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Download Collective Wisdom: Principles and Mechanisms by Hélène Landemore, Jon Elster PDF

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By Hélène Landemore, Jon Elster

James Madison wrote, "Had each Athenian citizen been a Socrates, each Athenian meeting could nonetheless were a mob." The participants to this quantity speak about and for the main half problem this declare by way of contemplating stipulations lower than which many minds may be wiser than one. With backgrounds in economics, cognitive technological know-how, political technological know-how, legislations, and historical past, the authors contemplate details markets, the web, jury debates, democratic deliberation, and using range as mechanisms for making improvements to collective judgements. whilst, they give thought to voter irrationality and paradoxes of aggregation as in all probability undermining the knowledge of teams. Implicitly or explicitly, the amount additionally bargains suggestions and warnings to institutional designers.

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Additional resources for Collective Wisdom: Principles and Mechanisms

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Moreover, not all crowds are wise. 5 How can we tell which crowds are wise and which are not? This theoretical question has everyday importance. To take one common example, how do we know that our Google search will display the “best” information related to a query? Usually, this is a matter of personal experience. After many trials with Google searches, we either arrive at or reject the conclusion that the results of search inquiries should be believed. 6 This scenario suggests one difference between evaluating epistemic and political systems.

And Wright, J. (1992). Anatomy of an experimental political stock market. American Economic Review, 82(5): 1142–61. Gruca, T. , Berg, J. , and Cipriano, M. (2008). Incentive and accuracy issues in movie prediction markets. Journal of Prediction Markets, 2(1): 29–73. Hanson, R. (1999). Decision Markets. IEEE Intelligent Systems (May–June 1999): 16–19. (2003). Combinatorial Information Market design. Information Systems Frontiers, 5(1): 107–19. Hopman, J. W. (2007). Using forecasting markets to manage demand risk.

In this design, the trading price captures the mean consensus estimate for a specific variable. Besides vote share, it has been used in various contexts to forecast a company’s quarterly sales, the number of bug reports for a software product, or the completion date of a large industrial project. Any continuous quantity can be forecasted in this manner. In some situations, however, it may be insufficient to forecast just the mean estimate for a variable. One may instead want to extract the full probability distribution for the outcome.

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