Welcome back.
For those of you just joining: I ended Part I where Stafford Beer’s Designing Freedom begins, in a “little house… in a quiet village on the western coast of Chile”. With the benefit of hindsight and history, we now know Beer is describing the town of Las Cruces — but we’ll get to all of that, in the fullness of time.
In rapid order, Beer deploys this pleasant coastal scene as grand metaphor, to introduce what will form the central thesis of the lectures that follow:
Although we may recognize the systemic nature of the world, and would agree when challenged that something we normally think of as an entity is actually a system, our culture does not propound this insight as particularly interesting or profitable to contemplate. Let me propose to you a little exercise, taking the bay I am looking at now as a convenient example. It is not difficult to recognize that the movement of water in this bay is the visible behaviour of a dynamic system: after all, the waves are steadily moving in and dissipating themselves along the shore. But please consider just one wave. We think of that as an entity: a wave, we say. What is it doing out there, why is it that shape, and what is the reason for its happy white crest? The exercise is to ask yourself in all honesty not whether you know the answers, because that would be just a technical exercise, but whether these are the sorts of question that have ever arisen for you. The point is that the questions themselves — and not just the answers — can be understood only when we stop thinking of the wave as an entity. As long as it is an entity, we tend to say, “Well, waves are like that”: the facts that our wave is out there moving across the bay, has that shape and a happy white crest, are the signs that tell me “It’s a wave” — just as the fact that a book is red and no other colour is a sign that tells me “That’s the book I want”.
The truth is, however, that the book is red because someone gave it a red cover when he might just as well have made it green; whereas the wave cannot be other than it is because a wave is a dynamic system. It consists of flows of water, which are its parts, and the relations between those flows, which are governed by the natural laws of systems of water that are investigated by the science of hydrodynamics. The appearances of the wave, its shape and the happy white crest, are actually outputs of this system. They are what they are because the system is organized in the way that it is, and this organization produces an inescapable kind of behaviour. The cross-section of the wave is parabolic, having two basic forms, the one dominating at the open-sea stage of the wave, and the other dominating later. As the second form is produced from the first, there is a moment when the wave holds the two forms: it has at this moment a wedge shape of 120°. And at this point, as the second form takes over, the wave begins to break — hence the happy white crest.
Now in terms of the dynamic system that we call a wave, the happy white crest is not at all the pretty sign by which what we first called an entity signalizes its existence. For the wave, that crest is its personal catastrophe. What has happened is that the wave has a systemic conflict within it determined by its form of organization, and that this has produced a phase of instability. The happy white crest is the mark of doom upon the wave, because the instability feeds upon itself; and the catastrophic collapse of the wave is an inevitable output of the system.
I am asking “Did you know?” Not “did you know about theoretic hydrodynamics?” but “did you know that a wave is a dynamic system in catastrophe, as a result of its internal organizational instability?” Of course, the reason for this exercise is to be ready to pose the same question about the social institutions we were discussing. If we perceive those as entities, the giant monoliths surrounding pygmy man, then we shall not be surprised to find the marks of bureaucracy upon them: sluggish and inaccurate response, and those other warning signs I mentioned earlier. “That is what these entities are like”, we tend to say — and sigh. But in fact these institutions are dynamic systems, having a particular organization which produces particular outputs. My contention is that they are typically moving into unstable phases, for which catastrophe is the inevitable outcome. And I believe the growing sense of unease I mentioned at the start derives from a public intuition that this is indeed the case. For people to understand this possibility, how it arises, what the dangers are, and above all what can be done about it, it is not necessary to master socio-political cybernetics. This is the science that stands to institutional behaviour as the science of hydrodynamics stands to the behaviour of waves. But it is necessary to train ourselves simply to perceive what was there all the time: not a monolithic entity, but a dynamic system; not a happy white crest, but the warning of catastrophic instability.
