Forecasting Argumentation Frameworks
This addresses forecasting challenges for domains like politics or economics, but it is incremental as it builds on existing argumentation and forecasting research.
The authors tackled the problem of improving forecasting accuracy by introducing Forecasting Argumentation Frameworks (FAFs), a method that allows agents to argue over time about outcome probabilities while flagging irrational behavior, and empirical evaluation showed potential for increased accuracy.
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 argumentation 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.