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Simulation has emerged as a popular method to study the long-term societalconsequences of recommender systems. This approach allows researchers tospecify their theoretical model explicitly and observe the evolution ofsystem-level outcomes over time. However, performing simulation-based studiesoften requires researchers to build their own simulation environments from theground up, which creates a high barrier to entry, introduces room forimplementation error, and makes it difficult to disentangle whether observedoutcomes are due to the model or the implementation.  We introduce T-RECS, an open-sourced Python package designed for researchersto simulate recommendation systems and other types of sociotechnical systems inwhich 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 onsociotechnical systems. We additionally show how T-RECS can be used to generatenovel insights with minimal overhead. Our tool promotes reproducibility in thisarea of research, provides a unified language for simulating sociotechnicalsystems, and removes the friction of implementing simulations from scratch.

T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems