Diversity and Complexity (Primers in Complex Systems) Illustrated Edition

Professor Scott E. Page's Diversity and Complexity (Princeton University Press, 2011) is a serious book about complexity science. Complexity science is an academic discipline of relatively recent origin, beginning last quarter of the last century, but really gathering momentum over the past twenty years. In large measure, what appears to have begun as granular studies in genetics and mutation of the genome of primitive living cells has expanded its horizons to include economic, social, and political activity that people enter as volitional activities. What began as close studies of the biochemical origins of life have expanded outward as researchers, and indeed philosophers of science have come to realize that the stochastic processes that are thought to be the origins of life itself here on planet Earth are subject to the same statistical laws and mathematical formularies that govern our understanding and analyses of large quanta of data at higher levels of abstraction and sophistication. It comes as no surprise that studies of complex systems, and the various forms of diversities that they embody, reflect parallel development of the Internet and the World Wide Web, whose fundamental purpose is to arrange, process, store, and transmit data.It is only been just over 160 years since Charles Darwin published his Origin of a Species, which basically turn the life sciences upside down. Darwin's approach was empirical observation and his use of deductive reasoning that natural living things have heritable characteristics, and that those characteristics are subject to natural forces of variation and change. Enthusiastic promoters of these new ideas soon developed ways of applying these newly popularized notions to economics and sociology. Oh course, they went overboard with simplistic characterizations of fitness for survival, predicated upon the supposition that they were at the apex of the heap in which society found itself at the time.Since then, scientists have worked to broaden and improve their understanding of how living things came to be. It wasn't until a century later when researchers James Watson and Francis Crick came up with their descriptive idea of the double helix in their investigation of the structure of DNA; that precipitated a flurry of new research into the structure and composition of the genome and associated nucleic acids found in the nucleus of living cells. Once that was done, biophysicists were then able to track not only how cell division worked, and how DNA replicated itself, but also how the biochemistry of inheritance work in detail. What first appeared to be design-driven replication turned out to be, at bottom, the product of random processes that at least inferentially could be traced back to the origins of life itself. Randomness, piled on randomness, piled on earlier randomness, strongly suggested that these layers of replicative activity had tendencies toward self-organization and structure that could be built upon as time went on, with variation occurring naturally, either through genetic mutation, or in response to environmental and competitive pressures in which those which survived had somehow adapted to their environment to be better able to withstand the vicissitudes of life, including the shocks that frequently occurred, sometimes resulting in mass extinction.Diversity and Complexity is denoted as a primer in a collection of works published by Princeton University Press intended to educate students, academics, and lay readers in the intricacies of complexity science. This volume, published in 2011, is an early contribution to this oeuvre. Nonetheless, despite its publication a decade ago, the book contains invaluable information about the meaning and contact of 'diversity'. Professor Page defines diversity as essentially a categorization of different types of variations to be found within the type of organism that is being investigated; that those variations lead to distinctive functions to be found both within type, and across type, as exemplified within the living communities in which they are found.Central to the understanding of how these different types of organism, and within which those aforementioned variations find themselves, is the process of 'averaging', using statistical techniques, regressions, linear or otherwise, and of course, have the balance of regularities and varieties are distributed within a population of a particular type of organism. Now, with the understanding that the random nature of interaction of components within the genome of a particular type are spontaneously creating changes within that genome that might or might not persist, either over time, or be distributed within the overall population of that type; and with the further understanding that environmental pressures and responses to environmental stressors produce their own changes in the nature and quality of that life form itself through the process known as emergence. We eventually have a hierarchically-based dynamic system that is multilayered, that responds to its environment, that is self-propagating and which seeks to expand its reach beyond the niche in which it may find itself initially, we ultimately have our complex adaptive systems comprised of diverse, interdependent entities whose behavior is determined by rules; whereby they may adapt, or do you not to; and whose interactions often produce phenomena that are greater than the sum of the parts. Complex entities are not predictable; and more importantly, they are capable of producing much larger events, which events occur far more frequently than might have been predicted simply by looking at Gaussian (bell curve) statistical techniques of prediction.Systems that produce complex outcomes tend to do so in crowded places, meaning that diversity creates complexity; and complexity is the nursery for generating and nurturing diversity: it is a two-way street that is augmented at both ends. Diversity allows complex systems become robust, allowing them to maintain functionalities in the face of systemic shocks. It drives forward every sort of innovation and improvement of productivity. Diversity makes possible economic and social progress through recombination of constituent parts; repurpose thing existing components; and drives forward scientific innovation, research, and understanding.Professor Page provides a helpful mnemonic to describe complexity: 'BOAR', meaning 'Between Order and Randomness'; and 'DEEP', meaning complexity