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Different: Escaping the Competitive Herd by Youngme Moon

  1. Consumers have become more thoughtful and hyperbolic marketing is more likely to backfire today. More refined advertising and better knowledge of what customers truly want is necessary. This book explores the companies that are different by taking this more refined path
Key Takeaways
  1. Rote memorization and “over knowing” things leads to mindlessness and us truly ceasing to know
  2. Two approaches to attempt to explain and understand complex things – extreme reductionism and adding layers of color from a variety of sources which can help us better grasp the topic
  3. Experts can better categorize and therefore distinguish nuances more quickly. Can become an expert either through deep immersion or early adoption in which case you mature and evolve and learn alongside the product.
    1. The connoisseur is the ideal customer as they are experts and aficionados but as category matures, even the experts may lose interest as products become too similar. Devotion declines and strict adherence to the product begins to feel silly – heterogeneous homogeneity.
    2. Microsegmentation to the point of senselessness. Change becomes a meaningless commodity.
    3. Hyper-maturity leads to falling brand loyalty – additional features begin to feel like annoyances rather than adding value
    4. Evolution of products tends to go from a couple major players to many but product proliferation does not mean product diversity. As a category matures, diversity decreases leading to homogeneity
  4. Once aberrations become a bit too frequent, conventional wisdom begins to evolve and transform – old truths become myths. Shift in business today from competing and collapsing into each other to competing to differentiate.
    1. The first to let go of the old myth has a massive advantage – these are the ones who understand the rules so well they know how to and when to break them
  5. A good description gets to the heart of what differentiates it and invites comparison. Experts often get lost in prose and don’t address what their customers really want
  6. Any competitive and widely adopted metric tends to incentivize herd behavior
  7. Differentiation or excellence is rarely an outcome of well-roundedness but rather of lopsidedness 
  8. Complex, self organizing systems, which companies and people are, tend to follow and often imitate what those closest to them are doing (flocking birds example). The micro determines the macro
  9. The what and why over the how. Give people high expectations and then let them loose. You’ll likely get the result but probably not in the fashion you imagined.
  10. Augmentation by addition – add features to products over time
  11. Augmentation by multiplication – introduce new product lines
  12. When our surroundings transform, so do we. First we make our environment, then our environment makes us
  13. Be a time shifter – replay your market, fast forward, put yourself above time and see what possible alternatives in your market may be
  14. Idea companies – the different companies often act orthogonally to expectations. They rise above competition and do something different than what is expected
    1. Reverse brands
      1. Google (cleanest home page possible); a reverse brand in that it said no to busy home page and attributes all else he but then had such amazing product it added to its mystique  – a beautiful oxymoron
      2. IKEA – withholds most benefits people would conceivably want but also do away with annoying sales reps. Thoughtfully reducing certain benefits is often very appreciated. Eliminate the needless and do a great job with the essential
      3. In-N-Out Burger also very minimalist but high quality and secret menu drives fanaticism
      4. Jet Blue got rid of classes but has luxurious seats and personal entertainment
    2. Breakaway brands – recognize people’s categorization are fragile but deeply drive consumption. These products break these archetypes and form new ones – deliver what we expect in a completely different way. Stereotypes give us the average rather than the variance. Breakaway brands take advantage of the variance. People “get” the brand immediately and love it. No explanation is necessary – they are paradoxes which marry seemingly contradictory traits
      1. Huggies as big kid underpants vs diapers
      2. Swatch a cool, cheap watch when other Swiss watches were all expensive).
    3. Hostile brands erect barriers to consumption. This seems counterintuitive but it can drive incredible loyalty as consuming the product shows how hard you worked to attain it. Traction requires friction and hostile brands add tension on purpose
      1. Red Bull’s taste got terrible formal market reviews but the founder went ahead anyway
      2. Marmite is either hated or loved but those who love it will go to great lengths to obtain it
  15. Important to know what competitors are doing but it cannot be main focus. Absolute focus on serving customer 
  16. Great ideas tend to be very fragile early on because often they are indistinguishable from crazy ideas. Suspending skepticism allows some of these fragile ideas to bloom and evolve
  17. These different companies never rely on formal market research
  18. They know what the customers have in abundance, what is scarce, what they value and deliver something very different which still meets expectations. They will also be intensely human – founders who deeply understand human nature. Intuition becomes increasingly valuable
What I got out of t
  1. Thought it was a really interesting read and will try to notice when brands are reversals, breakaways or hostile

