Prior to the 15th century, maps generally contained no empty spaces. Mapmakers simply left out unfamiliar areas, or filled them with imaginary monsters and wonders. This practice changed in Europe as the great age of exploration began. In Sapiens, Yuval Harari argues that leaving empty spaces on maps reflected a more scientific mindset, and was a key reason that Europeans were able to conquer and colonize other continents, in spite of starting with a technological and military disadvantage. Conquerors were curious, but the conquered were uninterested in the unknown. Amerigo Vespucci, after whom our home continent was named, was a strong advocate of leaving unknown spaces on maps blank. Explorers used these maps to move beyond the known, sailing into those empty spaces so they did not stay unmapped for long.
The same phenomenon occurs in business. In the Innovator’s Dilemma, Clayton Christensen shows why large established ostensibly well-run companies so frequently miss out on major waves of innovation. A key principle in the book is the difference between sustaining technologies, which merely improve the status quo, and disruptive technologies, which offer a new and unique value proposition. Large companies will frequently focus on sustaining technologies, and ignore disruptive technologies that serve fringe markets initially. Ultimately its disruptive technologies that define business history. Yet complacent companies don’t figure that out until its too late.
Companies whose investment processes demand quantification of market sizes and financial returns before they can enter a market get paralyzed or make serious mistakes when faced with disruptive technologies.
There are two parts to overcoming the innovator’s dilemma:
- Acknowledging that the market sizes and potential financial returns of a nascent market are unknowable and cannot be quantified (drawing the blank spaces on the maps) and;
- Entering the nascent market in the absence of quantifiable data- (travelling into the empty space)
Analogous ideas also apply to investing. In Investing in the Unknown and Unknowable, Richard Zeckhauser distinguishes between situations where the probability of future states is known, and when it is not. The former is the realm of academic finance and decision theory. The latter is the real world.
The real world of investing often ratchets the level of non-knowledge into still another dimension, where even the identity and nature of possible future states are not known. This is the world of ignorance. In it, there is no way that one can sensibly assign probabilities to the unknown states of the world. Just as traditional finance theory hits the wall when it encounters uncertainty, modern decision theory hits the wall when addressing the world of ignorance.
Human bias leads us into classic decision traps when confronted with the unknown and unknowable. Overconfidence and recollection bias are especially pernicious. Yet just because we are ignorant doesn’t mean we need to be nihilists. The essay has some key optimistic conclusions:
The first positive conclusion is that unknowable situations have been and will be associated with remarkably powerful investment returns. The second positive conclusion is that there are systematic ways to think about unknowable situations. If these ways are followed, they can provide a path to extraordinary expected investment returns. To be sure, some substantial losses are inevitable, and some will be blameworthy after the fact. But the net expected results, even after allowing for risk aversion, will be strongly positive.
Examples in the essay include David Ricardo buying British Sovereign bonds on the eve Battle of Waterloo, venture capital, frontier markets with high political risk, and some of Warren Buffet’s more non-standard insurance deals. Yet since even the industries that seem simple and steady can be disrupted, its critical to keep these ideas in mind at all times in order to avoid value traps.
The best returns are available to those willing to acknowledge ignorance, then systematically venture into blank spaces on maps and in markets.
Humans have flawed brains that cause them to act crazy sometimes. And in groups, people get even more crazy. Many smart people believe dumb things. Sometimes a group of otherwise completely sane people come together and do something insane. History is full of examples of the “ Extraordinary Popular Delusions and the Madness of Crowds, or Manias, Panics and Financial crisis. Humans sometimes join suicide cults. Human literally burned witches not that long ago. Groupthink is a helluva drug.
Financial markets provide an arena in which the biggest gains can be made betting against consensus. Yet statistically speaking, the consensus is usually right. The times when the crowd goes crazy are notable because they are exceptions. One must carefully decide when to be a contrarian.
Under what conditions is the consensus likely to be wrong? Lets invert the question: under what conditions is the crowd likely to be right?
Most of the time, a large group of people actually comes to a more accurate conclusion than any one individual. The success of Estimize, which crowdsources earnings estimates is one useful empirical example. “Wisdom of Crowds” is a well documented phenomenon, and well summarized in the book by the same title.
Why is the crowd so often right? What must happen for the crowd to be right ? Researchers have identified four interrelated conditions that encourage the wisdom of the crowds:Diversity, Information Availability, Decentralization, and existence of an Aggregation Mechanism.
Understanding these conditions can help one know when to follow the zeitgeist, and when to make a contrarian bet against it. A firm grasp of the facts on both sides of a controversy is necessary, but possibly not sufficient. One can never be sure of all the facts. Its also useful to understand the broader social forces, and how they influence the likelihood of the consensus being right or wrong. Searching for these conditions(or their absence), can be a useful method of avoiding cults and identifying opportunity, beyond just the facts of the individual situations.
