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.

中文翻译：

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T-RECS：研究推荐系统社会影响的模拟工具

模拟已成为研究推荐系统长期社会后果的流行方法。这种方法允许研究人员明确指定他们的理论模型并观察系统级结果随时间的演变。然而，进行基于模拟的研究通常需要研究人员从头开始构建自己的模拟环境，这会产生很高的进入壁垒，引入实施错误的空间，并且难以理清观察到的结果是由于模型还是实施。我们介绍了 T-RECS，这是一个开源 Python 包，专为研究人员模拟推荐系统和其他类型的社会技术系统而设计，在这些系统中，算法可以调解多个利益相关者（例如用户和内容创建者）之间的交互。为了证明 T-RECS 的灵活性，我们对之前的两项基于模拟的社会技术系统研究进行了复制。我们还展示了如何使用 T-RECS 以最小的开销生成新的见解。我们的工具提高了该研究领域的可重复性，为模拟社会技术系统提供了统一的语言，并消除了从头开始实施模拟的摩擦。