❄️Systems Thinking: The Web of Life (Quotes)

"Constantly regard the universe as one living being, having one substance and one soul; and observe how all things have reference to one perception, the perception of this one living being; and how all things act with one movement; and how all things are the cooperating causes of all things which exist; observe too the continuous spinning of the thread and the contexture of the web." (Marcus Aurelius, "Meditations". cca. 121–180 AD)

"As a net is made up of a series of ties, so everything in this world is connected by a series of ties. If anyone thinks that the mesh of a net is an independent, isolated thing, he is mistaken. It is called a net because it is made up of a series of a interconnected meshes, and each mesh has its place and responsibility in relation to other meshes." (Gautama Buddha)

"Omnia vivunt, omnia inter se conexa" 
"Everything is alive; everything is interconnected." (Cicero)

"It is thus that in the universe everything is connected; it is itself but an immense chain of causes and effects, which flow without ceasing one from the other." (Paul-Henri T d'Holbach [Baron d'Holbach], "The System of Nature, Or, Laws of the Moral and Physical World", 1770)

"Nature, displayed in its full extent, presents us with an immense tableau, in which all the order of beings are each represented by a chain which sustains a continuous series of objects, so close and so similar that their difference would be difficult to define. This chain is not a simple thread which is only extended in length, it is a large web or rather a network, which, from interval to interval, casts branches to the side in order to unite with the networks of another order." (Comte Georges-Louis Leclerc de Buffon, "Les Oiseaux Qui Ne Peuvent Voler", Histoire Naturelle des Oiseaux Vol. I, 1770)

"All knowledge is profitable; profitable in its ennobling effect on the character, in the pleasure it imparts in its acquisition, as well as in the power it gives over the operations of mind and of matter. All knowledge is useful; every part of this complex system of nature is connected with every other. Nothing is isolated. The discovery of to-day, which appears unconnected with any useful process, may, in the course of a few years, become the fruitful source of a thousand inventions." (Joseph Henry, "Report of the Secretary" [Sixth Annual Report of the Board of Regents of the Smithsonian Institution for 1851], 1852)

"All Nature is linked together by invisible bonds and every organic creature, however low, however feeble, however dependent, is necessary to the well-being of some other among the myriad forms of life." (George P Marsh, From Man and Nature, 1864)

"Brightness and freshness take possession of the mind when it is crossed by the light of principles, shewing the facts of Nature to be organically connected." (John Tyndall, "Six Lectures on Light Delivered in America in 1872-1873" 3rd Ed., 1901)

"When we try to pick anything out by itself, we find it hitched to everything else in the universe." (John Muir, "My First Summer in the Sierra", 1911)

"The hosts of living organisms are not random creatures, they can be classified in battalions and regiments. Neither are they isolated creatures, for every thread of life is inter-twined with others in a complex web." (John A Thomson, "The System of Animate Nature" Vol. 1, 1920)

"This whole electric universe is a complex maze of similar tensions. Every particle of matter in the universe is separated from its condition of oneness, just as the return ball is separated from the hand, and each is connected with the other one by an electric thread of light which measures the tension of that separateness." (Walter Russell, "The Secret of Light", 1947)

"We shall walk together on this path of life, for all things are a part of the universe, and are connected with each other to form one whole unity." (Maria Montessori, "To Educate the Human Potential", 1947)

"In fact, it is empirically ascertainable that every event is actually produced by a number of factors, or is at least accompanied by numerous other events that are somehow connected with it, so that the singling out involved in the picture of the causal chain is an extreme abstraction. Just as ideal objects cannot be isolated from their proper context, material existents exhibit multiple interconnections; therefore the universe is not a heap of things but a system of interacting systems." (Mario Bunge, "Causality: The place of the casual principles in modern science", 1959)

"We are seeking another basic outlook: the world as an organization.  This would profoundly change the categories of our thinking and influence our practical attitudes.  We must envision the biosphere as a whole with mutually reinforcing or mutually destructive interdependencies." (Ludwig von Bertalanffy, "Robots, Men and Minds", 1967)

"[...] everything is inter-linked. And therefore everything has numberless causes. The entire universe contributes to the least thing. A thing is as it is because the world is as it is." (Nisargadatta Maharaj, "I am That", 1973)

"We have since defined Gaia as a complex entity involving the Earth's biosphere, atmosphere, oceans, and soil; the totality constituting a feedback or cybernetic system which seeks an optimal physical and chemical environment for life on this planet." (James Lovelock, "Gaia: A new look at life on Earth", 1981)

"If we recognise that every ecosystem can also be viewed as a food web, we can think of it as a circular, interlacing nexus of plant animal relationships (rather than a stratified pyramid with man at the apex). [...] Each species, be it a form of bacteria or deer, is knitted together in a network of interdependence, however indirect the links may be." (Murray Bookchin, "The Ecology of Freedom", 1982)

"The new paradigm may be called a holistic world view, seeing the world as an integrated whole rather than a dissociated collection of parts. It may also be called an ecological view, if the term 'ecological' is used in a much broader and deeper sense than usual. Deep ecological awareness recognizes the fundamental interdependence of all phenomena and the fact that, as individuals and societies we are all embedded in (and ultimately dependent on) the cyclical process of nature." (Fritjof Capra & Gunter A. Pauli," Steering business toward sustainability", 1995)

"Today the network of relationships linking the human race to itself and to the rest of the biosphere is so complex that all aspects affect all others to an extraordinary degree. Someone should be studying the whole system, however crudely that has to be done, because no gluing together of partial studies of a complex nonlinear system can give a good idea of the behaviour of the whole." (Murray Gell-Mann, 1997)

"All living organisms must feed on continual flows of matter and energy: from their environment to stay alive, and all living organisms continually produce waste. However, an ecosystem generates no net waste, one species' waste being another species' food. Thus, matter cycles continually through the web of life." (Fritjof Capra, "The Hidden Connections", 2002)

"When we look at the world around us, we find that we are not thrown into chaos and randomness but are part of a great order, a grand symphony of life. Every molecule in our body was once a part of previous bodies-living or nonliving-and will be a part of future bodies. In this sense, our body will not die but will live on, again and again, because life lives on. We share not only life's molecules but also its basic principles of organization with the rest of the living world. Arid since our mind, too, is embodied, our concepts and metaphors are embedded in the web of life together with our bodies and brains. We belong to the universe, we are at home in it, and this experience of belonging can make our lives profoundly meaningful." (Fritjof Capra, "The Hidden Connections", 2002)

"Nothing exists in the universe that is separate from anything else. Everything is intrinsically connected, irrevocably interdependent, interactive, interwoven into the fabric of all of life." (Neale D Walsch, "Conversations With God: An Uncommon Dialogue", 2003)

"In our Gaian world, everything is connected to and influences everything else." (Tim Flannery, "The Weather Makers", 2005)

"We are beginning to see the entire universe as a holographically interlinked network of energy and information, organically whole and self referential at all scales of its existence. We, and all things in the universe, are non-locally connected with each other and with all other things in ways that are unfettered by the hitherto known limitations of space and time." (Ervin László,"Cosmos: A Co-creator's Guide to the Whole-World", 2010)

"I understand that everything is connected, that all roads meet, and that all rivers flow into the same sea." (Paulo Coelho, "Aleph", 2011)

"Deep ecology does not separate humans - or anything else-from the natural environment. It sees the world not as a collection of isolated objects, but as a network of phenomena that are fundamentally interconnected and interdependent. Deep ecology recognizes the intrinsic value of all living beings and views humans as just one particular strand in the web of life." (Fritjof Capra, "The Systems View of Life: A Unifying Vision", 2014)

"All living systems are networks of smaller components, and the web of life as a whole is a multilayered structure of living systems nesting within other living systems - networks within networks." (Fritjof Capra, "The Systems View of Life: A Unifying Vision", 2014)

"In other words, the web of life consists of networks within networks. At each scale, under closer scrutiny, the nodes of the network reveal themselves as smaller networks. We tend to arange these systems, all nesting within larger systems, in a hierarchical scheme by placing the larger systems above the smaller ones in pyramid fashion. But this is a human projection. In nature there is no 'above' or 'below', and there are no hierarchies. There are only networks nesting within other networks." (Fritjof Capra, "The Systems View of Life: A Unifying Vision", 2014)

🔖Knowledge Representation: Mental Models (Part III: Critical Notes I)

Mental Models Series

Despite the good intent and thorough research, the lack of appropriate definitions can easily make one mix concepts in the various explanatory pursuits. The best recent example is Adam Feel’s book on "Mental Models" in which the author doesn’t seem to correctly differentiate between mental processes, representations, concepts, models and the accessories used by mind in decisions and sense-making. Unfortunately, this is not an unique example, several books appeared recently on same topic seem to follow the same pattern.

