Notes from Capital Allocators: Basil Qunibi

Link: https://capitalallocatorspodcast.com/2018/03/04/novus/

About Basil: Founder of Novus which does analytics on managers and portfolios trying to disaggregate sources of edge/skill and quantify obliques risks such as crowding and liquidity.


Early Days

Initial Research

  • Early 2000s, Basil began studying underutilized data sets :
    1. Public filings (ie 13F, 13D etc) domestic and abroad
    2. Monthly exposure reports from managers
    3. Position level reports when available which provided full transparency

Meeting Resistance

  • “People hear with their amygdala”
    • Amygdala is the emotional center of the brain. His analysis was perceived as a threat as opposed to being received with commensurate rationality. Often his analysis contradicted narratives or perceptions

Novus’ Start

  • Initial Novus Products
    • Individual Manager Report
      • batting and slugging avg, long/short attribution, geographic/industry exposures
    • Overlap Report
      • Calculate overlap between candidate managers as well as the client allocator.
    • Aggregate/Look Thru Report
      • Analytics on an allocator’s entire portfolio of managers combined
  • Novus Framework Product: aimed at distilling manager skill, positioning (based on private data on 1500 funds)
  • Product aims to be “Moneyball for Allocators”

 

Moneyball for Allocators: Decomposing Manager Skill

Systematic Factors

Factors that depend on the broader market.

  • Exposure Management: How does gross exposure variation influence return? On average detracts 200 bps/yr
    • Manager’s variation in this aspect is not persistent; deviation from mean is mostly luck
  • Capital Allocation: How does exposure to capital structures or sectors contribute to performance?
    • This factor is also typically negative for most managers

Intrinsic Factors

More persistent and where the potential for alpha lies

  • Security selection: items picked out of the sectors or geographies
  • Sizing: This is compared to a control of equal-weighted portfolio
  • Trading: Tactical trading seen in flipping positions

 

Using the Framework to Make Better Allocation Decisions

  • If the allocation thesis for any fund is simply returns it will invariably hit a bad run. Mapping a fund’s skill to the environment is a better basis to decide whether to cut or increase exposure to the fund than simply returns.
    • For example, if the majority of a fund’s monthly alpha comes from trading but the data shows that the volume in the fund’s positions has been steadily dropping, it may indicate a lack of opportunity to capitalize on the fund’s strength.
  • The framework allows an allocator to evaluate a fund based on its stated intentions. If they claim they have an edge in security selection they can be rated on that dimension.
    • This shifts the evaluation from “thinking in T to thinking in N”.
    • It doesn’t make sense to compare a fundamental value strategy vs a high-frequency strategy at the same time horizons.
    • Large sample size of trades without any single trades dominating the results is easier to evaluate than strategies that make a few concentrated bets.
  • Benefit of increasing transparency also accrues to good managers since the story is about more than returns and the data can reveal that a bad run is just bad luck (ie losses coming from extrinsic non-persistent factors)

4 Measures of Crowding

  1. Conviction: largest position sizes amongst managers; names reported as > 5% positions within a fund
    • Best performing factor over time
    • From Faber’s interview with fellow Novus co-founder Altshuller, they constructed a ‘Conviction Index’ with Barclays based on impressive and still persistent performance of stocks which rank high on a sort of high conviction positions by hedge funds (stock > 7.5% of portfolio concentration)
  2. Concentration: How tightly held are the shares?
    • This is also a positive factor
  3. Consensus: how popular is the name?
    • This factor underperforms over time
  4. Crowdedness: How consensus is the name AND how much daily volume do they represent? “How crowded is the theater; how big is the exit?”
    • This is actually a factor which performs well over time but has massive skew

Scaling up as AUM Grows

How you can expect AUM growth to impact manager performance?

  • Increasing number of positions
    • If the manager has skill in sizing positions this will ‘flatten’ the alpha
  • Moving into higher market cap names
    • If the manager has skill in small cap, this is style drift
  • Increasing current position sizes
    • This deteriorates liquidity; while adding it can be a positive feedback loop but this is a double-edged sword. This is the most dangerous form of scaling if liquidity is overestimated

Notes From Invest Like the Best: Brian Christian

Link: http://investorfieldguide.com/christian/

About Brian: Author covering humans’ relationship with technology and AI


Q: What advice would you give to people, building careers. We’re in a political cycle now where things like basic income are being discussed. In your view, what are the most defensible areas of human activity, whether that’s some sort of creativity or asking great questions coming up with the objective functions that you then feed the machines? What would you recommend people focus on as they think about either early or late in their career, adding value?

A: There are sort of two ways that I can approach this question. My second book is called the Algorithms to Live By and it looks at things like career decisions from an explicitly algorithmic perspective.

1) Explore/Exploit Trade-off

Description

There’s this paradigm, called the “explore/exploit” trade-off, which is: How much of your energy do you spend gathering information vs how much do you spend committing based on the information? There’s a number of decisions that we face throughout life, that take the form of a tension or a balance between trying new things and committing to the things that seem to be the best. Where to go out to eat, go to our favorite restaurant and we try a new restaurant. Reach out to a new acquaintance we’d like to get to know better or spend time with our close family or best friend. The same thing is true in investing, the same thing is true in managing your time and your career.

Generalizing the Problem

The structure of this problem is an iterated decision that you get to make over and over again. Do you continue to put energy into the things that seem promising, or do you spend your energy trying new things? A clinical trial can have that same structure, and indeed the FDA has been increasingly interested in looking over the disciplinary fence at the computer scientists and saying, maybe those algorithms that you’re using to optimize ads, could also be used to optimize human lives. The way a computer scientist, approaches this question is through something that’s called the multi-armed bandit problem.

The Multi-armed Bandit Problem

Background

In the multi-armed bandit problem you walk into a casino that has all these different slot machines. Some of them pay out with a higher probability than others, but you don’t know which are which. What strategy do you employ to try to make as much money in the casino as you can. It’s going to necessarily involve some amount of exploration trying out different machines to see which ones appear to pay out more than others, and exploitation, which to a computer scientist doesn’t have the negative connotation that it has you know in regular English exploitation meaning, but just leveraging the information you’ve gained so far to crank away on those machines that do seem to be the best. Intuitively I think most of us would recognize that you need to do some amount of both, but it’s not totally obvious what that balance should look like in practice, and indeed for much of the 20th century, this was considered not only an unsolved problem but an unsolvable problem, and sort of career suicide to think about. During WWII, the British mathematicians joked about dropping the multi armed bandit problem over Germany in the ultimate intellectual sabotage. Just waste the brainpower and nerd snipe all of the German mathematicians. To the field’s own surprise, there came a series of breakthroughs on the multi-armed bandit problem through the second half of the 20th century.

Solution

Now we have a pretty good idea of what exact solutions look like given a number of constraints, but also what sort of more general flexible algorithms look like. The critical insight into thinking about this problem is that your strategy should depend entirely on how long you plan to be in the casino. If you feel that you have a long time ahead of you, then it’s worth it to invest in exploration, because if you do find something great, it has a long horizon to pay out. On the other hand, if you feel that you are about to leave the casino, then the return that you would get on making a great new discovery is going to be much smaller, because you have fewer opportunities to crank away on that handle once you find it. We should naturally transition from being more exploratory at the beginning of a process to more exploitative at the end. I think that’s an intuition that makes sense, but the math bears that out very concretely.

Observation of “Explore/Exploit” Trade-Off in Real Life

Psychology

It’s interesting to see this idea that emerges in computer science in the late 50s through the 70s getting picked up by psychologists and cognitive scientists who are interested in human decision making. For example, Alison Gopnik at UC Berkeley who studies infant cognition, has been thinking about the “explore/exploit” trade-off as a framework for how the infant mind works. If you think about how children behave, we have all these stereotypes about children are just kind of random, they’re generally incompetent at things, and there’s a huge literature that shows that they have what’s called a “novelty bias”. They’re relentlessly interested in the next thing and the next thing and the next thing. Rather than viewing that as a kind of low willpower or attentional control issue, you can view it as the optimal strategy. It’s as if you’ve just burst through the doors of life’s casino and you have 80 years ahead of you. It really does make a lot of sense to just run around wildly pulling handles at random. The same is true for being in the later years of one’s life. We have a lot of stereotypes about older people being set in their ways and resistant to change. There’s a psychology literature that shows that older adults, maintain fewer social connections than younger people, and it’s tempting to view that pessimistically. In fact if you build an argument from the mathematics, you can see that older adults are simply in the exploit phase of their life and they are again doing the optimal thing, given where they are in that interval of time. You have psychologists like Stanford’s Laura Carstensen appealing to the “explore/exploit” trade off to make this argument that older adults know exactly what they’re doing and they’re very rationally choosing a strategy that makes sense given where they are. They have a lifetime’s exploration behind them, they know what they really like, they know the people and the connections that matter to them, and they have a finite amount of time left to reap the fruits of some new connection or new discoveries so they’re very deliberately enacting the strategy. The math should predict that, on average, older adults are happier than young people. Despite our preconceptions, and her research bears this out, that appears to be the case.

Business

In business, the problem is very dynamic, which will classify it in the domain of the “restless bandit problem”. Since the research here is cloudier, researchers can invert the thinking to infer the conditions that lead to the business strategies we can observe.

Q: Interesting how this maps on to the life cycles of businesses. In the business context, “explore” might be innovation and “exploit” might be to run the same playbook to earn high returns on capital or something you know works. It seems like you always want to be handing off to a next batch of exploration or innovation, while thoughtfully maintaining something that you know works if you want to survive for very long time.

A: There’s a couple of things that I think are interesting in a business context. One is that implicitly the casino framing that I’ve described assumes that those probabilities are stable and fixed. Of course, we know that the world is not stable and not fixed that things change over time. This is true in our personal lives as well. Your favorite restaurant gets a new line cook and the burgers are not as good. These things shift. This is known as the “restless bandit problem”. How do you play this game when these probabilities are drifting on a random walk?