The biggest gains are available by being a contrarian who understands the crowd.
The key is understanding when the wisdom of crowds flips to the madness of crowds. And the essential insight is that it has to do with a violation of one or more of the core conditions for a wise crowd.Michael Mauboussin: Who is on the other side?
Diversity implies that each person has their own point of view and some private information, even if only their unique interpretation of the available public information. Diversity is important because it adds different perspectives and increases the amount of available information.The Value of Crowdsourcing: Evidence from Earnings Forecasts
Crowded trades have a tendency to crash- leading to no bid markets all the way down. Academics have noted that in a run up to a market crash diversity of population declines. The market becomes fragile, and eventually there is no one to buy from (or sell to).
One reason Estimize’ earnings estimates have tended to be better than Wall Street Sell side is that Estimize analysts are a diverse group of independent thinkers from around the world, holding a variety of different jobs. A lot of Wall Street analysts go to the same conferences, went to the same schools, etc.
When people come to the same conclusion from different backgrounds, logical methods, etc , their collective wisdom refines understanding of reality. The opposite occurs when people feel pressure to conform. Its important to note this is a genuine deep diversity of thought and perspective, not a superficial check the box diversity.
If multiple people with different viewpoints all come to similar conclusions, the odds of the opposite being true decrease substantially. On the other hand, if there is an obivous archetype of the thinker on the other side, then maybe a contrarian opportunity is available.
As Michael Maubossin has noted- conformity is a nonlinear process:
Scientists even have a sense of the neurobiological basis for conformity.Informational cascades occur when individuals follow the decisions of those who precede them without regard to their personal information.
Epidemiological models are useful here.
The wisdom of crowds does not emerge in groups of idiots. It only applies when there is widespread access to information necessary to reach a conclusion. The extreme opposite occurs in totalitarian societies (or large corporations), where information is tightly restricted. Prior to the internet, information sometimes diffused slowly through a market, leading to massive price discrepancies obvious at even a quick quantitative glance.
Quality of input is critical to the success of crowdsource analysis:
Alternatively, it is possible that the inclusion of forecasts from certain individuals, such as Non-Professionals, may provide no value, or worse, cause the Estimize consensus to deviate further from actuals. Surowiecki (2004) states that although diversity matters, assembling a group of diverse but thoroughly uninformed people is not likely to lead to wise outcomes.
Independence is related to diversity. People need to be able to freely analyze reality and discuss opinions. Conventional wisdom is more likely to be accurate when it is freely subjected to challenge. When there are institutional or social factors that make people extremely afraid to speak truth, what everybody says to be true, may be wrong.
Independence requires relative freedom from opinions and actions of others, not complete isolation. Independence enables people to actually express their diverse information and reduces potential bias in the group decision.
Decentralization allows people to specialize and draw on local knowledge, without any individual or small group dictating the process.
Diversity and independence all fit in nicely with decentralization. Through specialization, decentralization encourages independence and increases the scope and diversity of information. Decentralization reduces the risk that independence and diversity will go away. Similarly having capital flows from all around the world, not just from a small group of schools or similarly thinking firms, increases the likelihood that markets become more efficient.
Existence of an Aggregation Mechanism
Finally, an aggregation mechanism is necessary to collect the individual opinions and harness the ‘wisdom-of-crowds’ effect.
This is basically why capitalism has succeeded. The price mechanism aggregates facts about supply and demand better than any bureaucracy could. At the same time, this why often the best opportunities to earn an investment profit are in illiquid asset classes where the market does not function as an aggregation mechanism to make the price close to right.
Of course, just because the market consensus is wrong, doesn’t mean that is necessarily wise to bet against it today. Must also consider reflexivity, narratives, and capital flows etc, and maintain a balance sheet that allows one to survive long periods of mass delusion.
Postscript: This is all indirectly related earlier post on finding underfollowed opportunities: The hard thing about finding easy things . This linked the ideas of both Sun Tzu and Warren Buffett. Some of the specific opportunity sets mentioned in this post have since been too widely known and we’ve moved further into more esoteric off the beaten path ideas. Nonetheless the basic principal still holds: there are more likely to be opportunities where the crowd isn’t looking.
“My God, they’re purple and green. Do fish really take these lures?” And he said, “Mister, I don’t sell to fish.
Informational edge can drive fantastic alpha while it lasts. This explains the increasing investment industry focus on non-traditional data sources, aka alternative data. If you are the first to acquire a new alternative data set, you might be able to develop insights no one has.
Yet once a lot of people are using it, it is less likely to drive alpha. It might be table stakes to not get screwed, or it might be already be instantaneously reflected in the price of assets.