Hidden Order: How Adaptation Builds Complexity by John Holland

  1. Holland walks us through how coherence emerges from unstructured agents in environments of continuous flux; coherence under change and complex adaptive systems (CAS)
Key Takeaways
  1. Behavior depends much more upon interactions of agents than their actions
  2. Catalog of all activities does not equal understanding the effect of changes in the ecosystem
  3. Many complex systems show coherence in face of change through extensive interactions, aggregation of diverse elements and learning/adaptation
    1. Must understand the interactions and dynamics of the system before can hope to make any significant, lasting changes
  4. Theory can help detect lever points where small changes lead to big outcomes – Amplifier Effect
    1. Cross-disciplinary comparisons are vital as subtle characteristics in one context can be easily drawn out in others
  5. CAS systems made up of a large number of active elements diverse in form and capability
    1. Makes system stronger and more robust. Weeding out weak actors so that only the strong remain counter-intuitively leads to worse performance
  6. Rules are used to describe agent’s strategies – few, simple rules can lead to complex behavior
    1. A major part of the modeling effort for any CAS, then, goes into selecting and representing stimuli and responses, because the behaviors and strategies of the component agents are determined thereby. Once we specify the range of possible stimuli and the set of allowed responses for a given agent, we have determined the kinds of rules that agent can have
  7. Adaptation – process by which an organism best fits itself to its environment
    1. Time scale of adaptation varies drastically and they are very important to take into account in any system (humans vs. trees)
      1. The fast dynamics will shape the slow
    2. Overall, we will view CAS as systems composed of interacting agents described in terms of rules. These agents adapt by changing their rules as experience accumulates. In CAS, a major part of the environment of any given adaptive agent consists of other adaptive agents, so that a portion of any agent’s efforts at adaptation is spent adapting to other adaptive agents, co-evolution (Red Queen). This one feature is a major source of the complex temporal patterns that CAS generate
  8. The 7 Basics
    1. Aggregation
      1. Simplifies complex systems by grouping similar things which leads to constructing models as these are prime building blocks
      2. Emergence of complex, large-scale behavior from aggregate of small, simpler behaviors (ants and “intelligent” ant colony)
    2. Tagging
      1. Facilitates the formation of aggregates as tags manipulate symmetries (flag as a rallying cry which helps group people together)
      2. Tags enable us to observe and act on properties previously unobservable due to symmetries (spinning white cue ball harder to spot but when a stripe is added you can easily tell in which direction it is rotating)
      3. Facilitates selective interaction – filtering, specialization, cooperation leads to emergence of meta-agents and organizations through individual agents are always changing
      4. Tags are the mechanism behind hierarchies
    3. Non-linearity
      1. whole is greater than the sum of the parts
      2. Behavior in aggregate more complex than the parts would indicate
    4. Flows
      1. Nodes (processors, agents), connectors (designate possible interactions), Resource
      2. Adapt as time elapses and experience accumulates
      3. Tags almost always define the network by delimiting the critical interactions, the major connections. Tags acquire acquire this role because the adaptive processes that modify CAS select for tags that mediate useful interactions and against tags that cause malfunctions
      4. Multiplier Effect – resource injected in one node spreads over network which leads to chain of changes (big in network/flows modeling)
      5. Recycling Effect – the effect of cycles in the network can drastically increase output of the system over time as the system retains resources and these resources can be further exploited as they offer new niches to be exploited by new kinds of agents. This process leads to increasing diversity through increasing recycling (virtuous cycle)
    5. Diversity
      1. Each agent fills a niche which is determined based on interactions centering on that agent
      2. Nature abhors vacuums and will fill empty niche with new agent – typically similar in form and habit (the convergence effect, mimicry)
      3. CAS systems get diverse via adaptation which leads to further interactions and new niches – symbiosis, parasitism, mimicry, biological arms races
      4. Perpetual novelty is a hallmark of CAS
    6. Internal models
      1. Mechanisms CAS used to anticipate – eliminate details so that selected patterns are emphasized. Agent must select patterns in the torrent of input it receives and then must convert those patterns into change sin its internal structure
        1. A model allows us to infer something about the thing modeled
      2. Tacit and overt models
        1. Tacit simply prescribes a current action, under an implicit prediction of some desired future state
        2. Overt model is used as a basis for explicit, but internal, explorations of alternations, a process often called lookahead 
      3. Natural selection selects for better internal models
    7. Building blocks
      1. Deconstruct complex problem into simpler parts which can be used and reused in different circumstances
      2. The search for powerful building blocks is the most effective way to make the best internal models
        1. We can a significant advantage when we can reduce the building blocks at one level to interactions and combinations of building blocks at a lower level: the laws at the higher level derive from the laws at the lower level building blocks. This does not mean that the higher level laws are easy to discover but it does add a tremendous interlocking strength to understanding systems and hierarchies
  9. CAS exhibit coherence under change via conditional action and anticipation and do so with no central controller.
  10. Can discover lever points if can uncover general principles which govern CAS dynamics
  11. Agents must act somewhat similarly if a uniform approach to CAS is feasible
  12. Adaption – a rule’s ability to win based on its usefulness int he past – older rules are likely best as they’ve been tested by time
    1. Credit Assignment to best rules easiest when have immediate feedback – tests the rule’s utility
      1. Bucket Brigade – the credit assignment procedure which strengthens rules that belong to chains of action terminating in rewards
    2. Agent should prefer rules which use more information about a situation
      1. Higher specificity leads to stronger rules (higher in the hierarchy)
    3. Default hierarchy – early on, agents will depend on overly general default rules that serve better than random actions. As experience accumulates, these internal models will be modified by adding competing, more specific exception rules. These will interact symbiotically with the default rules and the resulting model is called a default hierarchy. Default hierarchies expand over time from general default to specific exceptions (the young man knows the rules, the old man the exceptions)
    4. Adaptation by rule discovery – trial and error may work but doesn’t leverage system experience
      1. Plausibility – take strong rules and apply to new areas which seem promising
        1. Innovation / creativity – simply combining tested building blocks in new ways
    5. Recombination of rules leads to discovery and occasionally mutation which can produce a more fit offspring
      1. More fit building blocks are used more frequently which are then passed on more often to succeeding generations
      2. More complicated building blocks usually formed by combining simpler blocks
        1. The higher level are typically composed of well-tested, above-average simpler blocks. Over time, the hierarchy becomes more elaborate, providing for the persistence of still more complex behavior. A
      3. Reproduction, recombination and replacement (genetic algorithm) found in nearly every CAS system
      4. Implicit parallelism – individuals (no matter how great) don’t recur but their building blocks do
        1. Evolution “remembers” combinations of building blocks which increase fitness
      5. Discovery of new building blocks leads to a slew of new innovations (punctuated equilibrium)
  13. With any model, must know what has been emphasized (exaggerated) to make a point and what has been left out to keep focused on that point
  14. Hierarchy – the appearance of new levels of an organization in this evolution depends on one critical ability: each new level must collect and protect resources in a way that outweighs the increased cost of a more complex structure. If the seeded aggregate collects resources rapidly enough to “pay” for the structured complexity, the seed will spread.
  15. Successful approach to any theory – interdisciplinary; computer-based thought experiments, a correspondence principle (models should encompass standard models from prior studies in relevant disciplines); a mathematics of competitive processes based on recombination
What I got out of it
  1. Fascinating book on how the universe seems to produce order for free via coherence, spontaneous self-organization and complex adaptive systems.

