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2016 – Up now, an overall of 1567 citations among 74 works (including 3 books) on GOOGLE SCHOLAR (https://scholar.google.com/citations?user=gSyQ-g8AAAAJ&hl=en) [with an Hirsh h-index=19, and an average of 160.2 citations each for any work on my top five] + 900 citations among 57 works on the new RESEARCH GATE site (https://www.researchgate.net/profile/Vitorino_Ramos).
Refs.: Science, Artificial Intelligence, Swarm Intelligence, Data-Mining, Big-Data, Evolutionary Computation, Complex Systems, Image Analysis, Pattern Recognition, Data Analysis.
“There is thus this completely decisive property of complexity, that there exists a critical size below which the process of synthesis is degenerative, but above which the phenomenon of synthesis, if properly arranged, can become explosive, in other words, where syntheses of automata can proceed in such a manner that each automaton will produce other automata which are more complex and of higher potentialities than itself“. ~ John von Neumann, in his 1949 University of Illinois lectures on the Theory and Organization of Complicated Automata [J. von Neumann, Theory of self-reproducing automata, 1949 Univ. of Illinois Lectures on the Theory and Organization of Complicated Automata, ed. A.W. Burks (University of Illinois Press, Urbana, IL, 1966).].
According to Baudrillard, Simulacra are copies that depict things that either had no reality to begin with, or that no longer have an original. While, Simulation is the imitation of the operation of a real-world process or system over time. “Simulacres et Simulation” is a 1981 philosophical treatise by Jean Baudrillard seeking to interrogate the relationship among reality, symbols, and society:
[…] Simulacra and Simulation is most known for its discussion of symbols, signs, and how they relate to contemporaneity (simultaneous existences). Baudrillard claims that our current society has replaced all reality and meaning with symbols and signs, and that human experience is of a simulation of reality. Moreover, these simulacra are not merely mediations of reality, nor even deceptive mediations of reality; they are not based in a reality nor do they hide a reality, they simply hide that anything like reality is relevant to our current understanding of our lives. The simulacra that Baudrillard refers to are the significations and symbolism of culture and media that construct perceived reality, the acquired understanding by which our lives and shared existence is and are rendered legible; Baudrillard believed that society has become so saturated with these simulacra and our lives so saturated with the constructs of society that all meaning was being rendered meaningless by being infinitely mutable. Baudrillard called this phenomenon the “precession of simulacra”. […] (from Wikipedia)
“Simulacra and Simulation” is definitely one of my best summer holiday readings I had this year. There are several connections to areas like Collective Intelligence and Perception, even Self-Organization as the dynamic and entangled use of symbols and signals, are recurrent on all these areas. Questions like the territory (cultural habitats) and metamorphose are also aborded. The book is an interesting source of new questions and thinking about our digital society, for people working on related areas such as Digital Media, Computer Simulation, Information Theory, Information and Entropy, Augmented Reality, Social Computation and related paradigms. I have read it in English for free [PDF] from a Georgetown Univ. link, here.
Figure – Hybrid Artificial Intelligent Systems new LNAI (Lecture Notes on Artificial Intelligence) series volume 8073, Springer-Verlag Book [original photo by my colleague David M.S. Rodrigues].
New work, new book. Last week one of our latest works come out published on Springer. Edited by Jeng-Shyang Pan, Marios M. Polycarpou, Emilio Corchado et al. “Hybrid Artificial Intelligent Systems” comprises a full set of new papers on this hybrid area on Intelligent Computing (check the full articles list at Springer). Our new paper “Second Order Swarm Intelligence” (pp. 411-420, Springer books link) was published on the Bio-inspired Models and Evolutionary Computation section.