This is a very interesting case where the theory is not yet consolidated but humans, in practice, seem to have no problem. If you put people in a lab and give them a restless bandit problem, they have no trouble making choices within that environment but we don’t yet know what the mathematics of the optimal solution looks like. So here’s the case where the computer scientists and the mathematicians are asking the cognitive scientists, what are your models for how humans are actually approaching this because there may be some insight that we can use from the theory side. One of the implications of thinking in this way that is particularly relevant in a business setting is if the interval of time you perceive yourself to be on determines the strategy that you should employ, then it should be the case that if you observe someone else’s strategy, you can infer the interval that they’re optimizing over.

Inferring The Explore/Exploit Strategy in a Restless Bandit Problem

Let’s give an example from Hollywood. Most people have noticed, it feels like we’re living through this deluge of sequels, such as Marvel movies. It turns out that this is objectively true. There’s a sea change in Hollywood. In 1982, 2 of the top 10 grossing films were sequels. By 1990 it was six. By the year 2000, it was eight, and I think most recently it was all ten. From that, we can infer that Hollywood has taken a very hard turn towards an exploitative strategy. They are milking their existing franchises, rather than investing money speculatively to try to develop new franchises that will last them into the next few decades. From that, it’s reasonable to infer that movie ticket sales are declining, which turns out to be the case. Hollywood correctly perceives itself to be at the waning time of the golden era of cinema-going. If that’s true, then they really should invest all of their money into just squeezing everything they can out of the existing franchises. More broadly, so you can look at different industries and different corporations to see if they cut their r&d budget. If they’ve given that money to marketing that’d be an indication that they feel that the area has matured or plateaued.

My thoughts

    1. Ahem, asset management, cough
    2. Reminds me of a great Peter Chernin interview where he suggests that every business must be trying to grow new opportunities faster than the the old ones die out. While you must do your best to milk the old, it’s imperative to develop the new.

2) Predicting the Impact of Automation

The second avenue is totally different from this way of thinking, which is just what will the impacts of something like AI or UBI be on the economy. I’m reminded of a McKinsey report on which jobs they thought would be the most robust. The big picture thing that was interesting to me is that it cuts across the traditional class lines. It is not a white-collar versus blue-collar thing. It’s not an upper middle class versus lower middle class thing. It’s very sector dependent. The most resilient or robust jobs at the top end was gardener, legislator, and psychotherapist. I thought that was very fascinating that it’s this eclectic mixture of things. I don’t think of myself as a prognosticator about these sorts of things but my way of thinking about it is that there’s a lot of kind of human machinery around how capital moves and how laws get made. How licensing and permitting happen. It’s still done at a human negotiation level. “I know a guy. I’ll talk to Joe and we’ll sort it out.” I think humans will maintain oversight of these kind of flows of power and capital, even if the actual value is being created by software. So position yourself closer to the flow of that value than the actual creation of the value, which may be counterintuitive.

As far as the question of UBI, I don’t have a great intuition for that. There is already a restlessness in the labor force. A lot of the careers that employ some of the most numbers of people are the most vulnerable. People who drive cars or trucks, people who work in warehouses. A lot of those jobs are just one innovation away, and it’s not clear to me that there’s going to be a political response as well as just a pure economic response. I grew up in New Jersey where there was a robust toll collector union yet they had machines where you could toss your change in a bin and it would automatically sort your change and give you whatever you needed back from that. There was an effective effort to unionize the toll collectors so that you still had a human being in the booth counting out your quarters. That’s an example where it’s not for lack of technology. We had a coin sorting machine, but there was a political process that was directing the actual level of implementation. People will fight to use licensing requirements and regulations to maintain those things. Despite the actual technological capability having radically changed, it’s very hard to know which areas will look shockingly different than the world looks today. Which things will be in some ways shockingly backwards for their time because we’ve had for political reasons to hold the line.

(Reminds me of how rent flows to the owner of a relationship in a competitive market that has been flattened by technology)

Algorithms to make other types of decisions

The mathematics is very instructive, both in a specific way but also has a broader set of principles.

Optimal Stopping Problem

Difference from “explore/exploit” trade-off

One thing that comes to mind is the idea called “optimal stopping”. The multi-armed bandit problem in the “explore/exploit trade off” presumes framing that’s highly iterative. You can pull the handles again and again and again. You can go from one machine to another and back. There are many decisions in life where you are forced to make a single binding commitment that could be anything as banal as pulling into a parking space. It could be something like purchasing a house or signing a lease. It could be something like marrying your spouse. There’s a separate mathematics of cases where you need to find the right moment in time to go all-in, commit to an option, and no longer gather any further information.

37% Rule

There’s this very famous result called the “37% rule”. Let’s say you’re looking for an apartment. And it’s a really competitive marketplace. You’re in a situation where you encounter a series of options one by one. And at each point in time, you must either immediately commit, and then never know what else might have been out there, or decide to walk away and keep exploring your options but lose that opportunity forever. What do you do to try to end up with the best thing possible, even though you, you won’t necessarily know at the time, whether you found the best option that might be out there? There’s this beautifully elegant result that says that you should spend the first 37% of your search non-committally exploring your options. Don’t bring your checkbook, don’t commit to anything No matter how good it seems you’re just purely setting a baseline. After that 37%, whether it’s 37% of the time that you’ve given yourself to make the decision or 37% of the way through the pool of options, be prepared to immediately commit to the very first thing you see that’s better than what you saw in that first 37%. This is not just an intuitively satisfying balance between looking and leaping, this is the mathematically optimal result.

Broader insights on algorithms

Elegant solutions under a range of narrow assumptions about goals and acceptable risks

There are strategies like that that I think are wonderfully crisp in the recommendation they give, but they, of course, rest on this bed of many different assumptions about exactly how the problem is structured and exactly what your goals are. This rule, presumes that your entire goal is to maximize the chance that you get the very best thing in the entire pool, but it comes with a 37% chance of course that you have nothing at all, because you’ve passed. Many people would find that unacceptable. We can go down the rabbit hole of how do you modify this and the solutions get less and less clean as you wiggle the assumptions around.

Intuition for how complex decision-making is can be strangely comforting

More broadly, one of the highest level takeaways for me, from working on the book and just thinking in computational terms about decisions in my own life, is some decisions are just hard. The classical optimal stopping problem, due to a weird mathematical symmetry, is that if you follow the 37% rule you will only succeed 37% of the time. The other 63% of the time you’ll fail, and that is the best possible strategy you could enact in that situation. In a weird way, that’s some measure of consolation because often, in real life, we find ourselves not getting the outcome we wanted. While we can rake ourselves over the coals or try to reconstruct our entire thought process, I think it’s some comfort that computer science and mathematics can, in effect, certify that you were just up against a hard problem. There is some measure of comfort that if you have the kind of the vocabulary to understand the type of problem that you’re facing, and you have some intuitions about the general shape of what optimal solutions look like, then even when you don’t get the outcome that you wanted you can in some sense rest easy because you knew that you followed the appropriate procedure or the appropriate process for dealing with that situation.

Notes From Saudi America

Saudi America
by Bethany McClean


On Aubrey McClendon, founder of Chesapeake

  • McClendon went all-in on shale buying land in Austin Chalk region when he saw Devon energy’s success drilling horizontally.
  • McClendon would overpay for land. His world-class salesmanship would allow him to raise capital more effectively than competitors.
  • He would be bailed out in 2000s when oil and gas prices rise and shale lands were in high demand.
  • By 2012, Chesapeake was trying to rebuild after the crash of 2008 and gas prices depressed. They would raise capital globally and make deals with pipelines that would require them to produce large quantities of gas regardless if price. They were basically funding themselves by selling the equivalent of naked puts on gas.
  • The board was replaced and McClendon stripped of chairmanship. Icahn even took a seat. But the company and governance were a disaster.
  • McClendon left and started AEP which would create a portfolio of companies with an interest in specific drilling operations.

​​EOG

  • Known as the “Apple” of oil
  • In 1999 Enron spun off EOG (“Enron oil and gas”) when Jeff Skilling became dismissive of businesses requiring hard assets.
  • EOG and Continental (Harold Hamm’s co) were first in the Bakken where production would increase 10x in the first decade of 2000s.
  • Encouraged that the fracking technology worked although unspectacularly (the industry and academics were still skeptical) they started acquiring land in Eagle Ford for under $500 an acre. Then in 2010, at the 4 Seasons Houston they announced there was 900mm barrels contained in Eagle Ford.
  • A look at EOG which has positive returns.
    • Only wells with at least 30% irr since about half of that is required to cover overhead including land and infrastructure.
    • Requires profitability at $40 oil so that it can survive full price cycles.
    • Means that there is limited capacity to invest since land prospects can be much worse (“better rock is exponentially better”)

Fracking is 2 technologies

  • Horizontal drilling and high pressure water to crack open rocks. A proppant (ie a sand usually from Wisconsin) is used to keep the crack open to let the oil or gas flow.

Shale depends on unsustainable economics

  • In 2015, Einhorn at the Sohn conference showed that even with oil at $100 the shale industry was incinerating cash. He called out Pioneer in particular (a descendant of a merger with Pickens Mesa Energy). Shale Wells have too steep a decline rate. A Bakken well declines 69% in year 1 and 85% in 3 years vs 10% pa for conventional). Einhorn argued that frackers’ pitches don’t account for cap-ex and cost to acquire leases. He showed that they embellish their estimates of reserves to investors (vs what they report to SEC standards). He and SailingStone of SF showed how comp was tied to production, not profitability. They both agreed that has was much better biz than oil.
  • Pension backed PE firms are financing shale 2.0 which is marked by better technology and more precise operations. 35% of horizontal drilling is done by private PE-backed companies. Rates of return remain unsustainable for all but the most efficient operators who are generating positive albeit still uninspiring returns.
  • Decline rates always mean you need to spend more to stay in place. The shale transformation was a Lollapalooza effect of Bakken, Eagle, and Permian hitting all at once. It may very well be a one-time price effect. And the more efficient we become on drilling the faster we deplete the wells.

