Partial solutions to the equations can predict the general location of the pathways that emerge in such a system, but the system of equations is said to be indeterminate because the exact future positions of the objects cannot be completely determined by the equations alone. (Location 182)
Though “unsolvable” in the strict mathematical sense of the term, such problems can be studied in depth through the relatively new approaches provided by complexity and chaos theory. (Location 185)
In these types of problems, small changes in the initial conditions result in wildly divergent outcomes. Faced with inconsistent results of this sort in his attempts to model weather systems, Lorenz coined the term “butterfly effect.” (Location 190)
patterns known as “strange attractors” do emerge as computer programs plot solutions to the equations. (Location 194)
Though unsolvable in the deterministic sense, we will see how strange attractors illustrate the path of emergence toward symmetry in living and non-living matter. (Location 195)
We will see how pathways of energy flow integrate through positive and negative feedback loops in the structure and behavior of complex adaptive systems. (Location 197)
To avoid the confusion generated by attempting to distinguish between living and nonliving systems in the traditional manner, the phrase “complex adaptive systems” will be used throughout this book to describe the coherent structures that emerge as the adaptive response to the environment at various levels of complexity. (Location 202)
will extend the meaning of metabolism to include any observed use of available energy by a complex adaptive system to maintain its dissipative structure. (Location 207)
Once the available energy is metabolized by the system, it can either collapse into the ground from which it emerged, or couple with another system in a symbiotic response to the depletion of energy in its environment. (Location 208)
The increased energy efficiency, formed in response to the depletion of an existing energy source, is the product of the resonant coupling of periodic oscillations in the metabolic pathways already established by the previously independent systems. (Location 211)
We further note throughout this book, that in nature, energy scarcity drives the emergence of more complex entrained systems. It is indeed the complexity and redundancy of the entrained information/matter networks of a complex system that gives it its selective advantage to persist in time. (Location 220)
It describes the retardation of energy dissipation through complex systems of information and matter. In this chapter, we explore the general process that leads to the stochastic emergence and selection of energy efficient, complex adaptive systems, as they evolve through fluctuations in Gibbs free energy, available in the environment. (Location 229)
It is demonstrated, that based on the principles of statistical mechanics, the interaction of complex systems with fluctuations in environmentally available energy, lead to the emergence of oscillating attractor basins, which direct the flow of energy to create Lorenz-type “strange-attractor paths.” (Location 233)
The law is called the conservation of energy. It states that there is a certain quantity, which we call energy that does not change in the manifold changes which nature undergoes. (Location 259)
However, this quantity was not universally conserved. In some cases, the motion of physical objects could be converted into heat, such as, for instance, in the act of rubbing our hands together to warm ourselves. Once the atomic theory was developed, heat was reinterpreted as a statistical quantity representing the vis viva of a large population of atoms. (Location 265)
There are two types of thermodynamic systems: open and closed. An open system, like most biological organisms, can exchange heat, energy, and matter with its environment, while a closed system, like a steam engine, can exchange heat and energy but not matter. (Location 276)
Carnot observed that energy does the same thing. Energy is in a state of equilibrium (equal balance) when it is evenly distributed throughout its environment.6 (Location 284)
If a system is composed of several subsystems, the entropy of the larger system is the sum of the entropies of the subsystems. S. is defined by the fact that when energy enters or leaves the system, the change in entropy is the ratio of the change in energy to the temperature of the system, (Location 288)
The fact that information can be understood using mathematical formulations which were developed to think about thermodynamics, also allows us to think about the thermodynamic concepts of energy and entropy, and the distributions thereof, in terms of information. (Location 439)
Thus, viewing neither thermodynamics nor information as the fundamental theory, but rather as manifestations of a more basic, wideranging theory, which we will build into Thermoinfocomplexity, one can develop useful insights into both fields that might not naturally occur. (Location 452)
In the context of a thermodynamic system containing both the organism and the sugars, the “information” bound up in the organism’s ability to digest things affects the amount of thermodynamic entropy in the sugars. Although their structures are nearly identical, when considered as part of a system with the organism, their entropies are very different. (Location 458)
The only difference in the systems is that information in the brain of Human A is of a lower entropy. The total potential energy in the rock in each situation is the same, but the dynamics possible in the first are not possible in the second. (Location 464)
He described reactions, which need an external heat source to occur, as “endothermic,” and reactions, which occur spontaneously and give off heat, as “exothermic.” (Location 467)
The Gibbs free energy represents the total available energy in the system. (Location 471)
He discovered that reactions in which the end product had a lower Gibbs free energy than did the same system before the reaction, were exothermic; they occurred without the need for any additional input of energy. (Location 472)
the amount of energy available to do work on another system. (Location 475)
