But that’s never enough for most serious thinkers. So it is that the grand theory arises: an extremely intelligent historian or social scientist ruminates on many historical examples, notices a pattern, and begins to test the pattern against other examples. Perhaps an important paper is published or a scholarly book printed.

Eventually, of course, a lay-person’s “pop history” book is written to bring the concepts to a wider market.

Into this role steps War and Peace and War: The Rise and Fall of Empires by Peter Turchin (also published with the subtitle The Life Cycles of Imperial Nations). This book which popularises Turchin’s own work in “cliodynamics”. And it makes for an interesting read.

Turchin’s Argument

Turchin core’s hypothesis is that the trajectory of an imperial nation is governed by the degree of “Asabiya”, an Arabic word he borrows from 1300s Islamic scholar Ibn Khaldun. Loosely, asabiya refers to social cohesion and national unity (One of my minor peeves with this book is that he didn’t just say … “cohesion”, or one of its synonyms in English. His description of asabiya seems insufficiently different from available English words that loaning a word would be necessary. On the other hand, not being steeped in Arabic or Islamic culture, I can’t know for sure).

Empires rise, and then fall. Consequently, Turchin’s account is broken into two major halves: formation (“imperiogenesis”) and dissolution (“imperiopathosis”).

Formation of imperial states, Turchin argues, occurs at “metaethnic fault lines” (again, what was wrong with “civilisations” as an existing term is beyond me). The more unalike two neighbouring peoples are, the stronger the warring pressure between them will be. This mutual hostility creates enormous adaptive pressure on both. Peoples which develop high asabiya will eventually conquer their neighbours and, for several centuries to follow, retain enough of a stock of asabiya to continually push back their boundaries.

Turchin has an excellent eye for historical anecdotes. He stocks this part of the book with fascinating case studies in the Russian-Tartar frontier and the sharp boundary of Greco-Roman civilisation on the Rhine. He notes in both such cases, subsequent empires have arisen at those critical geocultural boundaries, and not from the “safe” centres far away from those boundaries.

Turchin’s argument for the mechanics of dissolution are more complex, involving several distinct phenomena. Firstly there is the diminishing pressure on the founding imperial nation as it progressively pushes back its frontiers. Secondly, there are population dynamics which create agricultural pressure ripe for disruptive famines and disease. Thirdly, Turchin says that inequality between and within classes is corrosive of asabiya. Finally, an explosion in the size of the ruling class means that each member of the ruling class may need to be content with less and less than their ancestors — except for some few who become fantastically wealthy.

Turchin sees declines happening in a repeating mesocycle within the larger macrocycle of decline. Each such mesocycle is governed by a fathers-and-sons dynamic. Fathers live through a time of unrest. Once normalcy is restored, they exert great efforts to see its return. Their sons witness some of this, and it is impressed on them with great force how important stability is. Finally, the grandsons are born who have no firsthand knowledge of unrest and who discount the cost of radicalism. A new cycle of unrest commences.

These periodic bloodbaths trim the ruling classes, relieving some of the pressure; but eventually the steady erosion of asabiya across multiple mesocycles means that total collapse will eventuate in the third or fourth round of unrest. There is, as Adam Smith observed, a lot of ruin in a nation.

Some criticisms

Turchin sensibly accepts imprecision of such a general model. He carefully hedges his remarks with apparent counter examples. He doesn’t claim to be able to time to rise and fall of imperial nations; rather, he points out long-running trends that create enormous social momentum towards and then away from the holding of empire.

One area where I felt the book could have been improved would be the inclusion of diagrams. Turchin’s scholarly work is, he says himself, filled with formal charts, tables and formulae. Fine and well; but a simple diagram of the cycles within cycles, or an influence diagram of different elements affecting asabiya, or some example charts of trends in population, elite contention and so on would have solidified his case.

Another weakness was the book’s eurocentricity. As a criticism of scholarship I am generally wary of “eurocentrism”, as the whiff of lefty wankery is usually not far away. However outside of Europe and the United States, only China is mentioned as a nation with Imperial qualities. In essence, Turchin’s hypothesis is only tested against European civilisation — he does not look closely at civilisations on the other side of the European boundaries, or at other frontiers. It would be interesting to see if he has accounted for the empires of South East Asia, in the subcontinent or in South America.

Conclusion

Turchin’s narrative style makes for easy and interesting reading. If you enjoy grand theories of history, this would make a good read. Recommended.

A few months ago I partially tore the meniscus of my left knee. It’s been taking its sweet time healing up, in part because I kept performing certain exercises. I figured for about 8 weeks that if I didn’t feel pain, doing an exercise was OK. I was wrong.

So now I have dropped all work involving either knee extension or flexion. This includes the Olympic lifts, squats, deadlifts, romanian deadlifts, good mornings … in fact, all the exercises that form the staple of weightlifting training.

Instead I’ve been stranded with upper body work.

Combining Strength and Hypertrophy

Before I was injured, I became interested in the possibility of combining strength-oriented and hypertrophy-oriented training (influenced in part by Layne Norton’s PHAT program, Zatsiorsky & Kraemer’s textbook and seeing various studies being discussed in august fora). It’s common to use block periodisation to program first hypertrophy and then strength. However I have personally found that running a mesocycle of hypertrophy training causes me to get weaker, not stronger.

However, a current controversy in sports science is the degree to which hypertrophy training, involving high volumes of contraction, actually provokes myofibrillar hypertrophy versus sarcoplasmic hypertrophy. Some researchers believe that high-repetition training will increase the production of all protein elements more than the traditional heavy, low-repetition training approach of strength training.

Missing from this is the nervous system aspect. Strength is as much about nervous system adaptation as it is about increasing myofibrils. I’ve experimented with 10×10 and 5×12 schemes; in both cases seeing big increases in size but no strength gains (even reversions for the 5×12 experiments).

Also raising its head is the problem of sarcoplasmic hypertrophy (it too is a disputed hypothesis, but let’s move forward for now). To the degree that high-repetition training favours the storage of glycogen and fluid in the muscles without causing myofibrillar hypertrophy, it’s flatly useless for my sport. To snatch or clean & jerk weights I don’t need muscular endurance; and intramuscular water weight is dead weight that doesn’t produce contractile force.

