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Luck, Risk, and Life Chances
Published in Oscar H. Gandy, Coming to Terms with Chance, 2016
A fairly recent innovation in the financial world combines the predictive attributes of gamblers’ markets, such as those which enable sports betting, and financial markets in which investors wager on the rise, fall, or actual value of a stock exchange at its close. Prediction markets are used to establish forecasts, or predictions about outcomes, often presented in terms of probabilities. Prediction markets utilize contracts identifying what amount will be paid if a prediction comes true. So, for example, if the payoff for a contract is $1, the amount the “market” is willing to pay for that contract is a direct indicator of the probability, or chance that the prediction will be true. An active market predicting the outcome of the last presidential election is an example. Just as serious gamblers will bet on almost anything, there is considerable variation in the kinds of bets or contracts that can be made.
Smart Contracts: The Self-Executing Contracts
Published in Rajdeep Chakraborty, Anupam Ghosh, Valentina Emilia Bălaş, Ahmed A Elngar, Blockchain, 2023
It has been demonstrated that prediction markets can give better upcoming forecasts and speculative strategies. Smart contracts can be utilized in prediction markets because of their distributed consensus verification and immutability. Augur and Gnosis are two examples of typical uses. With the use of blockchain, Augur 8 has created a shockingly accurate forecasting tool.
Why We Play
Published in Julie A. Jacko, The Human–Computer Interaction Handbook, 2012
Games even make work fun. Players pay for the experience of being a waitress (Diner Dash), business owner (Lemonade Tycoon), dungeon master (Dungeon Keeper), professional sports player (Madden Football), or theme-park owner (Rollercoaster Tycoon). They even buy the opportunity to sort bugs by color (Tumblebugs), pick up their rooms (Katamari Damacy), or manage a city (Sim City). Games model life problems (The Sims) and improve performance during real work. In The ESP Game, developed at Carnegie Mellon University, people play a guessing game to enliven the otherwise boring task of providing text labels for images (von Ahn and Dabbish 2004). Idea or prediction markets use games to beat expert opinion polls. Prediction markets allow participants to express their opinions through buying and selling shares with either virtual or real money. One of the oldest, the University of Iowa’s Iowa Electronic Market, has allegedly beat expert polls in predicting U.S. presidential elections since 1988. Players of the Hollywood Stock Exchange use virtual money to predict what actor, director, or film will receive an Oscar nomination. At NewsFutures, players compete for prizes based on their ability to predict news events. Several companies, such as Google, use internal markets to predict launch dates and job openings. Other companies such as Newsfutures/Lumenogic have created public prediction markets and led the development of enterprise-class prediction market services. In a landmark study in 2004, it was demonstrated that play-money markets can be just as predictive as real-money ones (Servan-Schreiber et al. 2004). Not without controversy (gambling is illegal in many countries), markets have even been proposed to predict terrorist attacks (Hulse 2003). Because playing prediction markets accomplishes a real purpose, it changes how participants feel about playing in general. Like offering a prize for a competition to develop a solar car, having real money or reputation on the line increases the excitement. In Serious Fun, players accomplish real work for many reasons, including to meditate, lose weight, label every image on Internet, or beat Wall Street predictions.
Intelligent Collectives: Theory, Applications, and Research Challenges
Published in Cybernetics and Systems, 2018
Van Du Nguyen, Ngoc Thanh Nguyen
Prediction markets are markets mainly used for trading the outcomes of future events (Wolfers and Zitzewitz 2004). Referring to Tziralis and Tatsiopoulos (2012), prediction markets are defined as markets that are designed and run for the primary purpose of mining and aggregating information scattered among traders and subsequently using this information in the form of market values in order to make predictions about specific future events. Nowadays, prediction markets have widely applied in many companies such as HP, Microsoft, and Best Buy to achieve better predictions on sales in the future, the success of products (Cowgill, Wolfers, and Zitzewitz 2009). According to Surowiecki (2005), the necessary criteria for collectives to be intelligent: diversity, independence, decentralization. For such an assumption, predictive markets can be considered as a reliable source to harness the CI of crowds and improve the accuracy of predictions. Beyond these criteria, in Berg et al. (2008), researchers have revealed the necessary conditions for which prediction markets to be worked. They are (1) enough participants so that the aggregate of their knowledge can forecast the outcome of the event correctly; (2) effective market mechanism for revealing collective information. Moreover, prediction markets perform better if participants are more active, have many sources of information and the number of contracts is small.
Social Networks as Platforms for Enhancing Collective Intelligence
Published in Cybernetics and Systems, 2022
Van Du Nguyen, Van Cuong Tran, Hai Bang Truong, Ngoc Thanh Nguyen
The well-known application of CI is prediction markets which commonly used for trading the outputs of events in the future (Wolfers and Zitzewitz 2004). Prediction markets are: "markets that are designed and run for the primary purpose of mining and aggregating information scattered among traders and subsequently using this information in the form of market values in order to make predictions about specific future events" (Tziralis and Tatsiopoulos 2012). For such a task, prediction markets can be considered as an effective approach to aggregating widely dispersed information effectively (Plott, Wit, and Yang 2003), a vital part of collective intelligence (Palak and Nguyen 2017), harnessing CI of crowds to improve the accuracy of predictions (V. D. Nguyen and Nguyen 2018b) via trading activities. Even, the findings in (Snowberg, Wolfers, and Zitzewitz 2013) have indicated that prediction markets is better other professional predictors and polls in various prediction tasks. Due to their prominent roles, many prediction markets systems (The Iowa Election Markets-IEM, Cultivate Labs, iPredict, PredictIt) have been launched for predicting the outcomes of different future events including political and economic issues. In addition, PredictIt offers a Research Data Sharing program and is a partner of many universities such as Yale University, Harvard University, and University of Pennsylvania. Beside aforementioned characteristics of intelligent collectives, (Berg et al. 2008) have identified additional criteria that increase the performance of prediction markets systems: enough and more active participants; effective market mechanism for revealing collective information; diverse sources of information and small number of contracts.