cannot easily be 'Described, Evolved, Engineered, or Predicted'. Complexity may be visualized as a fluctuating middle ground between chaotic behavior that occurs on one side of a three-sided, triangular-shaped playing field; orderliness is the boundary of the second side of that triangle; and randomness constitutes the boundary of the third side of that triangle. Within that three-sided enclosure, complexity exists in an undulating, ever fluctuating environment, seemingly consistent, but to a limited extent; new elements arriving, but changing character as they are absorbed into the system itself; and some portion of which are forever on the brink of chaos. It is identifiable, sometimes measurable, but with a great deal of uncertainty. Without being the least sardonic, complexity is the place where pet theories and strict definitions go to die amid self-justification and controversy.Chapter 1 discusses and elaborates upon the technical meanings that scientists and researchers use to define or to describe what they mean by ‘complexity’ and ‘diversity’ as systems that consist of diverse rule-following entities whose behaviors are interdependent, interacting over a contact structure or network in which those entities are capable of adaptation to their environment. The emphasis here is on variation within biological systems, both within types and across types. Think of it as genetically-based fine-tuning that improves viability and robustness, and which could be expressed as physical differences within a type, such as size; a diversity of types or entities having a common ancestor branch off; or at the biological or molecular level, diversities of composition in which molecular arrangements change, leading to different outcomes.Out of those swirling masses of competing, diversifying, and adapting entities, some groups of survivors are able to go beyond the fixed limitations that held their forebears in place. This is known as emergent behavior whose newfound capabilities allow them to be more successful than those that came before. Simply put, emergent phenomena together with complexity emergence yield higher order structures and functionalities that arise from interactions between and among entities. These higher-level complex systems arise spontaneously, involving no foresight or future purpose. They just happen in which the populations benefited thereby would have no understanding of what they were experiencing. By way of example, prehistoric hunter gatherers were able to coalesce into clans that could socialize, and more importantly, cooperate with one another in hunting for food. They learned to migrate with the seasons. Having horizontally opposed thumbs, they were now capable of making tools and weaponry.Chapter 2 addresses ways of measuring diversity, of which there are several: variation, entropy, distance, attribute, and population measures. This chapter is somewhat technical, but it is worth paying attention to in order to internalize a common grammar and to understand what is actually meant. By way of example, the term ‘entropy’ is taken to mean something different from that used in the Third Law of Thermodynamics. Here, entropy measures capture distributions across types, and the evenness of the distribution across types. The ultimate purpose is to be able to differentiate among various species those attributes that remain constant, attributes that retain functional similarities, but which are expressed differently, those attributes that can be linked to some distant ancestor within diverse populations. It is basically the science of identifying and cataloging the development, growth, and dispersion of life forms on Planet Earth; but it is more than that. The mathematical and statistical techniques that trace the growth of life forms are also effective in analyzing social constructs, economics, and political imperatives and organization.Chapter 3 deals with the creation and evolution of diversity. Diversity abounds, but it has its limitations. There are mechanisms that produce and sustain diversity; but there are also forces that constrain diversity, and they apply to biological systems, economic systems, and systems of ideas. The differences are that biological systems evolve and are creative, but without purpose. Virtual systems, such as ideas about economics, are essentially informational and prescriptive. In biology, the gold standard is persistence over time and survivability; in economics and the social sciences, the standard measure of success appears to be academic tenure.In evolution, diversity is the result of random forces at work, creating differences where none existed before. Those variations can be attributed to mutation (a random change in the genome that by itself, or in combination with other genetic changes creates a new phenotype, meaning a set of observable characteristics resulting from interaction of its genotype with its environment. Inversion occurs when a genetic sequence is reversed; the same may occur in the realm of virtual systems where people sometimes get their ideas backwards. In either case, the innovation is unsuccessful. Recombination occurs in the genome when portions of bits from each of two DNA strings are combined to form a new strain different from its parents. In the virtual world of ideas, ideas from one realm are adapted to fit into another where Straussian lyrical concepts of ‘Wine, Women and Song’ are transmogrified into Chuck Berry’s ‘Drugs, Sex and Rock ‘n’ Roll’. Transfer is where a portion of one DNA sequence is transferred to another; and in the world of ideas, transferable concepts enhance marketability and desirability, such as cupholders in automobiles. Finally, there is representational diversity in which changes to the genome have a neutral effect on viability, but which add to the diversity of the genome.When he describes populations and the way they change, Professor Page invokes what he calls a Rugged Landscape Model, and its offspring the Dancing Landscapes Model. The former is a graphical representation of a function defined over several variables in which the elevation of any type corresponds to its ‘payoff’, or value. In short, it is the measure of success ensuring survivability. Within this chapter, Professor Page describes the various subdivisions and permutations about how these physical landscapes operate, and the ways in which their resident populations of entities adapt in order to survive. The science of plate tectonics tells us that in the distant past, non-aquatic life developed on a giant landmass