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Overcomplicated: Technology at the Limits of Comprehension
Why did the New York Stock Exchange suspend trading without warning on July 8, 2015? Why did certain Toyota vehicles accelerate uncontrollably against the will of their drivers? Why does the programming inside our airplanes occasionally surprise its creators?
After a thorough analysis by the top experts, the answers still elude us.
You don’t understand the software running your car or your iPhone. But here’s a neither do the geniuses at Apple or the Ph.D.’s at Toyota—not perfectly, anyway. No one, not lawyers, doctors, accountants, or policy makers, fully grasps the rules governing your tax return, your retirement account, or your hospital’s medical machinery. The same technological advances that have simplified our lives have made the systems governing our lives incomprehensible, unpredictable, and overcomplicated.
In Overcomplicated , complexity scientist Samuel Arbesman offers a fresh, insightful field guide to living with complex technologies that defy human comprehension. As technology grows more complex, Arbesman argues, its behavior mimics the vagaries of the natural world more than it conforms to a mathematical model. If we are to survive and thrive in this new age, we must abandon our need for governing principles and rules and accept the chaos. By embracing and observing the freak accidents and flukes that disrupt our lives, we can gain valuable clues about how our algorithms really work. What’s more, we will become better thinkers, scientists, and innovators as a result.
Lucid and energizing, this book is a vital new analysis of the world heralded as "modern" for anyone who wants to live wisely.
After a thorough analysis by the top experts, the answers still elude us.
You don’t understand the software running your car or your iPhone. But here’s a neither do the geniuses at Apple or the Ph.D.’s at Toyota—not perfectly, anyway. No one, not lawyers, doctors, accountants, or policy makers, fully grasps the rules governing your tax return, your retirement account, or your hospital’s medical machinery. The same technological advances that have simplified our lives have made the systems governing our lives incomprehensible, unpredictable, and overcomplicated.
In Overcomplicated , complexity scientist Samuel Arbesman offers a fresh, insightful field guide to living with complex technologies that defy human comprehension. As technology grows more complex, Arbesman argues, its behavior mimics the vagaries of the natural world more than it conforms to a mathematical model. If we are to survive and thrive in this new age, we must abandon our need for governing principles and rules and accept the chaos. By embracing and observing the freak accidents and flukes that disrupt our lives, we can gain valuable clues about how our algorithms really work. What’s more, we will become better thinkers, scientists, and innovators as a result.
Lucid and energizing, this book is a vital new analysis of the world heralded as "modern" for anyone who wants to live wisely.
256 pages, Hardcover
First published July 19, 2016
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April 4, 2018
This is a short book about the limits of our techniques for managing technology. I didn't care for it. The basic premise of the book is "our technology has gotten too complex for us to understand and we need new paradigms for managing it." Much of this book is true, much is thought-provoking, but alas the two subsets have limited overlap. The book also suffers from a lack of clear audience: it's a book about what technologists should do, written for non-technologists with limited grip on the problem.
The author's diagnosis of the problem is surely correct, but not very novel or deep. He points to two concerns: first, we keep tinkering with systems we half-understand, resulting in gradual increase of entropy, and second, we keep interconnecting systems, resulting in surprising couplings. I think most mid-career engineers already understand these points thoroughly. Nor are they new -- I think both points are already in The Mythical Man Month.
The author's advice about solutions is interesting but I think wrong. He says "rather than thinking like physicists who want general mathematical rules, we should be more like biologists, studying specific instances and specific interrelations." He also suggests more use of simulators. These two points, alas, contradict -- for a simulator to be useful, we need sound abstractions for the thing being simulated, which is the physics model.
Moreover, close observations of specifics and pairwise interactions has two other serious drawbacks -- it fails to scale as the systems do, and it doesn't let us extrapolate system behavior as various parameters change. Suppose you have a system and you want to know what happens as you vary some parameter -- data volume, number of nodes, whatever. The biology approach won't tell you much, and the physics approach will. The author approvingly cites Djikstra's remark about how computer science spans orders of magnitude in ways few other fields do -- but fails to take that into account.
In all, I am afraid this book gives the illusion of insight while in fact not telling anybody anything useful.
The author's diagnosis of the problem is surely correct, but not very novel or deep. He points to two concerns: first, we keep tinkering with systems we half-understand, resulting in gradual increase of entropy, and second, we keep interconnecting systems, resulting in surprising couplings. I think most mid-career engineers already understand these points thoroughly. Nor are they new -- I think both points are already in The Mythical Man Month.
The author's advice about solutions is interesting but I think wrong. He says "rather than thinking like physicists who want general mathematical rules, we should be more like biologists, studying specific instances and specific interrelations." He also suggests more use of simulators. These two points, alas, contradict -- for a simulator to be useful, we need sound abstractions for the thing being simulated, which is the physics model.
