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Worlds Hidden in Plain Sight: The Evolving Idea of Complexity at the Santa Fe Institute, 1984–2019
Over the last three decades, the Santa Fe Institute and its network of researchers have been pursuing a revolution in science.
Ignoring the boundaries of disciplines and schools and searching for novel fundamental ideas, theories, and practices, this international community integrates the full range of scientific inquiries that will help us to understand and survive on a complex planet.
This volume collects essays from the past thirty years of research, in which contributors explain in clear and accessible language many of the deepest challenges and insights of complexity science.
Explore the evolution of complex systems science with chapters from Nobel Laureates Murray Gell-Mann and Kenneth Arrow, as well as numerous pioneering complexity researchers, including John Holland, Brian Arthur, Robert May, Richard Lewontin, Jennifer Dunne, and Geoffrey West.
Ignoring the boundaries of disciplines and schools and searching for novel fundamental ideas, theories, and practices, this international community integrates the full range of scientific inquiries that will help us to understand and survive on a complex planet.
This volume collects essays from the past thirty years of research, in which contributors explain in clear and accessible language many of the deepest challenges and insights of complexity science.
Explore the evolution of complex systems science with chapters from Nobel Laureates Murray Gell-Mann and Kenneth Arrow, as well as numerous pioneering complexity researchers, including John Holland, Brian Arthur, Robert May, Richard Lewontin, Jennifer Dunne, and Geoffrey West.
396 pages, Paperback
Published April 27, 2019
355 people are currently reading
776 people want to read
About the author
David C. Krakauer
12 books54 followersDavid’s research focuses on the evolutionary history of information processing mechanisms in biology and culture. This includes genetic, neural, linguistic and cultural mechanisms. The research spans multiple levels of organization, seeking analogous patterns and principles in genetics, cell biology, microbiology and in organismal behavior and society. At the cellular level David has been interested in molecular processes, which rely on volatile, error-prone, asynchronous, mechanisms, which can be used as a basis for decision making and patterning. David also investigates how signaling interactions at higher levels, including microbial and organismal, are used to coordinate complex life cycles and social systems, and under what conditions we observe the emergence of proto-grammars. Much of this work is motivated by the search for 'noisy-design' principles in biology and culture emerging through evolutionary dynamics that span hierarchical structures.
Research projects includes work on the molecular logic of signaling pathways, the evolution of genome organization (redundancy, multiple encoding, quantization and compression), robust communication over networks, the evolution of distributed forms of biological information processing, dynamical memory systems, the logic of transmissible regulatory networks (such as virus life cycles) and the many ways in which organisms construct their environments (niche construction). Thinking about niche constructing niches provides us with a new perspective on the major evolutionary transitions.
Many of these areas are characterized by the need to encode heritable information (genetic, epigenetic, auto-catalytic or linguistic) at distinct levels of biological organization, where selection pressures are often independent or in conflict. Furthermore, components are noisy and degrade and interactions are typically diffusively coupled. At each level David asks how information is acquired, stored, transmitted, replicated, transformed and robustly encoded.
The big question that many are asking is what will evolutionary theory look like once it has become integrated with the sciences of adaptive information (information theory and computation), and of course, what will these sciences then look like?
Krakauer was previously chair of the faculty and a resident professor and external professor at the Santa Fe Institute. A graduate of the University of London, where he went on to earn degrees in biology, and computer science. Dr. Krakauer received his D.Phil. in evolutionary theory from Oxford University in 1995. He remained at Oxford as a postdoctoral research fellow, and two years later was named a Wellcome Research Fellow in mathematical biology and lecturer at Pembroke College. In 1999, he accepted an appointment to the Institute for Advanced Study in Princeton and served as visiting professor of evolution at Princeton University. He moved on to the Santa Fe Institute as a professor three years later and was made faculty chair in 2009. Dr. Krakauer has been a visiting fellow at the Genomics Frontiers Institute at the University of Pennsylvania and a Sage Fellow at the Sage Center for the Study of the Mind at the University of Santa Barbara. In 2012 Dr. Krakauer was included in the Wired Magazine Smart List as one of 50 people "who will change the World."