For a system to perpetuate itself over time, it must perform a variety of crucial functions. These functions can include any number of different things, from the repair of DNA in cells, to movement in the human body. (Location 476)
Consider the sugar-consuming organism discussed on page 50. For this organism to engage in its various vital processes, it needs to rearrange itself in order that the reactions are exothermic, or at least not endothermic. To do so, it needs to raise the amount of Gibbs free energy it contains within itself and its structure. (Location 480)
The GFE of the organism is now higher than the GFE of the hungry organism, and the GFE of the waste products is much lower than the GFE of the uneaten sugar. (Location 486)
it's also another way in which a system consumes GFE from its environment. The great utility of the concept of GFE is that it subsumes both the free disordered energy and the low-entropy organized energy under a single rubric. (Location 494)
In Thermoinfocomplexity, we will describe any process of energy transfer as the flow of Gibbs free energy. The law by which reactions occur spontaneously, if they lower GFE, will be treated as the fundamental axiom governing the dynamics of whatever system we study, and we will examine all of the complex behaviors that can arise from this simple rule. (Location 498)
a stochastic system, we mean a system which changes its state over time through a process of cumulative probability, and which means in turn that the future is determined by a combination of its present state and an element of probability. (Location 501)
When we say that reactions occur with a lower Gibbs free energy, what we mean is that although at any given point in time the system may change in ways that raise and lower its Gibbs free energy, developments in the system which bring it to an end state in which the Gibbs free energy is lower than it was in the beginning state, are more likely to occur than are developments in which the GFE is higher at the end than it was at the beginning. (Location 504)
However, most snowflakes (here we are dealing in probabilistic, statistical facts) have a hexagonal symmetry: the same pattern repeated six times. This derives from the structure and shape of the H2O molecule, though there is no hint of any such symmetry in the molecule itself. (Location 515)
However, some reactions will have middle states between their beginnings and their ends, and these have a higher Gibbs free energy than both the beginning and end states. (Location 523)
An excellent metaphor for this phenomenon is rain falling on a mountainside. The path which the water takes is determined at each point by the direction of steepest and easiest flow. (Location 537)
we can describe the various streams and channels by the volume of water that will pass through them, and we can say at what speed it will flow. (Location 539)
This continuous stream has the effect of deepening and smoothing the channels it follows, making it even more likely that water will flow through these paths in the future. Thus, if the water follows a certain pattern of flow, it is more likely to follow the same pattern (or a very similar one) at the next moment in time. Thus, this pattern is an “autocatalytic structure.” (Location 545)
A good example of a system with “disorganized complexity” is an avalanche. An avalanche is composed of immense quantities of molecules of ice and dirt and other substances moving down a slope and bouncing off of each other. (Location 552)
Disorganized complexity entails both a large number of variables, and individually erratic behavior for each of those variables. Only when composite variables are constructed by averaging or otherwise deriving information from the mass of basic variables, can interesting data about the system be discovered. (Location 555)
The algorithmic information contained in a certain sequence of symbols is the length of the shortest program which would cause the idealized computer to output that sequence. (Location 561)
With a great degree of regularity, repeating sequences may contain a large amount of information according to our definitions, but they will have a low algorithmic information content, because the computer could simply be told to repeat a certain sequence a number of times, making for a shorter program.14 (Location 563)
The irregularities in a system make it more complex, and these often arise because a system is governed by rules, which are not exact but probabilistic. (Location 573)
However, if we lack the ability to determine the conditions which govern it exactly, any deterministic system can be viewed as a stochastic system. (Location 578)
An attractor is a set of states which the system will tend to be attracted. There are several types of attractors, the description of which will make this definition more clear. (Location 580)
In thermodynamic systems, a point attractor is often a state of lowest Gibbs free energy, or at least a point that has lower Gibbs free energy from all nearby points. Due to the general law by which thermodynamic systems develop, the system will evolve in time to become closer and closer to this point. (Location 585)
Limit cycles are regular patterns that a system can take. Once a system is in any state contained in the limit cycle, it can never leave that limit cycle. (Location 589)
The system can spiral closer and closer toward a point attractor without ever reaching it, and a system can also wobble forever in a pattern that is very close to a limit cycle, but never actually fall within the limit cycle. (Location 590)
Attractors with fractional dimensions, “fractals,” are referred to as “strange attractors.” These are difficult to construct using top-down mathematical methods, but they frequently occur as emergent phenomena in systems that are described as the results of many interacting systems. (Location 593)
Positive feedback loops occur when an event makes itself even more likely to occur in the future, or likely to occur more intensely. (Location 599)
Combinations of positive and negative feedback loops occur more often in nature. A negative feedback loop occurs when an event makes itself less likely to occur in the future, or to occur less intensely. (Location 602)
