens of pricing strategies in a simulated market economy, where firms set prices reflecting their expectations of future demand. Building on the foundational work of Milton Friedman, Robert E. Lucas, we propose a novel approach that considers price-setting as an emergent property of limited rationality among firms. Using a simulation with n participants acting as firms, we examine the implications of five distinct pricing strategies Trial-and-error, Tit-for-tat,Forgiving, Competitive, and Random over 12 rounds, representing a financial year. The simulation incorporates a midgame demand shock, revealing how firms adapt their pricing decisions. Results demonstrate that limited rationality leads to short-run deviations from equilibrium, aligning with the expectations-augmented Phillips curve, and eventually converging to a long-run equilibrium, consistent with the classic Phillips curve. Our findings show that price adjustments, rather than demand increases, drive production in the long run, confirming the theoretical vertical Phillips curve. In contrast, short-run outcomes exhibit heterogeneity and a gradual convergence of prices following demand shocks. By simulating these games 10,000 times across varying participant numbers, we highlight the role of imperfect information and expectation formation in shaping economic outcomes. This study bridges microeconomic behaviour with macroeconomic theory, offering insights into the interplay between rational expectations and limited rationality in determining inflation and employment.