The Hacker and the Ants is a work of science fiction by Rudy Rucker published in 1994 by Avon Books. It was written while Rucker was working as a programmer at Autodesk, Inc., of Sausalito, California from 1988 to 1992. The main character is a transrealist interpretation of Rucker’s life in the 1970s (Rucker taught mathematics at the State University College at Geneseo, New York from 1972 to 1978. from Wikipedia). The plot follows:
(…) Jerzy Rugby is trying to create truly intelligent robots. While his actual life crumbles, Rugby toils in his virtual office, testing the robots online. Then, something goes wrong and zillions of computer virus ants invade the net. Rugby is the man wanted for the crime. He’s been set up to take a fall for a giant cyberconspiracy and he needs to figure out who — or what — is sabotaging the system in order to clear his name. Plunging deep into the virtual worlds of Antland of Fnoor to find some answers, Rugby confronts both electronic and all-too-real perils, facing death itself in a battle for his freedom. (…)
Interesting how this Samuel Beckett (1906–1989) quote to his work is so close to the research on Artificial Life (aLife), as well as how Christopher Langton (link) approached the field, on his initial stages, fighting back and fourth with his Lambda parameter (“Life emerges at the Edge of Chaos“) back in the 80’s. According to Langton‘s findings, at the edge of several ordered states and the chaotic regime (lambda=0,273) the information passing on the system is maximal, thus ensuring life. Will not wait for Godot. Here:
“Beckett was intrigued by chess because of the way it combined the free play of imagination with a rigid set of rules, presenting what the editors of the Faber Companion to Samuel Beckett call a “paradox of freedom and restriction”. That is a very Beckettian notion: the idea that we are simultaneously free and unfree, capable of beauty yet doomed. Chess, especially in the endgame when the board’s opening symmetry has been wrecked and the courtiers eliminated, represents life reduced to essentials – to a struggle to survive.”(*)
(*) on Stephen Moss, “Samuel Beckett’s obsession with chess: how the game influenced his work“, The Guardian, 29 August 2013. [link]
[…] Analogy is the core of all thinking. – This is the simple but unorthodox premise that Pulitzer Prize-winning author Douglas Hofstadter and French psychologist Emmanuel Sander defend in their new work. Hofstadter has been grappling with the mysteries of human thought for over thirty years. Now, with his trademark wit and special talent for making complex ideas vivid, he has partnered with Sander to put forth a highly novel perspective on cognition. We are constantly faced with a swirling and intermingling multitude of ill-defined situations. Our brain’s job is to try to make sense of this unpredictable, swarming chaos of stimuli. How does it do so? The ceaseless hail of input triggers analogies galore, helping us to pinpoint the essence of what is going on. Often this means the spontaneous evocation of words, sometimes idioms, sometimes the triggering of nameless, long-buried memories.
Why did two-year-old Camille proudly exclaim, “I undressed the banana!”? Why do people who hear a story often blurt out, “Exactly the same thing happened to me!” when it was a completely different event? How do we recognize an aggressive driver from a split-second glance in our rear-view mirror? What in a friend’s remark triggers the offhand reply, “That’s just sour grapes”? What did Albert Einstein see that made him suspect that light consists of particles when a century of research had driven the final nail in the coffin of that long-dead idea? The answer to all these questions, of course, is analogy-making – the meat and potatoes, the heart and soul, the fuel and fire, the gist and the crux, the lifeblood and the wellsprings of thought. Analogy-making, far from happening at rare intervals, occurs at all moments, defining thinking from top to toe, from the tiniest and most fleeting thoughts to the most creative scientific insights.
Like Gödel, Escher, Bach before it, Surfaces and Essences will profoundly enrich our understanding of our own minds. By plunging the reader into an extraordinary variety of colorful situations involving language, thought, and memory, by revealing bit by bit the constantly churning cognitive mechanisms normally completely hidden from view, and by discovering in them one central, invariant core – the incessant, unconscious quest for strong analogical links to past experiences – this book puts forth a radical and deeply surprising new vision of the act of thinking. […] intro to “Surfaces and Essences – Analogy as the fuel and fire of thinking” by Douglas Hofstadter and Emmanuel Sander, Basic Books, NY, 2013 [link] (to be released May 1, 2013).