This is a type of positive feedback loop. When glucose is high, insulin levels are high as well. But when the signal of glucose disappears, the insulin disappears with it. These two processes are entrained like the interlocking of the gears in a clock. (Location 621)
With insulin no longer being released, its concentration in the blood drops, while glucagon levels increase as per a negative feedback (Location 624)
Together, these two hormones work to keep blood sugar at a relatively constant level in order to maintain homeostasis in the body. (Location 628)
This cycle is an emergent phenomenon arising from the basic rules of how insulin and glucagon affect cells. (Location 634)
system of interacting components naturally and spontaneously leads to the emergence of interdependent fluxes and gradients within the system, with concomitant dynamic compartmentalization of the components of the system in space and time.”19 (Location 637)
Over time, automata following rules will sometimes develop into stable, highly ordered configurations, while other automata will continuously fluctuate in high-entropy configurations. (Location 642)
Wolfram posits that class 4 automata may be “universal computers,” meaning that “given a suitable initial program, its time evolution can implement any finite algorithm.” If cellular automata can be used as simple models for emergent behaviors, then that implies an important link between the theory of computation and the theory of complexity.21 (Location 650)
Why would they be so prevalent in the real world? We claim that greater degrees of complexity have an advantage, in that systems exhibiting them are more likely to persist through fluctuations of energy than are simpler structures. (Location 655)
Y ~ M-1/4 This means that the larger an organism gets, the more efficiently it makes use of energy. West et al. gave an explanation for this in organisms with circulatory systems by showing that in organisms with a more complex circulatory system, less energy dissipates into the environment; he also produced models that give the correct (Location 658)
power law.22 Similar power laws have been observed for non-biological organisms such as cities, which become more-efficient consumers of energy per capita as they grow.23 We believe that this sort of law, and efficiency, can be described in a scale-free way, and that this will apply to all of these contexts using the viewpoint of Thermoinfocomplexity. (Location 660)
As structures grow more complex, they are capable of recognizing more-complicated ininformation in the world around them. Other systems have higher cybernetic information and therefore lower entropy, and with lower entropy greater Gibbs free energy. (Location 669)
The ability to store and manipulate energy in low-entropy forms makes the fish much more efficient, and allows it to keep the energy stored for a longer period of time, thereby contributing to its longer life-span. This is true, too, for morecomplex organisms in the scale of evolution. (Location 678)
Here, one uses increasingly advanced understandings of the grammar and conventions of written language in order to be able to consume and absorb its information (and therefore its Gibbs free energy, if information and reduced entropy are held to be equivalent) more efficiently. (Location 685)
A more complex adaptive system captures and stores GFE in its information network more efficiently. (Location 689)
While simple organisms might starve within hours of not having access to food, a human being can survive for thirty days without it. Thus, more complex organisms are able to survive in less-predictable energy environments. Complexity allows one to endure boomand-bust cycles. (Location 690)
Complexity allows life to endure greater variation of available energy, but simplicity can survive at a much lower base level, as reproducing units. (Location 696)
Over the course of this book, further advantages will appear as we examine various contexts in which the viewpoint of Thermoinfocomplexity provides a greater understanding of evolutionary development. (Location 698)
Atoms use the energy from other reactions, sunlight, and electricity to form chemical bonds, just how organisms use nutrients to get to a state where they require less energy to survive. These bonds are like communications between atoms, sharing electrons as their message. (Location 772)
Between 10 and 15 percent of the system’s carbon had reassembled into organic compounds. Some of the carbon— about 2 percent—had even taken the form of amino acids, which are used by the machinery of modern cells to produce proteins. (Location 808)
Life required no special intervention outside of physics and probability. Order comes about from within (with a little energy from the sun). Through stochastic processes, selforganization begets complexity, and eventually, one of those complex physical systems meets our criteria for being “alive,” as it begins to interact with the molecules around it and becomes self-replicating. Although here, we should recall, that before life as we know it emerged, self-replication is described by the process of autocatalysis in RNA.28 (Location 822)
In each of the above-mentioned origin scenarios, complex forms arose from simpler ones in the presence of an energy supply, plus an agent (a type of catalyst) that lowers the activation energy of a reaction. (Location 832)
Protected from the outside, molecules inside the cell would be more likely to interact and chemically cascade into new molecules, making more of themselves through selfcatalysis (a type of reproduction to be discussed in more detail). (Location 888)
They would need structural stability. It would be all too easy for those chemical products (Location 912)
not destroyed by a harsh environment to simply fall apart within their lipid bubble, shortly after being produced. (Location 912)
A catalyst is a substance that increases the rate of a chemical reaction without going through a chemical reaction itself. Catalysts work like molecular matchmakers, facilitating new relationships between molecules. (Location 915)
