Robin Hanson

TL;DR: This work introduces market scoring rules, and considers several design issues, including how to represent variables to support both conditional and unconditional estimates, how to avoid becoming a money pump via errors in calculating probabilities, and how to ensure that users can cover their bets, without needlessly preventing them from using previous bets as collateral for future bets.

TL;DR: In practice, scoring rules elicit good probability estimates from individuals, while betting markets elicit good consensus estimates from groups as mentioned in this paper, and market scoring rules combine these features, eliciting estimates from individual or group, with groups costing no more than individuals.

TL;DR: The ability of groups of people to make predictions is a potent research tool that should be freed of unnecessary government restrictions.

TL;DR: A Bayesian method for constructing a super-resolved surface model by combining information from a set of images of the given surface by using a neighbor correlation model as well as pixel data from the image set is described.

TL;DR: This article study the effect of manipulators on an experimental market and find that manipulators are unable to distort price accuracy, and subjects without manipulation incentives compensate for the bias in offers from manipulators by setting a different threshold at which they are willing to accept trades.