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Myth  Flatland  Mr. Feynman Money master the game 51qwpkjNP7L._SY344_BO1,204,203,200_  7126 41ry6MoUc3L._SX331_BO1,204,203,200_Berkshire 51JNMx5G3iL._SY344_BO1,204,203,200_ 51K28NKVF3L._SY344_BO1,204,203,200_ 1984-by-opallynn-d4lnuoh 6596 17184 cover2 cvr9781451695182_9781451695182_hr Mastery_Cover movieposter PicofDorianGray-728143 Rye_catcher subliminal_seduction  TheJungleSinclair  The-Richest-Man-In-Babylon-George-Clason

On Health


  • See Bulletproof infographic – great and easy to follow graphic on what foods to eat most, what to avoid, when to eat and why

Exercise & Nutrition


  • 7-8 hours of sleep per night
  • Meditation
  • Foam roll
  • Power plate
  • Cryotherapy
  • Cold / hot exposure
  • Spike mat
  • Coherence / HRV
  • Occasional fasts

The Beautiful Game – Tribute to the San Antonio Spurs

Absolutely beautiful montage to Gregg Popovich and the San Antonio Spurs – teamwork, humility, sense of humor, ability to enjoy other’s success, work ethic, growth outside of basketball, complete trust, greatest success is found in building strong, lasting, trusting relationships

Pride in Performance by Les Schwab

  1. The autobiography of Les Schwab, founder of the Les Schwab supermarket tire store – his background, philosophy and views on life and business
Key Takeaways
  1. “I encourage you to share profits with your employees. I encourage you in every way possible to build people. If you do share, do it openly and honestly, and don’t get jealous if they start to make some money…that’s the whole idea. If you make people under you successful, what happens to you? Aren’t you also then successful? But if you think of yourself first, it just won’t work, and there’s no use attempting it. What nicer thing can you do with your life than to help young people build their lives into successful people, not just in money, but in all ways. The older I get the more proud I am of the profit sharing programs and other programs that I have created, or have helped to create. The best way to make it succeed is to share with people, to help people be successful people.”
    1. Started with over 50% of the profits going to the manager and each store operates as its own, separate business and the store employees share only in the profits of the store they work in
    2. Understood human nature, how to build trust and reciprocity with profit sharing program
    3. Also established a mandatory retirement trust with 15% of one’s earnings going into it
    4. Honesty clause – steal from the company and you lose everything you’ve saved in your trust
    5. Being generous pays off more than you’ll ever need – unselfish for selfish reasons!
      1. “I didn’t care about the money or who owned what, I just wanted to be successful”
      2. It is quite simple. Greed destroys
    6. Ardently believed that store managers and in-store employees were more important to the success of the company than the executives and paid them accordingly
  2. “Pride in performance. Pride in accomplishments well done. But never confused pride with ego. Pride commits us to do the job better. Ego tricks us into believing we can do no wrong. Concentrate on being the best each day, one day at a time, putting the customer first, and treating employees with respect. These are the traits that create pride in performance. These are the traits that will keep us humble.”
  3. Core tenets
    1. Be honest with ourselves
    2. Be hones with the people you work with. Be honest with your customer
    3. Be humble
    4. Have a desire to learn
    5. Tell the truth and have an open mind
    6. Be a man of action. Make some mistakes as this is the only way to learn
  4. Les had a tough family upbringing with little money, a drunk father and hard jobs (allowed him to see what the “bottom of the pyramid’s” world looks like
    1. Learned to work with people, to organize and promote, the power of recognition, importance of hiring, never taking advantage of customers or employees, and the power of centralized production as a newspaper circulation manager. He later implemented every one of this into Les Schwab Tires
  5. Learned early on to never get in over his head with debt
    1. Growing too quickly is often a huge mistake people make. Slow down and organically grow into your sales
  6. Importance of owning rather than leasing property (like Costco today)
    1. Time to buy a lot is when it is vacant
    2. Always wanted a 5 year lease with a 5 year option and the option to buy at the end of the lease
  7. “We always keep the customer happy in the best way possible”
  8. Importance of every day low pricing for everyone (Costco)
  9. Velocity – had odd tires to deliver to customers immediately. He didn’t make a profit necessarily but he always made a customer
  10. Fix flat tires for free for ladies. Even when made illegal, he continued to do it. No obvious, immediate profit but great goodwill and engendered loyalty – “we drove our competitors nuts”
  11. Decided to turn warehouse into the showroom (Costco)
  12. Complacency = death
  13. So important to have deep fluency and to think for oneself
  14. Most of his business dealings were simple handshake contracts
  15. Had a vengeance for the big tire companies as they mistreated him poorly early on – importance of dealing fairly with every constituent
  16. Life is hard for the man who thinks he can take shortcuts
  17. “Success in my mind comes from having a successful business, one that is a good place to work, one that offers opportunity for people and one that you can be proud to own or be associated with. Success in life is being a good husband, a good father and you end up being a second father to hundreds of other men and women.”
  18. I like to persuade people to do it my way. I don’t like to run the show myself. I like to work through people, and, unless you let them have almost the full power to make the final decision, you have a weak person working for you
  19. Once he was a bit older, he took 3 months off per year and always came back with fresh ideas on how to run a better business
  20. Set up policies for new stores which would help them get established by having old store help offset part of the costs of opening a new store
  21. Didn’t want to be known for being the cheap tire salesman or the most expensive. Wanted to be somewhere in the middle but hist customer service had to beat everyone
  22. Don’t be a “poor George” – a businessman who is not confident enough in his product or service, lowers the price at the customer’s request to the point that if he continued pricing this way, would eventually go out of business
  23. Holding grudges hurts you more than anyone else
  24. Whatever you do must be done with gusto and with volume
  25. People aren’t natural born leaders. Leadership is learned and I can’t explain fully how it is learned
  26. The decision making should always be made at the lowest possible level
  27. One thing that drives people is need – need to belong, to feel appreciated, to win, to grow. Find out what your employees need
  28. I’ve always wanted to be the best tire dealer, not necessarily the largest tire dealer
  29. The general customer tends not to fully understand tires so of course they’re going to buy from someone they trust
What I got out of it
  1. Growing people at the bottom of the business should be priority #1 for every company, open and honest communication is vital, establish profit sharing, keep decision-making at the bottom, total trust for everyone, don’t become a “poor George”

