Daniel Kahneman on Intuition and the Outside View

I had the privilege of attending another Santa Fe Institute “Risk Conference” at Morgan Stanley. There was a stellar lineup of accomplished speakers focusing on Old Wine in New Bottles: Big Data in Markets and Finance. The grand finale was “A Conversation with Daniel Kahneman” led by Michael Mauboussin. These two gentlemen are amongst the finest thinkers in finance and two of the most important influences in my effort to compound knowledge while remaining cognizant of my limitations. As Mauboussin is intimately familiar with the subject matter, he was the perfect person to elicit the deepest insights from Kahneman on the most important topics. Below are my notes, which are reproduced here in the form of a dialogue. When I started jotting these down in real-time, I had no visions of writing the conversation up in this form; however, I found myself writing an awful lot with the output resembling an actual transcript. I attempted to be as thorough as possible in keeping the language as consistent with the spirit of the spoken dialogue as possible, though this is hardly perfect. I apologize in advance for the lack of completeness and the tense shifts, but nonetheless I am delighted to share the following in hope that others will be able to learn as much from this conversation as I did.

Michael Mauboussin: When does intuition work or fail?

Daniel Kahneman: Intuition works less often than we think. There is no such thing as professional “expertise.” The Intuitions in chess masters develop with “big data” comes from experience. For people, the immediacy of feedback is especially important to learn the basis of expertise. When feedback comes closer in time to the decision, intuition tends to be a lot stronger. Gary Klein, author of The Sources of Power is hostile to Kahneman’s view. Together they studied the boundary between trustworthy and untrustworthy sources of intuition. Confidence of intuition is NOT a good guide of intuition. If you want to explore intuition, you have to ask “not how happy the individual is” but what domain they are working in. There are some domains where intuition works, and some domains where it does not.  You need to ask “did the individual have an opportunity to learn irregularities on the way to building intuition? In domains where a lot of people have equal degrees of high confidence, they often do not know the limits of their expertise. 

Mauboussin: People blend quantitative and qualitative intuition, but what about disciplined intuition? Is there a better structure to decision-making?

Kahneman: When you put human judgment against simple models, after reading Paul Meehl’s book which showed where the human has access to all of the data behind the model, the model still wins in making decisions. There are no confirmed counter-examples. Studied an interviewing system for combat units. Asked multiple interviewers to speak with each candidate with a focus on one topic only per subject. Previously the interviewers had experienced a looser system without restriction—one interviewer per subject, with a broad focus. Unfortunately the previous system had zero predictive value on subsequent performance. At first, when the interviewers were instructed on a “disciplined” focus/topical breakdown, they were furious. People like using their broad intuitions. The interviewers were given a rating scale of 1 to 5 in each area they were assigned to cover. Eventually we got the data on how performance turned out based on the revised interview process. It turned out that interviews done in this way had much better predictive value for subsequent performance.

The problem with intuitions is how they come too fast. They are subject to confirmation biases. If you look at just one thing independent of all else and reserve judgment until the very end, what ultimately comes to mind will be more valid than if you don’t have discipline. It’s important to stress the independence (focus on 1 topic) to resist and overcome associative coherence—aka the halo effect.

Mauboussin: Define regression to the mean and the problems with it (causality, feedback)? 

Kahneman: Regression is a familiar concept, but not well understood. We see articles like “Why do smart women marry men less smart than they are?” That is an effect without a cause. We can reformulate that question to say that “the distribution of intelligence in men and women is the same” but the sound/implication of the two statements is not equivalent. You have to rid yourself of causation in making such statements. There was a study of the incidence of kidney cancer which described it as mostly rural, Republican districts in the center and south of the USA. Why? Everyone has a theory. But, if you look at the areas where incidence is small, it’s the same answer—mostly rural, Republican districts in the center and south of the USA.  This is so because the rural counties have smaller samples (a lower “n”) so incidences of high and low are more pronounced.

Mauboussin: Talk about the inside vs outside view, and base rates…

Kahneman: Was involved in writing a textbook on decision-making without math for a high school curriculum. Asked the team: “when will we finish the book?” Everyone answered somewhere between 18 and 30 months. Asked another colleague how long it took to write other textbooks in similar situations. This colleague’s answer had been somewhere in the 18 to 30 month range. The answer: 1) not all textbooks ever finished, with somewhere around 40% of them having given up; and, 2) those that were completed all took more than 7 years.

There are two different ways to look at a problem: 1) make an estimate based on a plan and reasonable extrapolation of progress—the inside view. 2) Abstract to the category of the case and ask “what are its characteristics”—the outside view. Intuition prefers the inside view, while the outside view is non-causal and statistical. If you start your analysis from the outside view, with a known base rate, it gives you a fair anchor and  ballpark from which to work.

Mauboussin: People are optimistic. There was a story you told of a few product launch at a company. At what point do you balance optimism vs just giving up? Society wants risks and all the good things that come with them.