It’s true that explaining how the mind works is a hardy endeavor as the subject finds itself at the intersection of several cognitive and non-cognitive sciences and pseudo-sciences, however one can still make use of a dictionary to test definitions’ correctness and appropriateness. If the dictionary definitions don’t resemble one’s understanding, then more likely the gap between one’s explanations and reality increases, the deeper one goes into the subject.

In the respective book, the most important distinction is between process and representation. A process is a series of actions or steps taken in order to transform an input into an output, or reach from a point to another. In respect to the mind, the process as transformation makes more sense. Perception, sense-making, recollecting, thinking, depicting, imagining are examples of mental processes even if they can maybe split in further subprocesses. In contrast, a representation is a description and encoding of something, typically an aspect of external or internal reality. Therefore, mental processes use representations and other elements of the mental space as inputs and outputs.

When one considers as process a mental model, which is nothing but a form of representation, then the characteristics associated with the model are far from being correct. Mental models don’t interpret by themselves, they don’t disguise even if their lack of clarity of understanding complicate our mental processes. They do not dictate or predetermine an action but predisposes one to a set of actions. Besides that, the quality of one’s thinking processes has an important impact on mental models’ usability.

Each person has a certain understanding of the world with a degree of fuzziness attached to it. How one reflects and interprets reality is somehow reflected in the quality of the models held. Any model has impact on the decisions made, independently whether the model is correct or wrong. A wrong model can lead to positive results, and in certain situations is enough to address a situation, same as the use of a good model can lead to undesired outcomes. In the end each model has a degree of appropriateness and applicability usually interpreted as value of truth. Being aware of these aspects is important in knowing when to use a model.

A model by itself comes with no guarantees. It has a potential, though it’s in our power to explore and exploit that potential. Having more models for a situation increases in theory one’s chances to succeed, though there are further aspects to consider like chance, right timing, the competitors, etc. Having a set of models doesn’t automatically equate with better information or intelligence, better or faster problem solving, same as the whish of being in control if one’s life is just an illusion.

One can think of the multitude of models like the pieces a puzzle attempting to reflect contiguous pieces of reality, though more than one model fits in one place, while the pieces can often overlap and change their form depending on context. It’s more of a multilayered impossible to solve puzzle, but day by day one can grasp more from it, and get eventually a better understanding about world’s texture.

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🔖Knowledge Representation: Mental Models (Part II: Breaking of Complexity)

Mental Models Series

To understand how something complex works one has two main tools  - the mechanistic, respectively the holistic approach. The mechanistic approach assumes that something can be understood by breaking it into parts (aka analysis) and then by combining the parts to form the whole (aka synthesis). However, this approach doesn’t always account for everything as there’s behavior and/or characteristics not explainable by the parts themselves. Considering that the whole is more than its parts, the holistic approach studies the interactions of the parts that lead to such unexpected effects (aka synergies), the challenge being to identify those characteristics, circumstances or conditions that lead to or related to these effects. Thus, when these two tools are combined within multiple iterations one can get closer to the essence.

When breaking things into parts we need first to look at the thing or object of study from a bird’s eyes view and identify the things that might look like parts. Even if the object of study looks amorphous, the experience doubled by intuition and perseverance can offer a starting point, and from there one can start iteratively to take things apart until one decides to stop. When and where one stops is a question of possible depth, as allowed by the object itself, by the techniques available or our grasping, respectively by the intended depth  -  the level chosen for approximation.

Between the whole and the lowest perceived components, one has the luxury of experimenting by breaking things apart (physically and/or mentally) and putting things together to form unitary parts  - parts that typically explain one or more functions or characteristics, respectively the whole. In addition, one can play with the object, consider it in a range of contexts, extrapolate its characteristics, identify behavior not explainable by the parts themselves. In the process one arrives to a set of facts (things known or proved to be true), respectively suppositions expressed as beliefs (things hold as true without proof), assumptions (things accepted as true without proof) or hypotheses (things who’s value of truth is not known, typically because of limited evidence).

One builds thus a (mental) model, an abstraction of the object of study. The parts and relations existing between the parts form the skeleton of the model, while the facts and suppositions attempt to give the model form. Unfortunately, models seldom accommodate all the facts, therefore what one ignores or considers into the model can make an important difference on whether the model is of any use. One is forced thus to advance theories on how the skeleton can accommodate the form, how form reflects the facts and suppositions.

Simple models can prove to be useful, especially when they allow approximating the real thing within the considered context. However, the better approximations one needs and/or the broader the context is, more complex the models can become, especially when the number of facts considered as important increases. This can mean that two models or theories can be useful or correct when considered in different contexts but lose their applicability when considered in another context.

Having a repository of models to choose from is usually a helpful thing, especially in understanding more about the object studied. The appropriate usage of a model depends also on understanding its range of applicability within a context or across contexts, the advantages, and disadvantages of using the model. Knowing when to use a model is as important as knowing when not to use it, while understanding the measure of the error associated with a model can make us aware of the risks associated with a model and decisions made based on it.

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♾️Cognitive Science: On Swarm Intelligence (Quotes)

"The best and noblest bees are generated and bred out of the Lion, and the Kings and Princes of them do derive their pedigree and descent from the brain of the Lion, being the most excellent part of his body: it is no wonder therefore if they proceeding and coming from so generous a flock, do assail the greatest beasts, and being endures with Lion-like courage, do fear nothing." (Thomas Moffett, "Theatre of Insects", 1634)

"[….] a great number of […] living and thinking Particles could not possibly by their mutual contract and pressing and striking compose one greater individual Animal, with one Mind and Understanding, and a Vital Consension of the whole Body: anymore than a swarm of Bees, or a crowd of Men and Women can be conceived to make up one particular Living Creature compounded and constituted of the aggregate of them all."(Richard Bentley, "The folly and unreasonableness of atheism", 1699)

"Hence, following the comparison to a bee swarm, it is a whole stuck to a tree branch, by means of the action of many bees which must act in concert to hold on; some others become attached to the initial ones, and so on; all concur in forming a fairly solid body, yet each one has a particular action, apart from the others; if one of them gives way or acts too vigorously, the entire mass will be disturbed: when they all conspire to stick close, to mutually embrace, in the order of required proportions, they will comprise a whole which shall endure until they disturb one another." (Théophile de Bordeu," Recherches anatomiques sur la position des glandes et sur leur action", 1751)

"One could, following these authors, compare man to a flock of cranes which fly together, in a particular order, without mutually assisting or depending on one another. The Physicians or Philosophers who have studied and carefully observed man, have noticed this sympathy in all animal movements – this constant and necessary agreement in the interaction of the various parts, however disparate or distant from one another; they have also noticed the disturbance of the whole that results from the sensory disagreement of a single part. A famous physician [M. de Bordeu] and an illustrious physicist [M. de Maupertuis] likewise compared man, from this luminous and philosophical point of view, to a swarm of bees which strive together to hang to a tree branch. One can see them pressing and sustaining one another, forming a kind of whole, in which each living part contributes in its way, by the correspondence and direction of its movements, to sustain this kind of life of the whole body, if we may refer in this way to a mere connection of actions." (Ménuret de Chambaud, "Observation", Encyclopédie XI [by Diderot 318b-319a], cca. 1751 and 1772)

"Have you ever seen a swarm of bees leaving their hive? [...] The world, or the general mass of matter, is the great hive [...]. Have you seen them fly away and form a long cluster of little winged animals, hanging off the end of the branch of a tree, all clinging on to each other by their feet? [...] This cluster is a being, an individual, some sort of animal [...]. If one of these bees decides to pinch somehow the bee it is clinging onto, do you know what will happen? […] this one will pinch the next one; [...] as many pinching sensations will arise throughout the cluster as there are little animals in it; […] the whole cluster will stir, move and change position and shape […] someone who’d never seen the formation of a cluster like that would be tempted to think it was a single animal with five or six hundred heads and a thousand or twelve hundred wings [...]" (Denis Diderot," Rêve de D’Alembert", 1769)

"If we wish to form a mental representation of what is going on among the molecules in calm air, we cannot do better than observe a swarm of bees, when every individual bee is flying furiously, first in one direction, and then in another, while the swarm, as a whole, either remains at rest, or sails slowly through the air." (James C Maxwell, "Molecules", Nature, 1873)