However, my thinking has been to add a strength-biased hypertrophy component to my regimen. The thinking is firstly that additional stimulus at sufficiently high weight will provoke sufficient myofibrillar hypertrophy to justify any offsetting sarcoplasmic hypertrophy. Secondly, some of the side-effects of sarcoplasmic hypertrophy might create an environment that favours subsequent myofibrillar hypertrophy. Sarcoplasmic hypertrophy both expands the muscle fibres (creating more room for myofibrils to be created in) and leads to more mitochondria (increasing the in-cell turnover of energy and protein).

Therefore, my current approach is to alternate strength days and hypertrophy days.

Exercise Selection and Performance

First, a word about exercise selection. As I said above, I’ve injured my knee and been reduced to upper body work. I’ve focused on improving my pressing strength and retaining or improving pulling strength. The third focus of my selections is to keep the shoulder girdle, especially the rotator cuff, happy while undertaking a large amount of pressing volume and intensity.

The exercises chosen for strength days are the seated overhead press, band pullaparts, chinups, dips, pushups and face pulls. I also had seated good mornings, but these have proved to be too aggravating to the knee and I elected to drop them.

The exercises chosen for hypertrophy days are the incline dumbbell press, band pullaparts, face pulls, situps or crunches, batwings and pushups. I used to have left-hand side dumbbell shrugs to try to bring my left hand side upper trapezius into line with its substantially larger right hand counterpart. However this exercise has given me several episodes of painful neck twinges and I have dropped it for now.

Note that some of the exercises are repeated on both days. Pullaparts, batwings and face pulls help maintain scapular retraction strength and health for the both the internally and externally rotated position of the shoulder socket. This helps both to offset the punishment of high frequency pressing and retains and potentially improves strength I rely on in the snatch and the clean.

Pushups are included on both days because they are fun and have a better carryover to overhead pressing than bench pressing, while (with close hand and elbow placement) placing less stress on the shoulders.

In several exercises I use bands to either assist the movement or to provide resistance. I’ve been quite pleased with the usefulness of bands so far.

I aim to keep rest periods controlled. On strength day I aim to keep rest periods to under 3 minutes. On hypertrophy day I aim to keep the rest periods under 2 minutes. The former because 3 minutes is the minimum traditional figure given for the majority replenishment of the phosphagen energy system (a property I wish to train because of the limited rest period between lift attempts in competition). The latter because meta-studies have found that short rest periods — between 90 and 120 seconds — are positively correlated with overall hypertrophy.

Normally this amount of volume on both strength and hypertrophy days would take a very long time. To cut down “lost” time, and to stave off boredom, I have frequently supersetted exercises. In a superset the trainee performs one exercise, then immediately performs a different exercise, and sometimes a third and so on. There are many ways of programming these, mine is to pick antagonistic or unrelated exercise pairs. So the presses are followed by band pulls; the chinups by dips; pushups by face pulls; face pulls by situps; batwings by pushups.

Strength Days

The goal on the strength day is to preserve and improve the specification of strength, with a secondary goal of provoking myofibrillar hypertrophy.

The two key schemes used on strength day are 10×3 and 6×5.

I use 10 sets of 3 reps at a high intensity (the second and third reps should be difficult, but not grinders). 10 sets are used to provide sufficient stimulus for myofibrillar hypertrophy; triples are chosen to provide a working weight that challenges the nervous system to adapt.

Currently scheduled for 10x3s are seated overhead press and band pullaparts.

In the 6×5 section I use a slightly less intense weight (the 4th and 5th reps should be hard). This is chosen to allow the nervous system to become accustomed to movements I have not used in training before. Myofibrillar hypertrophy will be slightly less pronounced. For 6x5s I have been using band-assisted chinups, band-assisted dips, band-resisted pushups and unilateral band face pulls.

Lately I have added negative repetitions to the chinups. These involve jumping up and resisting the downward portion of the pull. I am not yet able to pause the downward movement, but overloaded negative or eccentric repetitions have been shown to provoke more myofibrillar hypertrophy than regular repetitions. They also lead to bloody unpleasant soreness.

Similarly, with dips, I have sometimes replaced or augmented the band-assisted dips with straight dips. In these cases I am aim for “AMRAP” — As Many Reps As Possible.

Hypertrophy Day

On hypertrophy day, the goal is provoke some myofibrillar and some sarcoplasmic hypertrophy. I have chosen as my basic pattern 8x8s — 8 sets of 8 repetitions.

In the lore of weight training, 8 repetitions is seen as where strength training ends and hypertrophy begins. In an 8-set the goal is for the last two or three repetitions to be difficult. In contrast to 10-sets or 12-sets, the 8-set allows a higher weight to be used, hopefully retaining a balance between the myofibrillar and sarcoplasmic hypertrophy. One should not be “feeling the burn” in an 8-set. It should be difficult to complete because of the heaviness of the selected weight, not because of a burning sensation.

8×8 provides a handy amount of volume to provoke hypertrophy in any case — a total of 64 repetitions per exercise.

Some confusion may arise amongst bodybuilding afficionados, for whom 3×8 is a popular pattern. The confusion arises because bodybuilders will often use multiple, different exercises for a muscle or group of muscles. Beginner trainees often ape the programs of advanced bodybuilders, unaware that the program listed is a snapshot of what the pro is doing right now to address some perceived aesthetic short coming. Bodybuilders have an enormous lore for bringing out this or that part of this or that muscle and the advanced and professional bodybuilders rely heavily on it to respond to the currently fashionable body shape.

But I don’t need that. What I’m trying to provoke is the development of functional mass — myofibrils and some sarcoplasm — that I can immediately train my nervous system to recruit on strength days. I don’t need some particular part of some particular muscle to “pop” or be bigger or smaller than some other muscle. I just need to add size and through it strength.

Hence the focus on keeping things simple. A few exercises, preferably multi-jointed and non-isolated, performed at a higher volume than a bodybuilder might use.