Reactions can still occur without a catalyst, but often at a much slower rate—much too slow to ever be of use within a living organism. Catalysts can speed up a reaction to over a million times faster, than the non-catalyzed speed, helping form stable products at a very fast rate and allowing information transfer to occur more efficiently. (Location 920)
Autocatalysis is a reaction whose product is the catalyst of the same reaction that made it. (Location 934)
This seems surprising, until one looks closer at the chemical structure of DNA, which indicates much more stability, making it a better candidate for the transfer of information to the next generation. (Location 952)
Neurobiologist, Richard Pico describes the world of the protocell in terms of the constant creation and destruction of protocellular membrane sacs.40 In this vision, each sac provides a short-lived protective microenvironment for chemical reactions to take place. Then, as each proto-membrane breaks apart, succumbing to entropy, it dumps its newly created contents back into the surrounding medium. These contents mix and recombine, before being thrust into a new protocell: a new spontaneous lipid sphere. (Location 1008)
Evolution selects for cooperative chemical networks that are more energy-efficient. In the fluctuations of energy availability in the environment, the forms that are selected and survive require less energy to evolve and persist. Their energy efficiency is facilitated by the network effect. (Location 1025)
In this sense, RNA is analogous to a speed reader. It acquires and translates the information, overlooking minor errors. (Location 1035)
Imagine that DNA is like a written instruction for someone that is usually written as “don’t yell.” (Location 1045)
In single-celled organisms, any mutation is passed on directly to their offspring because their DNA is copied and the offspring is a clone. However, in multicellular organisms, the mutation is only passed on to the offspring, if the sex cells―cells that combine together to form an offspring―carry the mutation. (Location 1050)
However, not all changes are detrimental. Some have little effect on the survivability of an organism, like the color of a mouse’s fur. Black, brown, and white are very common colors for mice, and in very dark places, the color of their fur usually has no effect. (Location 1057)
In this way, advantageous mutations continue to accumulate. This process is descriptive evolution, as described since Darwin. (Location 1066)
Competition, communication, cooperation, and selection worked hand-in-hand, at times limiting and at times amplifying the ordered complexity and structure of life forms in competition for the Gibbs free energy available in their environment. We may recall in Chapter One that in various complex structures, energy flow slows down by percolating through its complex information network. For (Location 1073)
Systems that are better at capturing Gibbs free energy and using it more efficiently will be more likely to survive. (Location 1077)
The history of life can now be thought of as the intersection of a variety of agents creating and changing the order of all matter, both inorganic and organic. The same principles that sparked life in its most primitive form continue to shape even the most complex. (Location 1079)
The process is scale-free and reiterated in stepwise emergences through the stochastic process of evolution that selects for complex adaptive systems that obey the laws of thermodynamics, as well as information, and complexity theories. (Location 1084)
Biological structures can be thought of as information networks, within the complexity of an adaptive system that helps the system to use less energy in the maintenance of its structure. Such systems follow the laws of thermodynamics, and when available energy is scarce, complexification serves as an adaptive mode, resulting in the selection of the most-energy-efficient forms. (Location 1087)
Each bacterium responds flexibly and even alters itself, by means of modifying its genetic expression patterns. (Location 1118)
Every day our bodies recycle a mass of ATP equal to our entire body weight.67 (Location 1395)
are using their own circular DNA and proteins to do so. They are reproducing as if they were bacteria. Strong evidence suggests that at one point, free-living bacteria joined together with or were engulfed by eukaryotic cells. (Location 1401)
It is an unintentional, physiochemical selection of the most energy efficient complex adaptive system. (Location 1437)
In birds, reptiles, amphibians, and fish, it comes from the battery-like power of egg yolk. Once an external energy source enables the reproduction of individual cells, the stage is set for a form of cellular evolution that takes place over the course of a single organism’s embryonic development. (Location 1453)
Simple programs are necessary, and form the foundation of stochastic selection, which in turn leads to more complex forms. (Location 1472)
So, in fact, all organs go through a compressed evolutionary expression in their development. (Location 1475)
These “junk DNA” codons, transposons, etc., may be in fact the historical library of evolutionary information that is conserved and reiterated and has useful functions throughout the growth and development of individuals. (Location 1480)
The stickiest cells adhere to the stickiest cells to form a ball. Now imagine the “second-most sticky” cell type. (Location 1504)
weaker adhesions are replaced by stronger ones, leading to higher levels of chemical stability. The structure always self-organizes, so that the total adhesive bonding energy between cells is maximized, resulting in the most stable structure. (Location 1510)
what mattered was their surface tension. (Location 1517)
Emergent, complex systems are highly dependent on the webs of communication and information networks imbedded within them. For the slime molds, cAMP concentrations make a world of difference. (Location 1567)
Rather, certain specific ratios arise from the specific organization of living things of varying complexity among prokaryotes, eukaryotes, or multicellular organisms. (Location 1652)
To put it simply, the more complex and intricate the network, the slower the dissipation of energy throughout. (Location 1654)
Nevertheless, the full explanation must take into account all the strange attractor pathways (Location 1663)