— Stafford Beer, “Designing Freedom”, p. 2-3 [emphasis mine]
Also towards the end of Part I, I’d shared a page from Beer’s “Notes in Support of the First Lecture”, featuring some people sat atop of some poles. Don’t worry; Beer has plenty more doodles like that in his lecture notes (and here they are!):
I mention them again so as to preface this next excerpt, taken from the conclusion of Beer’s first lecture:
Remember these aspects of our work together so far. A dynamic system is in constant flux; and the higher its variety, the greater the flux. Its stability depends upon its net state reaching equilibrium following a perturbation. The time this process takes is the relaxation time. The mode of organization adopted for the system is its variety controller. With these points clearly in our minds, it is possible to state the contention of this first lecture with force and I hope with simplicity. Here goes.
Our institutions were set up a long time ago. They handled a certain amount of variety, and controlled it by sets of organizational variety reducers. They coped with a certain range of perturbations, coming along at a certain average frequency. The system had a characteristic relaxation time which was acceptable to society. As time went by, variety rose — because the relevant population grew, and more states became accessible both to that population and to the institutional system. This meant that more variety reducers were systematically built into the system, until today our institutions are nearly solid with organizational restrictions. Meanwhile, both the range and the frequency of the perturbations has increased. But we just said that the systemic variety has been cut. This produces a mismatch. The relaxation time of the system is not geared to the current rate of perturbation. This means that a new swipe is taken at the ball before it has had time to settle. Hence our institutions are in an unstable condition. The ball keeps bobbing, and there is no way of recognizing where an equilibrial outcome is located.
If we cannot recognize the stable state, it follows that we cannot learn to reach it — there is no reference point. If we cannot learn how to reach stability, we cannot devise adaptive strategies — because the learning machinery is missing. If we cannot adapt, we cannot evolve. Then the instability threatens to be like the wave’s instability — catastrophic.
I said before that there are solutions, but I have also shown that they concern organizational modes. They concern engineering with the variety of dynamic systems. By continuing to treat our societary institutions as entities, by thinking of their organizations as static trees, by treating their failures as aberrations — in these clouded perceptions of the unfolding facts we rob ourselves of the only solutions.
— Stafford Beer, “Designing Freedom”, p. 5-6 [emphasis mine]
Getting Back to Bigness
Speaking of “a dynamic system in catastrophe, as a result of its internal organisational instability”, let’s get back to talking about the modern, commercial Internet. To borrow another of Beer’s turns of phrase, here are just a few of the “happy white crests” of the digital advertising economy today:
Show the baby, show the baby! See?! Show your baby.”
1: Ad Fraud
Fraud remains endemic to, and rampant throughout, the online ad supply chain, to such an extent that one may safely assume that about ten cents out of every dollar in digital ad-spend will end up going to fraudsters – that is, criminal and/or terrorist networks. More conservatively, advertisers might only be losing a nickel out of every dollar of their media budget to such fraud… but it might just as easily be a quarter of each dollar, or more.
Here I feel I should pause (already!) and bolster that claim a bit:
- In June 2016, the World Federation of Advertisers published its “Compendium of Ad Fraud Knowledge for Media Investors“, which found that “[t]he cost of ad fraud is estimated at $7.2 billion in this report, or approximately 5%, of the total global”, and grimly predicted: “ad fraud will, on the current trajectory, be second in revenue only to cocaine and opiates by 2025 as a form of crime.”
- The “2018-2019 Bot Baseline Report“, by the Association of National Advertisers and cybersecurity firm WhiteOps, claimed that “[w]e project losses to fraud to reach $5.8 billion globally in 2019”, and that “[t]oday, fraud attempts amount to 20 to 35 percent of all ad impressions throughout the year, but the fraud that gets through and gets paid for now is now much smaller”.
- A 2023 report by Juniper Research forecast that “the global potential advertising spend lost to fraud will rise from $84 billion in 2023 to $172 billion by 2028”. It further claimed that “North America will account for the highest proportion of advertising spend lost to fraud over the next five years”, and that “[i]n 2023… 17% of clickthroughs on PCs and desktops were illegitimate and could not provide ROAS [return on ad spend]”.
- It should be noted that the same firm had previously estimated the cost of ad fraud, as of 2019, at $42 billion, while projecting that this figure would reach $100 billion as of the year 2023. This would place their starting estimate well above the contemporaneous ANA/WhiteOps report, and suggests that their analysis has relied in the past on perhaps-overly pessimistic models of the rate of growth.