Moreover, close observations of specifics and pairwise interactions has two other serious drawbacks -- it fails to scale as the systems do, and it doesn't let us extrapolate system behavior as various parameters change. Suppose you have a system and you want to know what happens as you vary some parameter -- data volume, number of nodes, whatever. The biology approach won't tell you much, and the physics approach will. The author approvingly cites Djikstra's remark about how computer science spans orders of magnitude in ways few other fields do -- but fails to take that into account.
In all, I am afraid this book gives the illusion of insight while in fact not telling anybody anything useful.
September 4, 2016
É algo complicado perceber qual é o propósito deste livro. Promete ser uma reflexão sobre os sistemas complexos que suportam o nosso dia a dia, desde as infraestruturas tecnológicas aos códigos legais. Tem uma linha de pensamento muito clara: da colisão da expansão do conhecimento e desenvolvimento tecnológico surgiram sistemas de elevada complexidade, dos quais dependemos para manter a funcionar a economia, instituições, e cada vez mais, com a computação pessoal e a internet, as nossas vidas pessoais. Complexidade exponencial, que leva a que poucos sejam realmente capazes de compreender todos os elementos dos sistemas, e ao surgir de paralisações ou avarias inesperadas com causas difíceis de descobrir. Analisa o papel da progressiva especialização no domínio do conhecimento, reflectindo sobre a necessidade de abordagens transdisciplinares que preservem a flexibilidade de um pensamento generalista face ao isolamento trazido pela hiper-especialização.
São argumentos interessantes e pertinentes, mas o livro lê-se essencialmente como um longo resmungo acerca das condições da modernidade contemporânea. Os sistemas tecnológicos complexos que dão o tema ao livro são abordados de forma liminar, sem nada mais profundo do que pequenas histórias de momentos em que bugs ou anomalias fizeram algo correr mal. Ao falar de alguns tipos de sistemas complexos, sente-se até que o grande problema apontado pelo autor está na diversidade de necessidades humanas, que obrigam à complexidade bizantina dos sistemas legais. As leis seriam de facto mais simples se houvessem menos direitos a respeitar. Apesar de abordar questões pertinentes, acaba por ser ler como um misto de análise angustiada e síndrome de choque do futuro.
São argumentos interessantes e pertinentes, mas o livro lê-se essencialmente como um longo resmungo acerca das condições da modernidade contemporânea. Os sistemas tecnológicos complexos que dão o tema ao livro são abordados de forma liminar, sem nada mais profundo do que pequenas histórias de momentos em que bugs ou anomalias fizeram algo correr mal. Ao falar de alguns tipos de sistemas complexos, sente-se até que o grande problema apontado pelo autor está na diversidade de necessidades humanas, que obrigam à complexidade bizantina dos sistemas legais. As leis seriam de facto mais simples se houvessem menos direitos a respeitar. Apesar de abordar questões pertinentes, acaba por ser ler como um misto de análise angustiada e síndrome de choque do futuro.
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April 4, 2018I enjoyed this book, but put it down at some point and didn't pick it back up. I would say that's my fault more than the author's — it's getting harder and harder for me to finish a print book. Audiobooks dominate in my world.
November 27, 2016
Nunca mais vamos entender completamente a tecnologia. Essa foi a mensagem principal desse livro. Uma explicação rápida e bem direta de como sistemas se tornam complexos, da nossa língua a leis, de um programa de computador ao funcionamento de um Boeing 777, e como um sistema complexo e cheio de interações como esse (daí o complicado) se torna simplesmente incompreensível. De onde surgem bugs misteriosos, problemas sem solução e todo tipo de gambiarra criativa que constrói em cima do que já existe, já que muitas vezes é a única solução.
Gostei em especial da diferença que ele faz entre uma abordagem mais física – extrair grandes princípios integradores – e uma mais biológica – observar e catalogar a diversidade – e como as duas podem ser importante para navegarmos em um mundo mais complexo. Fiquei imaginando a profissão de um zoólogo de bugs... de computador.
Gostei em especial da diferença que ele faz entre uma abordagem mais física – extrair grandes princípios integradores – e uma mais biológica – observar e catalogar a diversidade – e como as duas podem ser importante para navegarmos em um mundo mais complexo. Fiquei imaginando a profissão de um zoólogo de bugs... de computador.
December 5, 2025
"Overcomplicated" tackles one of the most pressing intellectual challenges of our modern age: why the technologies we've built have become impossible for anyone, including the experts who created them, to fully understand. Arbesman makes a compelling and sometimes unsettling argument that should resonate with anyone who relies on technology, which basically means everyone today.