Kahneman: Entrepreneurs don’t take risks because they love risk. They do it because they don’t know the odds. They don’t fully appreciate the risks they are taking. Optimism is the engine of capitalism. When you look at big successes, it’s because someone tried something they shouldn’t have.

Everyone should wish their children be optimists. They are happier, persevere more. Though, I don’t want a financial advisor who is an optimist. 

Mauboussin: As we embrace big data, it suggests change. When baseball learned about Moneyball, scouts resisted. With loss aversion, how do you relate this with the degree to which people are willing to embrace big data?

Kahneman: Losses loom larger than gains. Disadvantages are more salient and heavily weighted. In the context of change, one thing is guaranteed: there will be losers and winners. We can know ahead of time that the losers will fight harder than the winners. Losers know what they will lose, winners are never sure exactly what they will gain. People who initiate change don’t appreciate the resistance they will encounter. When reform is done in the regulatory arena, the reforms often compensate the losers making change very expensive. The prescription is to take the outside view.

The endowment effect is strong. The selling price someone sets on a sandwich they already owns and possesses is higher than that same person would price one they do not own. Giving up is more painful than selling something. This is evident in the financial arena. Advisors are helpful, because when they do the selling on someone’s behalf they do not have the same possessive connection and there is no endowment effect. Loss aversion is emotional, so if you make a decision in an advisor role, you can do so without emotion.

Mauboussin: When we look at decision making in an organization, there is noise. What does “noise” mean and why does it matter?

Kahneman: We know why Meehl was right on formulas being better than judges. For example, there was a situation that for each judge, there was a model built to predict what the judge will rule based on their past decisions. You can then compare the judge’s actual decisions with the model. The model is better than the judge. This tells you why people are inferior to formulas. A formula always has the same output. People vary and vary over time. When x-ray readers are asked to view the same image two separate times, 20% of the time they conclude differently. That’s what noise is.

Many organizations have functionaries who decide, but in principle they are interchangeable (credit-rating agencies, etc.) We would want all people to be interchangeable. How many individuals would be random in their actions? 45-50% tend to be variable. That variability is costly. Noise is costly. Most organizations think their employees agree with each other, but they don’t. Experience doesn’t bring convergence, it brings increased confidence. Convergence and confidence are not the same. If a financial advisory asked their advisors to prioritize a list of clients, does each advisor list the same clients in order? Probably not. When there is no selection, noise is costly.

Mauboussin: Give us a synopsis of Philip Tetlock's Superforecasting.

Kahneman: His book Expert Political Judgment was very important. It looked at predictions 10 years after experts made them and concluded forecasters can’t do it. And, the more a forecaster thinks they can do it, they less they actually did. With that knowledge, Tetlock built an IARPA tournament with predictions that covered timespans 6 weeks to a few months out (see my notes from Tetlock’s talks at two past SFI conferences here). He ID’d the superforecasters (the top 2%), which included a wide range of experts and ability. Short-term prediction being possible isn’t revolutionary. What makes superforecasters? A mixture of the inside and outside view. Disciplined intuition. Independent judgment, collated. 

I am skeptical of applying these findings in the political area where political figures themselves take actions that can be deterministic and statements have to be crafted to multiple constituencies, but in the financial arena these findings are very interesting.

In Defense of Cash

There is a debate in the investment community about the merits of Schwab including a cash allocation in its new roboadvisor offering. Let us leave aside the merits of roboadvisors (short answer: they are great for some people, while terrible for others) and focus on the idea of an investor holding a steady cash allocation as a percentage of total investable assets. Betterfront and WealthFront, two of the early movers in the roboadvisor space, have piled on Schwab. The upstarts argue the cash allocation was merely a cynical ploy orchestrated by Schwab to generate higher revenues from client accounts. Schwab meanwhile argues this is merely a prudent allocation. So, is cash a good, or bad investment in a portfolio account? The answer to this debate holds implications not just for roboinvestors, but for all investors alike and sure enough, I think there is a conclusive answer (as the title suggests).

Here is Betterment’s argument against cash:

  • “Cash has a significant chance of a negative real return over time due to inflation risk.”
  • “Cash assets can present a conflict of interest when the investment manager is advising cash and then re-investing it for its own revenue.”
  • “You never hold cash at Betterment, as we use fractional shares. That ensures every dollar—down to the penny—is fully invested in a diversified portfolio of stocks and bonds.”

The crux of these points are ancillary to the true debate. In fact, Betterment’s argument boils down to a marketing stance, more so than an investment argument. Cullen Roche at Pragmatic Capitalism nicely demonstrates how over the very long run, cash does in fact generate a nice, non-correlated return for portfolios; yet, this is merely the tip of the iceberg in defense of cash. I will do my best to round out the case here.

First, it’s important to note that Warren Buffett would strongly disagree with the roboadvisor assessment of cash. Alice Schroder offers the following take on Buffett’s perspective: “he thinks of cash differently than the conventional investors. This is one of the most important things I learned from him: the optionality of cash. He thinks of cash as a call option with no expiration date, an option on every asset class, with no strike price.” [emphasis added]. Here we have one of the foremost authorities on Warren Buffett labeling a cash allocation as amongst “the most important” elements of Buffett’s investment prowess. If cash is so important to Warren Buffett, who are these roboadvisors to say otherwise? 