Image – The frontispiece of William King Gregory’s two-volume Evolution Emerging. Gregory, 1951, Evolution Emerging: A Survey of Changing Patterns from Primeval Life to Man, vol. 2, p. 757; fig. 20.33; [courtesy of Mary DeJong, Mai Qaraman, and the American Museum of Natural History].
Fig. – (book cover) “Emergence: The Connected Lives of Ants, Brains, Cities, and Software” (additional link) is a book written by media theorist Steven Berlin Johnson, published in 2001, Scribner. New York, NY.
“The patterns are simple, but followed together, they make for a whole that is wiser than the sum of its parts. Go for a walk; cultivate hunches; write everything down, but keep your folders messy; embrace serendipity; make generative mistakes; take on multiple hobbies; frequent coffee houses and other liquid networks; follow the links; let others build on your ideas; borrow, recycle; reinvent. Build a tangled bank.” — Steven Johnson.
Video – Animated short film (by Shulamit Serfaty) based on Italo Calvino‘s story “The distance from the moon“, in Le Cosmicomiche (Cosmicomics), 1st edition, Einaudi, Italy, 1965.
[…] In reality, from the top of the ladder, standing erect on the last rung, you could just touch the Moon if you held your arms up. We had taken the measurements carefully (we didn’t yet suspect that she was moving away from us); the only thing you had to be very careful about was where you put your hands. I always chose a scale that seemed fast (we climbed up in groups of five or six at a time), then I would cling first with one hand, then with both, and immediately I would feel ladder and boat drifting away from below me, and the motion of the Moon would tear me from the Earth’s attraction. Yes, the Moon was so strong that she pulled you up; you realized this the moment you passed from one to the other: you had to swing up abruptly, with a kind of somersault, grabbing the scales, throwing your legs over your head, until your feet were on the Moon’s surface. Seen from the Earth, you looked as if you were hanging there with your head down, but for you, it was the normal position, and the only odd thing was that when you raised your eyes you saw the sea above you, glistening, with the boat and the others upside down, hanging like a bunch of grapes from the vine. […], in Italo Calvino, “The distance from the moon“, Le Cosmicomiche (Cosmicomics), 1st edition, Einaudi, Italy, 1965.
Picture – (on the cover) “Calvino does what very few writers can do: he describes imaginary worlds with the most extraordinary precision and beauty…” – Gore Vidal, The New York Review of Books.
Finally one of the most recent Pixar animated short films, “La Luna” released last year. Directed by Enrico Casarosa, Pixar, June 2011:
Recent research have increasingly being focused on the relationship between Human-Human interaction, social networks (no, not the Facebook) and other Human-activity areas, like health. Nicholas Christakis (Harvard Univ. research link) points us that, people are inter-connected, and so as well, their health is inter-connected. This research engages two types of phenomena: the social, mathematical, and biological rules governing how social networks form (“Connection“) and the biological and social implications of how they operate to influence thoughts, feelings, and behaviours (“Contagion“), as in the self-organized stigmergy-like dynamics of Cognitive Collective Perception (link).
Above, Nicholas Christakis (in a 56m. documentary lecture produced by The Floating University, Sept. 2011) discusses the obvious tension and delicate balance between agency (one individual choices and actions) and structure (our collective responsibility), where here, structure refers not only to our co-evolving dynamic societal environment as well as to the permanent unfolding entangled nature of topological structure on complex networks, such as in human-human social networks, while asking: If you’re so free, why do you follow others? The documentary (YouTube link) resume states:
“If you think you’re in complete control of your destiny or even your own actions, you’re wrong. Every choice you make, every behaviour you exhibit, and even every desire you have finds its roots in the social universe. Nicholas Christakis explains why individual actions are inextricably linked to sociological pressures; whether you’re absorbing altruism performed by someone you’ll never meet or deciding to jump off the Golden Gate Bridge, collective phenomena affect every aspect of your life. By the end of the lecture Christakis has revealed a startling new way to understand the world that ranks sociology as one of the most vitally important social sciences.”
