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A day with SFI learning "Optimality vs Fragility"

Recently I had the privilege of attending Santa Fe Institute's latest joint conference with Morgan Stanley. This time, the topic was "Optimality vs Fragility: Are Optimality and Efficiency the Enemies of Robustness and Resilience?" The topic was both intriguing and timely, and the speakers were interesting, informative and a little bit more controversial than in years past. This made for an outstanding day. The audience in the room included some big names in finance and science alike, setting the stage for fascinating Q&As and stimulating conversations during the breaks.

This year, rather than writing one big post covering all of the lectures, I will break each down into its own entry. Here are the subsequent posts in order (and their respective links). Let this serve as your guide in navigating through the day:

Cris Moore--Optimization from Mt. Fuji to the Rockies

Nassim Taleb--Defining and Mapping Fragility

John Doyle--Universal Laws and Architectures for Robust Efficiency in Nets, Grids, Bugs, Hearts and Minds

Rob Park--Logic and Intent: Shaping Today's Financial Markets

Juan Enriquez--Are Humans Optimal?

Dan Geer--Optimality and Fragility of the Internet

I like to think about are how the lectures relate to what I do in markets and where there is overlap and dissention between the speakers. Further, I like to analyze how some of these lectures fit (or don't) with my preexisting views. I would love to hear what others think. Here are a few of my observations to get you all started:

  • Cris Moore's point that "best" is not necessarily optimal, and a confluence of models (what he calls data clusters) can yield better outcomes is extremely important in financial markets.
  • Nassim Taleb's suggestion that stress tests should focus on accelerating pain, rather than spot analysis is a powerful one that all risk managers should think about.
  • John Doyle's observation about the tradeoffs between robustness and efficiency is directly applicable to portfolio construction.
  • Rob Park's explanation of how algorithms are designed to express human intent, and the areas in which that can go has me rethinking my understanding of the risks from HFT.
  • Juan Enriquez opened everyone's eyes to how big the advances are in life science and the consequences this holds for the "secular stagnation" debate.
  • Dan Geer's explanation for why we have a choice between two of "security, convenience and freedom" online is both an enlightening and frightening call to action.

Again I will caution that these are my notes from the sessions. There is no guarantee of accuracy or completeness. I specifically focused on points that were intriguing to me, and purposely left out areas where the subject matter and terminology were too far removed from my competency. 


Dan Geer at SFI

"Optimality and Fragility on the Internet"


  • There are 3 professions that “beat practitioners into a state of humility—farming, weather, cyber security.”
  • Cybersecurity—there is a dual use inherent to all internet tools.
  • Offensive protection is where expensive innovation is happening today.
  • There is an outcome differential between good
  • “The most appealing ideas are not important, the most important ideas are not appealing.”
  • 10% of all internet traffic is unidentifiable by protocol, and more identification is simply not accurate.
  • Between security, convenience and freedom we can choose two, maybe, but not all three.
  • Some suggestions to help:
    • 1 Mandatory reporting—CDC has it with regard to disease appearances and they store data with skillful analysis. It would make sense to have mandatory reporting for cybersecurity problems. With real problems, hacks, require them to be reported. With attempted hacks/near misses we can build a reporting system like the FAA has for near misses. Let people report this anonymously and get voluntary entrants into the program. 
    • 2 Network neutrality—is Internet access an information or a communication service? So far we have not named it a communication service, but in reality, which is it? This has consequences for whether there will be common carrier protection or a duty to monitor. Right now, ISPs have it both ways. They should get one or the other, not both.
    • 3 Source code liability—“Security will be exactly as bad as it can be and still function.” There should be software liability regulation. “Intent or willfulness.” Build only liability for intent, not unintentional.
    • 4 Strike back—research the attacker, build cyber smartbombs to learn about them. The issue here is the shared infrastructure.
    • 5 Fall back on resilience. The code base on low-end routers today is 4-5 years old. Many networked components use old technology. Embedded systems should not be immortal.
    • 6 Vulnerability finding has been a good job for 8/9 years. We as a society should buy out (overpay) for finding vulnerabilities. This can expand the talent pool of vulnerability finding. Are “vulns” scarce or dense? “Exploitable areas are scarce enough.”
    • 7 Right to be forgotten. “We are all intelligence agents now…all our digital exhaust is identifiable.” Misrepresentation of identity online is getting harder and harder. The CIA wouldn’t have to fabricate an identity anymore, they can borrow one close to what they need. The new EU rule on this is appropriate, but doesn’t go far enough. “In public” means something very different today, than in the recent past.
    • 8 Internet voting. Most experts think it’s a bad idea.
    • 9 Abandonment. If a company abandons a code base (like Microsoft or Apple pulling support of an old OS), then it should become open source.
    • 10 Convergence. Are the physical and digital one world or 2? They are converging rapidly today. Need to ask “on whose terms will convergence occur?” The cause of risk today is dependence. We will be secure if there can be no unmitigable surprises.
  • Security breaches/viruses follow power law distribution. Target and Home Depot both fit on the curve.



