The end-to-end principle

Voice recognition, local and distant networks

We have hit the crazy summer season where JC will be bouncing around a bit more randomly than normal, and newsletters might become somewhat intermittent. Rest assured, much is going on behind the scenes at the Jolly Contrarian. Including the rollout of a new consulting arm! This Substack is reader-supported. To receive new posts and support my work, consider becoming a free or paid subscriber. Wither voice recognition? I’m trying something new for this post, and that is using the Wispr Flow [https://wisprflow.ai/]voice recognition application for dictating rather than writing. This does the useful but unglamorous job of voice recognition (a technology that is well developed) and overlaying some AI pattern matching to fix up and properly punctuate dictation. JC’s nasal mumbling comes out looking more presentable. For those who never learned to touch type or who regularly input significant data on a mobile device, voice recognition should be a real productivity opportunity. It is odd it has not caught on more quickly to date. Using AI to overcome some of the obvious limitations of dictation (that includes making errors, ums, ahs, and contradicting yourself: that is to say, applying a small amount of limited editorial control) is an ideal application for AI. It saves a good deal of incremental time. It is obviously testable and correctable at the time, so little or no risk of hallucination. Wispr Flow works pretty well. You can train it to correct my annoying verbal and linguistic tics. As do all lawyers, I tend to overcomplicate my sentences. It would be great to train AI Assistant to take my turgid rambling output and rectify it by reference to specific rules I specify. For example, “convert all nominalisations into direct verbal constructions”. “Replace all weak verbs with strong ones and avoid equivocal language like I think that — and this seems to be —”. These features help me eliminate awkward aspects of my natural speech. Getting the rules just right is important, but you can overdo it. Sometimes the output is nothing like what I said and feels generic and “sloppy”. It includes a handy feature: you can select your output text, and it will polish it according to your preferred rules. This will directly challenge Grammarly [https://www.grammarly.com/] and other spell‑checking applications. All this does prompt the question: why isn’t voice recognition more prevalent? Accurate voice recognition is hardly new. Maybe these tools will send it over the top. But the ongoing deprecation of office workspaces has something to do with it too. After all, forty years ago, voice recognition was the norm. We didn’t call it that: we called it “dictation”. We didn’t use tech—beyond cassette recorders. The intelligence was real: a human typist. In hindsight, having one employee dictating and another typing, and the inevitable iterative process between them to finalise a document, looks highly labour-intensive. Collapsing that down to a single employee composing and typing was less so—once the office manager [for younger readers, that’s the chief operating officer—Ed] got over the idea that “we don’t pay lawyers to type”. But dictating without having to type is surely quickest of all. So where is it? Why isn’t Wispr Flow eating the world? One factor may be the concurrent changes in the physical workplace that came with new technology: dedicated offices gave way to shared offices, then cubicles, then open‑plan spaces, and finally hot‑desking. Over that time we became quieter and more timid. Noise is distracting, though on a busy trading floor it is a fairly pleasant, comforting hubbub. Speaking aloud can feel slightly embarrassing. At the same time, dictation — indeed, all oral communication — has deprecated sharply. Desk phones have disappeared, replaced by Teams and Zoom. Phone calls — once ad-hoc, bilateral, analogue affairs — have given way to prearranged online meetings. Calling someone out of the blue, without arranging it in advance, is now the height of rudeness. This, incidentally, is one advantage—probably the only advantage, beyond the saved ground rental—of our post-COVID generational shift towards remote working: suddenly we all have our private spaces back, and voice recognition is a realistic prospect. Local and distant networks The above touches on the crux of the digital divide: since digital networks arrived in our working lives — the world-wide web broke its academic borders in the early 90s — the nature of our networks has changed. Networks in the pre-digital era The pre-digital era was based upon small, rich, accretive local networks. These networks were multilateral: they involved people interacting, in person, in the workplace, or in physical meetings. The longer such a network lasted the better it got, as relationships formed, ways of working evolved and “institutional capital” accumulated. Hence these were rich, accumulative networks. They were augmented by a thin layer of what I will call remote networks, which only got used were the local ones weren’t suitable. These these could be electronic — phone, fax, telex or telephone — or physical — snail mail or courier — but in any case were mainly bilateral in nature, and didn’t have the quite same “capital accumulating” nature. Building relationships was central to business in the pre-digital era. Local networks in the pre-digital era Local physical networks had distinct advantages over remote ones. They were immediate and multilateral — people who were on hand could convene quickly —the communications were rich — not just “text” but “speech”: imbued with relationships, institutional knowledge, body language and context — multilateral — as many people as you needed could attend — and instant — but at the same time informal: dialogue was not systematically captured, logged, audited, or recorded, allowing candour, correction and the safe removal of faux pas, irrelevance and error before creating any formal minute or record of the meeting — and free — in the sense that there was no incremental internal cost to having already-engaged employees meet together. (Employee cost attribution even today is minimal; in 1990 it was non-existent) since the employees were already on the payroll. Only where a physical meeting was not possible, would you opt for a remote communication. These were comparatively poor — usually bilateral, and at best (phone communication) conveying a fraction of the information of an in-person meeting; telex and fax even less so — or significantly delayed — telex and fax might take hours, mail correspondence days — and in any case incrementally expensive. Telephone and fax costs, postage and courier were meaningful costs per communication. Unlike labour costs, these were not baked in, and could be specifically accounted for. Therefore before the world wide web every business — rightly — would prefer the local rich, free networks available to it — over remote, poor, expensive bilateral networks. We organised ourselves accordingly. Firms organised into offices and branches, where people with a need to quickly interact could do so. Offices organised into compact districts where one could easily meet customers, counterparties, advisors and competitors. We built complex, rich, local, interlocking networks. We built relationships, reputations and trust. Much of the strength of the network was implicit — the shared history, knowledge, and common interests. By contrast, it was hard to build reputation and trust over a telephone line, and impossible by fax. So, these were the critical features of a local network: immediacy, speed, convencience, informality, memory, trust, and subtlety. These features are hard — impossible, really — to quantify. But before 1990, not everything in business needed to be quantified. There was a costs to doing business, and that was that. The wealth of local networks is “illegible”. You can’t see it in the financial accounts. It may was well not be there. That doesn’t mean it isn’t there, however. So: Local, physical, rich, fast, cheap, invisible networks good. Remote, electronic, impoverished, slow, expensive, visible networks: bad. Networks in the post digital era When the World Wide Web burst onto the public consciousness in around 1993 — I remember attending a seminar about it — everything changed, but nothing did. Local networks didn’t change at all. Everything changed about remote ones. Digital, not analogue Remote networks became now digital, not analogue. Whereas analogue communications occupied physical space and took time, and where they could be copied at all, quickly lost fidelity, digital communications occupied no space, could be copied, recorded or moved over any distance instantly and at zero marginal cost. Whereas analogue communications had to be embedded in a “substrate” — paper, mainly — digital data needs no substrate. It travels unaccompanied. Infinite audit While digital solved the problem of incremental cost and created an “opportunity”: infinite audit. As we will see, where there is “infinite audit” you no longer need “trust”: you have all the receipts. But trust turns out to be quite important. Everything, always, being on the record has a chilling effect on candid communication. Things you might once have said are no longer said. This can be a boon and a bane. There is one fork of the information revolution still cantering towards a fully trustless network: crypto. High bandwidth As the physical network has built out and chips have grown faster, bandwidth has exploded. In 1990 it took ten minutes over a 56k dial-up modem to download a day’s worth of email. In 2026 we can stream virtual reality to a mobile device in the scottish highlands. Multilateral In a distributed digital network, everyone is online — connected, and able to send and receive — all the time. Because communications are instant, costless and copied with no loss of fidelity, digital remote networks can be multilateral: we can quickly convene an all-hands video conference. Before the internet, remote networks — phones, faxes and letters — were a poor relation.They were expensive, slow, of poor quality, and cussedly bilateral. Now the remote networks are fantastic. They are fully audited, instantaneous, high-bandwidth, free and multilateral. Not only do they outperform the analog remote networks in every possible regard, they seem also to outperform the rich local networks. COVID as a live-fire experiment For the longest time, Chief Operating Officers around the world [for older readers, that’s the office manager—Ed] wondered about the stubborn cost of maintaining an office in the CBD. They had systematically rolled back employee benefits — “bring your own device [https://jollycontrarian.com/index.php/Bring_your_own_device]” is one thing: but even for McKinsey, “bring your own premises [https://jollycontrarian.com/index.php?title=Bring_your_own_premises]” seemed a step too far. Then came COVID, and we were all bounced into trying it whether we liked it or not. That live fire experiment ended more than 4 years ago, but things have not yet gone back to normal. There is an odd confluence: this suits many employees — especially us older ones, who quite