A bit too heavy on biographical details and the history of the Santa Fe institute, and not going deep enough into the weeds of complexity theory as I was hoping, when I first picked it up.
This is a wonderful introduction to all the people that contributed to the field, and amounts to a vibrant list of books, and articles and papers that I now want to read as well. This books, just opens the door, to go out and search for much much more, inviting the reader down into a wonderful rabbit hole.
The key idea is really, that complexity research, and the idea of emergent properties of systems, can be found everywhere, over and over and over again, from weather patterns, to human societies, to physical laws, to evolution and the emergence of life itself. It's everywhere and the mathematics that govern these systems, reflect the dynamic and everchanging nature of them, away from strict formalism, towards computational models and methods, embracing that analytical solutions won't be found for things that thrive at the edge of chaos.
You should pick up this book if you find things like brains, ant hills, economics, consciousness, cellular automata and the connections between them interesting. Because then this book will give you a spectacular, albeit not very deep overview of the theories that bind all of these and more together under a beautiful frame of thoughts and theories.
Visions of the Whole
Complexity research tackles questions that defy categorization.
Lots of different questions: Why did the Soviet Union collapse? Why the black Monday collapse on Monday? Why do species die out almost instantaneously? How did life arise? How did multicellular life arise? Why is there cooperation between individuals? How did eyes and kidneys evolve? What are minds? How does the brain give rise to the mind? Why is there a universe at all? And why does it have structure?
All of those are related by their complexness. They are governed by systems made up of many agents, interacting. Such systems can undergo spontaneous self-organization.
Groups of agents somehow manage to transcend themselves, acquiring collective properties such as life thought and purpose. — Mitchell Waldrop
These kind of systems exist at the edge between chaos and order. Complex enough to be surprising and new, but not so complex as to be unpredictable. That's why we find life among many other interesting phenomena here.
Chapter 1 - The Irish Idea of a Hero
A story about a revolutionary idea in economics that the "others" don't believe told from the view of Brian Arthur.
The Education of a Scientist
Building more hero story into it. Economics is an "Ersatz Physics". Mathematical Formalism trumps and hides the actual messiness of the world, man becomes a perfectly rational agent, markets have rigid rules by which they operate. But the real world is different! It's complex, and not easily modeled, the nuances are lost in the flawed assumptions that make the problem mathematically tractable.
Book Recommendation: The Population Bomb - Paul Ehrlich
Humans don't respond to economic incentives the way the economists would want them to. We are an irrational bunch. This brings to mind quite a few books Dan Ariely's Predictably Irrational as well as Daniel Kahnemanns Thinking Fast and Thinking Slow and Jonathan Haidts The Righteous Mind and of course also Nassim Taleb's The Black Swan. All of them deal with very similar ideas, how markets don't behave the way economist used to think they would, because humans are far from being rational, we have lots and lots of biases influencing our thinking, putting it far far away from the optimal choice in most cases.
Epiphany on the Beach
Book Recommendation: The Eight Day of Creation - Horace Freeland Judson
Energy put into a system can locally overcome entropy. Life is such a system, that builds complexity along that energy gradient.
Book Recommendation: Check out Ilya Prigogines writings
Positive reinforcement or positive feedback loops are what constitute self organizing systems, such as the economy, life, convection cells etc.
Negative and positive reinforcement together, two forces pushing and pulling in opposite directions create bistable order. Patterns. It's there at the edge of chaos and order, where complexity thrives.
Economies are complex systems arising out of the interactions between humans, reinforcement effects exist, market leads tend to amplify, successful regions attract yet more talent, but the market forces also lead to decay and dying of old structures, competitors can catch up, things shift and are ordered yet chaotic.
What's the point?
The essence of science lies in explanation, laying bare the fundamental mechanisms of nature. — Mitchell Waldrop
Economies of scale as well as lock in effects are real things in economy. Back then those seemed to be new ideas. High tech markets are different from more traditional markets because winner takes all effects are much stronger, thinking about internet things and network effects these ideas get even more amplified and you get giants like Google that classical economies couldn't predict.
Book Recommendation: Principles of Economics - Alfred Marshall
The idea of lockins chosen at random from lucky chance breaks reminds me a lot of the idea of Evolutionarily Stable Strategies from The Selfish Gene.
Violating Sacred Ground
The idea of free markets producing optimal outcomes is tied to the political idea of personal freedom, if you accept that free markets might not produce optimal outcomes you also endanger the idea of personal freedom being good.