- Lunio’s Wasted Ad Spend Report 2024 (produced in collaboration with IAS and Scope3) claimed that “[w]e evaluated a sample of more than 2.6 billion paid ad clicks…over the course of twelve months (May 2022 — May 2023)… 8.5% of all paid traffic was invalid”. They further estimated that about $55 billion had been lost due to what they term “invalid traffic” (IVT) in 2022, and predicted that about $71 billion in online ad-spend would be sacrificed to IVT globally in the coming year.
- This last example is especially noteworthy for having made some serious attempt at quantifying the prevalence and share of fraudulent ad-spend per advertising platform, as shown above.
2: Consent Management
Next year, in early March, the Digital Markets Act (or DMA) will impose new obligations on large digital companies operating in the European Economic Area (or EEA), notably bringing additional consent requirements. In addition, on the platform technology side, we know that third-party cookies have been deteriorating as browsers have been phasing them out, and customers choose not to allow them.
So these regulatory, platform, and technology changes are not “new news”, but 2024 is the inflection point for marketers where your digital marketing and measurement will rapidly lose effectiveness and functionality if you don’t take action.
— Jane Lascelles, EMEA Regulations Program Manager, Google Marketing Platform, November 28th, 2023
The ongoing public and technological shift towards “privacy by design” is rapidly obsoleting a great many of the methods by which digital marketers have traditionally gathered deterministic data and feedback on their efforts and their impact, whether for the purposes of planning, designing, executing, improving, or reporting on such work. This has been unambiguously great news, as far as human societies are concerned. For we online marketers, though, there have been certain headaches.
Here’s one: it is fairly common, in my recent experience, to find that 15-40 per cent of a given advertiser’s “ad clicks”, as reported by Meta Ads Manager (I am referring to the ‘link clicks‘ metric here), cannot be attributed to any subsequent “landing page view” event, as reported via the Meta Business Tools. To be clear, it is entirely feasible to achieve higher “ad-click-to-landing-page-view” ratios than these; it is also true that some variance between the two metrics is both normal and otherwise explicable. Still, my observation has been that many key stakeholders are simply unaware of any such loss in “signal quality” in their marketing attribution over recent weeks, months, and even years. And in those cases where the relevant stakeholders are alive to such changes, oftentimes they have not yet invested in needed repairs.
Many of the significant events and milestones in the adoption of “privacy by design” have taken place since 2018, when the EU’s “General Data Protection Regulation” (GDPR) came into force. I’ve already written at length (in this post) about the parallel histories of Canadian and European privacy law, and while I’m still happy to point anyone who might be curious to learn more over to read that one, I do have some notes, in hindsight:
- One thing I got right was how the GDPR establishes a much higher threshold for what constitutes “valid consent” to the collection and/or use of a user’s personal data online. The GDPR accomplished this by defining “consent” as… erm… well, consent. By contrast, online publishers and advertisers have traditionally relied on a basis of “implied consent” to their various data-harvesting practices, for marketing (and other) purposes. As you well know, “implied consent” was and is just bullshit.
- One thing I would’ve gotten right, but didn’t really explore was how Big Tech (but also Small-to-Mid-Size Tech) responded to GDPR, in essence, by turning around to their customers and saying “right, our systems are built to fully comply with all global privacy laws, so long as you never do anything or send us anything that violates any of those laws, so please tick this legally-binding checkbox confirming that you promise never to do that”. This legal sleight-of-hand enables firms like Google and Meta to continue offering (more or less) the same digital advertising toolsets as before, while offloading and transferring a great deal of their own liability risk onto customers.
- One thing I got wrong was my prediction that “data rights” would make its way into the broader public debate surrounding the 2019 federal election. My contention had been that any party forming government would be obliged to adopt GDPR-like protections under Canadian federal law, or else risk losing access to EU markets. In actual fact, five years later a watered-down version of the GDPR continues to toddle its way through Parliamentary committees, and it’s anybody’s guess whether it will pass before the next election. In short, we have still not done anything. I had dared to hope we might’ve.