"At the other far extreme, we find many systems ordered as a patchwork of parallel operations, very much as in the neural network of a brain or in a colony of ants. Action in these systems proceeds in a messy cascade of interdependent events. Instead of the discrete ticks of cause and effect that run a clock, a thousand clock springs try to simultaneously run a parallel system. Since there is no chain of command, the particular action of any single spring diffuses into the whole, making it easier for the sum of the whole to overwhelm the parts of the whole. What emerges from the collective is not a series of critical individual actions but a multitude of simultaneous actions whose collective pattern is far more important. This is the swarm model." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995)

"Light a fire, build up the steam, turn on a switch, and a linear system awakens. It’s ready to serve you. If it stalls, restart it. Simple collective systems can be awakened simply. But complex swarm systems with rich hierarchies take time to boot up. The more complex, the longer it takes to warm up. Each hierarchical layer has to settle down; lateral causes have to slosh around and come to rest; a million autonomous agents have to acquaint themselves. I think this will be the hardest lesson for humans to learn: that organic complexity will entail organic time." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995)

"Swarm systems generate novelty for three reasons: (1) They are 'sensitive to initial conditions' - a scientific shorthand for saying that the size of the effect is not proportional to the size of the cause - so they can make a surprising mountain out of a molehill. (2) They hide countless novel possibilities in the exponential combinations of many interlinked individuals. (3) They don’t reckon individuals, so therefore individual variation and imperfection can be allowed. In swarm systems with heritability, individual variation and imperfection will lead to perpetual novelty, or what we call evolution." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995)

"The internet model has many lessons for the new economy but perhaps the most important is its embrace of dumb swarm power. The aim of swarm power is superior performance in a turbulent environment. When things happen fast and furious, they tend to route around central control. By interlinking many simple parts into a loose confederation, control devolves from the center to the lowest or outermost points, which collectively keep things on course. A successful system, though, requires more than simply relinquishing control completely to the networked mob." (Kevin Kelly, "New Rules for the New Economy: 10 radical strategies for a connected world", 1998)

"Dumb parts, properly connected into a swarm, yield smart results." (Kevin Kelly, "New Rules for the New Economy", 1999)

"It is, however, fair to say that very few applications of swarm intelligence have been developed. One of the main reasons for this relative lack of success resides in the fact that swarm-intelligent systems are hard to 'program', because the paths to problem solving are not predefined but emergent in these systems and result from interactions among individuals and between individuals and their environment as much as from the behaviors of the individuals themselves. Therefore, using a swarm-intelligent system to solve a problem requires a thorough knowledge not only of what individual behaviors must be implemented but also of what interactions are needed to produce such or such global behavior." (Eric Bonabeau et al, "Swarm Intelligence: From Natural to Artificial Systems", 1999)

"[…] when software systems become so intractable that they can no longer be controlled, swarm intelligence offers an alternative way of designing an ‘intelligent’ systems, in which autonomy, emergence, and distributed functioning replace control, preprogramming, and centralization." (Eric Bonabeau et al, "Swarm Intelligence: From Natural to Artificial Systems", 1999)

"Agent subroutines may pass information back and forth, but subroutines are not changed as a result of the interaction, as people are. In real social interaction, information is exchanged, but also something else, perhaps more important: individuals exchange rules, tips, beliefs about how to process the information. Thus a social interaction typically results in a change in the thinking processes - not just the contents - of the participants." (James F Kennedy et al, "Swarm Intelligence", 2001)

"Just what valuable insights do ants, bees, and other social insects hold? Consider termites. Individually, they have meager intelligence. And they work with no supervision. Yet collectively they build mounds that are engineering marvels, able to maintain ambient temperature and comfortable levels of oxygen and carbon dioxide even as the nest grows. Indeed, for social insects teamwork is largely self-organized, coordinated primarily through the interactions of individual colony members. Together they can solve difficult problems (like choosing the shortest route to a food source from myriad possible pathways) even though each interaction might be very simple (one ant merely following the trail left by another). The collective behavior that emerges from a group of social insects has been dubbed 'swarm intelligence'." (Eric Bonabeau & Christopher Meyer, Swarm Intelligence: A Whole New Way to Think About Business, Harvard Business Review, 2001)

"[…] swarm intelligence is becoming a valuable tool for optimizing the operations of various businesses. Whether similar gains will be made in helping companies better organize themselves and develop more effective strategies remains to be seen. At the very least, though, the field provides a fresh new framework for solving such problems, and it questions the wisdom of certain assumptions regarding the need for employee supervision through command-and-control management. In the future, some companies could build their entire businesses from the ground up using the principles of swarm intelligence, integrating the approach throughout their operations, organization, and strategy. The result: the ultimate self-organizing enterprise that could adapt quickly - and instinctively - to fast-changing markets." (Eric Bonabeau & Christopher Meyer, "Swarm Intelligence: A Whole New Way to Think About Business", Harvard Business Review, 2001)

"Through self-organization, the behavior of the group emerges from the collective interactions of all the individuals. In fact, a major recurring theme in swarm intelligence (and of complexity science in general) is that even if individuals follow simple rules, the resulting group behavior can be surprisingly complex - and remarkably effective. And, to a large extent, flexibility and robustness result from self-organization." (Eric Bonabeau & Christopher Meyer, "Swarm Intelligence: A Whole New Way to Think About Business", Harvard Business Review, 2001)

"Many ants, all obeying simple rules, create the order that we see in an ant colony. This is an example of what has come to be known as swarm intelligence: behaviour or design that emerges out of simple responses by many individuals. Understanding how this happens is important in designing systems of components that have to coordinate their behaviour to achieve a desired result. Knowledge of the way order emerges in an ant colony, for instance, has been applied to create the so-called ant sort algorithm, which is used in contexts where items need to be sorted constantly, without any knowledge of the overall best plan." (David G Green, "The Serendipity Machine: A voyage of discovery through the unexpected world of computers", 2004)

"The most familiar example of swarm intelligence is the human brain. Memory, perception and thought all arise out of the nett actions of billions of individual neurons. As we saw earlier, artificial neural networks (ANNs) try to mimic this idea. Signals from the outside world enter via an input layer of neurons. These pass the signal through a series of hidden layers, until the result emerges from an output layer. Each neuron modifies the signal in some simple way. It might, for instance, convert the inputs by plugging them into a polynomial, or some other simple function. Also, the network can learn by modifying the strength of the connections between neurons in different layers." (David G Green, "The Serendipity Machine: A voyage of discovery through the unexpected world of computers", 2004)

"It is not only a metaphor to transform the Internet to a superbrain with self-organizing features of learning and adapting. Information retrieval is already realized by neural networks adapting to the information preferences of a human user with synaptic plasticity. In sociobiology, we can learn from populations of ants and termites how to organize traffic and information processing by swarm intelligence. From a technical point of view, we need intelligent programs distributed in the nets. There are already more or less intelligent virtual organisms {'agents'), learning, self-organizing and adapting to our individual preferences of information, to select our e-mails, to prepare economic transactions or to defend the attacks of hostile computer viruses, like the immune system of our body." (Klaus Mainzer, "Complexity Management in the Age of Globalization", 2006)

"Swarm Intelligence can be defined more precisely as: Any attempt to design algorithms or distributed problem-solving methods inspired by the collective behavior of the social insect colonies or other animal societies. The main properties of such systems are flexibility, robustness, decentralization and self-organization." ("Swarm Intelligence in Data Mining", Ed. Ajith Abraham et al, 2006)

"How is it that an ant colony can organize itself to carry out the complex tasks of food gathering and nest building and at the same time exhibit an enormous degree of resilience if disrupted and forced to adapt to changing situations? Natural systems are able not only to survive, but also to adapt and become better suited to their environment, in effect optimizing their behavior over time. They seemingly exhibit collective intelligence, or swarm intelligence as it is called, even without the existence of or the direction provided by a central authority." (Michael J North & Charles M Macal, "Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation", 2007)

"Swarm intelligence can be effective when applied to highly complicated problems with many nonlinear factors, although it is often less effective than the genetic algorithm approach discussed later in this chapter. Swarm intelligence is related to swarm optimization […]. As with swarm intelligence, there is some evidence that at least some of the time swarm optimization can produce solutions that are more robust than genetic algorithms. Robustness here is defined as a solution’s resistance to performance degradation when the underlying variables are changed." (Michael J North & Charles M Macal, "Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation", 2007)

"Swarm intelligence is sometimes also referred to as mob intelligence. Swarm intelligence uses large groups of agents to solve complicated problems. Swarm intelligence uses a combination of accumulation, teamwork, and voting to produce solutions. Accumulation occurs when agents contribute parts of a solution to a group. Teamwork occurs when different agents or subgroups of agents accidentally or purposefully work on different parts of a large problem. Voting occurs when agents propose solutions or components of solutions and the other agents vote explicitly by rating the proposal’s quality or vote implicitly by choosing whether to follow the proposal." (Michael J North & Charles M Macal, "Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation", 2007)