While cooperation is central to the success of human societies and is widespread, cooperation in itself, however, poses a challenge in both the social and biological sciences: How can this high level of cooperation be maintained in the face of possible exploitation? One answer involves networked interactions and population structure.
As perceived, the balance between homophily (where “birds of a feather flock together”) and heterophily (one where most of genotypes are negatively correlated), do requires further research. In fact, in humans, one of the most replicated findings in the social sciences is that people tend to associate with other people that they resemble, a process precisely known as homophily. As Christakis points out, although phenotypic resemblance between friends might partly reflect the operation of social influence, our genotypes are not materially susceptible to change. Therefore, genotypic resemblance could result only from a process of selection. Such genotypic selection might in turn take several forms. For short, let me stress you two examples. What follows are two papers, as well as a quick reference (image below) to a recent general-audience of his books:
1) Rewiring your network fosters cooperation:
“Human populations are both highly cooperative and highly organized. Human interactions are not random but rather are structured in social networks. Importantly, ties in these networks often are dynamic, changing in response to the behavior of one’s social partners. This dynamic structure permits an important form of conditional action that has been explored theoretically but has received little empirical attention: People can respond to the cooperation and defection of those around them by making or breaking network links. Here, we present experimental evidence of the power of using strategic link formation and dissolution, and the network modification it entails, to stabilize cooperation in sizable groups. Our experiments explore large-scale cooperation, where subjects’ cooperative actions are equally beneficial to all those with whom they interact. Consistent with previous research, we find that cooperation decays over time when social networks are shuffled randomly every round or are fixed across all rounds. We also find that, when networks are dynamic but are updated only infrequently, cooperation again fails. However, when subjects can update their network connections frequently, we see a qualitatively different outcome: Cooperation is maintained at a high level through network rewiring. Subjects preferentially break links with defectors and form new links with cooperators, creating an incentive to cooperate and leading to substantial changes in network structure. Our experiments confirm the predictions of a set of evolutionary game theoretic models and demonstrate the important role that dynamic social networks can play in supporting large-scale human cooperation.”, abstract in D.G. Rand, S. Arbesman, and N.A. Christakis, “Dynamic Social Networks Promote Cooperation in Experiments with Humans,” PNAS: Proceedings of the National Academy of Sciences (October 2011). [full PDF];
Picture – (book cover) Along with James Fowler, Christakis has authored also a general-audience book on social networks: Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives, 2011 (book link). For a recent book review, access here.
2) We are surrounded by a sea of our friends’ genes:
“It is well known that humans tend to associate with other humans who have similar characteristics, but it is unclear whether this tendency has consequences for the distribution of genotypes in a population. Although geneticists have shown that populations tend to stratify genetically, this process results from geographic sorting or assortative mating, and it is unknown whether genotypes may be correlated as a consequence of nonreproductive associations or other processes. Here, we study six available genotypes from the National Longitudinal Study of Adolescent Health to test for genetic similarity between friends. Maps of the friendship networks show clustering of genotypes and, after we apply strict controls for population strati!cation, the results show that one genotype is positively correlated (homophily) and one genotype is negatively correlated (heterophily). A replication study in an independent sample from the Framingham Heart Study veri!es that DRD2 exhibits signi!cant homophily and that CYP2A6 exhibits signi!cant heterophily. These unique results show that homophily and heterophily obtain on a genetic (indeed, an allelic) level, which has implications for the study of population genetics and social behavior. In particular, the results suggest that association tests should include friends’ genes and that theories of evolution should take into account the fact that humans might, in some sense, be metagenomic with respect to the humans around them.”, abstract in J.H. Fowler, J.E. Settle, and N.A. Christakis, “Correlated Genotypes in Friendship Networks,” PNAS: Proceedings of the National Academy of Sciences (January 2011). [full PDF].