Conclusion and the Future

Right now I have been working on this program for 3 weeks. I have noticed an accumulation of minor twinges and in particular, soreness in ligaments and tendons (which, due to their low blood circulation, typically take longer than muscles to adapt to stimuli). Thus I will be taking a traditional programming step: next week will be a programmed “deload” week. I will drop from 6 days to 3 days, I will drop several exercises from rotation, and I will drop the volume of work. I will however retain a high intensity on training days to maintain and consolidate any nervous system adaptations.

The thinking on this program has evolved slowly over several months, then quickly over the past few weeks of actually putting it into practice. It’s been an interesting change of pace and very demanding. I look forward to experimenting with these principles with more systemically demanding exercises (such as snatches, cleans, squats, RDLs) in future.

If you want to know, in soundbite terms, how my diet works, here’s Dr Rudi:

Dr Rudi addressed his remarks to women, but through one of those freak coincidences of biology, the basic principle works for men too.

The fancier version is that I’ve used the Cheat Mode diet plan. Cheat Mode can be explained very simply:

1. Skip breakfast.
2. Small, protein-dominant lunch.
3. Lift weights.
4. Reasonable dinner, carbs OK.

This plan kept me stable at 133kg for several months, after a very gentle drift down from 139kg (my peak weight).

What I’ve changed in the past few months is to replace a fully-laden lunch from the University cafeteria with a simple meal-replacement shake of my own recipe. The shake consists of two scoops of choc-flavoured casein powder, a heaped tablespoon of fibre (again of my own recipe — finely ground oats, psyllium husk, wheat germ and wheat bran) and a scoop of the expensive insurance policy, Biotest’s “Superfood”.

This works out, on my reckoning, to be around 700-800ish kilocalories. Because of the mix of a slow-digesting protein and lots of soluble and insoluble fibre, it’s quite filling. It replaces an admittedly delicious lunch of fatty meat and plentiful rice.

Occasionally I will engage in a 24 hour fast, usually on the weekends. To keep hunger down I allow myself a sip of full cream milk here or there. It’s not a strict fast, but then, it doesn’t need to be.

The author of the Cheat Mode diet has elaborate justifications for each design element, but I suspect that almost all of the effect on subcutaneous fat is down to the fact that it simply reduces calorie intake.

I’ve personally found that is easier to either skip meals or replace them with a modest, filling substitute. Actually controlling portions is an exercise in bloody torture; skipping meals is a doddle by comparison.

So there you have it. The diet that works for me: eating less.

“Thought provoking” is usually a shorthand used by buttered-up friends of the author to mean “I agree” or “he/she provided a great blurb for my dust jacket and now I’m returning the favour”.

But in this case, I found that the book provoked a lot of thought on my part. It tied to a lot of other books I’ve read in the past year or so, some of which I’ll name check.

So … what’s it about?

Dekker discusses how complex systems ‘fail’ in unforeseen ways. He characterises some of these failures as ‘drifts’. The system didn’t visibly zoom towards failure; there was no massive perturbation, no onrushing catastrophe, not even dark clouds on the horizon. In a drift-failure, the failure just happens, and only afterwards is there any chance of diagnosing the whys and hows.

Drift essentially cross two fields of work. The first is reliability / failure studies and the second is complex systems. I’m not very familiar with reliability studies except through a Chinese-whispers version that has been transmitted via software operations literature. I feel that I have a more-than-nodding acquaintance with systems theory through a uni course and my own reading in that area.

To a reader unfamiliar with either body of thought, this book might be a bit difficult. Dekker isn’t really addressing the book to the layperson, it’s really addressed to practitioners reliability/failure field. Dekker’s ultimate hypothesis is that a “Newtonian-Cartesian” approach to failure does not and cannot address failures in complex systems.

If you’re not from the reliability field, Dekker’s writing is a bit like being an atheist at a theological debate. Interesting, but a little hard to follow in parts. But boy does he have lots of points to make.

I respectfully disagree

I don’t think Dekker quite nails his case down. For the rest of the review I will try to explain why. Hang on, because it’s a long, circuitous ride.

Postmodernism

As I said above, Dekker posits that a Newtonian-Cartesian worldview can’t explain or predict failures in complex systems. Of most concern for yours truly is that, in addition to reaching for complex systems theory, he reaches out for postmodernism. I’m not a particular fan of postmodernism — I think that some of its insights can be usefully appropriated into modernist thinking, but its universalist claims are dangerously nigh to total bunkum. I don’t think Dekker needed it.

Dekker uses postmodernism to posit that failure is a negotiated label. A system isn’t “failed” until after a failure, and the very concept of failure is constructed as an agreement between observers and participants of the system. Hence: failure is subjective.

Well, yes. Certainly, failure is, after a fashion, transmitted backwards in time. But many of the systems humans build are purposive. The purpose is known ahead of time, in advance. Even before further negotiation between subjects take place, many failures are instantly recognisable as failures.

Local optimality, global optimality and failure

Dekker chose the “drift” metaphor because a system arrives at failure in small, locally-rational steps. In one case study, he examines Alaska Airlines flight 261 in great detail. In this case study, a series of small relaxations on safety standards eventually lead to a catastrophic system failure (sudden, unpredictable loss of human life).

Dekker asks: when did the system fail?

• Did it fail when the particular acme nuts failed?
• When maintenance was not performed?
• When times between scheduled maintenances were extended?
• When the design was made without accounting for the possibility of the above?

This goes back to the distinction between proximal and ultimate causes, popular amongst both reliability studies practitioners and lawyers. The proximal cause is clearly the acme nuts failing … but in this case, Dekker says, where is the ultimate cause? It’s diffused across the entire system, across a series of locally optimal solutions.

Local and global optimality is a classic human problem. In Daniel Kahneman’s excellent book Thinking, Fast and Slow he metaphorically describes different ‘selves’. One self is a fast, almost subconscious self; an intuitive rationaliser. It excels at locally optimal solutions. A second ‘consciously rational’ self must be aroused purposefully. “Math is hard”, as Barbie says, so let’s go shopping. Hence we almost never actually engage that second self, even in situations where we think we have. Kahneman includes lots of little fiendish self-tests for the reader that abundantly prove his case.