An idea that hasn't been published in an established journal doesn't officially exist. — Mitchell Waldrop
Chapter 2 - The Revolt of the Old Turks
The Santa Fe Institute pushed for a new kind of science - complexity science.
The culture of science does not mix well with the culture of politics. — Mitchell Waldrop
The president of the Santa Fe Institute was George A. Cowan, who played a crucial role in helping to develop better nuclear weapons for America. He thought that scientists lost a chance to take responsibility for their actions when they created the atomic bomb and that's one of the reasons why he founded the Santa Fe Institute. So that scientists can regain a position of power and redeem what has gone wrong in politics with their creations. It was about creating a sense of mission for scientists. To make the world a better place, to revolutionize the way science had been done.
Science cuts up the world into small pieces to understand those pieces. But it looses the interactions between pieces, the real world aspects in doing so. Science is practicing intellectual fragmentation, don't get broad strokes of explanations right, don't go from Zero to One but instead, go from 1234 to 1235. Very specific, small changes is how academia works, you don't think about the grand scale of things and connections between subjects, but just about your own narrow very small and perfectly defined world of a problem, that you try to solve to write a research paper that the 10 or so colleagues of yours can read and understand. Science in that way (more accurately the scientific establishment with it's papers, grants and Phd committees), is broken at it's core.
This fragmentation started to glue back together a little bit with the rise of computers and interdisciplinary work, between physicists and biologists for example. Computers could be used to simulate the messiness of the real world and have much more variables vary in equations that then form interesting behavior you wouldn't have expected from the raw equations alone. Chaos and Complexity theory have their origins here.
Nonlinear Dynamics! Something to dive in an learn more about.
Linear systems don't interact with themselves. Nonlinear systems do. And solving nonlinear equations without numerical approximation on a computer is pretty much impossible.
Another word I have to look up: Solitons
The Santa Fe Institute was born with the idea to glue together the fragmented pieces of the university departments again, to unite everything under one umbrella - complexity research, and have truly interdisciplinary work being done on that subject.
The group thinking about the potential Santa Fe Institute can't agree on what it should be about.
Murray unifies their efforts and gives the whole idea the vision it needed.
George Cowan becomes the head of the institute. The institute slowly forms.
More is Different - Science Magazine Article by Phil Anderson
Reductionism is necessary and good, however, world equations are not enough to explain emergent phenomena. There is something about a liquid that isn't covered by the particle physics equations. And that something is worth studying.
The universe is forming a kind of hierarchy. At each level of complexity, entirely new properties appear. And at each stage, entirely new laws, concepts, and generalizations are necessary, requiring inspiration and creativity to just as great a degree as in the previous one. Psychology is not applied biology, nor is biology applied chemistry. — Phil Anderson
The most interesting problems are at the intersection, the transition between levels, unifying the concepts together.
Phil Anderson joins.
What am I doing here?
Many "agents" fighting and cooperating with each other according to simple rules produce complex behavior. Molecules form cells, neurons form brains, corporations form economies, species form ecosystems etc.
Complexity Science -> science of emergence Question: What are the fundamental laws of complexity?
John Reed, CEO of Citibank enters the picture, asking the Santa Fe Institute to develop a better model for predicting the world economy more accurately.
Chapter 3 - Secrets of the Old One
Researcher: Stuart Kaufmann
Life is about order from chaos, self organization towards higher complexity in a world governed by entropy.
The strange thing isn't that birth defects happen, the strange thing is that most babies are born perfect and whole. — Stuart Kauffmann
Genes can be turned on an off forming a biochemical computer, that can react in real time. They form something like networks.
The idea of networks based on switches transcends genes and neurons and computers. That's one part where complexity theory shows, where it is substrate independent, just like computation and informational storage. See Max Tegmark Life 3.0
Networks have to be connected not to densely but also not too sparsely to show interesting patterns and behaviors. Two input networks stabilize into a sort of pattern usually. Randomly connected two input networks should just flicker almost randomly, in millions of different states, but that's not actually what they do, they "cycle" from one state to another. Typically these states are semi stable, maybe 10 or so for a network of 100 nodes.
Lookup: A Logical Calculus of the Ideas Immanent in Nervous Activity - Warren MCCulloch
Death and Life
Technology isn't really like a commodity at all. It is much more like an evolving ecosystem. Innovations never happen in a vacuum. — Brian Arthur
Technologies form interconnected evolving webs. Very similar to the neural and genetic networks from earlier. Parts of the network can "die out" if there connection to the rest is severed. More connections are added, some fade, and the weightings of this fading in and out is determined by the network itself.