How Nature Works by Per Bak

  1. Self-organized criticality (SOC) is a new way of viewing nature – perpetually out of balance but in a poised state, a critical state, where anything can happen within well-defined statistical laws. The aim of the science of SOC is to yield insight into the fundamental question of why nature is complex, not simple, as the laws of physics imply
Key Takeaways
  1. Manifestations of SOC – regularity of catastrophic events, fractals, 1/f noise, Zipf’s laws
    1. So similar that they can be expressed as straight lines on a double logarithmic plot – are they all manifestations of a single principle? Can there be a Newton’s law of complex behavior? Maybe SOC is that single underlying principle.
    2. Catastrophism – majority of changes take place mostly from catastrophic events, also known as punctuated equilibrium
    3. Fractal – nature is generally fractal, scale free
    4. 1/f noise – features at all time scales, found all over nature
    5. Zipf’s Law – straight line plot between rank and frequency
  2. Complex systems – systems with large variability
    1. Brain may be the most complex system of all as it is able to model the complex outer world
    2. Biggest puzzle of all may be how does complexity arise out of simple laws
    3. Because of the large sensitivity of the critical state, small perturbations will eventually affect the behavior everywhere (butterfly/Lorenz effect)
    4. Complexity is a consequence of criticality
    5. Complexity deals with common phenomena in different sciences so the study of complexity benefits from an interdisciplinary approach
  3. Chaos theory – shows that simple, mechanical systems show unpredictable behavior
    1. Chaos is not complexity – gas in a chamber is chaotic but not complex (no emergent properties where non-obvious consequences occur based on underlying dynamical rules. Small changes in initial value does not cause huge differences in the end)
  4. SOC systems evolve to the complex critical state without interference from any outside agent, an external organizing force. Criticality, and therefore complexity, can and will emerge “for free” without any watchmaker tuning the world
  5. The process of self-organization takes place over a very long transient period. Complex behavior, whether in geophysics or biology, is always created by a long process of evolution. It cannot be understood by studying the systems within a time frame that is short compared with this evolutionary process
  6. Once the poised state, the critical state, is reached, it is similar to that of a nuclear chain reaction
  7. Catastrophes can occur for no reason whatsoever
  8. Nature is SOC, the only known mechanism to generate complexity (sand pile metaphor and “avalanches” – punctuated equilibria)
    1. Punctuated equilibrium – rate of evolution occurs periodically in spurts. This idea is at the heart of the dynamics of complex systems (expect Black Swans!)
      1. This idea is contrary to Darwin’s original theory which proposed that evolution happens gradually, uniformly and steadily
      2. These fluctuations are unavoidable and cannot be repressed over the long-term and the most efficient systems show fluctuations of all sizes!
  9. Perhaps our ultimate understanding of scientific topics is measured in terms of our ability to generate metaphoric pictures of what is going on. Maybe understanding is coming up with metaphoric pictures
    1. All thinking is a type of analogy
  10. Laws of physics are simple but nature is complex – the philosophy of physics has always been reductionist
  11. Quality, in same way, emerges from quantity. But how? Maybe through the ever pressing laws of nature and scarcity. The fittest (most able to rapidly adapt) will survive and this becomes deemed as “quality”
  12. An unlikely event is likely to happen because there are so many unlikely events
  13. Must learn to free ourselves from biases and herd mentality in order to see things as they truly are
  14. The problem with understanding our world is that we have nothing to compare it with (Galilean relativity!)
  15. Systems in balance are not complex and generally have no emergent properties
  16. Earthquakes may be the cleanest and most direct examples of SOC in nature
    1. Faults form fractals; earthquakes follow power laws
    2. Crust of earth has self-organized to the critical state, as evidenced by the Gutenberg-Richter law (simple power law)
      1. The importance of this law cannot be exaggerated. It is precisely the observation of such simple empirical laws in nature that motivates us to search for a theory of complexity
    3. Pulsar glitches, black holes and solar flares also exhibit elements of SOC
  17. Real life operates at the point between order and chaos, the critical state. Punctuations, avalanches, are the hallmarks of SOC
    1. May be living in a highly nonlinear world where emergent events are very difficult, if not impossible, to predict.
  18. Nothing prevents further progress more than the belief that everything is already understood
  19. Science is often driven by sheer inertia. Science progresses “death by death”
  20. Adaptation at individual or species-level is the source of complexity in biology
  21. Fitness – we are “fit” only as long as the network/ecosystem exists in its current form. Fitness is not absolute and evolution cannot be seen as a drive towards a a more fit species
