What is a mental model?

A mental model is a simplified framework for understanding a situation, explaining how something works, or making a decision.

How should I use a mental models glossary?

Find a model relevant to your current problem, apply its central question, and check the result against evidence and other perspectives.

The Ultimate Mental Models Glossary: 100 Thinking Tools Explained Simply

A mental model is a simplified framework for understanding how something works or deciding what to do. This mental models glossary explains 100 thinking tools, from first principles to bounded rationality, with a practical use for each.

The collection includes reasoning methods, economic concepts, learning techniques, and cognitive biases. Biases describe predictable errors; understanding them helps you check your judgment. These entries are a working reference, rather than a claim that every situation fits one formula.

How to use this mental models glossary

Start with a decision you actually face. Find a relevant entry, turn it into a question, and write down an answer. Then use a second model to check what the first leaves out.

For example, suppose your team wants a new task-management tool. Opportunity cost asks what else you could do with the setup time. Bottlenecks asks whether the current tool causes the delays. Reversibility suggests a small pilot before moving every project. Three questions can produce a better decision than a longer list of features.

For a fuller introduction, read what a mental model is. For a repeatable routine, see how to use mental models in daily life.

Reasoning and problem solving

First principles. Break a problem into basic truths, then rebuild. Use it when inherited assumptions make a solution seem impossible.
Inversion. Work backward from failure. Before launching a project, identify what would ruin it and remove those conditions.
Second-order thinking. Consider what happens after the immediate result. Ask whether today's improvement creates tomorrow's recurring problem.
Map and territory. Descriptions simplify reality and leave things out. Check a dashboard against actual customer experiences before acting.
Circle of competence. Separate what you understand from what you only recognize. Seek help when a decision exceeds your knowledge.
Occam's razor. Prefer explanations that require fewer unsupported assumptions. Start with a simple diagnosis, then test whether it fits.
Hanlon's razor. Consider misunderstanding or error before assuming malice. Clarify an ambiguous message before responding with accusations.
Thought experiments. Explore a hypothetical situation to expose assumptions. Ask how your plan would change if your budget were halved.
Analogical reasoning. Transfer a useful pattern between similar situations. Check that the underlying mechanism matches before copying another industry's approach.
Abstraction. Remove details to reveal a reusable structure. Describe a scheduling problem as limited resources competing for time.

Uncertainty and evidence

Probabilistic thinking. Treat uncertain outcomes as possibilities with different likelihoods. Replace absolute predictions with ranges and update them as evidence arrives.
Bayesian updating. Revise a belief when new evidence appears, considering prior expectations. Ask how surprising that evidence would be under competing explanations.
Base rates. Start with how often an outcome occurs in comparable cases. Consult typical project completion times before trusting your own estimate.
Expected value. Weight possible outcomes by their probabilities. Compare repeated opportunities while also considering whether you can survive a bad outcome.
Margin of safety. Leave room for errors in your estimates. Build extra time into a deadline when delays would be costly.
Sensitivity analysis. Change one assumption and observe how the conclusion shifts. Identify whether your plan depends on an unusually optimistic forecast.
Calibration. Check whether your confidence matches your accuracy over time. Record predictions and compare your stated probabilities with eventual results.
Sample size. Small samples produce unstable estimates. Avoid rewriting a strategy because two customers happened to express the same preference.
Selection bias. The people or events observed may differ from those missing. Ask who did not answer your survey and why.
Survivorship bias. Visible successes hide failures that disappeared. Study unsuccessful attempts before copying the habits of a celebrated winner.

Biases and judgment

Confirmation bias. People favor evidence supporting existing beliefs. Search deliberately for observations that would show your preferred explanation is wrong.
Anchoring. An initial number can shape later judgments. Make an independent estimate before seeing a seller's opening offer.
Availability heuristic. Easily recalled events can seem more common than they are. Compare vivid anecdotes with broader evidence before estimating risk.
Loss aversion. Losses can feel more significant than equivalent gains. Define acceptable tradeoffs before emotional reactions dominate a choice.
Sunk cost fallacy. Past, unrecoverable spending can distort future decisions. Evaluate whether continuing makes sense from today onward.
Hindsight bias. Known outcomes make earlier uncertainty seem smaller. Review what information was actually available when the decision was made.
Outcome bias. A result can overshadow the quality of the process. Assess whether a decision was sensible given its original evidence.
Overconfidence. Certainty can exceed actual knowledge. State what you might be missing before making a difficult prediction.
Fundamental attribution error. People can overemphasize personality and underweight circumstances when explaining others' behavior. Ask what constraints shaped the action.
Framing effects. Equivalent information can produce different reactions depending on presentation. Restate a choice in terms of both gains and losses.