"Refers to a class of algorithms inspired by the collective behaviour of insect swarms, ant colonies, the flocking behaviour of some bird species, or the herding behaviour of some mammals, such that the behaviour of the whole can be considered as exhibiting a rudimentary form of 'intelligence'." (John Fulcher, "Intelligent Information Systems", 2009)

"The property of a system whereby the collective behaviors of unsophisticated agents interacting locally with their environment cause coherent functional global patterns to emerge." (M L Gavrilova, "Adaptive Algorithms for Intelligent Geometric Computing", 2009) 

"Is a discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, SI focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment." (Elina Pacini et al, "Schedulers Based on Ant Colony Optimization for Parameter Sweep Experiments in Distributed Environments", 2013)

"Swarm intelligence illustrates the complex and holistic way in which the world operates. Order is created from chaos; patterns are revealed; and systems are free to work out their errors and problems at their own level. What natural systems can teach humanity is truly amazing." (Lawrence K Samuels, "Defense of Chaos: The Chaology of Politics, Economics and Human Action", 2013)

"Swarm intelligence (SI) is a branch of computational intelligence that discusses the collective behavior emerging within self-organizing societies of agents. SI was inspired by the observation of the collective behavior in societies in nature such as the movement of birds and fish. The collective behavior of such ecosystems, and their artificial counterpart of SI, is not encoded within the set of rules that determines the movement of each isolated agent, but it emerges through the interaction of multiple agents." (Maximos A Kaliakatsos-Papakostas et al, "Intelligent Music Composition", 2013)

"Ants exhibit a 'neuron-like' behavior insofar as inactive ants have a low propensity to become spontaneously active, but can become excited by other ants with whom they come into contact. [...] Conversely, ants are prone to lapse back into inactivity if their activation is not sufficiently reinforced, and even exhibit a short refractory period (similar to neurons) before they can be reactivated – a mechanism which keeps the swarm from getting permanently 'locked' into an excitatory state." (Georg Theiner & John Sutton, "The collaborative emergence of group cognition", 2014) 

"These nature-inspired algorithms gradually became more and more attractive and popular among the evolutionary computation research community, and together they were named swarm intelligence, which became the little brother of the major four evolutionary computation algorithms." (Yuhui Shi, "Emerging Research on Swarm Intelligence and Algorithm Optimization", Information Science Reference, 2014)

"Collective intelligence of societies of biological (social animals) or artificial (robots, computer agents) individuals. In artificial intelligence, it gave rise to a computational paradigm based on decentralisation, self-organisation, local interactions, and collective emergent behaviours." (D T Pham & M Castellani, "The Bees Algorithm as a Biologically Inspired Optimisation Method", 2015)

"It is the field of artificial intelligence in which the population is in the form of agents which search in a parallel fashion with multiple initialization points. The swarm intelligence-based algorithms mimic the physical and natural processes for mathematical modeling of the optimization algorithm. They have the properties of information interchange and non-centralized control structure." (Sajad A Rather & P Shanthi Bala, "Analysis of Gravitation-Based Optimization Algorithms for Clustering and Classification", 2020)

"It is the discipline dealing with natural and artificial systems consisting of many individuals who coordinate through decentralized monitoring and self-organization." (Mehmet A Cifci, "Optimizing WSNs for CPS Using Machine Learning Techniques", 2021)

"Human beings suffer from a 'centralized mindset'; they would like to assign the coordination of activities to a central command. But the way social insects form highways and other amazing structures such as bridges, chains, nests (by the way, African fungus-growing termites have invented air conditioning) and can perform complex tasks (nest building, defense, cleaning, brood care, foraging, etc) is very different: they self-organize through direct and indirect interactions." (Eric Bonabeau)

"The most amazing thing about social insect colonies is that there's no individual in charge. If you look at a single ant, you may have the impression that it is behaving, if not randomly, at least not in synchrony with the rest of the colony. You feel that it is doing its own things without paying too much attention to what the others are doing." (Eric Bonabeau)

♾️Cognitive Science: On Collective Intelligence (Quotes)

"We must therefore establish a form of decision-making in which voters need only ever pronounce on simple propositions, expressing their opinions only with a yes or a no. […] Clearly, if anyone’s vote was self-contradictory (intransitive), it would have to be discounted, and we should therefore establish a form of voting which makes such absurdities impossible." (Nicolas de Condorcet, "On the form of decisions made by plurality vote", 1788)

"Collective wisdom, alas, is no adequate substitute for the intelligence of individuals. Individuals who opposed received opinions have been the source of all progress, both moral and intellectual. They have been unpopular, as was natural." (Bertrand Russell, "Why I Am Not a Christian", 1927)

"The collective intelligence of any group of people who are thinking as a 'herd' rather than individually is no higher than the intelligence of the stupidest members." (Mary Day Winn, "Adam's Rib", 1931)

"Learning is a property of all living organisms. […] Since organized groups can be looked upon as living entities, they can be expected to exhibit learning […]" (Winfred B. Hirschmann, "Profit from the Learning Curve", Harvard Business Review, 1964)

"A cardinal principle in systems theory is that all parties that have a stake in a system should be represented in its management." (Malcolm S Knowles, "The Adult Learner", 1973)

"Collective intelligence emerges when a group of people work together effectively. Collective intelligence can be additive (each adds his or her part which together form the whole) or it can be synergetic, where the whole is greater than the sum of its parts." (Trudy and Peter Johnson-Lenz, "Groupware: Orchestrating the Emergence of Collective Intelligence", cca. 1980)

"Cybernetic information theory suggests the possibility of assuming that intelligence is a feature of any feedback system that manifests a capacity for learning." (Paul Hawken et al, "Seven Tomorrows", 1982)

"The concept of organizational learning refers to the capacity of organizational complexes to develop experiential knowledge, instincts, and 'feel' or intuition which are greater than the combined knowledge, skills and instincts of the individuals involved." (Don E. Kash, "Perpetual Innovation", 1989)

"We haven't worked on ways to develop a higher social intelligence […] We need this higher intelligence to operate socially or we're not going to survive. […] If we don't manage things socially, individual high intelligence is not going to make much difference. [...] Ordinary thought in society is incoherent - it is going in all sorts of directions, with thoughts conflicting and canceling each other out. But if people were to think together in a coherent way, it would have tremendous power." (David Bohm, "New Age Journal", 1989)

"Civilization is to groups what intelligence is to individuals. It is a means of combining the intelligence of many to achieve ongoing group adaptation. […] Civilization, like intelligence, may serve well, serve adequately, or fail to serve its adaptive function. When civilization fails to serve, it must disintegrate unless it is acted upon by unifying internal or external forces." (Octavia E Butler, "Parable of the Sower", 1993)

"Great leaders reinforce the idea that accomplishment in our society comes from great individual acts. We credit individuals for outcomes that required teams and communities to accomplish." (Peter Block, "Stewardship", 1993)

"We must learn to think together in an integrated, synergistic fashion, rather than in fragmented and competitive ways." (Joanna Macy, Noetic Sciences Bulletin, 1994-1995)

"The leading edge of growth of intelligence is at the cultural and societal level. It is like a mind that is struggling to wake up. This is necessary because the most difficult problems we face are now collective ones. They are caused by complex global interactions and are beyond the scope of individuals to understand and solve. Individual mind, with its isolated viewpoints and narrow interests, is no longer enough." (Jeff Wright, "Basic Beliefs", [email] 1995)

"It [collective intelligence] is a form of universally distributed intelligence, constantly enhanced, coordinated in real time, and resulting in the effective mobilization of skills. I'll add the following indispensable characteristic to this definition: The basis and goal of collective intelligence is mutual recognition and enrichment of individuals rather than the cult of fetishized or hypostatized communities." (Pierre Levy, "Collective Intelligence", 1999)

The three basic mechanisms of averaging, feedback and division of labor give us a first idea of a how a CMM [Collective Mental Map] can be developed in the most efficient way, that is, how a given number of individuals can achieve a maximum of collective problem-solving competence. A collective mental map is developed basically by superposing a number of individual mental maps. There must be sufficient diversity among these individual maps to cover an as large as possible domain, yet sufficient redundancy so that the overlap between maps is large enough to make the resulting graph fully connected, and so that each preference in the map is the superposition of a number of individual preferences that is large enough to cancel out individual fluctuations. The best way to quickly expand and improve the map and fill in gaps is to use a positive feedback that encourages individuals to use high preference paths discovered by others, yet is not so strong that it discourages the exploration of new paths." (Francis Heylighen, "Collective Intelligence and its Implementation on the Web", 1999)