The crude oil export ban

  • The crude oil export ban had been in place since the 1970s.
  • But there was now a growing movement to overturn the ban backed by oil producers while refiners and environmentalists opposed it.
    • Many saw it as a counterweight to the political leverage of unfriendly powers such as Russia and the Middle East. Europeans were already grateful for the US agreeing to export LNG, keeping Russia from having a near monopoly on gas sales in Europe esp in Germany.
    • Many argued that the decline in global prices would more than makeup for the reduction of domestic supplies and that in boosting global supplies the political premium baked into oil prices would ease.
  • The collapse of oil prices in 2015, the “condensates” exemption, and being part of a larger spending bill provided cover from detractors as the bill was passed in Dec 2015. Environmentalists would gain an extension to solar and renewable subsidies.


My takes plus a Munger insight

  • The byproduct of commodity businesses is really an investment product.

A levered slice of volatile returns. The companies are closer to derivatives than they are businesses. The derivative is on the price of the commodity. The leverage is operational (call option struck on breakeven cost structure) and financial (companies have debt financing). The derivative is economically tied to the commodity via land rights which collateralize additional debt financing.

  • Sloppiness can occur when policies are seen as good for the public.

Greenspan saw rising energy prices as a threat to the economy and he suggested we invest in LNG import terminals. The fear of risings prices hastened the 2005 Energy Policy Act which exempted drillers from disclosing the chemicals used in fracturing.

  • Is Saudi’s timing of their portfolio rebalance performance-chasing?
Aramco IPO proceeds used to diversify Saudi America by creating a sovereign wealth fund. Shorting oil to invest in overpriced businesses a la SoftBank?  Strange move. The investments are not even in human capital and accumulation of tacit knowledge. It’s financial in nature only which seems not just expensive but shortsighted when the explicit goal of MBS is long term vision.
  • While increasing the security of US energy supplies seems like a net positive it’s unclear what the ramifications are longer term.

Shale for example has reduced our reliance in unstable regimes. But many of those regimes such as Nigeria were not major threats. If an insecure bully has some moderate economic leverage against you it may be better to pay them to control them. If you remove the leverage you also destabilize the equilibrium. An equilibrium you were ultimately in control of. You may find yourself now facing a desperate, rabid adversary who despite a smaller stature now has its back to the wall. You have exchanged a stable, modestly negative state for a wildly uncertain modestly incrementally positive (perhaps fleeting) state. This is especially true when the dog whose bark is louder than its bite is in a region concerned with terrorism and nuclear proliferation.

  • Munger: we should be conserving oil and delaying gratification until we are certain about the extent of its future needs.

This is because fertilizer, pesticides and other ag products are made with hydrocarbons and there is no technology that has replaced that. “Like the topsoil in Iowa, you wouldn’t want to use it as fast as possible” he believes the shale boom was lucky and we shouldn’t just consume the gift. He recommends we commit to production enough to remain competitive in its technology while maintaining reliance on foreign oil. Let them deplete their reserves until we have a better understanding of what the future barrel is worth. Remember that the US being the first to switch to unconventional oil and gas required it to be the first to run out of conventional oil and gas.

Shale has been a subsidy to the economy and it’s very unclear to what extent we should drill aggressively or more thoughtfully since the future demand for oil is devilishly uncertain (there is more certainty around gas supplies). Is this another example of being short-sighted and drilling at all costs while oil execs and consumers win or if we’d be better off with higher near term prices and renewables even more competitive?

Notes From Chop Wood, Carry Water

Chop Wood, Carry Water

by Jason Medcalf


Quick Summary and Review

A sensei teaches a modern day student to be a samurai archer in Japan. The focus on process is wax-on, wax-off a la Karate Kid. As an apprentice, the protaganist spends most of his time “chopping wood, and carrying water” which he is initially impatient with. Of course, as anybody familiar with Mr. Miyagi would forsee, the mental and physical effort is transferable to the mastery of archery. The lessons are communicated with contemporary parables. Sticky and timeless. This book would be a perfect gift for a teen or recent grad.

Why Try To Be Great

You have one life

Pain of regret lasts longer than the pain of failure.

Don’t pretend you get to live twice.

You will be irreplaceable even if you are not the best.

  • When you go on a self-directed journey you’re learning things and developing skills that make you highly irreplaceable.

You don’t find traffic after going the extra mile.

A bold new world

  • Things that used to be virtually impossible to build and create on your own can now be done with just a few clicks.
  • Very few people have woken up to that truth, because doing so requires getting uncomfortable and breaking away from much of what they’ve known their whole lives. They would rather have the perceived illusion of safety with a consistent job, even if it’s one where they are completely disengaged doing things they don’t believe in, all while complaining about how unfairly they’re being treated, despite being able to quit at any time. They have been so brainwashed by a system that encourages disengagement, passing tests, and buying stuff you don’t need to impress people you don’t like, that they can’t see the reality: that there are fields all around them, just waiting to be farmed.
  • Very few people understand this because they have been studying old maps their whole lives. The territory is new. 

 

Falling in love with process

Start small and focus on what you can control.

  • Chopping wood and carrying water is the price for admission to reach sustained excellence. Like the roots of a bamboo tree, it is a long and arduous process of invisible growth, where you are building the foundation that is necessary to sustain success. For many years it may feel as if nothing is happening, but you must trust the process and continue to chop wood and carry water, day in day out.

The importance of little things. Inches

“Now you see how much an inch is worth. Every adjustment at the firing line means the difference between hitting the target or missing it. And the same is true in life. Every little thing we do, no matter how mundane, matters greatly when it is multiplied by the number of times you do it. Over time, even the smallest habit or choice can change our lives immensely. Do you know what separates most wildly successful people from everyone else, John?”

“Luck?” Akira shook his head, no. “Hard work?” Again, no. “Coming from the right background?”

Akira just smiled, “Inches, John. That’s all that separates them.” (This was pretty much Al Pacino’s speech in Any Given Sunday).

Think about your group of friends from high school. You’re all from the same area, you’re all the same age, and most of you have had a very similar set of opportunities. And right now, someone might look at all of you and be unable to really see much of a difference in your lives. But in ten years, an underachiever might be incredibly successful, while another who flourished in high school may be struggling to even survive. But I can as the difference in their lives will always come down to inches.

Most people are so consumed with their day-to-day lives, that they never pause to see the big picture. And in the big picture, every single choice matters, no matter how small. Everything you choose to read, listen to, or look at. Everything you think about, dream about, or focus on. And especially, your circle the people you surround yourself with and allow to influence you can make all the difference in who you become. Inches might look small up close, but added up over the right amount of time, they can cover any distance in the universe.

You don’t rise to the occasion you sink to the level of your training.

What to Expect on the Path to Greatness

Loneliness

  • Don’t believe the myths. Greatness is far from sexy, it is dirty, hard work usually required to be done in the dark. When no one is watching your dreams are so far off they feel like fairy tales.
  • There will be distractions. There will be people who tell you that you are stupid or crazy for doing it.
  • Most people do not try to realize their potential because they’d rather protect their ego.
    • People won’t understand. Most are more invested in their ego than a mission.
    • Evil’s greatest weapon is discouragement.
    • When we see people who were like us achieve greatness we often retrofit labels like “talented” or “chosen”. This protects our egos which did not do what it takes to achieve greatness. (It reminds me of how people’s perception of Tom Sawyer when they discovered he had money. And how they retroactively applied that perception, seemingly having forgotten the orphan’s rascal behavior.)
  • Cites 74% of people knowingly give a wrong answer to not stand out.
  • “Greatness isn’t for the chosen few. It’s for the few who choose.” – Jamie Gilbert
  • Everyone wants to be great until it’s time to do what greatness requires.

Temptation to take shortcuts

  • There will be people who try and lure you off the path with quick fixes and get rich quick schemes. But you must be wise and stay the path, and continue to build your foundation by chopping wood and carrying water every day. Greatness isn’t sexy, it is the dirty, hard work that is often very boring. Your greatest challenge during your time here will be to faithfully keep your focus on the process, while surrendering the outcome.

Comparison traps

  • Comparison is the thief of joy and short-sighted. Compare to yourself. Your former self and your potential.

The grass is not greener on the other side. It is greener where you water it. 

Don’t give advice unless asked

  • There will be people who ask you for wisdom but you must never cross boundaries without an invitation.
    • When the student is ready, the teacher appears. Often a good idea to ask if the person is looking for advice or just venting and wanting to be heard. 
  • “The difference between a pest and a guest is an invitation.” – James Richards


Improve Your Mindset

Rewiring your brain for encouragement

  • We don’t remember experiences as objective events. We remember them by the stories we tell about them. By focusing on what we did well, we can turn our memory into encouragement. Rewiring is tedious work.
    • Example: writing down at least 15 positive things that you did or happened that day. This is an action which promotes a growth mindset.
    • Instead of seeing moments as tests, view them as opportunities to learn.
    • Another good habit: write at least 2 things you learned that day.

“Humility is not thinking less of ourselves it’s thinking of ourselves less.” – CS Lewis

  • To fuel yourself with encouragement watch your inputs: what you consume, who you surround yourself with, how you talk to yourself, what you visualize.

How to stop comparing

  • Enumerating what you are grateful for including basic like health and clean drinking water.

Reframing setbacks

  • Just as the dimples on a golf ball allow it to travel further, hardship prepares you to persist. Talent or winning a lottery can be a curse since it skips the character-building that is usually required to get far. So look at setbacks as investments which will pay off in the future.

Talent is nothing without character.

Talk to yourself instead of listening to yourself.

  • How you label your feelings affects how you filter their meaning which affects their impact. Are you “nervous” or “excited”?
  • Ask yourself, “what is one thing I can do to make the situation better?” rather than “why is this happening to me?”
  • Nobody is forcing you to do anything. You choose what you do.