Technological change is like the origin of DNA based life.
Book Recommendation: Design for a Brain - Ross Ashby
Networks can be modeled by nonlinear dynamics and the "stable" states are usually called attractors, think Lorentz Attractor.
In a pond of very simple organic chemistry there might be catalyzing loops giving rise to more complexity over time, because self catalyzing loops would in a way produce more of themselves, gradually growing in complexity, when steps of the catalyzation randomly acquired mutations that speeded up the process. Such a network/web of catalysing chemicals form an "autocatalytic set". In a way these webs are alive. Order arises for "free" from chemistry. Autocatalytic sets almost have to form in a pond with organic chemistry molecules in it, because the possible catalytic interactions grows exponentially with every new molecule added. Autocatalytic sets are little economies, converting "food" molecules into useful "products" - more of the set. Beyond thresholds of complexity - phase transitions happen. This reminds me of the Jump to Universality from The Beginning of Infinity. Trade raises complexity of economies so that they can undergo phase transitions and explode in complexity. Adding new variants - new technology to economies or new molecules to the chemical pond, can have tremendous consequences, because they change the possible interactions, sometimes drastically.
Book Recommendation: Chemical Evolution - Melvin Calvin
Discovering the secrets of the Old One is a phrase Einstein used to talk about science and the exploration of our universe. Complexity and how it forms "magically" as autocatalytic sets is such a secret.
Chapter 4 - You Guys Really Believe That?
Physicsts are confused by how economists can study the world with so much mathematical rigor, yet don't see the forest for the trees. Namely that their assumptions, necessary to make the problem mathematically tractable, moove their models infinitely far away from reality. "You're solving the wrong problem if that's not reality."
Pride in such an interdisciplinary group is super tough and hard to overcome. But it has to be overcome if true cooperation is to be achieved. If either group show the attitude of "hey kids, your stuff is easy, give us just a week to figure out" it's over.
Chapter 5 - Master of the Game
The Global Economy as an Adaptive Process by John H. Holland – couldn't quite find that talk specifically, however found this more modern talk instead.
There is also this paper on Complex Adaptive Systems by John Holland which came up with in Google.
Complex adaptive systems - agents interacting with each other, ever hanging environment, control is spread through all the agents - no single person knows how Paris is supplied with food, yet it is supplied with food. Cooperation and competition between agents produces the control, the behavior of the system. Whole systems can be the agents of new systems, a hierarchy level up. These systems also rearrange their parts over time, they adapt and evolve. They also try to anticipate the future. Building blocks can include "behavioral" patterns played out in the system as a whole if confronted with certain stimuli from the changing environment. These patterns of behavior also evolve. Within such systems are niches that can be filled by particular agents. Niches are dynamic, a niche being filled, often creates new niches. Adaptive systems therefore can't stabilize, the stability is only temporary until the system finds a better way to exploit it and improve, changing stability back to instability. Evolutionarily stable strategies only form so long until a mutation disrupts them.
Economics is like chess, simple rules giving rise to essentially infinite complexity and subtlety. There is no "perfect" play.
The Immense Space of Probabilities
In a world of circulating bits he could create an imaginary universe. All he had to do was to encode the proper laws and everything else would unfold. — Mitchell Waldrop
There it is again, this word, unfold, evolution is a continual unfolding of conplexity. This idea reminds me a lot of The Evolution of Everything.
Computers and brains are alike in a deeper and much more important sense: they are both information processing devices. — Mitchell Waldrop
Book Recommendation: The Organization of Behavior - Donald Hebb
Neurons organize themselves into cell assemblies, which can be overlapping and correspond to single thoughts, concepts, precepts. This behavior can be simulated with digital neurons in a computer.
Paper describing the idea of a von Neumann architecture - Preliminary Discussion of the Logical Design of an Electronic Computing Instrument
Another thing to read: Cycles in Logical Nets
Book Recommendation: Das Glasperlenspiel
Book Recommendation: The Genetical Theory of Natural Selection - R. A. Fisher
Evolution and learning are like games, they grow in complexity as they continue. There are rewards, certain states that are desirable, but attaining a "perfect" state with a perfect strategy is impossible. One can only get better.
Another technical paper: A Logical Theory of Adaptive Systems Informally Described
How can evolution explore the vast state of possible linked gene configurations to find improvements and adapt?
