Coaching practices for Rule Stability Risk
Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For Rule Stability Risk, these are the strongest matches in the current practice library.
Does this sound like the set of challenges you might be facing?
- I’m about to bet a big plan on the way things have always worked, and a quiet voice is asking whether the ground rules here could just shift out from under me
- I’ve got a contingency plan for every risk I could name, and yet every time it’s the thing I never listed that actually knocks me over
- My worst-case is basically just the worst thing that’s ever happened before, and I keep assuming nothing can be worse than that
- Something in my life keeps escalating and running away
- My plan feels solid until I notice it quietly depends on a whole stack of things just going my way
Practices that may help
- Check whether the rules of your domain are actually stable
Before applying any probability model, ask whether the rules governing outcomes could change mid-game.
The Ludic Fallacy: When You Mistake Real Life for a Game - Build plans with slack for outcomes outside your model
Reserve capacity for events that are not in your risk model — because the most damaging events usually aren’t.
The Ludic Fallacy: When You Mistake Real Life for a Game - Stress test plans against outcomes beyond the historical range
Ask how your plan holds up if the worst outcome is twice as bad as any historically observed case.
The Ludic Fallacy: When You Mistake Real Life for a Game - Strengthen corrective loops and weaken runaway ones
Find the balancing loop that should be correcting the problem — and ask why it is too weak.
Leverage Points - Name the assumptions that must hold for the plan to work
Every plan rests on assumptions — list them and ask how likely each one is.
Margin of Safety - Know when a bright line backfires
Rigid all-or-nothing rules can fuel disordered patterns — use them with judgment.
Bright-Line Rules: When "None" Beats "Some" - Design decisions so they work even if some things go wrong
Ask: does this plan require everything to go right? If so, redesign it.
Margin of Safety - Learn to distinguish necessary control from overcontrol
Not all restraint is overcontrol — identify when control is required and when it is costing more than it protects.
Radical Openness: The RO-DBT Approach to Overcontrol and Loneliness - Prefer positions with optionality over positions with precision
In uncertain environments, prioritize options to pivot over optimized fixed positions.
The Ludic Fallacy: When You Mistake Real Life for a Game - Use maximin reasoning for high-stakes, irreversible decisions under ambiguity
Choose the option whose worst plausible outcome is most survivable — when you can’t compute expected value, optimize the floor.
Ambiguity Aversion — Why Unknown Odds Feel Worse Than Bad Odds
Related concerns
- Ludic Fallacy
The ludic fallacy, named by Nassim Taleb in The Black Swan, is the mistake of applying the logic of well-defined games (known rules, bounded outcomes, stable probabilities) to domains where those assumptions do not hold — most of real life. The fallacy matters because standard risk models built on game-like distributions systematically underestimate the frequency and magnitude of extreme, unexpected events. This is Taleb’s analytical concept; the supporting evidence is largely observational and historical rather than from controlled experiments.
- Nassim Taleb Ludic Fallacy
The ludic fallacy, named by Nassim Taleb in The Black Swan, is the mistake of applying the logic of well-defined games (known rules, bounded outcomes, stable probabilities) to domains where those assumptions do not hold — most of real life. The fallacy matters because standard risk models built on game-like distributions systematically underestimate the frequency and magnitude of extreme, unexpected events. This is Taleb’s analytical concept; the supporting evidence is largely observational and historical rather than from controlled experiments.
- Plan Robustness
Ask: does this plan require everything to go right? If so, redesign it.
Design decisions so they work even if some things go wrong
- What Is The Ludic Fallacy
The ludic fallacy, named by Nassim Taleb in The Black Swan, is the mistake of applying the logic of well-defined games (known rules, bounded outcomes, stable probabilities) to domains where those assumptions do not hold — most of real life. The fallacy matters because standard risk models built on game-like distributions systematically underestimate the frequency and magnitude of extreme, unexpected events. This is Taleb’s analytical concept; the supporting evidence is largely observational and historical rather than from controlled experiments.
- Black Swan Risk Planning
Reserve capacity for events that are not in your risk model — because the most damaging events usually aren’t.
Build plans with slack for outcomes outside your model
- Game Vs Real World Uncertainty
Ask: would a domain expert still face this uncertainty? If not, the issue is a skill gap — not fundamental ambiguity.
Separate “the world is uncertain here” from “I don’t know enough yet”
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