Readiness, Resilience, and the Myth of Dynamics: 6 Adaptive Modelling as Readiness Stewardship

What complex systems modelling could become

The Limits of Better Simulation

When complex systems models fail, the default response is almost always the same:

increase resolution, add parameters, refine dynamics, simulate longer.

This response assumes that failure is a matter of insufficient detail.

But many modelling failures are not failures of precision. They are failures of readiness recognition.

The model continues because it can, not because it should.

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The Core Claim

Complex systems modelling should track readiness explicitly, not infer it indirectly from dynamics.

When readiness collapses, further simulation does not deepen understanding — it obscures responsibility.

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Readiness Checks as Modelling Constraints

A readiness-aware modelling practice would introduce explicit checks:

• How much relational capacity remains under the current construal?

• Are further distinctions meaningful, or merely formal?

• Is variation still absorbable, or being suppressed?

These are not numerical thresholds. They are relational diagnostics.

They ask whether the model still preserves room for interpretation, reorganisation, and alternative futures — or whether it is merely extending inclination beyond ability.

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Horizon Exhaustion as a Legitimate Stopping Condition

In current practice, models are often pushed past the point where their assumptions remain viable.

Divergence, instability, or extreme sensitivity are treated as problems to overcome rather than signals to heed.

A readiness-oriented approach treats horizon exhaustion as a legitimate stopping condition.

When the system cannot be meaningfully continued as construed, the appropriate response is not deeper extrapolation — it is a shift of framing.

Stopping is not failure.

It is epistemic responsibility.

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Shifts of Construal, Not Finer Dynamics

Rather than endlessly refining the same model, readiness stewardship encourages:

• re-describing the system,

• changing the level of abstraction,

• re-partitioning relations,

• or redefining what counts as relevant interaction.

This mirrors good practice already present — but rarely theorised — in successful modelling communities.

The difference is that these shifts are recognised as semiotic moves, not ad hoc fixes.

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What Must Be Rejected

A readiness-oriented modelling practice must explicitly reject:

• Hidden realism
  The idea that the “true dynamics” lie just beyond current resolution.

• Metric fetishism
  The belief that what matters most must be measurable.

• Dynamical inevitability
  The assumption that systems unfold according to internal necessity rather than relational constraint.

These commitments quietly reintroduce metaphysics where modelling discipline should stand.

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Modelling as Semiotic Practice

Seen relationally, modelling is not discovery of how systems really are.

It is a semiotic practice:

a disciplined way of cutting relational potential into intelligible form while preserving room for further meaning.

Accountable modelling acknowledges:

• what it stabilises,

• what it excludes,

• and when its own readiness has been exhausted.

This does not weaken science.

It strengthens it.

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Payoff

By treating readiness as a first-class concern, complex systems theory can become relationally responsible without losing explanatory power.

Models become tools for navigating possibility rather than engines of ontological assertion.

The gain is not humility for its own sake, but clarity about where understanding ends — and where new construals must begin.

In this light, complex systems theory finally names what it has always been tracking: not dynamics alone, but the fragile, exhaustible space in which systems can still become otherwise.