Found this little history lesson, follow the link for the rest: “In 1906, Italian economist Vilfredo Pareto created a mathematical formula to describe the unequal distribution of wealth in his country, observing that twenty percent of the people owned eighty percent of the wealth. In the late 1940s, Dr. Joseph M. Juran inaccurately attributed the 80/20 Rule to Pareto, calling it Pareto’s Principle. While it may be misnamed, Pareto’s Principle or Pareto’s Law as it is sometimes called, can be a very effective tool to help you manage effectively”
Complexity Risk Uncertainty Archives
I am working on the assumption that antifragility is a worthwhile goal for process development. But if that is not what processes are, then what are they? What other states – or more properly – meta-states can we define? I think we need to see the options and understand the contrasts.
Let us consider roughly three meta-states of a process on some sort of continuum: fragile – robust – antifragile.
Fragile is where the process I harmed by volatility, diversity and error. A fragile process is subject to breakage, disruption and malfunction. The process is not indifferent, but has little or no ability to recover on its own. Read More …
Not sure what triggered the tangential synaptial firing sequence, but it may have had something to do with the treacherous road conditions in town after a Chinook wind melted everything and then got covered by snow.
It’s not a shocker that we have ice and snow in a Canadian winter, of course, we should be eminently used to it rolling around year after year. No black swans here. Frozen swans, maybe, but that’s a whole other story.
Still, whenever feet abruptly slide out from under we are not prepared. We scramble and hopefully manage a recovery no worse for wear once the adrenaline flood has subsided. Read More …
Food is not the problem.
Housing ins not the problem.
The problem is lack of a sustainable – or at least stable – level of economic activity capable of bootstrapping the depressed or underdeveloped areas of the world to improved quality of life.
- If we have 1 billion people living in poverty with insufficient nutritious food and
- A person can survive on about a dollar a day and not the whole dollar goes to food, then
- The math to prove our hypothesis is ridiculously simple.
- 1 billion times $1 equals $1B per day or $365B per year.
- Spread out over the remaining 6 billion people, this comes out to about $60 per year per person or $5 per month.
We can do this
Could we not find a way to introduce a meaningful tax across the world to produce the equivalent of $5 per month per person in tax revenue to go to food and so on for the 1 billion poor? Come on, of course we can – if we want it enough, that is.
First some background to create the right mind frame. Most businesses are remarkably similar and much more similar than most owners, managers or operators will admit to. Trust me on this. This means that once we understand one business we have a great starting point for understanding the next one.
Nobody is as different as they claim
The critical difference between one business and the next is, well, the critical difference. This is the whole point: we have to understand how the business in front of us differs in one or more material and significant ways from the next one over. Then we have to learn what those key factors are.
What we all really want are not wants (or needs), but simply a payoff. This may sound crass, but hear me out. I am reading Nassem Nicholas Taleb’s book Antifragile at the moment, which is about how to think about and deal with risk and uncertainties.
To understand the options available to us, we need to understand our exposure to the risks and uncertainties we think we may face. The exposure is often most usefully expressed as the difference between costs and benefits, a measure of the expected net gain or loss or simply the ‘payoff’.
Payoff implies monetary outcomes, but that is just a mental convention or even delusion, take your pick. Just think of it as a measure of an outcome that can me good, bad or indifferent. We may find it useful to think of this in monetary terms, but as I said, that is a choice for a particular situation. Other situations may not lend themselves to specific monetary metrics.
What struck me as I was reading Taleb was that the payoff concept seems more practical than most other terms I’ve seen for expected outcomes (a term in point; see what I mean).
We the human race are generally terrible when it comes to understanding risk, complexity and uncertainty. We may get close to appreciating things like averages, but mostly fail to grasp the importance of variability (read: probability of occurrence, volatility, variance).
It gets really bad when it gets to uncertainties because by definition, we cannot predict most of them. We have nothing to base our analysis on. Just because something has not happened in the past does not mean it can’t or won’t.
What we can know is the extent of damage or exposure we face if an uncertain event might occur. (Oh, and uncertain events are typically events we can imagine, but cannot assign meaningful probabilities to.) What this means is that we may have a perfectly workable understanding of volatility or variance even if we’re missing the rest.
For example. we may not know when the next volcano eruption will occur in, say Mexico or Nicaragua, but we can say a lot with a high degree of accuracy of what the damage might be for various severities.
