Cambridge study shows that evidence-based facts might increase polarisation, rather than solve the argument.

The deVinery Isness Parallax introduces a new category of measurement that may solve this exact problem. 

We think that if we just prove the facts, the problem will just be solved, but a Cambridge experiment shows that evidence-based facts do not lead to agreement on solutions. In fact, intelligent, rigorous people can start from the same evidence and still become even further polarised while truly believing their political interpretation of the facts is the right one. They are willing to die on that hill to defend it because the interpretation of the facts are tied to their identity. To see the facts through any other lens is a threat to that identity.As cartoonist Ian Foley cleverly pointed out, we can see this exact issue playing out in the heated debates over data centres. 

This is normally where we assume that identity-based interpretation is too subjective to measure and that we should just heap more data on the pile. Kahan et al. concluded that better reasoning or more data does not necessarily overcome cultural identity; sometimes it serves it. Their solution is to protect scientific communication from becoming culturally identity-laden. 

But what if there is a way to measure the relational aspects of this we thought were inherently subjective? What if we could unclog the stalemate? 

The deVinery Institute is developing a way that reveals why intelligent people diverge on the interpretation of evidence, and offers a new category of measurement that may just make the political identity-based interpretation itself perceptible, measurable and accountable, and predict hitherto invisible downstream consequences. 

Until now, what we have not had is an invariant relational coordinate system that lets us compare those competing representations without quietly making one worldview the hidden standard of truth. That is what the deVinery Isness Parallax is being developed to do.

More data cannot compensate for a missing coordinate

Take the heated debate around data centres for example. A decision-making body may have all the evidence-based facts about economic output, technological necessity, investment, productivity, tax revenue and water, electricity, land, community costs, working people, environmental burden.

Yet one person might weigh this and conclude: 

This is dangerous. We need to slow down.

Another can look at the same data and say:

This is essential. We need to build faster.

So the conventional fight becomes:

Whose interpretation of the facts is stronger? Which methodology is better? Which consequence matters more?

Kahan helps explain why simply dumping more evidence into that fight may not resolve it. People can become better at defending the narrative through which they are already interpreting the evidence. (Cambridge University Press)

The deVinery Grammar of Desire proposes three invariants that act as relational coordinates. 

That is the new category. And that changes what it is possible to measure. 

The three locked invariants give you something that neither political side, neither scientific model, neither corporation, neither activist group gets to own:

Self v Self.Self v Other.Self v Collective.

They are not another viewpoint in the data-centre fight.

They are fixed coordinates that can be applied across the viewpoints.

The deVinery Institute has been developing a measurement instrument called the deVinery Isness Parallax.

It takes the narratives that stem from the three situated invariants and weights them, then measures the residual shape. 

The deVinery Parallax sits upstream of ordinary problem-solving.

Before asking:

What should we do?

It asks:

What is the perception gap between what we think is happening and what is actually playing out?

This changes the question being asked, and the new perception opens up the possibility space for new solutions. 

What makes the deVinery Parallax different

Historically, we tend to solve this category of problem by quietly appointing one narrative as the unspoken king at the table. And unacknowledged view from no where. We defer to:

Scientific materialism.

Institutional consensus.

Economic value.

Environmental protection.

Individual liberty.

Collective safety.

Whatever the dominant narrative happens to be.

We then measure other representations according to their distance from that assumed ground. But that is not neutral measurement. It is one situated representation being used as the ruler for all the others. The possibility opened by relational invariants is different.

If the three invariants act as coordinates that remain stable across competing narratives, no participant has to become the One True Way in order for us to navigate.

The invariants do not tell us which side is right. They allow us to examine the weighting occurring across the whole relational situation and then measure the residual between the stories being told about the thing and the thing that is actually being enacted.

That residual is what I call localised isness. No One True Way is made invisible king. 

Why this changes the data-centre argument

Return to data centres. If we remain inside the existing argument, we can spend years and a fortune rearranging the evidence.

More water studies.

More electricity projections.

More economic modelling.

More employment figures.

More community surveys.

More carbon accounting.

And those things may all be worth doing.

But if the underlying problem is representational, more information will not necessarily settle it.