Thinking in Systems by Donella Meadows

  1. A primer on problem solving on scales from local to global, how systems exist and react in the real world while acknowledging that all models are false although they help us simplify and at times make better predictions
Key Takeaways
  1. System – interconnected set of elements that is coherently organized in a way that delivers something (elements, interconnections, function/purpose)
    1. Systems can be self-organizing, self-repairing (up to a point), resilient and many are evolutionary (adaptive)
    2. Intangibles (such as school pride) are also part of systems
    3. Best way to deduce a system’s purpose is to watch it for some time to see how it behaves (avoid rhetoric and stated goals)
    4. Important function of nearly every system is its own perpetuation
  1. Systems thinking transcends disciplines and cultures and when it is done right, it over arches history as well
  2. Systems work so well due to:
    1. Resilience – ability to survive and persist in a variable environment
      1. Resilience in a system is restored through balancing feedback loops through different mechanisms, at different time scales and with redundancy
      2. A set of feedback loops that can restore or rebuild feedback loops is resilience at a still higher level – meta-resilience
      3. Even higher meta-meta-resilience comes from feedback loops that can learn, create, design and evolve ever more complex restorative structures. Systems that can do this are self-organizing
      4. A resilient system has a big plateau, a lot of space over which it can wander, with gentle, elastic walls that will bounce it back, if it comes near a dangerous edge. As a system loses resilience, this plateau shrinks
      5. Resilience often coupled with dynamism as static systems tend to become fragile
    2. Self-organization – leads to complexity, heterogeneity and unpredictability
      1. Like resilience, often sacrificed for productivity/short-term gain but drastically increases fragility of the system overall
      2. Few, simple organizing principles can lead to wildly different self-organizing outcomes
    3. Hierarchy – arrangement of systems and subsystems
      1. Complex systems can evolve from simple systems only if there are stable intermediate forms. The resulting complex forms will naturally be hierarchical. That may explain why hierarchies are so common in the systems nature presents to us. Among all possible complex forms, hierarchies are the only ones that have had the time to evolve
      2. Hierarchies are brilliant systems inventions, not only because they give a system stability and resilience, but also because they reduce the amount of information that any part of the system has to keep track of. In hierarchical systems relationships within each subsystem are denser and stronger than relationships between subsystems. Everything is still connected to everything else, but not equally strongly. If these differential information links within and between each level of the hierarchy are designed right, feedback delays are minimized. No level is overwhelmed with information. The system works with efficiency and resilience
      3. Hierarchies are partially decomposable and much can be learned by taking apart systems at different hierarchical levels and studying them separately
      4. Hierarchies evolve from the lowest level up. The original purpose of a hierarchy is always to help its originating subsystems do their jobs better. This is something which is easily forgotten and leads to malfunctioning hierarchies (suboptimal systems)
  3. External solutions help solve many problems (such as vaccines) but those deeply embedded in the internal structure of systems won’t go away unless we see the problem holistically, see the system as the cause of the problem and restructure it
  4. Individual rationalism can lead to collective insanity – why things happen much faster or slower than people expect and why systems can unexpectedly jump into a behavior you’ve never seen before (leaping emergent effects)
  5. Archetypes – common structures which produce characteristic behaviors
  6. The behavior of a system cannot be known just by knowing the elements of which the system is made
  7. Stock – accumulation of material over time, a memory of the history of changing flows in the system
  8. Dynamics – behavior over time
    1. Dynamic equilibrium stays the same though it is always changing (inflows exactly equal outflows)
  9. People tend to focus more on stock than flows (> inflow = < outflow)
    1. Stocks take time to change because flows take time to flow
    2. Changes in stocks set the pace of the dynamics in the system
    3. Stocks allow inflows and outflows to be decouple, independent and temporarily out of balance
      1. World is a collection of feedback processes
    4. The gap, discrepancy, between current and ideal state drives feedback loops and the bigger the gap the stronger the feedback loop
  10. 1 stock system – system with two competing, balancing loops (thermostat)
    1. The bigger the gap (between hot and cold in this case) the bigger the outflow
  11. Shifting dominance – one loop dominates and therefore drives behavior, oscillations and complex behavior
  12. Systems with similar feedback structures produce similar dynamic behavior
  13. 3 typical delays – perception, response, delivery
    1. These delays cause small changes to turn into massive oscillations
  14. 2 stock systems
    1. Renewable stock constrained by a non-renewable one (oil)
      1. Look for loops driving system and the loop that will ultimately constrain it (can be temporary, permanent and/or more than one)
    2. Renewable constrained by renewable (fishing)
  15. 3 important questions to ask to test the value of any model
    1. Are the driving factors likely to unfold this way?
    2. If they did, would the system react this way?
    3. What is driving the driving factors?
    4. Model utility depends not on whether its driving scenarios are realistic (since no one can know for sure), but on whether it responds with a realistic pattern of behavior
  1. Why hierarchies surprise us
    1. Everything we think we know about the world is a model (language, maps, books, databases, equations, computer programs, mental models) – nothing will ever be the real world
    2. Our models usually have a strong congruence with the real world
      1. Systems fool us by presenting themselves (or we fool ourselves by seeing the world) as a series of events. Like the tip of the iceberg above the water, events are the most visible aspect of a larger complex but not always the most important. We are less likely  to be surprised if we can see how events accumulate into dynamic patterns of behavior
      2. The behavior of a system is its performance over time – growth stagnation, decline, oscillation, randomness, evolution
      3. When a systems thinker encounters a problem, the first thing he does is look for data, item graphs, the history of the system. That’s because long-term behavior provides clues to the underlying system structure. And structure is the key to understanding not just what is happening but why
        1. Systems thinkers try to understand the connections between events and the resulting behavior and the mechanical characteristics of the structure
        2. Behavior based models are more useful than event based models but still flawed as they over focus on flows and under emphasize stocks. There is also no reason to expect any flow to bear a stable relationship to any other flow
      4. We are in sufficiently skilled at seeing in systems’ history the clues to the structures from which behavior and events flow
  2. Non-linear relationships do not change in proportion and changes the relative strength of the feedback loops (shifting dominance)
  3. Greatest complexities occur exactly at the boundaries – sources of diversity and creativity
    1. Boundaries are false, man-made but necessary to simplify and comprehend systems
  4. Most important input in a system is the one that is most limiting
  5. Growth itself depletes or enhances limits and therefore changes the limits themselves
  6. Bounded rationality – people make reasonable decisions based on information they have but since it is imperfect it leads to bad outcomes
    1. Change comes first from stepping outside the limited information that can be seen from any single place in the system and getting an overview. From a wider perspective, information flows, goals, incentives and disincentives can be restructured so that separate, bounded rational actions do add up to results that everyone desires. It’s amazing how quickly and easily behavior changes can come, with even the slightest enlargement of bounded rationality, by providing better, more complete, timelier information
    2. What makes a difference is redesigning the system to improve the information, incentives, disincentives, goals, stresses, and constraints that have an effect on specific actors. Must change the structure to change the behaviors
    3. However, and conversely, our models fall far short of representing the world fully
  7. You can’t navigate well in an interconnected, feedback-dominated world unless you take your eyes off short-term events and look for long-term behavior and structure; unless you are aware of false boundaries and bounded rationality; unless you take into account limiting factors, nonlinearities and delays. You are likely to mistreat, mis-design, or misread systems if you don’t respect their properties of resilience, self-organization and hierarchy 
  8. 3 ways to deal with policy resistance – overpower it, totally let go or find ways to align the goals of all the subsystems involved
    1. Tragedy of the commons – invisible or too long delayed feedback (educate / exhort, privatize or regulate the commons)
    2. Drift to low performance
      1. The trap is allowing performance standards to be influenced by past performance, especially if there is a negative bias in perceiving past performance. It sets up a reinforcing feedback loop of eroding goals that sets a system drifting to low performance
      2. Solution – Keep performance standards absolute and let standards be enhanced by the best actual performances instead of being discourage by the worst. Use the same structure to set up a drift of high performance
    3. Escalation – avoiding falling into it in the first place but if you are, refuse to compete or negotiate a new system with balancing loops to control the escalation
    4. Success to the successful – winners keep winning and enhance prospects of future prosperity. Diversification, strict limitation on the fraction of the pie any one winner may win (anti trust laws), policies leveling the playing field, policies that devise rewards for success that do not bias the next round of competition all good solutions
    5. Addiction – beware of symptom relieving or signal denying policies or practices that don’t really address the problem. Take the focus off short-term relief and put it on long-term restructuring
    6. Rule beating – design, or redesign, rules to release creativity you not in the direction of beating the rules, but in the direction of achieving the purpose of the rules
    7. Seeking the wrong goals – specify indicators and goals that reflect the real welfare of the system. Be especially careful not to confuse effort with result or you will end up with a. System that is producing effort, not results.
  9. Leverage point – point in system where a small change can lead to big shift in behavior
    1. The leverage point is often hidden and counterintuitive
    2. 12 examples of leverage points (from least to most effective)
      1. Numbers – constants and parameters such as subsidies, taxes and standards
        1. Least effective as changing these variables rarely changes the behavior of the system
      2. Buffers – the sizes of stabilizing stocks relative to their flows
        1. Big stocks relative to their flows are more stable than small ones
        2. Often stabilize a system by increasing the capacity of the buffer but if the buffer gets too big, the system gets inflexible
      3. Stock and flow structures – physical systems and their nodes of intersection
        1. The stocks and flows and their physical arrangement can have a tremendous effect on how the system operates
        2. The only way to fix a system that is laid out poorly is to rebuild it, if you can
      4. Delays – the lengths of time relative to the rates of system changes
        1. A delay in the feedback process is critical relative to rates of change in the stocks that the feedback loop is trying to control
        2. High leverage point except that delays are not often easily changeable
        3. Usually easier to slow down the change rate so that inevitable feedback delays won’t cause much trouble or oscillations
      5. Balancing feedback loops – the strength of the feedback is important relative to the impacts they are trying to correct
        1. One of the big mistakes is removing these “emergency” response mechanisms because they aren’t often used and they appear to be costly. May be no effect in the short-term but in the long-term you drastically reduce the range of conditions over which the system can survive
        2. For people, this means reducing personal rest, recreation, socialization, meditation, etc. for short-term productivity over long-term health
      6. Reinforcing feedback loops – the strength of the gain of driving loops
        1. Reinforcing loops are sources of growth, explosion, erosion and collapse in systems
        2. Slowing the growth is usually a more powerful leverage point in systems than strengthening balancing loops and far more preferable than letting the reinforcing loop run
      7. Information flows – the structure of who does and does not have access to information
        1. A new feedback loop to a place it wasn’t going before
      8. Rules – incentives, punishments, constraints
        1. Rules are high leverage points. Power over rules is real power
        2. If you want to understand the deepest malfunctions of systems, pay attention to the rules and who has power over them
      9. Self-organization – the power to add, change or evolve system structure
        1. The ability to self-organize is the strongest form of system resilience as it can evolve and survive almost any change, by changing itself
      10. Goals – the purpose or function of the system
        1. Everything further down the list from physical stocks and flows, feedback loops, information flows, even self-organizing behavior will be twisted to conform to the goal
        2. Single players who can change the system goal can affect the whole system
      11. Paradigms – the mind-set out of which the system (it’s goals, structure, rules, delays, parameters) arises
        1. Paradigms are the source of systems and harder to change than anything else about the system
        2. Best chance to change paradigms is to keep pointing at the anomalies and failures in the old paradigm
        3. Must get outside the system and force you to see the system as a whole (Galilean Relativity)
      12. Transcending paradigms
        1. Keeping oneself unattached in the arena of paradigms, to stay flexible, to realize that no paradigm is “true” gives a tremendous source of perspective when dealing with systems
  10. Systems can’t be controlled but they can be designed and redesigned 
  11. Guidelines for living in a world of systems
    1. Get the beat of the system – observe how it behaves before disturbing it. Forces you to focus on facts and long-term behavior rather than rhetoric and theories
    2. Expose your mental models to the light of day – judicious testing of theories allows you to faster admit uncertainties and correct mistakes leading to more flexibility. Mental flexibility, the willingness to redraw boundaries, to notice that a system has shifted into a new mode, to see how to redesign structure, is a necessity when you live in a world of flexible systems
    3. Honor, respect and distribute information
    4. Use language with care and enrich it with systems concepts – keep it concrete, meaningful and truthful
    5. Pay attention to what is important, not just what is quantifiable – quality over quantity and never ignore a part of the a system just because it can’t be counted
    6. Make feedback policies for feedback systems
    7. Go for the good of the whole – don’t optimize something which shouldn’t be done at all
    8. Listen to the wisdom of the system
    9. Locate responsibility within the system – design systems which are accountable for its own actions
    10. Stay humble, stay a learner – acknowledging uncertainty leads to more credibility
    11. Celebrate complexity
    12. Expand time horizons
    13. Defy the disciplines – be a multidisciplinary learner and thinker
    14. Expand the boundary of caring
    15. Don’t erode the goal of goodness
What I got out of it
  1. Systems consist of boundaries, inflows, stocks, and outflows. Must understand the structure and goals of the system as this affects its behavior and function. Systems work well due to resilience, self-organization and hierarchies. Delays (perception, response, delivery) cause oscillations and often people take the wrong course of action and cause higher oscillation. 3 important questions to test the value of any model. Focus on leverage points. Must take a long-term view and focus on the history of behavior to understand how and why systems function the way they do