Furthermore, most opportunities really hinge on a couple factors- more info isn’t always useful. That won’t stop the alternative data industry from doubling to $400 million by 2021, as a widely cited Tabb Group report predicts This is worth considering while one is caught up in an alternative data arms race.
Yet some data sets genuinely will provide an edge.
Perhaps a data set that no one else is looking at provides the edge you need. If a data set isn’t established as useful, the provider of that data will probably offer it cheaper in the early days of their business. There are so many alternative data providers out there, that marketing strategy is important for startups.
Ironically, the provider can charge higher price once word about its value gets out among investors.. So later adopters might pay more for an edge that is already gone.
Of course eventually someone will put all the data online for free and meta data of how investors use that alternative data can also be useful.
Now can I interest you in an alternative data feed that will make all your dreams come true?
Investing goes through fads. Investing strategies and fund structures(1) go in and out of style. Nowadays long/short hedge funds are out and infrastructure funds are in. Within the public equity markets, value is out, growth/momentum is in. Each time this happens, people forget how the cycle repeats.
In fact, one CIO contended that if he brought a hedge fund that paid him to invest to his board, the board would dismiss it without consideration — simply because it’s called a hedge fund, and hedge funds are bad.Institutional Investor
Hedge funds may have to do a name change if they want to raise capital.
Remember last time?
And yet people forget:
Allocators woke up craving the next rising hedge fund star and couldn’t invest enough at high and increasing management fees after the widespread success of long-short funds in the weak equity markets of 2000-2002. Board rooms back then castigated CIOs for not having long-short equity hedge funds in their portfolios.
This isn’t the first time:
People forget that 40 years ago, officials such as Paul Volcker of the Federal reserve wanted an active hedge fund industry to absorb the risk that was not well managed by state-insured banks.Financial Times
Each investment strategy picks up a certain type of risk(and potentially earns a profit in doing so)- if a strategy disappears that particular risk can become a systemic issue. Fortunately, around this time it also becomes more lucrative to bear the risk others are unwilling to bear. Eventually the risk reward tradeoff starts to make sense again.
Different, different, yet same
In the 1960’s Warren Buffett put up ridiculous returns, and Alfred Winslow Jones proteges profitably exploited anomalies in markets. By the mid 1970’s of there were many articles about hedge funds shutting down though. Industry AUM declined ~70% peak to trough. Nifty fifty boom and bust followed by the long nasty bear market. But as the institutional architecture of international trade and currency shifted we entered glory years of global macro/commodities traders. Then the 80’s were great for Graham deep value and Icahn style activist investing after the 70’s bear market left a huge portion of the market selling below liquidation value.
Likewise late 90’s again saw the death of hedge funds as day traders in pajamas earned easy returns from the latest dot-com- until the crash. Yet out of the rubble of the tech bubble rose a new generation of great hedge fund managers. There was rich pickings for surviving value hunters- and those with the guts and skills to execute became household names a few years later. Many value managers that nearly went out of business during the tech bubble put up ridiculous numbers 2000-2002 and through the next financial crisis. (See: The arb remains the same)
The greatly exaggerated death of a style gives rise to an environment where there is a plethora of opportunities for something similar to that style to work. Each time the narrative in the greater investment community favors some type of uniform strategy, and LPs give less capital to other strategies- causing them to nearly die off. But then the lack of people pursuing the out of fashion strategy makes its return potential more lucrative. Eventually someone finds a new method to pick up those dollar bills on the ground that shouldn’t exist.
Economics emphasizes rational actors and equilibrium. Yet the messy reality is far more complicated. Ecology is a far more useful mental model.
A giant self over-correcting ecosystem
There is in ecological function to speculative capital and over time there should be some excess returns to those willing to take mark-to-market lossesFinancial Times
Like biological species, financial strategies can have competitive, symbiotic, or predator-prey relationships. The tendency of a market to become more efficient can be understood in terms of an evolutionary progression toward a richer and more complex set of financial strategies.Market force, ecology and evolution
Ecology emphasizes interrrelationships between different individuals and groups within a changing environment, and indentifies second order impacts.
Thinking like a biologist
One can develop a useful framework by replacing species with strategy, population with capital, etc
Flows and valuation interact, self correct, and overshoot.
….capital varies as profits are reinvested, strategies change in popularity,and new strategies are discovered. Adjustments in capital alter the financial ecology and change its dynamics, causing the market to evolve. At any point in time there is a finite set of strategies that have positive capital; innovation occurs when new strategies acquire positive capital and enter this set. Market evolution is driven by capital allocation.
Market evolution occurs on a longer timescale than day-to-day price changes. There is feedback between the two timescales: The day-to-day dynamics determine profits, which affect capital allocations, which in turn alter the day-to-day dynamics. As the market evolves under static conditions it becomes more efficient. Strategies exploit profit-making opportunities and accumulate capital, which increases market impact and diminishes returns. The market learns to be more efficient.