David Krakauer also served as the Director of the Wisconsin Institute for Discovery, the Co-Director of the Center for Complexity and Collective Computation, and was a Professor of Genetics at the University of Wisconsin, Madison.
Source : Santa Fe Institute
Research projects includes work on the molecular logic of signaling pathways, the evolution of genome organization (redundancy, multiple encoding, quantization and compression), robust communication over networks, the evolution of distributed forms of biological information processing, dynamical memory systems, the logic of transmissible regulatory networks (such as virus life cycles) and the many ways in which organisms construct their environments (niche construction). Thinking about niche constructing niches provides us with a new perspective on the major evolutionary transitions.
Many of these areas are characterized by the need to encode heritable information (genetic, epigenetic, auto-catalytic or linguistic) at distinct levels of biological organization, where selection pressures are often independent or in conflict. Furthermore, components are noisy and degrade and interactions are typically diffusively coupled. At each level David asks how information is acquired, stored, transmitted, replicated, transformed and robustly encoded.
The big question that many are asking is what will evolutionary theory look like once it has become integrated with the sciences of adaptive information (information theory and computation), and of course, what will these sciences then look like?
Krakauer was previously chair of the faculty and a resident professor and external professor at the Santa Fe Institute. A graduate of the University of London, where he went on to earn degrees in biology, and computer science. Dr. Krakauer received his D.Phil. in evolutionary theory from Oxford University in 1995. He remained at Oxford as a postdoctoral research fellow, and two years later was named a Wellcome Research Fellow in mathematical biology and lecturer at Pembroke College. In 1999, he accepted an appointment to the Institute for Advanced Study in Princeton and served as visiting professor of evolution at Princeton University. He moved on to the Santa Fe Institute as a professor three years later and was made faculty chair in 2009. Dr. Krakauer has been a visiting fellow at the Genomics Frontiers Institute at the University of Pennsylvania and a Sage Fellow at the Sage Center for the Study of the Mind at the University of Santa Barbara. In 2012 Dr. Krakauer was included in the Wired Magazine Smart List as one of 50 people "who will change the World."
David Krakauer also served as the Director of the Wisconsin Institute for Discovery, the Co-Director of the Center for Complexity and Collective Computation, and was a Professor of Genetics at the University of Wisconsin, Madison.
Source : Santa Fe Institute
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Displaying 1 - 24 of 24 reviews
June 3, 2019
I'm a huge fan of complexity science and the Santa Fe Institute specifically, so this book was an easy pickup for me. It's a broad retrospective divided into three main parts: "Mavericks" (1984 - 1999), "Unifiers" (2000-2014), and "Terraformers" (2015+). However, the very thing that makes complexity science so exciting right now - the fact that it's a white-hot, fast-evolved field - sort of counts against it with the Mavericks section, which is overwhelmingly made up of tentative first steps that are desperately crying out for the more formalized language that sprung up in the decades to follow. Conversely, the "Terraformers" section seems to have all been essays adapted from a collaboration with Christian Science Monitor, and the magazine-ness shines through with the all-too-short chapter lengths and relatively unrigorous language.
But between the unsatisfying pieces of bread is some real cheese (listen - I eat plain cheese sandwiches, because I am an extremely boring person), with the Unifers section pumping out one hit after the other. It's especially good if you're a fan of evolutionary biology - chapters 11 (a meditation by Richard Lewonstin on the space of unevolved organisms) and 20 (Jessica Flack about the interplay of fast and slow variables on evolution) are both thunderously strong pieces. A book that's was nothing but the "Unifiers" section would probably be a 4/5 or 5/5 for me - so how to rate this one? On the one hand, the fact that the signposting is so clear could leave you to argue that I'm allowed to rate this as though I was only given the "Unifiers" section. But ultimately, I think it's fair to critique this book as it in on average, not on the best bit it's simple to pick out, and unfortunately I just found it lacking. Too much vagueness in the beginning and too much popcorn pop-sci at the end. If you're biologically inclined, I would still recommend this just for the "Unifiers" section - for everyone else, there are better broad-subject treatsies on complexity. (Many of them from SFI!)