like working out of the box room — and it rather suits the office managers, as it gives scope for downsizing premises — but you might wonder whether it rather misses the point about what is so good about rich local networks. I might return to the question of rich local networks and poor distant networks. All complex systems finds their own configuration, and if we participants don’t like where it ends up, all we can do is change our own behavior and see if that corrects it. If we prefer distant networks over local ones in our daily activities; if we accept the premise that cost, convenience, and auditability outweigh the value of slowly built relationships, trust, and wisdom, we should not to complain when the system treats us poorly. Wispr Flow redux Just wrapping up with Wispr Flow. Interestingly, the longer I wrote this newsletter, the less I relied on Wispr Flow, though I’m back to using it now for this closing section. I’m not sure whether this reflects a preference for typing or simply familiarity. Wispr Flow has a neat “polishing” function it does not depend on voice recognition. You can apply it to any text you’re editing, and you can also create your own rules and AI prompts for it. For example, I was exasperated at first that Wispr Flow did not handle “smart” punctuation (curly quotes and so on): I am a bit anal retentive about curly quotes, en-dashes and thing like that — but you can set a polishing prompt to convert everything to them. It strikes me this is how we should be using AI: not to replace human effort in toto, but rather to take away the boring faff of interacting online every day — getting typing and punctuation right. Thanks for reading! This post is public so feel free to share it. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit jollycontrarian.substack.com/subscribe [https://jollycontrarian.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2]

Computation, free will and the big bang

So remember, when you’re feeling very small and insecure—How amazingly unlikely is your birth.And pray that there’s intelligent lifeSomewhere up in space, becauseThere’s bugger all down here on Earth. — Galaxy Song, Monty Python’s Meaning of Life, 1983 This piece started out as a book review, of Professor Brian Klaas’ recent book Fluke: Chance, Chaos and Why Everything We Do Matters. It quickly would up as a rumination on a much, much bigger topic for which Fluke, is really only the occasion, not the subject, for it sets off one of the many bees in the JC bonnet. The bee in question is the presently fashionable world-view among tech-bros and people who spend too much time online that everything can be computed. This “computationalist” outlook, and its best practical example, the omnipresent Turing machine [https://jollycontrarian.com/index.php/Turing_machine], has wormed its way into contemporary intellectual life and is at risk of getting stuck there. This, I think, would be a tremendous pity. The computationalist view runs more or less as follows: * Turing machines are, by design, deterministic. * You can model the human brain as a Turing machine. * Therefore, the brain is a Turing machine. * Therefore, the human brain is deterministic. * Therefore, human agency is an illusion. Professor Klaas himself is a good case study, because much of his book is wise and — excuse the pun — enlightening, but he still winds up mistaking the map for the territory [https://jollycontrarian.com/index.php/The_map_and_the_territory] and concluding that since we can be made to seem like machines, we are machines, and are therefore constrained as machines. This seems like a typically dusty academic debate, but it has real-world implications for not just the hypothetical freedoms of metaphysical will, but the real-world freedoms of thought and action. Where you stand on this question makes a political and cultural difference to how you feel about where other people should be allowed to stand on it. We will turn to that at the end. My readers keep me going. For more posts like this — and even better ones — consider supporting the JC. Only half a pint a week and you get to feel like a Florentine arch-duke in 1470, and I get to feel like Sandro Botticelli. Plus, you get access to Premium JC and all kinds of neat stuff. A big “but” I have been grappling for some time with what is so troubling about the modern techno synthesis — End of History [https://jollycontrarian.com/index.php/The_End_of_History_and_the_Last_Man] millenarianism, truth [https://jollycontrarian.com/index.php/Truth], data modernism [https://jollycontrarian.com/index.php/Data_modernism], determinism [https://jollycontrarian.com/index.php/Determinism] and the oddly illiberal fix these logical ideas put us in — but have struggled to put my finger on it. In Fluke: Chance, Chaos and Why Everything We Do Matters, “disillusioned social scientist” Brian Klaas [https://jollycontrarian.com/index.php?title=Brian_Klaas&action=edit&redlink=1] helps. But not as he means to. This is a silly book. It makes a great show of being interesting, but then steers off a cliff in its final chapter, which presents as a big “but”. As G.R.R. Martin puts it, “Everything before the word ‘but’ is horseshit.” You might think I am being unfair or uninformed in my opinion, and so I might — it would hardly be the first or last time — but even Professor Klaas would have to admit it is not my fault: I can hardly do otherwise. It is written in the stars. So, you shouldn’t blame me, but if you do that’s not your fault, either: that too, is written in the stars. Indeed, Professor Klaas would oblige himself to concede, as he asks us to, that all of creation — all its galaxies and nebulae, all its tragedy and comedy, its infinite majesty and infinitesimal frippery exactly as it appears, right down to this silly article about that silly book — has been coming, unerringly and ineluctably, since the dawn of cosmic time itself. Which means I have an excuse — nay, a compulsion — to talk about metaphysics. God, mind and free will “And if we are to prevent the lights going out on our lives once more, we should ask ourselves crucial questions. Where are we? How did we get here? Why did we come? Where do we want to go? How do we want to get to where we want to go? How far do we have to go before we get to where we want to be? How would we know where we were when we got there? Have we got a map?” —Rowan Atkinson & Richard Curtis, Marcus Browning MP [https://youtu.be/P8ZQhB2DKQQ?], 1980 Anyone with a tertiary education who didn’t take at least one philosophy course missed the point of the exercise. The sine qua non of the university experience is, surely, stage one metaphysic [https://jollycontrarian.com/index.php/Metaphysics]s. Its three great questions — how did we get here, how does consciousness work, and do we have free will? — are not a million miles away from those posed by the hon. Marcus Browning. “God” and “mind” really collapse into “free will”, and that boils down to how you feel about the causal principle: has every action in the cosmos a complete and identifiable set of reliable mechanical causes, or is the universe some how unpredictable, biddable and capricious? Those who embrace the causal principle are broadly classed as “determinist”. Since, given enough information, one can precisely calculate the outcome of any interaction between any objects in the universe, the interaction of composite objects must be a function of their components. Since classical mechanics assumes that, besides elementary particles, all objects are composites it follows that the behaviour of everything can be reduced to the behaviour its components. It is not quite turtles all the way down, though: it stops at these subatomic particles. Their behaviour, theoretically, can be traced back to the singularity [https://jollycontrarian.com/index.php/Singularity] whence we, and the whole of creation, came. There is, therefore, nothing new under the sun. All was inevitable, Q.E.D. If this is right, certain popular beliefs about the cosmos cannot be true. There cannot be a non-material interventionist God: if there were, some events would not have a material cause. Humans cannot have free will: all human actions are the product of traceable electrochemical impulses in the brain. While much of the West has acclimatised to the first idea, the second one is taking some getting used to. If this all seems a bit desolate, there are a broad range of non-determinist philosophies that might suit you better. Beyond “non-determinist,” these philosophies have no catch-all label. If it would not lead to confusion you might call them “casual” as opposed to “causal”. They include most types of mysticism and religion and any belief in magic or the occult. They also take in the widely traduced “continental” philosophies of relativism [https://jollycontrarian.com/index.php/Relativism] and postmodernism [https://jollycontrarian.com/index.php/Post-modernism], but also some “rationalist” parts of the intellectual landscape: classical economics, much of behavioural and social science, systems theory [https://jollycontrarian.com/index.php/Systems_theory], chaos theory and — though it might horrify Professor Dawkins [https://jollycontrarian.com/index.php/Richard_Dawkins] to acknowledge it — evolutionary biology [https://jollycontrarian.com/index.php/Evolution_by_natural_selection]. These are all pragmatic, “emergent” philosophies that take the world as they find it. While they might find their explanations in granularities, they tend not to extrapolate actual predictions from them. Science’s insistence on analytical rigour in inferring causal principles from observed regularities, seems an incontestably good and valuable thing: it has vouchsafed flight, sanitation, aqueducts, viticulture and so on. But if we say, as Professor Dawkins does: “Show me a relativist at 30,000 feet and I’ll show you a hypocrite”. We throw quite a lot of baby out with the mystical bathwater. Like the idea we can have free will. So we are at a bit of an impasse. To enlightened liberals — often the same people as the modern rationalists — free will is also a generally good and valuable thing. All modern polities — not to mention all the ancient ones — depend on it for their basic coherence as social structures. There is no room for any immaterial agency inside a human that can override the eternal logic of cause and effect. To act a propos nothing but “animal spirit” must, at some synaptic level, violate Newton’s laws about conservation of energy. This is a great paradox: if we have free will, science is falsified. If we do not, the entire premise of civilisation is. Thus, the “free will versus determinism” debate presents a dilemma that has animated stage one philosophy classes as long as anyone can remember. Every now and then, someone comes along and tries to sort it out for once and all — the late Daniel Dennett [https://jollycontrarian.com/index.php/Daniel_Dennett] spent the last thirty years of his life trying to do so, for the determinists, but — in my mind at any rate — fell a long way short. Contending that freedom of thought and expression is an illusion is a hard sell. Nor is it one that, in the final analysis, makes a great deal of difference. Either