“There is only one thing you have to do in life, and that is die. You are always doing what you want to do because there is always a choice. You may not like the choices or the consequences, but you always have a choice. When you tell yourself that you have to do something it creates a negative internal energy, but when you realize you want to do something it creates a more beneficial internal energy.”

Remember this next time you think you don’t have enough time.

My Favorite Takeaways

The advice is simple and timeless

  • Just not simple to execute. Like losing weight. The strategy is straighforward but it is not easy.

Surrender what is out of your control while committing to what is.

Living by principles instead of feelings

  • Feelings are fickle.

Many days, you aren’t going to feel like working out and honing your craft. Many days, you aren’t going to feel like treating people really well. Many days, you aren’t going to feel like being unconditionally grateful. Many days, you aren’t going to feel like giving your very best.

But the principle says you are going to reap what you sow. The principle says that diligent workers are going to serve kings instead of ordinary men. The principle says to turn the other cheek. The principle says to seek wise counsel. The principle says to speak life and not death.

At the end of principles there is life, freedom, hope, joy, and peace.

The most you can expect from feelings is happiness. But like every other feeling, happiness doesn’t last. That’s exactly why trillion-dollar industries try to keep you chasing it because it is perpetually unavailable.

You are building your own house

  • The achitect parable

The architect, desperate to retire, is implored to design one final house. Since his heart is not fully in it, he cuts corners. Only at the end of the project does he learn that the firm that commissioned it, built the house for him. If he only know he was building his own house he would have designed it with maximal effort and pride. In life, we are all “building our own house”. 

Progress is not steady

  • Zoomed in it can look random with ups and downs and detours.
  • Zoomed out it is a step-function.

Remember this when you hit the inevitable detours and plateaus. 

Setbacks are investments

  • The grit it takes to overcome setbacks will be the basis of your confidence when hard times come. And they always do. By doing hard things you are training for life.

You are not as alone as seems

  • Curate your inputs and who you surround yourself with. Fill your environment with inspiration not naysayers.

Notes from Capital Allocators: Charley Ellis

Link: https://capitalallocatorspodcast.com/2018/07/29/ellis2/

About Charley: Charley Ellis is the founder of Greenwich Associates, author of 16 books, and one of the most sought-after industry advisors worldwide.

Otter Transcript (Link)


1. The Case for Indexing


The Evolution of Markets

Investing Environment 50 years ago

  • A device that reported the last, high and low prices and trading volume was cutting edge tech.
  • The money game used to be like stealing candy from children. 10% of trading at most was done by institutions.

Sparse Institutional Players

  1. Statewide branches were allowed but interstate branching was not allowed for banks. So every mid-sized and larger city had two or three trust departments.
  2. The second group would be the major insurance companies in Hartford
  3. There was a little bit of mutual fund activity up in Boston, a little bit New York and there might be some on the west coast.

Abundant Retail Investors

Were they hard to beat? No way. They were easy to beat. The secret to successful active investing is to have what’s called, it’s a little bit nasty term, but called “willing losers.”

  • Nice people who bought or sold once every year or two, usually an odd lot because that’s how much money they had. About half the time it was AT&T.
  • They bought because they’ve been given a raise or a bonus or an inheritance. And they sold because they’re sending kids off to college or buying a home or some other sensible purpose that had nothing to do with what’s going on inside the market.
  • They didn’t know very much, but that didn’t matter. They were buying a few blue-chip stocks that they read about in magazines.

Investing Today

The Talent Boom

  • Analysts

The number of people involved in active investment management, best I can tell, has gone from less than 5000 to more than 1 million over 56 years. A major securities firm might have had 10 or a dozen analysts back in 1962. What were they doing? They were looking for small-cap stocks and interesting companies that might be interesting investments for the partners of the firm. Did they send anything out to their clients? No, not anything. Goldman Sachs didn’t start sending things out until 1964 or 1965, and there was just one of the salesmen thought it might be interesting idea to put out. Today, any self-respecting security firm is worldwide with analysts in London, Hong Kong, Singapore, Tokyo, Los Angeles. 400, 500, even 600 people trying to come up with insights, information, data that might be useful to clients. Anything that might be useful. Demographers, economists, political strategists, portfolio strategist and every major industry team. Every major company will have 10, 12, 15 analysts covering that company. And of course, then if you go to the specialist firms, there are all kinds of people and then there are intermediaries with access to all kinds of experts in any subject you might like. We’ve got 2000 experts. And anytime you want to talk to any one of them, just let us know. Glad to provide so an unbelievable, flourishing amount of information of all kinds, all of which is organized and distributed used as quickly as possible. Instantaneously, everybody.

  • Global

The second thing is, Well, I hear about the CFA program. How’s that working out? Well, it’s off to a pretty good start. 135,000 people have passed the exams and another 250,000 people in the queue. The biggest crowd is the US, the second biggest crowd is China. The third biggest crowd is India. Its global. Yeah, of course its global. It’s all over the place — people want in on the good thing.

  • Trading

99% of trading is done by computers. Pros. We went from 3 million shares a day in 1960 to 5 billion today. And they know everything, you know, as soon as you know, and you can only buy from them and you only sell to them. How good a chance do you have of having this whole workout? And the answer is not very good.

Why so much competition?

  • Well, first of all, the investment world is probably the highest paid line of work for large numbers of people. It’s wide open to everybody. 
  • You don’t have to retire at 65 or 70. You can keep going to 80 or 85.
  • So the benefits around the edges that are quite nice. Anybody in the investment business knows once in a while, maybe once every 10 years, some unbelievably attractive opportunity, not really right for clients because it’s too small to specialize. But some really attractive opportunity comes up and says I would like you to invest in me. And it doesn’t always work out. But sometimes it can be beautiful. 

Paradox of Skill

So there you are professional investor, you have noticed that you’re getting better and better and better over the years because your skills keep getting better. You’ve got better tools to work with…You have research services like you’ve never had before…more sources of information, you get it very, very quickly, and can act anytime you want.

…So does everyone else

  • Flattening of skills

As more and more people get the same kind of computing power, the same kind of information, the same speed of access, more and more people get more and more equal to each other.

  • Flattening of info advantages

As recently as the 1980s, if you want to have a private meeting with senior management, all you had to do is show that you’ve done your homework that you were asking intelligent, probing questions, and that you had been coming back on a regular basis to this company. You were a serious investor. You could be invited to a dinner where the senior executives would talk about what their plans are for the future of the company. You could get a comparative advantage.

Today, the SEC now requires any public company that shares any useful information to any investor it must simultaneously make a diligent effort to be sure everybody gets that statement information. No more private conversations with management.

  • The increasing role of luck

All of us are in mixture of skills and good luck, when the old days, good luck when all that important, the skills really made a big, big difference. But the skills that they have might have diminished in their percentage or relative importance because they’ve got these fabulous tools in unbelievable supply. And it’s that that makes them all increasingly equal. Even though they’re getting better and better. They’re getting less and less different.

  • Playing bridge with all the cards face-up

Because everybody knows everything that everybody else knows, you may manage it a little differently, may make some mistakes a little differently. You may do some smart things a little differently. But it’s very hard to do significantly better than the other guys when they’ve got everything that you’ve got.

  • The genie isn’t going back in the bottle

Candidly, there isn’t any doubt in my mind that that transformation has already taken place so forcefully. And for really good, understandable reasons. It’s not going to reverse.

Indexing is the Logical Response

Unprecedented breadth and quality of competition means efficient pricing. Active management is a losing game when you consider that, adjusting for survivorship bias, 84% of funds underperform.

Benefits of Indexing

  • Top quartile performance, maybe top decile
  • Minimize fees which are a huge drag.
  • Lower taxes if you hold long-term

It’s passive and boring which helps you stay with it. Once you start transacting your behavioral biases undermine your goals.

“Passive” is poor label

He hates the word “passive” since it has labeled a great strategy with a very negative connotation word. Nobody wants to be called passive.

Caution on Smart Beta

  • These things do have real merit over the long, long, long term. But if you think about it for a minute, when will sales organizations ramp up their selling effort the most? And when will nice people who haven’t thought about it as carefully as they might be most tempted to say “let’s go with it”, of course, is after a very good period of rising prices. So if value has been working very, very well, the demand for interest in buying into and the supply interest in selling people on value factor investments will rise to a crescendo at the top and then people get disappointed.
  • The guys who have for years specialized in factor investing are going to find they can’t make as good a profit from doing it as they used to, because of the crowd. But they’ll still probably do a pretty good job for themselves and for their investors.
  • Beware those who are in it because it’s a good commercial opportunity. Intermediaries that are in it because they think “hey, this is a new way to beat the market” are going to create a self-disappointing experience and it’s a shame.

Indexing in China may be a mistake

  • The Chinese market is still dominated by retail investors which requires understanding how retail investors track past price performance and project future price performance.
  • You might find yourself being indexed with foolishness rather than an index with rigorous professional expertise.
  • This extends to indexing in emerging markets as well because there’s an unusual, different dynamic.

2. Concerns Over Indexing

Expensive Markets

There is concern that markets are not going to give what people need over the next 10 or 20 years. When Ted suggests that active management might outperform expensive public markets Charley replies with my favorite line in the interview:

Ted, Ted, Ted, you shouldn’t be talking that way.

He does not believe active will outperform.

How does Charley invest?

I’ve got two kinds of investment. One is index funds, which I’m happy with and just really comfortable with. And the second is an index fund equivalent in many ways –Berkshire Hathaway (he goes into his personal reasons for that).

Is there place for active management?

  • The bar for inclusion is exceedingly high. Charley, himself, has access to the brightest investors and sticks with indexing himself:

I’ve used myself as an example. I have probably as good a network of friends in the investments world as anybody, particularly in the active management world. I know, because of service on a whole bunch of different philanthropic investment committees, I happen to know an awful lot of guys who are really talented at picking talented people. And I meet with them on a regular basis. So you think, “hey, Charley, you are probably as good a position as anybody to be able to pick and choose terrific active managers.”

Why doesn’t he do more of the “clever” things that can be done?

