7 papers
Optimizing Regret
Irene Aldridge
Building on the identity that expected regret equals the covariance between costs and decisions, this paper develops a derivative theory of the covariance regret functional. We der…
Evaluating AI Investment Strategies
Irene Aldridge
We study the problem of auditing a black-box algorithmic decision-maker from observable inputs and outputs alone. Our main result is an exact decomposition: under precisely charact…
Comparing Market Mechanism Efficiencies
Irene Aldridge
We develop a game-theoretic framework that compares welfare efficiency across three market mechanisms: continuous double auctions with transparent order books (lit exchanges), opaq…
Multi-Dimensional Matching in Market Design
Irene Aldridge
This paper proposes a computationally efficient mechanism for multi-dimensional matching markets where agents report preferences over object features rather than complete utility a…
Regret Equals Covariance: A Closed-Form Characterization for Stochastic Optimization
Irene Aldridge
Regret is the cost of uncertainty in algorithmic decision-making. Quantifying regret typically requires computationally expensive simulation via Sample Average Approximation (SAA),…
Scaling the Queue: Reinforcement Learning for Equitable Call Classification Capacity in NYC Municipal Complaint Systems
Irene Aldridge, Ellie Bae, Siddhesh Darak +25
Municipal 311 call centers and complaint intake systems face a structural mismatch between incoming volume and classification capacity. The staff and heuristics available to triage…