Mental Model: Fitness Landscapes

Fitness Landscapes are used to visualize the relationship between genetic makeup (genotype) and evolutionary fitness (the ability to survive and reproduce). A fitness landscape is a vast landscape divided into a grid of billions of squares. Each square represents a genotype—some squares represent birds; some fish; some humans; with the majority being all the variations of genetic possibility that couldn’t survive in reality. Each square is very similar to its neighbors: two of the same species with a small variation, or two different but related species. The closer the squares, the more similar the genotype, and the further the squares, the more different. The fitness of each genotype is represented by its height on the landscape. Valleys represent low fitness, mountain peaks high fitness.

Fitness Landscape

Over time, species tend to move up the landscape to the nearest peak (A), where all future paths of variation lead downward. The peak that a genotype “settles” on is most likely to be a local optimum, which is not necessarily the highest peak in the landscape (a global optimum). This is because selection pushes fitness towards nearby peaks (what is called a basis of attraction), but lacks the foresight to select the highest peak.

To get to a higher peak, a species may have to reduce its fitness in the near term (C) as it slowly traverses across a valley in order to improve fitness in the long term. In order to make this shift, there has to be sufficient instability or challenge; otherwise, an organism will not opt to leave the intermediate peak and suffer the unknown prospects of the valley. If the valley is too low or the higher peak too far away, it may be unreachable as the low fitness hurdle can’t be overcome. (An example is the lack of wheeled animals, which although beneficial is inaccessible due to the valley of low fitness genotypes around it.)

Evolution usually moves in small steps, but occasionally it takes wild leaps—a single mutation might give a creature an extra pair of legs or another radically different feature. Most of the time these leaps result in much lower fitness (B), and therefore don’t last. But other times it allows the genotype to jump to a higher peak without the slow process of going down before going up.

Every landscape has different terrain that can be on a scale from flat to rugged. A rugged or coarse landscape has many local peaks and deep valleys, while a flat landscape has only very small hills (all genotypes have about the same success rates).

Landscapes don’t remain static—they shift over time due to either environmental changes or adjustments as organisms move across it. The movement can vary from being stable (relatively flat and slow to change) to roiling (likely rugged and changing quickly). Given the likelihood of ever-shifting landscapes, the evolutionary mix of small steps and occasional wild leaps is the best possible way to adapt to the environment.

Berkshire Hathaway Letters to Shareholders

Berkshire Letters CoverI’m excited to announce the release of a book I’ve been working on for about 6 months now, and first started in 2010.

It’s a compilation of every letter Warren Buffett wrote to the shareholders of Berkshire Hathaway. I first created it a few years ago for myself and friends. Last year I got Buffett’s endorsement — plus a few non-public letters — to publish the book for the benefit of fans and shareholders of Berkshire.

Here is the official page with all the details. There you can find a more detailed description, plus some sample pages and a chart detailing the performance of Berkshire’s insurance operations. (For any programmers out there, the chart was created with D3. You can check out the development version on GitHub.)

Features of the book:

  • Berkshire Hathaway annual shareholder letters from 1965 to 2012 (706 pages), including the 11 earliest letters not available on Berkshire’s website
  • Tabulated letter years so you can easily flip to the desired letter
  • Topics index
  • Company index
  • Person index
  • Charts of:
    • The growth in Berkshire’s book value and market price relative to benchmarks
    • Insurance float and performance
    • The operating businesses of Berkshire

The entire book is paginated, and has easy-to-flip-to labels for each letter’s year.

It is available for pre-order now. The first batch will be sold at the Berkshire Hathaway Annual Meeting on May 4 in the convention center. The rest of the copies will be available on Amazon on May 7.

Future projects

  • The obvious next step is to publish a digital version, easily readable on iPads or potentially Kindles. This is normally an easy transfer, but that’s not the case with this book due to the many tables that have to be converted. So no timeline on this but it will be forthcoming.
  • A book of letters to the partners of Buffett Partnership, Ltd., Buffett’s hedge fund he ran from 1957 to 1970. This will be a similar format to the Berkshire book, with indexes, page numbers, etc.