Four different snapshots (click to enlarge) from one of my latest books, recently published in Japan: Ajith Abraham, Crina Grosan, Vitorino Ramos (Eds.), “Swarm Intelligence in Data Mining” (群知能と データマイニング), Tokyo Denki University press [TDU], Tokyo, Japan, July 2012.
“… words are not numbers, nor even signs. They are animals, alive and with a will of their own. Put together, they are invariably less or more than their sum. Words die in antisepsis. Asked to be neutral, they display allegiances and stubborn propensities. They assume the color of their new surroundings, like chameleons; they perversely develop echoes.” Guy Davenport, “Another Odyssey”, 1967. [above: painting by Mark Rothko – untitled]
Remove one network edge and see what happens. Then, two… etc. This is the first illustration on Mark Buchanan “Nexus: Small-worlds and the ground-breaking science of networks” 2002 book – Norton, New York (Prelude, page 17), representing a portion of the food web for the Benguela ecosystem, located off the western coast of South Africa (from Peter Yodzis). For a joint review of 3 general books in complex networks, including Barabási‘s “Linked“, Duncan Watts “Small-Worlds” and Buchanan‘s “Nexus” pay a visit into JASSS – Journal of Artificial Societies and Social Simulation, ‘a review of three books’ entry by Frédéric Amblard (link).
Figure (click to enlarge) – Cover from one of my books published last month (10 July 2012) “Swarm Intelligence in Data Mining” recently translated and edited in Japan (by Tokyo Denki University press [TDU]). Cover image from Amazon.co.jp (url). Title was translated into 群知能と データマイニング. Funny also, to see my own name for the first time translated into Japanese – wonder if it’s Kanji. A brief synopsis follow:
(…) Swarm Intelligence (SI) is an innovative distributed intelligent paradigm for solving optimization problems that originally took its inspiration from the biological examples by swarming, flocking and herding phenomena in vertebrates. Particle Swarm Optimization (PSO) incorporates swarming behaviours observed in flocks of birds, schools of fish, or swarms of bees, and even human social behaviour, from which the idea is emerged. Ant Colony Optimization (ACO) deals with artificial systems that is inspired from the foraging behaviour of real ants, which are used to solve discrete optimization problems. Historically the notion of finding useful patterns in data has been given a variety of names including data mining, knowledge discovery, information extraction, etc. Data Mining is an analytic process designed to explore large amounts of data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. In order to achieve this, data mining uses computational techniques from statistics, machine learning and pattern recognition. Data mining and Swarm intelligence may seem that they do not have many properties in common. However, recent studies suggests that they can be used together for several real world data mining problems especially when other methods would be too expensive or difficult to implement. This book deals with the application of swarm intelligence methodologies in data mining. Addressing the various issues of swarm intelligence and data mining using different intelligent approaches is the novelty of this edited volume. This volume comprises of 11 chapters including an introductory chapters giving the fundamental definitions and some important research challenges. Chapters were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed. (…) (more)
Complex adaptive systems (CAS), including ecosystems, governments, biological cells, and markets, are characterized by intricate hierarchical arrangements of boundaries and signals. In ecosystems, for example, niches act as semi-permeable boundaries, and smells and visual patterns serve as signals; governments have departmental hierarchies with memoranda acting as signals; and so it is with other CAS. Despite a wealth of data and descriptions concerning different CAS, there remain many unanswered questions about “steering” these systems. In Signals and Boundaries, John Holland (Wikipedia entry) argues that understanding the origin of the intricate signal/border hierarchies of these systems is the key to answering such questions. He develops an overarching framework for comparing and steering CAS through the mechanisms that generate their signal/boundary hierarchies. Holland lays out a path for developing the framework that emphasizes agents, niches, theory, and mathematical models. He discusses, among other topics, theory construction; signal-processing agents; networks as representations of signal/boundary interaction; adaptation; recombination and reproduction; the use of tagged urn models (adapted from elementary probability theory) to represent boundary hierarchies; finitely generated systems as a way to tie the models examined into a single framework; the framework itself, illustrated by a simple finitely generated version of the development of a multi-celled organism; and Markov processes.
in, Introduction to John H. Holland, “Signals and Boundaries – Building blocks for Complex Adaptive Systems“, Cambridge, Mass. : ©MIT Press, 2012.