  22. Life only in cold places with little chemical activity, not a hot sizzling primordial soup with a lot of activity since this does not allow for large periods of stasis for complexity to emerge
  23. Gaia hypothesis – all Earth should be viewed as a single system as all organisms interact and co-evolve
    1. Red Queen effect – if all other species adapt and become more fit, you have to become more fit just to stay in the same place
  24. Regularity does not mean periodic. Just because a massive earthquake hasn’t happened in 5,000 years, does not mean we should expect one soon
  25. Acquiring insight is itself a worthwhile effort
  26. Insight seldom arises from complicated messy modeling, but more often from gross oversimplification. Once the essential mechanism has been identified, it is easy to check for robustness by tagging on more and more details
  27. Complex behavior can arise from a simple model through the SOC process
  28. Thought can be viewed as a punctuated equilibrium event as it occurs only once enough signal hits the brain
    1. Seek out challenges and important questions to focus on!
  29. Brain operates at the critical state where ideas are just barely able to propagate. Too little and nothing happens, too much and the brain would overload
    1. It appears that the human brain has not developed a language to deal with complex phenomena. We see patterns where there are none, like the man in the moon and the inkblots in a Rorschach test. We tend to experience phenomena as periodic even if they are not, gambling casinos and earthquakes. When there is an obvious deviation from the periodicity, like the absence of an event for a long time, we say that the volcano has become dormant. We try to compensate for our lack of ability to perceive the pattern properly by using words, but we use them poorly
  30. Economics shows many signs of being critical but has made the mistake of trying to be “scientific” where everything needs to be predictable – it cannot be predicted
    1. Shows periods of avalanches (financial crashes)
  31. Traffic jams also at critical state
    1. No cataclysm necessary to cause a jam
    2. Perfect 1/f noise – stop and go behavior
  32. SOC is a law of nature for which there is no dispensation – cannot suppress the fluctuations forever
    1. Critical state is the most efficient state that can happen dynamically
      1. Why does it occur all over nature? Because it is robust and efficient!!
      2. Fluctuations are not perfect but they are healthy for dynamic systems. An over-engineered system may be more efficient for some time but catastrophically unstable
What I got out of it
  1. Self-organized criticality stems from simple rules with no “blind watchmaker” and can lead to very complex outcomes. Exhibits criticality through occasional punctuated equilibria and emergent, non-linear properties (such as earthquakes). Fluctuations should be expected and are healthy! They are the most efficient way to run a dynamic system. Complexity can arise out of simple laws with no outside help and is seen all over nature. Chaos is not complexity.

Thinking in Systems: A Primer 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
  2. Systems thinking transcends disciplines and cultures and when it is done right, it over arches history as well
  3. 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)
  4. 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
  5. 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)
  6. Archetypes – common structures which produce characteristic behaviors
  7. The behavior of a system cannot be known just by knowing the elements of which the system is made
  8. Stock – accumulation of material over time, a memory of the history of changing flows in the system
  9. Dynamics – behavior over time
    1. Dynamic equilibrium stays the same though it is always changing (inflows exactly equal outflows)
  10. 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
  11. 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
  12. Shifting dominance – one loop dominates and therefore drives behavior, oscillations and complex behavior
  13. Systems with similar feedback structures produce similar dynamic behavior
  14. 3 typical delays – perception, response, delivery
    1. These delays cause small changes to turn into massive oscillations
  15. 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)
  16. 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
  17. 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
  18. Non-linear relationships do not change in proportion and changes the relative strength of the feedback loops (shifting dominance)
  19. 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
  20. Most important input in a system is the one that is most limiting
  21. Growth itself depletes or enhances limits and therefore changes the limits themselves
  22. 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
  23. 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 
  24. 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.
  25. Leverage point – point in system where a small change can lead to big shift in behavior (MAKE THIS A MENTAL MODEL)
    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
  26. Systems can’t be controlled but they can be designed and redesigned 
  27. 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