Investing: The Last Liberal Art by Robert Hagstrom

  1. Hagstrom walks the reader through why and how to incorporate fundamental principles from multiple fields to become a better thinker, decision maker, investor, etc.
Key Takeaways
  1. Worldly Wisdom
    1. Combine key ideas from all disciplines and then develop a latticework in head to ‘hang’ all mental models on
    2. Chances of good decisions improve when many, disparate models yield the same conclusion
    3. Educate self and then train to see problems by seeing/thinking differently
      1. Learn big ideas so well that they are always with you
    4. Key is finding linkages and connecting one idea to another
      1. Connectionism – we learn by analogy, more connections leads to more intelligence
      2. Massive number of connections more efficient than raw speed (small world networks are everywhere)
    5. Two keys to innovative thinking – understand basic disciplines we draw knowledge from and be aware of the benefits and uses of metaphors
      1. Concise, memorable, colorful way to depict thought, action, ideas and more importantly translate ideas into models – stimulating understanding and new ideas
  2. Physics
    1. The bridge between equilibrium in physics, economics and the stock market
    2. Equilibrium – state of balance between two opposing forces, powers or influences
      1. Static vs. dynamic
      2. Rational actions lead to stock market equilibrium – where the shadow price (intrinsic value) = stock price
        1. Now argue market is complex adaptive system – a network of many individual agents all acting in parallel and interacting with one another. The critical variable that makes a system both complex and adaptive is the idea that agents in the system accumulate experience by interacting with other agents and then change themselves to adapt to a changing environment
          1. Irrational, organic, not efficient
  3. Biology
    1. Evolution and natural selection to law of economic selection
    2. After crashes, market and economy best understood from a biological perspective as equilibrium could not account for them
    3. Struggle between species and individuals of same species leads to natural selection and evolution
    4. Schumpter – economics essentially an evolutionary process of continuous and creative destruction
      1. Innovation, a visionary and action-oriented entrepreneur and access to credit are all necessary
      2. Innovation leads to periods of punctuated equilibria – creative destruction
    5. 4 distinct features of economy
      1. Dispersed interaction – what happens in the economy is determined by the interactions of a great number of individual agents all acting in parallel
      2. No global controller
      3. Continual adaptation (co-evolution)
      4. Out of equilibrium dynamics – constant change leads to a system constantly out of equilibrium
    6. Evolution takes place sin stock market via economic selection and capital allocation
    7. Living systems make themselves up as they go along
    8. Efficiency and evolutionary / behavioral not necessarily exclusive – times of less emotions leads to more efficient market
  4. Sociology
    1. Study of how individuals function in society and ultimate goal is predicting group behavior
    2. Relationship between individual investor and stock market a profound puzzle
    3. All human interactions and systems are complex adaptive – can’t separate part from the whole and behavior constantly changes as agents and therefore system adapts
    4. Self-organization and self-reinforcement found in physics, biology, economics, etc.
    5. Emergence – larger entities arise out of interactions of simpler, smaller entities and have characteristics that the smaller entities do not exhibit
      1. Crowds can be collectively intelligent IF diverse and independent
      2. Smart and dumb agents lead to better outcomes than a group of just smart people
      3. Information cascades, which lead to diversity breakdowns happen when people make decisions based on others rather than private information and leads to inefficient system
        1. Can even happen with small groups if have a very dominant leader
      4. Self-organized criticality – market one example where instability is inherent, unpredictable and small fluctuations lead to big changes
        1. Different meta-models of reality (quant vs. fundamentally oriented…) leads to instability
      5. Complex adaptive, self-organization leads to emergence which leads to instability, unpredictability, criticality
  5. Psychology
    1. Anchoring, framing, overreaction, overconfidence, mental accounting, loss aversion key biases
    2. Equity risk premium is puzzling – people hold bonds because of loss aversion and mental accounting
    3. Loss aversion makes people short-term focused
    4. Longer investor holds an asset, the more attractive it becomes IF not evaluated frequently – advises checking prices only once per year!
    5. Information overload can lead to illusion of knowledge
    6. Don’t be  Walter Mitty investor – feed during difficult times!
    7. Decisions we make based on skill lead to higher risk taking and luck to lower
    8. Mental models are imprecise ways of modeling reality but very helpful and simplify life
      1. Mistakes – believe models equiprobable, focus on  few or one, ignore what is not easily seen
    9. Innate pattern seeking leads to magical thinking and superstitions by people trying to explain the unexplainable
      1. In this case, beliefs precede reasoning, beliefs dictate what you see
        1. Why people listen to forecasters – quells anxiety we hate to live with even if we rationally know how stupid it is
    10. Reduce noise via accurate communication of information makes for better rational decisions
      1. Correction device – get information from first-hand sources and then do your best to remove prejudices and biases
  6. Philosophy
    1. Forces us to think and can’t be transferred intact from one mind to another
    2. Metaphysics – ideas independent of space and time (God, afterlife)
    3. Aesthetics / ethics / politics three main branches
    4. Epistemology – study of the nature/limits of knowledge; thinking about thinking
      1. Develop rigorous, cohesive epistemological routines
    5. Failure to explain caused by failure to describe – Mandelbrot 
    6. Disorder simply order misunderstood
    7. Wittgenstein – world we see is defined and given meaning by the words we choose
      1. Reality is shaped by the words we select
      2. Stories very powerful description tools – beware of the overconfidence they can deliver
    8. Pragmatism – true belief defined by actions and habits it produces (William James)
      1. Idea or action is real, good, true if it makes a meaningful difference
        1. Our understanding of truth evolves as it is based on results
        2. No absolutes
  7. Literature
    1. Read selectively but analytically
    2. Always evaluate its worth in the larger picture and then either reject or incorporate what you learn into your mental models – the importance of reflection!
    3. Improves understanding (over fact collecting) and critical thinking
    4. Critical mindsets evaluate the facts and separate facts from opinion
    5. Fiction important because it helps us learn from others’ experiences
    6. Detectives best practices
      1. Develop a skeptic’s mindset; don’t automatically accept conventional wisdom
      2. Conduct a thorough investigation
      3. Begin an investigation with an objective and unemotional viewpoint
      4. Pay attention to the tiniest details
      5. Remain open-minded to new, even contrary, information
      6. Apply a process of logical reasoning to all you learn
      7. Become a student of psychology
      8. Have faith in your intuition
      9. Seek alternative explanations and redescriptions
  8. Mathematics
    1. Bayes’ Theorem – updating initial beliefs with new information leads to new and improved belief
      1. AKA Decision Tree Theory
    2. Probability theory – analysis of random phenomena
    3. Kelly Criterion – how to size bets
      1. 2p – 1 = x (p = probability of winning)
      2. To compensate people not having an infinite bankroll or time horizon, halve (or take some fraction) of the Kelly Criterion
    4. Never fail to take variation into account – trends of system vs. trends in system (individual winners even during sideways overall market)
    5. Never fail to take into account regression to the mean
  9. Decision Making
    1. Intuition helpful when situation is reliable enough to be predictable and when can learn regularities through prolonged practice (mostly linear systems)
      1. Intuition nothing more than recognition – increase store of knowledge and connections leads to improved intuition
    2. How you think more important than what you think
    3. Humans cognitive misers and stop thinking the minute they’re satisfied with an answer
    4. Building blocks from many disciplines used to form mental models must be dynamic and updated with new information
What I got out of it
  1. A fascinating read which was helpful to get a good, broad understanding of what it means to be a multi-disciplinary learner