the causal principle is true, or it isn’t, and we could have free will, or none, and it would not make any difference to how we must interact with the universe. Plato had this much right: we have only shadows on the wall of the cave to go by. It doesn’t much matter what causes them. That is why “God, mind and free will” is only a stage one course: it illustrates philosophy’s gift for paradox and at the same time, its deeply-rooted irrelevance. The debate is titillating but, at the same time, stale: causal principles seem to hold, and we seem to have moral agency, and humankind has managed the theoretical conflict ever since we first looked up at the stars. The transparently silly answers the paradox throws up suggest it is a silly question. So, for the longest time the scientists and humanities have kept out of each other’s way. But developments on our ways of being occasionally reopen the debate. The alarming advances in “compute” are doing that now: there is a generation of technologists who are once again yearning to solve the universe. Professor Klaas is here to help them. The great divide In modern academic discourse, there are two strands of thought: the icy rationalism [https://jollycontrarian.com/index.php?title=Rationalism&action=edit&redlink=1] of the STEM disciplines and the post-modernist [https://jollycontrarian.com/index.php/Post-modernist] mumbo jumbo of the humanities. This is, of course, an outrageous generalisation, but it also, kind of, isn’t. Occasionally, shots ring out across the aisle: mendacious physicists fox humourless sociology journals [https://jollycontrarian.com/index.php/Fashionable_Nonsense:_Postmodern_Intellectuals%E2%80%99_Abuse_of_Science] into publishing PoMo [https://jollycontrarian.com/index.php/Post-modernism]-tinged gobbledegook, while philosophers [https://jollycontrarian.com/index.php/Richard_Rorty] and historians [https://jollycontrarian.com/index.php/Thomas_Kuhn] ruffle biologists’ feathers by pointing out that their discipline cannot [https://jollycontrarian.com/index.php/The_Structure_of_Scientific_Revolutions] be as rational as they would have us believe [https://jollycontrarian.com/index.php/The_Structure_of_Scientific_Revolutions]. For all that, there is buyer’s remorse on either side: a clique of humanities academics, uncomfortable with Post-modernism [https://jollycontrarian.com/index.php/Post-modernism]’s reductio ad absurdum [https://jollycontrarian.com/index.php/Reductio_ad_absurdum] that nothing means anything, have organised themselves into a rationalist stance, huddled around the theory of evolution [https://jollycontrarian.com/index.php/Evolution]. Prominent among them are linguist Steven Pinker [https://jollycontrarian.com/index.php/Steven_Pinker] and late philosopher of mind Daniel Dennett [https://jollycontrarian.com/index.php/Daniel_Dennett] but their high priest — if you’ll forgive the expression — is biologist Richard Dawkins [https://jollycontrarian.com/index.php/Richard_Dawkins]. This group is outspoken in its criticism of religion, relativism [https://jollycontrarian.com/index.php/Relativism] and other ostensibly magical accounts of human ingenuity. You may wonder, as I do, whether there isn’t something ironic about this, given the contingency of “Darwin’s dangerous idea [https://jollycontrarian.com/index.php/Darwin%E2%80%99s_dangerous_idea]”. As Professor Dawkins explains it, the evolutionary process takes us away from an imperfect now; it does not converge upon a perfect later. It was Professor Dennett [https://jollycontrarian.com/index.php/Daniel_Dennett] who did the most to integrate evolutionary concepts into the humanities, sheeting them back to information theory [https://jollycontrarian.com/index.php/Information_theory] by means of that algorithm [https://jollycontrarian.com/index.php/Algorithm] — the “universal acid” that explains everything. Evolution [https://jollycontrarian.com/index.php/Evolution_by_natural_selection] is profoundly algorithmic — that is Darwin’s Dangerous Idea [https://jollycontrarian.com/index.php/Darwin%E2%80%99s_Dangerous_Idea], in a nutshell — and so, of course, are computers: Turing machines [https://jollycontrarian.com/index.php/Turing_machine]. So if this is how evolution works, and this is how computers work, why not brains? At the other end of the STEM spectrum, information theorists [https://jollycontrarian.com/index.php/Information_theory] and cyberneticists strive to apply their inorganic learnings to human systems. They work backwards towards the meatware, noting the calculable “Shannon entropy [https://jollycontrarian.com/index.php/Shannon_entropy]” of the symbol strings by which humans communicate. Cybernetics is a positivist sort of systems theory that strives to solve things from the middle — to solve the management conundrum — but there are better forms of systems theory that start at the edges of the network, with agents and their immediate interests, and let the middle of the system look after itself. See the end-to-end principle [https://jollycontrarian.com/index.php/End-to-end_principle]. Now, computation is a bounded, mathematical, calculable, zero-sum endeavour. It is fully causal. It is intensely deterministic. Therein lies the rugged beauty of the Turing machine [https://jollycontrarian.com/index.php/Turing_machine]: fast, cheap and utterly predictable. It doesn’t make mistakes. It doesn’t pick up the wrong end of the stick. It can’t. It is obliged to follow a determined causal chain of reactions. All steps can be reverse-engineered. This makes it quite a neat nomological machine [https://jollycontrarian.com/index.php/Nomological_machine]. This compels a worldview where everything is directly, bi-directionally causal: assuming a given operation, a machine state N’ can be computed by reference to machine state N that preceded it, and vice versa. One can journey up and down this chain of reactions indefinitely without losing fidelity or risking a different outcome. It would be great — some think — if we could quantise and optimise human intelligence the way we optimise computing. But while we understand pretty well how Turing machines [https://jollycontrarian.com/index.php/Turing_machine] work we don’t, very well, understand how brains do. In particular, we don’t understand how consciousness works. We don’t know what it is. We don’t even know if it is. Professor Dennett was not the first to see in Turing machines a framework for understanding human consciousness, but he pushed the idea harder and more successfully that anyone before him. But this is not so much to anthropomorphise machines as to robomorphise [https://jollycontrarian.com/index.php/Robomorphise] humans. For a Turing machine [https://jollycontrarian.com/index.php/Turing_machine] is a weak metaphor [https://jollycontrarian.com/index.php/Metaphor] for human intelligence. Turing machines [https://jollycontrarian.com/index.php/Turing_machine] are not like humans: as George Gilder drily noted, that is why we build them. Humans are inconstant, slow, easily bored and they take up space. They are hopeless in the environments in which machines excel. But in the chaotic, unbounded, inchoate complex [https://jollycontrarian.com/index.php/Complex] environments in which humans excel, machines — even generative AI — are hopeless. It is precisely human flexibility, imagination — our freedom from causal constraints that gives us this edge. Humans can imagine. Machines cannot. There is a flip side: as a result, humans are inconstant. They can “misinterpret”. They can pick up the wrong end of the stick. Fluke This is the category error [https://jollycontrarian.com/index.php/Category_error] Professor Klaas makes in the last part of his book, having made a great show of avoiding it in the first three quarters. There is much to agree with in his groundwork — he talks perceptively about the pervasive contingency of complex systems, but it turns out that he has deliberately taken up what he must regard as the wrong end of the stick to make a point. Because all computer operations are provably causal — garbage in, garbage out — and because computers superficially resemble brains, the temptation is to infer that humans are unavoidably causal too. This Oolon Colluphid [https://hitchhikers.fandom.com/wiki/Oolon_Colluphid]-style “puff of logic” prioritises causality. It rules out God, the mischievous supernatural and all those big-shirted French post-structuralists — but jettisons the principle of free will, too. Hence, if true, the inevitability, from the Big Bang, of this review. I had no choice but to read Professor Klaas’s book, and no choice but to be exasperated by it. If there is a God, she is mendacious indeed. But she is also as pointless as the universe she has created. Though the universe seems disinclined to random irregularities — it conforms, after a fashion, to our cosmological models — our sample size is minuscule in a spacetime of incomprehensible proportions. The data upon which we found our assumptions of universal causation is, for all intents, nil. And it is vulnerable to our own evolutionarily-conditioned selection bias [https://jollycontrarian.com/index.php/Selection_bias]. What we see is all there is [https://jollycontrarian.com/index.php/What_you_see_is_all_there_is]. We have evolved to truck in regularities. Ostensible irregularities we suppose, without evidence, can be explained by as yet undetected causes. But even that is a contingency. So, this is all a bit wishful — for those who don’t find it utterly desolate. Still, Fluke starts off brightly. There is good discussion of path dependence, contingency and convergence. Our existence here is the product of a colossal sequence of flukes. Things would not have had to be very different at any point in our evolution for us not to be here at all. For such a glib observation, Professor Klaas hammers it hard. Before long, we see why. He turns to chaos theory and invokes the familiar metaphor of Amazonian butterflies. But the lesson of chaos theory is not that “a butterfly’s wing-flap caused the Ottoman Empire to collapse”, but that for all we know a butterfly’s wingflap could have. That is illustrated by the marvellous contingencies Klaas illustrates — but he is looking out his rear window at them, as he drives away, by which stage, they are no longer contingencies. They are crystallised histories. They are no longer emblematic of chaos. They are now — to the extent they were recorded at all — fixed historical data. The contingent process — our observation of these data — is complete. What remains can be represented, without much loss, as a string of symbols. It is but a single path between two points in a garden not just of forking paths, but infinite, random, interconnections. To choose one path is to forgo all the others. This is the opportunity cost of existence. The string of symbols we are left with have no meaning until we run our information processing apparatus over them. It matches against familiar patterns and creates a narrative. In this way our symbol strings produce meaning. This is not a binary, Turing-style symbol-processing operation, but an imaginative one. It, too, is contingent, a willed extraction of one meaning from a vast plurality of possibilities. We