Energy efficiency results from the network effect, as evidenced by lower BMR/g. (Location 1665)
In other words it is the complexity of the total network that dictates the metabolic efficiency. This fact explains why humans, which have much smaller mass than elephants, are metabolically more efficient and have longer life-spans. (Location 1672)
sulfur, and a third uses ferric iron, in addition to sulfur. In the cells of more-complex organisms, there are only two pathways for cellular metabolism, and the cell will add the second only when oxygen is present. (Location 1685)
The complexity of its entrained, varied, multiple biochemical strange attractor paths that attract self-similar molecules, results in robust and efficient energy use. (Location 1694)
In the same way, organisms with efficient metabolisms are selected for and will thrive over time better than their lessefficient neighbors. (Location 1700)
At every level of complexity, life is organized into networks. Every living organism is an open system that ex changes information with its environment in the form of chemicals and energy. (Location 1718)
Large multicellular organisms tend to have the most complex information networks, and thus, have a lower metabolic rate per gram because they use, rather than dissipate, a larger fraction of the chemical energy they take in. (Location 1746)
It takes a long time for the energy captured in it’s structure to dissipate as heat. (Location 1754)
By keeping in mind what envi ronment these organisms evolved in, we can explain why certain animals, like camels, have a lower metabolic rate than, for example, kangaroo rats, in the same environment. (Location 1761)
yet birds, fish, insects, and even microorganisms group together without any executive direction. (Location 1789)
flock behavior obeys a set of basic rules that each individual in a flock follows. (Location 1790)
By making his boids fly according to a few simple rules, he was able to reproduce amazingly lifelike movement and coordination. (Location 1792)
In flocking patterns, the first priority of the bird is to avoid midair collisions, which besides the potential harm, wastes precious energy needed for breeding and the remainder of the flight. (Location 1796)
The result is a coherent, emergent structure with benefits in energy efficiency for the entire group. (Location 1813)
towards the lowest-possible energy configuration. (Location 1814)
These same principles apply to professional cyclists in the Tour de France who form a tight group, known as a peloton. (Location 1826)
The birds were responding to their neighbors’ movements faster than their own physiological reaction time. (Location 1840)
Each individual bird watches the wave spreading throughout the flock and then times its own maneuver to fit perfectly into that wave. (Location 1848)
And just like birds, they do it without a designated leader. Instead, each fish responds to a wide variety of movement cues from its neighbors. From the simple stimulus-and-response of each fish, the schooling phenomenon emerges. (Location 1852)
This ability to sate predators once every 13 to 17 years, and then breed in peace, is an example of one strange attractor’s rhythmic cycle among many leading to an emergent, roughly regular swarm lifecycle. (Location 1866)
entirety of the next generation, within a few days. Without any design or intentionality, the herding network provides defensive benefits to its members by the emergent properties it creates. A large group increases the chance of spotting a predator early, and a predator’s success depends on the difficult task of separating an individual from the group.9798 (Location 1893)
Foraging fish also receive the energy-saving benefits from the network effect, by sharing information about the surroundings among themselves. (Location 1927)
Group dynamics comes with emergent layers of complexity and enhanced communication. Over time, the more-energy-efficient forms will have specific advantages over less-efficient forms, and they are therefore selected to persist. (Location 1932)
These complex patterns cycle within strange attractor patterns. Strange attractors for our purposes here, may be defined as a path derived by a number of not clearly defined forces that collectively act upon the agents constituting a complex system, for example a swarm of locusts. (Location 1943)
this way, complex systems build upon one another in nature. The story of flux among various emergent stable systems is the story of evolution on Earth. (Location 1947)
specialization increases, so does the complexity of the hive system and its network of communication, resulting in greater energy efficiency. (Location 1992)
“Leafcutter” ants are known to actually cultivate entire fungus gardens, tending the fungus over time and defending it from predators. (Location 1996)
When a young queen leafcutter ant is ready to start her own colony, she carries a piece of the fungus with her and lets it grow in the new nest. (Location 1998)
The warrior caste brings in labor from the outside by stealing pupae from other colonies. When the pupae mature they become the slaves of their captors, working as though they were members of the colony. (Location 2001)
The answer is that the entire ant colony with its queen capable of passing on the genes of all ants, functions together as one superorganism. (Location 2026)
Though all the worker ants remain sterile, by helping the queen to lay more eggs, gathering her food, cleaning the nest, and defending the colony from predators, they are actually improving their own genes’ chances of making it to the next generation. (Location 2055)
next (Location 2058)
When an individual helps its relatives, it also helps copies of its own genes residing in its relatives’ bodies. (Location 2058)
This formula predicts that the closer the genetic relationship between two agents, the more we ought to expect altruism to have evolved as an evolutionarily stable strategy. (Location 2064)
local interactions are the foundation of the global emergent structure and determine the function of the superorganism, chemical tampering can cause a colony to devolve into warfare; (Location 2157)
