Leslie R. Fine

Predicting the Future

We present a novel methodology for predicting future outcomes that uses small numbers of individuals participating in an imperfect information market. By determining their risk attitudes and performing a nonlinear aggregation of their predictions, we are able to assess the probability of the future outcome of an uncertain event and compare it to both the objective probability of its occurrence and the performance of the market as a whole. Experiments show that this nonlinear aggregation mechanism vastly outperforms both the imperfect market and the best of the participants. We then extend the mechanism to prove robust in the presence of public information.

Inducing liquidity in thin financial markets through combined-value trading mechanisms

Previous experimental research has shown that thin financial markets fail to fully equilibrate, in contrast with thick markets. A specific type of market risk is conjectured to be the reason, namely, the risk of partial execution of desired portfolio rearrangements in a system of parallel, unconnected double auction markets. This market risk causes liquidity to dry up before equilibrium is reached. To verify the conjecture, we organized markets directly as a portfolio trading mechanism, allowing agents to better coordinate their orders across securities. The mechanism is an implementation of the combined-value trading (CVT) system. We present evidence that our portfolio trading mechanism facilitates equilibration to the same extent as thick markets do. Like in thick markets, the emergence of equilibrium pricing cannot be attributed to chance. Inspection of order submission and trade activity reveals that subjects manage to exploit the direct linkages between markets presented by the CVT system.

Forecasting uncertain events with small groups

We present a novel methodology for predicting future outcomes that uses small numbers of individuals participating in an imperfect information market. By determining their risk attitudes and performing a nonlinear aggregation of their predictions, we are able to assess the probability of the future outcome of an uncertain event and compare it to both the objective probability of its occurrence and the performance of the market as a whole. Experiments show that this nonlinear aggregation mechanism vastly outperforms both the imperfect market and the best of the participants.

Tycoon: A Market-Based Resource Allocation System

P2P clusters like the Grid and PlanetLab enable, in principle, the same statistical multiplexing efficiency gains for computing as the Internet provides for networking. The key unsolved problem is resource allocation. Existing solutions are not economically efficient and require high latency to acquire resources. We designed and implemented Tycoon, a market-based distributed resource allocation system based on an Auction Share scheduling algorithm. Preliminary results show that Tycoon achieves low latency and high fairness while providing incentives for truth-telling on the part of strategic users.

Privacy and Deviation

In spite of the widespread concerns expressed about the importance of privacy, individuals frequently give away or sell a myriad of personal data. How and why people decide to transition their information from the private to the public sphere is poorly understood. To address this puzzle, we conducted a reverse second-price auction to identify the monetary value of private information to individuals and how that value is set. Our results demonstrate that deviance, whether perceived or actual, from the groups average asymmetrically impacts the price demanded to reveal private information.