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Entries in robust (2)


John Doyle at SFI

"Universal Laws and Architectures for Robust Efficiency in Nets, Grids, Bugs, Hearts and Minds"


  • By making things more efficient you make things worse
  • Architecture flexibility achieves what is possible
  • Heroes: Darwin and Touring, dynamics and feedback
  • Efficiency and robustness are 2 aspects we want.
    • Sustainable=robust + efficient
  • Antifragile=adaptability and evolvability. 
    • Concrete, verifiable, testable.
    • “It’s much easier to bullshit at the macro level than micro.” 
  • Robustness, efficient and adaptive. 

  • What makes us robust is controlled and acute, what makes us fragile are those same features when they are uncontrolled and chronic.

  • Robust efficiency is at the heart of these trade-offs. 
    • On the cell level, we are robust in energy and efficient in energy use.
    • Big fragilities are unintended consequences of mechanisms designed for robustness. 
    • There are tradeoffs between the two. 
    • Fragility is due to the “hijacking” of robustness.
  • In the human transition to bipedialism, we became four times more efficient at running distance than chimps, but chimps are faster, better off in the shorter distances.
    • Similarly, if we go on a bike, we are 2x as fast as walking, but more fragile. 
    • Further, we can’t simply “add” a bike to ourselves to gain this speed. 
    • We must add the bike + learn how to ride it.
  • There was a visual demonstration, but for the purposes of these notes: imagine there is a wand that can get smaller or larger (or even better, try this with a pen). 
    • You can either hold it in your hand downwards, or balance it on top of your hand upwards (the balancing upwards is nearly impossible with the pen, though that’s part of the point).
    • Down is easy to control, up is hard and destabilizing. 
    • Up and looking away (ie don’t look at your hand, but look elsewhere entirely) is nearly impossible.
    • Gravity is a law. 
      • When we hold the wand downward, gravity is stabilizing. 
      • Stabilizing insofar as it holds it steady and straight. Gravity is destabilizing when holding it up.
    • Down=the easiest, up=harder, up and short want=the hardest (that’s why you can’t balance the pen upwards!).
  • We can look at the entropy rate exp(pt).  This explains quantitatively something qualitatively through a law.
  • Fragility depends on function (balanced movement in the case of the wand) and specific perturbation. 
  • There are hard tradeoffs between optimal lengths, but looking away is simply bad design.
  • Without an actuator, variability or extreme variability brings a crash imminently.
  • Markets are robust to prices, fragile to all else. 
    • For robustness, we want them to be fast and flexible, but these features cause the fragilities.
    • Much of nature is built on layered architecture between fast “apps” and robust hardware.
    • There are often horizontal transfers from one architecture to another, but only occasional novelty (think about the passing of genes vs the creation of new genes entirely; or similarly the passing of ideas from one discipline to another vs the discovery of novel ideas entirely). This accelerates evolution.
    • Such a system is fragile to exploitation. The more monoculture, the more this is amplified.
    • Our greatest fragility as a society are bad memes. People believe false, dangerous, unhealthy things.
    • These features are shared architectures between genes, bacteria, memes and hardware.
  • Hold your hand in front of your face. Move your hand back and forth real fast until the image blurs. Then hold your hand still, and move your head back and forth real fast until your hand blurs. (do this before reading on)
    • Notice that when you turn your head real fast it’s very challenging to get the hand to blur. This is because we have what is called the vestibular ocular reflex.
    • The illusion of speed and flexibility has been tuned to a specific environment. The head is automatically stabilized to see the hand clearly while moving. This is all happening subconsciously in the cerebellum.
  • There was another demonstration using colored circles that were adjacent at the midpoint of a screen. The slide was quickly switched and the color lingered for a while in your vision. (I was so intrigued by this, I did some googling afterwards and found the term afterimages. While I could not find the exact demonstration, this one using the American flag is quite cool and gives a sense of the effect covered for the following few lines).Color is the slowest transition. We don’t truly see in color, we simulate it.
    • This is a slow, inflexible, but cheap system (it doesn’t use a lot of resources)
    • It’s tuned to a highly specific environment, so we don’t notice it (it feels totally natural to us)
    • It is fragile to some environments, like the afterimage, but hopefully we don’t encounter that fragility in a context where it can hurt us.
  • Learning generally speaking is slow, so we have to evolve reflexes to go fast.