Complexity: A Guided Tour by Melanie Mitchell

  1. Seeks to explain how large scale, complex, organized and adaptive behavior can follow from simple rules among many individuals
Key Takeaways
  1. Complex systems – a system in which large networks of components with no central control and simple rules of operation give rise to complex collective behavior, sophisticated information processing and adaptation via learning or evolution
    1. How large numbers of relatively simple entities organize themselves, without benefit of any central controller, into a collective whole that creates patterns, uses information, and, in some cases, evolves and learns.
    2. Many simple parts are irreducibly entwined, and the field of complexity is itself an entwining of many different fields
    3. Systems in which organized behavior arises without an internal or external controller or leader are sometimes called self-organizing. Simple rules produce complex behavior in hard-to-predict ways, the macroscopic behavior of such systems is sometimes called emergent
    4. Another definition of complex systems – a system that exhibits nontrivial emergent and self-organizing behaviors
    5. Order is created out of disorder, upending the usual turn of events in which order decays and disorder (or entropy) wins out. A complete account of how such entropy-defying self-organization takes place is the holy grail of complex systems science
    6. Brain network, ants, immune system, world wide web, economy are all excellent examples of complex systems
      1. Ants are one of the simplest organisms but when millions of them are working together they can achieve “collective intelligence”
      2. Brains, like ant colonies, have billions of neurons (ants) working in parallel without central control
      3. Information processing has taken an ontological meaning similar to mass/energy, namely as a third primitive component of reality. In biology in particular, the description of living systems as information processing networks has become commonplace.
        1. Information processing seems to play a leading role in natural systems – immune system, ant colonies, cellular metabolism
    7. Prediction of complex systems impossible as can never know starting conditions precisely and small changes lead to huge differences in outcomes
      1. However, there are universal traits to chaotic systems: period doubling route to chaos (bifurcation) and Feigenbaum’s constant
  2. Revolutionary ideas from chaos
    1. Seemingly random behavior can emerge from deterministic systems, with no external source of randomness
    2. The behavior of some simple, deterministic systems can be impossible, even in principle, to predict in the long term due to sensitive dependence on initial conditions
    3. There is some “order in chaos” seen in universal properties common to large sets of chaotic systems
  3. Dynamical systems – description and prediction of systems that exhibit complex, changing behavior emerging from interaction of many components
  4. Nonlinear system – whole is different from the sum of the parts
    1. Attractors – fixed point, periodic, chaotic (logistic map)
  5. Entropy – energy which can’t be converted to work and turns to heat
    1. The second law of dynamics is said to define the “arrow of time” in that it proves there are processes that cannot be revised in time (heat spontaneously returning after work is done). The “future” is defined as the direction of time in which entropy increases. Why the second law of thermodynamics is different from all other physical laws in that it should distinguish between the past and future while all other laws of nature do not is perhaps the greatest mystery in physics
  6. Thermodynamics describes energy’s interaction with matter
  7. Reductionism great but it fails (so far) to explain chaos theory. Anti-reductionism systems are situations where the whole is more than the sum of its parts. Chaos theory, systems biology, evolutionary economics and network theory move beyond reductionism to explain how complex behavior can arise from large collections of simpler components. These disciplines require multi-disciplinary thinking from fields such as cybernetics, synergetics, systems science and complex systems
  8. How does intelligence and consciousness arise from nonmaterial and nonconscious substrates?
  9. Statistical mechanics – proposes that large-scale properties (heat) emerge from microscopic properties (motion of trillions of molecules)
  10. Information is processed via computation
  11. Turing’s accomplishments – defined notion of “definite procedure”; definition, in the form of Turing machines, laid the groundwork for the invention of electronic programmable computers; showed what few ever expected in that there are limits to what can be computed
  12. Darwin had single best idea ever – “in a single stroke, the idea of evolution by natural selection unifies the realm of life, meaning and  purpose with the realm of space and time, cause and effect, mechanism and physical law.”
    1. Evolution gives appearance of design with no “designer”
  13. Self-reference in DNA – complex cellular machinery – mRNA, tRNA, ribosomes, polymerases and so forth – that effect the transcription, translation, and replication of DNA are themselves encoded int that very DNA
    1. It is both information and input!
  14. Shannon entropy – one simple measure of complexity is size so Shanon entropy is the average information content or “amount of surprise” a message source has for a receiver
    1. The most complex entities don’t have the most order or randomness but fall somewhere in between
  15. Fractals have non-integer dimensions. Koch curve has 1.26 dimension and this is what makes them so strange
  16. Simon contends that evolution can design complex systems in nature only if they can be put together like building blocks – have hierarchy and are non-decomposable. Cell can evolve to become a building block for a higher level organ, which itself can become a building block for an even higher-level organ and so forth
  17. Most agree life includes autonomy, metabolism, self reproduction, survival instinct and evolution and adaptation
    1. Dual use of information (as instructions AND data) avoids self-referential loop (how DNA replicates)
    2. Van Neumann proved in principle that computers can self-replicate
    3. Holland studied if programs could breed, adapt and evolve (professor at UMich)
  18. Genetic algorithm – output is solution to a problem
    1. Job of genetic algorithm is to find (evolve) to good strategy once encoded
    2. Many real world applications and solves problem often hard for people to see why it works