The Fifth Discipline by Peter Senge

  1. The Fifth Discipline describes what a learning organization is and why it is vital in today’s world. It combines 5 core disciplines to help any organization gain a competitive advantage
Key Takeaways
  1. Communities survive and prosper because people work together
  2. A learning organization creates a community where the team learns together and shares the same vision. It creates interconnected thinking so everyone is on the same wavelength – ingenuity, flexibility, ability to think forward and innovate and adapt to new systems
  3. Team learning creates greater and more productive combined knowledge than individual, disparate insight
  4. Nature of constant change in business and in life makes constant learning imperative. Those who emphasize this get ahead and succeed in their fields
  5. Knowledge and experience is the foundation of intuition and the more you gain the stronger your intuition will be
  6. The 5 Core Disciplines
    1. Personal mastery – mastering one’s focus, energy and patience can go some way to creating a well rounded individual of great worth to any organization
      1. Promotes intellectual and problem-solving growth
      2. Promotes new skills
      3. Drives the individual to better themselves and those around them
      4. Form a clearer vision
      5. As we accumulate knowledge, we can form better intuitions – the more we learn the better our intuition becomes
    2. Mental models – understanding the role our ingrained mentality and prejudiced perceptions play in our decision making
      1. Altering mindsets has to come before altering reality
      2. Mental models exist solely in the mind, are never perfect, are resistant to change and affect actions
      3. To alter mental models must create alternatives, encourage new ways of thinking, become more self-aware of biases inherent in all mental models, get people to ask questions
    3. Building shared visions – a team-shared vision for the future is more beneficial to a company than a few disparate visions promoted by self-obsessed employees
      1. Many people have vision but pooling that passion into a shared vision can bring outstanding results
      2. Build shared vision by: suppressing egos, encourage people to share in the vision, allow the vision to grow over time but don’t avoid directing it when needed
      3. The shared vision is the centerpiece, the final expression of each individual
      4. “When you are immersed in a vision, you know what needs to be done. But you often don’t know how to do it.”
    4. Team learning – team work that brings together combined knowledge and expertise creates a fulfilling, powerful collective
      1. Team learning is all about collaborating and combining in order to point the organization, with all its acquired and assembled skills, in one clear direction, reaching all goals
      2. Foster team learning by: creating platform for open debates, encourage conflict, create learning platforms (come together in a fun, stimulating environment outside the office)
    5. Systems thinking – encourages businesses to look at the bigger picture, thereby providing sustainable long-term, rather than short-term, solutions to problem
      1. Systems thinking is the fulcrum, it is the driving force upon which the performance of the other disciplines hinge
      2. Encourages us to spot patterns that are affecting our performance and subsequently analyze them for any possible improvement. It does not simply look at the consequences of an event and seek to eradicate the problem ‘for now’
      3. All about preventing long-term problems
      4. The system is often the problem with a company’s poor performance so you should carefully examine the underlying issues plaguing poor business performance
      5. Systems thinking discourages quick fixes and says no to short-cut solutions
      6. Must focus on cause and effect – solve the root of the problem rather than always fighting fires
      7. Can often find small changes that lead to huge improvements in results – leverage points are key to find
        1. Leverage becomes possible when you consider the structure behind the results
  7. Crucial to overcome common problems – internal politics, exclusive power, lack of time for learning, difficulty in maintaining a good work / life balance, repeated mistakes, difficulty in leading a learning organization
    1. Learning organizations encourages its people to admit these problems exist so that solutions can be found
    2. Failure to acknowledge own mistakes leads to bad habits
    3. Businesses tend to react to the consequence of an event, rather than root out the cause of it
    4. Non-learning organizations are reactive rather than proactive and therefore repeat mistakes
    5. If everyone is given responsibilities and the chance to make decisions, your organization will reap the rewards as everyone will be inspired and motivated to come up with solutions and work harder
    6. It is imperative that businesses create time for learning – more effective in every sense in the long run than working in ignorance and creating bad habits
    7. Fostering a healthy work / life balance is paramount as it will lead to huge benefits in the long run for both individuals and the organization
    8. Leaders tend to be hard working and very ambitious but must blend in softer traits such as openness, foresight, open communication, creativity and patience
  8. Learning organizations are
    1. Active
    2. Forward thinking – continual learning irons out mistakes
    3. Dynamic – emphasis placed on team-work and shared learning
    4. Productive – because the whole team is learning, each member can feed off another’s strengths, leading to greater production
    5. Communal – shared knowledge and production is the key. Constant communication and sharing talents takes teams forward
    6. Innovative – they lead the way in genuinely effective improvements
  9. “Building learning organizations involves developing people who learn to see as systems thinkers see, who develop their personal mastery and who learn how to surface and restructure mental models collaboratively. Given the influence of organizations in today’s world, this may be one of the most powerful steps towards helping us ‘rewrite the code’, altering not just what we think, but our predominant ways of thinking. In this sense, organizations may be a tool not just for the evolution of organizations, but for the evolution of intelligence.”
  10. Learning organizations are a trial and error base in the sense that problems are confronted and attempts made to resolve them. They act almost as solutions providers
What I got out of it
  1. Continuously learning on an individual and organizational level is key to adapting to change and staying ahead of competitors. Important to schedule time to think deeply, learn, understand your mental models and its biases and prejudices and constantly think in systems

Complexity: The Emerging Science at the Edge of Order and Chaos by Mitchell Waldrop