"With the growing interest in complex adaptive systems, artificial life, swarms and simulated societies, the concept of “collective intelligence” is coming more and more to the fore. The basic idea is that a group of individuals (e. g. people, insects, robots, or software agents) can be smart in a way that none of its members is. Complex, apparently intelligent behavior may emerge from the synergy created by simple interactions between individuals that follow simple rules." (Francis Heylighen, "Collective Intelligence and its Implementation on the Web", 1999)

"Cultures are never merely intellectual constructs. They take form through the collective intelligence and memory, through a commonly held psychology and emotions, through spiritual and artistic communion." (Tariq Ramadan, "Islam and the Arab Awakening", 2012)

"[…] recent researchers in artificial intelligence and computational methods use the term swarm intelligence to name collective and distributed techniques of problem solving without centralized control or provision of a global model. […] the intelligence of the swarm is based fundamentally on communication. […] the member of the multitude do not have to become the same or renounce their creativity in order to communicate and cooperate with each other. They remain different in terms of race, sex, sexuality and so forth. We need to understand, then, is the collective intelligence that can emerge from the communication and cooperation of such varied multiplicity." (Antonio Negri, "Multitude: War and Democracy in the Age of Empire", 2004)

"Collective Intelligence (CI) is the capacity of human collectives to engage in intellectual cooperation in order to create, innovate, and invent." (Pierre Levy, "Toward a Self-referential Collective Intelligence", 2009)

"How is it that an ant colony can organize itself to carry out the complex tasks of food gathering and nest building and at the same time exhibit an enormous degree of resilience if disrupted and forced to adapt to changing situations? Natural systems are able not only to survive, but also to adapt and become better suited to their environment, in effect optimizing their behavior over time. They seemingly exhibit collective intelligence, or swarm intelligence as it is called, even without the existence of or the direction provided by a central authority." (Michael J North & Charles M Macal, "Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation", 2007)

"Some paths to superintelligence require great resources and are therefore likely to be the preserve of large well-funded projects. Whole brain emulation, for instance, requires many different kinds of expertise and lots of equipment. Biological intelligence enhancements and brain–computer interfaces would also have a large scale factor: while a small biotech firm might invent one or two drugs, achieving superintelligence along one of these paths (if doable at all) would likely require many inventions and many tests, and therefore the backing of an industrial sector or a well-funded national program. Achieving collective superintelligence by making organizations and networks more efficient requires even more extensive input, involving much of the world economy." (Nick Bostrom, "Superintelligence: Paths, Dangers, Strategies", 2014)

"By analyzing patterns in crowd behavior and honing methods of gauging fitness - or at least extricating it from popularity - the market can effectively use collective intelligence so that the cream always rises to the top." (Albert-László Barabási, "The Formula: The Universal Laws of Success", 2018)

❄️Systems Thinking: On Ecology (Quotes)

"[…] for as all organic beings are striving, it may be said, to seize on each place in the economy of nature, if any one species does not become modified and improved in a corresponding degree with its competitors, it will soon be exterminated." (Charles Darwin, "On the Origin of Species", 1859)

"Let it be borne in mind how infinitely complex and close-fitting are the mutual relations of all organic beings to each other and to their physical conditions of life. " (Charles Darwin, "On the Origin of Species", 1859)

"[…] without the theory of evolution all the big general series of phenomena of organic nature remain completely incomprehensible and inexplicable riddles, while by means of this theory they can be explained simply and consistently. This holds especially true for two complexes of biological phenomena which we now in conclusion wish to single out in a few words. These form the subject of two special branches of physiology which so far have been largely neglected, namely, the ecology and chorology of organisms." (Ernst Haeckel, "Generelle Morphologie der Organismen", 1866)

"By ecology we mean the body of knowledge concerning the economy of nature - the investigation of the total relations of the animal both to its inorganic and to its organic environment; including, above all, its friendly and inimical relations with those animals and plants with which it comes directly or indirectly into contact - in a word, ecology is the study of all those complex interrelations referred to by Darwin as the conditions of the struggle for existence." (Ernst Haeckel, [lecture] 1869)

 

"[employment of] exact or mathematical methods […] unfortunately is impossible in most branches of science (particularly in biology), because the empirical foundations are much too imperfect and the present problems much too complicated. Mathematical treatment of these does more harm than good because it gives a deceptive semblance of certainty which is not actually attainable. Part of physiology also involves problems which are difficult or impossible to resolve exactly, and these include the chorology and ecology of plankton." (Ernst Haeckel, “Plantonic studies”, 1891) 

"At a time when ecology and genetics are each racing swiftly towards one new concept after another, yet with little contact of thought between the two subjects, there may be some advantage in surveying, if only synoptically and in preliminary fashion, the largely uncharted territory between them." (Charles S Elton, 1938)

"If there are favourable habitats and favorable forms of association for animals and plants, as ecology demonstrates, why not for men? If each particular natural environment has has its own balance; is there not perhaps an equivalent of this in culture?" (Lewis Mumford, "The Culture of Cities", 1938)

"That land is a community is the basic concept of ecology, but that land is to be loved and respected is an extension of ethics." (Aldo Leopold, "A Sand County Almanac", 1949)

"In calling society an ecological system we are not merely using an analogy; society is an example of the general concept of an 'ecosystem' that is, an ecological system of which biological systems - forests, fields, swamps - are other examples." (Kenneth E Boulding, "A Reconstruction of Economics", 1950)

"The general study of the equilibria and dynamics of populations seems to have no name; but as it has probably reached its highest development in the biological study known as 'ecology,' this name may well be given to it." (Kenneth E Boulding, "A Reconstruction of Economics", 1950)

"Can any of us fix anything? No. None of us can do that. We're specialized. Each one of us has his own line, his own work. I understand my work, you understand yours. The tendency in evolution is toward greater and greater specialization. Man's society is an ecology that forces adaptation to it. Continued complexity makes it impossible for us to know anything outside our own personal field — I can't follow the work of the man sitting at the next desk over from me. Too much knowledge has piled up in each field. And there are too many fields. (Philip K. Dick, "The Variable Man", 1952)

If we have been slow to develop the general concepts of ecology and conservation, we have been even more tardy in recognizing the facts of the ecology and conservation of man himself. We may hope that this will be the next major phase in the development of biology. Here and there awareness is growing that man, far from being the overlord of all creation, is himself part of nature, subject to the same cosmic forces that control all other life. Man's future welfare and probably even his survival depend upon his learning to live in harmony, rather than in combat, with these forces. (Rachel Carson, "Essay on the Biological Sciences" in Good Reading, 1958)

"The thing the ecologically illiterate don't realize about an ecosystem is that it's a system. A system! A system maintains a certain fluid stability that can be destroyed by a misstep in just one niche. A system has order, a flowing from point to point. If something dams the flow, order collapses. The untrained miss the collapse until too late. That's why the highest function of ecology is the understanding of consequences." (Frank Herbert, "Dune", 1965)

"Evolution cannot be understood except in the frame of ecosystems." (Ramón Margalef, "Perspectives in Ecological Theory", 1968)

"For some years now the activity of the artist in our society has been trending more toward the function of the ecologist: one who deals with environmental relationships. Ecology is defined as the totality or pattern of relations between organisms and their environment. Thus the act of creation for the new artist is not so much the invention of new objects as the revelation of previously unrecognized relation- ships between existing phenomena, both physical and metaphysical. So we find that ecology is art in the most fundamental and pragmatic sense, expanding our apprehension of reality." (Gene Youngblood, "Expanded Cinema", 1970) 

"Ecology is the scientific study of the interactions that determine the distribution and abundance of organisms." (Charles J Krebs, "Ecology", 1972)

"To do science is to search for repeated patterns, not simply to accumulate facts, and to do the science of geographical ecology is to search for patterns of plants and animal life that can be put on a map." (Robert H. MacArthur, "Geographical Ecology", 1972)

"It is the intertwined and interacting mechanisms of evolution and ecology, each of which is at the same time a product and a process, that are responsible for life as we see it, and as it has been." (James W. Valentine, "Evolutionary Paleoecology of the Marine Biosphere", 1973)

"This paper introduces a concept of organizational ecology. This refers to the organizational field created by a number of organizations, whose interrelations compose a system at the level of the field as a whole. The overall field becomes the object of inquiry, not the single organization as related to its organization-set. The emergence of organizational ecology from earlier organization theory is traced and illustrated from empirical studies. Its relevance to the task of institution-building, in a world in which the environment has become exceedingly complex and more interdependent, is argued." (Eric Trist , "A concept of organizational eecolog", Australian journal of management 2 (2), 1977)