The next “shock” to most Canadian marketers, on the data/privacy front, will likely be Google’s roll-out of “Consent Mode v2”, which will become became compulsory in March 2024 for all Google Ads customers subject to Google’s “EU user consent policy” (which is to say, a lot of us). As one very new support doc in Google’s Tag Manager Help Center explains: “To keep using measurement, ad personalization, and remarketing features, you must collect consent for use of personal data from end users based in the EEA and share consent signals with Google”.
This is, of course, an altogether reasonable and valie request of the Big Tech firms to make of us. The real trouble will only come when an overwhelming majority of Canadian marketers come to realise that they must undertake several years of long-deferred “data stewardship” work, within critical project timelines which will be measured in weeks (if not days). Regarded as a class, I would hazard that the ‘average‘ Canadian organisation is a full decade behind the “state of the art” with regard to privacy compliance and corporate data governance today. Much of the knowledge work required of digital marketers in order to adapt to this nascent, more privacy-preserving Web remains incomplete. The circumstances are — in a word — pitiable.
3: Third-Party Data (or ‘Garbage In, Garbage Out’)
In addition to its impact on deterministic data (most often gathered in a “first-party” context), another natural consequence of the uptake of “privacy by design” by governments and regulators has been a step-change deterioration in “signal quality” for virtually all forms of probabilistic audience data (most often gathered in a “third-party” context). Nevertheless, probabilistic datasets continue to inform many of the key decisions digital marketers make every day, over media budgets, campaign strategy, audience targeting, and so forth.
One would expect that this should be (or have been, by now) a much bigger problem than it’s turning out to be in practice. To put things bluntly, if the audience data has become trash (I’m saying it is), but we’re still paying for and using a lot of it, then why haven’t more people noticed?
Here is perhaps a fitting time for me to bring up the compelling, peer-reviewed evidence (published five years ago) which suggested that third-party audience data tends to be less accurate than random guessing, even when seeking to segment users along fairly basic demographic factors, such as age and/or gender cohort. And that’s to say nothing on the accuracy or utility of the many much more granularly scoped “interest-based” segments offered all across the programmatic marketplace.
In other words, emerging technical and regulatory constraints that make user tracking and data-collection harder haven’t had as noticeable an impact on third-party audience data, because the third-party audience data was never not trash.
The reasons behind this are complex and manifold, but my personal favourite explanation comes from a 2020 episode of the wildly niche podcast What Happens In AdTech, featuring Pontiac Intelligence CEO Keith Gooberman. The key bit comes at 17:00 – 22:15; the clip below should start right about there, and I’ve transcribed it for you below. The fellow who tees him up at the start of the clip is Ryan McConaghy, the mind formerly(?) behind What Happens in Ad Ops and currently serving as “VP, Global Monetization Strategy” at Condé Nast.
Companies start, these data companies. So they realise through cookies that they can cookie all types of sites, and find out all types of detail of the users that come through there, and segment them. Okay, so this is as remarketing and third-party data, this is getting going 2005-10, they’re starting to realise what they can do. Big cookie footprints. Okay.
So let’s take one of these companies that started out in the data, DMP space, okay? They already raised $50-$100 million. They’re already in debt. It’s a venture play, venture money behind it, they’re down $100 million. Okay? So, if you’re down $100 million, you’d better build yourself a scalable business. You can’t just build a business that makes $1 million a year and be like “We did it!”, right? It’s like “No no, sir, we’re gonna need a bigger business than that. You took a hundred million of our dollars”.
…So let’s say one of these companies, these early DMPs, okay? They go to all the car websites. So they go to KBB[.com], Edmonds[.com], and Cars[.com], and they say “Hey, I got a proposal for ya. I’m gonna put a pixel on your site. We’re gonna collect data of who looks at a Mercedes S-Class. We’re gonna turn around, we’re gonna sell the data to BMW and Mercedes, we’re all gonna share the money, everybody goes home happy.”
The car websites are like “Yes! What a great idea! No more ads! I love it, pixels, all day, let’s go”. They then do this, okay? They get all the pixels down. And they get an audience together of people that’ve looked at the Mercedes-Benz in the last three weeks, on all these major car retailers, okay?
