Published in Research Policy, 2026
This paper develops a game-theoretic model to analyze the pre-doctoral academic labor market dynamics in the age of AI.
Recommended citation: Wang, S. (2026). "Hope, signals, and silicon: A game-theoretic model of the pre-doctoral academic labor market in the age of AI." Research Policy, 55(8), 105568. https://doi.org/10.1016/j.respol.2026.105568.
Working paper; available on SSRN
This paper models how dividing residual control over cross-client reuse and post-exit continuity shapes complementary investment in customer-specific AI learning assets across firm boundaries.
Presented at AOM Annual Meeting 2026, Philadelphia, USA (TIM Division)
This paper shows how allocations of AI access, improvement, exclusivity, and forking rights shape relationship-specific investment incentives and underinvestment across alternative governance regimes.
Under review at Economics Letters
This paper establishes sharp identified sets for latent-class moments in selected-at-random positive-unlabeled data without valid instruments.
Preparing for journal submission
This design science study develops an auditable inference-time control layer that detects compassion deficits in LLM counseling responses and applies minimal targeted interventions.
Presented at ICIS 2025, Nashville, USA
This paper formalizes when interaction-generated signals improve human-AI forecasting ensembles and validates an online fusion algorithm in simulation.