fox ourselves that the story we chose is the “true” one. That is the pattern we draw on the side of the Texan barn [https://jollycontrarian.com/index.php/Texas_sharpshooter]. But we are not Turing machines. It is not inevitable until it is made. At least with history there are data. We have at least something to draw our fancy casual chains over and declare sacred and “true”. This is not true of the future. Here our uncertainty is “aleatory” — it has not happened, so we cannot know it — and not merely “epistemic” — it has happened, unobserved, so we don’t know it. In a complex adaptive system [https://jollycontrarian.com/index.php/Complex_adaptive_system] filled with intentional agents, building that narrative and generating consensus about what has already happened is hard enough. Predicting what will happen next is much harder and, as the horizon recedes, quickly becomes hopeless. Complex adaptive systems adapt. They have autonomy. Intentional agents intend. They have agency. Pretending they are mechanical ducks for the sake of a commitment to causation gets us nowhere. Professor Klaas even states this, outright: he cites a neat experiment in which social scientists were presented with sets of historical input data about human subjects and asked, based on their own published theories, to predict behavioural outcomes. They were, of course, systematically unable to get anything right. Physical scientists laugh up their sleeves at social science, but the outcome would be no better were you to ask a biologist, in a double-blind test, to predict the present form of juramaia sinensis, a tiny, nocturnal, insectivore from the Jurassic era. And nor would the mechanical physicists do any better. We are not puzzled that professors of ballistical mechanics are no better at cricket than anyone else. Here we must be careful not to make another category error. There is, thereby, little practical difference between “unknown events in the past” — epistemic uncertainties — and “unknowable events in the future” — aleatory uncertainties. But — and this is rather the point — there are different things to take from that similarity. Professor Klaas proves it by drawing an opposite conclusion to the one I would have: rather than taking it to illustrate how unreliable historical data is, he takes it to show that the future that is no less set in stone than the past. At this late stage Professor Klaas declares, as might a newly-minted philosophy undergraduate, that there are two alternatives: either the causal principle holds, the cosmos flies by calculable wire, and the reason we can’t better predict it is due to our own inadequacy and lack of suitable data, or there is free will, science is worthless and the universe is random. Doing without causation seems unthinkable, and the cosmos appears to be ordered and science seems to be worthwhile, so Professor Klaas, with more exhilaration and less regret than I would, embraces causality and gives free will the old heave-ho. The thing is, on their face, both are plainly preposterous positions. Professor Klaas has boxed himself in with a silly stage one a priori [https://jollycontrarian.com/index.php/A_priori] thought experiment. Given that he calls his blog “the garden of forking paths [https://www.forkingpaths.co/welcome]” — named for a Borges short story [https://en.wikipedia.org/wiki/The_Garden_of_Forking_Paths]— seems to be all about contingency and unpredictability of complex systems, so it is a bit baffling that Professor Klaas still concludes that everything is pre-ordained. Since, even if so, we have no practical ability to predict what will happen, and we seem to have autonomy, and in any case there is no way of knowing, then what do we achieve by saying “well, it is all predetermined”. How does that even help us? The computationalist view, the infinite, and the end of political freedom “It wasn’t infinity, in fact. Infinity itself looks flat and uninteresting. Looking up into the night sky is looking into infinity — distance is incomprehensible and therefore meaningless. The chamber into which the aircar emerged was anything but infinite, it was just very, very, very big, so big, that it gave the impression of infinity far better than infinity itself.” —Douglas Adams, The Hitch-Hiker’s Guide to the Galaxy [https://jollycontrarian.com/index.php/The_Hitch-Hiker%E2%80%99s_Guide_to_the_Galaxy] The question is not without its practical consequences. In one sense it is a desolate view, hankering to get to the end to the journey we define ourselves by being on: life. The computationalist view offers the same desolate resolution as do the Abrahamic religions: an end state, where everything is solved, everything is managed, a benevolent higher intelligence silently ensures everything is optimised, and no-one wants for anything. Religions promise that in an afterlife: computational techbros promise it here, if not now, then in the foreseeable future. But a universe without problems to be solved, lots to be improved or games to be played — with no riddles, mysteries or hazards — of unlimited accommodation is hell as much as it is heaven. Utopia and dystopia are the same. No-one would like such a world, and we can already see what happens when we approach it: people wilfully ruin it. Francis Fukuyama [https://jollycontrarian.com/index.php/The_End_of_History_and_the_Last_Man] — he has had some bad press, including from me — captured it well: Experience suggests that if men cannot struggle on behalf of a just cause because that just cause was victorious in an earlier generation, then they will struggle against the just cause. They will struggle for the sake of struggle. They will struggle, in other words, out of a certain boredom: for they cannot imagine living in a world without struggle. And if the greater part of the world in which they live is characterised by peaceful and prosperous liberal democracy, then they will struggle against that peace and prosperity, and against democracy. Heaven would be awful. The Devil has all the best tunes. But there’s a more insidious implication, too. A causal, solvable universe implies a single truth. It means those who have that truth are justified in suppressing anyone who promotes a different idea. Scientism leads to this: we see harmless forms of it in Richard Dawkins [https://jollycontrarian.com/index.php/Richard_Dawkins]’ grumpy (and uninformed) tracts against religion [https://jollycontrarian.com/index.php/The_God_Delusion]. Elsewhere, the religious are just as guilty of the same kind of intellectual imperialism. But, either way, it is an illiberal instinct: to assert a single solution — let alone to enact it — is to stop people from living their lives as they would choose. Citizens should be free to make their own mistakes: who knows? We might all learn from them. Preventing it might deprive us of the serendipities that have characterised scientific discovery over centuries. In any case we should not wish ourselves to a utopian endpoint that none of us would like much if we got there. We are not non-player characters. We are not hopeless invalids better hooked up to a pleasure machine for our own wellbeing. We do not want to spend an eternity making small-talk with do-gooders. We came here to struggle and make sense of things. I’ve wittered on enough and have to finish this now to get out the door to a talk by Iain McGilchrist, whose marvellous book The Master and his Emissary [https://jollycontrarian.com/index.php/The_Master_and_his_Emissary] gives a much more hopeful perspective on this very question. See also * Robomorphism [https://jollycontrarian.com/index.php/Robomorphism] * The Master and his Emissary [https://jollycontrarian.com/index.php/The_Master_and_his_Emissary] Thanks for reading! This post is public so feel free to share it. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit jollycontrarian.substack.com/subscribe [https://jollycontrarian.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2]

The end-to-end principle

This is a free preview of a paid episode. To hear more, visit jollycontrarian.substack.com [https://jollycontrarian.substack.com?utm_medium=podcast&utm_campaign=CTA_7] “I once got all the way from Glasgow to Edinburgh without a ticket. I walked.” — Sid Snot, The Kenny Everett Video Show. “There are more network use-cases [https://jollycontrarian.com/index.php/Use-case_obsolescence] in heav’n and earth, Horatio, than are dream’t of in your philosophy.” — Shakespeare, Spamlet I, vi An iron-fisted Romanian “The Bickerings,” ancestral home of the Contrarian clan, is freezing old pile in Squatney Green. It is cold enough, but made worse on account of the JC’s missus, the Contesă Birgită von Sachsen Rämmerstein, who controls the central heating with an iron fist. The Contesă grew up in a stone castle in the high Transfăgărășan, her father was a tyrant and she has therefore grown accustomed to a chilly ambience. The family was grand but impecunious, and she habitually regards any attempt to put temperatures into double figures as evidence of immutable moral decay. “Eef you are cold,” she is fond of saying, “you should put on a hat.” I am, by these standards, weak. I am often tempted into defiance when she is not looking. Until now my meagre resistance has been mainly useless: the Contesă is gimlet-eyed, and immeasurably helped by our central heating system which was designed about the time they built the computers for the Apollo programme, and it has similar functionality. While it can, I am told, schedule and regulate temperatures this requires an advanced facility with algebra that I, alas, do not have. Nor will the Contesă countenance my occasional suggestions that we upgrade to a modern central heating system with an intuitive user interface. That would involve massive expenditure and, besides, capitulate to my lack of Transylvanian fibre. But recently things have changed. I have identified a way of fitting inexpensive replacement valves on our radiators. They are wifi-enabled and fitted with a smart thermostat. They can be programmed, controlled and adjusted from an app. I used the meagre allowance the Contesă grants me and bought a set of smart valves. As the northern hemisphere winter grinds its saturated way to a squelchy close, retailers are trying to shift their inventory before the spring arrives, the world warms up and it is too late. The valves are currently on sale. I bought seven and I got a bargain: they were half price. Thanks for reading! This post is public so feel free to share it. The problem with central heating systems Until there was the internet, the problem with upgrading a traditional central heating system was exactly that: it is a centralised system. It has a heavy structure. There is a single central brain, a designed-in “nervous system” and it is integrated and not articulated: if you want to upgrade any part, you need to upgrade the lot. The brain controls two systems: a water system, that sends hot water from the boiler out to spur radiators around the house, and an electrical system that measures temperatures around the house with remote thermostats and sends that information back to brain. The brain has a “preferred setting” from which it controls how much water