Decision principle · v0.1 · 2026-09-12
Path Expansion
Evaluate the move by what becomes possible after it.
A Choseverse decision principle by John Brajer, published through MyMindMine.
Core idea
An action can matter beyond its immediate payoff because it changes the state from which the next decision will be made. Path Expansion evaluates an action partly by the number, diversity, and quality of desirable future actions and states that become reachable afterward.
Which action most improves the future possibility distribution?
More branches are not automatically better. Useful expansion must remain connected to the actual objective, constraints, risk, and evidence.
Trajectory model
Current State → Action → Weight Shift → New State → New Possibilities
Path Expansion focuses on the final transition. A choice changes state; the new state changes which later actions and outcomes remain materially reachable. Immediate utility still matters, but it is not the only term in the decision.
What to inspect
- Quantity: materially relevant futures newly reachable.
- Quality: desirability and strategic value of those futures.
- Diversity: meaningfully different routes rather than one fragile path.
- Preservation: valuable options that remain open.
- Closure: valuable paths made harder or impossible.
- Reversibility: ability to recover if the forecast is wrong.
- Information gain: evidence created for the next decision.
- Capability gain: assets, access, relationships, skills, infrastructure, or resources created.
- Confidence: strength of evidence supporting the predicted expansion.
These are v0.1 operationalization dimensions. No universal numeric formula is asserted.
Perspective Expansion and Path Expansion
Perspective Expansion: Am I seeing the relevant actions that are available?
Path Expansion: If I take this action, what desirable futures become reachable, remain reachable, or close?
Perspective Expansion operates before selection by improving the candidate action set. Path Expansion evaluates the future-state consequences of those candidate actions.
System relationships
The Trajectory Engine can use Path Expansion alongside money, network, visibility, risk, confidence, reversibility, information gain, alignment, and current-state constraints.
Possibility Reserve extends the orientation into longer-horizon search through Path-Expansion Search, where a branch can earn attention partly because of the possibility-space it opens.
BRMFA can make those state transitions more causally interpretable by modeling actions as interventions on mechanisms.
Failure controls
- Do not maximize branch count without regard to quality.
- Do not present speculative futures as established outcomes.
- Do not ignore immediate material, legal, financial, or ethical constraints.
- Do not assume every new option is beneficial.
- Do not postpone action indefinitely in pursuit of perfect optionality.
- Do not collapse uncertainty into false precision.
Evaluation protocol
- What state does this action most plausibly create next?
- What valuable actions become newly reachable from that state?
- What valuable actions remain reachable?
- What valuable paths close or become materially harder?
- Does the action create reusable capability, information, assets, access, relationships, or authority?
- How reversible is the action if the forecast is wrong?
- How strong is the evidence for the predicted expansion?
- Does the expansion serve the actual objective, or only create motion?
Provenance
Path Expansion recurs across John Brajer's August 2026 Choseverse, Trajectory Engine, Possibility Reserve, and systems-analysis records. The established source material defines its central move as evaluating an action by how it changes access to desirable future states and actions, rather than by immediate payoff alone. Later integrations with Perspective Expansion, Path-Expansion Search, BRMFA, and opportunity scoring are preserved as downstream relationships.
This public v0.1 adds an operational evaluation checklist but does not claim scientific novelty, mathematical optimality, or empirically superior decision performance.