Once you see the distinction between the locally optimal and the globally optimal, cases jump out of the woodwork everywhere you look. It’s funny, because I learnt the concept of local/global optima at university but never really clicked to it before reading Kahneman.

And as with optimality, so too rationality. What is rational locally may transpire to have irrational global consequences. Little agents optimising their corner of a large system can cause failed systems. Part of Dekker’s broader hypothesis is that assigning blame is a bit rich in such circumstances — everyone was just acting according to sensible rules within their own situation. It’s all so complicated, give them a break.

I’m not so sure. Take for example the question: “when was the system in a failed state?”

By itself, that question supposes a binary logic. The system IS in a failed state, OR the system IS NOT in a failed state. Dekker sees what anyone can see as a bit of a nonsense and pushes it downwards to our notion of blame. I prefer to look at it and push it up to a narrow conception of logic.

To explain what I mean, I need to make two diversions.

Diversion I: Fuzzy Logic

Here’s where fuzzy logic pops in (and also where, based on the title of this subsection, I lose both of the readers who got this far without giving up out of boredom).

The core insight of fuzzy logic is that we can think of things as belonging to “fuzzy sets”. In normal logic, sets are cut-and-dried. Remember Venn Diagrams? They all looked like this:

Look at all that sharp delineation! Any “thing” in that diagram indisputably in one of five possible states:

• Blue
• Yellow
• Blue AND Yellow
• Blue OR Yellow
• NEITHER Blue NOR Yellow

Traditionally we ignore that last condition — the neither/nor — because that way we get a neat formula for calculating the possible number of states for any number of exclusive sets or logical variables.

There’s a lot to like about conventional logic. It’s the granite foundations of the field I hold a degree in — Computer Science. Given ANDs, ORs, NOTs and some ones and zeros, one can build essentially infinitely complex systems (I’ll return to this point later on).

But it doesn’t actually describe a heap of common problems in the actual world.

And speaking of heaps — here’s a classic philosophy question: is this a pile of sand?

Well yes. And if I remove a grain? Still yes. In computer science terms I’ve performed an inductive step, it’s now “turtles all the way down”. The pile of sand is always a pile of sand, perhaps until I remove the last grain.

But we know that’s not “true”, in the every day sense. A few grains of sand does not a pile make. And it gets worse, for when does the pile become a dune? And when does the dune become a desert?

Fuzzy logic sidesteps the issue by saying that the pile of sand has a degree to which it is a pile of sand. This is expressed with a “membership function”. To what degree does this pile of sand belong to the set of all piles of sand? Well in this case, I think we can all agree that it’s a pile of sand, so we grant it a high membership and say it’s a member of that set to a degree of 0.9.

When it gets small, we lower its membership degree. A few handfuls of sand might only rate 0.05 in the membership function. And as it grows very large, its membership degree again shrinks to a low number, even as its membership of the set of dunes grows larger.

Hence in Venn diagram terms, fuzzy sets look a bit more like this:

It’s … fuzzy, as you’d expect. Membership in the blue and yellow sets is not a binary proposition, there are degrees of membership.

Diversion II: Phase Space

Why do we care about sets all of a sudden? Because sets are one way to represent systems. More accurately, any given system has many states, and states can be grouped in various ways as sets.

First, let’s look at a very simple system: a switch. It has two possible states, on and off. The system can be described with a graph, like so:

This is a phase space, a space of all possible states of the system. The phase space diagram here is simple. It has one axis — one dimension — because the system only has one controlling variable. It has two states — two coordinates in phase space — because it’s a discrete binary variable. The switch is on or off. That’s it.

Systems of interest are, unsurprisingly, more complex than that.

Suppose now we have a control panel with one dial. It controls a vent which emits cold air. Next to the dial is a temperature gauge. The dials and gauge are wired to a room which you cannot directly observe. Your job is to reach a certain temperature.

A phase space diagram here would have two axes: one for the dial and one for the temperature. You need both axes to fully describe the configuration of a system at any given point in time.

What does that look like? A bit like this (warning, unsexy diagram):

Now suppose you twiddle the dial. You have changed the configuration of the system — you’ve moved through phase space to a new set of coordinates. We draw a line on the diagram to represent that:

After a while, the temperature falls:

Not the most stunning of diagrams, I grant you. But this is broadly how phase diagrams work. The line is implied to be a span of time, the points are particular configurations of the system.

So when is a system in a state of failure?

Dekker says that systems drift and that from inside the system, such drift isn’t visible until the failure occurs. But we still try to back track to discover “causes”, even when it might make no sense to.

Suppose a 2-variable system drifts to failure:

Dekker posits that in the Newtonian-Cartesian paradigm, we aim to trace that line backwards in time to discover who and what failed. But this is insensible, says Dekker, because in fact the causes can be so diffused over the entire system and not individuals or components.

The “Newtonian-Cartesian mindset”

Dekker decries the “Newtonian-Cartesian” mindset of trying to find discrete causes for failure. Instead each step can be sensible in itself, or causes too diffuse to tease out, or insufficient information to work it out.

I don’t think that Dekker really refutes N-C mindset at all. Just because a step was locally, but not globally optimal, doesn’t excuse it. If global reasoning was available, it should exercised. Causes that are diffuse are still causes. Causes that can’t be detected due to lack of evidence or lack of instruments can still be considered causes (“hidden variables”, in physics parlance).

But Dekker wants to excuse a lot of such cases because, he posits, the Newtonian-Cartesian paradigm is itself broken.

I don’t think he proves his case. Worse still, he handwaves a lot of the rough edges of his argument away. Complex systems are hard to govern, he says. Why are they hard to govern? Because they’re complex. It’s a circular logic.

Ultimately Dekker’s logic relies on the incomplete conception of logic I gave above. In Dekker’s conception, a system is or is not failed. The observable paradoxes of meaning that this generates are then resolved by slapping a “warning: complex system!” tag on it, plus a dose of postmodern voodoo.