On that note - it feels kind of unpleasant to have the inaugural review of a book on Goodreads be a critical one, especially from an org I admire so strongly. ("Scale" by Geoffrey West, the former president of the Santa Fe Institute, was my 2018 book of the year.) I guess I ultimately just think that the mission of this book is one that was maybe doomed to result in kind of a flawed product. That's not really a slight on the people who put it together - it's just why I can't full-throatedly recommend this to everyone.
But between the unsatisfying pieces of bread is some real cheese (listen - I eat plain cheese sandwiches, because I am an extremely boring person), with the Unifers section pumping out one hit after the other. It's especially good if you're a fan of evolutionary biology - chapters 11 (a meditation by Richard Lewonstin on the space of unevolved organisms) and 20 (Jessica Flack about the interplay of fast and slow variables on evolution) are both thunderously strong pieces. A book that's was nothing but the "Unifiers" section would probably be a 4/5 or 5/5 for me - so how to rate this one? On the one hand, the fact that the signposting is so clear could leave you to argue that I'm allowed to rate this as though I was only given the "Unifiers" section. But ultimately, I think it's fair to critique this book as it in on average, not on the best bit it's simple to pick out, and unfortunately I just found it lacking. Too much vagueness in the beginning and too much popcorn pop-sci at the end. If you're biologically inclined, I would still recommend this just for the "Unifiers" section - for everyone else, there are better broad-subject treatsies on complexity. (Many of them from SFI!)
On that note - it feels kind of unpleasant to have the inaugural review of a book on Goodreads be a critical one, especially from an org I admire so strongly. ("Scale" by Geoffrey West, the former president of the Santa Fe Institute, was my 2018 book of the year.) I guess I ultimately just think that the mission of this book is one that was maybe doomed to result in kind of a flawed product. That's not really a slight on the people who put it together - it's just why I can't full-throatedly recommend this to everyone.
March 19, 2020
Explaining the complexity of evolution in biological and non-biological systems
This is an edited book that discusses the evolutionary science of complex systems that includes diverse subjects as, matter (non-life) to life transitions, and evolution, which includes biological evolution, and evolution of, economics and technologies, educational system, rural and urban structures, political structures, and banking systems. There are 37 chapters from various teams active in complex science research, and many are from the Santa Fe Institute in New Mexico. The editor of this book is a leading researcher in the field, and I found many chapters very illuminating. This new and emerging area of science finds commonality in the birth and evolution in biology, economics and technology, and other systems.
The take-home message from this book is as follows: Biological and non-biological systems seem to be unrelated. However, when we consider concepts such as non-equilibrium thermodynamics, entropy, Shannon’s information theory and statistical mechanics, they yield surprisingly similar results for the evolution of life and non-life systems alike. From one perspective, dynamical systems can be viewed as obeying the laws of physics (and chemistry for biological systems). From another perspective, they can be viewed as processing information and the operation of non-linear statistical mechanics. This is how complex adaptive systems come into existence and solve problems to control its own environment. This is illustrated by an example of a robot that is trying to catch an irregularly bouncing ball. It must decide what information is relevant, and the best way to use that in a model of task, and how can it learn to perform that task in real time? Similar challenges are relevant to biological systems undergoing natural selection or to any system that processes information in order to adapt. The fact that the total information contains both order and disorder information. We must identify where order increased at the expense of disorder. A system that controls its environment successfully adapts by constructing models that allow it to decide what information is necessary and how to act on it.
Thermodynamics is not a dynamical theory, it offers no explanation for the mechanistic origins of its macroscopic variables, such as pressure, temperature, volume, entropy, etc. But statistical mechanics offers microscopic basis for these macroscopic variables. The statistical mechanics establishes the conditions for non-equilibrium states, that is for dynamical/irreversible processes for counting microscopic configurations of a system and then connecting to its macroscopic averages of thermodynamic/macroscopic variables. The evolution of pattern formation under these conditions becomes relevant in system learning. Then terms such as, ''individual species," the boundaries of "community," the functional scale at which to characterize the "ecosystems," and the interface between "natural selection and self-organization," becomes more meaningful.