it should send out to the radiators. If the thermostats say, “it is too hot” the central system shuts off. If they say, “it is too cold” the central system opens up. There is no great intelligence in the system: it has some kind of a time scheduling function and a temperature gauge, and that is it. More sophisticated systems divided the house into temperature zones, each controlled by a single thermostat. But beyond that, to micro-manage their local environment, users would have to manually adjust the radiators. Each has its own analog thermostatic valve connected to a switch that gates the pipes running into the heater. If it opens, water flows in. If it closes, water stops. But the manual valves are not connected to the central brain: if a radiator’s local valve is fully off, the radiator will not come on, whatever the central system tells it. The electronic thermostats that talk to the central system’s brain are overriden by the manual ones that do not. On the other hand, if the central system thinks the zone is too hot, it won’t send any water to the radiators, so it won’t matter how the local radiator valves are set. The system is, therefore, something like a binary logic gate: a radiator heats only if both the electronic and the manual valves open. It is what lawyers, and grammarians, would call conjunctive: an “and [https://jollycontrarian.com/index.php/And],” not an “or” [https://jollycontrarian.com/index.php/Or]. It all takes quite a lot of — well — plumbing and wiring to install such a system, and therefore quite a lot of disruption if you want to replace it. The electronic thermostats are hardware-controlled and connected by cable, chased into the walls of the house. God forbid should I suggest we move a thermostat and upset the Contesă’s Farrar & Ball™ elephant spunk™ skim coat wall finish. Since our control panels were designed in the late 60s, they have little of the functionality we are used to these days. They were not designed to be upgraded. They are not modular. Their programming is hard-coded into ugly little devices dotted around the house. Not just ugly, but dysfunctional: they hail from a time before “user experience” was any kind design criteria. There are four buttons, embossed with hieroglyphics I don’t understand, and a small liquid crystal display panel that displays different hieroglyphics that I don’t understand either. It isn’t clear what any of them do. How we originally programmed them is now lost to posterity, and for some years now we have just tolerated the meagre assistance they provide in the depths of winter. For the Contesă, this is business as usual. Over the years I have invested in knitwear. The heating comes on when it deigns to come on, goes off when it deigns to go off and that is that. The Contesă and I shuffle around our frigid house, wrapped up in mittens and scarves. The problem is solvable because of the ingenious design of the valves. They accord with a principle of network design called the “end-to-end principle”. It is quite unintuitive but, when you get your head around it, utterly brilliant. The design of the internet is fastidiously based on the end-to-end principle. But — and this is the beautiful thing about design — the internet’s construction in the 1960s long preceded theory that made it viable. The end-to-end principle explaining why the internet works was not identified or formalised until 1984. How to design networks When creating a network of dispersed “users” — call them “endpoints” and the system a “distributed network” — you have design choices to make. Different network designs have different pros and cons and different consequences for scaling, efficiency and task management. It is all rather mathematical. Direct point-to-point networks The simplest, in theory, is to link every endpoint in the network directly. We can see this rapidly gets complicated. With a two endpoint network there is one link. Adding a third endpoint, requires two new links. Adding a fourth requires three. The problem grows arithmetically as you add new users. Given a total userbase of N, the number of new connections needed to add a single user is N - 1. The more endpoints, the more links required to add a single new user. The application for which the network is used is important. If all users will be interacting with all other users all the time, this may be the maximally efficient design. An example of this kind of network is a high-performance computing GPU cluster used for AI training: here the point is parallel processing, where every node exchange data directly with every other node on the “network” (a series of gates on a graphics processor) at maximum speed with minimal latency. But it is a pretty unique case. There aren’t many cases where a point-to-point network is a great design choice. Most human networks are not like that. We only have a certain amount of personal bandwidth. We can only read one book at a time, or watch one film at a time. Our interaction with a given network is highly selective, and in fact unique: how I experience and interact with London is unique: I go to the Cherry Tree [https://east-finchley.com/directory/cherrytree/] in Ost Finkelstein for my apples. The Contesă goes to an odd little Russian shop [https://dachashop.co.uk/?] to get ingredients for her borscht. She does not need a link to my greengrocer. I don’t need a link to her cabbage purveyor. In this case a fully-connected network becomes progressively harder to scale and less efficient. The more endpoints in the network, the less likely user is to communicate along a given link. A directly linked network, therefore, contains a great deal of redundancy. Hub and spoke Another way of designing networks is a hub and spoke model where local users are connected to a single large hub which has a much greater bandwidth connection to other hubs, to which other local users are connected. This is how, for example, railway networks work: There are a small number of “nodes” — stations — and these have limited set of very-high bandwidth connections between them. Endpoints — passengers — must make their own way to a node. But “adding new users” is therefore, from a “hub and spoke” network’s perspective, a low-cost, low complexity activity. It carries a predictable, low marginal cost. building additional hubs and connectors between them — that is, rails and tunnels — is obviously more expensive, but it is a one-time expenditure that happens infrequently and supports a greater capacity to handle users on the network. It is much, much less wasteful than a point-to-point network. But hub-and-spoke models have some odd inefficiencies of their own. For one thing, connection routes on the network may be much longer and more complicated than is needed to cross the physical distance between user endpoints in real space. The London Underground is famous for this sort of thing. Visitors who take the journey from Wood Lane, on the Circle Line, to White City, on the Central Line — which takes about three quarters of an hour via Liverpool Street, or over half an hour with two changes, via Notting Hill Gate and Edgware Road —deposits them across the road from where they started. Furthermore, knocking out a single hub can break the whole network, at least for anyone connected to it, or depending on it for a through link to another person. The hub-and-spoke model is, nonetheless effective in most cases, at least where nodes are not very close to each other. Airlines run a similar arrangement, with regional airports feeding central hub airports like Heathrow and Chicago, which handle long-haul flights between them. Postal services, too, are hub-and-spoke models, often with several layers of hubs arranged as spokes around each other. But typical social networks are not like that. In urban communities a lot of different networks live on top of each other. There are all kinds of random intersections and interconnections between disparate networks. It is all very fluid. There’s no central control: networks arise and die back as individuals need and use them. These networks don’t have any intelligence of their own: all the intelligence lives within the individual members of the communities. At network endpoints, in other words. Community members figure out which networks to join and what to use them for. Neither the point-to-point or hub and spoke networks are efficient when people are often close to each other and sometimes distant, and where network needs are constantly in flux. In a dynamic, fluctuating community users need something that can do a bit of both. Mesh network There is, as Tony Blair once said, a third way. (There are doubtless others, but I don’t think you would thank me for embarking on a comprehensive survey of all network ontologies.) In this case, there are a great number of nodes, and most endpoints function as nodes too. the only difference between a true endpoint and a node is that an endpoint only has a single connection. Because there are countless nodes, nodes are not all interconnected but, instead, connected only to nearby nodes. Distant nodes are only indirectly connected through one or more intermediate nodes. Now there are any number of indirect connection paths between any two nodes. The more nodes in the network, the more possible connection paths between them. This solves all three of the problems identified above, and quite quickly. Firstly, it is easy, and cheap to add new nodes and endpoints to the network — each needs a small number of connections,: it may be as few as one, so the “arithmetic increase in cost to connect an additional user” problem does not exist. The network is easy to scale. The marginal cost of adding users is static, and it is borne by the connecting user, not the rest of the network. User pays. Secondly, it solves the “single point of failure” problem of a hub-and-spoke model. As a mesh network scales, what does increase, geometrically, is “the number of potential connections between any two points”. The bigger the network, therefore — the more nodes it has, and mesh networks tend to have a lot — the more robust it is. The more resilient to failure. This means that there are no single, or significant points of failure. If you knock out a node, that only impacts that node, and any endpoints connected only to that node. This is, indeed the fundamental problem that the U.S. Department of Defense’s Advanced Research Projects Agency — DARPA — was trying to solve when it formulated the principles for the ARPAnet, on which the modern internet was founded. The goal was to create a network that could sustain operation during its partial destruction, such as by nuclear strike. A mesh network is largely immune to targeted attack. If you want to knock out the network you must take out all its nodes. The more nodes the network has, the harder it is for a single impulse to destroy it. Thirdly, it solves the hub-and-spoke model’s “stupid-way-of-crossing-the-road” problem, too: since all nodes are connected directly to other local nodes and will always be connected to the ones closest to it, there will never be a need to go from Wood Lane to White City via Liverpool Street. Problems with mesh networks Of course, nothing is perfect and mesh networks have their