Easy answer. I’m not good enough. I don’t know enough.

  • If not for Charley, who is active for?

Let’s be candid. You and I both have tremendous admiration for David Swensen. He should not be indexing. You know, why? He’s got all kinds of competitive advantage. Everybody loves David Swensen. Everybody loves the idea of working for Yale. He’s got the best team on his side, working with best managers anybody ever had. If you have a relationship with Yale, you know, it’s going to be a long term relationship, they average 10 years, their mentor relationship, something like 14 years. That’s the average, even though they usually invest with people at day one or before day one when they get into business. If you look at a list of their investment managers, you say, “geez, I don’t recognize most of those names.” That’s right. Nobody else does either. So a very unusual kind of investing. Guts, intellectual precision, and they do slightly better on asset mix. Half of 1%, maybe slightly better on manager selection, half of 1%, maybe got a team of 30 guys who are working to be sure they keep it up. And they got a network of friends and admirers all over the world, they get the best call, they do all kinds of vendors. So there are organizations like that and they’re not very many of them. Who else? There are pockets of managers who are extemely specialized and have their own money in the game, maybe some from outside investors. Basically a handful of managers with no real competition.

“If you are really an exception, you don’t have to index you can do something really different. But you have to be really good at your exceptional way of doing things. And you have to be not very widely followed or copied, because you’ve got to be almost alone doing it.”

  • How about private equity?

He makes a criticially important point here about the inversion in the supply/demand of capital :

The real competition today is not by the investment managers to get your investment money. It’s by the people who’ve got money to get access to the best investment managers. And that’s been true for the last decade and venture capitalist have clearly true in private equity. And that’s a really important differentiation.

He continues:

I don’t think there’s any very large pool of capital that has not addressed the following question. We have a commitment to a higher rate of return than we’re now getting. What can we do to increase our rate of return? Answer: Private equity. Great, so why don’t we put not 10%, but 20 or 30%. Let’s say 33% into private equity. And we’ll do it in a very imaginative way. We’ll have a couple of specialists who work on selecting the private equity funds employed by US, Canada, we can’t pay very high salaries because our fund structure doesn’t allow us to do that. But we’ll do the best we can. And we’re going to make a major commitment to private equity. Fine. So does everybody.

What is the PE landscape as a result?

They have more money than they’d really like to have. In fact, they’ve got cash balances they can’t use yet. They’re competing with other guys with a lot of money too. So the entry price for private equity has been going up and up and up.

Some people say “we’re not going to raise more money, we’re going to stay as small as we can, we’re going to specialize in our particular niche, and we’re not going to take new accounts.” So that takes them out of the equation. There’s another group that says, “Well, you know, if everybody wants us, we’ll have to be opening up more capacity. We’ll just take the money. Let’s see if we can find a way to invest it as we go along. But might be difficult, but we’re gonna try and you know, it’s always worked so far.”

Well, you could have a huge flood of cash going into private equity. You can ruin anything by raising the entry price.

The theoretical limit of indexing

  • What has to happen for the price discovery function to fail?

First, if you have 30% of the value of the market indexed, that’s not 30% of the trading activity, it’s a much smaller fraction. So what you have to do is have enough assets indexed to reduce the trading activity enough so that enough of that million plus people who are making their living as active investors comes down and down and down. Enough people need to decide “I’ve looked at it very carefully, and I’ve decided I’m quitting the business. I’m going into medicine, law, farming, ranching…”

What is it that people are going to go into that they think they’re going to find a more satisfying? Honestly, it’s not as good as it was, but it’s still the best game in town. It’s gonna be very hard to get people to give up on going into active investing.

  • If they do cut back a lot, how much do you have to cut back?

You’d have to cut back so far that there was not a residual group of people who are pretty darn good at price determination. My own guess would be somewhere around 85% would have to quit, just because it doesn’t make sense anymore.

Mix in the combination of interest of active managers, overconfidence, and people’s desire to be better than average and it doesn’t seem likely that the competition would abate enough to undermine price discovery.

3. The Pension Crisis

Scope of the Problem

Public pension plans are impossibly underfunded

If you look at what are the biggest problems we as a nation have in the investments world, it’s pensions or retirement security. You can see it easily in the state and city funds that are seriously underfunded. They need 7.5% rate of return which they’re not going to get because they’ve got 25% in 2.5-3% bonds. They’re just not going to get it.

Households are underfunded

If you look at individuals, half the population does not have a retirement plan. For those that do 401k is increasingly dominant, taking over from defined benefit system. The average person approaching age 63.5 which is the retirement age in this country is thinking:

“I’ve got 165,000 smackers in my account. Why my wife and I are going to Florida to play some golf, some tennis, have some fun. We’re gonna have great years. We’ve earned it. It’s been a long long working run, but we’ve earned it it’s going to work out just fine.”

Except…

Anybody with any knowledge about investing knows right away — $165,000 if you take money out, from 63 years old to 85 or 90 is not enough. You’re not going to have anywhere near enough per year, cobbled together with social security to make anything like a decent connection.

Something over 65% of your life time health expenses are spent in your life six months. Well, that’s where half the bank for personal bankruptcies come from all kinds of trauma that goes with that as well assisted living expensive and dementia. So we’re going to have a real problem with old age, retirement security.

Political nightmare

So what are people gonna say?

“God damn it. I worked hard all my life I played by the game rules as everybody laid them out. And I was supposed to be able to retire at a decent age and enjoy retirement. That’s part of the deal.”

But the answer they will get back?

“Sorry, but nobody else understands that to be the part of the deal. And you’re on your own.”

So you will have a giant generation that is angry, focused, and motivated to do something about this false promise.

If you think we’ve had divisive politics in the past, imagine what it would be if you had millions of people and their relatives all saying “It isn’t fair. It isn’t right. These guys got screwed.” I think we’re going to have a terrible societal problem, political problem.

How Did We Get Here?

The retirement problem is rooted in an era of different needs and circumstances.

History of the retirement age

  • Age 65 came from Social security which dates back to 1935,

which came from:

  • Railroad Retirement act in 1923,

and even before that:

  • Churchill and Chamberlain jointly put forward in the United Kingdom retirement at 70, but people thought that was unfair because the Germans used 65.

And here’s where we get to the root…

  • German’s retirement age dates back to early 1880s

Baron Von Bismark tried to unify the German municipalities via technology namely the telegraph and the railroads. The telegraph combined with the post office allowed instantaneous communication anywhere in Germany.

We’re going to bring coal and iron ore from the rural and other areas to where the steel mills are and we’re going to build steel mills and have tremendous industry. And then railroads are going to be able to bring people from the cities out to the countryside for weekends, vacations  can be normal, and we will bring from the countryside, fresh fruit, fresh vegetables, all kinds of wonderful things that for people to eat, it’s going to make everything terrific. That’s great.

But where are you going to get the workers to work on the railroad?

Offer lifetime employment.

You get them to come out of the forest because they can get lifetime employment. That’s terrific. What do you call that? That’s guaranteed. This is a commitment. It’s the honor of Germany. Okay. Let’s go.

So what happened?

Well after a couple of years there were accidents on the railroads. Trains ran into each other, people were killed. Public outrage and scrutiny.

What’s going on?

Well, let’s send a study group and find out what the heck is going with these accidents. Well, we found out what the answer is in the work. Laying tiles, lifting heavy ties, brailles, shoveling coal, all kinds of heavy work. They’re saying to the older guys in their late 50s and 60s, your too old for this kind of work. You take the easy job. You’ll be in charge of the switches.

Then what happened?

So the switches are being manned by guys in their early 60s. A beautiful summer’s day and no trains coming in for the next couple of hours, why not take a little nap? And they’re just taking a nap, forget to wake up, and the accident happened. 

The solution?

Guarantees for life. Pay them not to work. To be cost effective find the min-max where it costs not too much to solve most of the problem. And the answer was 65. Most people don’t live to 65 in those days in Germany, but those who do are really doddering, so they will only last for another couple years after 65 anyway.

An obsolete model

We have inherited and retained a retirement model that is a poor fit for our post-industrial circumstances.

    • People live longer now. The ratio of non-working to working years has increased.
    • People are able to work longer as manual labor’s share of the economy has declined.

Dealing with the Crisis

Extend your savings

  • Take social security later…instead of 62 if you wait until 70.5 you make 76% more inflation-protected for the rest of your life. If you wait, you have fewer years in retirement, so they’re willing to give you a larger amount.
  • Continue funding your 401k in your 60s. These are the easiest years to save money. So you can ramp up your savings, dump it into the 401k as fast as you could. (also there are catch-up allowances)

Do all of these things and your chances of being in serious financial trouble in retirement go from awful to not too bad. So if we act soon, we could make a big, big difference in what could otherwise be one of the worst problems our society has ever faced.

Why has this been so challenging to solve?

The big problem is nobody’s paying attention to it. It’s too late. Congress is dealing with politically urgent issues. We need to agree to raise the retirement age to 70 but it’s easy to say that when you are not a ditch digger or coal miner.

Lessons from Tom Sawyer

Some select highlights from Tom Sawyer by Mark Twain


Human Psychology

When Tom tricks his friends into painting the fence…

He had discovered a great law of human action, without knowing it — namely, that in order to make a man or a boy covet a thing, it is only necessary to make the thing difficult to attain.

How money influences perception…

There are wealthy gentlemen in England who drive four–horse passenger–coaches twenty or thirty miles on a daily line, in the summer, because the privilege costs them considerable money; but if they were offered wages for the service, that would turn it into work and then they would resign.

When the approval of others is worth more than the prize…

It is possible that Tom’s mental stomach had never really hungered for one of those prizes, but unquestionably his entire being had for many a day longed for the glory and the eclat that came with it.

How admiration from others encourages you until the point of fraud…

They began to tell their adventures to hungry listeners-but they only began; it was not a thing likely to have an end, with imaginations like theirs to furnish material.

Reverse psychology defined…

Now he found out a new thing-namely, that to promise not to do a thing is the surest way in the world to make a body want to go and do that very thing.