"We argue that in order to deal with the various inertial pressures the adaptation perspective must be supplemented with a selection orientation. We consider first two broad issues that are preliminary to ecological modelling. The first concerns appropriate units of analysis. Typical analyses of the relation of organizations to environments take the point of view of a single organization facing an environment." (Michael T Hannan, "The Population Ecology of Organizations", 1977)

"The social dynamics of human history, even more than that of biological evolution, illustrate the fundamental principle of ecological evolution - that everything depends on everything else. The nine elements that we have described in societal evolution of the three families of phenotypes - the phyla of things, organizations and people, the genetic bases in knowledge operating through energy and materials to produce phenotypes, and the three bonding relations of threat, integration and exchange - all interact on each other." (Kenneth E Boulding, "Ecodynamics: A New Theory Of Societal Evolution", 1978)

"The world is a complex, interconnected, finite, ecological–social–psychological–economic system. We treat it as if it were not, as if it were divisible, separable, simple, and infinite. Our persistent, intractable global problems arise directly from this mismatch." (Donella Meadows,"Whole Earth Models and Systems", 1982)

"Ultimately, uncontrolled escalation destroys a system. However, change in the direction of learning, adaptation, and evolution arises from the control of control, rather than unchecked change per se. In general, for the survival and co-evolution of any ecology of systems, feedback processes must be embodied by a recursive hierarchy of control circuits." (Bradford P Keeney, "Aesthetics of Change", 1983)

"Scientific ecology works along a very great number of different lines - perhaps a typical feature of this discipline. In ecological research each element of these different approaches to work and the of the varied opinions formed about them plays its part. (Wolfgang Haber, Universitas: A Quarterly German Review of the Arts and Sciences Vol. 26, (2), 1984)

"The ecological principle of unity in diversity grades into a richly mediated social principle; hence my use of the term social ecology." (Murray Bookchin,"What Is Social Ecology?" , 1984)

"The existing literature usually stresses the capacity of organizations to learn about and adapt to uncertain, changing environments. We think this emphasis is misplaced. The most important issues about the applicability of evolutionary-ecological theories to organizations concern the timing of changes. Learning and adjusting structure enhance the chance of survival only if the speed of response is commensurate with the temporal patterns of relevant environments." (Michael T Hannan,"Organizational ecology", 1989)

"To halt the decline of an ecosystem, it is necessary to think like an ecosystem." (Douglas P Wheeler, EPA Journal, 1990)

"Ecological Economics studies the ecology of humans and the economy of nature, the web of interconnections uniting the economic subsystem to the global ecosystem of which it is a part." (Robert Costanza, "Ecological Economics: the science and management of sustainability", 1992)

"When the study of the household (ecology) and the management of the household (economics) can be merged, and when ethics can be extended to include environmental as well as human values, then we can be optimistic about the future of humankind. Accordingly, bringing together these three 'E's' is the ultimate holism and the great challenge for our future." (Eugene Odum," Ecology and our endangered life-support systems", 1993)

"Progressively higher levels of organization are attained as catalytic cycles on one level interlock and form hypercycles: these are systems on a higher level of organization. Thus molecules emerge from a combination of chemically active atoms; protocells emerge from sequences of complex molecules; eukaryotic cells emerge among the prokaryotes; metazoa make their appearance among the protozoa and converge in still higher-level ecological and social systems." (Ervin László,"Vision 2020: Reordering Chaos for Global Survival" , 1994)

"The new paradigm may be called a holistic world view, seeing the world as an integrated whole rather than a dissociated collection of parts. It may also be called an ecological view, if the term 'ecological' is used in a much broader and deeper sense than usual. Deep ecological awareness recognizes the fundamental interdependence of all phenomena and the fact that, as individuals and societies we are all embedded in (and ultimately dependent on) the cyclical process of nature." (Fritjof Capra & Gunter A. Pauli," Steering business toward sustainability", 1995)

"In the past several years, social work has increasingly focused on an ecological model. This model integrates both treatment and reform by conceptualizing and emphasizing the dysfunctional transactions between people and their physical and social environments. Human beings are viewed as developing and adapting through transactions with all elements of their environments. An ecological model gives attention to both internal and external factors. It does not view people as passive reactors to their environments but, rather, as being involved in dynamic and reciprocal interactions with them." (Charles Zastrow, "The practice of social work", 1995)

"Economics emphasizes competition, expansion, and domination; ecology emphasizes cooperation, conservation, and partnership. (Fritjof Capra, "The Web of Life", 1996)

"A major clash between economics and ecology derives from the fact that nature is cyclical, whereas our industrial systems are linear. Our businesses take resources, transform them into products plus waste, and sell the products to consumers, who discard more waste […]" (Fritjof Capra, "The Web of Life", 1996)

"These, then, are some of the basic principles of ecology - interdependence, recycling, partnership, flexibility, diversity, and, as a consequence of all those, sustainability... the survival of humanity will depend on our ecological literacy, on our ability to understand these principles of ecology and live accordingly."(Fritjof Capra, "The Web of Life", 1996)

"Understanding ecological interdependence means understanding relationships. It requires the shifts of perception that are characteristic of systems thinking—from the parts to the whole, from objects to relationships, from contents to patterns. [...] Nourishing the community means nourishing those relationships. (Fritjof Capra, "The Web of Life", 1996)

"Ecology, as it is currently practiced, sometimes deals with human impacts on ecosystems, but the more common tendency is to stick to 'natural' systems."(Robert Costanza & Janis King, "The first decade of ecological economics", Ecological Economics 28 (1), 1999)

"Ecological rationality uses reason – rational reconstruction – to examine the behavior of individuals based on their experience and folk knowledge, who are ‘naïve’ in their ability to apply constructivist tools to the decisions they make; to understand the emergent order in human cultures; to discover the possible intelligence embodied in the rules, norms and institutions of our cultural and biological heritage that are created from human interactions but not by deliberate human design. People follow rules without being able to articulate them, but they can be discovered." (Vernon L Smith, "Constructivist and ecological rationality in economics",  2002)

"Organizations need to undergo fundamental changes, both in order to adapt to the new business environment and to become ecologically sustainable." (Fritjof Capra, "The Hidden Connections", 2002)

"Ecology, on the other hand, is messy. We cannot find anything deserving of the term law, not because ecology is less developed than physics, but simply because the underlying phenomena are more chaotic and hence less amenable to description via generalization." (Lev Ginzburg & Mark Colyvan," Ecological Orbits: How Planets Move and Populations Grow", 2004)

"Limiting factors in population dynamics play the role in ecology that friction does in physics. They stop exponential growth, not unlike the way in which friction stops uniform motion. Whether or not ecology is more like physics in a viscous liquid, when the growth-rate-based traditional view is sufficient, is an open question. We argue that this limit is an oversimplification, that populations do exhibit inertial properties that are noticeable. Note that the inclusion of inertia is a generalization—it does not exclude the regular rate-based, first-order theories. They may still be widely applicable under a strong immediate density dependence, acting like friction in physics." (Lev Ginzburg & Mark Colyvan, "Ecological Orbits: How Planets Move and Populations Grow", 2004)

"It is science that brings us an understanding of the true complexity of natural systems. The insights from the science of ecology are teaching us how to work with the checks and balances of nature, and encouraging a new, rational, limited-input, environmentally sound means of vineyard management that offers a third way between the ideologically driven approach of Biodynamics and conventional chemical-based agricultural systems." (Jamie Goode," The Science of Wine: From Vine to Glass", 2005)

"An ecology provides the special formations needed by organizations. Ecologies are: loose, free, dynamic, adaptable, messy, and chaotic. Innovation does not arise through hierarchies. As a function of creativity, innovation requires trust, openness, and a spirit of experimentation - where random ideas and thoughts can collide for re-creation." (George Siemens, "Knowing Knowledge", 2006)

"Knowledge flow can be likened to a river that meanders through the ecology of an organization. In certain areas, the river pools and in other areas it ebbs. The health of the learning ecology of the organization depends on effective nurturing of flow." (George Siemens, "Knowing Knowledge", 2006)

"Nodes and connectors comprise the structure of a network. In contrast, an ecology is a living organism. It influences the formation of the network itself." (George Siemens, "Knowing Knowledge", 2006)

"[ecology:] the scientific study of the distribution and abundance of organisms and the interactions that determine distribution and abundance." (Michael Begon et al, "Ecology: From individuals to ecosystems", 2006)

