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首页> 外文期刊>Journal of Business Research >Testing weighting approaches for forecasting in a Group Wisdom Support System environment
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Testing weighting approaches for forecasting in a Group Wisdom Support System environment

机译：在小组智慧支持系统环境中测试用于预测的加权方法

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Decision makers usually seek the best possible information to support their decisions. Yet the more experts a decision maker consults, the more divergent opinions he or she might collect. In particular, the approach of attaining an adequate level of information is of crucial importance for many stakeholders such as financial and political institutions as well as sales departments. Inspired by fact that simple heuristics oftentimes outperform complex optimization models, we test and compare several simple forecast-combining methods, including multiple equally weighted approaches, an "imitate-the-successful" heuristic as well as several other weighting approaches (based on self-assessment, knowledge, and hit rate). Forecasts are collected and processed from the crowd using a novel Group Wisdom Support System (GWSS), which provides an entire forecast distribution and information on the consensus evolution over time. We find that the equally weighted triangular forecasts, a simple 1/N heuristic, delivers the most accurate results. (C) 2016 Elsevier Inc All rights reserved.
机译：决策者通常会寻求最好的信息来支持他们的决策。然而，决策者咨询的专家越多，他或她可能收集到的分歧也就越大。尤其是，对于许多利益相关者（例如金融和政治机构以及销售部门）而言，获得足够信息水平的方法至关重要。受简单启发式算法通常优于复杂优化模型的启发，我们测试并比较了几种简单的预测组合方法，包括多种平均加权的方法，一种“模仿成功”的启发式方法以及其他几种加权方法（基于自我评估，知识和点击率）。使用新颖的团体智慧支持系统（GWSS）从人群中收集并处理预测，该系统可提供整个预测分布以及有关随时间推移共识发展的信息。我们发现，同样加权的三角预测（简单的1 / N启发式）可提供最准确的结果。 （C）2016 Elsevier Inc保留所有权利。

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