disadvantages too. For one thing, the route any signal takes across the network is likely to be circuitous. That is a problem if what you are sending is somehow secret. Everyone in the communication chain will get to see it. It’s also a problem if you are a control freak or, for some other reason, you need a predictable route. A mesh network is all very-seat-of-the-pants, make-it-up-as-you-go-along and ad hoc. Furthermore, should there be a time or cost implication of sending a message, then mesh networks can be quite inefficient. The larger one gets, the more expensive, and slow, sending “content rich” messages becomes. But there has been an information revolution in the last 40 years. Electronic signals move down a wire at the speed of light. Speed was not the constraint it once was. But the resource impact of sending a message across a node — not speed of communication, but volume and format of information sent — presented another problem. The variety of human communications There is a down-side to there being an almost infinite number of pathways across a network. It means, to route a given message, every one of those pathways needs to be able to handle the message. Say you built a physical “mesh” network that employed those cute little Citroën Amis to shuttle your messages between individual loading bay nodes on the network. The vehicles are smart, they drive themselves, using an algorithm to determine which nodes to use on the network pass. As long as you are transporting small people and the odd parcel it will work serviceably well. But if you want to transfer a live dolphin, the network cannot manage. You would need to re-engineer the whole network, and every point on it, to cope. You are stuck. You would have to start again. Unless you can figure out a way of working around the chunkiness implicit in a live dolphin. So, whatever your network topology there is always a design decision to be made: what is the universe of items that can conceivably be transported across this network? It is an optimising function, rather like the one we take when buying a car. We know most of car our journeys will be short and involve one occupant with little luggage. For these, a Citroën Ami would be perfectly adequate. Better, in fact, as long as our friends don’t see us. You don’t need a Land Rover with a snorkel to get around the Hampstead Garden Suburb. But there will be times when we need to collect the kids from karate practice, take old furniture to the dump, or go off-roading in Wales. It is worth “solving” for these contingencies. But every now and then it might be useful to have a minibus, or a tractor. But we don’t optimise for these extremes: we just hire in the equipment, or the man with a van, as we need it. The “network” has its limits. Designers of physical networks — even for mesh networks — must do the same exercise. They will optimise for known use-cases, but cannot be expected to predict future use-cases that might come along as technology develops. This is a shortcoming of all models of network design — if you build tunnels that are only ten metres wide, that forever precludes putting eleven-metre wide vehicles on your railway. So, along with a rail network (hub-and-spoke) there is a road network, which is much more like a mesh. The railway is very good at certain transport functions — passenger commuting, or hauling coal around – but not good for nipping up to the highstreet to collect your dry cleaning, or ingredients for borscht. Because the link count in a mesh network is so large, and chaotic, capacity constraints are a particular limitation. This leads to different arrangement of structure and intelligence. For a hub-and-spoke network there is a real advantage to heavily engineering and controlling the central parts. It doesn’t matter of some things can’t go on the railway because there are aways other networks: the road, sea, and air, that can accommodate them. So railways and their designed-on rolling stock are heavily engineered to work together, and closely controlled by a centralised, intelligent monitoring system. But central control of a system has its drawbacks. It is a single point of failure. Any London commuter will know that a central signalling failure can lead to widespread disruption. End users can’t work around it unless they get off the network and use the roads — being a different kind of engineering proposition. The engineering of roads is minimal, and while in urban settings they are controlled, it is lightly. If all the traffic signals go down, the network functions: drivers just have to be a bit more careful. In any case there are two design principles: engineering and intelligence in the middle, or intelligence and engineering at the edges. A railway is a heavily engineered, centrally controlled, intelligent network. All the intelligence is in the middle, and the edges are really easy. You don’t need any particular kit to ride a train other than a ticket. You can just sit there. You just have to remember where to get off. A road is simple, mainly dumb network, with little central intelligence. All the complication, design and intelligence is “at the edges”. Users must bring their own vehicles, and they have to operate them. They have to figure out where to go, by which route, and how to operate their vehicle. The road network is mainly passive. It just sits there. You have to worry about where you are going. The road doesn’t care. Internet as a dumb network So there are smart networks and dumb networks. What about the internet? You could be forgiven for presuming the world wide web—surely the most sophisticated distributed network in the known universe—is highly intelligent. In fact, it is not. It is a supremely dumb network. That, indeed, is its very brilliance. The world-wide web could hardly be stupider. All the brilliance is at the edges. This is partly a function of its genealogy. They built the digital world wide web on a network that was already there, that was designed with a completely different use-case in mind: analog telephone signals. A traditional telephone mouthpiece worked by converting sound waves into an analog electrical signal—a continuously varying voltage describing those sound waves that travelled to the exchange, passed through a series of switches and down another wire to the other caller, where the receiver’s ear piece speaker does the reverse: converting the analog signal back into sound waves. An analog system was a continuous pipe. The exchange would physically dedicate a continuous electrical circuit between callers for the duration of the call. It was like a private, dedicated tunnel. It persisted whether anyone was speaking. It was inefficient for data. The internet wanted to send binary digits — lots of ones and zeros — down the pipe. It did that by converting them into audible tones that the phone line was expecting. That is the famous modem noise — youngsters probably don’t remember it, but for people of about JC’s age it was a thing of marvel and wonder. This Substack is reader-supported. To receive new posts and support my work, consider becoming a free or paid subscriber.

Traitors, prejudice & how to get promoted

Reality TV competitions like the BBC’s Traitors [https://jollycontrarian.com/index.php/Traitors] offer valuable insights into group dynamics and decision making under situations of uncertainty. Thanks for reading! This post is public so feel free to share it. For those bewitched by the unravelling convictions of postmasters, LIBOR rate setters and antenatal nurses Traitors is a superb, but unsettling model. It shows how easily we — be we contestants, viewers, witnesses, prosecutors, judges, juries or poundshop Poirots — can be mistaken, even about very obvious things. How inevitably, where the facts and social dynamics before us are inchoate or contrived, or calculated to mislead, we will be misled. The same cognitive habits and heuristics, that serve us well as we navigate our ordinary worlds of straightforward surfaces and familiar social relationships, lead us astray when we are asked to play strange games of misdirection with unfamiliar participants. These are “games” not in the senses of parlour games like bridge or chess or even poker, but language games: hermeneutic [https://jollycontrarian.com/index.php/Hermeneutic] constructs built from artificial conditions and contradictory and only partially disclosed rules. Where players can reach their objectives only obliquely, while appearing to head in the opposite direction. There are some parlour games like this: Secret Hitler [https://www.secrethitler.com/] — great fun as long as you don’t mind being accused of fascism — has a similar dynamic. There are other common situations like this in our lives. The workplace — often a nest of sharp-elbowed misdirections — is one. So is any political organisation: the clue is in the name. The criminal justice system is another. Traitors is a fully-designed exercise in the wilful perpetration of injustice. Of the twenty-two initial contestants, three — according to the canonical rules — “deserve” to be banished. The others are pure in heart, if not in deed. But even that is a misdirection: all players in the game have the same object— to win it — and that, whether you are traitor or faithful, involves eliminating all the other players. The trick is to to be seen to do as little of the eliminating as possible. The winner is the best at misdirection. How to play For those living under a rock, the comatose and the deeply uninterested in popular culture, here are the principles of the game. Calling them “rules” is a bit of a stretch. Twenty-two contestants convene at a neo-gothic castle near Inverness, hosted by Claudia Winkelman. Having bonded briefly, the players are sat around a round-table and blindfolded whereupon, theatrically, Winkelman assigns 3 of them the secret role of “traitor”. The remainder are the “faithful“. The traitors will shortly meet in private, so have certain knowledge know who each other are and therefore, who are faithful. The faithful know neither. They only know their own status and, about that, to other faithful they are unreliable witnesses. This is a key information asymmetry. It gives traitors an enormous advantage. For the faithful, their putative objective — we will come to why it isn’t their actual objective — is to identify and eliminate traitors. They have one opportunity each day to do that, during a banishment session convened at the round table where players debate who seems suspicious. At the conclusion of the roundtable debate players vote to eject one of their number, based on whatever meagre information presented to the table that best persuaded the congregation. But all participants, including the unknown traitors, participate in the roundtable. The odds are therefore somewhat stacked against faithfuls even in the use of their own most powerful weapon. Each person who pleads her own case, or casts aspersions about another’s, is an unreliable witness. The roundtable is like a jury in every respect but one: “jurors” are players, guilty and innocent, and therefore also participants in, or witnesses to, the crimes alleged. Each has a stake in the outcome in a way a juror does not. Given