506 citations · 1.3k across the 28 of their papers we have counts for
8 papers · 1 filter
Decentralized Aggregation of LLM Predictions via Wagering Mechanisms
Yuhong Luo, David M. Pennock, Xintong Wang
It is increasingly common to aggregate predictions from multiple LLMs, each with domain expertise or access to private tools and data, to improve collective prediction performance.…
Crowdsourced Outcome Determination in Prediction Markets
Rupert Freeman, Sebastien Lahaie, David M. Pennock
A prediction market is a useful means of aggregating information about a future event. To function, the market needs a trusted entity who will verify the true outcome in the end. M…
Representing Aggregate Belief through the Competitive Equilibrium of a Securities Market
David M. Pennock, Michael P. Wellman
We consider the problem of belief aggregation: given a group of individual agents with probabilistic beliefs over a set of uncertain events, formulate a sensible consensus or aggre…
Logarithmic Time Parallel Bayesian Inference
David M. Pennock
I present a parallel algorithm for exact probabilistic inference in Bayesian networks. For polytree networks with n variables, the worst-case time complexity is O(log n) on a CREW…
Graphical Representations of Consensus Belief
David M. Pennock, Michael P. Wellman
Graphical models based on conditional independence support concise encodings of the subjective belief of a single agent. A natural question is whether the consensus belief of a gro…
Modelling Information Incorporation in Markets, with Application to Detecting and Explaining Events
David M Pennock, Sandip Debnath, Eric Glover +1
We develop a model of how information flows into a market, and derive algorithms for automatically detecting and explaining relevant events. We analyze data from twenty-two "politi…