ARPI Insight

When Algorithms Optimise Noise and Miss Coherence

Why engagement-driven systems destabilise democracy — and how constraint-based intelligence offers a different path

Algorithms are not inherently harmful. They are powerful tools for pattern recognition, coordination, and scale. But the way we currently deploy them — particularly in social, economic, and informational systems — is creating unintended consequences that are no longer marginal. They are becoming civilisationally significant.

At the heart of the issue is a simple misalignment: most large-scale algorithms are optimised for measurable activity, not for human or societal coherence.

They reward what can be counted — clicks, impressions, engagement, velocity — rather than what actually sustains democratic, peaceful, and caring societies: trust, shared reality, thoughtful disagreement, long-horizon understanding, and mutual recognition.

This is not a moral failure of individuals. It is a structural property of optimisation without sufficient constraint. Optimisation without coherence produces instability

In complex systems — biological, ecological, or social — optimisation applied to a narrow goal tends to produce fragility elsewhere. When throughput is maximised without regard for boundary conditions, systems accelerate toward breakdown.

In today’s algorithmic environments, this appears as:

• amplification of outrage over nuance,

• reward for certainty over curiosity,

• visibility for speed rather than care,

• fragmentation of shared meaning,

• and increasing volatility in public discourse.

These effects are not accidents. They are emergent properties of systems trained to privilege noise because noise is easy to detect and monetise.

Coherence, by contrast, is quieter. It spreads through recognition rather than amplification. It builds slowly, person to person. It rarely spikes — but it endures.

The problem is not intelligence — it is constraint

A democratic civilisation depends on stability under disagreement. It requires systems that can tolerate difference without incentivising rupture. This means intelligence must be shaped not only by what it can optimise, but by what it must preserve.

Current algorithmic systems are rarely designed with such preservation in mind. Their purposes emerge from incentive structures — profit, growth, engagement — rather than from explicit commitments to social coherence, human agency, or long-term wellbeing.

As a result, responsibility becomes diffuse. Creators, users, and institutions find themselves adapting to opaque feedback loops they do not control. When livelihoods, visibility, or social standing are tied to algorithmic performance, agency quietly erodes. Volatility becomes normalised. Instability propagates downward into real lives.

This is not sustainable — socially, psychologically, or democratically.

A different path: constraint-based intelligence

From an ARPI perspective, intelligence — whether biological, technological, or civilisational — must be understood as a constraint-satisfying process. Healthy systems do not merely accelerate; they remain in phase with the conditions that allow them to persist.

In living systems, coherence is not enforced externally. It is maintained because misalignment degrades function. This is autopoietic alignment: systems that regulate themselves by respecting the boundaries that keep them viable.

Applied to algorithms, this implies a shift in design philosophy:

• from maximising engagement to preserving shared reality,

• from rewarding volatility to supporting stability,

• from extracting attention to sustaining agency,

• from short-term metrics to long-term coherence.

Such systems would not eliminate disagreement or emotion. They would simply stop amplifying rupture as a default survival strategy.

Visibility is not value

One of the quiet distortions of algorithmic culture is the conflation of visibility with meaning. What is most amplified is not necessarily what is most true, wise, or caring — only what is most reactive.

But human resonance does not obey those metrics. It travels through understanding, not virality. One thoughtful exchange can shape a worldview more deeply than a thousand impressions.

A civilisation that mistakes noise for value risks optimising itself away from what makes it humane.

Choosing the future we want

Algorithms do not decide their own purpose. Their purpose emerges from the values we encode, the incentives we tolerate, and the constraints we fail to set.

If we want a democratic, peaceful, caring future, then intelligence — human and artificial — must be designed to slow when coherence degrades, not accelerate through rupture.

The question is no longer whether algorithms are powerful.

It is whether we are willing to shape that power in service of life, agency, and shared meaning — rather than noise?

That choice is still ours.