Psychology
Cognitive Biases in Expert Forecasting of Election Outcomes
Quick fact
Studies show that expert political forecasters are often only slightly more accurate than random guessing, yet they express high confidence in their predictions; for instance, in the 2016 U.S. presidential election, most experts gave Hillary Clinton a 70-99% chance of winning, and they were wrong.
Why this is interesting
You might assume that top political forecasters, armed with data and models, would predict elections accurately. Yet they are often spectacularly wrong—how can that be?
Read the full explanation
Understanding Cognitive Biases in Expert Forecasting of Election Outcomes
Imagine a forecaster as a navigator on a ship. They have a map (data and models), but their perception of where the ship is headed is warped by mental shortcuts known as cognitive biases. These biases are like distorted lenses on the navigator's binoculars—they make some landmarks appear closer or more important than they really are. In election forecasting, an expert might anchor on the first polls they see, giving them undue weight, even when later data suggests a shift. Or they might fall in love with a favored candidate (wishful thinking) and interpret ambiguous evidence as supporting their hope. This isn't about dishonesty; it's about how our brains naturally process information to reduce complexity, but in doing so, they introduce systematic errors.
A deeper explanation
Cognitive biases are systematic patterns of deviation from rationality in judgment. Experts are not immune; in fact, their expertise can amplify certain biases. Overconfidence arises because experts have a deep knowledge base that makes them blind to their own fallibility. They rely on mental models that have worked in the past, so they stop questioning. Confirmation bias leads them to seek out evidence that supports their preferred outcome and dismiss contradictory data. When they must make a prediction, they anchor on initial information, such as early polls or past election results, and adjust insufficiently. Additionally, social dynamics like groupthink within forecasting teams reinforce these biases, as individuals conform to the group's consensus to avoid being the outlier. These biases matter because forecasts shape public discourse, investor decisions, and even policy planning. Recognizing them is a first step to counteracting them through structured forecasting methods like prediction markets or Bayesian updating.