The point of all this is that we can manage rare events, especially the unpleasant ones, not by predicting them, because that really doesn’t do anything, but by taking specific actions to avoid harm from their occurrence.
In the long run, is it cheaper not to locate anyone too close to a volcano than to relocate and rebuild after a disaster? Due to the screwed up way to count costs and benefits, the ‘total cost of ownership’ usually ends up misstated because we fail to take into account what economists call externalities. E.g., real estate developers don’t want to take on the higher cost of volcano eruption earth quake protection. The result is that the costs of most disasters are transferred to the tax payers.
I’m expecting that at some point in the future the voters will become sufficiently versed in these matters to finally put and end to this by forcing some much needed policy changes. I’m not going to offer a prediction, though, for when.
Robustness is different from antifragile. Robustness implies an imperviousness to change; the ability to withstand forces being brought to bear. Resilient is different. It impies the ability to change and accommodate.
Look at it this way: robust resists and endures, then breaks when finally overcome. Resilient bends and twists to avoid and adapt, then breaks when finally overcome.
Fragile implies something that will break under comparatively light stress. In the words of Nassim Nicholas Taleb, author of The Black Swan and Antifragile, fragile is at best unharmed; at worst it will break (i.e., be harmed).
Taleb makes the good point that the opposite of fragile is not robustness or resilience; it is something that is at worst unharmed. He calls that Antifragile. I guess a catchy name is always useful and it does force us to think harder. Antifragile is not a word in common everyday use (except perhaps in derivatives trading circles, Taleb’s original haunts).
This is all very nice, of course, but why should we care so much about word definitions? What struck me as I sorted out these terms in my own head was the notion that when we make plans, we should think about risk and uncertainty in ways that will let us say something about our proposed solution or outcome in terms of robustness, resilience, fragility and antifragility.
Here’s one way to make this work. As we formulate the solution we imagine alternative outcomes; possible alternate futures. These alternate futures are what we call scenarios. While the future literally has the capacity to surprise us with any one of an infinite variety of outcomes, in most cases, some outcomes are more likely than others.
3-4 scenarios are usually more than enough. More than that and we spend a lot of time on analysis that may be wasted. Better to be clear about a couple than so-so about many.
When developing scenarios, avoid being overly specific. Describe scenarios in broad terms that indicate trends or general conditions, then identify a handful of indicators that will tell us about which way we are actually heading.
For each scenario, outline our possible responses to changes in the prevailing conditions. Ideally, we will come up with a solution that has an inbuilt strategy that allows us to make changes to adapt to the future we are getting without these changes being contradictory or wasteful.
Ideally, if we do it right, we will be prepared with at least a robust solution. Better still if it is resilient to adapt to change. Best case is where the worst imagined outcome still brings us success. This is being antifragile.
Of course, not all situations or futures are antifragile. Taleb’s argument is simply that if you can find those that are, you will have a successful outcome no matter what happens. The unstated assumption is that we don’t mess up the execution of our plan. Unfortunately, that is sometimes a strong assumption.
I have been curious for some time about what might be the main trends to follow for the century we’re in. The it occurred to me to ask myself what is a trend anyway?
The short definition of a trend is this:
“A trend signifies the general direction in which something is developing or changing.”
That’s nice, but begs the follow-up question: How do you know it’s real?
Here’s my current attempt at an answer.
The basic rules of trend spotting are as follows:
- A “direction” implies a starting point. This means we have to understand the boundaries for data collection or for analysis (or both). If we start or end at the wrong points, we may be missing some data or introducing meaningless clutter.
- “Development” or “change” implies a rate or, more technically, a slope of a curve. This means that we need sufficient data to plot a meaningful line or curve.
- “Sufficient data” implies that we have enough to say something about the size of the sampling error. Any sample will be an imperfect representation of the universe of events we are studying. The degree of difference is the error we need to know about. Statistical techniques will tell us something about this error and whether we have statistically significant results, or in plainer language: can we trust our plot?
- Knowing the size of the sampling error implies we can say something about what observed changes lie within the band of natural or expected variation and what lies outside this band. The stuff outside the band of natural variation is what determines whether we have a trend or just random behavior or “noise.”
The above four rules should be applicable as an acid test for pretty much all trend claims. If we cannot get acceptable answers, we should not trust the trend claim. “Acceptable” implies judgment and is an important qualification of the above procedure. The rules may look very clear and scientific, but trend spotting is seldom a plain vanilla statistical, computational exercise.