Steve Jobs on learning to code

From Robert X. Cringley’s “Steve Jobs: The Lost Interview”:

When we were designing our blue box, we wrote a lot of custom programs to help us design it, you know, and to do a lot of the dog work for us in terms of calculating master frequencies with subdivisors to get other frequencies and things like that. We used the computer quite a bit to calculate, you know, to calculate how much error we would get in the frequencies and how much could be tolerated.

So we used them in our work, but much more importantly, it had nothing to do with using them for anything practical. It had to do with using them to be a mirror of your thought process; to actually learn how to think.

I think the greatest value of learning how to—I think everybody in this country should learn how to program a computer—should learn a computer language, because it teaches you how to think. It’s like going to law school. I don’t think anybody should be a lawyer, but I think going to law school would actually be useful, because it teaches you how to think in a certain way, in the same way that computer programming teaches you in a slightly different way how to think. And so I view computer science as a liberal art.

Google Glass and the Segway Paradox

Google Glass

The customer rearely buys what the company thinks it is selling him. — Peter Drucker

Google Glass was finally announced to the public yesterday.

Glass is a solution looking for problems. It’s too hard to say what jobs-to-be-done Glass will be hired to do at this stage, or how widely used it will be. We’ll only know after it’s released.

The lean startup way of thinking heavily emphasizes the reverse sequence: find a problem (job), think of a way(s) to solve that problem, test your hypothesis using a minimum viable product, repeat. This method should work for most startups. It worked well for companies like Microsoft (Problem: I need an Operating System to put on the computers I sell so people can use them. Solution: Build/Buy/Copy Basic/DOS/Windows).

But there are some innovations where the solution=>problem sequence is necessary — anything that requires a lot of R&D and isn’t easily demo’ed on a large scale. Google Glass, Tesla cars, Segway, iPad, Lytro, etc. These are physical, more capital intensive examples, but the same still holds for some smaller software projects. Sometimes you just need to build the full version to see what it’s best used for.

One of the problems of this method is what I call the “Segway Paradox“: a new technology with huge initial interest and possibilities turns out to only be used in a few niche cases.

This may happen for a number of reasons (see Paul Graham’s The Trouble with the Segway). I think Google Glass may fall prey to this problem.

There are a few use cases I can think of that may make Glass worth the cost:

  1. Hands-free sports — biking, skiing, football, climbing
  2. Search & rescue, emergency — alerting the user to visual/audio anomalies
  3. Jobs that require detailed visual instructions (“advanced checklists”)

But it seems from the videos that Google is focusing more on everyday consumer uses, competing more with smartphones.

Mistakes = information

Mistakes can help us learn

Re-posted from the Atlastory blog.

In Nassim Taleb’s new book “Antifragile,” there’s an interesting segment about how an entire system can be antifragile (benefiting from variability / disorder / stressors) precisely because its individual parts remain fragile (harmed by variability). A few examples:

The engineer and historian of engineering Henry Petroski presents a very elegant point. Had the Titanic not had that famous accident, as fatal as it was, we would have kept building larger and larger ocean liners and the next disaster would have been even more tragic. So the people who perished were sacrificed for the greater good; they unarguable save more lives than were lost. . . . Every plane crash brings us closer to safety, improves the system, and makes the next flight safer.

Thankfully the errors we encounter while developing Atlastory don’t involve anyone dying. But the same principle applies — every bug, problem, server crash, chokepoint, or design flaw we encounter leads to a better system. We want to run into problems, because that means we know about them and can now fix them — eventually making the user experience better as a result.

“Some businesses love their own mistakes,” Taleb continues. “Reinsurance companies, who focus on insuring catastrophic risks . . . manage to do well after a calamity . . . All they need is to keep their mistakes small enough so they can survive them.”

The more you benefit from low-downside mistakes, the more “antifragile” your business is. I see this as a function of both the industry you’re in and the internal culture of the company.

If everyday work and life is viewed as a science experiment (the circle of observe > guess > test > interpret), then any screw-ups or failures are a good thing in the end. You know something’s wrong, and you can work on fixing it. Taleb again: “…every attempt becomes more valuable, more like an expense than an error. And of course you make discoveries along the way.”

Continual improvement is everyday life in software development, but it is only just catching on for personal development.


