Game-Theoretic Simulations Meet AI: Fast Policy Recommendations Under Data Scarcity (Student Abstract)

Abstract

We tackle the challenge of setting smart prices and advertising budgets for dairy products sold through three retail channels—General Trade (GT), Modern Trade (MT), and E-commerce (EC)—in markets where data is scarce. Traditional economic models can capture the complex relationships between price, trust, and advertising, but solving these models every time a manager wants to ask a question is slow and impractical. Our solution is to turn a game-theoretic market simulator into training data: we generate 10,000 market scenarios, solve for the best pricing and ad strategies, and train an AI to imitate those decisions. On unseen scenarios, the AI remains accurate (price RMSE ≤ 0.047, ad RMSE ≤ 0.031) and economically sound (Ratio of Means = 1.0010, Regret = 0.40%). To make it easy to use, we add a simple natural-language interface: users can say things like “trust is low, ad cost is high,” and the system returns price and ad suggestions along with confidence ranges. This creates a practical bridge between AI and economic rigor—delivering defensible decisions in seconds, even when data is limited.