It should also be emphasized that the methods of dynamical systems theory are derived from deterministic classical mechanics. In contrast, the methods of information theory are non-deterministic which are based on probabilities. An example should serve as a useful exercise; In some monkey societies, it has been observed and reported that individuals estimate the future cost of social interaction by encoding the average outcome of past interactions in special signals and then summing over these signals that help them to take next steps in social interactions!
The authors in the edited book help us take a closer view of the world we live in. The physical reality we see, and experience is not just a product of the laws of physics and chemistry but also information dynamics, statistical mechanics and thermodynamics of systems. The non-deterministic probability component of statistical mechanics is ever present. No body could have modelled the path of biological evolution if there were intelligent beings studying planet Earth 65 million years ago! Highly recommended to readers interested in understanding the parallels in biological and non-biological evolution.
This is an edited book that discusses the evolutionary science of complex systems that includes diverse subjects as, matter (non-life) to life transitions, and evolution, which includes biological evolution, and evolution of, economics and technologies, educational system, rural and urban structures, political structures, and banking systems. There are 37 chapters from various teams active in complex science research, and many are from the Santa Fe Institute in New Mexico. The editor of this book is a leading researcher in the field, and I found many chapters very illuminating. This new and emerging area of science finds commonality in the birth and evolution in biology, economics and technology, and other systems.
The take-home message from this book is as follows: Biological and non-biological systems seem to be unrelated. However, when we consider concepts such as non-equilibrium thermodynamics, entropy, Shannon’s information theory and statistical mechanics, they yield surprisingly similar results for the evolution of life and non-life systems alike. From one perspective, dynamical systems can be viewed as obeying the laws of physics (and chemistry for biological systems). From another perspective, they can be viewed as processing information and the operation of non-linear statistical mechanics. This is how complex adaptive systems come into existence and solve problems to control its own environment. This is illustrated by an example of a robot that is trying to catch an irregularly bouncing ball. It must decide what information is relevant, and the best way to use that in a model of task, and how can it learn to perform that task in real time? Similar challenges are relevant to biological systems undergoing natural selection or to any system that processes information in order to adapt. The fact that the total information contains both order and disorder information. We must identify where order increased at the expense of disorder. A system that controls its environment successfully adapts by constructing models that allow it to decide what information is necessary and how to act on it.
Thermodynamics is not a dynamical theory, it offers no explanation for the mechanistic origins of its macroscopic variables, such as pressure, temperature, volume, entropy, etc. But statistical mechanics offers microscopic basis for these macroscopic variables. The statistical mechanics establishes the conditions for non-equilibrium states, that is for dynamical/irreversible processes for counting microscopic configurations of a system and then connecting to its macroscopic averages of thermodynamic/macroscopic variables. The evolution of pattern formation under these conditions becomes relevant in system learning. Then terms such as, ''individual species," the boundaries of "community," the functional scale at which to characterize the "ecosystems," and the interface between "natural selection and self-organization," becomes more meaningful.
It should also be emphasized that the methods of dynamical systems theory are derived from deterministic classical mechanics. In contrast, the methods of information theory are non-deterministic which are based on probabilities. An example should serve as a useful exercise; In some monkey societies, it has been observed and reported that individuals estimate the future cost of social interaction by encoding the average outcome of past interactions in special signals and then summing over these signals that help them to take next steps in social interactions!
The authors in the edited book help us take a closer view of the world we live in. The physical reality we see, and experience is not just a product of the laws of physics and chemistry but also information dynamics, statistical mechanics and thermodynamics of systems. The non-deterministic probability component of statistical mechanics is ever present. No body could have modelled the path of biological evolution if there were intelligent beings studying planet Earth 65 million years ago! Highly recommended to readers interested in understanding the parallels in biological and non-biological evolution.
April 17, 2020
On the topic of complexity, I recently finished World Hidden in Plain Sight, a collection of essays and papers on complexity.
