Financialisation

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“There’s a war going on. The battlefield’s in the mind, and the prize is the soul. So be careful.”

— Prince, Don’t Be Fooled By The Internet (1999)

Financialisation
/faɪˈnænʃᵊlaɪˈzeɪʃᵊn/ (n.)

1. General: The increasing importance of financial markets, motives, institutions, and elites in the operation of the economy and its governing institutions.
2. JC’s own meaning: The high-modernist goal of reducing ineffable things to calculable, and manipulable quantities.

The financialising world

We take it as not needing detailed argument that we are amidst — a long way through — a generational stampede towards the financialisation of everything. This is to commoditise, rationalise, systematise and scale activities, artefacts, goods, services and cultural experiences and measure them by standard, simplified scales. To lose intractable ineffability and reduce things to computable units.

This is, for example, to convert what David Graeber might call “social indebtedness” — the nebulous set of cross-pollinating reciprocal favours we do to each other that both define our “community of interest” and bind it together, with a view that our debts to each other are never discharged — into “monetary indebtedness” — specific, delimited obligations that have a defined value, time cost and term, and whose price must be paid in full. To try to jettison that ineffable social dimension, notwithstanding the irony that the very continued existence of a financial indebtedness depends on the ongoing “social” indebtedness and undischarged reciprocity — exactly the sort of unmeasurable, ineffable relationships of trust that financialisation would seek to eradicate.

The ultimate financialisation: the dispensation with the need for trust in a system with devices like the distributed ledger and permissionless Blockchain

Even those who warn us most cogently about system intractability tend to financialise their analysis, so management consultants can grok it: hence by way of articulating this intractability, Russell Ackoff breaks the world down into “messes, problems and puzzles”:

Helping the machines to read us

The most manipulable, fungible, calculable, aggregable articulation of value known to Western society is cash — fiat cash, sorry cryptobros — and it is the common language in which we describe our interrelationships. Hence “financialisation”.

But we are talking metaphorically here: there is “financialisation” in a broader sense that need not involve money as such: SAT scores, A-level grades, Out-of-five product reviews, performance appraisals, RAG statuses, net promoter scores, QR codes, implied carbon footprints, gender pay differentials — any numerically measurable criteria that convert the messy, idiosyncratic, intractable life experience into ordered columns, pivot tables, and scatter plots that can be averaged, extrapolated, enriched, Pareto triaged, standard deviation plotted, and put into ranked, tranched order.

When you are building a technologised process — seeking inputs and calculating outputs — free text is not your friend. You can’t do anything with free text, beyond bucketing it as “other”. “Other” conceals an infinitude of richness and variability. So does “A*”, “Male”, “needs improvement”, “amber” and “British Asian”.

By our process design, we are elect to assign that variability a numerical value of zero within a category, and 1 beyond it, even though the actual variability might transgress the original categorisation. A “male” and a “female” might have more in common than they differ, depending on the reason for categorisation. (For example, when the categorisation is of “financial services professionals” this is almost certainly the case.)

In this way, our own model determines the types of biases we see as much as the data. (We will never know if recreational cricketers, left-handers, introverts, or people who live more than twenty kilometres away are discriminated against because this data is not gathered).

In data we trust

“In God we trust. All others must bring data. ”

The fatuous “truism”, allegedly but not actually coined by W. Edwards Deming brings this whole thing to a head.

Firstly, as has been observed anon, data tells you a limited, and coloured , story about the past, not a comprehensive picture of the future.

Secondly, that word: “trust”. It is the sine qua non of commercial enterprise — of society. This has even been proven out by game theorists, in a limited case, between people who do not know when their next interaction will be, or whether there will be one. This is a dependency on an unknown future. Data plays no part in it.

But part of the assessment you must make in a game is as to the state of your opponent’s mind. You must assess whether she understands the rules, whether she recognises the longer term benefits of repeated cooperation over the short term sugar rush of defection, and whether she believes there will be future interactions. One must, that is, trust your opponent. That is a delicate assessment. It requires knowledge of history — it might seem that data can help there, but the important knowledge is informal — of the depth of your relationship, your interconnections and mutual dependencies, your shared history, shared values and that ineffable assessment of whether this is someone I can trust.

No large language model can do that. As information technology always does at all points where humans react in ineffably human ways, an algorithm must run some kind of proxy calculation of that assessment. At scale you might make a numerical assessment that, say, seventy percent of transactors will honour their bargain, and elect to go one, but this is gamable.

Computers cannot trust. They don't have

There are no straight lines in nature

In which we call to mind Robin Williams’ great scene in Dead Poets Society, and restate it: just as there is no machine for judging poetry, there is no machine for judging commerce either.

