Herding vs Independent Forecasting: When Consensus Hurts
What herding means
Herding is when you move your forecast toward a visible market consensus or crowd estimate mainly because it is there, not because you gained new evidence.
In prediction markets and forecasting tournaments, herding is common because:
• consensus is always visible
• it feels safer to be wrong with the crowd
• it reduces embarrassment from being an outlier
Why herding is not always bad
Consensus can be a strong baseline. If you have no information edge, moving toward consensus can improve your accuracy.
In scorecard terms, herding often improves raw Brier score compared to wild independent guesses, especially for beginners.
When herding helps
Case 1: you have no signal
If you do not have time, data, or expertise, the crowd is often better than you.
In that case, herding is a rational shortcut.
Case 2: the market is liquid and well informed
In liquid markets with diverse participants, consensus can compress a lot of information quickly.
Using it as a prior can be more accurate than starting from your own story.
Case 3: you are learning calibration basics
If your main goal is learning how probabilities behave, anchoring on consensus can reduce the variance of your mistakes while you build discipline.
When herding hurts
Problem 1: you stop learning
If you always copy the market, you do not generate independent hypotheses. Your forecasting journal becomes empty and you do not discover where your model is wrong.
Problem 2: you hide miscalibration
Herding can mask overconfidence or underconfidence because you are outsourcing probability levels to the crowd.
You can end up with “okay” Brier score but still have weak personal calibration.
Problem 3: you become late and reactive
Many people herd by waiting for price moves, then updating.
If your scoring rewards late updates, this becomes a strategy. That is why evaluation checkpoints matter.
Problem 4: you herd into thin market noise
In thin markets, the displayed price can be noisy or manipulable. Herding into a low quality consensus can be worse than sticking to a base rate.
See Liquidity and Thin Markets.
A practical approach: consensus as prior, not as answer
A clean workflow:
• start from a prior (base rate or consensus)
• write what new evidence you have
• move away from the prior only if you can justify the update
This keeps you anchored while still being independent.
How to detect herding in your own data
Simple diagnostics:
• your forecasts cluster tightly around market price
• you rarely disagree by more than a few points
• your updates lag price moves instead of leading them
• your journal evidence entries are thin
Herding is not automatically bad, but you should know when you are doing it.
Designing scorecards that reduce harmful herding
If you want a platform that measures skill, not copying, use:
• fixed checkpoints (reduce late updates)
• show consensus only after entry deadline in some modes
• require brief reasoning notes in training mode
• track coverage and horizon splits
Takeaway
Herding can improve accuracy when you have no edge, but it can also block learning, hide miscalibration, and reward waiting. Use consensus as a prior, not as a replacement for thinking, and design checkpoints so copying late does not win.