the paucity of information, players’ pet theories are inevitably bunk. This is obvious. Everyone can see it. I’ve lost count of the numbers of times I’ve heard people say, “it’s mad they all get het up because the elimination process is basically random.” When, occasionally, they do stumble on the truth — an inspired aspect of the show is the confessional segments wherein individual players disclose their innermost suspicions to the audience —contestants usually trip over it. Being no surer of themselves than any other players, those who are onto something are usually talked out of action. There is another elimination mechanism. The traitors, most days, confer in a secret conclave to agree upon the “murder” of a faithful. Murders take place unwitnessed and off-stage: the traitors leave no direct evidence [https://jollycontrarian.com/index.php/Direct_evidence]: If the faithful want to catch a traitor, they must use their powers of deduction [https://jollycontrarian.com/index.php?title=Deduction&action=edit&redlink=1] and inference [https://jollycontrarian.com/index.php/Inference] on the strength of whatever weak circumstantial evidence [https://jollycontrarian.com/index.php/Circumstantial_evidence] they can find that give the traitors give away: tics, oral slips, guilty looks, conspiratorial behaviour — that kind of thing. In the meantime the characters participate in “missions” where they must cooperate to add money to the prize pool. Here, traitors’ and faithfuls’ interests are aligned. This is in some ways clever, but in others, a weakness in the show’s format: it would be better if the traitors stood to benefit by jeopardising the faithfuls’ prize pool somehow. It might give faithful more concrete material to go on at the roundtable. As it is, there is precious little: before and after the mission contestants have time to interact, air their suspicions and eke out information about each other but only from an inert “data set” that does not really contain any useful information. The game is carefully constructed to avoid traitors ever leaving unambiguous evidence of their identities, except by accident. As long as the traitors have been circumspect, there will no meaningful clues from which anyone could draw a sound conclusion. In any case, at least one of the traitors is certain to survive to the “final five”— the rules are, literally, rigged as the game progresses to ensure this: television schedules, and not game dynamics, require it. But players should nonetheless factor it in: there will be traitors at the death. There must be. The game would not work without them. A game of chance What is fascinating is how players approach a situation in which they must make important decisions with almost no reliable information. Traitors is a show about deception, and it perpetrates its own deceptions on the players, in plain sight, from the outset. It tells them their objective is to identify and eject traitors. But it is not: traitors will in any case “respawn” if their numbers dwindle. Each player’s objective — traitor and faithful alike — is simply, and only, to survive. They should do nothing that jeopardises that objective, including displaying skill at identifying traitors, and thereby presenting an apparent threat. The best strategy is to keep your head down, keep your opinions to yourself, and say nothing unless spoken to. Be the zebra in the middle of the herd. For Traitors is a game of ostensible, but not actual, strategy. It is, by contrast, a game of pure chance. At the outset there is maximum uncertainty: players have nothing to go on but resting probabilities. These are easy enough to calculate: once their roles are nominated, each faithful should know there is a 3 in 21 chance — that’s 1 in 7, for the hard of mathematics — of any other player being a traitor. And then the game commences. Of the 19 original innocenti, at least 15 must, by the rules of the game, be thrown under the bus. They will not all be murdered: more than half will be ejected by the faithful at the roundtable. Murder victims are necessarily faithful — the traitors cannot murder each other — but it is a necessary consequence of the game that most round-table ejectees will also be faithful. They are “murdered” too, only by a council comprising a large majority of faithful. All that really differs between traitors and faithful, therefore, is their means of killing other players: traitors by murder, faithful by banishment. Presuming they don’t cheat, “traitors” are no less “deserving” of success than “faithful”. For the thing is: as far as players have any control over outcomes, the elimination process isn’t just basically random: it’s completely random. Players, like viewers, must know this, yet they persist in believing they can anticipate and even influence outcomes — and, for the sake of watchability, just as well: if they did not, the show would not work. The game obliges players to willingly suspend their own disbelief. For all the good their uninformed machinations do them they would be better, and happier, were they to leave things to chance. If faithful players can discipline themselves into thinking in terms of probabilities, they will note some reliable posterior information does emerge as the game goes on: as the roles of eliminated players are evealed — all murdered are ipso facto faithful, the banished declare their allegiance as they depart — so remaining faithful can update their “priors” somewhat, though, again, their information is incomplete. Faithful players’ odds systematically shorten — get worse — as the game progresses and contestants are whittled down. Elimination overweights the faithful, so as players disappear, the higher the probability that remaining competitors are traitors. By the time of the final five, at least one and probably two of the players must be traitors — that can be deduced from the fact of ongoing nightly murders. This means, for a given faithful, the “traitor ratio” amongst the remaining players increases over the game from about 14% to between 25% and 50%. That is an inevitable consequence of game play. Most seem unaware of this. They must surely know it, at some level, but if they do, they don’t seem to care. For despite it, remaining players form strengthening bonds. Their sense of “ordeal camaraderie” is at least as strong as their willingness to suspect their comrades are traitors. Thanks to their improbable longevity surviving players, whatever their allegiance, have more in common with each other than any player has with a fallen comrade. There is a good reason for this: when push comes to shove, individual faithful are no less incentivised to murder — or, by the end, guilty of it — than traitors. Traitors is a gave of push and shove. It is also a fantastic illustration of just how hard it is to make good decisions in times of uncertainty. No killer facts The game is carefully constructed so that the faithful are never presented with unambiguous evidence of treachery. It can happen, but only by a traitor’s unforced error. As long as the traitors are not careless, they can avoid leaving direct clues and the faithful must form suspicions based on inferences that are basically bunk. This, as Traitors series across the world — there are editions in the US, Ireland, Australia and even little old New Zealand — has consistently illustrated, is incredibly difficult to get right, except by fluke. It’s little wonder: the faithful are (mostly!) perfect strangers to each other. They don’t know how each other behave in normal social situations, let alone times of social stress or the state of prolonged contrived deceit that Traitors forces them into. Their suspicions are usually wildly wrong. Viewers, who know who the traitors are, find the faithfuls’ utter guilelessness at the same time mesmerising and exasperating. We howl at our televisions. We clutch our heads in exasperation. “How can you possibly miss it?!” But this is a perfect example of hindsight bias. Of course it is obvious when you know who the traitors are. We are deceiving ourselves if we think we could do better. The game environment is highly artificial: all players, not just traitors, are motivated to lie and disguise their true opinions in ways they ordinarily would not. A faithful who believes she is “onto” a traitor will keep her opinions from the traitor, but will readily share them with others — who may include the traitor’s confederates. This obligation to engage in duplicity leads even the faithful to spin and perpetuate dishonesties the same way traitors do. This is a neat design feature: the natural advantage the faithful would otherwise have, of having nothing to hide, is extinguished. Some are better at this then others, but the cognitive load in trying to draw inferences from minimal available information often manifests in erratic behaviour, as faithful scrabble helplessly to get some purchase on who is who and what is what in the game. This erratic behaviour is often mistaken as “traitorous” and those exhibiting it banished. Usually, it is quite the opposite: with their superior information and greater sense of jeopardy, traitors tend to have a much better “game plan” than faithful. They are generally more careful and rational because they do have a plan. This, ironically, tends to stave off suspicion! The faithful tend systematically to banish each other on dismal pretexts, while the traitors continue to get away with murder, literally, undetected and even unsuspected. As the game unfolds players tend to form alliances. Across the Traitors’ regional franchises, the way they do this differs in a way that, amusingly, reinforces cultural stereotypes: the the Brits are self-effacing, charming, polite and deferential, especially at the beginning. They tend to eject players who are not polite. Irish are cheerfully idiomatic in their interactions. Australians, from the first morning, are brutal. The British “celebrity” edition of Traitors, in 2025, was a nadir Britishness. Random British contestants are pretty bad at detecting traitors, but do they tend to get some. British celebrities, as you might expect from a bunch of luvvies, grovel disingenuously to each other at all times, in contrived mutual deference, and prove therefore quite useless when it comes to identifying traitors. There are some learnings from this. An obvious one: in situations of epistemic uncertainty, when people you cannot trust are motivated to present a particular view of the world, we are really bad at figuring out who is telling the truth. Worse even than a choice at random. This has real-world implications. Traitors might seem contrived, but it tracks the commonplace. For most of us, complex situations of factual uncertainty where conflicted agents spin facts to suit their own agendas, is an everyday experience. This is how parliament works. It is how the media works. It is how most workplaces work. And it is, explicitly how justice works: the “traitors’ dilemma” is exactly the scenario faced by a criminal jury. Who is faithful? Who is a traitor? Who is spinning? What is relevant? What is a red herring? Like the faithful, jurors have limited information to go on. It may not be everything. It may be wrong. It may invite prejudicial