  19. Parallel traced scan – many, if not all, complex systems in biology have a fine grained architecture, in that they consist of large numbers of relatively simple elements that work together in a highly parallel fashion. Several possible advantages arise out of this type of architecture including robustness, efficiency and evolvability. One additional major advantage is that a fine-grained parallel system is able to carry out a parallel traced scan which is a simultaneous exploration of many possibilities or pathways in which the resources given to each exploration at a given time depend on the perceived success of that exploration at that time. The search is parallel in that many different possibilities are explored simultaneously, but is “terraced” in that not all possibilities are explored at the same speeds or to the same depth. Information is used as it is gained to continually reassess what is important to explore
    1. Allows many different paths to be explored and allows the system to continually change its exploration paths since only relatively simple micro-actions are taken at any time
    2. The redundancy inherent in fine-grained systems allows the system to work well even when the individual components are not perfectly reliable and the information available is only statistical in nature. Redundancy allows many independent samples of information to be made and allows fine-grained actions to be consequential only when taken by a large number of components
    3. Continuous interplay of unfocused, random explorations and focused actions driven by the system’s perceived needs. Early explorations, based on little or no information are largely random and unfocused. As information is obtained and acted on, exploration gradually becomes more deterministic and focused in response to what has been perceived by the system.
    4. This balancing act between unfocused exploration and focused exploitation has been hypothesized to be a general property of adaptive and intelligent systems
  20. Meaning – the meaning of an event is what tells one how to respond to it
  21. Computers, unlike humans, lack sensitivity to context, a lack of ability to use analogies
    1. Humans are very good at perceiving abstract similarities
  22. Idea Models – relatively simple models meant to gain insights into a general concept without the necessity of making detailed predictions about any specific system
    1. Maxwell’s demon – exploring the concept of entropy
    2. Turing machine – defining “definite procedure” and exploring computation
    3. Logistic model and logistic map – minimal models for predicting population growth, dynamics and chaos in general
    4. Von Neumann’s self-reproducing automaton – exploring the “logic” of self-reproduction
    5. Genetic algorithm – exploring the concept of adaptation. Sometimes used as a minimal model of Darwinian evolution
    6. Cellular automaton – complex systems in general
    7. Koch curve – exploring fractal-like structures such as coastlines and snowflakes
    8. Copycat – human analogy making
  23. Prisoner’s dilemma – pursuit of self-interest for each leads to poor outcome for all
    1. Tit for Tat is the best strategy with the first being cooperation
    2. Predictability is important for cooperation
    3. Close proximity aids cooperation
  24. People have poor intuitive understanding of coincidence
  25. Network thinking will permeate through all human activity and inquiry 
    1. Scale-free degree distributions, clustering and the existence of hubs are the common themes. These features give rise to networks with small-world communication capabilities and resilience to deletion of random nodes. Each of these properties is significant for understanding complex systems, both in science, technology and business
    2. Means focusing on relationships between entities rather than entities themselves
    3. A major discovery to date of network science is that high-clustering, skewed degree distributions and hub structure seem to be characteristic of the vast majority of all the natural, social and technological networks that network scientists have studied
      1. Hubs – high-degree nodes and are major conduits for the flow of activity or information in networks (Google)
      2. Small-world property – a network with relatively few long distance connections but has a small average path-length relative to the total number of nodes
        1. A network with 1,000 nodes, slightly rewired with random links brings down the average path length from 250 to 20…
        2. Evolved because information needs to travel quickly within the system and creating and maintaining reliable long-distance connections is very energy expensive. Nature has selected for it – robust, resilient, effective, efficient, energy-cheap…
        3. Web is scale-free, small world network (fractal) – relatively small number of very high-degree hubs (Google), nodes with degrees over a very large range of different values (heterogeneity of degree value), self-similarity
        4. Scale-free network = power-law degree distribution
    4. What seems to generate the complexity of humans as compared to plants is not how many genes we have but how those genes are organized into networks
    5. Focus on the hubs as that is where the power, influence, network, etc. falls to and relies on (Google, Facebook, GrubHub, LinkedIn, Zillow, Amazon, etc.) Winner take all systems!!
    6. Dangers of networks is that a small problem can quickly balloon into a major one if it is allowed to reach its tipping point
  26. Scaling – how one property of a system will change if a related property changes. The scaling mystery in biology concerns the question of how the average energy used by an organism while resting – the basal metabolic rate – scales with the organism’s body mass
    1. Metabolic rate proportional to body mass ^3/4
      1. Larger animals are more efficient than smaller ones and this leads to heart having to work less hard and the larger animal, on average, to live longer
    2. Circulatory system is fractal
    3. Metabolism is universal to all life so this touches every aspect of biology
  27. Evo-Devo (evolutionary development) – genetic switches main cause for large differences between species with very similar DNA. “Junk DNA” and allows for punctuated equilibrium in evolution
  28. Life exists at the edge of chaos
  29. Natural selection is in principle not necessary to create a complex creature. Once a network becomes sufficiently complex, that is, it has a large number of nodes controlling other nodes, complex and self-organized behavior will emerge
  30. Life has an innate tendency to become more complex which is independent of any tendency of natural selection
  31. Good, short overview of networks
What I got out of it
  1. Awesome book on chaos and complexity, how it arises, what its real-world implications are, how they might shape our world moving forward, the importance of networks and hubs, scaling, parallel traced scan, some idea models