The book's greatest strength lies in its accessible yet rigorous exploration of technological complexity. Arbesman brilliantly introduces the concept of "the Entanglement"- the idea that we've crossed into an era where massive technological systems are interconnected in ways we can no longer comprehend. He doesn't just explain this abstract idea; he grounds it in real, concrete examples that stick with you. On July 8, 2015, United Airlines suffered a computer outage that grounded flights. That same day, the New York Stock Exchange halted trading, and the Wall Street Journal's website went down. Nobody knew what was happening. Twitter exploded with speculation about cyberattacks, but the culprit was actually just buggy software that nobody fully understood. This opening sets the tone perfectly, we're not dealing with science fiction doomsday scenarios; this is happening right now.
What particularly stands out is how Arbesman dissects the forces that drive technological complexity. He identifies three main culprits: accretion (adding more parts over time), interaction (connecting those parts together), and edge cases (exceptions that demand special handling). His examination of accretion is especially illuminating. The FAA discovered in the late 1990s that its air traffic control systems relied on IBM 3083 mainframes installed in the 1980s, running software from years before that. When they needed to fix the Y2K bug, they found only two IBM employees who understood the microcode and both were retired. The IRS, meanwhile, was still using computer systems developed during the Kennedy administration. Even the final space shuttle mission was supported by five IBM machines that had less computing power than today's average smartphone. You realize quickly that we don't design these systems once and then move on; we keep patching them, building on top of them, creating what Arbesman calls "kluges" - Rube Goldberg contraptions that work but are far from elegant.
The book's middle chapters, particularly the discussion of "The Origins of the Kluge," provide excellent case studies. Arbesman walks through how the Internet was never designed for secure commercial transactions, so we've had to bolt on layer upon layer of encryption and security protocols. HTML was never meant to support interactive applications like Google Docs, yet engineers have somehow made it work through sheer determination and complexity. Even email, which seems relatively simple, has evolved into something far more convoluted than its original designers imagined, with features like message threading grafted awkwardly onto outdated foundations.
Another strength is his willingness to embrace nuance. Rather than suggesting we're heading toward some dystopian robot takeover, Arbesman instead proposes something more subtle and actually more troubling: we're building systems so complex that they behave in ways we cannot predict or control. He describes the Therac-25 radiation machine scandal of the 1980s, where a radiation therapy machine irradiated six patients with massive overdoses, killing some of them. The heartbreaking part? The safety analysis didn't even consider software as a potential failure point. The engineers assumed software "does not degrade due to wear, fatigue, or reproduction process," completely missing the fact that software is complex and can fail in countless ways. This wasn't malice or incompetence; it was a fundamental misunderstanding of complexity.
The book does have some weaknesses worth noting. In the later chapters, when Arbesman shifts toward solutions, his suggestions feel somewhat thin. He advocates for "biological thinking" about technology, essentially treating tech systems like biologists treat living organisms, studying them through careful observation and experimentation rather than trying to understand them all at once through pure logic. While philosophically interesting, this feels more like wisdom than actionable guidance. If I'm an engineer building a system, how exactly does "biological thinking" help me prevent the next disaster? The book gestures toward answers but doesn't provide concrete methodologies.
Additionally, the discussion of "walking humbly with technology" in the final chapter, while philosophically satisfying, feels a bit abstract after the very concrete problems outlined earlier. Arbesman essentially argues that we should embrace humility, accepting that we can never fully understand these systems while still trying to understand them incrementally. This is wise counsel, but the gap between diagnosis and solution feels wider in these closing sections than in the earlier, more tightly argued chapters about why systems become complex in the first place.
What makes this book genuinely important is not that it solves the problem of technological complexity , it doesn't but that it clearly articulates the problem in a way that speaks to anyone who's ever felt frustrated by the incomprehensibility of the systems they depend on. This book validates a common experience in our time: technology is becoming less understandable, not more, despite (or perhaps because of) the brilliance of individual engineers and developers.
Arbesman's central insight is counterintuitive: the more engineers work within complicated systems to fix problems or add features, the more complex those systems become. Each individual decision makes sense, but collectively, they create a baroque mess. He illustrates this beautifully with the story of trying to build a simple calendar application. How hard can it be, right? Just track days, handle leap years, include time zones. But then you need to account for daylight saving time, except Arizona doesn't observe it. What about holidays? Some move every year because they follow lunar calendars. If you want historical accuracy, you need to account for how different regions adopted different calendar systems at different times. The Russian October Revolution is celebrated in November in Russia because Russia was still using the Julian calendar when it happened. By the time you're done, your "simple" calendar app has become a labyrinth of special cases and workarounds
This book is essential reading for anyone working in technology, particularly software engineering and system design. But it's also valuable for anyone who cares about the future which should include policymakers, business leaders, and informed citizens. In an age where we're increasingly dependent on systems we don't understand (algorithms determining what content we see, AI making medical decisions, autonomous vehicles deciding when to brake), understanding why these systems resist understanding is crucial.