“critical period” of their development, would become imprinted onto the young geese. Essentially, a young goose would act as if the object were its mother, whether that object was a wire-frame, a puppet, or a picture of an adult goose. (Location 2172)
When placed together, worker ants with a limited communication network maintained a higher metabolic rate, which scaled according to a power law of one: identical to single-celled eukaryotes. (Location 2190)
This demonstrates that the energy efficiency we see in the ant colony superorganism, which scales like a single, multicellular organism, results from specialization and information exchange to create specialized ant groups performing specific tasks. (Location 2192)
We can see that increased communication, higher degrees of interconnectedness, and information transfer among specialized groups are at the root of energy efficiency in superorganisms, and these traits combine to give the superorganism higher genetic fitness. (Location 2197)
efficient.123 (Location 2200)
Intense specialization makes the colony behave interdependently, and the superorganism that emerges from this interdependence is more energy efficient as a result. (Location 2206)
By looking at how social animals interact with one another over the course of their lives, we will gain insight into one of matter’s most profound organizational jumps between molecule and human: the jump that allows for truly social communities, with large gains in energy efficiency. (Location 2229)
The recipient bat would gain about 18 hours until the starvation point, compared with the six lost by the donor. This study shows mathematically what already makes intuitive sense: the metabolic benefit of additional food is far greater to a starving individual, than is the cost of giving up that same quantity of food to a well-fed al.128Nonetheless, (Location 2243)
Long-term cooperative relationships pay off metabolically. Pairs of unrelated vampire bats are known to develop a “buddy system,” by which two bats regularly share food with one another. (Location 2267)
Each regional environment in the global ecosystem exhibits differential selective pressures on the societies that inhabit it. (Location 2435)
of how complexity emerges in response to energy scarcity. (Location 2440)
Here we have a prime example of energy efficiency, as the driver of natural selection. (Location 2450)
Throughout this book, we’ve made the argument that new complexities as they arise are selected because they are energy efficient in the face of energy scarcity. (Location 2459)
Unlike most of the social organisms we have studied this far, human beings have developed behavioral patterns of hunting, foraging, and sharing food that are not purely instinctual; they must be learned through culture. (Location 2475)
From the Thermoinfocomplexity point of view, the distinction between “biological” and “sociological” is immaterial, yet the sociological structures that characterize the human superorganism exhibit additional complex macrostates that must be examined at each additional level of emergent complexity of various social structure. (Location 2477)
That the evolution of human societies reflects the same evolutionary processes we have observed from atoms to organisms is testament to the grand consilience of nature at all levels of complexity. (Location 2485)
emergence of complexity in different human societies. Cultural networks naturally emerge as complex adaptive systems self-organizing through the sharing and distribution of food, energy, and information between people in a society. (Location 2488)
The nested arrays of micro-macrostate structures into which families congregate, exhibit self-similar, fractal-like organizational patterns that are repeated across vastly different cultural and environmental conditions. (Location 2495)
This metabolic efficiency scales in a similar manner to the metabolic scaling of organisms that we have discussed in previous chapters. (Location 2510)
The !Kung have adapted to life in this landscape, maintaining a broad diet by optimizing adaptive strategies to maximize their access to seasonally available food resources, while minimizing the effort required to do so. (Location 2537)
When the local resource supply becomes depleted to such a degree that the effort required to gather food from that campsite becomes more than the effort required to move the camp to a new location, the camp will move. (Location 2541)
Though the circuit never repeats itself exactly, the overall pattern of annual “orbits” through the landscape is an attractor pathway. (Location 2553)
Here, again, we find the self-similar pattern of maximally efficient metabolic organization observable at all scales, operating at the level of human societies. (Location 2562)
Larger organisms will require greater foraging areas, but like metabolism, the area used by groups of organisms does not necessarily scale linearly with increasing numbers of organisms; it is, rather, characterized by a scaling exponent, (Location 2570)
The primary characteristic that distinguishes foraging societies from more-complex and hierarchical human social networks is the extent to which foragers rely on social ties to minimize risk in the face of energy scarcity, rather than on the physical storage of food. (Location 2586)
Each family keeps whatever it catches on its own, and differences in the accumulation of bounty are eventually evened out during obligatory shared feasts. (Location 2687)
The women and children are served by the women, who dole out the soup evenly among all those present, ensuring each gets an equal portion. The men, however, follow a hierarchical procedure. (Location 2691)
The overall pecking order and the customs that emerge through the process of sharing food and information, reflects the social value of experience and knowledge that resides (Location 2705)
Emergent fractal patterns of regional economies and political structures, reflect additional environmental energy captured into bonds that bind fluid familial relationships into more-rigid forms of complex social organization. In the case of the Machiguenga, pressure from (Location 2729)
At higher population densities, the society was no longer mobile. Fighting soon broke out among the people who soon had to travel steadily increasing distances in order to find wild foods, and to continue farming land that was producing rapidly diminishing returns. (Location 2739)