What I propose as an alternative is that systems have degrees of failure. Just because, in the every day sense, they have not “failed”, nevertheless within the phase space are fuzzy sets of states that represent all possible failures. And every state in the phase space has some degree of membership in each of those failure states. It might look like this:

(An alternative rendering would be to add the “disaster” membership degree as another axis, but my graphics skills extend only so far).

Going back to Alaska Air Flight 261, when the plane crashed, the aviation safety system was obviously belonged to the “tragedy” failure set to a 1.0 degree. But before the crash, its degree of membership in that set grew steadily as the system drifted towards it.

My formulation does not excuse actors and components in a complex system. They are, where any degree of global insight is possible, still on the hook.

Legalism and Realism

Dekker describes the hunt for a single person or component causing a failure is something he describes as being a “legal view” of things. Which is funny, because lawyers have been grappling with complex systems for thousands of years. They’ve got some tricks up their sleeves.

One debate amongst lawyers is in what role a judge should play. One classic doctrine is “Legalism”, most famously demanded by Sir Owen Dixon during his tenure as Chief Justice of the High Court of Australia:

Close adherence to legal reasoning is the only way to maintain the confidence of all parties in federal conflicts. It may be that the court is thought to be excessively legalistic. I should be sorry to think that it is anything else. There is no safer guide to judicial decisions in great conflict than strict and complete legalism.

Legalism meant that, in considering a case, judges should strive to ignore all considerations but the law. This is, in a strict sense, impossible. The world is too mixed up in the law, the law to mingled with the affairs of the world. Judges are mere humans; a sea of passions with a few stony outcrops of reason. Legalism is, like Newton’s laws stretched to their limits, strictly impossible.

That last argument leads us to Realism, which basically says: judges are biased. Judges make law, in practice. Get used to it.

But the funny thing is that, when we zoom out, which better serves society at large? I would personally argue legalism, imposing as it does much lower uncertainty costs and politicking costs on society at large. And that was Sir Dixon’s point. The loosey-goosey “broadness” of Realism turns out, upon closer inspection, to be founded on a narrower view of society than Legalism. The Legalist embraces an important impossibility because it serves a higher good.

My analogy here is that Dekker is poo-pooing the analogical Legalism of the Newtonian-Cartesian world view — that causes can be ultimate derived from computation and analysis — in favour of a kind of Realism. Systems are complex, he says. Get used to it.

But like the Realists, his analysis is too narrow. Even if he is right (and I think he is only half right, as I will go on to say below), his postmodern / complex system view nurses dangerous seeds. Embracing the concept that there is always a cause or a set of causes leads to better systems, even if it isn’t true.

What is a Complex System, anyhow?

Dekker never really makes this clear, perhaps because he lacks the fuzzy logic terminology to point out that it’s a matter of degree.

I suggest that a “complex” system is any system which successfully confounds human understanding. That’s a fuzzy statement already: which human? What counts as confounding? What counts as understanding? But if we accept the fuzzy logic worldview, it’s less of a problem. Systems will belong to the “complex systems” set with a different level of degree.

But I suggest that there is no qualitative change. It’s just that some problems are too big for humans. Some problems are too big for any computational device, as computer science has discovered — some problems cannot be solved at all by a computing device; some can’t be solved before the heat death of the universe.

But suppose availability of sufficiently advanced hypercomputer (or more quaintly, a god). What could it predict? How deep a system? What level of complexity? Newtonian — really Einsteinian — physics breaks down at the limit because of the uncertainty principle. But supposing it could be done, would this universe be predictable?

I think so. And that’s the most complex conceivable system there is — ie, the System of Everything. No qualitative shift has occurred. It’s a matter of (very, very, very large) quantitative differences.

So in fact “complex” systems are a human phenomenon, a label given to things that exceed 1) our ability to observe and 2) our ability to compute.

Epistemological Confusion

Dekker’s contest between the Newtonian-Cartesian vs Complex-Postmodern worldviews
is really akin to the debate of atheism vs agnosticism.

Newtonian-Cartesianism says “this is reality, this is what is objective” — it’s a statement of belief. Postmodernism/Complexitism is “it’s unknowable, it’s constructed between subjects, it can’t realistically be done that way”. That’s a statement of epistemology, about what is knowable.

But these are talking past each other. Reality is, in a sense, both. There’s an objective reality, broadly a Newtonian-Cartesian reality at the humanly experienceable macroscale. And there’s our understanding of that reality. In a sense complexity just means “intractably difficult to compute”. Dekker has confused a statement of fact (“the world is not Newtonian-Cartesian at the macroscale”) with a statement of epistemology (“the world is not truly knowable at a complex scale”).

To me, a mechanistic universe does not preclude complexity, it predicts it. I can only imagine that a non-mechanistic universe would have no emergent phenomena and would resemble mere randomness. A non-mechanistic universe is entropic in an information-theoretic sense. No information arises from it, and therefore any claims of complexity are meaningless in a postmodern sense.

For example, the mechanistic nature of computers (Turing machines) belies the experienced complexity of modern computer systems. Alan Turing wrote a paper to discuss an important mathematical question, and as a side-effect invented one pillar of the modern world. At the basic level Turing’s hypothetical machine is extremely simple: a tape, a tape reader, a pen and some agreed symbols that can be read or written on the tape. Modern computers, at their most basic and fundamental level, still resemble a pastiche of the Turing machine.

Yet from this very modest little well springs a fountain of complexity. Modern software systems are stupendously complex. Failure is their normal condition; trying to exhaustively test every combination of factors is so vast a task that it is laughed out of polite company. Yet we can test the common cases. Better yet, with some deft mathematical footwork we can simply eliminate whole swathes of phase space from consideration. This is the Newtonian-Cartesian paradigm at work, busily mending its own fences.

Should you read this book?

Yes, I think so. But critically. Dekker’s book makes fascinating reading and I greatly enjoyed it. I may have attacked it here, but that’s only because I think he fell short of elucidating and proving his case. A fine book can still be a fine book even if its contents or conclusions are, in one’s own opinion, wrong (cf. Plato’s Republic).

