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Examination of 155 poll forecasts in 68 national elections since 1949 shows that errors average nearly twice what statistical theory would indicate. Polls predict the division of vote between major parties better than individual party %s, leading to 85% success in picking the winner. The worst failures occurred in a few elections where most polls went wrong. Liberal party votes were correctly forecast, but those of conservatives were slightly underestimated. Improved polling methods have not led to better forecasts. 1 Table, 6 References. AA

The election night vote estimation device used in the 1962 Iowa gubernatorial election by Robert W. Clyde, William J. Hemmerle, & T. A. Bancroft (see SA 0827/A8902) was modified & tested by the author in the 1966 Minn gubernatorial election. This system represents a low-cost method for estimating rapidly & accurately final vote ratios in statewide election contests. Minn's 87 counties were placed into homogeneous groupings on the basis of pop & historical voting patterns. Pop statistics from the 1960 census were used. Each county's total Democratic vote was added up for the past 3 gubernatorial elections & divided by the total votes cast, thus yielding a 'historical %' for each county. Strata were defined by pop & historical % to create a minimum N of cells necessary to yield sufficient representation early in the evening, yet enough cells to satisfy reasonably the assumption of homogeneity. The values of estimated final Democratic % & estimated final Republican % were established by computer. County-by-county returns were telephoned to the authors at the computer site throughout election night. Results lead to the conclusion that it is possible to predict aggregate electoral behavior with a high degree of accuracy without explicit reliance on SE variables. No such factors as age, religion, income, occup, etc, were included. It is suggested that in rapid vote estimating, broader sampling categories (eg, historical tendencies) may minimize some of the disruptive & transitory variables which often haunt samples based upon narrower & more select SE factors. 2 Tables. M. Maxfield.

Prediction markets sind generell bekannt als ein Sondertyp der Kapitalmärkte, an welchen der Wert der gehandelten Vermögenswerte durch den Ausgang von unsicheren, zukünftigen Ereignissen bedingt wird. Die Masterarbeit hat sich zum Ziel gesetzt, die berichtenswertesten Anwendungsbefunde in Bezug auf Prediction markets systematisch vorzustellen. Um dies zu verwirklichen wurde folgende Forschungsfrage formuliert: Was sind die berichtenswertesten Anwendungsbereiche für Prediction Markets und welche Verhaltensaspekte der Menschen zeigen sich bei der Verwendung derartiger Prognosemechanismen? Die Masterarbeit untersucht und stellt methodologisch zusammen sowohl die neuesten akademischen Ansätze, als auch die praktischen Implementationen von Prediction Markets mit dem Ziel, diesem wenig systematisierten Wissenschaftsfeld eine gewisse Kontinuität zu verschaffen. Politik, Sport und zahlreiche Anwendungen innerhalb verschiedenen Unternehmen stellen den Kern dieser Masterarbeit dar und liefern folgende Befunde: Im Bereich der Vorhersage politischer Ereignisse liefern Prediction Markets regelmäßig bessere Leistungen als Umfragen und Experten. Im Sportbereich sind Prediction Markets in der Lage, die allgemeine Auffassung über die Prognosefähigkeit der Wettquoten zu widerlegen. Anwendungsbeispiele aus der Praxis liefern glaubwürdige Belege dafür, dass Prediction Markets erfolgreich verstreute Information aggregieren können. Favorite longshot bias, reverse favorite longshot bias, herding bias und optimistic bias sind Verhaltensmuster, die durchgehend festgestellt werden können. Auf der negativen Seite sind das Nichtvorhandensein eines geeigneten Benchmark-Mechanismus, ein experimenteller Charakter und das Fehlen rechtlicher Rahmenbedingungen hervorzuheben. Die fehlende Übereinstimmung über theoretische Grundlagen, weitere konkrete Anwendungen und allgemeine Popularisierung der Thematik drängen sich schließlich als forschungsbedürftige Themen auf. ; Prediction markets are generally being referred to as a special case of asset markets where the value of the traded asset is contingent upon the outcome of some uncertain event at or before some pre-specified point in time. The master thesis aims at systematically presenting the newsworthiest application findings related to prediction markets. In order to succeed in such an endeavor, the following research question finds itself at the heart of the thesis: What are the newsworthiest prediction markets application fields, and which aspects of human behavior become apparent with the use of such forecasting mechanisms? The thesis compiles and methodically examines both the latest academic as well as practical efforts related to the application fields of prediction markets, with the aim of adding continuity to this still somewhat disorganized scientific field. Application fields of politics, sports and various types of corporate applications represent the core of the paper and provide the following findings: the field of politics shows that prediction markets are able to outperform polls and experts on a consistent basis. In the field of sports, prediction market studies refute the general belief that betting lines represent predictions. Numerous corporate applications provide substantial evidence for successful aggregation of scattered information and the use of collective wisdom. Behavioral patterns in the form of the favorite-longshot bias, reverse favorite-longshot bias, herding bias, and optimistic bias are consistently being detected throughout the different application fields. The frequent absence of an appropriate benchmark mechanism, an experimental character, and a deficiency of a legal framework come into the forefront on the negative side. To conclude, the thesis identifies the following topics as requiring further research: fundamental theoretical postulates, further practical implementations, and the popularization of the field. ; Miroslav Tepavac ; Zusammenfassungen in Deutsch und Englisch ; Karl-Franzens-Universität Graz, Masterarbeit, 2018 ; (VLID)2945882