Any metrics, balance sheets, org charts, projections or discounted cashflow analysis — any formal accounting for the intensely human activity of doing business — jettisons much of potential value. The map can never be more than a brief schematic: it cannot convey the grandeur — or the horror — of the territory. The jettisoning is part of the exercise: it is, itself to judge what is and is not important. It is to extract a signal from noise. That signal is ad hoc, imaginary, a creative work, and by no means exclusive to the hubbub: there are infinite array of signals we could take out of the hubbub; the ones we do are determined by our cultural fabric, which is made of all the decisions, signals, and institutions we have already built.

This relativity terrifies “right-thinking people”, but there is no way around it: it is best just to ignore it: bucket it up with all that other irrelevant and inconvenient stuff, as “other”.

Map and territory

That much is obvious: it is not the lesson we should draw. We should already know it.

The lesson is this: if we mistake the map for the territory — if we organise our interests and judge outcomes only by reference to the map we have made, we thereby change the territory. There is a feedback loop at work here. Gradually, by degrees, behaviour will change and territory will converge on the map.

This appears to be excellent news, in the short term, for machines and those who deploy them — cartographers — as it makes their life easier in the “ordinary run” of things. The mountain comes to Mohammed. For those out in the territory, it leads to two kinds of bad outcomes.

1. Making life easier for devices that work by “algorithm” and see the world in terms of numbers — call these “financialisation machines” — thus removing variability does not make our lives richer. It makes them generic. It diminishes the benefit of network nodes that can handle ineffability — this is good, right, because those nodes — call them subject matter experts, or even humans — are expensive, slow and unpredictable. Now of course we can assign humans to algorithmic roles — where there is peripheral intractability in a system, we have no choice — and as the territory redraws itself to the map, we further marginalise that intractability, and can deploy cheaper, more interchangeable humans, and at the limit, we can replace them altogether. Where the “intractability” is relatively low-risk — for example, interpreting, triaging and responding to unstructured requests from low-value customers, as in a consumer helpline — then techniques like AI can already handle it. It doesn’t much matter if the AI isn’t much good. (For a complaints line, it is ideal if the AI isn’t much good — it is a perfect accountability sink). By agreeing to behave like machines, to be categorised according to numerical terms, to be financialised — we surrender to machines. If you are worried that your job will be replaced by tech, then this is the way it will happen.
2. But in those exceptional cases that turn out not to be the ordinary course it leads to bad outcomes in the territory. No better example than the Post Office Horizon scandal, the internal territory — how managers behave had so closely converged on the map as to utterly lose sight of the territory. As long as the territory was unaligned, scattered, unconnected and could not fight back, this did not matter at all. But the territory has a habit of overwhelming mapmakers who lose sight of their original purpose, which was to functionally reflect the territory. Our roll of honour refers.

Things that can’t be ranked and counted — that aren’t “legible” to this great high powered information processing system — have no particular value to the system, in the system’s terms — it can't digest them or extract value out of them — it literally cannot “process” them — this is so whether or not these have any value to us.

Value

Forgive me a postmodern moment but value is a function of cultural and linguistic context — sorry, Professor Dawkins, but it just is — the richer the language, the more figurative, the more scope for imagination, the more scope for alternative formulations of value.

Conversely, inflexible languages, with little scope for imaginative reapplication have much less scope for articulating values — it is much harder to capture all that richness of meaning. The closer a language is to one-way symbol processing, the more it resembles code.

Ineffability is that “betweenness”, the informal, the uncountable, it follows that machine languages cannot handle ineffability. (This is a highly relativistic sense of value by the way. Guilty as charged.)

If you render human experience in machine language, let alone in the constrained parameters of international financial reporting standards, you are losing something. You are losing a lot.

But if energy is free, you can afford to be wasteful with it. If your financialising techniques generate enough financial value — money — for the same amount of effort, who cares how much extra ineffable value leaks out of the system as you go? This is the promise of scale, and the interconnected network promises a lot of scale. Go Taylor Swift, forget about the Nietzsche-loving doom metal merchant from Austin.

In the eyes of the financialising machine the unique differing pleasures we derive from Marcus Aurelius’ Meditations — or listening to Keegan Kjeldsen talking about Robert Michel's iron law of oligopoly — or, damnit, even Taylor Swift —reduce precisely to the unit cost for which items of that cultural artefact can be sold, over the cost of producing and distributing it. Any greater value — the life lesson, aphorisms and fortitude it magically confers that guide you through through your heaviest seas and blackest storms count for nothing. It is hardly a novel idea to regret that something is being lost hereby.