While in Perth I worked on my first investment pitch and sought feedback from family. One family member is involved in Serious Business and had some excellent suggestions to give, including talking to various commercialisation outfits.

Of the four potential groups we identified:

1. The NT Government is only really interested in quite local businesses,
2. CDU doesn’t really have a commercialisation arm,
3. UWA’s commercialisation office were very helpful but said that commercialising undergraduate work is not really their thing, leaving
4. Commercialisation Australia, a division of AusIndustry, a division of the Department of Who Knows What They’re Calling Themselves This Week, a Ministry of the Crown in Right of the Commonwealth of Australia

(I may have goosed up the last bit).

I intend absolutely to apply to Commercialisation Australia, if only to give more ammunition to people who remember that I’m a bit of a libertard. However I decided first that it would be helpful to talk to one of their professionals. So I sent this email:

Subject: Is there a Darwin office?
Hello;

I’m interested in applying for the Skills and Knowledge
program. Do you have a Darwin office I could visit?

Cheers,

JC.

A few days later I received this:

FW: Is there a Darwin office? [SEC=UNCLASSIFIED]

Hi Jacques,

Thankyou for your email.

To meet with one of the Commercialisation Australia Case Managers, you must first complete a Stage 1 Application Form. Completing a Stage 1 Application form will help determine the eligibility and merit of your application. A Case Manager will then review the Stage 1 Application Form and contact you with advice on your project proposal and, where appropriate, provide you with a copy of the Commercialisation Australia Stage 2 Application Form.

If you feel that it is not appropriate to complete the Stage 1 Application Form at this point in time, please contact the Commercialisation Australia hotline on 13 22 56 and you will be referred to your nearest Case Manager who can have a discussion with you about whether Commercialisation Australia can assist you. They can also discuss other programs and services offered by AusIndustry, the primary program delivery division in the Department of Industry, Innovation, Science, Research and Tertiary Education.
Kind regards,

[Name of helpful person with what seems to be a very senior role redacted]

So that’s what they’re called.

I can’t fault that they have a process to be followed, but if I want to know if there’s a Darwin office, I can apply. Or alternatively I can ask to see a Case Manager first … and then find out where they are.

Sometimes, you have to wonder.

Update: 3 minutes on the phone with the hotline establishes that a) no, they don’t have a Darwin office (was that so hard?) and b) they really, really mean it when they say they want the Level 1 Application filled out first.

I resolved to begin doing so, starting today. The last book I read to completion was Venture Deals: Be Smarter Than Your Lawyer and Venture Capitalist, by Brad Feld and Jason Mendelson; that’s where I’ll begin.

Some background is necessary. Since completing my honours, I’ve been planning to commercialise my research. To do so requires time, and of course, money is a socially-acceptable substitutable for time in a wide variety of situations. The practical upshot was that I’ve being considering how to raise money.

I’ve flown to Perth this week to graduate. Located here is this new venture capital fund, who are probably the first such outfit in Perth.

My original plan was: fly to Perth for the week, and during that week, saunter into their office with some vague hand-waving and wander out with a cheque for squillions of dollars. That’s how some folk in Silicon Valley seem to do it: why not me?

The part of me which is most like my father decided that perhaps I could do just a smidge of background reading; the part of me which used to be a law student heartily agreed. So I bought Venture Deals to get the background.

So: the book.

The meat of the book is a discussion of the Term Sheet, a document which usually embodies the key points of agreement when a venture capital investment is made. The authors helpfully point out that term sheet clauses fall into three categories: economics, control, and other. The first two are important.

The book takes a tour of these different clauses, exposing in particular the trickier bits of boilerplate that a VC might try to slip in. Parts of this are very detailed — the sort of thing that’d be clearer with an actual offer from an actual VC.

Also covered is a useful discussion of the tactics and timing matters of negotiating, an illuminating discussion of the internal structure and dynamics of venture capital firms and a chapter on Letters of Intent — a cousin of the term sheet sent when another company offers to buy yours out.

Unsurprisingly, the book is totally US-centric. Most VC firms are in the USA, most companies who take VC funding eventually incorporate in the USA and operate under US laws. Nevertheless, I think the authors might have made a clearer distinction between what is done in VC everywhere, and those particulars applying to US-based companies only.

Quality

Self-published books sometimes need more loving attention than they receive. This one is actually pretty reasonable — I didn’t notice any typos or egregious grammos. There were some sections where massaging the explanations might have helped me to get a clearer understanding of the process in hand.

One thing that would make a nice addition to a future edition would be a charts detailing common negotiation lifecycles and flow charts for trading off different clauses. Flow charts and diagrammatic representations are tools I have found very useful both when studying law and in my daily work as an analyst/programmer. Sometimes you need a picture to know what’s happening.

The most important thing

What I really took away from this book is: it’s too soon for me to be worrying about venture capital. I should be looking at raising money in smaller amounts from a different class of investor.

However, if you’re a US-based company looking for funds from a US-based venture capital firm, I think this book would provide a short, useful introduction to the issues.

Introduction

There is a tide in the affairs of men
Which, taken at the flood, leads on to fortune…
Julius Caesar, Act IV, Scene 3.1 [fn1]

From an ancient greek philosopher musing about the perfect Republic, to a Rennaissance diplomat giving advice to Princes, through to the Bard’s character studies, leadership has occupied a central place of honour in great literature throughout history.

Software, being a human endeavour, lends itself to discussions of leadership also. What was true of Julius Caesar might be true of Steve Jobs. What was true of Archimedes might be true of John Carmack. We mine our history for parallels to the present day; we draw from old heroes lessons of heroism.

But does leadership make any difference? Or can these outcomes be explained in other ways?

The central thrust of this essay will be to explore the hypothesis that plain luck is as important a factor in leadership as any other traditionally-listed quality. Mere chance can govern the leader’s environment, the leader’s team mates and the leader’s task. Mere chance can be the determinant of success.