Sync: How Order Emerges from Chaos in the Universe, Nature and Daily Life by Stephen Strogatz

  1. Strogatz describes the universality of sync in nature, human biology, social networks, etc. and how it might come to be. “For reasons I wish I understood, the spectacle of sync strikes a chord in us, somewhere deep in our souls. It’s a wonderful and terrifying thing. Unlike many other phenomena, the witnessing of it touches people at a primal level. Maybe we instinctively realize that if we ever find the source of spontaneous order, we will have discovered the secret of the universe.”
Key Takeaways
  1. At the heart of the universe is a steady, insistent beat – the sound of cycles in sync. It pervades nature at every scale and spontaneously, almost as if nature has an eerie yearning for order
  2. Spontaneous order baffles scientists as thermodynamics seems to predict the opposite – greater disorder and entropy rather than order
  3. Synchrony – explaining order in time. We interpret persistent sync as a sign of intelligence, planning and choreography and it gives humans intrinsic happiness to witness and be a part of something in sync
  4. Chaos – seemingly random, unpredictable behavior governed by non-random, determinate laws. Occupies an unfamiliar middle ground between order and disorder. Looks erratic superficially, yet it contains cryptic patterns and is governed by rigid rules. It’s predictable in the short run but unpredictable in the long run. And it never repeats itself: it’s behavior is non-periodic
    1. Linear = whole is equal to sum of the parts
    2. Non-linear = whole is greater than the sum of the parts
    3. Chaotic systems can sync! No rhythmic it’s (periodic cycles) and scrambling communication lines is one example
    4. Tends to exhibit self-organized criticality which leads to cascade effects as increasing pressure builds up and overcomes a threshold (earthquakes)
  5. Small world networks – most networks resemble each other in design with most everyone connected by a short chain of intermediaries with hubs having the most connections
    1. Small world networks are ubiquitous in nature, technology, social interactions, etc. They are resilient, robust, reliable, efficient, effective, cheap. Nature has selected for it
    2. At an anatomical level – the level of pure, abstract connectivity – we seem to have stumbled upon a universal pattern of complexity. Disparate networks show the same three tendencies: short chains, high clustering, and scale-free link distributions. The coincidences are eerie, and baffling to interpret 
  6. Structure always affects function. The structure of social networks affects the spread of information and disease; the structure of power grids affects the stability of power transmission. The same must be true for species in an ecosystem. The layout of the web must profoundly shape it’s dynamics
    1. Average path length (degree of separation) and clustering (how big, how incestuous) are two important factors
    2. Small-world networks are small networks and highly clustered, scale-free link distributions (brain, power grid, social networks)
  7. Phase transitions (tipping points) – “If the network is too sparsely connected, it fragments into tiny islands and cascades can’t spread beyond any of them. At a higher, critical level of connectivity – the first tipping point – the islands abruptly link together into a giant mesh and global cascades become possible. An initial seed can now trigger an epidemic of change that ultimately infects much of the population. With further connectivity, the cascades at first become even larger and more likely, as one might expect, but then – paradoxically – they become larger yet rarer, suddenly vanishing when the network exceeds a critical density of connections. This second tipping point arises because of a dilution effect: when a node has too many neighbors, each of them has too little influence to trigger a toppling of its own. The more neighbors there are, the less impact any one of them has in a fractional sense. Just before this second tipping point, the outcome is extremely unpredictable in much the same way real fads are. Seems highly stable and resistant to outside disturbances but then another fad comes