When an ecoystem is overpopulated with a certain species, it eventually overshoots and results in mass starvation. Populations fluctuate wildly across decades, and sometimes species go extinct or evolve into something that seems new.
New conditions give rise to new dominant species.
(1) Although I am frequently pedantic about the differences between structure, strategy, and sector, many in the media seem to use these interchangeably when discussing reversion to mean situations. Fortunately they all exhibit the same boom/bust phenomenon, so I am using them interchangeably here.
Imagine if Warren Buffett of 1960 puts down the deadtree 10-K he got in the mail and time travels forward to 2019. Then he looks over the shoulder of an analyst at present day O’Shaughnessy Asset Management. He would find the scene unrecognizable.
Or, if the original Jesse Livermore time traveled from the 1920s stock exchange to the present day trading floor of DE Shaw or Renaissance. Again, completely unrecognizable.
Back in the day people went to the SEC office in the Washington DC to access annual reports faster. That was how one got a fundamental edge. Now people scrape filings the minute they come out. Or use satellites and credit card data to get an edge on information before it hits regulatory filings. People used to gauge momentum by looking at the facial expressions of other traders, now they use complex computer models. People mine market and fundamental data around the globe looking for a bit of an edge. New techniques, same thing.
Over time there is the change in the physical activities, and words we use to describe the process of identifying and exploiting market inefficiencies. Nonetheless the ecological function is the same. Investors are just looking for mispriced risk, and exploiting it till its no longer mispriced.
Around the world there are unfair coins waiting for someone to flip them. Arbitrageurs will need to use weirder and weirder methods to find and exploit them. Methods change, but the arb remains the same.
- Technological Revolutions and Financial Capital
- Capital Returns
- Modern Monopolies
- How the Music Got Free
- Laws of Human Nature
- Debt’s Dominion: A History of Bankruptcy Law in America
- Thinking in Bets: Making Smarter Decisions When You Don’t Have All the Facts
- The Coffee Trader
- Three Body Trilogy :
- Pattern Recognition
Technological Revolutions and Financial Capital did more to advance my understanding of the modern economy than any other book I read in 2018. Traditional economics treats sudden changes of technology as a separate exogenous factor. This mode of thinking is pretty much useless for anyone forced to allocate capital under uncertain conditions. In reality there is strong empirical evidence that financial markets and technology interact in repeated cycles . Carlota Perez shows the pattern in these cycles by breaking them down into Installation and Deployment Phases. She identifies four main parts of each technological cycle: Irruption, Frenzy, Synergy and Maturity. The book illustrates these patterns in the most important disruptions in economic history, including: the industrial revolution , steam engines/railways, steel/electricity/heavy engineering, automobiles/mass production, and information/telecom. Marc Andreesen hailed Technological Revolutions and Financial Capital as the single best book for understanding the software industry. Since software is eating the world, its probably also the best book for understanding any industry.
Capital Returns is about how competitive advantages change over time, and how changes in capital availability can alter industry dynamics. It covers a fund manager’s thoughts and activities from 2002- 2015, including periods of disruption and volatility in a variety of industries. Ultimately the book is about how mean reversion occurs and and impacts investors.
Modern Monopolies deserves most of the hype that it has received. Many business decision makers were schooled in “linear” business models, but the most valuable and disruptive businesses are platforms, business model that facilitate the exchange of value between two or more user groups, a consumer and a producer. This book covers how technology has enabled more platform business models, and what the implications are for investors and entrepreneurs.
How the Music Got Free tells the story of how streaming music and piracy upended the music industry. It also covers the story of how the MP3 format struggled to gain acceptance for many years before becoming ubiquitous. The contrasting attitudes and actions of the winners and losers in the story are an entertaining juxtaposition , and a useful case study. As a teenager I had been one of the early pirates, but was less aware of what was going on inside the old line music companies, and had not zoomed out to consider the broader industry implications.
Laws of Human Nature is an essential guide for understanding how people act and how society functions. It teases out key lessons from psychology along with successes and failures throughout history. Reading one Robert Greene book provides a reader with the benefits of reading dozens of business biographies and history books. It provides ideas for avoiding certain problematic tendencies in oneself and exploiting traits in others. Laws of Human Nature looks at the dark reality of patterns in thought and behavior that don’t conform to an idealistic rational expectation:
As long as there are humans, the irrational will find its voices and means of spreading. Rationality is something to be acquired by individuals, not by mass movements or technological progress. Feeling superior and beyond it is a sure sign that the irrational is at work.
Debt’s Dominion: A History of Bankruptcy Law in America, is a bit wonkish, but well worth the slog for the insight into the American bankruptcy system. Its easier to understand the subtle nuances of the bankruptcy process and navigate opportunities in the distressed debt market when I think about how the system evolved. Indirectly the book is also about the slow process of institutional change.
