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

arXiv: https://arxiv.org/abs/2107.08959 · HTML: https://mlsanity.com/paper/2107.08959/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 88 references that are in the map.
- First posted on arXiv 19 Jul 2021.

## Builds on (most cited first, 23 of 23)

- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility](https://mlsanity.com/paper/1710.11214/) (2017)
- [Collaborative Deep Learning for Recommender Systems](https://mlsanity.com/paper/1409.2944/) (2014)
- [RecSim: A Configurable Simulation Platform for Recommender Systems](https://mlsanity.com/paper/1909.04847/) (2019)
- [Beyond Parity: Fairness Objectives for Collaborative Filtering](https://mlsanity.com/paper/1705.08804/) (2017)
- [Joint Deep Modeling of Users and Items Using Reviews for Recommendation](https://mlsanity.com/paper/1701.04783/) (2017)
- [Multisided Fairness for Recommendation](https://mlsanity.com/paper/1707.00093/) (2017)
- [Degenerate Feedback Loops in Recommender Systems](https://mlsanity.com/paper/1902.10730/) (2019)
- [RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising](https://mlsanity.com/paper/1808.00720/) (2018)
- [Auditing Radicalization Pathways on YouTube](https://mlsanity.com/paper/1908.08313/) (2019)
- [A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers](https://mlsanity.com/paper/1806.00955/) (2018)
- [Do Offline Metrics Predict Online Performance in Recommender Systems?](https://mlsanity.com/paper/2011.07931/) (2020)
- [Recommendations and User Agency: The Reachability of Collaboratively-Filtered Information](https://mlsanity.com/paper/1912.10068/) (2019)
- [RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems](https://mlsanity.com/paper/2103.08057/) (2021)
- [Measuring Personalization of Web Search](https://mlsanity.com/paper/1706.05011/) (2017)
- [How algorithmic popularity bias hinders or promotes quality](https://mlsanity.com/paper/1707.00574/) (2017)
- [Human Interaction with Recommendation Systems](https://mlsanity.com/paper/1703.00535/) (2017)
- [LensKit for Python: Next-Generation Software for Recommender System Experiments](https://mlsanity.com/paper/1809.03125/) (2018)
- [Modelling Opinion Dynamics in the Age of Algorithmic Personalisation](https://mlsanity.com/paper/1811.03341/) (2018)
- [Balancing Information Exposure in Social Networks](https://mlsanity.com/paper/1709.01491/) (2017)
- [Homophily and minority size explain perception biases in social networks](https://mlsanity.com/paper/1710.08601/) (2017)
- [An Agent-Based Model of Message Propagation in the Facebook Electronic Social Network](https://mlsanity.com/paper/1611.07454/) (2016)
- [Network segregation in a model of misinformation and fact checking](https://mlsanity.com/paper/1610.04170/) (2016)

## Built on by (most cited first, 12 of 12)

- [Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation](https://mlsanity.com/paper/2205.04610/) (2022)
- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [Preference Dynamics Under Personalized Recommendations](https://mlsanity.com/paper/2205.13026/) (2022)
- [Fair ranking: a critical review, challenges, and future directions](https://mlsanity.com/paper/2201.12662/) (2022)
- [Reward Reports for Reinforcement Learning](https://mlsanity.com/paper/2204.10817/) (2022)
- [Synthetic Data-Based Simulators for Recommender Systems: A Survey](https://mlsanity.com/paper/2206.11338/) (2022)
- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)
- [Upvotes? Downvotes? No Votes? Understanding the relationship between reaction mechanisms and political discourse on Reddit](https://mlsanity.com/paper/2302.09540/) (2023)
- [Recommendation System Simulations: A Discussion of Two Key Challenges](https://mlsanity.com/paper/2109.02475/) (2021)
- [Simulating News Recommendation Ecosystem for Fun and Profit](https://mlsanity.com/paper/2305.14103/) (2023)
- [We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Later](https://mlsanity.com/paper/2509.09414/) (2025)
- [Evaluation of Agents under Simulated AI Marketplace Dynamics](https://mlsanity.com/paper/2604.14256/) (2026)

## Abstract (arXiv)

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.