We want what we can’t have. There’s a lesson about artificial scarcity here…

however-there was something in that. He could drink and swear, now-but found to his surprise that he did not want to. The simple fact that he could, took the desire away, and the charm of it.

Status alters our perceptions…

Mr. Benton, an actual United States Senator, proved an overwhelming disappointment-for he was not twenty-five.

When Tom and Huck are found to be rich (they found stolen $$) confirmation bias sets in and the narratives about them are retrofit to account for their newfound brilliance…

Wherever Tom and Huck appeared they were courted, admired, stared at. The boys were not able to remember that their remarks had possessed weight before; but now their sayings were treasured and repeated; everything they did seemed somehow to be regarded as remarkable; they had evidently lost the power of doing and saying commonplace things; moreover, their past history was raked up and discovered to bear marks of conspicuous originality. The village paper published biographical sketches of the boys.

Sometimes you just need someone to talk to…

It would be some relief to unseal his tongue for a little while; to divide his burden of distress with another sufferer.

On taking loved ones for granted…

Tom, you’ll look back, some day, when it’s too late, and wish you’d cared a little more for me when it would have cost you so little.

On the temperment of mob psychology. Here Potter goes from being considered guilty without a trial to be exonerated on hearsay…

As usual, the fickle, unreasoning world took Muff Potter to its bosom and fondled him as lavishly as it had abused him before. But that sort of conduct is to the world’s credit; therefore it is not well to find fault with it.

Mental well-being means physical well-being…

such a laugh was money in a-man’s pocket, because it cut down the doctor’s bill like everything.

Mocking customs

When Tom purchases his way to an honor that was meant to be earned…

Tom was therefore elevated to a place with the Judge and the other elect, and the great news was announced from headquarters. It was the most stunning surprise of the decade, and so profound was the sensation that it lifted the new hero up to the judicial one’s altitude, and the school had two marvels to gaze upon in place of one. The boys were all eaten up with envy–but those that suffered the bitterest pangs were those who perceived too late that they themselves had contributed to this hated splendor by trading tickets to Tom for the wealth he had amassed in selling whitewashing privileges. These despised themselves, as being the dupes of a wily fraud, a guileful snake in the grass. The prize was delivered to Tom with as much effusion as the superintendent could pump up under the circumstances; but it lacked somewhat of the true gush, for the poor fellow’s instinct taught him that there was a mystery here that could not well bear the light, perhaps; it was simply preposterous that this boy had warehoused two thousand sheaves of Scriptural wisdom on his premises–a dozen would strain his capacity, without a doubt.

Customs are often illogical…

Often, the less there is to justify a traditional custom, the harder it is to get rid of it.

On the banality of conformity…

A prevalent feature in these compositions was a nursed and petted melancholy; another was a wasteful and opulent gush of “fine language”; another was a tendency to lug in by the ears particularly prized words and phrases until they were worn entirely out; and a peculiarity that conspicuously marked and marred them was the inveterate and intolerable sermon that wagged its crippled tail at the end of each and every one of them. No matter what the subject might be, a brainracking effort was made to squirm it into some aspect or other that the moral and religious mind could contemplate with edification. The glaring insincerity of these sermons was not sufficient to compass the banishment of the fashion from the schools, and it is not sufficient today; it never will be sufficient while the world stands, perhaps.

How mindless ritual corrodes ingenuity (Twain is critical of organized religion)…

Tom went about, hoping against hope for the sight of one. blessed sinful face, but disappointment crossed him everywhere. He found Joe Harper studying a Testament, and turned sadly away from the depressing spectacle. He sought Ben Rogers, and found him visiting the poor with a basket of tracts. He hunted up Jim Hollis, who called his attention to the precious blessing of his late measles as a warning. Every boy he encountered added another ton to his depression; and when, in desperation, he flew for refuge at last to the bosom of Huckleberry Finn and was received with a Scriptural quotation, his heart broke and he crept home and to bed realizing that he alone of all the town was lost, forever and forever.

Humans are susceptible to quackery and superstition

Describing his Aunt Polly who falls for remedies…

She was one of those people who are infatuated with patent medicines and all new-fangled methods of producing health or mending it. She was an inveterate experimenter in these things. When something fresh in this line came out she was in a fever, right away, to try it; not on herself, for she was never ailing, but on anybody else that came handy. She was a subscriber for all the “Health” periodicals and phrenological frauds; and the solemn ignorance they were inflated with was breath to her nostrils. All the “rot” they contained about ventilation, and how to go to bed, and how to get up, and what to eat, and what to drink, and how much exercise to take, and what frame of mind to keep one’s self in, and what sort of clothing to wear, was all gospel to her, and she never observed that her health-journals of the current month customarily upset everything they had recommended the month before. She was as simple-hearted and honest as the day was long, and so she was an easy victim. She gathered together her quack periodicals and her quack medicines, and thus armed with death, went about on her pale horse, in disguise, to the suffering neighbors.

And how confirmation bias reinforces their gullibility. Here’s Aunt Polly falling for Tom’s claim of ESP…

Sereny Harper shall know of this before I’m an hour older. I’d like to see her get around this with her rubbage ’bout superstition. Go on, Tom!

The bizarre epistemology that the superstitious employ…

The other boys agreed that there was reason in what Tom said, because an ignorant lump of bread, uninstructed by an incantation, could not be expected to act very intelligently when set upon an errand of such gravity.

Observations of people

There will always be people whose lenience will seem excessive of the facts…

Injun Joe was believed to have killed five citizens of the village, but what of that? If he had been Satan himself there would have been plenty of weaklings ready to scribble their names to a pardon-petition, and drip a tear on it from their permanently impaired and leaky water-works.

Careful what you wish for. On miswanting…

Huck Finn’s wealth and the fact that he was now under the Widow Douglas’ protection introduced him into society-no, dragged him into it, hurled him into it-and his sufferings were almost more than he could bear.

He had to eat with a knife and fork; he had to use napkin, cup, and plate; he had to learn his book, he had to go to church; he had to talk so properly that speech was become insipid in his mouth; whithersoever he turned, the bars and shackles of civilization shut him in and bound him hand and foot.

I got to wear them blamed clothes that just smothers me, Tom; they don’t seem to any air git through ’em, somehow; and they’re so rotten nice that I can’t set down, nor lay down, nor roll around anywher’s; I hain’t slid on a cellar-door for-well, it ‘pears to be years; I got to go to church and sweat and sweat-I hate them ornery sermons! I can’t ketch a fly in there, I can’t chaw. I got to wear shoes all Sunday. The widder eats by a bell; she goes to bed by a bell; she gits up by a bell-everything’s so awful reg’lar a body can’t stand it.” “Well, everybody does that way, Huck.” “Tom, it don’t make no difference. I ain’t everybody, and I can’t stand it. It’s awful to be tied up so. And grub comes too easy-I don’t take no interest in vittles, that way. I got to ask to go a-fishing; I got to ask to go in a-swimming-dern’d if I hain’t got to ask to do everything. Well, I’d got to talk so nice it wasn’t no comfort-I’d got to go up in the attic and rip out awhile, every day, to git a taste in my mouth, or I’d a died, Tom. The widder wouldn’t let me smoke; she wouldn’t let me yell, she wouldn’t let I never see such a woman! I had to shove, Tom-I just had to. And besides, that school’s going to open, and I’d a had to go to it-well, I wouldn’t stand that , Tom. Looky-here, Tom, being rich ain’t what it’s cracked up to be. It’s just worry and worry, and sweat and sweat, and a-wishing you was dead all the time. Now these clothes suits me, and this bar’l suits me, and I ain’t ever going to shake ’em any more. Tom, I wouldn’t ever got into all this trouble if it hadn’t ‘a’ ben for that money; now you just take my sheer of it along with your’n, and gimme a ten-center sometimes-not many times, becuz I don’t give a dern for a thing ‘thout it’s tollable hard to git-and you go and beg off for me with the widder.” “Oh, Huck, you know I can’t do that. ‘Tain’t fair; and besides if you’ll try this thing just a while longer you’ll come to like it.” “Like it! Yes-the way I’d like a hot stove if I was to set on it long enough. No, Tom, I won’t be rich, and I won’t live in them cussed smothery houses. I like the woods, and the river, and hogsheads, and I’ll stick to ’em, too.

The value of time depends on your wealth…

Huck was always willing to take a hand in any enterprise that offered entertainment and required no capital, for he had a troublesome superabundance of that sort of time which is not money.

Adults and children are fundamentally different (and some humor on marriage as the end of the line)…

SO endeth this chronicle. It being strictly a history of a boy , it must stop here; the story could not go much further without becoming the history of a man . When one writes a novel about grown people, he knows exactly where to stop-that is, with a marriage; but when he writes of juveniles, he must stop where he best can. Most of the characters that perform in this book still live, and are prosperous and happy. Some day it may seem worth while to take up the story of the younger ones again and see what sort of men and women they turned out to be; therefore it will be wisest not to reveal any of that part of their lives at present.

Notes on Statistics Done Wrong

Statistics Done Wrong
Alex Reinhart

https://www.statisticsdonewrong.com/


P Values

  • Measure of ‘surprise’. The smaller the p the larger the ‘surprise’. P values work by assuming that there is no difference between the 2 samples. If you want to show a drug works you counterintuitively show that “the data is inconsistent with the drug not working”
  • P values say nothing about the magnitude of the effect. A small p can reveal a massive effect or a tiny effect with great certainty (say if you collected massive data). Statistical significance does not mean practical significance! Similarly, statistical insignificance does not mean zero and is simply the best evidence-based on the trial data you studied.
  • Neyman-Pearson uses p-values in a conceptually different way. They estimate an acceptable false positive rate called ‘alpha’. Statistical significance allows us to reject the null hypothesis for the established alpha or false positive rate. This rate is informed by the experimenter’s understanding of the procedure.