Human societies are complex adaptive systems that facilitate the transfer of energy from repositories in the environment (edible plants and animals) to repositories in their social networks (families, villages, towns, and cities). (Location 2768)
As we have seen with the Machiguenga, at a certain level of population and environmental pressure, the loosely bound social networks of foraging societies become insufficient to reliably meet the basic metabolic needs of the people. (Location 2772)
scarcity. New complexities resulting in new energy efficiency must emerge within the social network if (Location 2775)
In every transition, selection favoring increased levels of complexity is opposed by genetic conflicts between aggregated cell lineages. (Location 2784)
a stable adaptive system is formed that is able to access repositories of environmental energy that were previously unavailable to the ancestral forms. (Location 2786)
In turn, complex hierarchies of political order further shifted the attractor pathways that govern communication and energy exchange throughout the social network of Hawaiian society. (Location 2795)
political power was a function of access to energy surplus, which was stored during times of relative plenty and redistributed during times of scarcity. Here we observe a process in the human superorganism that is akin to the function of fat cells in a multicellular organism. (Location 2825)
In the face of energy scarcity due to population pressure, dry land agricultural systems were created to grow taro root and other crops in marginally productive landscapes on the leeward side of the islands. (Location 2828)
The annual New Year celebration—the mata-fiti or “first fruit” offering—was an energy exchange system that established a pattern of attractor pathways in the social network. (Location 2848)
All this changed as the society transformed in the face of energy scarcity. (Location 2852)
Control over the accumulation of and access to surplus energy is the foundation of political (Location 2857)
These “ritual attractors” constellated the paths through which environmental free energy was transformed from agricultural surplus into tribute, organizing the society around the redistribution of energy and power throughout the social network. (Location 2873)
Tellingly, and in accordance with the Thermoinfocomplexity thesis, the most powerful chiefs emerged on the most unproductive islands, whose relative scarcity of available Gibbs free energy required more-efficient hierarchical networks. (Location 2890)
we note that everywhere agriculture emerged, attractor pathways, i.e. the paths of least resistance for energy flow, dictated the timing as well as the precise locations of agricultural projects. (Location 2903)
Intensive agricultural practices have deepened these channels over time, redirecting much of the energy that flows through Earth’s ecosystems into the information and energy exchange networks that constitute human societies. (Location 2923)
To illustrate this process, we will now examine the emergence and evolution of trade networks, which have evolved over the course of millennia, from footpaths following the path of least resistance between ancient settlements, to flight paths following geodesic arcs between modern cities. (Location 2950)
emerged through the prolonged activity of human travelers seeking paths of least resistance, maximizing the dissipation of concentrated energy sources by exchanging goods and services throughout the socio-energetic network. (Location 2959)
The organizational pattern we find in trade routes was not designed in a top-down manner. As people continued to use the routes, or energy and information distribution channels, the networks of activity self-organized and reinforced their structures, regardless of the specifics of the primary commodity being traded. (Location 2961)
Every major roadway first emerged under conditions determined by the circulation of energy and information in the form of goods and services, (Location 2967)
The evolution of complex trade networks, alliances between societies, and even empires proceeds through this process of exaptation: the co-option of an existing phenomenon to perform a new function. (Location 2970)
The coherence of commerce throughout such a vast network emerged through the positive feedback channels of an autocatalytic network composed of multiple urban centers united by a shared system of organized religion and bureaucratic institutions. (Location 2980)
The empire emerged after several states were integrated politically by a war that subsequently carved the path of least resistance for the flow of energy. These attractor paths channeled the flow of energy in the superorganism. (Location 2983)
As in Hawaii, rulers often recited their royal lineages, thereby acting as channels for concentrating and reflecting cultural information. (Location 2998)
attractor pathway governing information and energy flow was redirected from Cyrus to Cambyses. (Location 3004)
complexity, speaks to a fundamental underlying mechanism: the selection of cultural expressions as the most energy efficient forms able to utilize channels of existing energy flow. (Location 3022)
The most energy efficient forms are preserved along their passage through channels carved by thermodynamic flows of energy, captured in information networks, and preserved in the formation of complex adaptive systems. (Location 3024)
The energy and information exchange networks enabled by these technologies further deepened the attractor pathways that contributed to the Industrial Revolution and the emergence of the “Modern West.” (Location 3042)
By sharing a common language, the Arabic world was much more integrated, and information flowed more easily in the Arabic medium. (Location 3047)
The culture rewarded those who could copy accurately and completely. (Location 3063)
These large-scale patterns of organization embodied by Muslims, not only united the Muslim superorganism under the law of God, but also laid the energy-information infrastructure for many other societies of varying degrees complexity. (Location 3067)