Nassim Taleb at SFI

Nassim Taleb

Defining and Mapping Fragility


  • Black swans are not about fat tail events. They are about how we do not know the probabilities in the tail.
  • The absence of evidence vs evidence of absence is very severe
    • Too much is based on non-evidentiary methods
  • Financial instruments (options) are more fat-tailed than the function suggests
    • P(x) is non-linear
    • Thus the dynamics of exposure are different than the dynamics of the security
    • To that end law of large numbers doesn’t apply in options
  • “Anyone who uses the word variance does not trade options”
    • The measure of a fat tail is a distribution’s kurtosis
  • There was a great chart of 50 years of data across markets
    • In the S&P in particular, 80% of the kurtosis can be represented by 1 single day (1987 crash)
    • This would not converge in your data studying a broad look at the S&P
    • One  can only talk about variance if the error coefficient of the variance is under control
    • In Silver, 98% of its 50-year variance comes from 1 observation
  • EVT-extreme value theory is very problematic because we don’t know what the tail alpha is.
    • In VAR, a small change can add many 0000s
    • There is no confidence at all in the tails of these models
    • The concentration of tail events without predecessors means that such events do not occur in the data. Tails that don’t occur are problematic.
  • A short option position pays until a random shock. Asymmetric downside to defined, modest upside. This bet does not like variability (dispersion), volatility.
  • Look at the level of k (believe kurtosis??) and see sensitivity to the scale of the distribution. This is fragility.
    • Volatility = the scale of the distribution
    • The payoff in the tail increases as a result of sigma
  • If you define fragility, you can measure it even without understanding the probabilities in the tail
    • Nonlinearity of the payoff in the tail means that the rate of harm increase disproportionately to an instance of harm
    • What is nonlinear has a negative response to volatility
  • Fragility hates 2nd order effects. For example: if you like 72 degree room temperature, 2 days at 70 degrees is better than 1 at 0 and the next at 140.
  • Lots of nature demonstrates “S” curves
    • In the convex face of the s-curve, we want dispersion. In the concave face we do not (stability)
  • How to measure risk in portfolios: takes issue with IMFs emphasis on stress tests looking at a “worst” past instance, which is a stationary point in time.
    • Dexia went out of business shortly after “passing” such a stress test
    • Solution: do 3 stress tests and figure out the acceleration of harm past a certain point, as conditions get worse. 
      • We should care about increasing levels of risk, not degree
      • Risk increases asymmetrically, so if the rate of acceleration is extreme, this is stress.
  • Praised Marty Liebowitz for “figuring out convexity in bonds”
  • “Convex losses, concave gains --->thin tials ---> robust”
  • Antifragile=convex, benefits from variability
  • Can take the past to see the degree of fragility. You get more information and more measurable data from something that went down and then back up in the past, than something that went down and stayed at 0. 
  • Adding information is concave (N). Convex is when we add dimensions (D), spurious correlations increase.
    • There is a large D, small N problem in epidemiology.
    • NSA is one of the few areas that uses data well, but this is so because they are not interesting in many things, only the few that have value to what they’re trying to do.
  • PCA analysis, variations are regime dependent. 
  • We can lower nonlinearity of a price (buying options) 
    • Hard to turn fragile into anti-fragile, but can make it robust (tea cup can put lead in it).
    • Robust requires the absence of an absorption barrier – no O, no I in transition probabilities. Don’t stay or die in a specific state.
  • “Small is beautiful”
  • Q that “VAR is the best we have”:
    • A pilot says on a flight to Moscow: “We don’t have a map of Moscow, but we do have one of Paris.” You get off that plane. We don’t take random maps for that reason, and same logic applies to VAR.
    • Using VAR under this logic is troubling because it encourages people to take more risk than they really think they are taking. They anchor to the probabilities of VAR, not reality.
  • Liquidation costs are concave. There are diseconomies of scale from massive size.