It may simply give each side more ammunition.

That is why one of the lines in my recent submission to the Australian Senate inquiry into Artificial Intelligence and Data Centres, was:

More data cannot compensate for a missing coordinate.

The deVinery question is therefore not:

Are data centres good or bad?

Nor is it:

Which side has the better facts?

It is:

What relational situation are all these facts describing, how is each participant weighting that situation, what disappears under those weightings, and are we actually solving the problem we think we are solving?

That can change the question itself.

And once the question changes, different interventions become possible.

A disagreement that looked like:

economic development versus environmental protection

may turn out to contain a different problem entirely.

A policy intervention that looked obviously necessary may turn out to place the cost somewhere nobody had represented.

An apparently unavoidable trade-off may turn out to have been produced by the way the problem was categorised.

Or the original concern may survive the Parallax completely and become stronger because we can now see more clearly where the actual harm lies.

The instrument does not guarantee a comfortable answer.

That is rather the point.

We are beginning to see predictive potential

The deVinery Institute is still at an early research stage with this application. In fact the deVinery Institute is still currently being founded so its online presence is still fittingly rudimentary.

But the case studies conducted so far are showing something I consider important enough to test properly.

The Parallax appears not only to reveal information that was previously missing from a representation. Once the weighting becomes visible, it begins to show signs of predicting the shape of what is likely to happen next.

If this information continues to be made cheap, who is likely to pay?

If this is the problem we think we are solving, what kind of intervention will appear obvious?

If that intervention is aimed at the wrong target, where will the cost reappear?

And when that consequence becomes the starting condition for the next decision, what happens then?

That takes us beyond a single decision.

It gives us the possibility of following a chain.

Representation → weighting → intervention → consequence → new relational situation → next representation → next intervention.

The deVinery Institute is now developing a proposed research programme to test whether the Parallax can track those chains prospectively rather than merely explain them afterward.

In other words:

Can we see the shape of the failure before we pay for it?

Can we identify that we are about to cut the fabric in a way that makes a dress?

Or are we actually shredding the fabric, hiding the pieces under the rug, and congratulating ourselves on having solved the problem?

This is why the deVinery Institute is seeking research funding

The research question is whether this new category of measurement works.

The deVinery Institute already has an emerging body of case studies examining representational failure at smaller scales.

The next stage requires proper experimental development: larger comparative studies, prospective predictions, longitudinal decision chains, independent testing and application to consequential real-world problems.

Data centres are one obvious field.

AI decision-making is another. Particularly as AI becomes physical and robot are walking around making decisions in our spaces. 

Public policy, institutional failures, moral panic, conflict and peace studies all contain versions of the same underlying problem:

Intelligent people (and AIs) can act on accurate information and still cause enormous harm because they have located the problem incorrectly.

The deVinery Isness Parallax would give us something we badly need in arguments like the one now surrounding data centres and physical AI.

Test the deVinery Isness Parallax for Yourself

The tests in the case studies are deliberately usable. Take your own interaction, policy, decision, dataset, AI output, organisational problem or conflict and run the same questions over it. See what becomes visible. You may discover that the problem you were trying to solve was not the actual problem at all.

That can be useful on its own. You will also hit a ceiling.

The deVinery Isness Parallax is one part of a much larger instrument. It sits inside the deVinery Grammar of Desire and its associated navigation methods, which can be applied across personal desire, relational systems, organisations, AI, governance and other categories and scales.

In other words:

You have the cookie crumb. There is a whole cookie, and a cookie factory, over here.

Use and licensing

You are welcome to use the published Crumb to test the Parallax privately on your own material and evaluate what it reveals.

Publication of this test does not grant permission to:

• use the deVinery Isness Parallax commercially

• implement it inside an organisation or product

• provide consulting, training or services using it

• reproduce or adapt the instrument as your own methodology

• build derivative tools, models or systems from the proprietary method

Commercial, organisational, research-partnership, training and implementation use requires a licence from the deVinery Institute.

Contact the deVinery Institute:

Artemisia de Vinedevineryinstitute.com ORCID: https://orcid.org/0009-0001-6420-5561DOI: 10.5281/zenodo.22717964