Transportation technologies such as shipping permitted the flow of goods (energy), information, and people throughout the world. (Location 3088)
Within India, “portfolio capitalists” invested in handicraft, commercial, and tax-farming initiatives that integrated trade networks across multiple regional courts. (Location 3105)
The increased population pressure of an urbanized people in search of new energy resources impelled the British to venture beyond the European continent. (Location 3124)
This internal population pressure, coupled with the abundance of Gibbs free energy provided by the island’s coal deposits, provided the expansive forces necessary to propel a society of seafaring adventurers and merchants into a prominent position in an emerging global information and energy exchange network. (Location 3127)
During the Napoleonic Wars, London became a safe haven for skilled workers and capital from mainland Europe, perhaps because of the difficulty of attacking Britain across the English Channel. (Location 3141)
We have repeatedly observed how populations grow to the carrying capacity of their environments, determined by an abundance of Gibbs free energy available in forms to which the society has adapted. (Location 3153)
Because these were not the sorts of innovations that could easily be patented, technological improvements in energy production circulated readily in urban London. (Location 3172)
occurred through bottom-up innovation rather than topdown organization and planning. (Location 3173)
found locally in abundance and unknown elsewhere in the world. (Location 3175)
Interestingly, the infrastructure of social metabolic networks—whether of the agro-economic structures of the Hawaiian chiefdoms, the satrapies of the Persian Empire, or the megalopolises of modern states—expands sub-linearly with respect to population density. (Location 3182)
However, the slope of the scaling was superlinear (greater than 1) with respect to energy efficiency. A city ten times larger than another is not just ten times more innovative; it is 17 times more so. (Location 3191)
As discussed in previous chapters, Kleiber’s law reflects that as living organisms grow larger and more complex, metabolism per unit of body mass slows down. (Location 3193)
Human societies are adaptive, dynamic systems that select for the most energy efficient social structures in the face of energy scarcity at a variety of levels of energy density and corresponding complexity. (Location 3200)
Despite the remarkable diversity of human societies, the organizational patterns of human populations are remarkably self-similar across all scales of complexity. (Location 3219)
The slime mold builds highly efficient networks in a self-organized manner, through the elegant process of energy and information exchange by means of positive and negative feedback loops in the extended plasmodium. (Location 3228)
complex adaptive systems in nature is quite the opposite. Evolution is a bottom-up process with no designer. It is spontaneous, elegant and surprising. (Location 3262)
changing physical environment, is beyond predictability. No top-down system could harness all the trillions of probable variables in a stochastic, dynamic process of energy and matter interaction adaptable to the ever-changing biosphere. (Location 3266)
About 10,000 years ago, the rise of specialization among individuals and the extensive communication between them led to the emergence of agricultural society, which provided the foundation for increased productivity and rapid population growth. (Location 3281)
Humans have more cells within the cerebral cortex (11.5 billion cells) than any other animal, with the exception of elephants and certain whales. (Location 3295)
powerful biological information-processing machine. The human cortex is thicker and contains a much higher density of neurons than that of any other animal. (Location 3304)
In short, the human brain is efficient because it is a highly organized, efficiently networked system. (Location 3314)
Some explanations for our shrinking brains postulate that the invention of agriculture, over the last 10,000 years, encouraged mental disuse, causing individuals to become less intelligent on an individual basis, regardless of the increased group intelligence that may have arisen from specialization, cooperation, and communication. (Location 3332)
Another possibility, of course, is that although brains have gotten smaller, they have become more complex and efficient in their use of energy. There is no evidence that larger brains necessarily correlate with greater intelligence. (Location 3336)
Whoever can convert energy into information most efficiently, and effectively communicate this information, will be more likely to survive. (Location 3346)
Or, like the brain of ants, the human brain may have become more specialized and energy efficient, allowing for a lower BMR/g among individuals, in addition to greater processing power in specific temporal regions. (Location 3348)
Our various specialized cells communicate with each other in multiple ways. Individual cells live out their normal lifespans, like individual ants, and through their specialization contribute to the much longer average lifespans of human organisms. (Location 3360)
In the evolution of complex adaptive (Location 3368)
systems, complexity plateaus, and then as it evolves further, often jumping to a new, emergent state that is qualitatively different from the previous one. (Location 3368)
We can see that in the history of Walmart, the growth rate has increased continuously, whereas in most corporations, as the sales of the corporation increases, the number of employees also increases and the corporation gradually reaches static profitability, until eventually, its profitability decreases. (Location 3427)
The accumulation of wealth (surplus energy) at the top of the hierarchy is an interesting phenomenon observed in the structure of all human social hierarchies, from the (Location 3429)
The system we will describe is simply based on a long trajectory of the emergence of complex adaptive systems, progressing toward energy efficiency. (Location 3545)
Now, let us ask the obvious question: why didn’t silicon evolve into a life form? The answer is simple—it is not nearly as versatile an element. (Location 3590)