I would give certain essays in the is book two or three stars and others four or five. As someone who has read a lot on the topic, I still found quite a few new insights and a deeper exploration of concepts like ergodicity.
I would highly recommend about half of this book, specifically, chapters: 1,6,8, and 9-19 are fantastic. Many of the early chapters struggle to articulate the ideas coherently and the later chapters restate them without saying much new. If you want the really short version, Ch. 9, 13, 15 and 17 were outstanding.
My favorite new idea was the notion of a response threshold. Neurons and individuals can respond to various stimuli. Responses are based on stimulus thresholds; stimuli below some threshold result in no response while stimuli above a threshold can elicit a reaction (kind of like Wiley Coyote?). Once generated, the action potential propagates at full intensity.
Individuals do not respond to a stimulus until it is stronger than some minimum threshold. One dirty dish won't get me to do the dishes but beyond some point where they stack up and start to look gross, I'll do all of them.
The response threshold is a fundamental organizing property of complex systems ranging from neurons and individuals.
It made me think about why division of labor naturally arises in confounders or between companies. If I see something poorly written, I can't help but start fixing it whereas poor visual design doesn't really bother me. As a result, I tend to work on the writing aspect of projects and not the design side.
Thinking about your own and colleagues' response thresholds seems a helpful way to assign out duties.
I would give certain essays in the is book two or three stars and others four or five. As someone who has read a lot on the topic, I still found quite a few new insights and a deeper exploration of concepts like ergodicity.
I would highly recommend about half of this book, specifically, chapters: 1,6,8, and 9-19 are fantastic. Many of the early chapters struggle to articulate the ideas coherently and the later chapters restate them without saying much new. If you want the really short version, Ch. 9, 13, 15 and 17 were outstanding.
My favorite new idea was the notion of a response threshold. Neurons and individuals can respond to various stimuli. Responses are based on stimulus thresholds; stimuli below some threshold result in no response while stimuli above a threshold can elicit a reaction (kind of like Wiley Coyote?). Once generated, the action potential propagates at full intensity.
Individuals do not respond to a stimulus until it is stronger than some minimum threshold. One dirty dish won't get me to do the dishes but beyond some point where they stack up and start to look gross, I'll do all of them.
The response threshold is a fundamental organizing property of complex systems ranging from neurons and individuals.
It made me think about why division of labor naturally arises in confounders or between companies. If I see something poorly written, I can't help but start fixing it whereas poor visual design doesn't really bother me. As a result, I tend to work on the writing aspect of projects and not the design side.
Thinking about your own and colleagues' response thresholds seems a helpful way to assign out duties.
September 11, 2020
Lots of little readable packages of ideas. Yum.
The essays (although they read more like short talks) on complexity cover areas like biology, markets and society. Some I just skimmed with interest ("Why people become terrorists", "Beehives and voting booths") and some I want to follow up in more detail.
The essays (although they read more like short talks) on complexity cover areas like biology, markets and society. Some I just skimmed with interest ("Why people become terrorists", "Beehives and voting booths") and some I want to follow up in more detail.
Want to read
May 14, 2020recommendation: [Transcript of Episode 10 - David Krakauer - The Jim Rutt Show](https://www.jimruttshow.com/the-jim-r...)
March 13, 2020
The book says a lot with a little payoff in terms of understanding, maybe because of the broad areas it delves into. However, I did enjoy a couple of the essays to the point of noting them down for further review, which was probably the idea behind this all along. (And these are a goldmine of good references to read further)
Would not recommend if you're looking for something that looks at complexity in a more formal fashion. Would recommend if you're already working on something related and are looking for inspiration.
Would not recommend if you're looking for something that looks at complexity in a more formal fashion. Would recommend if you're already working on something related and are looking for inspiration.
