Coaching practices for Case Based Reasoning
Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For Case Based Reasoning, these are the strongest matches in the current practice library.
Does this sound like the set of challenges you might be facing?
- I keep facing the same shape of decision over and over and starting from scratch every time, and I know I’ve lived through situations just like it that turned out a certain way
- I’m about to lock in a judgment and I’m only seeing the evidence that backs it
- If I’m honest, I knew where I was going to land before I’d looked at anything
- I’m staring at a case where following the rule to the letter would clearly produce the wrong result
- One compelling story or one impressive candidate completely swings my whole read, and afterward I can’t tell whether real new facts moved me or I just got swept up in how vivid and memorable that one case was.
Practices that may help
- Build a library of cases and reason from them
Accumulate a diverse set of cases with known outcomes, and retrieve structurally similar ones when facing a new problem.
Analogical Reasoning - Consider the opposite before finalizing a judgment
Deliberately generate the case for the conclusion you are not reaching.
Motivated Reasoning - Catch yourself working backward from a conclusion
Notice when the conclusion appeared before the evidence — and treat that as a red flag.
Motivated Reasoning - Practice equity: bend general rules to particular cases
Apply rules with awareness that no rule perfectly fits every situation; the wise person corrects for the fit.
Phronesis: Aristotle’s Practical Wisdom - State your estimate before encountering vivid case information
Lock in a prior probability estimate before reading a compelling story or meeting a specific candidate.
Attribute Substitution: When Your Brain Answers a Different Question - Accumulate varied situational episodes to move from novice to competent
Build a library of real cases — not just more rules — to develop situational recognition.
The Dreyfus Model: Five Stages from Novice to Expert - Update beliefs by degrees, not wholesale
Treat new information as evidence that shifts probabilities, not as proof that changes everything.
Base-Rate Neglect: Why We Ignore the Odds - Distinguish directional from accuracy goals before you start
Before reasoning about something, ask whether you are trying to reach a specific answer or the correct one.
Motivated Reasoning - Examine the evidence for and against
Systematically list what supports your interpretation and what doesn’t — like a fair-minded lawyer, not a prosecutor.
Check the Facts, Made Practical - Name the prototype you’re comparing against before deciding
Make the prototype you’re using as a reference explicit — hidden templates bias decisions without scrutiny.
The Representativeness Heuristic — Judging by Resemblance
Related concerns
- Backward Reasoning
Notice when the conclusion appeared before the evidence — and treat that as a red flag.
Catch yourself working backward from a conclusion
- Extreme Case Reasoning
In debates and narratives, an extreme position is often introduced to make a moderate position seem reasonable — identify this before updating.
Recognize decoys in political and narrative framing
- Reasoning Bias Prevention
Motivated reasoning, studied extensively by Ziva Kunda and others, is the tendency to reason toward a conclusion you want to reach rather than wherever the evidence leads. It is not conscious deception — the brain generates genuinely felt arguments for the preferred conclusion. Detection and structured debiasing techniques reduce but do not eliminate it; awareness alone is insufficient.
- When Argument Mapping Add Objections Formally
For each supporting reason, find the best counter and put it on the map.
Add the strongest objection to every major supporting reason
- Authority Bias Reasoning
Deliberately generate the case for the conclusion you are not reaching.
Consider the opposite before finalizing a judgment
- Case Based Learning Decisions
Accumulate a diverse set of cases with known outcomes, and retrieve structurally similar ones when facing a new problem.
Build a library of cases and reason from them
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