No LLM generated, heavily assisted/co-written, or otherwise reliant work.
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(my human written version)
I’m not sure if this is novel, or if I’ve just rediscovered something from The Sequences in a clumsy way.
But I’ve noticed a pattern in myself that I haven’t seen described cleanly.
I don’t usually quit projects.
I restart them.
Example: I build a weekly system. It works for ~5–7 days. Then I miss one day. Not a catastrophic failure — just travel, bad sleep, distraction, whatever. And instead of simply resuming, I feel this weird pressure to redesign everything.
New rule set. New constraints. New “this time it’s clean.”
Objectively, the plan barely changes.
Subjectively, it feels necessary.
I’m trying to understand what’s going on there.
One hypothesis:
When I build a plan, I’m not just making a schedule. I’m making a self-claim.
“I am now someone who operates at X standard.”
When I miss, that claim becomes slightly false.
The dissonance is small in magnitude but surprisingly sharp. The restart reduces the dissonance because it reframes the failure as belonging to an “old version.”
This might just be identity-protective cognition dressed up in productivity clothing.
But it doesn’t feel identical to classic akrasia.
In akrasia, the problem is failing to do what you intend.
Here, I do what I intend — until interruption. Then I reinitialize the intention itself.
It’s like my agency is brittle under variance.
Another angle: activation cost.
After a miss, the system feels heavier. There’s backlog. There’s implicit judgment. There’s a tiny narrative of “you slipped.”
Restarting collapses the perceived complexity. It makes the state-space feel small again.
This might just be a local optimum: minimize psychological load now, at the cost of compounding later.
If that’s right, then the variable that matters isn’t streak length but return latency.
Time between miss and re-entry.
I don’t have data. This is introspection + pattern-matching across a few friends.
Possible counterpoint: maybe frequent restarts are just rational updating. Maybe the system actually was suboptimal.
But in my case, the changes are often cosmetic. Slight rule shifts. Slight reframing. Nothing that justifies the reset cost.
Which makes me think the restart is serving identity coherence more than optimization.
If this is real, the fix probably isn’t “try harder.”
It’s something like:
Ban catch-up.
Force one small action within 24 hours of a miss.
Prohibit redesign for 48 hours.
Basically, increase robustness to noise.
This feels adjacent to discussions here about self-model stability and local vs global optimization. I just haven’t seen it framed exactly this way.
If this is already a named thing, I’d appreciate a pointer. It’s possible I’m reinventing a wheel.
(my human written version)
I’m not sure if this is novel, or if I’ve just rediscovered something from The Sequences in a clumsy way.
But I’ve noticed a pattern in myself that I haven’t seen described cleanly.
I don’t usually quit projects.
I restart them.
Example: I build a weekly system. It works for ~5–7 days. Then I miss one day. Not a catastrophic failure — just travel, bad sleep, distraction, whatever. And instead of simply resuming, I feel this weird pressure to redesign everything.
New rule set. New constraints. New “this time it’s clean.”
Objectively, the plan barely changes.
Subjectively, it feels necessary.
I’m trying to understand what’s going on there.
One hypothesis:
When I build a plan, I’m not just making a schedule. I’m making a self-claim.
“I am now someone who operates at X standard.”
When I miss, that claim becomes slightly false.
The dissonance is small in magnitude but surprisingly sharp. The restart reduces the dissonance because it reframes the failure as belonging to an “old version.”
This might just be identity-protective cognition dressed up in productivity clothing.
But it doesn’t feel identical to classic akrasia.
In akrasia, the problem is failing to do what you intend.
Here, I do what I intend — until interruption. Then I reinitialize the intention itself.
It’s like my agency is brittle under variance.
Another angle: activation cost.
After a miss, the system feels heavier. There’s backlog. There’s implicit judgment. There’s a tiny narrative of “you slipped.”
Restarting collapses the perceived complexity. It makes the state-space feel small again.
This might just be a local optimum: minimize psychological load now, at the cost of compounding later.
If that’s right, then the variable that matters isn’t streak length but return latency.
Time between miss and re-entry.
I don’t have data. This is introspection + pattern-matching across a few friends.
Possible counterpoint: maybe frequent restarts are just rational updating. Maybe the system actually was suboptimal.
But in my case, the changes are often cosmetic. Slight rule shifts. Slight reframing. Nothing that justifies the reset cost.
Which makes me think the restart is serving identity coherence more than optimization.
If this is real, the fix probably isn’t “try harder.”
It’s something like:
Basically, increase robustness to noise.
This feels adjacent to discussions here about self-model stability and local vs global optimization. I just haven’t seen it framed exactly this way.
If this is already a named thing, I’d appreciate a pointer. It’s possible I’m reinventing a wheel.