Recency bias is the tendency to give the latest events more weight than they deserve and to assume the near future will look like the recent past. It is related to the recency effect in memory research, where the last items in a list are recalled best, documented in Bennet Murdock‘s 1962 study of the serial position effect.
Recent events are easy to recall, so they feel more common and more important.
02
Extrapolation
People draw a line through the last few data points and project it forward.
03
Neglect of base rates
The long record, which usually says more, is pushed aside by the latest headline.
Where you see it today
Markets and AI stocks
Survey measures of investors’ expected stock returns rise and fall with past returns, yet they move against the returns that standard models predict (Greenwood and Shleifer, 2014). AI stocks invite the same pattern: a strong quarter reads as a permanent trend, a weak one as a bubble bursting.
Inside the models
Language models have a positional bias of their own. Research on long contexts (Liu et al., 2023) found they use information at the beginning and end of a long prompt better than information in the middle. Where you place a key fact can change the answer.
AI as a judge
Studies of language models used to grade answers (Zheng et al., 2023) found position bias: the order in which two answers are shown can sway the verdict. If AI ranks options for you, shuffle the order and compare.
Performance reviews
Managers tend to remember the last few weeks best, so a strong or weak finish can outweigh a year of work. AI tools that summarise a full year of notes can help, if the notes were kept all year.
Recency bias vs related biases
Recency bias
What it doesOverweights the latest events
ExampleSelling after one bad quarter
Recency effect
What it doesLast items in a list are recalled best
Before deciding, pull the longest data series you have, not just the last quarter.
Write down what you would have decided a month ago, and ask what has really changed.
Keep a decision journal so you can compare predictions against outcomes over time.
Put critical facts at the start or end of long AI prompts, and test whether moving them changes the answer.
When AI ranks or compares options, run it again with the order shuffled.
The Business Engineer lens
AI news moves weekly, but capex, adoption and business-model change play out over years. The Business Engineer reads each launch or sell-off against the longer cycle, which is the only way to tell a real turn from noise.
The tendency to give recent events more weight than they deserve and to expect the near future to look like the recent past.
What is the difference between recency bias and the recency effect?
The recency effect is a memory finding: the last items in a list are recalled best. Recency bias is the broader judgement error of overweighting recent events.
Do AI models have recency bias?
They show positional biases: models often use information at the start and end of long prompts better than in the middle, and LLM judges can be swayed by answer order.
How do investors fall for recency bias?
Studies of survey data find investors' return expectations follow past returns and the level of the market, while moving against model-based expected returns.
The complete explainer, cleaned up and reformatted for mobile.
Recency bias is a cognitive bias that affects how individuals process and evaluate information. It occurs when people rely heavily on the most recent information or experiences they have encountered, believing that these recent events are more relevant or significant than older ones. Essentially, recency bias leads individuals to prioritize the present or immediate past when making decisions, often overlooking historical or long-term data.
The Causes of Recency Bias
Recency bias can be attributed to several psychological and cognitive factors:
1. Salience of Recent Events
Recent events tend to be more salient and vivid in our memory compared to older ones. This heightened salience makes them more accessible when we need to recall information or make judgments.
The availability heuristic is a mental shortcut where people assess the likelihood of an event based on its ease of retrieval from memory. Because recent events are more readily accessible, individuals tend to overestimate their significance and relevance.
3. Human Memory Limitations
Human memory has limited capacity, and as a result, individuals may not have easy access to historical data or events. This limitation pushes people to rely on readily available recent information.
4. Emotional Impact
Recent events often carry a stronger emotional impact than older ones. Emotionally charged experiences are more likely to be remembered and influence decision-making.
Effects of Recency Bias
Recency bias can have a significant impact on various aspects of decision-making and judgment:
1. Investment Decisions
In the world of finance, recency bias can lead investors to make decisions based on recent market trends rather than considering long-term historical data. This can result in buying high and selling low, leading to financial losses.
2. Hiring and Promotion
Recency bias can influence hiring managers and HR professionals when evaluating candidates. A recent accomplishment or event in a candidate’s career may overshadow their overall qualifications and experience.
3. Performance Evaluation
In the workplace, recency bias can affect performance evaluations. Managers may place undue emphasis on recent performance, overlooking an employee’s consistent long-term contributions.
