Title: Simulating Environments for Evaluating Scarce Resource Allocation Policies. Abstract: Consider the sequential decision problem of allocating a limited supply of resources to a pool of potential recipients: This scarce resource allocation problem arises in a variety of settings characterized by "hard-to-make" tradeoffs– such as assigning organs to transplant patients, or rationing ventilators in overstretched ICUs. Assisting human judgement in these choices are dynamic allocation policies that prescribe how to match available assets to an evolving pool of beneficiaries– such as clinical guidelines that stipulate selection criteria on the basis of recipient and organ attributes. However, while such policies have received increasing attention in recent years, a key challenge lies in pre-deployment evaluation: How might allocation policies behave in the real world? In particular, in addition to conventional backtesting, it is crucial that policies be evaluated on a variety of possible scenarios and sensitivities– such as distributions of recipients and organs that may diverge from historic patterns. In this work, we present AllSim, an open-source framework for performing data-driven simulation of scarce resource allocation policies for pre-deployment evaluation. Simulation environments are modular (i.e. parameterized componentwise), learnable (i.e. on historical data), and customizable (i.e. to unseen conditions), and– upon interaction with a policy –outputs a dataset of simulated outcomes for analysis and benchmarking. Compared to existing work, we believe this approach takes a step towards more methodical evaluation of scarce resource allocation policies.