Thinking in Bets is basically a long essay on behavioral economics, with one valuable message. Under a plethora of entertaining anecdotes about professional poker it provides an immediately applicable framework for functionning in an uncertain world. Basically its a slightly less nerdy and less nuanced companion to Fortune’s Formula. It also fits in well with some of the more important behavioral finance books, such as…. Misbehaving, and Hour Between Dog and Wolf, Kluge, etc. (more notes here)
The Coffee Trader is historical fiction taking place in 17th century Amsterdam, when coffee was first brought to Europe and the modern concept of stock and derivative markets were just beginning to form. It tells the story from the vantage point of Miguel Lienzo, a small time speculator who must navigate complex social structures, and deal with shady counter parties and aggressive creditors and competitors while trying to get rich in a nascent market. He is also a Jewish refugee who fled the Portuguese inquisition but then struggles with overly conservative Jewish leaders in Amsterdam, who constantly threaten to shun him. Complicating matters further, he has a conflict with his brother, who is at the beginning a far more successful and respected member of the community.
The first person narration includes gems like this one:
There was no shortage of my kind in Amsterdam. We were as specialized as taverns, each of us serving one particular group or another: this lender serves artisans; that one, merchants; yet another, shopkeepers. I resolved never to lend to fellow Jews, for I did not want to travel down that path. I would not want to have to enforce my will on my countrymen and then have them speak of me as one who had turned against them. Instead, I lent to Dutchmen, and not just any Dutchmen. I found myself again and again lending to Dutchmen of the most unsavory variety: thieves and bandits, outlaws and renegades. I would not have chosen so vile a bunch, but a man has to earn his bread, and I had been thrust into this situation against my will.
Three Body Trilogy is a deep character study of humanity itself in the form of an epic science fiction story spanning multiple centuries. It ultimately focuses on the way people act in situations of duress and conflict, both within and between different countries and groups. The Three Body Problem starts slowly, opening during the Chinese Cultural revolution, and tells of the initial contact with alien civilizations. The Dark Forest explores the paradoxical alignment of incentives in high stakes inter galactic diplomacy. The ultimate conclusion for the future of civilization in Death’s End is pessimistic, but getting there is thrilling.
Pattern Recognition is a thriller set in the early 2000s that feels like a science fiction take on modern media. The protagonist is a branding consultant/corporate spook who gets sent on a mission to uncover the anonymous creator behind a series of video clips that have spawned an obsessive subculture of fans. She tracks people and clues down in London, Tokyo and Moscow meeting many bizarre and unseemly characters along the way.
As with all Gibson novels, the dialogue and descriptions are fantastic.
“Of course,” he says, “we have no idea, now, of who or what the inhabitants of our future might be. In that sense, we have no future. Not in the sense that our grandparents had a future, or thought they did. Fully imagined cultural futures were the luxury of another day, one in which ‘now’ was of some greater duration. For us, of course, things can change so abruptly, so violently, so profoundly, that futures like our grandparents’ have insufficient ‘now’ to stand on. We have no future because our present is too volatile.” He smiles, a version of Tom Cruise with too many teeth, and longer, but still very white. “We have only risk management. The spinning of the given moment’s scenarios. Pattern recognition.
“To Destroy the Ego,
One Must First Find it”
-Wu Hsin, Aphorisms for Thirsty Fish
One ego suspension trick I find useful is to separate myself from my ideas. Once I think of and share an idea I mentally treat it as this thing that is different and separate from me.
So if someone says the idea sucks- I can view their feedback objectively. They’re not attacking me. The way forward is not a choice between “my” idea and someone elses idea. Its just a choice between ideas.
The same goes for actions already completed. If someone questions a move I made in backgammon, or someone I hired at my company, or a major life choice, I can view it objectively and potentially improve in the future.
Its kind indirect way to achieving the benefits of Kipling’s call to:
“… trust yourself when all men doubt you
But make allowance for their doubting too. “
I just make it easier by stepping outside and not being attached to the “you”. The criticism and criticized are in the distance.
Sometimes this leads to better insight. There is no need to preserve an identity that may be attached to a particular idea. Just find the idea that is better according to some criterion and go there.
This isn’t a perfect system. There is only human software operating here. But it helps. Its a cruder version of what many great thinkers in the past have known.
Around 200 BC Chinese strategist Zhuge Liang first used a sales trick still re purposed by consultants and lawyers today.
Zhuge Liang was an amateur meteorologist, and he used this fact to convince people that he could control the weather. His knowledge of meteorology was very basic, something any farmer who paid attention would have known. Nonetheless his enemies didn’t have this knowledge. So it was easy to bamboozle them.