"The living world can be viewed as a biological hierarchy that starts with subcellular particles, and continues up through cells, tissues and organs. Ecology deals with the next three levels: the individual organism, the population (consisting of individuals of the same species) and the community (consisting of a greater or lesser number of species populations). At the level of the organism, ecology deals with how individuals are affected by (and how they affect) their environment. At the level of the population, ecology is concerned with the presence or absence of particular species, their abundance or rarity, and with the trends and fluctuations in their numbers. Community ecology then deals with the composition and organization of ecological communities." (Michael Begon et al, "Ecology: From individuals to ecosystems", 2006)

"We need to renegotiate our contract with nature. Ecology is a unifying force that can diminish intolerance and expand our empathy towards others - both human and animal." (Gregory Colbert, "Peace and Harmony: The Message of Our Discovery", [Photo No. 427] 2006)

"When we focus on designing ecologies in which people can forage for knowledge, we are less concerned about communicating the minutiae of changing knowledge. Instead, we are creating the conduit through which knowledge will flow." (George Siemens, "Knowing Knowledge", 2006)

"Any new dominant communications medium leads to a new information ecology in society that inevitably changes the way ideas, feelings, wealth, power and influence are distributed and the way collective decisions are made." (Al Gore,"The Assault on Reason", 2007)

"Social ecology is based on the conviction that nearly all of our present ecological problems originate in deep-seated social problems. It follows, from this view, that these ecological problems cannot be understood, let alone solved, without a careful understanding of our existing society and the irrationalities that dominate it. To make this point more concrete: economic, ethnic, cultural, and gender conflicts, among many others, lie at the core of the most serious ecological dislocations we face today—apart, to be sure, from those that are produced by natural catastrophes." (Murray Bookchin, "Social Ecology and Communalism", 2007)

"In ecology, we are often interested in exploring the behavior of whole systems of species or ecosystem composed of individual components which interact through biological processes. We are interested not simply in the dynamics of each species or component in isolation, but the dynamics of each species or component in the context of all the others and how those coupled dynamics account for properties of the system as a whole, such as its persistence. This is what people seem to mean when they say that ecology is ‘holistic’, an otherwise rather vague term." (John Pastor, "Mathematical Ecology of Populations and Ecosystems", 2008)

"Much of what we deal with in ecology are rates of change of biological objects: growth of an organism, decay of a dead leaf, fluctuations in populations, accumulation or erosion of soil, increases or decreases in lake levels, etc. But rates of change are some of the hardest things to measure. What we measure are static properties such as the sizes of objects at different times and then infer that change has taken place between those two measurements." (John Pastor, "Mathematical Ecology of Populations and Ecosystems", 2008)

"Therefore, mathematical ecology does not deal directly with natural objects. Instead, it deals with the mathematical objects and operations we offer as analogs of nature and natural processes. These mathematical models do not contain all information about nature that we may know, but only what we think are the most pertinent for the problem at hand. In mathematical modeling, we have abstracted nature into simpler form so that we have some chance of understanding it. Mathematical ecology helps us understand the logic of our thinking about nature to help us avoid making plausible arguments that may not be true or only true under certain restrictions. It helps us avoid wishful thinking about how we would like nature to be in favor of rigorous thinking about how nature might actually work." (John Pastor, "Mathematical Ecology of Populations and Ecosystems", 2008)

"This new model of development would be based clearly on the goal of sustainable human well-being. It would use measures of progress that clearly acknowledge this goal. It would acknowledge the importance of ecological sustainability, social fairness, and real economic efficiency. Ecological sustainability implies recognizing that natural and social capital are not infinitely substitutable for built and human capital, and that real biophysical limits exist to the expansion of the market economy." (Robert Costanza, "Toward a New Sustainable Economy", 2008)

"The answers to the human problems of ecology are to be found in economy. And the answers to the problems of economy are to be found in culture and character. To fail to see this is to go on dividing the world falsely between guilty producers and innocent consumers." (Wendell Berry, "What Are People For?: Essays", 2010)

"Economists don't seem to have noticed that the economy sits entirely within the ecology." (Carl Safina, "The View from Lazy Point: A Natural Year in an Unnatural World", 2011)

"Shallow ecology is anthropocentric, or human-centered. It views humans as above or outside of nature, as the source of all value, and ascribes only instrumental, or ‘use’, value to nature. Deep ecology does not separate humans - or anything else-from the natural environment. It sees the world not as a collection of isolated objects, but as a network of phenomena that are fundamentally interconnected and interdependent. Deep ecology recognizes the intrinsic value of all living beings and views humans as just one particular strand in the web of life." (Fritjof Capra, "The Systems View of Life: A Unifying Vision", 2014)

"When we chop nature into bits in an attempt to understand it, we lose sight of the relationships among those bits. But ecological healing is all about the healing of relationships." (Charles Eisenstein, "Climate: A New Story", 2018)

"Ecology is] the science of relations between organisms and their environment." (Ernst Haeckel)

🔖Knowledge Representation: Defining Mental Models (Part III)

Mental Models Series

Packing one or more labels (expressing the same thing) together with a set of characteristics into discrete cognitive units within a context is what defines a concept. Therefore, one or more labels can be associated with a concept, while a label can be associated with more concepts when different contexts apply. 

Concepts are associated with physical objects (doors, keys, latches, knobs), subjects (doorman, locksmith), scenarios and actions (opening a door, opening a door with a key, opening a door without a key, closing a door, breaking a door). As previously discussed, this mix forms together a ‘mental model’, however more precision is needed if we want to delimit the borders of a mental model.

When one or more labels are associated with one or more actions, or a chain of causality exists, then a mental model can be more likely extrapolated (aka build). Minimally we can consider only one object (the door) and only one action (opening the door) with two states (door open, door closed). One can consider the action of opening a door also as an objective, while in addition a basis heuristic is also implied (pushing the door). Thus, to open the door, I push it, and it will open or will remain closed. This is a description of the simplest mechanical model I can build. 

Of course, I can consider more factors: if I don’t apply enough force, the door might not open, so the force applied needs to be higher than the resistance. However, “force” and “resistance” are indirectly considered in our model and for the sake of simplicity can be ignored. Still, I can extend the model with one more heuristic – trying again. I can try again by applying the same force, or by pushing the door with my body’s force (maximal force) if the door didn’t open the first time. In the later case we deal thus with a third heuristic. A fourth heuristic might be to slightly increase the push force. 

When one talks about repeatability of an action a loop is implied with an exist condition - I push the door, it doesn’t open, then I try to apply more force. When just enough force is applied the door opens, and this is the exit criterion from the loop. If after several tries the door doesn’t open, then one has the choice of persisting or of breaking the option – another exit criterion. Typically a few tries are enough, though one might be tempted to try again later, though this behavior might include other drivers and probably the existence of other models. 

One can extend the model by including a door latch and with it adding more heuristics, pushing down/up the latch, if a simple push of the door doesn’t work. With it the number of states increases – latch push down, respectively up. Also here the force applied plays an important role and a loop is implied. The more objects are involved, more complex the model becomes. Of course, one doesn’t need to consider all the heuristics and states, just the more relevant or more probably one. 

On the other side, the model we are trying to build even if reflects to some degree how the mind works, is deeply submerged into our cognitive space to the degree that it become an automatism. However, we can become aware of the entire mechanism as soon we try to describe it into a functional unit, even if the process might not always be that straightforward as we would like to. In addition, the are many elements which we are forced to ignore or simplify. 

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🔖Knowledge Representation: Defining Mental Models (Part II)

Mental Models Series

Even if a good definition for a concept is available this doesn’t mean that people are able to recognize concept’s characteristics, usage and area of applicability. Therefore, the easiest way to understand what a concept like ‘mental model’ is about is by having one or more representative examples. Even if the examples aren’t always sufficient, they allow us in theory more chances to succeed. With this in mind, let’s consider the scenario of ‘opening a door’. 

If I need to open the door, then I can use the door knob or latch to do that. If I try that and the door doesn’t open, then I might also need a certain key (or one deals with something that isn’t a door, e.g. a false door). Only a specific key will work unless I have a master key which can open any door within the building, though not in other buildings. Supposing that I have other special skills, I could open a locked door with other objects – a big and hard object that will break the door, maybe a picklock, or any other device that will help me achieve that. Otherwise I can call somebody who has the skills or means to do it. I still need to differentiate between the different types of doors, keys or on whether I am or not entitled to enter the building.

We deal thus with a set of objects (doors, keys, knobs, latches, picklock), subjects (doorman, security man, locksmith), actions (opening, breaking, entering), heuristics (trying the knob first, first trying by myself then ask for help) and contexts (am I entitled to open the door?, can I ask for help?) which, when put together form a mental model. If we look at this mix, one can break it down to smaller units – how to open a door, a lock, how to bypass security, how to break a door, etc. All these can be considered as separate mental models which when put within a context or situation can form another mental model. Therefore, the delimitation between mental models is really thin as many mental models overlap or aggregate to handle more complex situations. 