How to separate luck and skill

These are some of my notes from the book “The Success Equation” by Michael Mauboussin. This book was spotted on Warren Buffett’s desk in this tour of his office. There’s lots more interesting stuff in the book, but these notes in particular answer the question “How do you separate luck and skill?” We’ll start off with some definitions:

Luck is a chance occurrence that affects a person or a group (e.g., a sports team or a company). Luck can be good or bad. Furthermore, if it is reasonable to assume that another outcome was possible, then a certain amount of luck is involved. In this sense, luck is out of one’s control and unpredictable. Randomness and luck are related, but there is a useful distinction between the two. You can think of randomness as operating at the level of a system and luck operating at the level of the individual. Luck is a residual: it’s what is left over after you’ve subtracted skill from an outcome.

The definition of skill depends on how much luck there is in the activity. In activities allowing little luck, you acquire skill through practice of physical or cognitive tasks. In activities incorporating a large dose of luck, skill is best defined as a process of making decisions. Here, a good process will have a good outcome but only over time. Patience, persistence, and resilience are all elements of skill.

Separating luck and skill

Luck-Skill Continuum
At the heart of making this distinction lays the issue of feedback. On the skill side, feedback is clear and accurate, because there is a close relationship between cause and effect. Feedback on the luck side is often misleading because cause and effect are poorly correlated in the short run.

In most cases, characterizing what’s going on at the extremes is not too hard. As an example, you can’t predict the outcome of a specific fair coin toss or payoff from a slot machine. They are entirely dependent on chance. On the other hand, the fastest swimmer will almost always win the race. The outcome is determined by skill, with luck playing only a vanishingly small role.

Continue reading “How to separate luck and skill”

Instacart: analysis of a startup

InstacartInstacart is a seed-stage startup that delivers groceries and other basic items in a very short timeframe. They are the “ with a 1 hour delivery.” At the moment their current market is only San Francisco and the Silicon Valley area. Customers can place either a 3-hour order ($3.99) or a 1-hour order ($14.99).  Orders are routed to shoppers who work for Instacart, who then pick up the items at a local store and deliver them within the timeframe.

In October they raised $2.3 million from Canaan Partners and Khosla Ventures. Below is a  a very brief analysis if I were considering a potential investment in Instacart.

Quick analysis

So basically Instacart uses software (algorithms & data analysis on the back-end, with good UI design on the front-end) to connect “deliverers” in need of cash with “buyers” who need quick delivery of basic items.

Opportunity: arbitraging the demand for instant satisfaction and convenience, using software + crowdsourcing. This will be disrupting convenience stores on the low-end, and potentially grocery stores in the future. It is taking advantage of the trends in mobile computing, data analysis, and e-commerce (willingness to trust online vendors).

Potential moatsbrand habit developed through repeated purchases. Learning curve — should remain ahead of competition on the learning curve because of technology (software) advantage. This is a business where it pays to have lots of data on: customer habits, traffic, prices, store traffic, etc. It is a virtuous circle: the learning curve reinforces customer experience, which improves the brand. These advantages are all geographically local, so it will be best to roll out to new cities as quickly as possible once the kinks are worked out.

Management: with only doing minimal due diligence with public information on the founders, I didn’t see any red flags. Apoorva Mehta has worked on the Amazon supply chain, so he has some experience in the business. All founders on the surface seem to be very talented. What am I looking for? Amar Bhide found that the most important traits for the founders of a typical startup are the dichotomies of: (1) seeking uncertainty while being risk averse; and (2) persevering while being adaptable.

What could go wrong: (1) other cities are not as receptive to the concept; (2) Amazon or other grocery company catches on and preempts their growth in new cities.

Investment edge: structural (not very many participants at this early stage) and psychological (grocery delivery has failed many times in the past, sometimes spectacularly — Webvan — investors are turned off by the concept because of these past failures).

Final note

This seems like a company with a good future ahead of it. That usually makes it a good investment, especially at this stage. I’m not sure what the valuation of the company is at the moment. But for a startup at this stage, the precise valuation you invest at isn’t usually as important as how well the company does (within limits, of course — refer to the internet bubble).

Disclosure: I have no ownership in Instacart.


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