We study how AI workers behave inside organizations, markets, and institutions — through controlled simulation, formal theory, welfare accounting, and translation into deployment-ready design rules.
We build controlled economies for AI workers — artificial organizations, markets, crisis rooms, commons, and governance environments — and measure what happens. Welfare-accounted simulations, formal baselines, behavioral benchmarks, and deployable governance rules.
Place AI workers in high-stakes social and organizational environments and observe how they cooperate, defect, trust, punish, free-ride, and fail.
Connect observed behavior to structure: incentives, horizons, information, payoff geometry, consequence regimes, and governance rules.
Measure not only whether tasks succeed, but who benefits, who bears risk, and when local success produces collective harm.
Translate findings into concrete levers: bystander visibility, accountability horizons, review windows, memory structures, trust repair, and consequence design.
What AI workers do when the cost of being wrong is real — not what they say they would do in the abstract.
The same model can cooperate or self-destruct depending on the institution around it. We map which rules matter.
Who benefits, who bears risk, who is invisible to the prompt. The metrics that matter for an AI workforce on behalf of people.
We look hardest for the role-, horizon-, and visibility-level fixes that change outcomes without changing the model.
How do AI workers build, lose, and recover trust across repeated interactions in an organization? We study the conditions under which a single early failure leaves a lasting mark — and the structural choices (reasoning effort, memory, verification protocols, trust-repair mechanisms) that shape whether teams of AI workers can coordinate at all when the stakes are real.
Cooperation in an AI workforce is not a property of the model — it is a property of the institution around the model. We map how different consequence regimes (proportional, progressive, all-or-nothing, regressive) shape cooperation, exploitation, and catastrophic failure, and identify the configurations where each regime quietly breaks.
When environments contain absorbing failure states, optimal policies start to look strikingly human — risk-averse near the cliff in growth regimes, risk-seeking near the cliff in decline. We derive the structural conditions that produce these patterns and connect them to long-standing puzzles in behavioral economics. What looks like model bias is often institutional structure.
Many real deployments give AI workers fixed terms, finite horizons, or short-window incentives. We study what happens when these conditions meet a shared resource: when does a worker extract too much, rationalize doing it, and become invisible to the people it harms? And which deployment-time choices reverse the pattern cheaply?
Most evaluation asks whether tasks succeed. We ask who benefits, who bears risk, and when local success produces collective harm. Welfare-accounted simulations and benchmarks for AI workforces operating on behalf of stakeholders who never see the prompt.
Translating experimental findings into design rules for the people deploying AI workforces. Bystander visibility, accountability horizons, review windows, memory structure, trust repair, consequence design — the levers, the failure modes they prevent, and the evidence behind each rule.
Every domain — healthcare, finance, education, defense — has its own failure modes and trade-offs for AI labor. We are building tailored evaluation frameworks that move beyond one-size-fits-all checklists toward governance shaped by the structure of each setting.
The patterns we find in artificial workers — negativity bias, short-horizon extraction, bystander invisibility — are not the model's invention. They are inherited from us. We use multi-agent experiments as a diagnostic tool for the institutions, incentives, and blind spots of the societies that built the training data.
Publications
New work from the lab is in preparation. Papers and preprints will be listed here as they are released.
Collaborate
Interested in our research or want to collaborate on AI workforce governance? Reach out at hello@reigndragon.ai