Improving decision-making performance through argumentation: An argument-based decision support system to compute with evidence
作者：
Highlights：
• We measure subjects’ ability to predict housing market trends using an argument based tool with belief aggregation
• The population mean performance improves by 36% when using the system
• Decision structures generated by subjects outperform the subjects themselves on held-out problems
• Individual performance when using the tool does not correlate with performance when not using the tool
• We tentatively conclude that the tool replaces, rather than amplifies, the user’s internal decision process
摘要
While research has shown that argument based systems (ABSs) can be used to improve aspects of individual thinking and learning, relatively few studies have shown that ABSs improve decision performance in real world tasks. In this article, we strive to improve the value-proposition of ABSs for decision makers by showing that individuals can, with minimal training, use a novel ABS called Pendo to improve their ability to predict housing market trends. Pendo helps to weight and aggregate evidence through a computational engine to support evidence-based reasoning, a well-documented deficiency in human decision-making. It also supports individuals in the creation of knowledge artifacts that can be used to solve similar problems in the same domain.
论文关键词：Computer-supported argumentation,Evidence-based reasoning,Dempster–Shafer belief aggregation,Housing market prediction
论文评审过程：Received 15 February 2013, Revised 3 March 2014, Accepted 22 April 2014, Available online 4 May 2014.
论文官网地址：https://doi.org/10.1016/j.dss.2014.04.005