Confidence Intervals

  • These are preferable to p-values since they provide a point estimate and a measure of uncertainty and if they can be supplied instead of just p-values they should.
  • They are less common in the literature perhaps b/c they can be very wide

Statistical Power

  • The probability that a study of a given amount of data is capable of showing statistical significance. For example, how many coin flips do you need to be 95% sure that your experiment can reveal a biased coin that is 60% weighted towards heads? Statistical power is a function of :
    • The size of the effect; a smaller effect requires more data
    • Sample size; more data means the study has higher statistical power
    • Measurement error; more subjective measurements have less power
  • Many studies are underpowered because there is not enough data. This often occurs because it is expensive/risky (ie drug studies) or unethical (studies on animals)
  • While an antidote for multiple comparison problems can be to require lower p values, the trade-off is that studies will become underpowered
  • The concept of power is often forgotten because it is not taught in intro stats and is not readily intuitive. Again, confidence intervals which are wide can reveal a lack of statistical power again supporting their use over p values.
  • Truth inflation or type M error (‘magnitude’) is the effect of there being many experimenters ‘competing’ to publish extreme results.
  • Small samples have more variance; be careful to draw conclusions from them since they are more likely to be underpowered
    • Rural states have counties with both the lowest AND highest kidney cancer rates; this is likely due to small populations, not a real effect. The same is true for test scores in smaller schools; we may interpret them to be ‘better’ based on test scores but this is because their average extremes are higher than the average extremes of bigger schools!
    • Remedies for this include shrinkage. Weighting the average from a small sample with a weighted average from a larger population (ie weighting a small county with a higher weighted national average). This will, unfortunately, bias truly abnormal cases too much towards normal. The best remedy is to try to find a larger sample (ie use congressional districts instead of counties). Shrinkage is a good technique in measuring average product reviews (products with few reviews are shrunk towards a generic version of the product).

Pseudoreplication

  • Using additional measurements that are highly dependent on highly correlated to previous data. This form of replication doesn’t allow you to generalize inferences.
  • If you cannot eliminate hidden sources of correlation between variables you must try to statistically adjust for confounding factors.

Base rate fallacy

  • A low p-value is often touted as evidence of significance but significance also depends on the base rate. Consider Bayesian examples like mammogram testing. If mammograms have a false positive rate of 5% and a 90% chance of accurately identifying cancer then if you test 1000 people and 50 of them test positive then it is still quite unlikely that most of those people have cancer. Why? Because the base rate is a mere 1%. Only 10 people in that sample have cancer and we expect 9 of them to be accurately identified but more than 50 will test positive! When testing for conditions with very low base rates false-positive rates will swamp true positive rates.
  • An extreme example of these false discovery rates you are looking for an effect which definitely does not exist, no matter how low you set your p threshold we know your so-called significant results are still false positives, and you are bound to record significance results with a large enough sample.
  • Combatting false discovery rates with multiple comparisons is challenging but important since you expect many false discoveries. Tips include:
    • Remember p < .05 doesn’t mean there’s a 5% chance your result is false
    • When making multiple comparisons using a procedure such as Bonferroni or Benjamin-Hochberg will make your required p values much more conservative by accounting for the number of tests
    • Be aware of stat techniques specific to your field for testing data
    • Have an idea of base rates to estimate how prevalent false positives are likely to be

Confounding variables

  1. Correlation is not causation
  • Because you do not know if there is a confounding variable. If you create a model that predicts heart attack rates based on weight, exercise, and diet it’s tempting to say that if you change one of them x% that the heart attack rate will change by y%. However, that is not what you tested. You didn’t change the variables in a real experiment and measure the outcomes. It is not clear that a confounding variable is actually influencing the heart attack rate.
  • Also, to say a variable changes all else equal is a fantasy. In reality, it is unusual for single variables to change in a vacuum.
  1. Simpsons Paradox
  • When a trend in the data disappears when the data is divided into natural groups. It tends to occur in observational studies with biased samples thus obscuring a confounding variable.
  • Examples:
    • Berkeley admission bias against women in 1973. In aggregate, women looked discriminated against but at the department level, the opposite was true. The bias occurred bc women applied in higher numbers to competitive, underfunded departments. The bias happened earlier in the process: women were systematically pushed towards these fields
    • Penicillin appeared to improve outcomes for meningitis cases in the UK. At a closer look, the sample was biased since it was only administered to children who were not rushed to the hospital so they were the milder cases. Isolating the sample to those who visited a general practitioner first we find that penicillin, in fact, seemed to correlate with worse outcomes (there are theories about the breakdown of the contagion causing shock but there aren’t experiments for testing if penicillin actually causes meningitis patients to die)
    • Looking at aggregate data United flights are delayed more frequently than Continental. But at individual airports, the trend reverses. The aggregate data doesn’t account for the fact that United flys out of more airports with bad weather.

Notes on How Not to Be Wrong: The Power of Mathematical Thinking

How Not to Be Wrong: The Power of Mathematical Thinking
by Jordan Ellenburg


  • Math gives you tools to extend your common sense of reasoning and logic. He uses the analogy of Ironman’s suit.
  • Competitions including war are often decided by small edges. Being 5% better at x or y can decide the outcome over a long enough game. (Similar to my experience with boardgames. A more efficient engine in a game of 7 Wonders or Settlers can save you actions; like reducing the cost of capturing winning victory points.)
  • Zooming in on points of a curve they look and can be approximated by lines. Trajectories are curves influenced by gravity but objects appear to move in straight lines. Using lines to approximate curves is the basis of calculus and even the derivation of pi as the area of a circle (Archimedes did this by iteratively computing the edges of a circumscribed circle as a polygon. Imagine an octagon, then a polygon with 64 sides, and so forth until it looks like a circle. You can then use trigonometry to compute the area of the triangles you keep creating until the sum of all the area approximates the area of the circle)
    • Critics of this method like Zeno highlighted the uncomfortable paradox of constantly halving something until you get nowhere. Comically, the skeptic Diogenes countered Zeno by simply walking across the room to make the point that motion is indeed possible!
  • The law of large numbers explains why South Dakota can have the highest rate of brain cancer and North Dakota the smallest. They both have small populations. Be careful when comparing quantities from 2 very different sample sizes. Small samples are more volatile. If you flip 10 coins, your odds of getting 8 heads is unlikely but real. But flip 1000 coins it’s nearly impossible to get 800 heads.

Data mining 

“The more chances you give yourself to be surprised the higher your threshold for surprise had better be.”

“A significance test is a scientific instrument and like any other instrument, it has a certain degree of precision. If you make the test more sensitive by increasing the size of the studies population, for example, you enable yourself to see ever-smaller effects. That’s the power of the method but also the danger”

  • An underpowered study has the opposite problem. You dismiss an effect that your method was too weak to see. A good example is the original 1985 hot hand studies. They rejected the idea of a hot hand but it turns out the methods they used rejected a hot hand even on data sets that were generated by simulations that deliberately baked in a hot hand! In fact, their methods failed to notice even the effects of good vs bad defenses which we know influences offensive shooting percentages.
    • The final verdict is there may be some hot hand effect but it is too difficult to detect because if it exists it is very small. In fact, players who think they are hot take harder shots and perform worse so it’s best for them to not believe in the effect since it will be more than offset by an unjustifiably confident shot selection.

The Bayesian examples in the book are great.

  • In a Bayesian framework how much you believe something after you see the evidence depends not just on what the evidence shows but much you believed it to begin with. Posterior probabilities still depend on the strength of your priors.
  • On conspiracy theories: “If you do happen to find yourself partially believing a crazy theory, don’t worry — probably the evidence you encounter will be inconsistent with it, driving down your degree of belief in the craziness until your beliefs come in line with everyone else’s. Unless, that is, the crazy theory is designed to survive the winnowing process. That’s how conspiracy theories work”.

Tradeoffs and cost of perfection

  • Stigler type arguments that optimal decisions often leave a margin for error. Getting to the airport early enough to have a 100% chance of making the flight is probably so conservative it’s wasteful (depends on your utility curve but almost certainly wasteful to be 100% certain vs say 95%). When you read a story about social security overpaying people bc they were actually dead, it turns out that mistake represents less than 1 basis point of payments. In other words, they do a great job not making this mistake and the cost of being 100% compliant may simply not be cost-effective to be worthwhile.

St Petersburg and the role of expected utility

  • Fran Lebowitz utility curve of money: she would drive a cab each month until she could eat and pay rent. Afterwards, she would write. In other words, she had a linear utility curve which flattened abruptly. If you raise her taxes she works more as opposed to someone with a logarithmic curve who is at the point of indifference between work and leisure
  • Ellsburg Paradox highlights the limitations of utility theories. It highlights the difference between what Rumsfeld called “known unknowns” or what mathematics refers to as risk vs “unkown unknowns” or uncertainty. Utility theory may help with uncertainty but formal math is less useful.

Regression to the mean explains many phenomena that are usually attributed to another reason.

  • Examples, best-performing companies (competition attribution), musician/writer sophomore slump, RB after signing a big contract, dietary fiber speeding or slowing digestion, Scared Straight juvenile detention program, diet effects when people are at their peak weights. When something is at an extreme we should expect reversion simply bc of math and therefore be very careful of attributing to an intervention.

Correlations between variables reduce the information content of the variable.

  • You try to identify criminals by foot and hand size you are choosing highly correlated variables.
  • Strong correlations lie behind how we compress images and music files. A green pixel is probably next to a green pixel.

Berkson’s Paradox

  • (via Wikipedia) The most common example of Berkson’s paradox is a false observation of a negative correlation between two positive traits, i.e., that members of a population which have some positive trait tend to lack a second. Berkson’s paradox occurs when this observation appears true when in reality the two properties are unrelated—or even positively correlated—because members of the population where both are absent are not equally observed.
  • Wikipedia summarized Ellenberg’s attractiveness example:

Suppose Alex will only date a man if his niceness plus his handsomeness exceeds some threshold. Then nicer men do not have to be as handsome to qualify for Alex’s dating pool. So, among the men that Alex dates, Alex may observe that the nicer ones are less handsome on average (and vice versa), even if these traits are uncorrelated in the general population. Note that this does not mean that men in the dating pool compare unfavorably with men in the population. On the contrary, Alex’s selection criterion means that Alex has high standards. The average nice man that Alex dates is actually more handsome than the average man in the population (since even among nice men, the ugliest portion of the population is skipped). Berkson’s negative correlation is an effect that arises within the dating pool: the rude men that Alex dates must have been even more handsome to qualify.

