This paper shows how agent-based stochastic approaches can provide a complementary and more flexible approach to study investment incentives and price dynamics in a real options framework. We particularly study the case of two-stage production chains in which one sector produces an intermediate...

We explore the macro-financial consequences of the disruption of a supply chain in an agent based framework characterized by two networks, a credit network connecting banks and firms and a production network connecting upstream and down-stream firms. We consider two scenarios. In the first one,...

We apply Multi-Agent Deep Reinforcement Learning (MADRL) to inventory management problems with multiple echelons and evaluate MADRL's performance to minimize the overall costs of a supply chain. We also examine whether the upfront-only information-sharing mechanism used in MADRL helps alleviate...