“Coders are now habitat providers for the rest of the world.” ~ Vitorino Ramos, via Twitter, July, 17, 2012 (link).
Video lecture – Casey Reas (reas.com) at Eyeo2012 (uploaded 2 days ago on Vimeo): From a visual and conceptual point of view, the tension between order and chaos is a fertile space to explore. For over one hundred years, visual artists have focused on both in isolation and in tandem. As artists started to use software in the 1960s, the nature of this exploration expanded. This presentation features a series of revealing examples, historical research into the topic as developed for Reas‘ upcoming co-authored book “10 PRINT CHR$(205.5+RND(1)); : GOTO 10″ (MIT Press, 2012, book link; cover above), and a selection of Casey‘s artwork that relies on the relationship between chance operations and strict rules.
Fig. – (Organizational Complexity trough History) Four forms behind the Organization and Evolution of all societies (David Ronfeldt TIMN). Each form also seems to be triggered by major societal changes in communications and language. Oral speech enabled tribes (T), the written word enabled institutions (I), the printed word fostered regional and global markets (M), and the electric (digital) word is empowering worldwide networks (N). [in David Ronfeldt, “Tribes, Institutions, Markets, Networks: A framework about Societal Evolution“, RAND Corporation, Document Number: P-7967, (1996). PDF link]
[…] Organizational complexity is defined as the amount of differentiation that exists within different elements constituting the organization. This is often operationalized as the number of different professional specializations that exist within the organization. For example, a school would be considered a less complex organization than a hospital, since a hospital requires a large diversity of professional specialties in order to function. Organizational complexity can also be observed via differentiation in structure, authority and locus of control, and attributes of personnel, products, and technologies. Contingency theory states that an organization structures itself and behaves in a particular manner as an attempt to fit with its environment. Thus organizations are more or less complex as a reaction to environmental complexity. An organization’s environment may be complex because it is turbulent, hostile, diverse, technologically complex, or restrictive. An organization may also be complex as a result of the complexity of its underlying technological core. For example, a nuclear power plant is likely to have a more complex organization than a standard power plant because the underlying technology is more difficult to understand and control. There are numerous consequences of environmental and organizational complexity. Organizational members, faced with overwhelming and/or complex decisions, omit, tolerate errors, queue, filter, abstract, use multiple channels, escape, and chunk in order to deal effectively with the complexity. At an organizational level, an organizational will respond to complexity by building barriers around its technical core; by smoothing input and output transactions; by planning and predicting; by segmenting itself and/or becoming decentralized; and by adopting rules.
Complexity science offers a broader view of organizational complexity – it maintains that all organizations are relatively complex, and that such complexity arises that complex behavior is not necessarily the result of complex action on the behalf of a single individual’s effort; rather, complex behavior of the whole can be the result of loosely coupled organizational members behaving in simple ways, acting on local information. Complexity science posits that most organizational behavior is the result of numerous events occurring over extended periods of time, rather than the result of some smaller number of critical incidents. […] in Dooley, K. (2002), “Organizational Complexity,” International Encyclopedia of Business and Management, M. Warner (ed.), London: Thompson Learning, p. 5013-5022. (PDF link)
The Internet has given us a glimpse of the power of networks. We are just beginning to realize how we can use networks as our primary form of living and working. David Ronfeldt has developed the TIMN framework to explain this – Tribal (T); Institutional (I); Markets (M); Networks (N). The TIMN framework shows how we have evolved as a civilization. It has not been a clean progression from one organizing mode to the next but rather each new form built upon and changed the previous mode. He sees the network form not as a modifier of previous forms, but a form in itself that can address issues that the three other forms could not address. This point is very important when it comes to things like implementing social business (a network mode) within corporations (institutional + market modes). Real network models (e.g. wirearchy) are new modes, not modifications of the old ones.