Asymmetric domination effect

  • Aka “decoy effect”. When an clearly inferior option is introduced in one’s menu of choices it makes the clearly dominant choice appear even better than it did against its prior competitor. He uses the example of slime mold behavior which implicitly rak preferences between more food (oats) vs dark environments.

Marketing is the most common domain of the decoy effect, but it’s also present elsewhere.

• Price tables: These, like The Economist example above, frequently display the decoy effect.

• Menus and wine lists: Putting an expensive option at the top of a menu makes the other meals seem cheaper (remember anchoring?). Similarly, wine lists make use of the decoy effect: “People often order the second cheapest wine on the list and not the cheapest because they don’t want to look too stingy. Most of the time, the second cheapest wine is the one that has the highest profit margin.”

• Romance: Ariely offers some dating advice: If you are looking to meet that special someone in a social setting, he recommends bringing someone who looks similar to you but is less attractive. They will act as a decoy, making you seem more attractive by comparison. And if a “similar but better-looking friend of the same sex asks you to accompany him or her for a night out, you might wonder whether you have been invited along for your company or merely as a decoy.”

• Elections: Studies show that third candidates and minor parties influence your voting preferences by acting as decoys.

Democracy as tool for rightness not fairness

This was an interesting comment on what is at heart a question of whether democracy is both positive and normative. And how many ideas about fairness are never considered in light of their normative merit.

Condorcet thought that questions like “Who is the best leader?” had something like a right answer, and that citizens were something like scientific instruments for investigating those questions, subject to some inaccuracies of measurement, to be sure, but on average quite accurate. For him, democracy and majority rule were ways
not to be wrong, via math.

We don’t talk about democracy that way now. For most people, nowadays, the appeal of democratic choice is that it’s fair, we speak in the language of rights and believe on moral grounds that people should be able to choose their own rulers, whether these choices are wise or not.

This is not just an argument about politics—it’s a fundamental question that applies to every field of mental endeavor. Are we trying to figure out what’s true, or are we trying to figure out what conclusions are licensed by our rules and procedures? Hopefully the two notions frequently agree but all the difficulty, and thus all the conceptually interesting stuff happens at the points where they diverge.

Notes From Lessons of History

The Lessons of History
by Will and Ariel Durant


Geography

  • As technology evolves, the influence of geography is diminished
  • The prosperity of civilizations is closely tied to geography notably rivers and coasts
  • Ultimately, humans create culture, not the earth

Biological lessons of history

  • Life is competition
  • We rely on the protection of our tribes and may cooperate within its confines but this is adaptive behavior in service of a wider competitive landscape. Until a group is as large as a state it will “continue to act like individuals and families in the hunting stage”
  • Life is selection
  • Nature knows nothing of our egalitarian ideals or Bill of Rights. “Freedom and equality are sworn enemies”. If people are free, relative advantages will grow nearly geometrically. Inequality is inborn, we can only aspire to ideals such as equal access to education and justice.
  • Natural selection benefits from the diversity and range of natural ability as it is the basis of evolution.
  • Life must breed; birth rates shape history
  • Nature cares only about the species, not the individual. Diversity and large “litters” are the fertile grounds that natural selection needs as fuel. Nature’s check on overpopulation is famine, pestilence, war.
  • Higher birth rates can give rise to stronger tribal powers while progression into a higher standard of livings and industry appears to slow the birth rates as the tribe advances.
  • To track the future of ideas, it may be instructive to watch birth rates; weakened ties to ethnicity or tribalism can leave the incumbent group vulnerable (Caesar and Augustus were aware of this and sought to penalize birth control; Italy’s ethnicity diluted over time making the empire vulnerable to its neighbors)
  • On race: “A knowledge of history may teach us that civilization is a cooperative product, that nearly all peoples have contributed to it; it is it common heritage and debt; and the civilized soul will reveal itself in treating every man or woman, however lonely, as a representative of one of these creative and contributory groups.”

Government

  • Monarchy has been the historical norm while democracies have been ‘hectic interludes’
    • Roman democracy crumbled under class wars giving way to Pax Romana, a 200 year succession of benevolent dictators until the murder of Caesar. This period was followed by disgraceful monarchs including Caligula before giving way to the greatest succession of monarchs ever, the last being Marcus Aurelius. Some of these monarchs had no heirs and promoted by merit until Aurelius died. Afterward his son Commodus ruled when no heir was named.
    • Monarchy has a mixed record, typically at its worst is when it is determined by blood and accompanying incompetence
  • Most modern, complex governments  are oligarchies — ruled by a minority
    • Aristocracy by birth
    • Theocracy by religion
    • Democracy by wealth
  • Plato reduced the cycle to monarchy->aristocracy->democracy->dictatorship. Repeat. This was based on Greece and repeated with the Romans. Democracies are overthrown or conquered as envy amongst the majority tires of the ‘sham’ of having a vote and uses the state to seize wealth. Other oligarchies fall when they wield their great power to narrowly or incompetently.
  • The US democracy started from a wider base and in unity against British rule. Rural land-owning enhanced the sense and dedication to freedom, while geographic isolation created a national sense of freedom. These conditions have given way as land as sparse and cities have grown “Every advance in the complexity of the economy puts an added premium upon superior ability and intensifies the concentration of wealth and [power]”
  • Democracy is a difficult form of gov’t since effective mob rule requires widespread intelligence to avoid manipulation by ‘the forces that mold public opinion’.
  • Democracy, however, has done the most good and it can maintain its promise only if it affords equal opportunity for education [my own thought is this will always be uneven, and the unevenness grows with population size. For a small population, it is reasonable that the average access to education can be even]
  • “If a race or class war divides us into hostile camps, changing political argument into blind hate, one side…may overturn the hustings with the rule of the sword. If our economy of freedom fails to distribute wealth as ably as it has created it, the road to dictatorship will be open to any man who can persuasively promise security to all”

History and War

  • Competition via war is often for the same reasons as competition amongst individuals however while individuals are restrained by law and morals a state ‘acknowledges no substantial restraint either because it is strong enough to defy any interference…or because there is no superstate to offer its basic protection, and no international law or moral code wielding effective force’
  • Religious wars in the 16th century and the wars of the French Revolution were battles between aristocracies leaving the masses to maintain mutual respect for their foreign counterparts while wars of the 20th century in accordance with technology and ‘means of indoctrination’ made war all-consuming ‘struggles between people’ and completely destroying centuries of labor and property.
  • He presents the general case for the inevitability of war — competitive human nature, envy, beliefs so fundamentally different that cannot be settled by negotiation. Perhaps and only if we were united against aliens could we imagine our species not warring with each other. The opposing view is that the imperative to avoid large scale war because of the destructive power of current technology will prevail over any disagreements over ways to organize our lives and economies.

Progress

  • Are we the ‘same trousered apes’ merely armed with increased tech, locomotion, and knowledge but weighed down by the persistent features which moor us to our primitive ancestors? Progress is defined not by happiness or similar measure but to the degree in which we may control our environment. By this standard, as evidenced by our increased adaptability and lifespans we have made tremendous progress. While subject to lapses and regressions, the avergae human has been the beneficary of an accumulated history and knowledge which has been succeeds in being recorded and transmitted to subsequent generations so that they may continue to build on this heritage from its most advanced point.

Mauboussin on Making Better Comparisons

Michael Mauboussin Guide to Making Better Comparisons (Link)


Comparing via analogies (steps and common mistakes)

  1. Select the source for analogy
    • Common pitfall at this stage: Undersampling due to availability bias
  2. Map the source to target to make inferences usually looking for similarities
    • A common pitfall at this stage: mistaking correlation for causality
      • Consider the first attempts at flight were people putting feathers on their arms, not studying lift
      • Confusing a star performer with talent in a volatile field
  3. Adjust for differences between source and target
    • Tversky showed we place more emphasis on similarity than differences
    • The framing problem: We are influenced by which differences and similarities we are prodded to focus on (ie framing)
  4. Learn by the success or failure of the analogy
    • Pitfalls in heuristics and intuition
      • Intuition works great in chess since it is a form of pattern recognition, but works poorly in investing where outcomes are non-linear
      • Recency bias influences sample
      • Choice-supportive or confirmation bias taps into our need to be consistent; we create stories after the fact to validate our decisions
      • Hyperbolic discount rates prevail when we study inter-temporal decision making
        • Stress is good response for crisis because it focuses on immediate needs; chronic stress causes decisions to be short-sighted
    • Using poor reference points in comparison
      • 2 companies in different industries can be more similar than their peers within their own industry; this shows how we can conflate attributes with what is actually driving value
      • Not recognizing that widely varied values can be justifiable. For example, 2 companies with the same earnings growth can trade at justifiably different valuations if underlying returns on invested capital are very different
      • Anchoring bias
      • When I went to Capital Camp, Mauboussin discussed T Theory. The top row of a category have more in common with each other than the average in the category. This articulates how I think about investors! Warren Buffet, Annie Duke, and Sam Hinkie have more in common with each other than other people in the same category.

So how to get better?

Instead of relying on analogies drawn from memory we can use “similarity-based” forecasting.

  • Inputs
    1. A wide sample for the reference class rather than 1 or 2 examples from memory
      • Additional refinement by weighting the results of the most similar samples more heavily
        • Ways to quantify the similarity
          • “nearest neighbor” algorithm (requires identifying relevant axes for the dimensions)
          • “connectionist” technique for weighting features by similarities and differences
    2. A statistical “base rate” drawn from the outcomes of varied reference classes