The writing itself is clear and engaging. Arbesman avoids jargon when possible and explains technical concepts in accessible terms. He uses excellent examples that make abstract ideas concrete. This isn't a book written only for specialists; it's genuinely readable for a broad audience.
The book's greatest strength lies in its accessible yet rigorous exploration of technological complexity. Arbesman brilliantly introduces the concept of "the Entanglement"- the idea that we've crossed into an era where massive technological systems are interconnected in ways we can no longer comprehend. He doesn't just explain this abstract idea; he grounds it in real, concrete examples that stick with you. On July 8, 2015, United Airlines suffered a computer outage that grounded flights. That same day, the New York Stock Exchange halted trading, and the Wall Street Journal's website went down. Nobody knew what was happening. Twitter exploded with speculation about cyberattacks, but the culprit was actually just buggy software that nobody fully understood. This opening sets the tone perfectly, we're not dealing with science fiction doomsday scenarios; this is happening right now.
What particularly stands out is how Arbesman dissects the forces that drive technological complexity. He identifies three main culprits: accretion (adding more parts over time), interaction (connecting those parts together), and edge cases (exceptions that demand special handling). His examination of accretion is especially illuminating. The FAA discovered in the late 1990s that its air traffic control systems relied on IBM 3083 mainframes installed in the 1980s, running software from years before that. When they needed to fix the Y2K bug, they found only two IBM employees who understood the microcode and both were retired. The IRS, meanwhile, was still using computer systems developed during the Kennedy administration. Even the final space shuttle mission was supported by five IBM machines that had less computing power than today's average smartphone. You realize quickly that we don't design these systems once and then move on; we keep patching them, building on top of them, creating what Arbesman calls "kluges" - Rube Goldberg contraptions that work but are far from elegant.
The book's middle chapters, particularly the discussion of "The Origins of the Kluge," provide excellent case studies. Arbesman walks through how the Internet was never designed for secure commercial transactions, so we've had to bolt on layer upon layer of encryption and security protocols. HTML was never meant to support interactive applications like Google Docs, yet engineers have somehow made it work through sheer determination and complexity. Even email, which seems relatively simple, has evolved into something far more convoluted than its original designers imagined, with features like message threading grafted awkwardly onto outdated foundations.
Another strength is his willingness to embrace nuance. Rather than suggesting we're heading toward some dystopian robot takeover, Arbesman instead proposes something more subtle and actually more troubling: we're building systems so complex that they behave in ways we cannot predict or control. He describes the Therac-25 radiation machine scandal of the 1980s, where a radiation therapy machine irradiated six patients with massive overdoses, killing some of them. The heartbreaking part? The safety analysis didn't even consider software as a potential failure point. The engineers assumed software "does not degrade due to wear, fatigue, or reproduction process," completely missing the fact that software is complex and can fail in countless ways. This wasn't malice or incompetence; it was a fundamental misunderstanding of complexity.
The book does have some weaknesses worth noting. In the later chapters, when Arbesman shifts toward solutions, his suggestions feel somewhat thin. He advocates for "biological thinking" about technology, essentially treating tech systems like biologists treat living organisms, studying them through careful observation and experimentation rather than trying to understand them all at once through pure logic. While philosophically interesting, this feels more like wisdom than actionable guidance. If I'm an engineer building a system, how exactly does "biological thinking" help me prevent the next disaster? The book gestures toward answers but doesn't provide concrete methodologies.
Additionally, the discussion of "walking humbly with technology" in the final chapter, while philosophically satisfying, feels a bit abstract after the very concrete problems outlined earlier. Arbesman essentially argues that we should embrace humility, accepting that we can never fully understand these systems while still trying to understand them incrementally. This is wise counsel, but the gap between diagnosis and solution feels wider in these closing sections than in the earlier, more tightly argued chapters about why systems become complex in the first place.
What makes this book genuinely important is not that it solves the problem of technological complexity , it doesn't but that it clearly articulates the problem in a way that speaks to anyone who's ever felt frustrated by the incomprehensibility of the systems they depend on. This book validates a common experience in our time: technology is becoming less understandable, not more, despite (or perhaps because of) the brilliance of individual engineers and developers.
