Comparing our predictions to the actual votes for the Toronto mayoral election

We value constructive feedback and continuous improvement, so we've taken a careful look at how our predictions held up for the recent mayoral election in Toronto.

The full analysis is here. The summary is that our estimates weren't too bad on average: the distribution of errors is centered on zero (i.e., not biased) with a small standard error. But, on-average estimates are not sufficient for the types of prediction we would like to make. At a ward-level, we find that we generally overestimated support for Tory, especially in areas where Ford received significant votes.

We understood that our simple agent-based approach wouldn't be enough. Now we're particularly motivated to gather up much more data to enrich our agents' behaviour and make better predictions.