During one battle , he realized that the wind was likely to switch direction in a manner that was highly favorable for his army.
He made sure the enemy saw him do an elaborate ceremony that looked like black magic. He kept at it until the wind changed direction. As a result his reputation as a fearsome indispensable strategist grew massively.
This was featured in the historical fiction Romance of the Three Kingdoms . The phrase “Borrow the East Wind (借东风) refers to this story. Its sometimes used to described taking advantage of a situation.
A bit of dancing, drumming and smoke. Zhuge Liang took basic observation skills and sold them as black magic.
Modern knowledge work
I think of this anytime I see a knowledge worker selling their work makes it look more complex than it really is.
Jargon, chartporn and powerpoint replaces dancing drumming and smoke. Or alternatively with legal and compliance work, fear of regulatory risk leads to a company paying high fees to avoid problems. Even if all that is needed is filing a simple form at the right time.
There is a risk of a similar phenomenon in any business where there is a huge knowledge gap between seller and customer. Will the seller take advantage of that gap in a way that harms the buyer?
It may seem like there is one fundamental problem with this comparison: Zhuge Liang was a diplomat/military strategist. A sales call isn’t a war. Its not supposed to be adversarial!
That might actually be the problem. An honest sales process is about helping the client see the value. The battle is against any misperception not against the client. A dishonest sales process is about taking as much from the client as possible.
Zhuge Liang’s life was on the line. And warfare (against sentient opponents) is all about deception. Deceiving competitors is justifiable. But deceiving customers is not. Some businesses may feel their life is on the line, but I bet they could make a good living by reducing complexity rather than playing it up. I know I’m willing to pay up for reduced complexity!
Dealing with this issue has proved to be a major challenge in dealing with lawyers, compliance consultants and technology contractors. I’ll ask around and get quoted absurdly large price ranges for the same set of work.
I’m getting better at asking the right questions in order to see what services are really worth.
I place great value on lawyers, consultants and developers who can cut through the bullshit.
The idea of having “morning routine” is a favorite topic on the interwebs these days. Its now at the point where I can barely tell apart parody from serious articles. I think Tim Ferriss started it all by asking every single one of his guests their morning routine. Most people couldn’t resist listening. I know I couldn’t.
When a “high performer” provides a detail on their morning routine, they are merely providing an example of a thing that works for a very specific set of circumstances. Nothing more nothing less. Just because it worked for them, doesn’t mean it will work for you.
Nonetheless it is still a useful data point. Weird low cost ideas are usually worth trying.
Eliminating decision fatigue
I view morning routines as constrained optimization problem that will have different solutions for different people. It might even have different answers on different days for the same person.
I’m generally skeptical of anything overly complicated. The key benefit of a morning routine is it reduces decision fatigue. The morning routine is a “default setting” . But if one is obsessed with following it, it becomes a burden and prevents serendipity.
Towards an antifragile morning routine
The ability to adapt to changing circumstances is more important than having a set routine. Some days you have to work late. Other days you may have an early meeting. Or maybe your child gets sick. What if your spouse or mistress needs your support? What do you do when you travel or are awoken by a phone call? What then?
It doesn’t make sense to rigidly expect the world to conform to your plan. A fragile morning routine is worse than no morning routine. A robust morning routine is better. An antifragile morning routine is best.
Personally I have a “default setting” to do when I’m home and nothing special is happening. I have a handful of different algorithms I can enact depending on other circumstances that come up. This gives me the comfort of a routine without rigidity. If I’m travelling I’ll usually explore the local area just a bit.
Sometimes an emergency leads to a new insight.
The secret is to make peace with walking around in a world where we recognize that we are not sure and that’s okay. As we learn more about how our brains operate, we recognize that we don’t perceive the world objectively. But our goal should be to try.
Annie duke’s “Thinking in Bets” is basically long essay with an extremely valuable message. Under a plethora of entertaining anecdotes about professional poker it contains a valuable framework for making decisions in this uncertain world. This requires accepting uncertainty, and being intellectually honest. Good decision making habits compound over time
Thinking in Bets is a slightly less nerdy and less nuanced compliment to pair with “Fortune’s Formula”. It also fits in well with some of the more important behavioral finance books, such as…. Misbehaving, and Hour Between wolf and dog, Kluge, etc.
I’ve organized some of my highlights and notes from Thinking in Bets below.
The implications of treating decisions as bets made it possible for me to find learning opportunities in uncertain environments. Treating decisions as bets, I discovered, helped me avoid common decision traps, learn from results in a more rational way, and keep emotions out of the process as much as possible.
