Balance Agents in Capacity-Constrained Industrial Games A Stackelberg Control Framework

Recent debates increasingly describe AI-driven industries through narratives of bubbles, expectations, and valuation cycles. This work takes a different route. We study industrial systems in which economic interaction is constrained not primarily by prices or beliefs, but by physical capacity, ramp rates, and irreversible infrastructure commitments. In such environments, standard symmetric game-theoretic models become insufficient. We introduce a class of capacity-constrained industrial games governed by a balance agent operating as a Stackelberg leader. The model captures how upstream bottlenecks shape the feasible operating region of downstream actors, induce paced growth, and generate stabilization effects independent of market sentiment. The framework is fully non-normative and does not assume coordination or intent. It provides a structurally grounded extension of game-theoretic analysis suitable for modern industrial cascades. The paper is intended for researchers in game theory, industrial organization, and applied economics interested in feasibility-driven dynamics beyond expectation-dominated models