And they package the audience, and then they go to BMW, and they get their agency to buy against this audience, okay? They do it, they do the whole thing. The agency says “Yes, sounds interesting. Let me test $25,000 against this, okay? I’ll test— I’ll pay, I’ll put $25,000 into the media buy, I’ll pay ya $3,000 for the data”, okay?
So they run the campaign. It’s the best performing campaign that BMW has ever run on display ads, ever. It’s unbelievable! It’s the greatest thing they’ve ever seen! They’re like “This is amazing!”. They’re like “Hey, we did $25,000 last month. Let’s do $250,000 this month”. And the data guy, salesmen, you know these people, right? He’s like [rubbing hands together] “ohh mmy ggoo, oh, YES!“. Like, “Turn it UP!“.
So he goes back to the AdOps department, okay, the AdOps, y’know… these are the people like us, who’re sittin’ there, looking at the numbers. And they’re like “I got BMW, they want every person who’s looked at a Mercedes, give ’em all three million cookies so they can do this buy!”.
And the data guy sits there, and he’s like [mimics typing]: “…nah, forty-seven thousand, bro. Only 47,000 people looked at the Mercedes.”
And he’s like, “No no no, I need three million, I need three million! That’s… they’re not gonna be able to spend more” — are you ready for the punchline? — “they’re not gonna be able to spend more than $2000 with us a month if we only have forty-seven thousand”. And the data guy goes, “Uhh, I can’t make up people who like Mercedes. You said you wanted people who like Mercedes!”.
And what do you think happened in that room? You think that they ended up serving three million cookies that included anybody that’s ever looked at a car, and other people that they could find that fit the description, so they could sell it through to the client? Or you think they held true to word, and only gave ’em the Mercedes audience?
…The joke is, is that the CEO who raised $100 million was like “I can’t, I can’t make enough money back if it’s not, if we don’t, if we don’t dilute these audiences. I can’t make this work if we don’t dilute the audiences“. And that discovery, my friend, there’s your reason why all the audiences are worthless. I get it! Listen, people look at cars. You know how many people are in the “auto intender” audience in most of these things? 144 million cookies. I’ll tell ya, it’s a big business. It ain’t that big of a business.”
— Keith Gooberman, CEO, Pontiac Intelligence
4: Recommender Discrimination Systems
The choices an online publisher makes as to which content they present (or “surface”) to a given user are the most direct and effective lever they have available to them to influence how that audience “engages” with them – and thus, to engineer their own profitability. In order to better predict what sorts of content will keep a given user “engaged” (and thus eligible to be advertised at), such systems run experiments by presenting some “test subjects” (that is, you or I) with some given stimuli, and then observing their interactions (or lack thereof), noting these for future study.
The accuracy of such predictions tends to improve as the scale of experimentation increases; so too does the potential to logically segment and stratify users, on the basis of some shared set of observed data-traits. This process goes on continuously and forever — or at least the machines doing this work are often designed so as to assume that it will. This is the essence of the wargame in which our individual minds now contend, against sophisticated networks of computers programmed by one or more teams of clever and amply funded “domain experts”, whenever we thumb idly through our varying “feeds”.
The algorithms tasked with making such decisions are termed “recommender systems“, but insofar as these work to monopolize an ever-greater share of each individual user’s attention, in pursuit of maximising the online publisher’s profits, these might more usefully be termed “discrimination systems” — for the sole animating purpose of such a system is to mark a population of users out from one another.
What these systems tend to work out, of course, is how the sort of content which best captures our attention (and this is essential: within the contexts they are tasked with “optimising”) tends to be strongly emotively charged — I’m talking “fight, flight, or freeze”-type stuff. And so these systems can (and often do) become adept at surfacing content in our feeds which prompt us to feel angry, or happy, or sad, or any number of more (and less) nuanced emotions. They also show an exceptional knack for doing so just as our own, individual attention threatens to wane; they’ve bought themselves another scroll, and possibly another after that. In these respects, recommender systems perform superlative work.