Genes can form self reinforcing autocatalytic sets and clusters. It's not just genes, but any kind of agent group that interacts with itself. Stable clusters within the group can become building blocks in even larger clusters. Building Blocks clustering forming new building blocks for something another level up, is emergence.
Layers of abstraction are a fundamental feature of the world. Because having solid building blocks means the combinatorial states of combining them in various ways are almost endless.
Hence evolution in nature finds a few good genes and then combines those so they interact nicely with each other, that way forming lots of different animals.
Idea of a Genetical algorithm harvests that combinatorial power to find novel solutions to hard problems. Essentially the binary digit machine instructions are the DNA of a Program and you can randomly shuffle it for mutations and implement natural selection and differential reproduction of these programs. Sexual recombination of those program "genes" can then find novel solutions that are still similar to the old solutions, gradually improving a gene that performs well into a better and better building block.
Emergence of Mind
Book Recommendation: Adaptation in Natural and Artificial Systems - John Holland
Adaptation in the mind and adaptation in nature are just two different aspects of the same thing. — Mitchell Waldrop
Agents play games with the environment, they try to predict it while the environment gives feedback. Predictions need models of the world. Bacterias have implicit models encoded in their DNA, things about the environment that can be exploited. Every skill humans have is an implicit model.
These implicit models, can be summarized as knowledge or "affordances" as Daniell Dennett would call them. They encode reasons - why things behave in a certain way. The book From Bacteria to Bach and Back deals with the question how affordances could arise from things that don't have them - i.e. how do molecules come to build implicit models with a lot of encoded knowledge and reasoning about the world? How can competence give rise to comprehension and how can both be born from a primordial soup?
The answer in short - evolution.
Selection on random combination produces insight.
Feedback loop between agent with random thoughts/behavior and environment creates knowledge.
Thinking and learning are two aspects of the same thing in the brain. — Mitchell Waldrop
Concepts and percepts are emerging structures from the constant adjusting of weights of neuronal connections.
Those concepts might disagree, picking one over the other, i.e. controlling behavior from conflicting/messy input, is also a learned, emergent property of neural networks.
Competition and cooperation may seem antithetical, but at some very deep level, they are two sides of the same coin. — John Holland
Classifiers interacting, in a kind of marketplace, posting bids for the action that should be taken, being taxed for that bid from all the other classifiers the bid relied on and only rewarded by reality if their bid turned out to be good. Rewards spread down the chain of classifiers because some are really useful and build a "backbone" of the economy, almost like infrastructure, being heavily used by many many other classifiers to do their tasks. New classifiers can be randomly added to the mix, by randomly cutting apart and combining old rules.
This sounds a lot like the Dopamine system in the brain.
Book Recommendation: Induction - Richard Nisbett
A Place to Come Home To
Life at the Edge of Chaos
Epiphany at Massachusetts General
The Self-Assembly of the Brain
You just got squeezed out of the tube of high school onto the toothbrush of college. — Chris Langton
Book Recommendation: The Lives of a Cell - Lewis Thomas
I could see these disconnected patterns self-organize, come together, and merge with me in some way. It was as if you took an ant colony and tore it up, and then watched the ants come back together, reorganize, and rebuild the colony. — Chris Langton
The Evolution of Belief - Paper by Chris Langton
Book Recommendation: The Theory of Self Reproducing Automata - John von Neumann
Book Recommendation: Essays on Cellular Automata - A. W. Burks
Book Recommendation: Cellular Automata - Ted Codd
A system that can replicate itself needs two things - a constructor + a description copier. Genetic material has to serve two roles: Algorithm for construction + data that is passed down to offspring. DNA does both.
Cellular Automata - grid space, discrete time steps, state transition of grids between states, based on rules, taking into account surrounding states + own state. Special Case - Conway's Game of Life
Self Reproducing Cellular Automata can exist.
The Edge of Chaos
Connection of Cellular Automata to Nonlinear Dynamics, probably connected to Steven Wolfram's Ideas in A New Kind of Science.
4 Universality Classes:
- Doomsday, Single Point Attractors, Stable State
- Stagnation, but with a pattern/repetition, Periodic Attractors
- Too Lively, Chaos, Noise, "Strange Attractors"
- Lively things, never settling down, also not repeating, also not chaotic. They don't exist in dynamical systems /nonlinear dynamics theory
Rules are organized. 1 - 2 - 4 - 3 Order - Complexity - Chaos
Complexity and life exists at the edge between order and chaos, at a lambda value of 0.273. Where lambda is the average probability of a cell being dead in the next timestep.
