Evidence guide: Forecasting and estimation

The decision question

How can I make forecasts and estimates without letting one vivid case or target number dominate?

Reviewed lenses

Hot–Cold Empathy Gap – when one state is a poor guide to choices in another — The hot–cold empathy gap describes difficulty predicting preferences, behavior, or experience across different visceral or affective states. In a relatively cold state, people can underappreciate how pain, hunger, sexual arousal, craving, fear, anger, and other hot states will change motivation and choice; in a hot state, they can also overproject the current state into the future. The pattern can be intrapersonal or interpersonal and should not be reduced to a generic claim that emotion always causes bad decisions.
Planning Fallacy – when plans make completion times look too optimistic — The planning fallacy is a well-documented tendency for people to predict their own task completion times too optimistically. The effect has been observed across different kinds of tasks, but it is not a rule that every plan will run late. Project overruns can also come from changing scope, dependencies, incentives, poor data, deliberate underestimation, or genuinely unusual events, so a late project should not automatically be diagnosed as a planning fallacy.
Subadditivity Effect – the whole seems less likely than the sum of its parts — People often give a larger total probability when an event is unpacked into separate possibilities than when the same event is judged as one packed category. The effect is not universal: how the possibilities are described and how typical they are can change or even reverse an unpacking effect.
Anchoring Effect – when a starting number pulls later estimates toward it — Anchoring is a well-supported effect in which an initial numerical value can pull a later estimate toward it. A 2026 meta-analysis covering 2,601 effect sizes found a large overall effect, but also substantial variation across studies. The effect should not be treated as a rule that every number changes every judgment: incidental anchors, anchors from a different dimension, clearly random values, incentives, and some debiasing conditions were associated with smaller or null effects.
Impact Bias – when you overestimate how long or intense your emotions will be — People often overestimate how intense or long their emotional reactions to future events will be, especially when the focal event crowds out everything else that will also shape daily experience. The literature is broader than a rule that people always overpredict emotion: forecasting errors vary by event, time horizon, emotion, and what exactly is being predicted.
Loss Aversion – when a loss can carry more weight than an equivalent gain — Loss aversion is a central component of prospect theory and many studies estimate losses as receiving greater subjective weight than gains around a reference point. However, the effect should not be summarized with one universal coefficient. A 2024 interdisciplinary meta-analysis of 607 estimates reported a mean coefficient near 1.96, while another 2024 meta-analysis of individual risky-choice datasets estimated about 1.31. A 2025 re-analysis of the larger dataset found little evidence of loss aversion in some symmetric, unordered designs, showing that task structure and analysis can materially change the result.
Projection Bias – when you assume your future self will want what you want now — Projection bias describes a tendency to overproject current tastes or visceral states onto future preferences. People often understand that tastes will change but underestimate how much they will change. The construct is most directly supported in intertemporal choice and consumer settings; it should not be stretched into a generic explanation for every bad prediction about one's future self.
Availability Heuristic – when easy-to-recall examples shape frequency judgments — Availability is a judgment heuristic: people can use how easily examples or scenarios come to mind when estimating frequency or probability. That shortcut is not automatically an error because memorable or accessible examples can correlate with real frequency. Bias appears when accessibility is driven by factors that are not diagnostic of the quantity being judged.
Conservatism Bias – when new evidence doesn't change old beliefs — Here, conservatism means under-updating: new evidence changes a probability judgment less than a Bayesian benchmark would predict. It should not be confused with the separate memory phenomenon in this corpus that pulls remembered extreme values toward the middle.
Curse of Knowledge – when what you know distorts what you expect others to know — Knowing more can make it harder to estimate what a less-informed person knows or understands, but the size and mechanism of the effect depend on the task. The useful claim is not that experts are unable to teach beginners; it is that one's own knowledge can contaminate judgments about another person's knowledge unless the perspective gap is made explicit.

Decision guides

How to use this page

Start with the decision, not with a label for a person. Open the evidence behind the lenses that fit, compare nearby concepts when needed, and keep uncertainty visible.