Tag Archives: Chaos Theory

Resilience: Why Things Bounce Back by Andrew Zolli and Ann Marie Healy

  1. A resilient structure or system is one which can bounce back to its original form after some stimulus. This book describes how to make more resilient systems and businesses in order to better deal with our increasingly volatile world. Resilience is a common characteristic of dynamic systems which persist over time which is why most organisms embody characteristics of resilience to varying degrees
Key Takeaways
  1. Volatility is increasing and here to stay. The details are different but they share certain common characteristics and are always the result of many complex interactions. Can’t control this type of disruption but we can build better systems by making them more resilient, having the ability to rebound and adapt. Continuity and recovery in the face of change
  2. To improve your resilience is to increase the effort it takes for a stimulus to force you off your baseline while also increasing your ability to adapt and bounce back once it happens. Preserving adaptive capacity. Truly resilient systems change dynamically to achieve its purpose as well as the scale at which it operates. Diversifying the resources in which the system operates makes it more resilient to change as it allows for modularity. Diverse at their edges but simple at their core – modularity, simplicity and interoperability vital
  3. The ways to adapt and the stimuli which force change are both nearly infinite
  4. Resilience is not robustness – robustness typically entails hardening the assets of a business. Redundancy is keeping a backup but is not resiliency either. Resilience is also not the recovery of a system to its initial state.
    1. Think of a tree which is strong but has no give. It can withstand a lot until it snaps. This is robust but not resilient
    2. Now, imagine bamboo. It is thin, flexible and can return to its original state given pretty much any wind. This is resilience
  5. Failures are often helpful to release resources and reset and trying to stop these small failures make systems more fragile and will eventually lead to a massive failure. A seemingly perfect system is often the most fragile and the one which fails often but in small ways may be the most resilient
  6. Psychic resilience comes from habits of mind and is able to be learned and improved upon over time.
    1. Optimism and confidence are some of the best traits to deal with depression and to become more resilient
    2. People exhibiting ego-resilience and ego-control are best at delaying gratification, being resilient and overcoming obstacles
    3. Hardiness – believe can find a meaningful purpose in life, one can influence one’s surrounding and events, both positive and negative events will have lessons one can learn from. People of faith tend to be more resilient partially due to their “hardiness”
    4. Mindfulness meditation is a great tool to improve our resilience as it helps us create a space between our events, thoughts, emotions – an external “witness observer”
  7. Strong social resilience is found in societies with a lot of trust, a translational leader at it’s core promoting adaptive governance
  8. Holism – bolstering the resilience of only one part of the system sometimes adds fragility to another area. To improve resilience you often need to work in more than one mode and one scale and one silo at a time. Take the granular and the global into account simultaneously
  9. 4 stages of adaptive growth – Fast growth (resources coming together), conservation (efficiency of resources used but less resilience), release (fall of system), reorganization (process starting over)
  10. Robust yet fragile – systems which are resilient to anticipated danger or change but not to the unanticipated. It is often thousands of small decisions which aggregate rather than one massive event which brings down a system
  11. Must be able to measure health of a system as a whole and not just its pieces to know if fragility is sneaking in
  12. In risk management, risks tend to be modeled as additive but in reality they are multiplicative. One failure makes future failures multiples more likely
  13. Signs of a system flip – becomes unstable near its threshold, too much synchrony or agents acting in union (over correlation and people must make similar choices to survive)
  14. The timing of force, change and its effects is often more important than its scope
  15. Real time data, better monitoring and isolation upon any sign of cascading failure are three important design features
  16. Protocols are the lingua Franca of systems
  17. There are universal scaling laws for biological organisms so that the larger the organism the slower the metabolism and the longer the average life span. The power of clustering comes from a similar phenomenon but in the case of cities, the larger they get, the “faster” they become and the average income increases but certain quality of life markers decrease – there are increasing returns to scale, super linear scaling. However, as this part of life increases, the pace of innovation needs to speed up too or else the city may spiral downwards. The increasing diversity helps with this
  18. Respect is the cheapest concession you can give in relationships and negotiation. It is also a positive sum trait where your dispersal of respect only increases the total
  19. Improving resilience is not about removing every possible disturbance. In fact, facing challenges which test you or your organization are vital. They show where improvements need to be made and can clear the path for creative destruction
What I got out of it
  1. A thorough overview of what resilience entails and many examples of both fragile and antifragile people, ecosystems, institutions, organizations and more

