Forecasting Argumentation Frameworks

TL;DR

Forecasting Argumentation Frameworks (FAFs) introduce a new argumentation-based method that enables agents to collaboratively forecast outcomes over time, identify irrationality, and improve accuracy through structured argument types and semantics.

Contribution

FAFs present a novel argumentation framework with five argument types and adapted semantics, integrating rationality checks and aggregation for improved forecasting accuracy.

Findings

Empirical evaluation shows FAFs can enhance forecasting accuracy.

FAFs effectively flag irrational behaviour in forecasting agents.

The framework supports dynamic, multi-agent argumentation for complex outcome prediction.

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 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.