Arbesman's central insight is counterintuitive: the more engineers work within complicated systems to fix problems or add features, the more complex those systems become. Each individual decision makes sense, but collectively, they create a baroque mess. He illustrates this beautifully with the story of trying to build a simple calendar application. How hard can it be, right? Just track days, handle leap years, include time zones. But then you need to account for daylight saving time, except Arizona doesn't observe it. What about holidays? Some move every year because they follow lunar calendars. If you want historical accuracy, you need to account for how different regions adopted different calendar systems at different times. The Russian October Revolution is celebrated in November in Russia because Russia was still using the Julian calendar when it happened. By the time you're done, your "simple" calendar app has become a labyrinth of special cases and workarounds
This book is essential reading for anyone working in technology, particularly software engineering and system design. But it's also valuable for anyone who cares about the future which should include policymakers, business leaders, and informed citizens. In an age where we're increasingly dependent on systems we don't understand (algorithms determining what content we see, AI making medical decisions, autonomous vehicles deciding when to brake), understanding why these systems resist understanding is crucial.
The writing itself is clear and engaging. Arbesman avoids jargon when possible and explains technical concepts in accessible terms. He uses excellent examples that make abstract ideas concrete. This isn't a book written only for specialists; it's genuinely readable for a broad audience.
January 14, 2019
Worth reading, if a bit repeitive
Certainly a timely and fairly interesting book, but the author seemed to repeat himself a lot about all modern technology becoming increasingly hard to comprehend in entirety. There were two or three genuinely interesting bits. He talks about "physics thinking" vs "biological thinking", ie, the difference between finding grand overarching patterns vs examining the specifics. He also used the lens of complex systems to find unifying themes in areas as diverse as law, linguistics, and software, which seemed like a good way to approach problems that have lots of interacting parts.
Overall, I'm glad I read this book, but it wasn't as challenging or thought-provoking as I would have hoped.
Certainly a timely and fairly interesting book, but the author seemed to repeat himself a lot about all modern technology becoming increasingly hard to comprehend in entirety. There were two or three genuinely interesting bits. He talks about "physics thinking" vs "biological thinking", ie, the difference between finding grand overarching patterns vs examining the specifics. He also used the lens of complex systems to find unifying themes in areas as diverse as law, linguistics, and software, which seemed like a good way to approach problems that have lots of interacting parts.
Overall, I'm glad I read this book, but it wasn't as challenging or thought-provoking as I would have hoped.
May 23, 2017
Introduces the Entanglement in which complex systems are so interconnected that they are essentially not understandable. This leads to mythical entities like "bugs" and "glitches" that seem to be somewhat random (based on our lack of knowledge). The author notes how complexity thinking in science (biology) might be more useful to gain understanding of tech than a relationship thinking (physics). Yet, a mix is likely reasonable. Finally, given the complexity, the author notes the problems with awe and fear. Instead, humbly trying to understand is a more useful outlook.
March 3, 2020
The subtitle of this book – "Technology at the limits of comprehension" – could pass for my personal knowledge and inclinations on this topic notwithstanding, as Samuel Arbesman points out here, that I may be expecting too much of the complex cobbled-together systems that operate throughout my life.
His theme, and use of the term "kluge" has also been applied in the area of brain research, where presumptions about brains in general can suggest that it's much more perfect than what it is,particularly as an acme of evolution. "The Whiggish view of progress" – more accurately Herbert Butterfield's "Whig view of history" – is a term used by Arbesman in his technological context and can readily be applied in neuroscience, evolution and elsewhere.
At any rate, there are systems built on systems, methods built on methods, to the extent that noone can know what might go on or happen, bugs and otherwise. A mundane example might be the loss of office memory when someone departs, or downsizing occurs, a reason why corporate and utility language is fairly incomprehensible, even on what ought to be basic letter writing, but it's much more complex than that and as a consequence, the author labels the current state of affairs the Entanglement, a word that I first encountered in biology-related reading.
In fact, Arbesman recommends the greater use of biological methods (his background) to complement physics methods, partly because the latter tends to discount exceptions and exclusions. Really, it doesn't matter what labels you put on the thought and method he recommends, it seems fundamental to me.
The book contains a variety of examples and some personal information and reflection, as seems routine these days. The text is competent and predominantly clear, with some curious words used at times, which may indicate I'm too old or out of touch with technology..
His theme, and use of the term "kluge" has also been applied in the area of brain research, where presumptions about brains in general can suggest that it's much more perfect than what it is,particularly as an acme of evolution. "The Whiggish view of progress" – more accurately Herbert Butterfield's "Whig view of history" – is a term used by Arbesman in his technological context and can readily be applied in neuroscience, evolution and elsewhere.
At any rate, there are systems built on systems, methods built on methods, to the extent that noone can know what might go on or happen, bugs and otherwise. A mundane example might be the loss of office memory when someone departs, or downsizing occurs, a reason why corporate and utility language is fairly incomprehensible, even on what ought to be basic letter writing, but it's much more complex than that and as a consequence, the author labels the current state of affairs the Entanglement, a word that I first encountered in biology-related reading.
