ArguCast: A System for Online Multi-Forecasting with Gradual Argumentation

Judgmental forecasting is a form of forecasting which employs (human) users to make predictions about specified 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 field which excels in the representation and resolution of conflicting 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 filtering out of irrational opinions before obtaining group forecasting predictions by means commonly used in judgmental forecasting.