At Home in the Universe by Stuart Kauffman

  1. Natural selection is important, but it has not labored alone to craft the fine architectures of the biosphere. Self-organization is the root source of order and is not merely tinkered, but arises naturally and spontaneously because of the principles of self-organization. Self-organization works together with natural selection to help shape and drive evolution in species
Key Takeaways
  1. Science has taken away our paradise – purpose and values are ours alone to make – job today is to reinvent the sacred and Kauffman believes that complexity may contain the answer
  2. Complexity suggests that not all order is accidental and is responsible for much of the spontaneous order seen throughout the world
    1. May lie at the heart of the origin of life and leads to order found in organisms today
    2. Life, therefore, is to be expected and is not an accident if it arises out of fundamental self-organizing principles
    3. Spontaneous order and natural selection have always worked together
  3. Second law of thermodynamics – order tends to disappear in equilibrium systems
  4. Best models explain and predict but failure to predict does not equal failure to understand or explain, especially with chaotic systems. Can find deep theories without knowing every detail (don’t have to know every detail of ontogeny (development of an adult organism) but we can understand it – spontaneous order which then natural selection goes on to mold)
  5. For most systems, equilibrium = death
  6. Order for free – order arises spontaneously and naturally and leads to self-organized systems and emergent properties
    1. Life would then be able to emerge full-grown from a primordial soup and would not need to be built one component at a time – life emerges whole and not piece meal
    2. Life is a natural property of complex chemical systems and that when the number of different kinds of molecules in a chemical soup pass a certain threshold, a self-sustaining network of reactions – an autocatalytic metabolism – will suddenly appear
      1. Life did come from non-life – reduces biology to physics and chemistry
      2. Must pass the subcritical / supracritical threshold
    3. Life exists in between order and chaos – in a kind of phase transition where it is best able to coordinate complex activities and evolve
    4. The very nature of coevolution is to attain this edge of chaos, a self-organized criticality, a web of compromises where each species prospers as well as possible but where none can be sure if its best next step will set off a trickle or a landslide
      1. This world does not lend itself to long-term prediction, we cannot know the true consequences of our own best actions. All we players can do is be locally wise, not globally wise
  7. All living things seem to have a minimal complexity, below which it is impossible to go
    1. Matter must reach a threshold of complexity in order to spring to life – this is inherent to the very nature of life
  8. Living organisms are autocatalytic systems – living organisms began as a system of chemicals that had the capacity to catalyze its own self-maintaining and self-reproducing metabolism once a sufficiently diverse mix of molecules accumulates. Once this threshold is reached, a vast web of catalyzed reactions will crystallize. Such a web, it turns out, is almost certainly autocatalytic – almost certainly self-sustaining, alive. Life emerges as a phase transition once the subcritical threshold of reactions to chemicals is breached
    1. The spontaneous emergence of self-sustaining webs is so natural and robust that it is even deeper than the specific chemistry that happens to exist on earth; it is rooted in mathematics itself
    2. There is an inevitable relationship among spontaneous order, robustness, redundancy, gradualism, and correlated landscapes. Systems with redundancy have the property that many mutations cause no or only slight modifications in behavior. Redundancy yields gradualism. But another name for redundancy is robustness. Robust properties are ones that are insensitive to many detailed alterations. Robustness is precisely what allows such systems to be molded by gradual accumulation of variations – the stable structures and behaviors are ones that can be molded
  9. Homeostasis, the ability to survive small perturbations, required for life to survive
  10. Small-world, sparsely connected networks are extremely efficient at connecting agents and trend toward internal order
  11. Complexity – orderly enough to ensure stability but flexible enough to adapt and exhibit surprises – evolution takes life to the edge of chaos
    1. Organisms evolve to the subcritical-supracritical boundary which exhibit a power law distribution of events
  12. Be smart by being dumb – have a huge sample set and choose what serves your purpose (don’t be ideological, go with promising evidence over beautiful theory)
  13. Immune system is a universal tool box – ability to produce 100m + antibodies allows you to recognize and respond to any threat
