1 citations · 2 across the 9 of their papers we have counts for
11 papers
A Constant-Factor Approximation to Multidimensional Consumer Utility
Kira Goldner, Taylor Lundy, Thodoris Tsilivis
Motivated by social services where consumers pay with non-transferable ordeals, we study mechanisms that maximize consumer utility for multiple unit-demand buyers and heterogeneous…
QuickScope: Certifying Hard Questions in Dynamic LLM Benchmarks
Taylor Lundy, Narun K. Raman, Kevin Leyton-Brown
LLM benchmarks are increasingly dynamic: instead of containing a fixed set of questions, they define templates and parameters that can generate an effectively unlimited number of q…
Reasoning Models are Test Exploiters: Rethinking Multiple-Choice
Narun Raman, Taylor Lundy, Kevin Leyton-Brown
When evaluating Large Language Models (LLMs) in question answering domains, it is common to ask the model to choose among a fixed set of choices (so-called multiple-choice question…
NFTs as a Data-Rich Test Bed: Conspicuous Consumption and its Determinants
Taylor Lundy, Narun Raman, Scott Duke Kominers +1
Conspicuous consumption occurs when a consumer derives value from a good based on its social meaning as a signal of wealth, taste, and/or community affiliation. Common conspicuous…
STEER-ME: Assessing the Microeconomic Reasoning of Large Language Models
Narun Raman, Taylor Lundy, Thiago Amin +2
How should one judge whether a given large language model (LLM) can reliably perform economic reasoning? Most existing LLM benchmarks focus on specific applications and fail to pre…
Multidimensional Bayesian Utility Maximization: Tight Approximations to Welfare
Kira Goldner, Taylor Lundy
We initiate the study of multidimensional Bayesian utility maximization, focusing on the unit-demand setting where values are i.i.d. across both items and buyers. The seminal resul…
STEER: Assessing the Economic Rationality of Large Language Models
Narun Raman, Taylor Lundy, Samuel Amouyal +3
There is increasing interest in using LLMs as decision-making "agents." Doing so includes many degrees of freedom: which model should be used; how should it be prompted; should it…
Pay to (Not) Play: Monetizing Impatience in Mobile Games
Taylor Lundy, Narun Raman, Hu Fu +1
Mobile gaming is a rapidly growing and incredibly profitable sector; having grown seven-fold over the past 10 years, it now grosses over $100 billion annually. This growth was due…
UNSAT Solver Synthesis via Monte Carlo Forest Search
Chris Cameron, Jason Hartford, Taylor Lundy +4
We introduce Monte Carlo Forest Search (MCFS), a class of reinforcement learning (RL) algorithms for learning policies in {tree MDPs}, for which policy execution involves traversin…
The Perils of Learning Before Optimizing
Chris Cameron, Jason Hartford, Taylor Lundy +1
Formulating real-world optimization problems often begins with making predictions from historical data (e.g., an optimizer that aims to recommend fast routes relies upon travel-tim…
Limitations of Incentive Compatibility on Discrete Type Spaces
Taylor Lundy, Hu Fu
In the design of incentive compatible mechanisms, a common approach is to enforce incentive compatibility as constraints in programs that optimize over feasible mechanisms. Such co…