inferences that are not justified. Misconceptions Traitors is so beguiling because it is based on a couple of misdirections. For one thing, the faithful are not the good guys: the “faithful” and “traitor” labels are a misdirection. There are no innocents in Traitors. The inevitable probabilities of the round table gives the lie to the idea that the “faithful” are really the good guys. Over a series there will be some 12 round tables. A banishment at each is compulsory. The dynamics of the game require contestants, faithful or not, to winnow themselves down to three finalists. The faithful have no power to save each other to avoid this. This means the faithful must compete for survival against each other just as fiercely as they must against the unseen traitors. The traitors, conceivably, could all make it to the final. They have slightly more incentive to be collegiate than do the faithful, which is ironic. Over the course of the game, the faithful typically eliminate more of their own than do the traitors. The familiar refrain, “I’m faithful, 100%” is not quite the ringing endorsement of probity its seems. Being faithful just means you intend to eliminate people in public, not private. The faithful is, in no sense, a “team”. Unconscious bias? In recent times, collated game statistics across five seasons of Traitors have prompted questions as to whether the collective decisions made in roundtables and the ”turret” reveal the unstated, even unconscious, prejudice? Banishment data from early rounds invites the inference that there is mild bias against minorities and older players, who are often ejected first. We should not be surprised at this. It does not prove prejudice. Firstly, in a novel situation of great uncertainty, informed decision making us impossible: literally there is no information. The players know the baseline probabilities — there’s a 1 in 7 chance of another player being a traitor, so a given contestant is, most likely, not a traitor. This is a dissonance though, because the players also know that three definitely are traitors. A good Bayesian [https://jollycontrarian.com/index.php/Bayesian_reasoning] uses what information she can find to provisionally improve those odds. This is a subjective process, to which she will bring all her life experiences. A person who appears easy to trust has a marginal advantage. Here “in-groups” and “out-groups” might make a difference. We are all, instinctively, inclined to trust those with whom we are familiar — those our accumulated experience of the world tells us are likely to share our experiences, impressions and values — and those we form an interpersonal connection with. These will often be people who most resemble us — by age, sex, cultural background, occupation, interests, geographic origin. This is no kind of positive discrimination against those who don’t resemble us — they keep their base line odds — but a concession towards those who do. Those common connection points are often cultural. Ethnic, religious and racial identities often follow cultural ones. I can illustrate this with my own “minorityship”: though I live in the UK, I am from New Zealand. There are not many Kiwis in the UK — come to think of it, there aren’t that many in New Zealand either — but in the UK we make up about 0.1% of the population, though, like sand in a picnic rug we do tend to get everywhere. Though my own connection with Aotearoa is slim — I’ve spent the vast majority of my life in London — should I encounter another New Zealander in the UK, we will quickly connect. We have shared experiences. We can make assumptions about how each other will think. We’re also likely to have been to school with each other’s cousins but that is a different story. The connection might not last — some kiwis are jerks — but all other things equal it is a good starting basis. This is exactly what is happening on traitors. The great majority of contestants are under 45. The cast reflects the ethnic diversity of the UK, which is predominantly Caucasian, and geographic make up: there are always a couple of Welsh and Scottish but a majority from England. We should expect these people to instinctively bond with in-groups, the same way ex-pat New Zealanders do. No surprise, the “bias” effect in the data wears off after a few days, by which time participants have got to know each other and have adjusted their perceptions based on actual evidence. We are natural Bayesians. We update our priors. As the game wears on contestants get no better at picking traitors, however. They consistently allow obvious confirmation bias [https://jollycontrarian.com/index.php/Confirmation_bias] to override their better judgment. We are astounded at their credulity. We should let it tell us more about our own. Complex system Traitors is a perfect model of a complex system. Not only are their autonomous agents making uncontrollable decisions and stark, but shifting asymmetries in information, but the “rules” of the game are opaque and amorphous. Some are disclosed late, others are never disclosed and some change without warning or notice. Generally the rule changes are engineered to favour the traitors, but not always. Secret traitors are introduced. Players are unexpectedly ejected before the game starts, and then reintroduced, just as unexpectedly, later. Players are therefore in a situation of uncertainty [https://jollycontrarian.com/index.php/Uncertainty], not risk [https://jollycontrarian.com/index.php/Risk]. Risks you can manage; uncertainty you cannot. The players’ efforts to manage uncertainty and work each other out are doomed not only to fail, but to sow seeds of doubt and resentment in other players. This rancour ossifies into factions, and hostile subgroups. Of course, the traitors merrily stir up this rancour. The net effect is that the faithful get even worse at guessing traitors than random. Players may as well be in a lottery, where an elimination is drawn from the group, and a murder victim selected from the faithful, at random each day. If they all resigned themselves to that fate, they could relax, enjoy the game, enjoy each others’ company, and let fate’s cold hand decide, without blaming it on any player. This would completely spoil the spectacle for viewers, of course: who wants to watch a bunch of random strangers having a nice time in a Scottish castle? We, and the networks, can therefore be grateful it never occurs to any of the participants that they have no control over the game. They carry on as if they can beat the game, and each other, with their cunning. Even after the Faithful have ejected eight of their own and just one traitor — even when players they profess to be convinced are lying repeatedly turn out not to be — it never occurs to anyone to abandon the psychodrama and just draw lots. Traitors as a model for the workplace Similar group dynamics exist in the workplace, especially where it comes to promotion and preferment. If you can influence outcomes with certainty, it informs how you “play the game”: being political may pay off — forming and then tactically defecting on alliances, exaggerating your role on things you were involved with, and taking credit for things you were not — even if this destroys relationships with those whom you are outmanouevring — makes sense. It is — should be —management’s job to impose incentives and structures inside the system that discourage this kind of behaviour. Most management fails to. For if you can’t influence outcomes — if the rules are shifting and unclear — if the decision-makers to whom you appeal are themselves subject to just the same game-playing and caprice, whose fortunes, like yours, may ebb and flow — then you cannot know whether your gamesmanship, like that of a “faithful” in a game of traitors — will pay off or sink you. You are better to let the river take you where it will, building as you go enduring and healthy relationships around you. Being useful, agreeable and unthreatening is a sensible tactic for a safe but unspectacular career. Most people in professional services have long since figured that out. The workplace is different from Traitors and Squid Games in an important respect: Traitors is a finite game; the workplace is an infinite one. There is no equivalent to Traitors’ known common general objective of elimination. There is no end-point at which a player wins. At work, the objective is just to keep playing. Relative advantages are often transient. We think we know “the rules of the game”, but the game is complex, the rules are opaque, and they continually change with the continually changing market outside and internal organisations and priorities within. From where most of us sit, the “rules” — if there even are any — that govern our advancement may as well be random. This is what propels the experimental finding from 2010 that organisations that promote people at random do no worse than those with extensive performance appraisal processes. Curiously, “the rules being random” may be a better outcome either way, if it leads to staff prioritising cooperation, collaboration, informal relationships and trust over “playing the game”. This Substack is reader-supported. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit jollycontrarian.substack.com/subscribe [https://jollycontrarian.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2]

Satellite of Love: Live Aid, Bad, Bono and the tragic triumph of irony

Welcome to a new experiment in gratuitous discursion about music, culture and modernity. Thanks for reading! This post is public so feel free to share it. This is a little different that the usual JC fare, and in lieu of any strengths plays to my weaknesses, which is the ability to get utterly sidetracked by the simplest questions. On a train up to see my daughter Antagonista I happened across U2’s album The Unforgettable Fire. A guilty, embarrassing pleasure, but halfway though the six-minute, two-chord epic Bad it struck me what a magnificently great song, and performance it is, and that called to mind U2’s legendary, notorious performance of Bad — all ten minutes of it — at Live Aid. I have a theory that Live Aid was a fundamental cultural touchpoint for people of my generation — that it changed the world in ways we do not often acknowledge – and this pulls on a few JC strings, and I started writing it up as a sort of appreciation of rock bands, guitar rock, digital delays, ambient music, plonkers, irony one of the great vocal performances — and and realised it would work much better as full audio surround sound experience. So here it is. Hope you enjoy! If this goes well I might do a bit more of this sort of thing. The original text — though it isn’t half as much fun — is here [https://jollycontrarian.com/index.php/Satellite_of_Love:_Live_Aid,_Bad,_Bono,_and_the_tragic_triumph_of_irony]. This Substack is reader-supported. To receive new posts and support my work, consider becoming a free or paid subscriber. There is a Spotify playlist of the forty seven — FORTY SEVEN FOLKS — songs name-checked and sampled in this podcast! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit jollycontrarian.substack.com/subscribe [https://jollycontrarian.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2]