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Persistence Bias

Persistence bias is the statistical asymmetry in transition probabilities by which a system, under fixed dynamics, constraints, and noise statistics, is more likely to remain within or return to previously occupied regions of effective state space than to transition into radically reconfigured regions.

It is not a force. It is an emergent bias in the stochastic flow of trajectories produced by barriers, constrained reachability, and the combinatorial difficulty of dismantling persistent distinctions.

Persistence bias is scale-relative: what counts as “the same region” depends on coarse graining and the effective description used.

Through this role, persistence bias:

• increases dwell times and recurrence in metastable basins,
• makes large-scale reconfiguration exponentially unlikely relative to local fluctuations,
• enables cumulative structure by favoring continuity over wholesale rearrangement.

Distillation

What already exists is easier to keep than to rebuild.

Why it matters

• Persistence: Explains why metastable structures dominate observed histories via suppressed escape rates.
• Entropy: Specifies why entropic exploration produces mostly local variation rather than constant global rearrangement.
• Existence: Separates causally active patterns from fleeting fluctuations by recurrence and durability.
• RDD coherence: Provides a non-teleological account of “stickiness” without redefining gravity or invoking agency.

Links

Related atoms

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Used in molecules

Conflicts with

• Teleological “selection” principles that treat persistence as purpose-driven rather than rate-driven.
• Reframing “gravity” as a general persistence concept (category error: physical gravity remains spacetime curvature and interaction, not a statistical bias).
• Accounts that treat persistence bias as epistemic preference rather than ontic transition asymmetry.

Sources

• Source: Anderson, P. W. (1972). More Is Different.

  • Key: @andersonMoreDifferentBroken1972
  • Use here: Emergence and stability as real, higher-level constraints on dynamics.

• Source: Sethna, J. P. (2021). Statistical Mechanics: Entropy, Order Parameters, and Complexity.

  • Key: @sethnaEntropyOrderParameters2021
  • Use here: Energy landscapes, basins, and why systems spend most time in metastable regions.

• Source: Einstein, A. (1916). Die Grundlage der allgemeinen Relativitätstheorie.

  • Key: @einsteinGrundlageAllgemeinenRelativitatstheorie1916
  • Use here: Anchor for physical gravity to avoid conceptual drift when using “bias” metaphors.

Re-contextualization Log

• 2025-12-29

  • context: Needed operational definition to prevent persistence bias from sounding like a new force
  • effect: refined
  • note: Redefined persistence bias as an asymmetry in transition probabilities over effective state space; made scale-relativity and measurement hooks (dwell times, recurrence, escape rates) explicit.

• 2025-12-26

  • context: Separation of dynamical bias from gravitational interaction
  • effect: renamed
  • note: Reframed prior “gravity” language into persistence bias to avoid category error and restore physical specificity.