ArguCast: a system for online multi-forecasting with gradual argumentation
File(s)
Published version
Author(s)
Gorur, Deniz
Rago, Antonio
Toni, Francesca
Type
Conference Paper
Abstract
Judgmental forecasting is a form of forecasting which employs (human) users to make predictions about specied future events. Judgmental forecasting has been shown to perform better than quantitative methods for forecasting, e.g. when historical data is unavailable or causal reasoning is needed. However, it has a number of limitations, arising from users’ irrationality and cognitive biases. To mitigate against these phenomena, we leverage on computational argumentation, a eld which excels in the representation and resolution of conicting knowledge and human-like reasoning, and propose novel ArguCast frameworks (ACFs) and the novel online system ArguCast, integrating ACFs. ACFs and ArguCast accommodate multi-forecasting, by allowing multiple users to debate on multiple forecasting predictions simultaneously, each potentially admitting multiple outcomes. Finally, we propose a novel notion of user rationality in ACFs based on votes on arguments in ACFs, allowing the ltering out of irrational opinions before obtaining group forecasting predictions by means commonly used in judgmental forecasting.
Date Acceptance
2023-07-04
Citation
Proceedings of the First International Workshop on Argumentation and Applications (Arg&App 2023), 3472, pp.40-51
Publisher
CEUR-WS.org
Start Page
40
End Page
51
Journal / Book Title
Proceedings of the First International Workshop on Argumentation and Applications (Arg&App 2023)
Volume
3472
Copyright Statement
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)
License URL
Identifier
https://ceur-ws.org/Vol-3472/paper3.pdf
Source
Knowledge Representation 2023
Publication Status
Published
Start Date
2023-09-02
Finish Date
2023-09-08
Coverage Spatial
Rhodes, Greece
Date Publish Online
2023-09-02