  1. Explanations of complexity, self-organization, emergence, order and chaos and some of the pioneers behind this work. It also details the history of the Santa Fe Institute
Key Takeaways
  1. Complex systems – many individual agents interacting and outcomes difficult to predict
    1. Complexity is the science of emergence
  2. Spontaneous self-organization (organization with no central conductor) found all over nature
    1. Complex systems all over nature have somehow acquired ability to bring order and chaos into a special kind of balance – the edge of chaos. The components of the system never lock into place yet never dissolve into turbulence either. the edge of chaos is where life has enough stability to sustain itself and enough creativity to deserve the name of life. The edge of chaos is where new ideas and innovative genotypes are forever nibbling away at the edges of the status quo and where even the most entrenched old guard will eventually be overthrown; where eons of evolutionary stability suddenly give way to wholesale species transformation. the edge of chaos is the constantly shifting battle zone between stagnation and anarchy, the one place where a complex system can be spontaneous, adaptive and alive.
    2. Self-organization is the most powerful force in biology and living systems operate at the edge of chaos
      1. Evolution always seems to lead to the edge of chaos
  3. Them that has, gets – domino effect once tipping point hits leads to cascades and often winner-take-all systems
  4. The crucial skill is insight. The ability to see connections
  5. At some fundamental level that Brian Arthur didn’t yet understand, the phenomena of physics and biology are the same
    1. Self-organization found everywhere! – positive feedback, increasing returns, lock-in (more niches dependent on a technology, the harder it is to change that technology until something vastly better comes along), unpredictability, tiny events that have immense consequences all seem to be a re-requisite for life itself
  6. Must look at world how it is, not as some elegant theory says it ought to be
  7. Essence of science lies in explanation more than prediction
  8. Increasing returns prominent when marginal cost is minimal (software for example)
  9. Nearly everything and everybody caught up in non-linear web of incentives, constraints and connections
  10. Innovations never happen in a vacuum and often come from someone who is outside the field
  11. Catalysis everywhere and life wouldn’t be possible without it – molecules could have catalyzed the formation of other molecules so that those in the web would have taken over. The web would keep growing and would have catalyzed its own formation, it would become an autocatalytic set – order for free
    1. Autocatalytic set can bootstrap its own creation and evolution by growing more and more complex over time and will also experience booms and busts from small changes
  12. Complex adaptive systems – characterized by perpetual novelty; dispersed, hierarchical, learn / adapt / evolve, anticipate the future
    1. Can never get to equilibrium as new opportunities are always being created by the system – always unfolding, always in transition
  13. Emergence is hierarchical – building blocks at one level combining into new blocks at a higher level. Hierarchies are one of the fundamental organizing principles of the world. Found everywhere because a well-designed hierarchy is an excellent way of getting some work done without any one person being overwhelmed or having to know everything. Also, utterly transforms a system’s ability to learn, evolve and adapt – can reshuffle building blocks and take giant leaps. Can describe a great many complicated things from relatively few building blocks
  14. Adaptive agents always playing game with environment for fitness requires feedback and prediction
    1. In order to learn, must be able to take advantage of what the world is trying to tell it
  15. Implicit expertise – a huge, interlocking set of standard operating procedures that have been inscribed on the nervous system and refined by years of experience
    1. Competition much more essential than consistency
    2. Competition and cooperation may seem antithetical but at some very deep level, they are two sides of the same coin (leading to symbiosis across nature, tit for tat strategy)
  16. Self-reproduction requires medium to be both data and instructions (DNA)
    1. von Neumann and cellular automata
  17. Spectrums:
    1. Dynamical systems: Order – Complexity – Chaos
      1. Complexity is emergent, dynamical, characterized by phase transitions
      2. Interesting things always happen at the edge of chaos
    2. Matter: Solid – Phase Transition – Liquid
      1. First and second order phase transitions – sharp and precise phase transitions (molecules forced to make either or choice between order and chaos) compared to second order which is much less common in nature – much less abrupt because molecules don’t need to make an either-or choice, they combine order and chaos (fluid with pockets of solid or vice versa)
    3. Computation: Halting – “Undecidable” – Nonhalting
    4. Life: too static – “life / intelligence” – too noisy
  18. Life is based to a great degree on its ability to process and store information and then mapping it out to determine proper action
  19. Always ask, “optimal relative to what?
  20. Artificial life – effort to understand life by synthesis, putting together simple pieces to generate lifelike behavior in man-made systems. Its credo is that life is not a property of matter per se, but the organization of that matter
    1. ‘Aliveness’ lies in the organization of the molecules and not the molecules themselves
    2. Fact that simple rules leads to unpredictability is reason trial and error (Darwinian natural selection), although somewhat crude and ‘wasteful’ is the best strategy in nature and evolution
    3. If organization determines life, it shouldn’t matter what it is made of if properly organized
    4. Complex, life-like behavior is the result of simple rules unfolding from the bottom up
  21. Emergence – somehow, by groups of agents cooperating and seeking self-accommodation, they manage to transcend themselves and become something more where the whole is greater than the sum of the parts
  22. Power truly lies in connections – exploitation (improving what you already have) vs. exploration (taking big risk for big reward)
  23. Edge of chaos – found right in between order and chaos, aka complexity
    1. Stable enough to store information but evanescent enough to transmit it
    2. Observe systems in terms of how they behave instead of how they are made
    3. Systems which are too controlled, too stagnant, too locked in will perish
    4. Healthy economies and societies must balance order and chaos via feedback and regulation while leaving room for creativity, change and response to new conditions – “evolution thrives in systems with a bottom-up organization which gives rise to flexibility”
    5. Information has to flow from the bottom-up and from the top-down
    6. Learning and evolution move agents along the edge of chaos towards ever greater complexity, sophistication and functionality
      1. One of the greatest questions and mysteries is why life gains ‘quality’ and becomes more complex over time. It is also one of the most fascinating and profound clues as to what life is all about
  24. Complex phenomena of life only associated with molecular scale due to variety and reactivity
  25. Tao of complexity – there is no duality between man and nature, we are all part of this interlocking network
    1. Once this is realized, conversation changes from optimality to co-adaptation and accommodation – what would be good for the system as a whole
    2. You keep as many options open as possible and go for what’s workable, rather than what’s ‘optimal’
    3. Optimization isn’t well defined anymore. Rather, what you’re trying to do is maximize robustness, or survivability, in the face of an ill-defined future
What I got out of it
  1. Ties together a lot of fascinating concepts and drew some more light on phase transitions and complexity for me

Running the Amazon by Joe Kane

  1. Joe Kane describes his adventure trekking through the Amazon from its source in the Andes to the Atlantic Ocean
Key Takeaways
  1. The river – the word hardly does justice to the churning mess enveloping you – the river tumbles you like so much laundry. It punches the air from your lungs. You’re helpless. Swimming is a joke. You know for a fact that you are drowning. For the first time you understand the strength of the insouciant monster that has swallowed you. That is River Lesson Number One. Everyone suffers it. And every time you get the least bit cocky, every time you think you have finally figured out what the river is all about, you suffer it all over again.
  2. The idea is not to beat the river. The river always wins. It does not care. We try the river because we must try. White water is, how do you say it, like you are bleeding…It gets in your blood. Yes. It is in your blood. it is a thing you are never forgetting.
  3. And deep down in my stomach, the place where as a child I had believed my soul could be found, burned the knowledge that what I despised in others was that which I feared in myself.
What I got out of it
  1. An inspiring read about great vision and endurance

The Manual of Ideas: The Proven Framework for Finding the Best Value Investments by John Mihaljevic