To open a door, one even doesn’t need to know how the objects or subjects are called. Being able to use a language gives us this luxury. In contrast, a cat or a dog can open an unlocked door (when that is possible) and probably there must have some cognitive structure that allows them to recognize that they deal with something that can be opened, how it could be opened, and repeat the action when needed. Of course, they could also mimic behavior they have seen (us opening the door), though there must be a small sprung of intelligence in recognizing that by reaching to the door latch and grabbing it the door will open.

Being able to use language allows us to describe and communicate how to open a door, how to arrive from A to B, how to solve problems, etc. Labeling things and agreeing on the labels with other people helps in the process, to the degree that we can use the respective labels to communicate with people we don’t even know, or cope with unknown situations based on descriptions using the labels we know (e.g. opening a door with a touch key). It helps also that the labels carry with them some characteristics that define them and that characteristics apply to a set of objects sharing them. There are also characteristics that can slightly differ, while upon case these variances can be ignored or make a considerable difference.

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🔖Knowledge Representation: Mental Models (Part I: An Early Retrospective)

Mental Models Series

Browsing through the various material available on mental models it’s hard not to observe the frequency with which relatively modern scholastic sources like Craik [46], Johnson-Laird [47] or Boulding [48] are considered as starting points in elaborating the ideas. One is tempted to believe there's nothing else before them. However, as soon one leaves the standard paths of cognitive sciences and adventure on the paths of philosophy or pseudosciences, one is surprised to find a rich of material attempting to describe how the mind perceives, represents und understands reality, respectively the phenomena we deal with. 

One can agree that Craik’s work was a milestone within this context, as he considered that organisms and not only humans carry ‘small-scale model’ of external reality (aka mental models). However the term can be rooted back to antiquity if we consider Aristotle’s phantasmata (mental images) - perceptual states without matter used by intellect to think and associated with the imagination faculty [4] [5]. Similar interpretations appear in Augustinus [8], Avicenna [9] [10] [11], Maimonides [12], Aquinas [13] and much later St. Thomas [14] or Spinoza’s [16] works. Probably many of these sources have as direct or indirect source Aristotle’s work.

One can be entitled to suppose that there are also earlier similar attempts to explain how the mind reflects the reality, for example Plato’s 'images of beauty' [3] and model of resemblance [2]. Further early references are met in the works of Cicero [6] or Plotinus’ Enneads [7]. The available translations are maybe copies that haven’t kept the original or the meaning were adapted to modern times. References to the 'eye of the mind' or the 'thinking soul' are indicators for such attempts, typically in the context of treating imagination and perception themes.

One should not neglect the scriptures of the East, probably the earliest knowledge sources which attempted to describe metaphorically the inner workings of the mind. Patañjali 'Yoga Sūtras' [1] is maybe the best-known example of such sources, available in various translations with rich commentaries. Despite its character of pseudoscience and its interpretational complexity, the depth of the work could provide food for thought for the scientist and nonscientist altogether.

Hobbes [15], Berkely [17] [18], Hume [19], Reid [21] or Coleridge’s [22] [23] works seem to be more elaborated and have the advantage of eliminating the translator from the process, being to some degree easier to consume, despite the complex logical constructs of the literary style and the muddy character of the advanced ideas. In opposition with them, Kant [20] and later Nietzsche’s [25] works form the groundwork on which the modern German and European philosophy was built upon. Kant and  Nietzsche’s schemas and schemata reflect the purposeful and structural character of such representations when associated with concepts.

Starting with Helmholtz’s 'Anschauungsbild' [24] (mental image) the term starts being use by scientists like Hertz [26] [31], Boltzmann [29] [35], Heisenberger [44] or Dirac [43] in respect to the modelling of phenomena. In parallel the works of Galton [27], Frege [28] [39], James [30], Peirce [32] [37] [38], Bergson [33], Wundt [34], Ribot [36], Wittgenstein [40], Freud [41], Piaget [42] or Sartre [45] represent a new wave into the development of cognitive sciences. It’s a really long list of precursors, probably incomplete, and ignoring them in the detriment of Craik, Johnson-Laird, Boulding, or any others, is an injustice made to the former.

Whether one talks about phantasmata, (mental) images, pictures, models or representations, schema, schemata, diagrams, conceptual schemes/models, or frames, they are all metaphors with similar meaning. It’s important to stress the fact that the metaphors used across the centuries and geographies reflect also the vocabulary available and the languages used to express them. The deeper one dives into the early ages’ cultural heritage, the more one discovers such metaphors. Unfortunately, without appropriate keys to decipher them and minds to explore them, the sources can become lost in the thread of time. 

References (the quotes are available here):

[1] Yoga Sūtras of Patañjali, cca. 500 BC-400 CE

[2] Plato, “The Symposium", cca. 385–370 BC)
[3] Plato, “Timaeus”, 360 BC

[4] Aristotle, "De Anima" III, cca. 350 BC

[5] Aristotle, “De Memoria et Reminiscentia” [On Memory and Recollection], 4th century BC

[6] Marcus Tullius Cicero, "De Natura Deorum" ["On the Nature of the Gods"], 45 BC

[7] Plotinus, “Enneads”, cca. 270 AD

[8] Aurelius Augustinus, "The City of God", early 400s

[9] Avicenna Latinus [Ibn Sina], "A Compendium on the Soul", cca. 996-997

[10] Avicenna Latinus [Ibn Sina], "Liber De anima", cca. 1014-1027

[11] Avicenna Latinus [Ibn Sina], "Pointer and Reminders", cca. 1030

[12] Moses Maimonides, “The Guide for the Perplexed”, 1190

[13] Saint Thomas Aquinas, “De Anima” III, cca. 1268

[14] John of St. Thomas, “Tractatus de signis”, 1632

[15] Thomas Hobbes, “Leviathan”, 1651

[16] Baruch Spinoza, "Ethics", 1677

[17] George Berkeley, "Principles of Human Knowledge", 1710

[18] George Berkeley, "Three Dialogues", 1713

[19] David Hume, “Treatise of Human Nature”, 1738

[20] Immanuel Kant," Critique of Pure Reason", 1781

[21] Thomas Reid, "Essays on the Intellectual Powers of Man", 1785

[22] Samuel T Coleridge, "On the Principles of Genial Criticism", 1814

[23] Samuel T Coleridge, "The Statesman's Manual", 1816 

[24] Hermann von Helmholtz, "Tonempfindungen" ["Sensations of Tone"], 1863

[25] Friedrich Nietzsche, "On Truth and Lie in an Extra-Moral Sense", 1873

[26] Heinrich Hertz, "The Facts in Perception", 1878

[27] Francis Galton, “Mental imagery”, 1880

[28] Gottlob Frege, "The Foundations of Arithmetic", 1884

[29] Ludwig E Boltzmann, “On the Significance of Theories”, 1890

[30] William James, “The Principles of Psychology”, 1890

[31] Heinrich Hertz, “The Principles of Mechanics Presented in a New Form”, 1894

[32] Charles S Peirce, “Kinds of Reasoning”, cca. 1896

[33] Henri Bergson, "Matter and Memory", 1896

[34] Wilhelm M Wundt, “Outlines of Psychology”, 1897

[35] Ludwig Boltzmann, "On the development of the methods of theoretical physics", 1899

[36] Théodule-Armand Ribot, "Essay on the Creative Imagination", 1900

[37] Charles S Peirce, “Fallibility of Reasoning and the Feeling of Rationality”, cca. 1902

[38] Charles S Peirce, “On Existential Graphs, Euler's Diagrams, and Logical”, 1903 

[39] Gottlob Frege, [in "On the Foundations of Geometry and Formal Theories of Arithmetic" 1971] cca. 1903-1909

[40] Ludwig Wittgenstein, “Tractatus Logico-Philosophicus”, 1922

[41] Sigmund Freud, "The Ego And The Id", 1923

[42] Jean Piaget, "The Language and Thought of the Child", 1926

[43] Paul A M Dirac, "The Principles of Quantum Mechanics", 1930

[44] Werner K Heisenberg, "The Physical Principles of the Quantum Theory", 1930

[45] Jean-Paul Sartre, “The Psychology of Imagination”, 1940)

[46] Kenneth Craik, “The Nature of Explanation”, 1943

[47] Kenneth E Boulding, "The Image: Knowledge in life and society", 1956

[48] Philip Johnson-Laird, "Mental models: Toward a cognitive science of language, inference, and consciousness", 1983