4. Marketing and Consumer Behavior
Marketers often leverage recency bias by emphasizing the latest features, trends, or testimonials in their advertising. Consumers may be more inclined to purchase products or services that appear to be in the spotlight.
5. Political Decision-Making
In politics, recency bias can shape public opinion. Recent events, speeches, or news stories can disproportionately influence voters’ decisions, potentially swaying elections.
6. Sports and Entertainment
Recency bias can also affect sports and entertainment awards, such as MVP selections or film awards. Recent standout performances may overshadow a performer’s body of work.
Mitigating Recency Bias
Recognizing and mitigating recency bias is essential for making more rational and informed decisions:
1. Consider Historical Data
Actively seek out and consider historical data or information when making decisions. This can provide a broader perspective and help balance the influence of recent events.
2. Maintain a Decision Journal
Keeping a decision journal can help individuals record their thought processes and rationales when making choices. This practice encourages reflection and can reveal the presence of recency bias.
3. Seek Diverse Perspectives
Engage with others who have different viewpoints or experiences. This can help counter the influence of recency bias by introducing alternative perspectives and information.
4. Implement Decision-Making Processes
Establish structured decision-making processes that involve evaluating both recent and historical data. Encourage the use of systematic approaches that consider a range of factors.
5. Slow Down Decisions
Avoid making snap judgments or decisions based solely on recent information. Taking the time to reflect and gather additional data can reduce the impact of recency bias.
6. Conduct Periodic Reviews
Regularly review and assess decisions made in the past. Analyze whether recency bias played a role in those decisions and learn from them.
Real-World Examples of Recency Bias
Recency bias can be observed in various real-world scenarios:
1. Stock Market
Investors often react to recent market fluctuations, leading to periods of irrational exuberance and panic selling. This behavior can result in market bubbles and crashes.
2. Sports Betting
In sports betting, recency bias can lead individuals to place bets based on a team’s recent performance, ignoring historical data or factors that may influence the outcome.
3. Political Campaigns
During political campaigns, candidates highlight recent accomplishments or events to sway voters. This can influence public perception and voting decisions.
4. Product Releases
Tech companies frequently promote the latest features and innovations in their products, capitalizing on consumers’ recency bias to drive sales.
5. Hiring Decisions
Recency bias can affect hiring decisions when interviewers focus on a candidate’s most recent job or achievement, overlooking their overall qualifications.
Overcoming Recency Bias
Overcoming recency bias is an ongoing process that requires self-awareness and deliberate effort. By recognizing the influence of recent events on decision-making and implementing strategies to mitigate its effects, individuals and organizations can make more rational and well-informed choices.
Key Highlights
Definition: Recency bias is the tendency to prioritize recent events or experiences over older ones when making decisions.
Causes: It stems from factors like the salience of recent events, the availability heuristic, human memory limitations, and the emotional impact of recent experiences.
Effects: Recency bias can impact investment decisions, hiring and promotion processes, performance evaluations, consumer behavior, political decision-making, and awards selection in sports and entertainment.
Mitigating Strategies: Strategies to mitigate recency bias include considering historical data, maintaining decision journals, seeking diverse perspectives, implementing structured decision-making processes, slowing down decisions, and conducting periodic reviews.
Real-World Examples: Recency bias can be observed in various domains such as the stock market, sports betting, political campaigns, product releases, and hiring decisions.
Overcoming Recency Bias: Overcoming recency bias requires self-awareness and deliberate effort to recognize its influence and implement strategies to mitigate its effects.
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Gennaro Cuofano is a CRO and tech executive who has worked in AI since late 2015, starting with bringing NLP, natural-language generation, voice and chatbot products to the marketing industry. His background is in law and finance: he holds a Master's degree in Law and an International MBA with an emphasis on corporate finance (LUISS Business School and the University of San Diego, 2012), and worked as a financial analyst at a real-estate investment firm in San Diego and as an assistant controller. His work focuses on business model strategy, business engineering and, more broadly, structural analysis: how companies make money, read from their own filings. He created FourWeekMBA and leads research there and at The Business Engineer, his newsletter on AI and business strategy, with over 95,000 subscribers and more than 1,000 published analyses. His writing has also appeared on Entrepreneur (for example, How to Digitize a Family Business in Europe) and HackerNoon. Find him on LinkedIn and Substack. See how we source.
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