# Toward an AI-Powered Computational Testbed for Workforce Policy

arXiv: https://arxiv.org/abs/2605.19064 · HTML: https://mlsanity.com/paper/2605.19064/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 18 May 2026.

## Builds on (most cited first, 7 of 7)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs](https://mlsanity.com/paper/2311.04892/) (2023)
- [Cultural Bias and Cultural Alignment of Large Language Models](https://mlsanity.com/paper/2311.14096/) (2023)
- [A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias](https://mlsanity.com/paper/2404.00929/) (2024)
- [Mitigating Cultural Bias in LLMs via Multi-Agent Cultural Debate](https://mlsanity.com/paper/2601.12091/) (2026)

## Built on by (most cited first, 1 of 1)

- [Benchmarking Psychological Dynamics in Generative Agents](https://mlsanity.com/paper/2610.04246/) (2026)

## Abstract (arXiv)

Workforce transformations are difficult to forecast and costly to mismanage. In particular, the integration of artificial intelligence into knowledge work currently affects a substantial share of the global workforce, yet this transition proceeds without tools to forecast how individual employees will respond psychologically and behaviorally. We combine recent advances in LLM-powered generative agents with foundational management science and organizational behavior research to propose dynamic employee agents. Among consenting populations, these agents can be seeded with HR records, validated psychometric measures, and digital activity data to simulate employees' cognitive, emotional, and behavioral trajectories across successive workdays during planned organizational changes. In this article, we detail the computational architecture required to construct this simulation platform and define the privacy, accuracy, and representativeness safeguards necessary for responsible deployment. We argue that establishing this prospective forecasting infrastructure is a critical technical requirement for managing the current global workforce realignment around AI.