along, seemingly indistinguishable from the first, yet this one triggers a massive cascade. In other words, near this second tipping point, fads are rare but gigantic when they do occur. A subset of connected nodes in the network, called the vulnerable cluster, shapes how fads percolate through the rest of the structure. The vulnerable cluster in humans are “early adopters.” Most people really in the “early and late majority” and not the vulnerable cluster but because the network is so densely connected near the second tipping point, a spark that happens to ignite the vulnerable cluster is able to create enough momentum to detonate nearly everyone else.”
  8. Nature uses every means to allow oscillators to communicate which leads to sync. Oscillators when they freeze into sync, line up in time, not space
  9. Fireflies self-organize with no conductor or intelligence – internal metronome and then adjust based on other firefly’s oscillators
  10. Even lifeless things can synchronize pulses and communicate. Pendulums sync through minute vibrations of the medium
    1. Inanimate sync stems from deepest laws of math and physics
    2. Lasers, power grids, pendulums, moon/earth, asteroids/planets, electrons all examples
  11. Poincare is considered the father of chaos theory
  12. Sync almost always occurs regardless of the number of oscillators or how it started
  13. In any population, oscillators must be somewhat similar or no sync occurs
  14. Great geniuses often have a vision for how the world should work, strip it to its essence and then search until they find it
  15. Human biological clock is like an enormous orchestra with the circadian pacemaker acting as the conductor. Sync at cellular level, sync between various organs and sync between our bodies and the world around us (entrainment)
  16. Without external cues, circadian rhythm a little longer than 24 hours and body temperature varies accordingly
    1. Nearly everyone desires a nap after being awake between 9-10 hours
    2. High alertness correlated with high body temperature
    3. 3-5am trough of circadian rhythm and body temperature
    4. REM tied to body temperature and not sleep (early morning has most REM)
    5. Many disparate rhythms controlled by same biological clock, the circadian pacemaker
    6. People are the least alert around 5am and between 1-4pm
  17. Serendipitous discoveries are always made by people who are focused and alert yet calm and relaxed. They’re searching for something but just happen to find something else
  18. Downside of sync is the domino effect of failure – becomes a vicious cycle which reinforces itself
  19. Accurate time allows for precise positioning (GPS relies on atomic clocks)
  20. Super conductivity is a type of perpetual motion machine which doesn’t defy thermodynamics due to electrons ability to pair up and sync
  21. Bose-Einstein condensate – near absolute zero, bosons will intermingle and act as one (quantum sympathy) and merge into one “super atom” and “sing in unison”
    1. Lasers are an example of technology relying on this principle
    2. Electrons are fermions (recluse) but once they pair, they become bosonic (gregarious)
  22. Fractions of a degree make all the difference in phase transitions (water freezing, electrons lining up for super conductivity)
  23. There is beauty and wonder in recognizing hidden unity
  24. A dumb rule in a smart architecture can achieve world-class results
    1. Importance of structuring properly aligned habits, incentives, environment, thoughts, actions, etc.
  25. Power laws naturally arise from network growth (Geiger Scale)
  26. People are terrible at estimating probabilities of rare events
  27. Evidence that insights occur when different parts of the brain sync. Some guess that thinking and consciousness is a byproduct of sync
What I got out of it
  1. Synchrony can be found universally from lasers to electrons to human biological clocks to pendulums. The sync of inanimate objects is an appearance of some of the deepest laws of math and physics at work. Small world networks, phase transitions, superconductivity. Structure always affects function – before trying to change behavior, look at the environment in which people are in and try to change that first