Another key point of this framework is that Tribes exist within Institutions, Markets and Networks. We never lose our affinity for community groups or family, but each mode brings new factors that influence our previous modes. For example, tribalism is alive and well in online social networks. It’s just not the same tribalism of several hundred years ago. Each transition also has its hazards. For instance, while tribal societies may result in nepotism, networked societies can lead to deception. Ronfeldt states that the initial tribal form informs the other modes and can have a profound influence as they evolve:
Balanced combination is apparently imperative: Each form (and its realm) builds on its predecessor(s). In the progression from T through T+I+M+N, the rise of a new form depends on the successes (and failures) achieved through the earlier forms. For a society to progress optimally through the addition of new forms, no single form should be allowed to dominate any other, and none should be suppressed or eliminated. A society’s potential to function well at a given stage, and to evolve to a higher level of complexity, depends on its ability to integrate these inherently contradictory forms into a well-functioning whole. A society can constrain its prospects for evolutionary growth by elevating a single form to primacy — as appears to be a tendency at times in market-mad America. [in David Ronfeldt, “Tribes, Institutions, Markets, Networks: A framework about Societal Evolution“, RAND Corporation, Document Number: P-7967, (1996). PDF link]
Finally, on these areas (far behind the strict topic of organizational topology and complex networks), let me add two books. One his from José Fonseca, a friend researcher I first met in 2001, during a joint interview for the Portuguese Idéias & Negócios Magazine, for his 5th anniversary (old link) embracing innovation in Portugal. His book entitled “Complexity & Innovation in Organizations” (above) was published in December that year, 2001 by Routledge. The other one is more recent and from Ralph Stacey, “Complexity and Organizational Reality: Uncertainty and the Need to Rethink Management After the Collapse of Investment Capitalism” (below), Routledge, 2010. Even if, Ralph as many other past seminal books on this topic. Both, have worked together at the Hertfordshire University.
(…) The Four Mists of Chaos, the North, the East, the West, and the South, went to visit Chaos himself. He treated them all very kindly and when they were thinking of leaving, they consulted among themselves how they might repay his hospitality. Since they had noticed that he had no holes in his body, as they each had (eyes, nose, mouth, ears, etc.), they decided each day to provide him with an opening. At the end of seven days, Kwang-tse tells us, Chaos died. (…) in Indeterminacy – Ninety Stories by John Cage (Transcript of story number 27), With Music, ca. 26’00” to 27’00”, From John Cage’s [1958] Lecture ‘Indeterminacy’, 26’00” to 27’00”, in Die Reihe No. 5, English edition on p.120.
“Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!” ~ The Red Queen, at “Through the looking-glass, and what Alice found there“, Charles Lutwidge Dogson, 1871.
Your move. Alice was suddenly found in a new strange world. And quickly needed to adapt. As C.L. Dogson (most known as Lewis Carroll) brilliantly puts it, all the running you can do, does not suffices at all. This is a world (Wonderland) with different “physical” laws or “societal norms”. Surprisingly, those patterns appear also to us quite familiar, here, on planet Earth. As an example, the quote above is mainly the paradigm for Biological Co-Evolution, in the form of the Red-Queen effect.
In Wonderland (1st book), Alice follows the white rabbit, which end-ups driving her on this strange habitat, where apparently “normal” “physical” laws do not apply. On this second book however, Alice now needs to overcome a series of great obstacles – structured as phases in a game of chess – in order to become a queen. Though, as she moves on, several other enigmatic personages appear. Punctuated as well as surrounded by circular arguments and logical paradoxes, Alice must keep on, in order to found the “other side of the mirror“.