Put another way, I will propose that the qualities we see as leadership are necessary but not sufficient elements of the achievements of famous leaders, both in general and the software industry particularly. That we are sometimes confounded by leaders who followed a ‘recipe’ and still failed will be seen as an example of survivorship bias.

Necessities

Before we can address the question of mere luck, it’s necessary to study the question of what precedes luck. What qualities are required to lead a successful technology company, and in particular a software-dominant company?

Technologists

A common thread that can be discerned throughout the history of software is successful companies being founded and co-founded by technologists. Apple was founded part-founded by Steve Wozniak, Microsoft part-founded by Bill Gates, Google by two PhD students, Yahoo! by two electrical engineering students, SAS by a group of programmers, Facebook by a hobby programmer and so on.

So our first predictor of success is technological aptitude. Software is in particular different enough from conventional lines of business that it will frustrate the efforts of a conventional manager. An MBA student studies factories, retailers and hourly service firms. None of these models maps neatly to software development (see “Make a Cheeseburger, Sell a Cheeseburger” in PeopleWare 2nd Edition by DeMarco and Lister).

In contrast, a technologist will have some intuition for what is possible. This allows them to both dismiss the impractical and to embrace possibilities that non-technologists might not, as yet, have deduced.

Vision

Some of the most famous technology companies in the world have simple but all-encompassing visions. Microsoft under Gates and Allen saw its mission as putting a computer on every desk and in every home in the world. Steve Jobs set Apple’s mission as producing “insanely great” computers. Google sets out to organise all the world’s information.

Embracing a large vision requires a certain amount of immodesty and immunity to ridicule. It is not enough to mouth the words; the visionary sincerely believes that the vision is possible and that they, the visionary, will make it happen. This quality is closely related to boldness.

Boldness

Fortes fortuna adiuvat.

“Fortune favours the bold”. This saying, probably derived from a play by the Roman playwright Terence, neatly illustrates the relationship between luck and boldness.

The handmaiden of vision is the boldness both to entertain a grand vision and to carry it out. Visions like those of Bill Gates and Steve Jobs are hubristic. But without the self-confidence of their leaders, Apple, Google, Microsoft and the like would have been footnotes in the history of modern technology.

So, for example, Apple is famous for being bold in embracing and abandoning technology. Under Jobs, Apple was the first major hardware company to embrace the 31⁄2 inch floppy disk. Under Jobs, they were the first to abandon it.

Luck

I returned, and saw under the sun,
that the race is not to the swift,
nor the battle to the strong,
neither yet bread to the wise,
nor yet riches to men of understanding,
nor yet favor to men of skill;
but time and chance happens to them all.
Ecclesiastes 9:11

It was not enough for Gates, Jobs, Brin, Zuckerberg et al to be bold, visionary technologists. They also had to be fortunate.

Firstly, timing matters. Gates and Jobs began their work in the formative days of personal computing. The market for personal computers didn’t yet exist; therefore the scope of their ambitions could encompass the entire world. By contrast, companies entering into the crowded web and mobile applications markets today often have very narrow missions. There is still a need for the qualities outlined above, but the richest spoils of the fundamental revolutions in technology have been apportioned to those who had the luck to be on the US West Coast in the 1980s and 1990s.

It is possible to be too early with a technology. We sometimes say of technologies that failed that they were “ahead of their time”. A critical component in luck is to bring a technology to market in the right way at the right time.

When Mark Zuckerberg began working on Facebook, social networking was an established concept. LiveJournal, Friendster and MySpace had already been operating for several years. Furthermore, while Zuckerberg is an unusual individual, there are probably hundreds of individuals with his mix of education, connections, intelligence, experience and boldness. So why did Facebook succeed while pioneers of social networking foundered, faded or failed?

Lucky timing, in part. Facebook, a refinement of the social networking concept, struck at a time when constant engagement with the internet was becoming the norm amongst incoming university students. Before long Facebook had an almost total penetration of the US university system; before long it spread from there to become the most successful company of its kind.

Secondly, boldness to seize the opportunities that arise at random. The most important moment in the history of Microsoft came in 1980 when IBM told Microsoft that it was looking for a BASIC interpreter — and an operating system — for its upcoming PC project. Microsoft seized the opportunity to create the operating system but retained the copyrights. To do so required cunning but also boldness: the boldness to both bite at the IBM opportunity but also to be prepared to undermine them in future.

But boldness by itself is not enough, an unconstrained boldness becomes foolhardiness. Around the bright stars of successful leaders and companies there is far more dark matter composed of the bold and unfortunate. Luck is still the deciding factor.

Survivorship Bias

So how is it that mere chance does not get top billing in any discussion of successful leaders? The most likely answer is survivorship bias. We judge history based on examples and records that have reached us from posterity. But then we are biased what has managed to survive the passage of time.

Take the complaint that “they don’t make things like they used to”. Perhaps the complainant is referring to a particularly sturdy antique chair. How can it be that chairs made in the 1800s have lasted for centuries, but a $30 chair won’t last for 2 years?

The mistake here is to generalise from this one, exceptional chair, to all chairs of that vintage. If chairs from the 1800s were “made like they used to”, then in fact the world would be awash with such chairs.

By only studying successful leaders and trying to infer common qualities and patterns, we run the risk of introducing survivorship bias. Unless we also study abject failures, we cannot determine whether or not any given quality is necessary, sufficient or merely coincidental. We have no control group: the evidentiary standard is too low to draw any scientific conclusion.

It has been said that “History is written by the victors” (traditionally attributed to Winston Churchill). In the case of leadership studies, history has been written about the victors. Very little ink has been spent illuminating the much larger pool of failures. Those failures who do receive attention were at first victors. Nobody writes about Commodore International merely because it failed, but because it thrived first. We seek out Rise and Fall, not Fall and Stay Fallen.

Measuring Greatness

The other problem with determining the properties of a great leader is to identify them in the first place. We must define how we know they’re “great”. This too introduces problems, because any assessment must be based in time.