Outcome quality vs decision quality
We can get better at separating outcome quality from decision quality, discover the power of saying, “I’m not sure,” learn strategies to map out the future, become less reactive decision-makers, build and sustain pods of fellow truthseekers to improve our decision process, and recruit our past and future selves to make fewer emotional decisions. I didn’t become an always-rational, emotion-free decision-maker from thinking in bets. I still made (and make) plenty of mistakes. Mistakes, emotions, losing—those things are all inevitable because we are human. The approach of thinking in bets moved me toward objectivity, accuracy, and open-mindedness. That movement compounds over time
Thinking in bets starts with recognizing that there are exactly two things that determine how our lives turn out: the quality of our decisions and luck. Learning to recognize the difference between the two is what thinking in bets is all about.
Why are we so bad at separating luck and skill? Why are we so uncomfortable knowing that results can be beyond our control? Why do we create such a strong connection between results and the quality of the decisions preceding them? How
Certainty is an illusion
Trying to force certainty onto an uncertain world is a recipe for poor decision making. To improve decision making, learn to accept uncertainty. You can always revise beliefs.
Seeking certainty helped keep us alive all this time, but it can wreak havoc on our decisions in an uncertain world. When we work backward from results to figure out why those things happened, we are susceptible to a variety of cognitive traps, like assuming causation when there is only a correlation, or cherry-picking data to confirm the narrative we prefer. We will pound a lot of square pegs into round holes to maintain the illusion of a tight relationship between our outcomes and our decisions.
There are many reasons why wrapping our arms around uncertainty and giving it a big hug will help us become better decision-makers. Here are two of them. First, “I’m not sure” is simply a more accurate representation of the world. Second, and related, when we accept that we can’t be sure, we are less likely to fall
Our lives are too short to collect enough data from our own experience to make it easy to dig down into decision quality from the small set of results we experience.
Incorporating uncertainty into the way we think about our beliefs comes with many benefits. By expressing our level of confidence in what we believe, we are shifting our approach to how we view the world. Acknowledging uncertainty is the first step in measuring and narrowing it. Incorporating uncertainty in the way we think about what we believe creates open-mindedness, moving us closer to a more objective stance toward information that disagrees with us. We are less likely to succumb to motivated reasoning since it feels better to make small adjustments in degrees of certainty instead of having to grossly downgrade from “right” to “wrong.” When confronted with new evidence, it is a very different narrative to say, “I was 58% but now I’m 46%.” That doesn’t feel nearly as bad as “I thought I was right but now I’m wrong.” Our narrative of being a knowledgeable, educated, intelligent person who holds quality opinions isn’t compromised when we use new information to calibrate our beliefs, compared with having to make a full-on reversal. This shifts us away from treating information that disagrees with us as a threat, as something we have to defend against, making us better able to truthseek. When we work toward belief calibration, we become less judgmental .
In an uncertain world, the key to improving is to revise, revise, revise.
Not much is ever certain. Samuel Arbesman’s The Half-Life of Facts is a great read about how practically every fact we’ve ever known has been subject to revision or reversal. We are in a perpetual state of learning, and that can make any prior fact obsolete. One of many examples he provides is about the extinction of the coelacanth, a fish from the Late Cretaceous period. A mass-extinction event (such as a large meteor striking the Earth, a series of volcanic eruptions, or a permanent climate shift) ended the Cretaceous period. That was the end of dinosaurs, coelacanths, and a lot of other species. In the late 1930s and independently in the mid-1950s, however, coelacanths were found alive and well. A species becoming “unextinct” is pretty common. Arbesman cites the work of a pair of biologists at the University of Queensland who made a list of all 187 species of mammals declared extinct in the last five hundred years.
Getting comfortable with this realignment, and all the good things that follow, starts with recognizing that you’ve been betting all along.
The danger of being too smart
The popular wisdom is that the smarter you are, the less susceptible you are to fake news or disinformation. After all, smart people are more likely to analyze and effectively evaluate where information is coming from, right? Part of being “smart” is being good at processing information, parsing the quality of an argument and the credibility of the source. So, intuitively, it feels like smart people should have the ability to spot motivated reasoning coming and should have more intellectual resources to fight it. Surprisingly, being smart can actually make bias worse. Let me give you a different intuitive frame: the smarter you are, the better you are at constructing a narrative .
… the more numerate people (whether pro- or anti-gun) made more mistakes interpreting the data on the emotionally charged topic than the less numerate subjects sharing those same beliefs. “This pattern of polarization . . . does not abate among high-Numeracy subjects.
It turns out the better you are with numbers, the better you are at spinning those numbers to conform to and support your beliefs. Unfortunately, this is just the way evolution built us. We are wired to protect our beliefs even when our goal is to truthseek. This is one of those instances where being smart and aware of our capacity for irrationality alone doesn’t help us refrain from biased reasoning. As with visual illusions, we can’t make our minds work differently than they do no matter how smart we are. Just as we can’t unsee an illusion, intellect or willpower alone can’t make us resist motivated reasoning.
