Abstract: A system and a method for allocating limited resources to agents that reveal their stochastic demands on arrival over a finite horizon in a proportionally fair manner that exhausts available resource budgets are provided. The method includes: receiving, from an entity, a request for a resource for which a predetermined maximum amount of the resource is available for allocation; estimating a number of future requests and amounts of the resource to be requested within a predetermined time interval; adjusting a result of the estimating by calculating a standard deviation of an uncertainty of at least one future request; and determining, based on the estimates and the adjustment, an amount of the resource to allocated to the entity by applying an algorithm with respect to the adjusted estimates and the predetermined maximum available amount of the resources.

Abstract: A method for creating a realistic agent-based simulation of an over-the-counter market and using data from the real market to calibrate the simulator is provided. The method includes: assigning, to each of a group of agents, a type value that relates to a state of the respective agent; receiving agent-specific data that relates to market-based observations, market-based actions, and market-based rewards; generating a market simulation based on the assigned type values and the received agent-specific data; and calibrating the market simulation based on actual market data.

Abstract: Systems and methods for risk-sensitive reinforcement learning are disclosed. In one embodiment, a method for a method for training a risk-sensitive reinforcement learning policy may include: (1) receiving, from a data source, a plurality of sets of training data for a plurality of time steps; (2) receiving a training budget comprising a plurality of episodes, a risk aversion coefficient, and an end state; and (3) calculating a correction factor using the training data. The correction factor may minimize stochasticity based on a risk aversion coefficient.