Third analogy in matter, specifically in Second Order Phase Transitions. Islands of Solid in a Fluid, constantly dissolving and restructuring, but at just the right temperature, just the right lambda, continually perpetuating, growing, living? So cellular Automata undergo phase transitions mathematically speaking, when they increase in lambda values.
Complexity exists at the edge between chaos and order.
Computations can happen only in class IV cellular Automata. The universe is similar to a class IV automaton, hence it allows for life.
Life is to an incredible degree based on its ability to process information. It stores information. It maps sensory information. And it makes complex transformations on that information to produce action. — Chris Langton
Cellular Automata Classes: I & II -> IV -> III
Dynamical Systems: Order -> Complexity -> Chaos
Matter: Solid -> Phase Transition -> Fluid
Computation: Halting -> Undecidable -> Nonhalting
Life: Stasis -> Life/Intelligence/Computation -> Noise
Life is between order and chaos - Jordan Peterson has it eerily right in his book 12 Rules for Life he comes from a different place of reasoning but arrives at the very same conclusion.
Evolution is life learning to better balance on that edge + making the edge itself broader?
Go, Go, Go, Yes, Yes!
Book Recommendation: The Selfish Gene - Richard Dawkins
Chapter 7 - Peasants Under Glass
What is "true" emergence if everything boils down to the rules of physics? What about physics gives rise to chemistry and eventually life?
Use boid like agent based code to simulate economies.
The Fledgling Doctor
The Santa Fe Approach
Agents can improve rationality in economic settings, they learn to make better decisions if that's what's necessary to survive. Interestingly, to produce Zero to One kind of decisions one sometimes needs to think "outside the box" in other words, one has to act semi randomly.
Book Recommendation: The Eight Day of Creation
In real life, people have to make decisions in a complicated, muddled world, without all the date, limited time and resources, decisions that are far from optimal, with unforseen circumstances arising on the fly. We have to adapt plans and learn from the past, but sometimes, the past is not a good predictor of the future. That whole mode of learning is inductive as well as deductive, it's based on hunches and logic.
Prediction is not the essence of science. The essence is comprehension and explanation.
The Darwinian Principle of Relativity
Paper: Complex Adaptive Systems and Spontaneous Emergence by John Holland
Co-Evolution is an important property of evolutionary systems. It's dynamics create the richness of ecosystems, the subtle and beautiful interactions between organisms. Things like symbiosis and the ideas from Matt Ridley's "The Red Queen" come into play here.
Book Recommendation: The Evolution of Cooperation - Robert Axelrod
Tit for Tat as a stable and successful strategy in extended prisoners dilemma games. And it is self reinforcing over time.
See also Richard Dawkins book "The Selfish Gene" for a much more extended discussion of this topic.
Wet Labs for the Mind
The Stock Market is a form of artificial life.
Chapter 8 - Waiting for Carnot
The A-Life Papers
Book Recommendation: Santa Fe Institute Book on Complexity - Foreword by Chris Langton
Computation is substrate independent. It's not tied to a particular implementation within matter, any matter suffices to do it, as long as it is organized in a way that corresponds (models/stores) the information. Hence computers can do computations but so can DNA, or neural networks. That idea is also heavily treated in Max Tegmark's Life 3.0
The "machineness" of the machine is in the software, not the hardware.
The "aliveness" of an organism is also in the software - in the organization of molecules, not the molecules themselves.
Complex behavior need not have complex roots. — Chris Langton
Lifelike behavior arises from simple building blocks interacting.
Life is computation.
Generalized genotypes - programs - unfold by doing computation, into their generalized phenotypes. Instructions materialize as patterns in the environment.
Life is computation.
Programs are unpredictable, before the computation is run, we don't know what it is going to do.
Finding mappings between specific geno and phenotypes is therefore equally impossible to do. You don't know what the result of a program is before you run it. Trial and error (even mental trial and error) is the only way to go.
Computer viruses are a form of matter organized in the right way to be considered alive. They reproduce, they have metabolism, they react, they evolve. Only their ecosystem and material basis is different from us.