  14. Cambrian pattern of evolution – It is a general principle that innovations are followed by rapid, dramatic improvements in a variety of very different directions followed by successive improvements that are less and less dramatic.
    1. Learning curve – After each improvement, the number of directions for further improvement falls by a constant fraction – an exponential slowing of improvement (applies to technology, evolution, business, mastering skills, any improvement!)
      1. The more complex the system, the more difficult it is to make and accumulate useful drastic changes through natural selection
      2. Correlation length – taking massive jumps can lead to fitter mutations if land at a fitter peak – explore and try vastly different areas to possibly get outsized rewards (deep fluency in many fields and iterate constantly with small bets and pursue promising areas – parallel traced scan)
        • When fitness is average, the fittest variants will be found far away but as fitness improves, the fittest variants will be found closer and closer to the current position. Expect to find dramatically different variants emerging during early stages of an adaptive process but later the fitter variants that emerge should be ever less different
        • When fitness is low, there are may directions uphill. As fitness improves, the number of directions uphill dwindles. Thus we expect the branching process to be bushy initially, branching widely at its base, and then branching less and less profusely as fitness increases
  1. Optimal solutions to one part of the overall design problem conflict with optimal solutions to other parts of the overall design. Then we must find compromise solutions to the joint problem that meet the conflicting restraints of the different subproblems
  2. Coevolution itself evolves over time as fitness landscape changes – maybe towards Red Queen or Evolutionary Stable Strategy
    1. Evolution pushes towards edge of chaos, towards phase transitions
    2. Highest fitness occurs right between chaos and order
  3. Mill-mistake – mistaking the familiar for the optimal
  4. A central directing agent is not necessary to life, life results as an emergent property
  5. The tools we make help us make tools that in turn afford us new ways to make tools we began with
  6. Technological revolution is coevolution – niche creation and combinatorial organization
    1. Diversity begets diversity and growth but must first cross the supracritical threshold to hit the autocatalytic phase transition
      1. Diversity (resources, goods, trade, skills, etc.) great predictor of economic growth
  7. Patch Procedure
    1. Take a hard, conflict-laden task in which many parts interact and divide it into a quilt of nonoverlapping patches. Try to optimize within each patch. As this occurs, the couplings between part in two patches across patch boundaries will mean that finding a “good” solution in on patch will change the problem to be solved by the parts in adjacent patches… – models coevolving ecosystems
      1. If a problem is complex and full of conflicting constraints, break it into patches and let each patch try to optimize such that all patches coevolve with one another
    2. May not give us the solution to the real problem but may teach us how to learn about the real problem, how to break it into quilt patches that coevolve to find excellent solutions
    3. Ignoring certain subsets of restraints may be helpful at times – should not please all of the people all of the time but you should pay attention to everyone some of the time
What I got out of it
  1. Spontaneous self-organization is a deep, fundamental principles of math, physics, life. Order for free, patch procedure, learning curves and the Cambrian diversity principle, subcritical and supracritical threshold breach is the same thing as phase transition, all we can do is be locally wise and not globally wise since our system is too complex to predict

On Complexity

I spent a couple months reading deliberately on complexity and its many off-shoots and applications. After summarizing the books I read and wrangling with the concepts for some time, I have attempted to make a distilled “teacher’s reference guide” or cheat sheet which (hopefully) describes the key terms and ideas in a clear, concise and applicable manner.

On Complexity

*The vast majority of the content is from the books and not my own words. I’ve simply distilled, compiled, and added a few notes. This is clearly my amateur attempt which I’m sure has many points that experts would refute or disapprove of. Please reach out with any suggestions as I plan to iterate and improve this document over time.

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

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

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

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.

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