While this point is merely an appeal to (quite the) authority, it might be worth exploring how and why this is not merely fallacious thinking. For that, we can turn to Claude Shannon, also known as “the father of information theory.” I first cited Shannon in my post explaining how the Kelly Criterion can be used to size positions. Unsurprisingly, this was not Shannon’s only investment insight. One of the more interesting conclusions Shannon came to about investing demonstrates how it is possible to “make money off of a random walk” with cash being the secret weapon. 

First let us look at the chart Betterment offered to support its case against cash, as it helps set the stage for why cash is so effective and what Betterment and WealthFront may be missing in building their story:

 Notice something about both lines? There is nothing jagged or wavelike to them. Have you ever observed a stock moving in such fashion? Has any actual historical performance visually appeared as smooth these two lines other than Madoff’s fund? Sure, this is standard operating procedure for presenting simulations of what forward performance could look like in an optimized portfolio, but this is only effective as a rough guide. Reality assuredly will be different, and while no one can guarantee the end return will be different (despite this likely being the case), we all can guarantee that the path in getting from the bottom left to the top right will be different. The fact is, the path of stock price movements have consequences for portfolio returns (human behavioral consequences aside--this alone could be its own extended blog post). There is considerable evidence behind the notion that in the short run, stock market movements are merely a random walk. This is another way of saying that stock price movements will be noisy and volatile, with up and down days scattered across time following no real, predictable patterns. In many respects, this is one of the more important philosophical underpinnings behind the existence of roboadvisors in the first place. It should then be no wonder that this fact has serious consequences for the benefits of cash as a strategic allocation.

The following is an explanation for how cash can effectively boosts returns from Fortune’s Formula by William Poundstone:

Shannon described a way to make money off a random walk. He asked the audience to consider a stock whose price jitters up and down randomly, with no overall upward or downward trend. Put half your capital into the stock and half into a “cash” account. Each day, the price of the stock changes. At noon each day, you “rebalance” the portfolio. That means you figure out what the whole portfolio (stock plus cash account) is presently worth, then shift assets from stock to cash account or vice versa in order to recover the original 50-50 proportion of stock and cash.

To make this clear: Imagine you start with $1,000, $500 in stock and $500 in cash. Suppose the stock halves in price the first day. (It’s a really volatile stock.) This gives you a $750 portfolio with $250 in stock and $500 in cash. That is now lopsided in favor of cash. You rebalance by withdrawing $125 from the cash account to buy stock. This leaves you with a newly balanced mixed of $374 in stock and $375 cash. 

Now repeat. The next day, let’s say the stock doubles in price. The $375 in stock jumps to $750. With the #375 in the cash account, you have $1,125. This time, you sell some stock, ending up with $562.50 each in stock and cash.

Look at what Shannon’s scheme has achieved so far. After a dramatic plunge, the stock’s price is back to where it began. A buy-and-hold investor would have no profit at all. Shannon’s investor has made $125.

This scheme defies most investor’s instincts. Most people are happy to leave their money in a stock that goes up. Should the stock keep going up, they might put more of their free cash into the stock. In Shannon’s system, when a stock goes up, you sell some of it. You also keep pumping money into a stock that goes down.

Poundstone then offers a chart of Shannon’s performance in a 50/50 cash/stock portfolio rebalanced once per each unit of time:

It turns out the rebalanced portfolio beats the fully invested portfolio while also minimizing volatility. The example above is clearly a far more extreme version of the cash allocation and stock volatiltiy Schwab (or most investors) would take on in a real portfolio; however, even in more subtle form the effect is noticeable and real. Note how jagged, rather than smooth, these lines are. Jagged lumpiness is a reality we all must contend with in financial markets.

Long-term investors of all kinds need to acknowledge how hard it is to predict short-run movements in stocks. Even in a good value investing opportunity with an impending catalyst, one can never know with certainty which way a stock will move. We can rely on “asset classes” in the most general sense to earn a positive return over long enough timeframes, but we never can now in advance how long that long-run needs to be. Further, we must also acknowledge the unfortunate reality that it is possible for decades of stagnation on price appreciation even with a growing “intrinsic value”—we call this multiple compression. In such an environment (more so than in a trending environment), cash serves as imposed discipline: one systematically buys low and sells high when this kind of rebalancing is automatic.

Clearly rebalancing is part of the roboadvisors' strategy in switching between stocks and bonds when an allocation leaves a tolerance band; however, there are long stretches of time when stocks and bonds are correlated and meaningful periods of time where cash would offer not just a strong buffer against volatility, but an actual enhancer of return. Poundstone references how counterintuitive Shannon’s methodology appears. As counterintuitive as it may be, it is assuredly true and the benefits are both actual and behavioral. If you like better returns, with less volatility, then cash must be an important component of your portfolio.

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.