Book Recommendation: Frankenstein
The New Second Law
Paper: Artificial Life - The Coming Evolution - Doyne Farmer
Book Recommendation: The Final Question - Isaac Asimov
Norbert Wiener - Cybernetics, Ilya Prigogines - self-organization, Hermann Haken - synergetics
Why does the universe give rise to order, even though the 2nd law of thermodynamics works against it? Possible answer from Max Tegmark's Life 3.0 - Structure is a Subgoal of Replication, and Replication is a Subgoal of Dissipation.
Flying bouda adapt to the actions of their neighbors, thereby becoming a flock. Organisms cooperate and compete in a dance of coevolution thereby becoming an exquisitely tuned ecosystem. Atoms search for a minimum energy state by forming chemical bonds with each other, thereby becoming the emergent structures known as molecules. Human beings try to satisfy their material needs by buying, selling, and trading with each other, thereby creating an emergent structure known as a market. Humans likewise interact witheqch other to satisfy less quantifiable goals, thereby forming families, religions and cultures.
Paper: A Rosetta Stone for Connectionism - Doyne Farmer
Different models (classifier systems, autocatalytic sets, genetic algorithms, neural networks, cellular automata, immune system models, artificial stock markets etc.) can be mapped to networks of connected nodes. Emergence is a property of the way these networks are connected up.
The Edge of Chaos
These connected networks can live at the edge of chaos. Based on the connectivity they have, they either go off into chaos or into order or with just the right amount - into very complex, unpredictable yet interesting, sometimes patterned behavior. They can be at the phase transitions.
There exists something akin to a twin of the second law of thermodynamics. Namely one that describes the arising of order out of chaos. The arising of conplexity as a physical law.
The Growth of Complexity
Life exists at the infinitely thin region between order and chaos. Right at the edge. Evolution moves things closer to that edge. Sitting on that edge with high precision requires complexity.
The Arc of Howitzer Shell
Book Recommendation: The Origins of Order - Stuart Kauffmann
Paper: Adaptation to the Edge of Chaos
Living systems are only close to the edge of chaos. Natural selection is pushing them closer.
The edge of chaos is where useful computation can happen.
Is it the whole universe's purpose to move closer to that edge?
Many systems exist at another edge, that of criticality. A continuous input of energy, water, sand, strain or whatever into a system leads many into the state. Those systems follow power laws. Examples are avalanches on a pile of sand, a mass of plutonium or earthquakes. Waves of change in these systems follow power laws. Some waves are huge and disruptive, but they are rare. Punctuated equilibrium.
Ecosystems could be thought of as being in such a critical state as well. Long periods of silence, interspersed with some small changes, and some really big ones. That's what the fossil record also shows, relative stasis, followed by evolutionary explosions, every once in a while.
Maximum evolutionary fitness for all the organisms in an ecosystem exists at the edge of chaos. Therefore evolution moves towards complexity which also sits at that edge. Complexity is self-organizing, because it is beneficial to the organisms.
Why is life only existent (at least evidently so) at the level of chemistry?
- Variety. Chemistry is the only thing that can produce so many permutations that react with each other that it can become complex enough to move towards the edge of chaos meaningfully.
Interactions between subsystems produces autocatalytic behaviors, and autocatalytic groups can interact with other such groups on a "higher" level. Again forming autocatalytic groups at that higher level and so on, marching up layers of abstraction and going towards layered complexity.
Higher order complex entities are better at surviving, that's why they get selected for.
At Home in the Universe
Science is the accumulation of facts and data. It's the construction of logically consistent theories to account for the facts. It's the discovery of new materials and new technologies.
Life is the natural expression of complex matter.
Life is, on average, doing best, on the edge of chaos. We are at home in the universe.
Chapter 9 - Work in Progress
The Tao of Complexity
Science is about creating new metaphors, frames of mind, to make sense of the world.
Systems are inherently messy, and that messiness can not be reduced. Gödel Incompleteness and Turings Undecidability or Chaos Theory are examples.
Complex lifelike behavior is the result of simple rules unfolding from the bottom up.
The universe is unfolding, simple parts, always changing, recombining, shuffling, moving, living. It's complex and taoist in nature.
Observe then act, maximize for non-wasted motion.
It's not man against nature. People coexist within nature and therefore "maximizing" gain from it changes the ecosystem and we have to adapt to those changes. You forego optimization for robustness. In a way that difference sounds very familiar, from Nicholas Nassim Taleb's Antifragile.
The Hair Shirt
Stability is Death.
Book Recommendation: The Dance of Life - Havelock Ellis
It's a fundamental part of human nature to believe that we can shape the future.
A Moment in the Sunlight
Last chapter, closing words, nothing much of note here.