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Recency Bias

Overweighting recent events over historical patterns

Memory

What is it?

Recency bias is the tendency to give recent events or information more weight than earlier data when forming judgments. It arises because recent memories tend to be more vivid and easier to recall. In performance evaluations, it leads managers to overweight the last few weeks and overlook months of earlier work. In investing, recent market performance can dominate expectations, encouraging people to buy high after rallies and sell low after crashes. The bias affects hiring (emphasizing the most recent interview impressions), relationships (letting a recent conflict overshadow years of positive history), and strategic planning (extrapolating recent trends indefinitely). It is particularly costly in environments with high variability or mean reversion, where recent performance is a poor predictor of future results, and it makes it harder to learn from historical patterns. Counteracting it requires collecting data systematically over longer periods, using evaluation frameworks that force consideration of the whole period, and deliberately asking "is this recent trend representative of the longer pattern?"

Example

Judging an employee mostly on their last month. Expecting stock trends to continue after a recent rally or crash. Forgetting years of good service after one bad experience.

References

Murdock, B. B., Jr. (1962). The Serial Position Effect of Free Recall. Journal of Experimental Psychology, 64(5), 482-488.

Glanzer, M., & Cunitz, A. R. (1966). Two Storage Mechanisms in Free Recall. Journal of Verbal Learning and Verbal Behavior, 5(4), 351-360.

How to Prevent It

Doxa uses AI and can make mistakes. How it's built

Question

What does the full historical record show?

Question

Am I overweighting recent events?

Question

How typical is this recent period compared to longer trends?

Question

What was happening 6 months or a year ago that I've forgotten?

Question

Would my assessment change if I reviewed older data first?

Technique

Look at data over longer time periods systematically.

Technique

Use structured performance reviews covering the full period.

Technique

Take notes as things happen, so you can refer to them instead of memory.

Technique

Decide in advance how much weight the recent period deserves, and why.

Technique

Review historical data before looking at recent performance.