In the span of the first few years after Japan's defeat in World War II, five of Japan's leading earth scientists came forward to warn the nation that major earthquakes would soon occur. They (almost) never did. This article focuses on those predictions to highlight the debates that shaped early postwar efforts in Japan to make scientists, and earth scientists in particular, guardians of the public's safety. It draws on multiple archival collections, participant accounts and popular media coverage to explore the tensions between individual scientists and newly formed, officially sanctioned bodies charged with coordinating earthquake prediction research. These tensions, I argue, reflect both a long-standing ambivalence within the field toward prediction's legitimacy, and the emergence of a new set of research and policy imperatives for Japan's earth scientists that privileged it. The legacies of the Occupation-era encounters with prediction include the 1962 publication of Earthquake Prediction: Current Status and a Plan for Development, the formation of the Coordinating Committee for Earthquake Prediction in 1969 and the passage of the Large-Scale Earthquake Countermeasures Act in 1978.

With the tech policy landscape showing no signs of slowing down in 2024, AEI's tech policy team is taking a moment to make some predictions for the year ahead.
The post 2024 Tech Policy Predictions appeared first on American Enterprise Institute - AEI.

It's of some interest to see how polls and prediction markets are viewing the presidential race, and how this links with economic sentiment. First polls and sentiment. Source: TheHill, accessed 4/17/2024. Source: U.Michigan via FRED, TradingEconomics, accessed 4/17/2024. Now, a prediction market. Below, I show PredictIt odds for Biden vs. Trump (omitting RFK Jr.), for […]

AbstractThis article discusses recent moves in political science that emphasise predicting future events rather than theoretically explaining past ones or understanding empirical generalisations. Two types of prediction are defined: pragmatic, and scientific. The main aim of political science is explanation, which requires scientific prediction. Scientific prediction does not necessarily entail pragmatic prediction nor does it necessarily refer to the future, though both are desiderata for political science. Pragmatic prediction is not necessarily explanatory, and emphasising pragmatic prediction will lead to disappointment, as it will not always help in understanding how to intervene to change future outcomes, and policy makers are likely to be disappointed by its time‐scale.

If probation officers are to be involved in collecting data to compile prediction scores they must be aware of how the final data is to be used and interpreted. Tessa Webb, Probation Officer in Essex, argues that practitioners need to be able to place research findings in context and highlight their limitations, to ensure that their work is not evaluated and interpreted on narrow and unreliable grounds.

The assumption that one of a set of prediction models is a literal description of reality formally underlies many formal econometric methods, including Bayesian model averaging and most approaches to model selection. Prediction pooling does not invoke this assumption and leads to predictions that improve on those based on Bayesian model averaging, as assessed by the log predictive score. The paper shows that the improvement is substantial using a pool consisting of a dynamic stochastic general equilibrium model, a vector autoregression, and a dynamic factor model, in conjunction with standard US postwar quarterly macroeconomic time series.