BibTex format
@inproceedings{Irwin:2022,
author = {Irwin, B and Rago, A and Toni, F},
pages = {1636--1638},
publisher = {ACM},
title = {Argumentative forecasting},
url = {http://hdl.handle.net/10044/1/95443},
year = {2022}
}
Citation
RIS format (EndNote, RefMan)
TY - CPAPER
AB - We introduce the Forecasting Argumentation Framework (FAF), anovel argumentation framework for forecasting informed by re-cent judgmental forecasting research. FAFs comprise update frame-works which empower (human or artificial) agents to argue overtime with and about probability of scenarios, whilst flagging per-ceived irrationality in their behaviour with a view to improvingtheir forecasting accuracy. FAFs include three argument types withfuture forecasts and aggregate the strength of these arguments toinform estimates of the likelihood of scenarios. We describe animplementation of FAFs for supporting forecasting agents.
AU - Irwin,B
AU - Rago,A
AU - Toni,F
EP - 1638
PB - ACM
PY - 2022///
SP - 1636
TI - Argumentative forecasting
UR - http://hdl.handle.net/10044/1/95443
ER -
Contact us
Artificial Intelligence Network
South Kensington Campus
Imperial College London
SW7 2AZ
To reach the elected speaker of the network, Dr Rossella Arcucci, please contact:
To reach the network manager, Diana O'Malley - including to join the network - please contact: