Tag Archives: Chaos Theory

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

Summary
  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

Summary
  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

*This is clearly my amateur attempt which I’m sure has many points that experts would refute or disapprove of.  I will continue to iterate, add to and improve this document over time. I have found this topic to have widely universal appeal and applicability and hope you find it as helpful and interesting as I have!

Hidden Order: How Adaptation Builds Complexity by John Holland

Summary
  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

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

Investing: The Last Liberal Art by Robert Hagstrom

Summary
  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

Summary
  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

Summary
  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

Summary
  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

Summary
  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