The Learning Loop
Thinking rationally is a lot about revising, and refuting beliefs(link to reflexivity) By going through a learning loop faster we are able to get an advantage. This is similar to John Boyd’s concept of an OODA loop.
We have the opportunity to learn from the way the future unfolds to improve our beliefs and decisions going forward. The more evidence we get from experience, the less uncertainty we have about our beliefs and choices. Actively using outcomes to examine our beliefs and bets closes the feedback loop, reducing uncertainty. This is the heavy lifting of how we learn.
Chalk up an outcome to skill, and we take credit for the result. Chalk up an outcome to luck, and it wasn’t in our control. For any outcome, we are faced with this initial sorting decision. That decision is a bet on whether the outcome belongs in the “luck” bucket or the “skill” bucket. This is where Nick the Greek went wrong. We can update the learning loop to represent this like so: Think about this like we are an outfielder catching a fly ball with runners on base. Fielders have to make in-the-moment game decisions about where to throw the ball.
Key message: How poker players adjust their play from experience determines how much they succeed. This applies ot any competitive endeavor in an uncertain world.
The best players analyze their performance with extreme intellectual honesty. This means if they win, they may end up being more focused on erros they made, as told in this anecdote:
In 2004, my brother provided televised final-table commentary for a tournament in which Phil Ivey smoked a star-studded final table. After his win, the two of them went to a restaurant for dinner, during which Ivey deconstructed every potential playing error he thought he might have made on the way to victory, asking my brother’s opinion about each strategic decision. A more run-of-the-mill player might have spent the time talking about how great they played, relishing the victory. Not Ivey. For him, the opportunity to learn from his mistakes was much more important than treating that dinner as a self-satisfying celebration. He earned a half-million dollars and won a lengthy poker tournament over world-class competition, but all he wanted to do was discuss with a fellow pro where he might have made better decisions. I heard an identical story secondhand about Ivey at another otherwise celebratory dinner following one of his now ten World Series of Poker victories. Again, from what I understand, he spent the evening discussing in intricate detail with some other pros the points in hands where he could have made better decisions. Phil Ivey, clearly, has different habits than most poker players—and most people in any endeavor—in how he fields his outcomes. Habits operate in a neurological loop consisting of three parts: the cue, the routine, and the reward. A habit could involve eating cookies: the cue might be hunger, the routine going to the pantry and grabbing a cookie, and the reward a sugar high. Or, in poker, the cue might be winning a hand, the routine taking credit for it, the reward a boost to our ego. Charles Duhigg, in The Power of Habit, offers the golden rule of habit change….
Being in an environment where the challenge of a bet is always looming works to reduce motivated reasoning. Such an environment changes the frame through which we view disconfirming information, reinforcing the frame change that our truthseeking group rewards. Evidence that might contradict a belief we hold is no longer viewed through as hurtful a frame. Rather, it is viewed as helpful because it can improve our chances of making a better bet. And winning a bet triggers a reinforcing positive update.
Note: Intellectual Honesty thinking clearly= thinking in bets
Good decisions compound
One useful model is to view everything as one big long poker game. Therefore the result of individual games won’t upset you so much. Furthermore, good decision making habits compound over time. So the key is to always be developing good long term habits, even as you deal with the challenges of a specific game.
The best poker players develop practical ways to incorporate their long-term strategic goals into their in-the-moment decisions. The rest of this chapter is devoted to many of these strategies designed to recruit past- and future-us to help with all the execution decisions we have to make to reach our long-term goals. As with all the strategies in this book, we must recognize that no strategy can turn us into perfectly rational actors. In addition, we can make the best possible decisions and still not get the result we want. Improving decision quality is about increasing our chances of good outcomes, not guaranteeing them. Even when that effort makes a small difference—more rational thinking and fewer emotional decisions, translated into an increased probability of better outcomes—it can have a significant impact on how our lives turn out. Good results compound. Good processes become habits, and make possible future calibration and improvement.
At the very beginning of my poker career, I heard an aphorism from some of the legends of the profession: “It’s all just one long poker game.” That aphorism is a reminder to take the long view, especially when something big happened in the last half hour, or the previous hand—or when we get a flat tire. Once we learn specific ways to recruit past and future versions of us to remind ourselves of this, we can keep the most recent upticks and downticks in their proper perspective. When we take the long view, we’re going to think in a more rational way.
Life, like poker, is one long game, and there are going to be a lot of losses, even after making the best possible bets. We are going to do better, and be happier, if we start by recognizing that we’ll never be sure of the future. That changes our task from trying to be right every time, an impossible job, to navigating our way through the uncertainty by calibrating our beliefs to move toward, little by little, a more accurate and objective representation of the world. With strategic foresight and perspective, that’s manageable work. If we keep learning and calibrating, we might even get good at it.