In fact, Arbesman recommends the greater use of biological methods (his background) to complement physics methods, partly because the latter tends to discount exceptions and exclusions. Really, it doesn't matter what labels you put on the thought and method he recommends, it seems fundamental to me.
The book contains a variety of examples and some personal information and reflection, as seems routine these days. The text is competent and predominantly clear, with some curious words used at times, which may indicate I'm too old or out of touch with technology..
August 14, 2018
مع التطور التكنولوجي لابد من دفع ضريبة هي التعقيد, الذي يزداد على نحو أسي مع كل كشف علمي.
ويزداد حتى نصل إلا نقطة يتعذر على عقولنا فهم هذه الأنظمة, فمثلا لا يوجد أحد على وجه الأرض قادر على فهم كل جزء من نظام التشغيل, لأن بعض مكوناته مكتوبة بلغة برمجة قديمة لم تعد مستخدمة.
التعقيد هو صفة أساسية لكل نظام, لأن هذا النظام ليس إلا تجميعة عدة أنظمة أبسط هي في الأساس معقدة بما يكفي.
و مع هذه الأنظمة المكونة تأتي الأخطاء, وبجمع هذه الأنظمة بطريقة معينة تنتج أخطاء جديدة, وهكذا فمن المستحيل تفادي الأخطاء.
وأحيانا تكون هذه الأخطاء قاتلة, وقد تسبب خسائر في الأرواح والممتلكات تقدر بالملايين.
كتعطل برنامج لتسيير البورصة, فتتعطل المقايضات لساعات. أو تعطل برنامج للعلاج بالأشعة, أو نظام الدفع في سيارة, أو نظام إطلاق صاروخ.
يعرض الكاتب هذه الأخطاء و يناقش أسبابها.
وفي النهاية يقدم خلاصة خبرته أنه علينا التوقف عن محاولة فهم الأنظمة المعقدة بطريقة الفيزيائيين التقليدية و أن نتبنى فلسفة علماء البيولوجيا الذين يعتمدون على الوصف والملاحظة من أجل فهم البنيات الحيوية المعقدة.
February 3, 2017
Summary: "the world is complicated, nobody can understand it, but that's ok".
August 26, 2016
Why cars, websites, and other cool technological things, go batshit crazy and what to do about it (sort of).
January 17, 2019
Interesting read for those who may not work with technology. Sadly I am in that field and, although I agreed with most of his points, I didn’t find them to be particularly insightful.
August 4, 2018
I thought this was a nice introduction to some concepts in complexity theory and computer science. I thought at times it was repetitive i.e. technology is creeping towards the limits of our understanding if not already surpassed. However, I also thought the author provided some great insight that should make any reader think (interoperability, entanglement, emergence, dark code, hapax legomena, etc). I also liked the correlations to linguistics and a primer on some NLP concepts and machine learning.
So how do we deal with this complexity? The author had some solutions like the return of generalists, simulations, interdisciplinary teams, humility not fear, etc. Whether or not any of them will actually work who knows, but I think it's a start and we should have more people thinking about this. How many times can we layer our knowledge upon generation and generation without any loss of context?
I also think dividing two types of thinking up into biological vs physical is true to an extent but also strips away all the details becuase it is such an overarching generalization. Of course any good scientist/engineer will tinker with the abstractness and messiness of reality(biological) but also look for patterns and universality using rigor and mathematics(physical). I guess it is true we need all types of people with different kinds of thinking. Also, you could argue physics forms the basis of all biological activity so there is that to ponder too.
Overall this should be entertaining for someone who wants to have a gentle introduction but if you seek a deeper dive and have good foundational knowledge look elsewhere. The author does provide a lot of further readings and notes for the reader which is helpful.
So how do we deal with this complexity? The author had some solutions like the return of generalists, simulations, interdisciplinary teams, humility not fear, etc. Whether or not any of them will actually work who knows, but I think it's a start and we should have more people thinking about this. How many times can we layer our knowledge upon generation and generation without any loss of context?
I also think dividing two types of thinking up into biological vs physical is true to an extent but also strips away all the details becuase it is such an overarching generalization. Of course any good scientist/engineer will tinker with the abstractness and messiness of reality(biological) but also look for patterns and universality using rigor and mathematics(physical). I guess it is true we need all types of people with different kinds of thinking. Also, you could argue physics forms the basis of all biological activity so there is that to ponder too.
Overall this should be entertaining for someone who wants to have a gentle introduction but if you seek a deeper dive and have good foundational knowledge look elsewhere. The author does provide a lot of further readings and notes for the reader which is helpful.