  1. Describes some of the world’s most respected investors’ proven, proprietary frameworks for finding, researching, analyzing, and implementing the best value investing opportunities
Key Takeaways
  1. Each investor must carve out a personal way to invest in order to succeed
  2. a share of a stock is a share in the ownership of a business
  3. investors tend to buy after a period of good performance and withdraw after a period of bad performance, which is bad for the funds results
  4. Those considering an investment in a hedge fund may first wish to convince themselves that their prospective fund manager can beat Buffett. Doing this on a prefee basis is hard enough; on an afterfee basis, the odds diminish considerably.
  5. Becoming a smart asset allocator is key to managerial success
  6. believe that our investment decisions affects the world
  7. Losses have a perverse impact on long-term capital appreciation, 20% drop in book value requires a 25% subsequent gain in order to offset the loss
  8. Increase in size makes it increasingly difficult to maintain same level of success
  9. thinking like a capital allocator is coupled with thinking like an owner. looking to the business rather than the market for return on investment
  10. Graham-style investing starts with price of a stock.  If it does not look like a bargain based on tangible metrics, Graham-style investors are not interested.
  11. Eugene Fama and Kenneth French have found through their studies that equities with high book-to-market ratios outperform those with low ratios.
  12. Uncovering equities that provide both asset protection on balance sheet and own businesses with high returns on capital are treasures.  This is hard to find unless the business has experienced a steep near-term profit decline.
  13. by prioritizing return of cash to shareholders, low-return businesses can assist investors in earning a strong investment return, assuming the equity purchase price was favorable.
  14. Investors may overestimate liquidation values, as the reality of a dying business tends to hide some nasty surprises
  15. Acceptance of discomfort can be rewarding in investing, as fearful equities frequently trade at exceptionally low valuations.
  16. investing in asset-rich but low return business, time may be working against you.  As long as management can hold on to the assets and keep reinvesting at low returns, shareholders may earn unimpressive returns despite a bargain purchase price. As result, catalysts become a relevant consideration.
  17. businesses trading at deep value prices are among those most likely to be creatively destroyed. It seems unwise to allocate a large portion of investable capital to any one deep value opportunity, even if it promises a large expected return
  18. Several considerations may augment the likelihood that a Graham-style screen yields a list of market-beating investment candidates.  Share re-purchases, insider buying, and cash generated through working capital shrinkage may be used as screening factors.
  19. When we value a company based solely on readily ascertainable balance sheet values, we run the risk that those values erode over time, negatively impacting future equity value.
  20. Many companies can be appraised most accurately by analyzing each of their distinct businesses or assets separately and then adding up those components of value to arrive at an estimate of overall enterprise or equity value.
  21. A reason for the market’s occasional mispricing of companies with multiple sources of value may be investors’ unwillingness to value assets that differ materially from a company’s core assets.
  22. Companies with distinct components of value often enjoy greater strategic flexibility, as they may divest a fairly valued asset to improve the balance sheet, repurchase undervalued shares, or reinvest capital in a high-return business.
  23. Sometimes investors, in their zeal to create a sum-of-the-parts opportunity, slice a company into too many parts, creating an attractive investment thesis in theory but not in reality
  24. We normally do not require a catalyst, but we find that situations with multiple sources of value are more prone to becoming value traps in the absence of strategic action.
  25. It matters tremendously whether the offer is “buy one get one free” or if it is “buy ten get one free”.  As shoppers we recognize the former as a more compelling offer. As investors, we often overlook this important distinction.
  26. sum-of-the-parts opportunities come in a few different flavors, each of which demands a slightly different approach to screening. excess assets typically consist of cash and cash like assets, stakes in other businesses, real estate holdings, or a combination of these asset types.
  27. Some hidden asset stories are so compelling that they attract quite a few smart investors, potentially eliminating both the valuation discount and the hidden nature of the assets.  Investors may become patsies by failing to realize how many other smart investors have bought into the same story of hidden value.
  28. Whenever hidden assets motivate us to consider an equity security, the question of how the assets will cease to be hidden becomes important.  In this context, we are less interested in the speculative question of what will prompt other investors to see what we are seeing.  rather, we focus on the economically important issue of how the value inherent in the hidden assets will accrue to us as shareholders– and when.
  29. Buying good companies when they are cheap is invaluable advice, as demonstrated in Greenblatt’s “the Little Book That Beats the Market”.
  30. Higher return on capital employed indicates a good business.  Typically calculate capital employed as net working capital plus net fixed assets.
  31. Greenblatt’s use of operating income to enterprise value as the way of determining cheapness is congruent with his use of operating income to capital employed as the way of determining quality, as the effects of leverage and taxes are stripped from both calculations.
  32. Greenblatt’s magic formula suggests that it will keep outperforming markets over time despite the fact that its success, in theory, would attract a flock of investors and therefore eliminating its prospective attractiveness.
  33. Mr. Market makes two mistakes with some consistency: it over values high-return businesses whose returns on capital derive from explosive but ultimately transitory trends or fads. On the flip side, the market may undervalue unhyped quality businesses with sustainable high-return  reinvestment opportunities.
  34. The future is what counts in investing, and while historical data has the advantage of certainty, forward-looking estimates have the advantage of relevance.
  35. high returns on existing capital – the capital already employed in a business – are almost meaningless without the ability to invest new capital at above-average returns.  Returns on existing capital, whether high or low, are already reflected in a company’s operating income.
  36. Business executives can distinguish themselves in two ways: business value creation and smart capital allocation
  37. Distinguish between business performance and stock price.  Better management results generally mean better business outcomes, but in terms of the stock beating the market also depends on market quotation at time of investment.
  38. Eliminate the bad actors when it comes to finding better management, even when some are esteemed by the business establishment.
  39. Some factors that reflect CEO attitude towards owners
    1. communication with shareholders open and honest
    2. composition of board of directors
    3. what does financial leverage tell us about the management
  40. Determinations like shareholder friendliness, alignment of interests and the ability to run a business not only involve many variables but also an element of judgment.  while we cannot exactly screen for jockey stocks, we can use screens to move a step closer toward finding companies with good management
  41. Screening for close alignment involves two proxies, stock ownership and insider buying activity.
  42. Most capital allocators view reinvestment as a default option, giving little consideration to the alternatives.
  43. Making a list of great capital allocators represents a continuous process of discovery and curation.  Corporate executives come and go, and seemingly great managers may reveal themselves as not so great over time.
  44. Subjective assessment of management in a one on one meeting likely adds value to the investment process, assuming the investor is aware of the biases involved and judges correctly that awareness will render inconsequential any biases.
  45. in addition to selecting a proper focus for a meeting, investors may want to prioritize meetings likely to produce incremental, differentiated insights.
  46. hedgefundletters.com
  47. One of the best pieces of advice “Do your own work and do not trust the tips of others”. nonetheless, following the moves of super investors can be both smart and profitable, if done correctly.
  48. Common traits of super investors, Other than remarkable returns, are clear thinking, lucid communication, a visible passion for the process of investing and surprisingly humble attitude toward success.
  49. Even if we accept that super investors are likely to outperform the market, it is not entirely clear that copying superinvestors also leads to outperformance
  50. The problem is not that all investors make mistakes but also that our ability to stick with an investment is diminished if we have not done the research to give ourselves a certain level of conviction in an idea.
  51. Investors may invest in macro theme or political outcome that makes them invest in individual stocks that they may not be entirely sure about.  Viewing these stock purchases as endorsements from such investors would be a mistake
  52. Factors to track superinvestors: decide which type of investors to track, concentration of portfolio, average portfolio turnover, propensity to employ short selling, and congruence between one’s own investment approach and that of a superinvestor
  53. Turnover is important because as outside observers we receive only delayed notice of other investors’ buy-sell activity.  the higher the turnover, the higher the chance that an investors is considering selling a holding by the time we consider buying it.
  54. Context is paramount when assessing the purchase and sale activity of superinvestors.  imagine three investors, each of whom has invested five percent of their respective equity portfolios in Bank of America.  It would be wrong to infer that each investor’s position has comparable significance for our purposes.
  55. Several key developments have created opportunities for small stock investors, including an increase in the size of institutional portfolios, an escalation of compensation expectations, exclusion of small stocks from major market indices, and scant research coverage by sell-side firms.
  56. Major shareholders have more influence on small-company CEOs than they do on their large-company counterparts, as more investment firms can credibly put small companies in play.
  57. We find that small stocks outperform large stocks by a statistically significant margin over time.  while the results differ based on the time periods examined and the definitions used, the verdict is clearly in favor of small stocks.
  58. Even if small caps as a group stop outperforming large caps, the differential between top and bottom performers should continue to be greater in the case of smaller stocks, providing opportunities for research-driven investors.
  59. While underfollowed situations generally offer fertile ground for research-driven investors, it is not always necessary that many people analyze an investment for pricing inefficiency to be eliminated.
  60. In small cap arena, moving beyond quantitative screens is valuable because few professionals are willing to start at A and work through Z in their appraisal of qualitative value drivers of small companies.
  61. Small company executives are also generally more forthcoming than are corporate executives whose ability to communicate spontaneously has been lawyered into oblivion.  Ask a small company CEO how business is going and you might get an answer.
  62. one well-known drawback of small stock investing is the, at times, severely constrained trading liquidity of smaller companies.  Wider bid-ask spreads, greater market impact, and perhaps greater trading commissions conspire to make entering and exiting the equity of small companies a costly affair.
  63. Many of the best small stock opportunities elude discovery by quantitative screens.  the reasons include rapid change in company fundamentals, the disproportionate impact of management quality on value, and the tendency of small companies to lump nonrecurring items into financial reports.
  64. we may uncover hidden inflection points by scouring the small-cap landscape for companies with two or more businesses, one of which is typically a large, declining legacy business.  if the other business is a profitable growth business, we may have found a compelling opportunity.
  65. Special situations encompass equities whose near to medium term stock price performance is largely independent of the performance of equity markets.
  66. the flood of talent and capital has taken some areas of special situation investing from obscurity to popularity, reducing prospective investment returns.
  67. the more obscure a market niche, the higher the likelihood that diligent investors will generate market beating returns.
  68. in markets that exhibit informational inefficiency, rewards may accrue to those who make the effort to obtain timely, accurate and relevant information.
  69. Analytical inefficiencies may play an even greater role in driving outperformance in special situations. While information is generally available to investors willing to dig for it, many market participants struggle to overcome the analytical hurdles.
  70. Investing rules, as distinct from laws, need to be broken occasionally in the pursuit of investment excellence. In this context, rules include the financial formulas we have memorized along the ways.
  71. Some insights can be gained only if we launch the process of inquiry at the relevant point in time.  If we do so, we may enrich the process with new insights at a later date, but if we fail to launch the process, we may never capture the available insights.
  72. Special situations are one of the few investment areas in which it makes sense to pay at least as much attention to the time component of annualized return as to the absolute return expected in a particular situation.
  73. Special situations crystallize the meaning of value.  In a liquidation, value is determined solely by when and how much cash we will receive in exchange for the cash we give up today. When no terminal value remains, we cannot base the investment thesis on what other investors might pay for a business.
  74. In the absence of identifiable drivers of inefficiency, the probability may be higher that our appraisal of value contains an oversight or flaw.  If we can identify a non-fundamental factor that explains the low valuation, we gain confidence in an estimate of value that differs from the market price.
  75. Passive returns to investing in leveraged equities reveal little about the merits of such an approach.  On the other hand, the all-but-certain wide dispersion of returns strikes us as crucial.
  76. It would be difficult to overstate the importance of judgment in this area.  Even if all investors possessed comprehensive data on equity stubs, their investment decisions, and outcomes, would differ materially.
  77. We need to be careful not to overreach when our judgment turns out to be correct.  The payoffs in equity stubs may exert an intoxicating effect on the successful investor.
  78. Do to the lopsided payoff in leveraged equities, the probability of winning on any one investment may be well under 50 percent.  The low batting average increases the size of the sample required to estimate the ex ante likelihood of success with any confidence.
  79. It helps to commit our investment theses to paper, and then test and refine them over time. In leveraged equities, experience can be an investor’s key asset, it interpreted properly.  We add this qualification because a danger exists that we overlearn.
  80. The tendency of investors to think about the likely outcome rather than the range of possible outcomes represents a key stumbling block to success in leveraged equities.
  81. Assuming we wish to wade into treacherous but potentially rewarding equity stubs, one of the key considerations in each situation is the ownership of the debt on a company’s books.
  82. We distinguish between two types of equity stubs for screening purposes: first, we look for companies that have been designed as equity stubs, namely, private equity type investments available in the public market.  Second, we target companies that have become equity stubs due to some kind of stumble.
  83. Our experience suggests that industry- side sell-offs represent better hunting grounds for
What I got out of it
  1. Good overview of where to look for and how to look for value investments