Systems and feedback

Feedback loops. A system's outputs influence its future behavior. Identify whether a reaction amplifies a change or pushes it back toward balance.
Stocks and flows. Stocks accumulate; flows add to or subtract from them. Distinguish your unfinished work from the rate new tasks arrive.
Bottlenecks. One constrained stage can limit the entire process. Improve the slowest necessary step before speeding up everything around it.
Leverage points. Some interventions change a system more than others. Look for rules or incentives causing repeated problems before treating individual symptoms.
Nonlinearity. Inputs and outcomes do not always change proportionally. Avoid assuming that doubling effort will necessarily double useful output.
Emergence. Interactions can create patterns absent from individual parts. Study team routines as well as the abilities of each member.
Path dependence. Earlier choices shape the options available later. Consider switching costs before adopting a tool that will organize your workflow.
Delays. Effects may appear long after their causes. Wait for a process to respond before making another large adjustment.
Unintended consequences. An intervention can change behavior in unexpected ways. Ask how people might adapt to a new rule or reward.
Local versus global optimization. Improving one part can worsen the whole. Evaluate a team's performance by shared outcomes as well as individual speed.

Tradeoffs and resources

Opportunity cost. Choosing one option gives up the best available alternative. Compare a meeting's value with the work it displaces.
Marginal thinking. Evaluate the next unit of effort or spending. Ask whether another hour adds enough value to justify its cost.
Diminishing returns. Additional inputs can yield progressively smaller benefits. Stop polishing when the next improvement matters less than another unfinished task.
Comparative advantage. People can specialize where their relative tradeoff is most favorable. Divide work by what each person gives up to do it.
Incentives. Rewards and penalties shape behavior. Check whether a performance metric encourages the behavior your organization actually wants.
Externalities. An action can impose costs or benefits on outsiders. Include the burden your process places on another team.
Transaction costs. Searching, negotiating, and coordinating consume resources. Count administrative overhead when comparing an internal solution with buying a service.
Economies of scale. Larger output can spread fixed costs across more units. Check whether demand justifies the added capacity and complexity.
Supply and demand. Availability and willingness to buy jointly influence prices. Ask whether a shortage is temporary before redesigning a plan around it.
Scarcity. Limited resources force prioritization. Name the real constraint, whether it is money, attention, space, or time.

Strategy and cooperation

Game theory. Outcomes depend on how others respond to your choices. Consider their incentives before assuming they will follow your preferred plan.
Prisoner's dilemma. Individual incentives can undermine mutually beneficial cooperation. Create conditions where keeping agreements makes sense for everyone involved.
Repeated games. Future interactions change the value of today's behavior. Protect trust when you expect to work with someone again.
Positive-sum thinking. Some arrangements create more total value for participants. Look for compatible needs before arguing over how to divide existing resources.
Zero-sum thinking. In some contests, one participant's gain matches another's loss. Identify the fixed prize before treating every relationship as competition.
Credible commitments. Promises carry more weight when backed by observable constraints. Use clear milestones and accountability to support an agreement.
Signaling. Actions can communicate information that words alone cannot verify. Look for relevant evidence of capability rather than impressive claims.
Information asymmetry. Participants may hold different information. Ask what the other party knows that could change your assessment of an offer.
Principal-agent problem. Someone acting for you may have different incentives. Align responsibilities and rewards before relying on delegation.
Coordination problems. People may want compatible outcomes but struggle to align actions. Establish a shared schedule or convention everyone can follow.

Learning and improvement

Compounding. Gains can build on earlier gains over repeated cycles. Maintain practices whose benefits improve your ability to make further progress.
Deliberate practice. Focused work on a specific weakness can improve performance. Choose a difficult subskill and seek useful feedback on repeated attempts.
Learning loops. Action, feedback, and revision turn experience into improvement. Review what happened and change your approach before the next attempt.
Feynman technique. Explaining a concept simply can reveal gaps in understanding. Rewrite confusing parts after discovering what you cannot explain clearly.
Spaced repetition. Reviewing information over separated intervals supports retention. Revisit important material instead of relying on one long study session.
Retrieval practice. Trying to recall information exercises memory. Answer a question from memory before looking at your notes.
Transfer of learning. A skill learned in one setting may help elsewhere. Test whether the new situation requires the same underlying capability.
Explore and exploit. Balance discovering new options with using known effective ones. Reserve some effort for experiments while maintaining reliable routines.
Plateaus. Visible progress can stall while constraints remain unresolved. Diagnose the limiting factor before assuming more repetition will fix it.
Error budgets. Define an acceptable amount of failure for a process. Use that limit to balance experimentation with reliability.