July 25, 2024
Complex adaptive systems, or studies more commonly thought of as complex systems, are easy to define. They are, as defined in an accessible Wikipedia article "a system composed of many components which may interact with each other. Examples of complex systems are Earth's global climate, organisms, the human brain, infrastructure such as power grid, transportation or communication systems, complex software and electronic systems, social and economic organizations (like cities), an ecosystem, a living cell, and, ultimately, ... the entire universe." The Santa Fe Institute is a theoretical research organization based in Santa Fe, New Mexico. The Institute was founded in 1984 with the stated purpose of organizing a new interdisciplinary research discipline into complexity theory or complex systems. This volume of forty collected essays reflects much of that interdisciplinary research from 1984 until 2019. The essays are all relatively brief. They cover a range of topics from evolutionary biology to city planning to economics. The essays are all accessible providing a summary sense of the theoretical research done at the Institute. The essays are not, however, light reading. The "hidden in plain sight" of the book's title has real understanding David Krakauer's brief introduction is read and digested.
August 31, 2019
Complexity science has been around in some of its nascent forms for over a century, but it picked up a lot of speed in the last thirty-odd years. In essence, it’s an attempt to understand and learn to deal with complex interactions that cannot be boiled down to reductionist determinism, such as weather forecasting, economics, ecology, and human psychology. When enough things are mutually influencing one another, you can’t fully understand the system or predict its behavior by finding out everything there is to know about its components. The system takes on a life of its own that doesn’t conform to simple laws and can’t be summed up with a few equations.
This book is a chronological collection of essays and articles by members of the Santa Fe Institute, a group of scientists studying complexity, written from 1984 to 2019. It serves as a sampler for the subject and its various applications as well as a historical survey of progress. Complexity science is an important new avenue because it leads to methods of solving problems that more traditional science has been knocking its head against. I was disappointed mostly because the book presents history rather than new advances.
This book is a chronological collection of essays and articles by members of the Santa Fe Institute, a group of scientists studying complexity, written from 1984 to 2019. It serves as a sampler for the subject and its various applications as well as a historical survey of progress. Complexity science is an important new avenue because it leads to methods of solving problems that more traditional science has been knocking its head against. I was disappointed mostly because the book presents history rather than new advances.
July 5, 2020
I loved a lot of this. My felt rating was at a 4 moving to 5 until I hit the last third, which is a collection of many 6-page articles originally published in Christian Science Monitor. With a few exceptions, these pieces were oversimplified or over-condensed to the point where, despite being enjoyable, they almost approached pseudo-science in their tone (the effort to make complexity "digestible" is probably a worthy goal, but it cannot avoid being somewhat oxymoronic). But then I got to the last piece, written by Krakauer. It is one of the most accessible and clear characterizations of what a complexity-literate approach to solutions design looks like in contrast to standard design/engineering paradigms. It's brilliant. More people looking to solve "wicked problems" need the kind of understanding provided in this piece, in particular, the final call for approaches that move desired outcomes into "a space of desirable, albeit never optimal, performance" (p. 355).
October 3, 2020
Lovely book summarising research and elegant ideas over the last three decades.
It’s full of wonderful insights such as ‘nearly every assumption listed in the engineering axioms is violated by complex systems. So, what leads us to believe that we can use the insights of classical engineering to predict and control these systems?’
But it does have awful unreadable sentences such as ‘we begin the search for cognitively principled effective theories using what we know about component cognition to inform how we coarse-grain and compress the circuits’
It’s full of wonderful insights such as ‘nearly every assumption listed in the engineering axioms is violated by complex systems. So, what leads us to believe that we can use the insights of classical engineering to predict and control these systems?’
But it does have awful unreadable sentences such as ‘we begin the search for cognitively principled effective theories using what we know about component cognition to inform how we coarse-grain and compress the circuits’
February 23, 2020
Essays of unequal value
A large collection of essays by various authors is sure to include some stories among the pearls. Some pearls were riveting and yielded dozens of highlighted passages. You have to be willing to sort through the stories. In the end I found what I was looking for.
A large collection of essays by various authors is sure to include some stories among the pearls. Some pearls were riveting and yielded dozens of highlighted passages. You have to be willing to sort through the stories. In the end I found what I was looking for.
October 13, 2020
An enjoyable overview
This isn't an academic work. It provides an overview of some of the thinking from the SFI.