November 4, 2017
This short book discusses the problems caused when technology becomes so complex that few people can understand all aspects of a given invention. The author discusses the ubiquity of errors in software and historical examples where this has caused problems as well as additional problems caused by inter-connectedness.
The author doesn't much posit solutions to this and merely observes what he calls thinking like a biologist and thinking like a physicist in terms of understanding elements vs a system's gestalt. He also proposes that we have more generalists to help tackle problems as narrow solutions become harder to come by.
I wasn't particularly impressed by this book. The author has a point but doesn't bring enough commentary to the table to justify this being a book. I've read other books that comment on the problems in certain hard technologies and do much better jobs both explaining what makes them hard as well as discussing how we deal with that complexity.
The book does avoid catastrophism and avoids lamenting the loss of "a simpler time" but that's not enough to stand on. Skip.
The author doesn't much posit solutions to this and merely observes what he calls thinking like a biologist and thinking like a physicist in terms of understanding elements vs a system's gestalt. He also proposes that we have more generalists to help tackle problems as narrow solutions become harder to come by.
I wasn't particularly impressed by this book. The author has a point but doesn't bring enough commentary to the table to justify this being a book. I've read other books that comment on the problems in certain hard technologies and do much better jobs both explaining what makes them hard as well as discussing how we deal with that complexity.
The book does avoid catastrophism and avoids lamenting the loss of "a simpler time" but that's not enough to stand on. Skip.
September 26, 2017
We build systems (software, legal frameworks, etc) and we keep adding features and exceptions to them, each addition triggering an exponential increase of interactions between individual parts. And that's when we lose the ability to understand or anticipate all possible pathways through the system. Our systems and technologies become black boxes.
Once our technological creations reach such high complexities that we don't understand them anymore, we either resort to fear or awe (the modern day sublime). Plus unexpected behaviour and/or bugs creep in and we simply can't make sense of them anymore. Arbesam makes the point that we need to find ways of keeping up with our creations, by becoming T-shaped people (specialist with generalist knowledge) and by designing more transparent, more easily-readable systems (see "explainable A.I.")
This is a pretty fascinating topic. The book could have maybe used some harsh editing and could have worked better as an elongated essay, but all in all he's collecting interesting points.
Once our technological creations reach such high complexities that we don't understand them anymore, we either resort to fear or awe (the modern day sublime). Plus unexpected behaviour and/or bugs creep in and we simply can't make sense of them anymore. Arbesam makes the point that we need to find ways of keeping up with our creations, by becoming T-shaped people (specialist with generalist knowledge) and by designing more transparent, more easily-readable systems (see "explainable A.I.")
This is a pretty fascinating topic. The book could have maybe used some harsh editing and could have worked better as an elongated essay, but all in all he's collecting interesting points.
August 4, 2024
This book taught me new words and concepts, such as kluge, naches, and dark code. The author’s diagnosis of our technology’s ever-expanding complexity is surely right, but the prescriptions don’t feel very satisfying. Yes, we need more generalists, but our educational system and our economy don’t incentivize the cultivation of generalists. Yes, we need to be more comfortable with uncertainty and nuance and varying degrees of understanding, but that sounds like it will take a whole cultural revolution. Plus, this 2016 book already feels a bit outdated. I wish Samuel Arbesman would talk to Meghan O’Gieblyn about her 2021 book “God Human Animal Machine.” They could riff about how technology is often equated (rightly) to magic, and I could listen in, and we’d all have fun.
October 19, 2020
In the beginning I really loved this book, because it acknowledged what I've seen time and time in software development. The most carefully laid plans turning into kluges. But in time the book mostly repeated itself and lost my attention. I think it had value because this isn't something you're taught in school. I feel better about my own work and my own limits as a mere human. But I'm still wanting to learn how some of these mistakes could be avoided, or mitigated once they crop up, ideally in a practical way, since this book did once again confirm that while abstraction has its place, it also can be too vague.
February 13, 2017
I was surprisingly underwhelmed by this book. To sum up - there is lots of systems out there, they are complicated, no, one person can know everything, all good. Press repeat, and repeat again, insert an anecdote, then tell us that there are lots of systems out there and its all very complex - but use a few different words. Kind of reminded me of a 10th grade english essay - where you are just learning to construct the argument and defend it (not well).
I thought the premise of this book was great - but there seemed to be so little substance or real evidence or information.
I thought the premise of this book was great - but there seemed to be so little substance or real evidence or information.
July 12, 2018
Pretty good synopsis regarding the new normal of Project Management, Expert Systems, and current norms and practices in the world. Truth be told, I had an idea like this a few years' back but nothing ever came of it (go figure).
