Prediction Markets as an Information Aggregation Tool for Effective Project Management in Defense Acquisition Projects

2011 ◽  
Author(s):  
Ricardo Valerdi ◽  
Matthew Potoski
Keyword(s):  
Project Management ◽  
Information Aggregation ◽  
Prediction Markets ◽  
Defense Acquisition

Educational Prediction Markets: Construction Project Management Case Study

2013 ◽  
Vol 139 (2) ◽  
pp. 134-138 ◽  
Author(s):  
Ivan Damnjanovic ◽  
Vahid Faghihi ◽  
Chyllis Scott ◽  
Erin McTigue ◽  
Kenneth Reinschmidt
Keyword(s):  
Project Management ◽  
Construction Project ◽  
Prediction Markets ◽  
Construction Project Management ◽  
Management Case Study

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.

Exploring the Use of Prediction Markets as Digital Games to Support Learning in a Project Management Context

2019 ◽  
pp. 239-260
Author(s):  
Herbert Remidez ◽  
Michael Stodnick ◽  
Sri Beldona
Keyword(s):  
Project Management ◽  
Digital Games ◽  
Prediction Markets ◽  
Game Based Learning ◽  
Aggregate Information ◽  
Complex Projects ◽  
Management Context ◽  
Future Events ◽  
Potential Risks ◽  
Learning Principles

A growing area of study is the management of complex projects involving stakeholders dispersed across organizations. Key to the success of complex projects is encouraging stakeholders to learn and communicate useful information about work progress and potential risks. Increasingly, companies are using a gaming approach to encourage workers to learn and communication useful information. This chapter looks at one such gaming vehicle, namely prediction markets. Prediction markets are games in the form of marketplaces that adapt many of the same structures found in stock markets to aggregate information about the probability of future events. This chapter traces the developmental history and application of prediction markets, discusses issues in marketplace design, and explores how game-based learning principles can support the use of prediction markets in this context. The concluding section discusses the application of a prediction market to support the management of an IT project.

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.

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

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.

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