Forecasting argumentation frameworks
File(s)
Accepted version
Author(s)
Irwin, Benjamin
Rago, Antonio
Toni, Francesca
Type
Conference Paper
Abstract
We introduce Forecasting Argumentation Frameworks
(FAFs), a novel argumentation-based methodology for
forecasting informed by recent judgmental forecasting
research. FAFs comprise update frameworks which empower
(human or artificial) agents to argue over time about the
probability of outcomes, e.g. the winner of a political
election or a fluctuation in inflation rates, whilst flagging
perceived irrationality in the agents’ behaviour with a view
to improving their forecasting accuracy. FAFs include five
argument types, amounting to standard pro/con arguments,
as in bipolar argumentation, as well as novel proposal
arguments and increase/decrease amendment arguments. We
adapt an existing gradual semantics for bipolar argumen-
tation to determine the aggregated dialectical strength of
proposal arguments and define irrational behaviour. We then
give a simple aggregation function which produces a final
group forecast from rational agents’ individual forecasts.
We identify and study properties of FAFs and conduct
an empirical evaluation which signals FAFs’ potential to
increase the forecasting accuracy of participants.
(FAFs), a novel argumentation-based methodology for
forecasting informed by recent judgmental forecasting
research. FAFs comprise update frameworks which empower
(human or artificial) agents to argue over time about the
probability of outcomes, e.g. the winner of a political
election or a fluctuation in inflation rates, whilst flagging
perceived irrationality in the agents’ behaviour with a view
to improving their forecasting accuracy. FAFs include five
argument types, amounting to standard pro/con arguments,
as in bipolar argumentation, as well as novel proposal
arguments and increase/decrease amendment arguments. We
adapt an existing gradual semantics for bipolar argumen-
tation to determine the aggregated dialectical strength of
proposal arguments and define irrational behaviour. We then
give a simple aggregation function which produces a final
group forecast from rational agents’ individual forecasts.
We identify and study properties of FAFs and conduct
an empirical evaluation which signals FAFs’ potential to
increase the forecasting accuracy of participants.
Date Issued
2022-07-31
Date Acceptance
2022-04-15
Citation
Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning, 2022, pp.533-543
ISSN
2334-1033
Publisher
IJCAI Organisation
Start Page
533
End Page
543
Journal / Book Title
Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning
Copyright Statement
Copyright © 2022 International Joint Conferences on Artificial Intelligence Organization
Identifier
https://proceedings.kr.org/2022/55/
Source
19th International Conference on Principles of Knowledge Representation and Reasoning (KR 2022)
Publication Status
Published
Start Date
2022-07-31
Finish Date
2022-08-05
Coverage Spatial
Haifa, Israel
Date Publish Online
2022-07-31