دانلود مقاله ISI انگلیسی شماره 76807
ترجمه فارسی عنوان مقاله

یکپارچه سازی بازار پیش بینی و روش دلفی در داخل یک سیستم پشتیبانی آینده نگری - بینش از یک بازی آنلاین

عنوان انگلیسی
Integrating prediction market and Delphi methodology into a foresight support system — Insights from an online game
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
76807 2015 18 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Technological Forecasting and Social Change, Volume 97, August 2015, Pages 47–64

ترجمه کلمات کلیدی
آینده نگری؛ بازار پیش بینی؛ یکپارچه سازی اطلاعات؛ روش دلفی، سیستم تصمیم و پشتیبانی آینده نگری
کلمات کلیدی انگلیسی
Forecasting; Prediction market; Information integration; Delphi method; Decision and foresight support system
پیش نمایش مقاله
پیش نمایش مقاله  یکپارچه سازی بازار پیش بینی و روش دلفی در داخل یک سیستم پشتیبانی آینده نگری - بینش از یک بازی آنلاین

چکیده انگلیسی

In this paper, we introduce a novel approach, an electronic combination of a prediction market and Delphi methodology, to create a foresight support system (FSS). While the Delphi method has been a widely accepted foresight technique, prediction markets have been a very recent innovation to the existing decision and foresight support systems. Though, traditional prediction markets have been quite successful recently, our extension to the traditional prediction market methodology allows us to extract more valuable market information than any other prediction market since our approach provides not only a market forecast but also delivers an entire forecast distribution. The forecast distribution is generated by the aggregation of another unique characteristic of the suggested market structure namely that financial market professionals have to submit an interval forecast rather than point forecast. Based on our analysis, we demonstrate that our market is weak-form efficient and hence contains all publicly available information. In terms of forecasting accuracy, we conclude that the precision of our market improved over time and overall outperformed its benchmark.