In studying the great leaders of history, and especially antiquity, this is not a problem. The details of their life have been exhaustively canvassed, the consequences long since unfolded into the tapestry of history. But in software we are mostly studying leaders who are still alive, who can be expected to remain alive for some time yet, who are still shaping the world we live and work in.

So any selection of “greatness” must deal with the now. Today Steve Jobs is admired as a brilliant leader of Apple; but it was not so long ago that Apple was considered to be doomed. Did his qualities as a leader change in that time? Probably not. We took the revenues, profits and market capitalisation of Apple under Jobs as a proxy for his greatness. We studied outputs to choose the leaders and only then did we ask what the qualities of leadership were.

We can compare contemporary leaders with historical leaders, but we must be careful. When looking at Julius Caesar we can integrate his life and the centuries that followed; but for a living leader we can only take a particular slice of time. There will ups and downs and the sum of their efforts and achievements will not be known for some time.

This may seem like a very fine distinction, worthy of angels dancing on a pinhead. But it matters because it means our survivorship bias will whip around almost from year to year. We exalt this year’s winners and excoriate the losers, but who will sing the praises of the steady improver? We won’t know for some time — if ever [fn2].

When luck plays its role and elevates some obscure software company into the spotlight, do they suddenly have better leaders? Is there something about the leaders of that company that made it destined to succeed? Of course not. So why do we measure leadership greatness purely according to outcomes, which are so heavily influenced by luck and timing?

What About Process?

Perhaps companies become elevated because they crossed their Ts and dotted their Is. Where then does leadership end and process begin? Can luck be tamed by process?
Suppose we line up Microsoft, Google and Facebook on a spectrum of adherence to process; do we find some dependable gradient of successfulness? No. Instead we find very different lessons of process.

Many parts of Microsoft, including its Windows division, have followed something like a iterative waterfall process for decades. But when that process led to the expensive delays and poor results of the Vista project, Microsoft adopted a heavily revised process for the more successful Windows 7 process, but the concepts of working to a plan, assigning tasks centrally and so on remained in force.

Then contrast Google: “From a high level, Google’s process probably does look like chaos to someone from a more traditional software development company”. Yet Google relies heavily on automation, scalable tools and code reviews to ensure that the code they release into production is of as high a quality as possible.

Perhaps most radical of all is Facebook, where engineers can launch entire features, live, at any time. From Google they borrow a free-ranging engineering-led culture, but with almost zero central coordination. “[E]ngineers can modify specs mid-process, re-order work projects, and inject new feature ideas anytime”.

Microsoft and Google are diversified companies making billions in profits; Facebook is generally regarded as a near-future cash fountain. Yet their engineering processes have few things in common. More divides Google and Microsoft, for example, than unites them. Yet all three companies have had episodes of substantial luck.

Beyond a certain point process has ceased to be the predictor of success; at best it is a constraint on growth. Leadership here has been about creating the conditions to embrace change and dynamism, but luck has still played the central role in the very existence of these companies.

Conclusion

Cause and effect are easily distinguished, when they occur in succession; but are often confounded, when the operation is continuous and simultaneous.
— Jean-Baptiste Say

Are we then doomed to mere wandering? To casting our die? To muttering deo volente every time we sit down to write code or launch a business venture?

It is the impenetrable complexity of humans, of human systems, and the history of those systems, that drives software engineering. In history there have been two great technological revolutions: the industrial revolution, where for the first time musclepower was replaced by machinery, and now the information revolution, where brainpower is being replaced by machinery. The consequences of the latter revolution have some time yet to run. Just as conventional engineering was born in the crucible of steam and iron, so too will software engineering emerge from software itself.

Variance and fortune arise from the systems of the world. They are often unpredictable, given our level of understanding. Sometimes we can deduce causes in retrospect, but other times we will not. We will ascribe some outcomes to “luck”, simply because we are unable to deduce causes.

This is deeply unsatisfying. Leadership must not merely be about relying on luck, but about creating luck, about preparedness to embrace fortune when it arrives. When in 1995 Microsoft performed a radical about-face on the issue of Internet connectivity, they demonstrated that the wise leader sometimes senses the tide of history and rides it on to fortune. Microsoft foresaw misfortune and manoeuvred to avoid the shallows.

Without boldness, without vision and without technologists at the helm, Microsoft might not have made that change. Today they might have faded into history like DEC or Sun or Wang Laboratories or hundreds of other technology companies whose time has passed. In actual fact, luck, by itself, was not enough.

Microsoft had luck, and Google, and Facebook; and a lot of their luck has been extrinsic. But the rest they made themselves. And that, perhaps, is the most fortunate lesson of all.

Footnotes

fn1: About the quotations. As I grow older, various things I once read and considered trite come back and strike me with new forcefulness. In writing this essay, many such quotes came back to my recollection. I have included them both to illuminate my points and to show off my thin erudition. I sincerely hope I will not be mistaken for one of those lazy students who merely hit a quotations book in order to spice up a dull essay.

fn2: Note for contrast the vast business literature on Toyota’s rigorous self-improvement focus. But until Toyota was successful, who cared what they did?

“Programming is like cooking”, we say. “We write recipes, and the computer carries them out”.

And sometimes methodologists apply it to our work too. “Our method is like a recipe”, goes the sales pitch to management. “Apply our method, fill out these 9 reports, these 4 strategies and those 27 reports and the soufflé will rise.”

Well, maybe. But ask a chef about the secret of fine gastronomie, and they will tell that it’s the ingredients that count.

And if you ask any experienced PM or software engineering research about methodologies, they’ll tell you it’s the people that count.

The recipe is useful to prevent known, avoidable flavours. But no method can rescue a soufflé made with fish sauce and pine bark. You have to have the right ingredients first, or nothing good will come of the recipe.

Alchemist:dissertation jacques$ date; texcount -nosub dissertation.tex
Wed 26 Oct 2011 20:15:06 WST
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What’s left: one project, one presentation, one poster, one exam.

Wed 19 Oct 2011 10:30:15 WST
File: dissertation.tex
Encoding: ascii
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Now for two massive assignments, a presentation, a poster and an exam.