Motivated by equilibrium models of labor markets, we develop a formulation of
causal strategic classification in which strategic agents can directly
manipulate their outcomes. As an application, we compare employers that
anticipate the strategic response of a labor force with employers that do not.
We show through a combination of theory and experiment that employers with
performatively optimal hiring policies improve employer reward, labor force
skill level, and in some cases labor force equity. On the other hand, we
demonstrate that performative employers harm labor force utility and fail to
prevent discrimination in other cases.

通过对劳动市场的平衡模型进行研究，我们发展了一种因果战略分类的表述，在这种模型中，战略代理人可以直接操纵他们的结果。作为应用，我们比较了预计劳动力战略反应和不预计劳动力战略反应的雇主。我们通过理论和实验的结合表明，具有表现最优的招聘政策的雇主可以提高雇主回报率、劳动力技能水平，并在某些情况下实现劳动力公平。另一方面，我们证明具有行动表现的雇主会损害劳动力效用，并且在其他情况下无法防止歧视。

反向学习在具有影响力的双边市场中的应用

Learning In Reverse Causal Strategic Environments With Ramifications on  Two Sided Markets

Large Language Models (LLMs) have made rapid progress in recent months and
weeks, garnering significant public attention. This has sparked concerns about
aligning these models with human values, their impact on labor markets, and the
potential need for regulation in further research and development. However, the
discourse often lacks a focus on the imperative to widely diffuse the societal
benefits of LLMs. To qualify this societal benefit, we assert that LLMs exhibit
emergent abilities to humanize technology more effectively than previous
technologies, and for people across language, occupation, and accessibility
divides. We argue that they do so by addressing three mechanizing bottlenecks
in today's computing technologies: creating diverse and accessible content,
learning complex digital tools, and personalizing machine learning algorithms.
We adopt a case-based approach and illustrate each bottleneck with two examples
where current technology imposes bottlenecks that LLMs demonstrate the ability
to address. Given this opportunity to humanize technology widely, we advocate
for more widespread understanding of LLMs, tools and methods to simplify use of
LLMs, and cross-cutting institutional capacity.

这篇文章着重探讨了大型语言模型对人类价值、劳动力市场的影响以及需不需要对它们进行监管等问题，同时，它们也能更好地人性化技术，并且可以克服当前技术所面临的瓶颈问题。因此，我们应该更广泛地了解和推广 LLMs 以及简化 LLMs 使用的工具和方法。