There are, alas, other and peskier dimensions of human psychology for which recommender systems would seem to be proving less helpful. They appear to have a tendency to amplify and overweight extremist perspectives and/or other “fringe” views, and to inspire body dysmorphic disorders in children. At a population level, they’ve had the general effect of fracturing our civic spaces, corroding our politics, and degrading our mental health. Many would consider these to be negative aspects of such a system; something worth looking further into, and perhaps somehow correcting. Unfortunately, there is little overlap between this growing body of thought and the profit-driven actions and motives of those publishers I have been mentioning.
To our great and well-deserved shame, the same is also true of advertisers — yes, us — who blithely encourage the pervasive and uncritical deployment of such systems, whose workings we do not and cannot even claim to know, in the hopes of accessing an ever-greater supply of potential ad impressions, addressable to the individual mind, with ever-greater accuracy and precision.
5: Generative AI-yai-yai
…Look, you don’t really need me to churn out any more words on the potential impacts of “generative AI” tools on Field X , do you? Please, let’s just assume you’d rather that I didn’t, and say only that the advent of large language models (LLMs), deployed at scale, is likely to exacerbate all of the problems I’ve just listed above.
That’s not to say that the technologies themselves are nefarious (Beer would object to such a premise), even if there are compelling arguments that the most popular contemporary systems are. What I am suggesting is that among the “first movers”, the most enthusiastic adopters of such technologies, are likely to be those who seek to leverage them towards nefarious ends. We must conclude, then, that many of these shall do quite well for themselves.
Variety: the Spice Melange of Life
While I promise not to make a habit of this, there are some times when any further editorialising only muddles the meaning of one’s source. So, in much the same way that I began this post by quoting far too much from Beer’s first lecture, I shall end it by quoting far too much from his second:
Our culture has had nearly 300 years to understand the problems of Newtonian physics. It has had more than half a century to get its grip on relativity theory and the second law of thermodynamics, knowing that it is at any rate possible to make general statements about the physics of the universe. Not all of us, I dare say, would care to answer basic questions about these two, although one might have supposed that the culture would have imbibed them by now. The observed fact is that the culture takes a long, long time to learn. The observed fact also is that individuals are highly resistant to changing the picture of the world that their culture projects to them.
I am trying to display the problem that we face in thinking about institutions. The culture does not accept that it is possible to make general scientific statements about them. Therefore it is extremely difficult for individuals, however well intentioned, to admit that there are laws (let’s call them) that govern institutional behaviour, regardless of the institution. People know that there is a science of physics; you will not be burnt at the stake for saying that the earth moves round the sun, or even be disbarred by physicists for proposing a theory in which it is mathematically convenient to display the earth as the centre of the universe after all. That is because people in general, and physicists in particular, can handle such propositions with ease. But people do not know that there is a science of effective organization, and you are likely to be disbarred by those who run institutions for proposing any theory at all. For what these people say is that their own institution is unique; and that therefore an apple-growing company bears no resemblance to a company manufacturing water glasses or to an airline flying aeroplanes.
The consequences are bizarre. Our institutions are failing because they are disobeying laws of effective organization which their administrators do not know about, to which indeed their cultural mind is closed, because they contend that there exists and can exist no science competent to discover those laws. Therefore they remain satisfied with a bunch of organizational precepts which are equivalent to the precept in physics that base metal can be transmuted into gold by incantation—and with much the same effect. Therefore they also look at the tools which might well be used to make the institutions work properly in a completely wrong light. The main tools I have in mind are the electronic computer, telecommunications, and the techniques of cybernetics. . .
Now, if we seriously want to think about the transmutation of elements in physics, we will recognize that we have atom-crackers, that they will be required, and that they must be mobilized. We shall not use the atom-crackers to crack walnuts, and go on with the incantations. But in running institutions we disregard our tools because we do not recognize what they really are. So we use computers to process data, as if data had a right to be processed, and as if processed data were necessarily digestible and nutritious to the institution, and carry on with the incantations like so many latter-day alchemists.
The invitation to face up to these realities is a necessary one if there is to be any real chance of perceiving the proper role of currently available tools. For it is not something scintillatingly clever that I am proposing, not a complicated new extension of mind-blowing techniques that are already beyond most people’s understanding, not a “big brother” that will alienate us still further from the monstrous electronic machinery that by now seems to govern our lives.