Personal History by Katherine Graham

  1. Katherine Graham, long time CEO of The Washington Post, recounts her story, her struggles and her rise to running this acclaimed newspaper
Key Takeaways
  1. Never forget or underestimate the role of chance in your life
  2. Parents had impossibly high standards but she receives good emotional support from parents where siblings didn’t
  3. Father bought the Washington Post in an auction
  4. Single most strengthening thing in her life was her fathers unconditional love and belief
  5. It is much more fun to fight to get to the top to fight to keep at the top
  6. Married Phil Graham in 1940 and he was adamant that if they married he wouldn’t accept any of her family’s money. He was able to cut through formality and connect with anyone, regardless of age, race, career, etc. Phil would soon join the Post anyway as Graham’s fathers deputy and soon take over the business. He worked so hard and put so much pressure on himself that he soon had a nervous breakdown. He worked closely with LBJ to pass the Civil Rights Act
  7. Relationships work best when there is most equality
  8. Katherine soon found out that Phil was having an affair
  9. Phil committed suicide after a bout of depression in the family’s home. Katherine found him and that was one of the most traumatic experiences of her life. Sometimes you don’t decide, you simply move forward
  10. Her friend gave her the confidence to believe that she could truly run the company after her husband died
  11. She knew a lot about publishing but began learning the rest by nibbling at the edges, making many mistakes and learning from them. She had to overcome her insecurities and many ingrained assumptions about women which were prevalent at the time
  12. The Post decided to go public in the early 1970s and Buffett bought about 10% of the company shortly after
  13. The Pentagon Papers scandal quickly brought the post attention and credibility as it refused to stop publishing papers which were damaging to the government
  14. The Post’s Woodward and Bernstein soon staged one of the most impressive investigative journalist efforts of all time in unlocking the Watergate Scandal. Graham’s courage and confidence during these times again propelled the Post to great national fanfare. A union strike soon destroyed some of the presses but Graham was able to quickly start printing again using other non union facilities. This was some of the most stressful times of her life and Buffett offered camaraderie at this time. He said he was looking for the tipping point of when she would lose the company for being down for too long
  15. Katherine turned over the role of Publisher to her son Don so she could focus on CEO duties. She stepped down as CEO in 1991 and was considered one of the best CEOs in the country and the Post one of the most respected companies
What I got out of it
  1. Amazing how Katherine was able to rise above her doubts, insecurities, stigmas around women, etc. in order to become one of the best CEOs in recent history!