Instructor Rating Markets

Abstract

We describe the design of Instructor Rating Markets in which students trade on the ratings that will be received by instructors, with new ratings revealed every two weeks. The markets provide useful dynamic feedback to instructors on the progress of their class, while at the same time enabling the controlled study of prediction markets where traders can affect the outcomes they are trading on. More than 200 students across the Rensselaer campus participated in markets for ten classes in the Fall 2010 semester. We show that market prices convey useful information on future instructor ratings and contain significantly more information than do past ratings. The bulk of useful information contained in the price of a particular class is provided by students who are in that class, showing that the markets are serving to disseminate insider information. At the same time, we find little evidence of attempted manipulation of the liquidating dividends by raters. The markets are also a laboratory for comparing different microstructures and the resulting price dynamics, and we show how they can be used to compare market making algorithms.

Improved Price Oracles

“…The reported price can be easily computed in the common case where the trading set can be written in the form of (7), in which case the reachable set is constant after any optimal trade since every optimal trade is never in the dominated interior, which, in turn, implies that an optimal trade always leaves the reachable set unchanged by the definition of path independence. We can then use (7) to write the reachable sets as…”

“…One of the most popular scoring rules is Hanson's logarithmic market scoring rule (LMSR) [18]. This rule has been implemented in numerous online settings including online ad auctions [15], prediction markets [5,31], and instructor rating markets [7].…”

Improved Price Oracles: Constant Function Market Makers

“…One of the most popular scoring rules is Hanson's logarithmic market scoring rule (LMSR) [21]. This rule has been implemented in numerous online settings including online ad auctions [20], prediction markets [6,34], and instructor rating markets [9].…”

Profit-charging market makers with bounded loss, vanishing bid/ask spreads, and unlimited market depth

“…In many markets, there may not be enough organic liquidity to support active trade, or the market may encompass enough events that buyers and sellers have trouble finding one another. Markets mediated by automated agents have successfully predicted the openings of buildings [Othman and Sandholm 2010a], point spreads in sports matches [Goel et al 2008], anticipated the ratings of course instructors [Chakraborty et al 2011], etc. Automated market makers are also used by a number of companies (e.g., Inkling Markets) offering private corporate prediction markets to aggregate internal information.…”