Conversely, things that can be counted can acquire “value” even if they don't have value. There are plenty of examples of this — things that sell at a greater margin than they cost — carbonated soft drinks, bottled water — or bitcoin, fashion, cosmetics professional sports, commercial music. Followers. Subscribers. Eyeballs. Clicks. Diversity reduced to a set of arbitrary criteria, characteristics, that can then be catalogued, categorised and analysed. The system can only understand diversity by homogenising it. I mean, talk about irony.

This will to financialisation distils down to a worldview that the analogue, informal, unique, different, the diverse — all those things that require judgment, patience , understanding, — that take “metis” are therefore expensive, troublesome, irksome, difficult, slow and unscalable and therefore bad.

JC has said this before: if we reorganise our values to suit the machines, we will lose to the machines. Do not surrender before kick-off.

These things used to be premium. Now we have premium mediocre — artificially scarce, disingenuously novel, that sapping word, “content” — generated for its own sake, that we pay for, or value, for its own sake — see above.

We are all out here desperately searching for meaning, and it is up to us what we settle for. But if we settle for the premium mediocre the authentic — the real meaning, value — will wither and die.

Our own attitudes, and the stories we tell ourselves, and each other, matter. If we settle for premium mediocre that is what we will get. Until we are replaced.

The ineffable value of uncertainty and the difference between risk (a calculable probability) and uncertainty (intractable, black box, non-linear).

The modernist yen — imperative — need — to reduce uncertainty to risk, and the false comfort this gives. The Viniar problem.

But non-linear loss is the consequence, and corollary of non-linear opportunity and vice versa. If we put ourselves on a linear track that approximates the non-linear reality we will be fine until there is actual event at either end. Persuading everyone else to get on the linear track is a good strategy. As long as it works. If you can persuade everyone in the system to behave, the system will “behave” — in the sense of not producing unexpected outcomes, and not necessarily optimising, or producing particularly good outcomes. Volatility will drop. As long as everyone behaves.

What if I turn out to be wrong?

Consequences of this instinct

• Private equity
• Outsourcing/management consulting

The desire for digital certainty

James C. Scott’s observation that a top-down organisation can only operate by what it sees, which necessarily misses nuance. Centrally planned states have the blessing and the curse of scale. A relatively small governing class can effectively accommodate — satisfice — the needs of a great many people as long as everyone’s needs are suitably generic. The more generic they are the better margins can maintain.

The normal offsetting effects of competition are muted in an interconnected world where the scale advantage can usually drown out market entrants as long as the market/product demand stays relatively constant. There are few but significant disruptions (computers, internet, mobile internet — not yet clear whether AI is another one). Beyond these market dominators can generally defend their positions.

Robert Michels’ iron law of oligopoly, that at all organisations concentrate “power” and become top-down

The madness of crowds and our interconnectedness: if it was hard to be exceptional before the internet, it is so much harder now. Yet we kid ourselves that we are all exceptional. If we are all competing at the same thing, we have almost no chance of excelling. These are the Bayesian priors. But everyone of us is different.

Averagarianism

Outsourcing and offshoring as the relentless financialisation of the internal firm.

The Peter Principle that we rise to our own level of incompetence so will be dispositionally bad at the hard parts of our job. The basic narcissism or Dunning Krugerism of those prepared to do what it takes to climb the greasy pole required to want to be a chief executive officer or politician - those who want the job enough to get it

Data modernism and the conviction that everything now can be solved, and mankind is something to be overcome.

Fundamental ineffability

Stand for something, or you’ll fall for anything.

—Anon.

It is there but we really have to want it - and stand up for it.

James C Scott: Metis.

Chris Anderson’s The Long Tail: How Endless Choice is Creating Unlimited Demand: there really is a long tail out there — proverbial doom metal merchants lecturing insightfully on Nietzsche — but we are allowing it to wither on the vine. Our moral responsibility, if we want to keep it, is to support it. But are they dying out like local bookstores? We need to nurture them.

The informal and formal lines of information in any organisation - in this take Jane Jacobs, desire lines

Countability

The conversion of ineffable things into fundamental fungible countable things comes at the cost of being able to quantify them for stop they become a subject to arithmetical manipulation colon maxima comma median mean mode, upper courtyards and lower quartiles. They become comparable with each other and once comparable hey cannot help but being evaluated.

This is perhaps the fundamental lesson of the Israeli daycare experiment: the quantification of a moral obligation fundamentally changes it.

Things that are fungible and countable and comparable appear as redundancies rather than strengths.

See also

• Misbehaviour as arbitrage