There are other funny parallel moves, also. The story goes on that Lewis Carroll gave a copy of “Alice in Wonderland” to Queen Victoria who then asked him in return to send her his next book as she fancied the first one. The joke is that the book was (!) … “An Elementary Treatise on Determinants, With Their Application to Simultaneous Linear Equations and Algebraic Equations (link)”. Lewis Carroll then went on to write a follow-on to Alice in Wonderland entitled “Through the Looking-Glass, and what Alice found there” that features a chess board on his first pages, and where chess was used to gave her, Queen Victoria, a glimpse on what Alice explored on this new world.
In fact, the diagram on the first pages contains not only the entire book chapters of this novel as well how Alice moved on. Where basically, each move, moves the reader to a new chapter (see below) representing it. The entire book could be found here in PDF format. Besides the beauty and philosophical value of Dogson‘s novel on itself, and his repercussions on nowadays co-Evolution research as a metaphor, this is much probably the first “chess-literature” diagram ever composed. Now, of course, pieces are not white and black, but instead white and red (note that pieces in c1 – queen – and c6 – king – are white). Lewis Carroll novel, then goes on like this: White pawn (Alice) to play, and win in eleven moves.
However, in order to enter this world you must follow the “rules” of this new world. “Chess” in here is not normal, as Wonderland was not normal to Alice’s eyes. Remember: If you do all do run you could do, you will find yourself at the same place. Better if you could run twice as fast! First Lewis Carroll words on his second book (at the preface / PDF link above) advise us:
(…) As the chess-problem, given on a previous page, has puzzled some of my readers, it may be well to explain that it is correctly worked out, so far as the moves are concerned. The alternation of Red and White is perhaps not so strictly observed as it might be, and the ‘castling’ of the three Queens is merely a way of saying that they entered the palace; but the ‘check’ of the White King at move 6, the capture of the Red Knight at move 7, and the final ‘check-mate’ of the Red King, will be found, by any one who will take the trouble to set the pieces and play the moves as directed, to be strictly in accordance with the laws of the game. (…) Lewis Carroll, Christmas,1896.
Now, the solution, could be delivered in various format languages. But here is one I prefer. It was encoded on classic BASIC, running on a ZX Spectrum emulator. Here is an excerpt:
1750 LET t$=”11. Alice takes Red Queen & wins(checkmate)”: GO SUB 7000 (…)
9000 REM
9001 REM ** ZX SPECTRUM MANUAL Page 96 Chapter 14. **
9002 REM
9004 RESTORE 9000 (…)
9006 LET b=BIN 01111100: LET c=BIN 00111000: LET d=BIN 00010000
9010 FOR n=1 TO 6: READ p$: REM 6 pieces
9020 FOR f=0 TO 7: REM read piece into 8 bytes
9030 READ a: POKE USR p$+f,a
9040 NEXT f
9100 REM bishop
9110 DATA “b”,0,d,BIN 00101000,BIN 01000100
9120 DATA BIN 01101100,c,b,0
9130 REM king
9140 DATA “k”,0,d,c,d
9150 DATA c,BIN 01000100,c,0
9160 REM rook
9170 DATA “r”,0,BIN 01010100,b,c
9180 DATA c,b,b,0
9190 REM queen
9200 DATA “q”,0,BIN 01010100,BIN 00101000,d
9210 DATA BIN 01101100,b,b,0
9220 REM pawn
9230 DATA “p”,0,0,d,c
9240 DATA c,d,b,0
9250 REM knight
9260 DATA “n”,0,d,c,BIN 01111000
9270 DATA BIN 00011000,c,b,0
(…) full code on [link]
This is BASIC-ally Alice’s story …
Chemoton § Vitorino Ramos' research notebook 
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