A More Beautiful Question by Warren Berger

  1. A beautiful question is an ambitious yet actionable question that can begin to shift the way we perceive or think about something and that might serve as a catalyst to bring about change
Key Takeaways
  1. Process: slowing down, stepping back, noticing what others miss, challenging assumptions (including your own), gaining a deeper understanding of the situation or problem at hand, question the questions you’re asking, taking ownership of a particular question and trying to shift perspective in order to see your own life – and the problems, opportunities and challenges worth tackling – more clearly
  2. “Why – What If – How” model for forming and tackling big, beautiful questions. It’s not a formula but more of a framework designed to help guide one through various stages of inquiry because ambitious, catalytic questioning tends to follow a logica progression, one that often starts with stepping back and seeing things differently and ends with taking action on a particular question
    1. Why – helps you question the status quo and see potential new possibilities; backwards steps
    2. What If – helps you see things other than they currently are; leaps of imagination
    3. How – helps you take your question and make it actionable; action, failure, iteration
      1. Must narrow down to most promising question at this point
      2. Fail fast, get feedback, improve, repeat – establish a minimum viable product
      3. Important to study failures but equally important to study and understand what went right. Am I failing differently each time?
  3. Great products, companies, innovations, industries stem from a single question
  4. Questions today are more important than answers but people are asking less and worse questions than ever before (much like “deep work”)
  5. Question everything! Fundamentals and own assumptions especially
  6. Questions often disrupt hierarchy which is why most companies don’t truly like it
  7. Best questioners refuse to accept current reality
  8. Tends to be inverse relationship between expertise and good questions. Most breakthrough innovations come from “outside the field”
    1. Important to stop “doing” and “knowing” in order to truly start asking
  9. Big step to go from questioning to determining to take action – must almost get to the point of desperation and realize that nobody else is going to do it if you don’t
  10. A good question is like a lever for effort and curiosity
  11. Must have an awareness of what we don’t know in order to ask great questions
  12. Questions open up, direct and focus thinking
  13. Open questions with the right tone is important to draw the most out of people
  14. Questions allow people to think and act in the face of adversity
  15. Innovative questioning – confronting, formulating and framing the initial question that articulates the challenge at hand and trying to get some understanding of the context
    1. Why does a present situation exist?
    2. Why does it present a problem or create a need or opportunity, and for whom?
    3. Why has no one addressed this need or solved this problem before?
    4. Why do you personally want to invest more thinking about, and formulating questions around this problem?
  16. 4 stage process of creativity
    1. Preparation
    2. Incubation
    3. Illumination
    4. Implementation
  17. Must be comfortable sitting with questions and unknowns for long periods of time
    1. New insights take time to percolate and form. Don’t rush this process!
  18. Combinatorial thinking / connective inquiry – thinking with both connections and questions in mind
  19. Neotemy – “beginner’s mind” allows you to see things without labels or assumptions. Detached from self, ego, patterns and allows for flexibility, creativity, no assumptions taken
    1. Must learn to withhold judgment while exploring new ideas and big questions
  20. Children are asking less questions due to too much structure at home and at school
  21. Today’s education system was designed during the Industrial Age in order to produce workers, not creative questioners who are self-learners and what the world needs today
  22. 5 learning skills or habits of mind
    1. Evidence – how do we know what’s true or false? What evidence counts?
    2. Viewpoint – how might this look if we stepped into other shoes, or looked at it from a different direction?
    3. Connection – is there a pattern? Have we ween something like this before?
    4. Conjecture – what if it were different?
    5. Relevance – why does this matter?
    6. Approach questions, situations, answers with skepticism and empathy
  23. Fear is the enemy of curiosity – importance of creating a calm, stable, reassuring environment at home and at work
  24. Ownership of a question is very important as it drives you to find the answer
  25. Helpful to be questioned because it forces you to simplify and synthesize your core ideas
  26. Question your own questions – often takes 5 consecutive “why’s?” To get to the core
  27. Narrow, broaden, reshape to yes/no questions are good techniques to help look at questions from different perspectives
  28. Context is important – get first hand experience to better understand what the true issues are
  29. You must quiet the logical mind sometimes to get to the core of issues and reach the true question which needs to be answered
  30. Multi-disciplinary learning with rests interspersed is very helpful. Must be able and willing to live with difficult questions for years and let it marinate in your subconscious
  31. Purposely trying to think “wrong”, what you want to avoid or not accomplish is often helpful
  32. Learn to rely on other’s expertise and know when to ask for help
  33. Real potential for breakthrough innovation tends to be at the low end of the market
  34. Strive for efficiency often reduces questions and big idea thinking – over celebration of simply getting things done
  35. Tend to do your best creative inquiry when you are relaxed, informal and not really trying
    1. Have informal brainstorms to generate questions over answers. “How might we?…”
  36. Critical for leaders to embrace ambiguity
  37. Mission question rather than a mission statement shows that the company is striving towards ambitious end and that it might never “get there” although it is their goal
  38. Nature abhors a vacuum and business hates ambiguity
  39. Great way to stimulate curiosity is by exposure to as many original ideas and unusual points of view as possible – outside teachers, brain questioning sessions…
  40. Must determine what people actually want and need rather than what you think they want and need
  41. Definite Chief Aim – mission statement, life goal, what you are all about, what makes you tick
  42. Make sure you’re climbing the right “mountain” by understanding the true why and that it’s aligned with your goals and values
    1. What is truly worth doing regardless of failure or success?
  43. There is no substitute for self-questioning
  44. A repeatable questioning and action process is key as you don’t just “find” answers to complex life problems. You work your way, gradually, toward figuring out those answers, relying on questions each step of the way
  45. Developing a family mission statement can be a good bonding exercise
  46. Questions can be propulsive, help generate momentum
  47. When you find your beautiful question, stick with it
  48. “Thinking means concentrating on one thing long enough to develop an idea about it. It’s only by concentrating, sticking to the question, being patient, letting all the parts of your brain come into play, that you arrive at an original idea.” – William Deresiewicz
  49. It’s easier to act your way into a new way of thinking than to think your way into a new way of acting
What I got out of it
  1. I love the “Why – What If – How?” framework, self-inquiry vital, 5 why’s, understand the why behind everything you do, figure out what you would do regardless of failure or success and go tackle it