I am proposing simply that society should use its tools to redesign its institutions, and to operate those institutions quite differently. You can imagine all the problems. But the first and gravest problem is in the mind, screwed down by all those cultural constraints. You will not need a lot of learning to understand what I am saying; what you will need is intellectual freedom. It is a free gift for all who have the courage to accept it. Remember: our culture teaches us not intellectual courage, but intellectual conformity.
Let’s get down to work, and recall where we were. A social institution is not an entity, but a dynamic system. The measure we need to discuss it is the measure of variety. Variety is the number of possible states of the system, and that number grows daily, for every institution, because of an ever-increasing range of possibilities afforded by education, by technology , by communications, by prosperity, and by the way these possibilities interact to generate yet more variety. In order to regulate a system, we have to absorb its variety. If we fail in this, the system becomes unstable. Then, at the best, we cannot control it- as happened with the bobbing ball on our elaborated tennis trainer; at the worst, there is a catastrophic collapse -as happened with the wave.”
— Stafford Beer, “Designing Freedom”, p. 11-12 [emphasis mine]
I have already suggested a list of three basic tools that are available for variety amplification: the computer, teleprocessing, and the techniques of the science of effective organization, which is what I call cybernetics. Now I am saying that we don’t really use them, whereas everyone can assuredly say: “Oh yes we do.” The trouble is that we are using them on the wrong side of the variety equation. We use them without regard to the proliferation of variety within the system, thereby effectively increasing it, and not, as they should be used, to amplify regulative variety. As a result, we do not even like the wretched things.
[…] It’s obvious really, once the concept of variety and the law of requisite variety are clear. The computer can generate untold variety; and all of this is pumped into a system originally designed to handle the output of a hundred quill pens. The institution’s processes overfill, just as the crest of the wave overfills, and there is a catastrophic collapse. So what do we hear? On no account do we hear: “Sorry, we did not really understand the role of the computer, so we have spent a terrible lot of money to turn mere instability into catastrophe.” What we hear is: “Sorry, but it’s not our fault—the computer made a mistake.”
Forgive my audacity, please, but I have been “in” computers right from the start. I can tell you flatly that they do not make mistakes. People make mistakes. People who program computers make mistakes; systems analysts who organize the programming make mistakes; but these men and women are professionals, and they soon clear up their mistakes. We need to look for the people hiding behind all this mess; the people who are responsible for the system itself being the way it is, the people who don’t understand what the computer is really for, and the people who have turned computers into one of the biggest businesses of our age, regardless of the societary consequences. These are the people who make the mistakes, and they do not even know it. As to the ordinary citizen, he is in a fix—and this is why I wax so furious. It is bad enough that folk should be misled into blaming their undoubted troubles onto machines that cannot answer back while the real culprits go scot free. Where the wickedness lies—and wickedness is not too strong a word—is that ordinary folk are led to think that the computer is an expensive and dangerous failure, a threat to their freedom and their individuality, whereas it is really their only hope.
There is no time left in this lecture to analyse the false roles of the other two variety amplifiers I mentioned—but we shall get to them later in the series. For the moment, you may find it tough enough to hear that just as the computer is used on the wrong side of the variety equation to make instability more unstable, and possibly catastrophic, so are telecommunications used to raise expectations but not to satisfy them, and so are the techniques of cybernetics used to make lousy plans more efficiently lousy.
— Stafford Beer, “Designing Freedom”, p. 14 [emphasis mine]
Well, you’ve heard the fellow. In Part III (stay tuned!), I still hope to focus on the fourth of Beer’s lectures, titled “Science in the Service of Man“. With this post, I’ve mainly focused on the problems facing digital marketers right now, as we enter another new year. In the next, I aim to explore (what I see as) their underlying causes, and why — despite their solutions being relatively obvious — any definitive “fix” remains unlikely, barring some dramatic shift in the prevailing industry paradigm. And, because I’ve now managed to go some 10,000 words without making a direct reference to Project Cybersyn (which, holy shit you guys, Project Cybersyn!!), I’ll try to work that in somewhere too.
Thanks for reading,
-R.