  Simulation has emerged as a popular method to study the long-term societal
consequences of recommender systems. This approach allows researchers to
specify their theoretical model explicitly and observe the evolution of
system-level outcomes over time. However, performing simulation-based studies
often requires researchers to build their own simulation environments from the
ground up, which creates a high barrier to entry, introduces room for
implementation error, and makes it difficult to disentangle whether observed
outcomes are due to the model or the implementation.
  We introduce T-RECS, an open-sourced Python package designed for researchers
to simulate recommendation systems and other types of sociotechnical systems in
which an algorithm mediates the interactions between multiple stakeholders,
such as users and content creators. To demonstrate the flexibility of T-RECS,
we perform a replication of two prior simulation-based research on
sociotechnical systems. We additionally show how T-RECS can be used to generate
novel insights with minimal overhead. Our tool promotes reproducibility in this
area of research, provides a unified language for simulating sociotechnical
systems, and removes the friction of implementing simulations from scratch.