that split apart and recombined in ways that caused their resident life forms to respond in order to survive. The process that Professor Page describes and analyzes illustrates the logic behind those changes. The logical landscapes that he uses to illustrate his points are no different from the physical landscapes we see out in the world; nothing ever remain static for any length of time, and those who live there need to be able to respond to whatever comes along.Chapter 4 concerns constraints on diversity, which could include matters of scale and individual size; the size of the food chain and relative demand; and at higher levels of existence, interdependence and coordination needed for success or survival. Greater interdependence reduces the amount of diversity. We speak of matters of scale, the nature of physical matter itself is a limiting factor. Bone structure can absorb only so much weight before it collapses; the number of heartbeats a living energy can have during its lifetime remains a relative constant, regardless of whether that entity is a Blue Whale or a tiny shrew. Smaller animals have faster heart rates, and the smallest have the fastest of all. In manufactured goods and products, roughly the same rules apply.Chapter 5 speaks to variation in complex systems, discussing robustness, meaning the ability of an entity to continuing functioning after experiencing a systemic shock; the trade-off between exploration and exploitation, meaning the extent to which an entity has unused or underused abilities to explore those capabilities to good advantage, versus exploiting its known capabilities in order to gain food and ensure its survival. In human terms, the storied explorers of old were not those who stayed at home and tended the farm. Typically, they pursued their passion relentlessly, and they frequently died young.Variation and stability within complex systems are frequently influenced by interactions and feedbacks. Positive feedbacks are synergistic in the sense that they create conditions that accelerate the rate of change so that the total output is greater than the sum of its parts. This is the effect of power laws, and compound interest. Negative feedback, on the other hand, occurs when the net value of system output is less than the sum of the value of all inputs, thereby putting a drag on the system, placing inefficiencies like rush-hour traffic jams on urban freeways.Chapters 6 through 9 discuss diversity’s inescapable benefits, first, in averaging the total effect of all effects of diverse complex systems. Of particular note is the Central Limit Theorem which holds that in the aggregate, a complex system behaves like a Gaussian normal distribution in which all variations cancel out. This is sometimes referred to as the Law of Large Numbers. If the numbers of a statistical population grow sufficiently large, the value of any sample taken from the population is equivalent to the true mean of the entire population. The distribution is a normal bell curve, with a standard deviation. It assumes that all variables are random and that they are independent. Likewise, its counterpart in economics, the Factor Limit Theorem assumes that all stocks in an investment portfolio are influenced by common factors, and that correlations of success are based upon common functionalities and attributes. Here, however, there are grounds for caution and skepticism, as although financial markets tend to exhibit random-like movements on a day-to-day basis, the ‘perfect storms’ that market manipulation creates are anything but random.The other positive attribute that diversity provides is diminishing returns to types: more begets more, but typically less of it as each new quantum of resources added to the pile produces a fractionally less amount of benefit. What really is being discussed is the matter of scalability, because as scale increases, the energy and resources needed to sustain that level of organization gets distributed downward into the whole, because without that augmentation, the system would not be able to go the extra distance. Geoffrey West calculated that the strength of effort in order to increase the height or size of an entity is needed to be done on a volumetric basis, with the result that for every unit of input used to augment that entity, the resulting benefit amounts to approximately two-thirds of that number on an upward sloping curve that flattens as returns to scale diminish.Finally, diversity’s impact on conflict systems includes many benefits that include comparative advantage; learning by doing, responsiveness and competition; various synergies and economies of scale; multiple landscapes; diverse production capabilities and improve collective knowledge; improved robustness in the form of functional redundancy and degeneracy; firewalling and modularity; and crosscutting cleavages that add to what amounts to crosswise bracing that counter the tendencies of type -dependent entities to split apart at their boundaries. However, diversity is subject to the limits of scalability as described in the preceding paragraph. Diversity increases the information load that the system must maintain, even if it’s benefits, manifest as they are, are often held in reserve for emergency situations. To be effective, these diverse functionalities must be a force in being, and cannot be expected to be created from scratch in the face of outside assault. The current coronavirus pandemic that the world is experiencing is the product of false economies that assumed that overseas production on demand will remain effective, that national leaders will pay close attention to developing events overseas; and will not attempt to fool themselves into thinking that what they have in reserve is ‘safe enough’. None of the foregoing was true then, nor will it be for the foreseeable future.I can say without hesitation that I truly enjoyed Professor Page's book, so much that I spent the entire week notetaking on a set of 50-odd, 4 by 6 index cards (mostly both sides), because I knew what I was finding there would be information that I would find useful and valuable elsewhere. If this were taught at the university level, my guess would be that this book, and any supplementary materials on the course reading list would probably be a 30-hour course; an entirely worthwhile investment of time and effort. It would require some familiarity with stochastic processes and knowledge of statistics. For those interested in the life sciences, this is fundamental knowledge that needs to become second nature.

