We consider how to make probability forecasts of binary labels. Our main mathematical result is that for any continuous gambling strategy used for detecting disagreement between the forecasts and the actual labels, there exists a forecasting strategy whose forecasts are ideal as far as this gambling strategy is concerned. A forecasting strategy obtained in this way from a gambling strategy demo...

Information from crop disease surveillance programs and outbreak investigations provides real-world data about the drivers of epidemics. In many cases, however, only information on outbreaks is collected surrounding healthy crops are omitted. Use such to develop models that can forecast risk/no risk therefore problematic, as relating no-risk status missing. This study explored a novel applicati...

Prediction markets provide an efficient means to assess uncertain quantities from forecasters. Traditional and competitive strictly proper scoring rules have been shown to incentivize players to provide truthful probabilistic forecasts. However, we show that when those players can cooperate, these mechanisms can instead discourage them from reporting what they really believe. When players with ...

This chapter covers different approaches that may be taken when building an ensemble method, through studying specific examples of each approach from research conductded by the author. A method called Negative Correlation Learning illustrates a decision level combination approach with individual calssifiers trained co-operatively. The Model level combination paradigm is illustrated via a tree c...

The paper introduces a modified version of the classical Coupon Collector’s Problem entailing exchanges and cooperation between multiple players. Results of the development show that, within a proper Markov framework, the complexity of the Cooperative Multiplayer Coupon Collectors’ Problem can be attacked with an eye to the modeling of social strategies and community behaviors. The cost of coop...

In recent years, authors have focused on modeling and forecasting volatility in financial series it is crucial for the characterization of markets, portfolio optimization and asset valuation. One of the most used methods to forecast market volatility is the linear regression. Nonetheless, the errors in prediction using this approach are often quite high. Hence, continued research is conducted t...