Second-Generation Prediction Markets for Information Aggregation: A Comparison of Payoff Mechanisms

2011 ◽  
Vol 31 (6) ◽  
pp. 469-489 ◽  
Author(s):  
Christian Slamka ◽  
Wolfgang Jank ◽  
Bernd Skiera
Keyword(s):  
Second Generation ◽  
Information Aggregation ◽  
Prediction Markets

scholarly journals FaRM: Fair Reward Mechanism for Information Aggregation in Spontaneous Localized Settings

2019 ◽  
Author(s):  
Moin Hussain Moti ◽  
Dimitris Chatzopoulos ◽  
Pan Hui ◽  
Sujit Gujar
Keyword(s):  
Prior Knowledge ◽  
Incentive Mechanism ◽  
Information Aggregation ◽  
Prediction Markets ◽  
Consistency Score ◽  
Aggregate Information ◽  
Reliability Score ◽  
Random Pairing ◽  
History Of ◽  
Reward Mechanism

Although peer prediction markets are widely used in crowdsourcing to aggregate information from agents, they often fail to reward the participating agents equitably. Honest agents can be wrongly penalized if randomly paired with dishonest ones. In this work, we introduce selective and cumulative fairness. We characterize a mechanism as fair if it satisfies both notions and present FaRM, a representative mechanism we designed. FaRM is a Nash incentive mechanism that focuses on information aggregation for spontaneous local activities which are accessible to a limited number of agents without assuming any prior knowledge of the event. All the agents in the vicinity observe the same information. FaRM uses (i) a report strength score to remove the risk of random pairing with dishonest reporters, (ii) a consistency score to measure an agent's history of accurate reports and distinguish valuable reports, (iii) a reliability score to estimate the probability of an agent to collude with nearby agents and prevents agents from getting swayed, and (iv) a location robustness score to filter agents who try to participate without being present in the considered setting. Together, report strength, consistency, and reliability represent a fair reward given to agents based on their reports.

REAL-MONEY VS. PLAY-MONEY FORECASTING ACCURACY IN ONLINE PREDICTION MARKETS – EMPIRICAL INSIGHTS FROM IPREDICT

2012 ◽  
Vol 4 (3) ◽  
pp. 21-58 ◽  
Author(s):  
Sebastian Deimer ◽  
Joaquin Poblete
Keyword(s):  
Stock Exchange ◽  
Forecast Accuracy ◽  
Information Aggregation ◽  
Prediction Markets ◽  
Trading Activity ◽  
Market Prices ◽  
Forecasting Accuracy ◽  
Real Money ◽  
Incentive Schemes ◽  
Online Trading

Prediction markets are online trading platforms where contracts on future events are traded with payoffs being exclusively linked to event occurrence. Scientific research has shown that market prices of such contracts imply high forecasting accuracy through effective information aggregation of dispersed knowledge. This phenomenon is related to incentives for truthful aggregation in the form of real-money or play-money rewards. The question whether real- or play-money incentives enhance higher relative forecast accuracy has been addressed by previous works with diverse findings. The current state of empirical research in his field is subject to two inherent deficiencies. First, inter-market studies suffer from market disparities and differences in the definition of underlying events. Comparisons between two different platforms (one for play-money contracts, one for real-money contracts) are potentially biased by different trading behaviour. Second, the majority of studies are based upon identical datasets of market platforms (IOWA stock exchange, Tradesports/Intrade, NewsFutures).This paper contributes new insights by analysing 44,169 trading observations on ipredict, where real-money and play-money contracts are traded on a variety of events. Forecasting accuracy is analysed on overall trading activity as well as comparison of equal contracts under different monetary incentive schemes. Statistical models are built to analyse the influence of order volumes and days to expiry under both incentive schemes. Ignoring different events in underlying trading activity, play-money contracts imply statistically insignificant excess accuracy. In direct comparison of equal events, real-money contracts, however, real-money contracts predict at significantly higher accuracy. This paper finds a relationship between order volumes and forecasting accuracy whereas the influence of days to expiry and aggregated volumes showed lower R² than was expected by formed hypotheses.

Ideas Markets: A Literature Review and Classification Scheme

2014 ◽  
Vol 8 (2) ◽  
pp. 76-88
Author(s):  
Eoin McDonagh ◽  
Patrick Buckley
Keyword(s):  
Literature Review ◽  
Current Literature ◽  
Classification Scheme ◽  
Information Aggregation ◽  
Prediction Markets ◽  
Market Mechanisms ◽  
Current Trends ◽  
New Ideas ◽  
Future Events ◽  
Uncertain Future

Prediction markets have been positioned in the literature as efficient and scalable information aggregation mechanisms. The increasing interest in the use of market mechanisms to enable decision making has led to attempts to use these mechanisms to stimulate innovation in a number of organisational contexts. These tools, usually referred to as Ideas Markets are seen as a potentially powerful method of sourcing and evaluating new ideas. Whereas traditional Prediction Markets allow participants to trade on the outcome of uncertain future events, Ideas Markets’ provide a platform for the generation and evaluation of ideas through the trading of virtual stocks representing products and concepts.  In this paper, we study the evolution of research on Idea Markets though a comprehensive literature review. We develop a classification scheme, which enables thorough analysis of current trends within Ideas Markets research. Our results show that case studies detailing corporate applications of Ideas Markets dominate the current literature. The paper contributes by providing a comprehensive guide to the extant literature on Ideas Markets. This serves a number of purposes, including providing practitioners and academics with a convenient bibliography of the current literature. The issues highlighted by this literature review also serve to both motivate and enable further research.

Pari-mutuel Information Aggregation Mechanisms

2012 ◽  
Vol 4 (2) ◽  
pp. 23-43
Author(s):  
Jordi McKenzie ◽  
Jared Bullen
Keyword(s):  
Information Structure ◽  
Technical Assistance ◽  
Experimental Studies ◽  
Public Information ◽  
Information Aggregation ◽  
Prediction Markets ◽  
The Public ◽  
Aggregate Information ◽  
Private And Public ◽  
Knowledge Bias

A number of experimental studies have found that pari-mutuel markets possess the ability to aggregate information privately held by individuals and therefore act as prediction markets.  However, all previous studies have assumed that information is privately and independently distributed. In real world environments the distribution of information is unlikely to take this form. This paper investigates, experimentally, an information structure in which there is both private and public information. It is found that this structure induces a ‘public knowledge bias’ which limits the market’s ability to aggregate information to the extent that the public information reduces the market’s predictive performance.The authors wish to thank Murali Agastya, Andrew Coleman, Pablo Guillen, Stefan Palan, Charles Plott, Kunal Sengupta, and Robert Slonim for useful comments and technical assistance.  We are also extremely grateful to Katarina Kálovcová and Andreas Ortmann for supplying their Ztree program for use in developing our own.

Second-Generation Antidepressants May Be Safer

2008 ◽  
Vol 41 (14) ◽  
pp. 23
Author(s):  
SHERRY BOSCHERT
Keyword(s):  
Second Generation
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