i love this book!!!! was fantastic to read and so understood what he meant when he said the ambiguity of definitions can get one stuck in the mud and trying to understand this topic. Then he does show, definitions are, relevant to context of discussion. the definitions have to be tweaked evovle with the topic in general as applied to different scenarios.My opinion only, this book is a classic,, and just one of many on this topic that is good to read. But I like the style of writing, insights, not to difficult to understand, but you will have to stop and think about some of his thoughts, but i like books that make you do that, feels like conversation. He does try to get away from pure mathematics but at same time he includes it, but you can still get much from reading this book even if the math is not your favorite topic, the way you process understanding ideas. I suggest, try the sample first, then buy the book if you like the sample. You feel a wisdom in the writing. that admits what is known, and not known. many today write not willing to admit limitations in knowledge, and that gets annoying because good research always admits limitations. Again, my opinion only, and no i'm not new to this topic, so i did have prior knowledge on this topic before I started reading this book.

I was disinclined to like this book even though I am a product of one of Page's academic departments at Michigan.Too many books based on formal modeling are based on assumptions that have little bearing on what actually happens in the world I work in where, models or not, our job is to foster cooperation in conditions of diversity and complexity.Much too my surprise, I really like his use of formal modeling techniques and his ability to bring them down (near) the level of mere mortals who lack his economic and mathematical sophistication. I agreed with his conclusion at least in part because I wanted to.But seriously, the logic in his arguments contains germs of ideas we who work in the applied field should pay attention and even more than critics of cooperative problem solving ignore at their empirical and normative peril.

I like the book. In this work Page provides insight not necessarily answers.Definitions are easy, insight isn't. Definitions I can get anywhere or make them up myself. The invitation Page provides is simple: here's a couple of ways to think about the phenomenon under consideration. And he does so with a comfortable degree of depth and rigor. I have to admit, I didn't spend a lot of time thinking about the role of diversity in complex systems. So maybe now it isn't an afterthought.Page is no Tom Clancy, but neither is the book boring. A bit of a warning though, if you do not know much about complexity, maybe it would be a good idea to read a primer first. Page gives an intro on complexity, but its a bit shallow. I think a novice ought to have a better appreciation for connectedness, inter-dependency, adaptability, etc. in general to provide a richer context.Let's face it, a lot of concepts in complexity science are axiomatic at best, and ill-defined/poorly understood to any degree of depth. I think it is very much worthwhile to take a characteristic of complex systems, i.e. diversity, and spend some time and effort exploring its nuances. Good job and a worthy addition to anyone's library on complexity.If I lost my copy, is the book worthwhile enough to buy it again? Yes.

One of the best works on systems theory I've read. I flagged everything of interest, which ended up being almost every page of the book. An amazing work from start to finish.

Scott E Page has made some real insightful discoveries about complexity science and how important diversity really is for teams to solve complex problems as well as how diversity is not caused by race, gender or culture but correlates with it.

平易な英文で高度な内容を記述してあり複雑系のなかでも多様性をどう考えるのかがよく分かります。

excellent read

A good book, but a difficult subject brought to life with examples. The complication of this book is the moving between different systems from ecosystems to economies to illustrate the author's points, always hard to do with ease, but in places the author nails it, in others his message gets hidden.