Predictably, some of it is groundbreaking and offers new paradigms in the authentically Khunian sense, while others are a little more pedestrian.
A fun read so in all.
This isn't an academic work. It provides an overview of some of the thinking from the SFI.
Predictably, some of it is groundbreaking and offers new paradigms in the authentically Khunian sense, while others are a little more pedestrian.
A fun read so in all.
December 30, 2020
This book is primarily aimed at people who are interested in Complexity Science and/or the Santa Fe Institute and want to read through 25 years of notable essays on topics in complexity science.
As I am interested in Complexity Science (as a hobby more so than anything), I very much enjoyed reading the book, seeing essays from notable experts I have previously read about, and adding to my depth of knowledge of how to model things from a complexity science perspective.
As I am interested in Complexity Science (as a hobby more so than anything), I very much enjoyed reading the book, seeing essays from notable experts I have previously read about, and adding to my depth of knowledge of how to model things from a complexity science perspective.
August 12, 2025
some very cool ideas and concepts inside, but a large percentage of the essays could have conveyed their ideas in simpler forms and without unnecessarily cryptic language. academic decoder ring activate. additionally some of the metaphors and analogies were vague to the point of uselessness. but it’s hard to strike a balance in such an abstract interdisciplinary field I would guess
August 1, 2020
A great review of the exciting work on nonlinear dynamic systems conducted at the Santa Fe Institute in the past decades. The book is extremely accessible with the third section, especially written for a general audience.
August 25, 2020
Most of the scientists Jeffrey Epstein donated money to were associated with the Santa Fe Institute.
It's not complex, they are pervs and the science part of the institute is basically a cover for the rape part of the institute.
It's all a game to them, it's in their culture.
It's not complex, they are pervs and the science part of the institute is basically a cover for the rape part of the institute.
It's all a game to them, it's in their culture.
January 25, 2022
Has some interesting essays on complexity science with examples from all sorts of fields. Everything is connected when it comes to complexity.
The whole book doesn't feel properly woven together, but good enough if you want some new ideas/perspectives.
The whole book doesn't feel properly woven together, but good enough if you want some new ideas/perspectives.
July 23, 2022
great introduction to the complexity theory
I see this book as a thought provoking collection of articles . The objective here is to stat one on the journey of learning more about complexity.
I see this book as a thought provoking collection of articles . The objective here is to stat one on the journey of learning more about complexity.
December 5, 2019
Unexpectedly great list of essays on complexity science. Many fresh ideas from different areas. These guys from Sante Fe Institute know their stuff. The future is in complexity science - no doubt!
June 23, 2020
A collection of essential writing from an essential institution.
November 16, 2020
Intricate essays that dwell nicely in your thoughts over time and which you find yourself returning to your shelf to peruse in leisurely pensive moments.
February 10, 2022
Changed the direction of my life.
July 1, 2025
Chapters vary widely in utility and depth, but overall a good introduction, as designed.
And I suppose by its design SFI will always investigate the fortunate skim that includes us rather than the overwhelmingly more important microbial world that individually and collectively lie much closer to important physical and chemical constraints so many of its associates strain to emphasise. To what degree can their developments be used to understand this world, its constraints, predict its development, and understand the degree to which we depend upon it?
Like, for example, Steven Frank’s new book Microbial Life History ? Frank is excellent and I’m very glad he’s turned his mind to this subject, but his book is the kind of book SFI scholars should have been writing for years. If only.
And I suppose by its design SFI will always investigate the fortunate skim that includes us rather than the overwhelmingly more important microbial world that individually and collectively lie much closer to important physical and chemical constraints so many of its associates strain to emphasise. To what degree can their developments be used to understand this world, its constraints, predict its development, and understand the degree to which we depend upon it?
Like, for example, Steven Frank’s new book Microbial Life History ? Frank is excellent and I’m very glad he’s turned his mind to this subject, but his book is the kind of book SFI scholars should have been writing for years. If only.
April 7, 2023
Selectively read....the early articles are more than well written...they are revelatory....lots of notes in my margins.
Displaying 1 - 24 of 24 reviews