Juan Enriquez at SFI

"Are Humans Optimal?"


  • Historically on the planet there have been several hominins existing at a time. Right now humans are the only species of hominins.
    • Typically when there is only one species, that is a sign of impending extinction.
  • The difference between humans and Neanderthals is less than 0.004% on the genomic level.
    • Differences are in sperm, testes, smell and skin
  • There was an experiment in Russia to try and breed domesticated wild foxes. They took only the friendliest foxes and bred them amongst each other. Within a few generations they got tame and were worthy of being pets (more on that here).
  • We can now sequence and acquire genetic data 3x quicker than our capacity to store it. We’ve sequenced about 10,000 human genes today. We will start to find more differences soon.
  • Life is imperfectly transmitted code.
  • We can now build just mouth teeth (or human teeth with stem cells from a lost tooth). We can build an ear, a bladder, a trachea.
  • Homo evolutis:
    • For better or worse, we’re beginning to control our own evolution
    • This is “unnatural selection or actual intelligent design”
    • We have to live with the consequences, whether they be good or bad.
    • So far, using these technologies we have taken ourselves out of the food chain and doubled lifespans. In this respect, it’s been good for us so far.
  • While we conventionally speak about how great the digital revolution has been, the revolution in life sciences is and will be magnitudes greater.
  • Co-founded Synthetic Genomics with J. Craig Venter (One of the first to have sequenced the human genome)
    • Synthetic Genomics has developed a cell built that can operate like a computer system. It’s a cell that executes life code.
    • It may be possible to reprogram a species to become another species.
    • It’s like a software that makes its own hardware.
    • Algae is the best scalable production systems for energy development in a constrained world.
  • “We are evolving ourselves.” In science, “there are decades when nothing happens and weeks when everything happens.” (a questioner in the audience pointed out this quote comes from Lenin).
  • Q: “Do we have secular stagnation?”
    • Enriquez: A resounding no. Today there are people who are smart, creative, with scale and ambition. Lots of great things are happening in the sciences. We are as advanced as ever, and increasingly so. 1 problem is that with technology, our interest in sex different than it used to be, and sex is not keeping the developed world population moving upwards fast enough.



Rob Park at SFI

"Logic and Intent: Shaping Today's Financial Markets"

  • Started program trading with a spread algo between Deere and Caterpillar, under the assumption that fundamental drivers were similar and spreads will revert to mean. 
    • In executing this algo, felt orders were being copied by someone else.
  • Today, 70% of total US volume is algos.
  • How do algos introduce risks?
    • Problems occur when you can’t predict.
  • The algo ecosystem: the number of possibilities grow exponentially when algos interact with other algos.
    • 1 runaway algo problem. Example-on Amazon there was a $1 million book. Someone raised the price in marketplaces of another ever so slightly and that triggered a cascade where this book ended up listed for $20 million (the story of how this happened is fascinating and told here)
    • 2 Flash crash – unpredictable interaction of algos
  • What is an algorithm? It is a sequence of logic statements. All algos are created by humans. They do what people intend them to do. Intent=important. Humans are driven by incentives, algorithms are driven by human intent.
    • The technologist needs to understand the human goal, or else risk is introduced into the system.
  • IEX introduced a 350 microsecond delay on an order reaching the exchange.
  • The broker’s dilemma: brokers were matching orders between buyers and sellers, so brokers created dark pools. Broker A gets the buy, Broker B gets the sell, what’s the incentive for Broker A to trade with B?
  • In today’s market there are 11 exchanges, 40+ dark pools (IEX right now is a dark pool, but will try to become an exchange eventually).
  • Exchange dilemma: exchanges facilitate issuers with investors. Exchanges are supposed to be neutral to all participants, but now are for-profit companies who build services for specific customers. This is not the intended purpose of exchanges, and biases these exchanges towards one kind of participant (HFTs) over another.
  • There have been three generations of market algos so far:
    • 1 automatic traders flow, algos execute upon traders’ ideas, helping these traders focus on “their work” as opposed to execution
    • 2 gaming automatic trader-based algos. These algos took advantage of transparent inefficiencies in the first generations functionality.
    • 3 counteract generation 2. A trader who wants to buy size needs to game level two algos in order to hide intent and execute efficiently.
  • Participants send orders, but they don’t arrive at the actual exchange at the same time.
  • At the micro level, markets are deterministic (opposite of physics).
  • Latency arb—in a distributed system, race conditions matter. HFT aims to exploit the race. Exchanges need to know where the market is before pricing a transaction.  Introducing the 350 microsecond delay through a fishing-line like fiber. In doing so, assume the order is not fast. And then figure out where the market is.
  • Resistance to IEX so far has come from 2nd generation algo programmers. 



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.