Resilience and risk

Redundancy. Backup capacity can protect against a single failure. Keep an alternative for a tool essential to your work.
Diversification. Different exposures can reduce dependence on one outcome. Check whether supposedly separate risks would fail for the same reason.
Optionality. Some choices preserve useful future paths. Prefer a reversible trial when uncertainty is high and learning is valuable.
Reversibility. Decisions differ in how easily they can be undone. Spend more preparation on choices with lasting consequences.
Risk of ruin. A catastrophic loss can prevent future participation. Avoid stakes that would make recovery impossible even if average returns look attractive.
Robustness. A robust plan works across several plausible conditions. Test your schedule against ordinary disruptions rather than one ideal day.
Antifragility. Some systems benefit from certain stresses or variation. Design small experiments that teach you without exposing the whole project to collapse.
Precaution. Potential severe harm can justify extra care under uncertainty. Examine plausible consequences and alternatives before committing to an irreversible action.
Failure modes. A system can break in different ways. List likely failure paths and choose safeguards matched to each one.
Stress testing. Simulate difficult conditions to expose weaknesses. Ask what fails first if demand doubles or a key contributor becomes unavailable.

Measurement and causality

Correlation and causation. Two things moving together does not establish that one causes the other. Consider shared causes and reverse causation.
Confounding. A third factor can distort an apparent relationship. Ask whether different starting conditions explain the results you are comparing.
Counterfactual thinking. Compare what happened with a plausible alternative. Ask what would likely have occurred without your intervention.
Regression to the mean. Unusually extreme observations often become less extreme on repetition. Avoid crediting every rebound to your latest intervention.
Goodhart's law. A measure can lose usefulness when people optimize it as a target. Check whether a metric still reflects the underlying goal.
Proxy measures. An observable indicator stands in for something harder to measure. Verify that clicks still represent the engagement you care about.
Leading and lagging indicators. Some measures track inputs; others track later outcomes. Pair regular practice with evidence that performance actually improves.
Falsifiability. A testable claim allows evidence that could contradict it. Specify what observation would make you revise your explanation.
Causal chains. Outcomes arise through connected intermediate steps. Trace how an action is supposed to produce its promised benefit.
Controlled experiments. Comparing conditions while limiting other differences helps test causes. Define the outcome before observing which approach performs better.

Attention and everyday decisions

Pareto principle. A minority of inputs may account for much of an outcome. Find the high-impact contributors without assuming an exact ratio.
Parkinson's law. Work can expand to fill available time. Set a reasonable time limit and define what finished looks like.
Decision fatigue. Repeated decisions can make sustained choosing harder. Reduce routine choices and schedule demanding judgments when you can concentrate.
Default effects. People often stay with a preselected option. Review defaults before accepting them, especially when their consequences accumulate.
Status quo bias. Familiar arrangements can seem preferable simply because they already exist. Compare staying and changing using the same criteria.
Commitment devices. An advance arrangement can support a future intention. Schedule a work session with someone who expects you to attend.
Implementation intentions. A specific cue-action plan makes an intention easier to execute. Decide when, where, and how you will begin.
Mental accounting. People categorize resources and treat equivalent units differently. Evaluate spending against your overall priorities rather than an arbitrary label.
Reciprocity. People often respond to help with help. Build relationships through useful contributions without treating kindness as a guaranteed transaction.
Bounded rationality. Time, information, and attention limit analysis. Choose a satisfactory option when further searching costs more than its likely benefit.

Put one model to work today

Choose one decision and write three lines: what you believe, which model you are using, and what evidence would change your mind. If you are considering another weekly meeting, use opportunity cost to identify the displaced work, then incentives to ask whether attendance rewards appearances more than useful contributions.

Avoid collecting model names without changing your behavior. A definition is a starting point; a useful model helps you notice something, compare options, or test an assumption. Different models can disagree because they emphasize different parts of a situation. Resolve that tension with context and evidence.

If you want to study these thinking tools more deeply, 100 Mental Models offers a practical guide to applying mental models in everyday decisions.