Arbesman has a good grasp of software engineering, traffic management, the grid all current sectors undergoing rapid and irreversible change. This Information Age has all the makings of a full blown man made disaster, but I'm ever the optimist.
Good read about the dangers(and opportunities) that modern industry and business have on society. Not an exaggerated read.
Arbesman has a good grasp of software engineering, traffic management, the grid all current sectors undergoing rapid and irreversible change. This Information Age has all the makings of a full blown man made disaster, but I'm ever the optimist.
Good read about the dangers(and opportunities) that modern industry and business have on society. Not an exaggerated read.
August 2, 2019
from over complicated to entanglement. All starting from simplicity to complex.
Biologist field for technology.
Learning from bugs.
Technology humility
01 欢迎来到这个纠缠的时代
什么是复杂系统 /从“启蒙时代”到“纠缠时代”/ 抽象的局限 /
02 复杂系统形成的 4 个原因
原因 1:吸积 /原因 2:交互 /原因 3:必须处理的例外情况 / 原因 4:普遍的稀有事物 / 越来越多的复杂系统
03 为什么复杂系统越来越难以理解了
力不从心的大�� /认知的极限 / 最后一个无所不知的人
04 令人费解的 bug
并不是所有 bug 都能被消除 / 从错误中学习 /像生物学家一样思考
05 为什么需要生物学思维
复杂的技术系统需要生物学思维的 3 个原因/技术领域的“生物学家”/当物理学遇见生物学 /复杂性科学的视角 / 思维方式的进化 /我们需要通才
06 生物学思维是理解复杂世界的一把金钥匙
不要被表象迷惑 /以欣慰感看待不理解的事物 / 谦卑之心 + 迭代的生物学思维
Biologist field for technology.
Learning from bugs.
Technology humility
01 欢迎来到这个纠缠的时代
什么是复杂系统 /从“启蒙时代”到“纠缠时代”/ 抽象的局限 /
02 复杂系统形成的 4 个原因
原因 1:吸积 /原因 2:交互 /原因 3:必须处理的例外情况 / 原因 4:普遍的稀有事物 / 越来越多的复杂系统
03 为什么复杂系统越来越难以理解了
力不从心的大�� /认知的极限 / 最后一个无所不知的人
04 令人费解的 bug
并不是所有 bug 都能被消除 / 从错误中学习 /像生物学家一样思考
05 为什么需要生物学思维
复杂的技术系统需要生物学思维的 3 个原因/技术领域的“生物学家”/当物理学遇见生物学 /复杂性科学的视角 / 思维方式的进化 /我们需要通才
06 生物学思维是理解复杂世界的一把金钥匙
不要被表象迷惑 /以欣慰感看待不理解的事物 / 谦卑之心 + 迭代的生物学思维
December 19, 2021
This is a great read that helps the reader understand the underlying complexity of the technologies and networks we live with. Mr. Arbesman's prose illustrates we he calls the Age of Entanglement and the disconnect some of us may experience from our technology. The book provides the reader with the general understanding necessary to visualize the complexity of the underlying systems within our technology and the accretion inherent in its development and implementation. I recommend this book to anyone interested in our place in the Entanglement.
January 11, 2018
The author took an overcomplicated look at overcomplicated things. Examples were general enough to give weight to the narrative that, although simple things start simply, they evolve with complicated requirements, and thus, complexity. The author repeatedly gives little solution besides, enjoy the ride and forget about resetting the clock to simpler times.
December 20, 2020
A walk through the green field of complexity theory. While the book contains good ideas and examples that make you think, it's not fully internally consistent and could have been edited better. Looking forward to a second edition when the field has matured and the author has had more time to develop their ideas.
February 28, 2024
We could learn more from how biologists, naturalists understand complexity of nature, and apply it to understanding complexity of technology.
As an improvement, author used too much unnecessary words. Sentence could be shorten a lot, and some repetitive sentences repeated too many times, which could also be shorten.
As an improvement, author used too much unnecessary words. Sentence could be shorten a lot, and some repetitive sentences repeated too many times, which could also be shorten.
March 11, 2024
Is a poor supposed book, that tries to show that technology is complicated, just by giving a few examples, of very well-known technology that fails, but doesn't prove any point. Technology is complicated but if you use your brain is not that complicated just requires effort that many people like the author don't want to do.
March 30, 2025
While I thoroughly enjoyed this book and agree with its overall thesis, I found it too long and overburdened with too many examples that weren't necessary to convey that thesis.
On the other hand, this book had "preaching to the quire" effect on me, so, perhaps, the examples would have been useful if I needed more convincing.
On the other hand, this book had "preaching to the quire" effect on me, so, perhaps, the examples would have been useful if I needed more convincing.
June 11, 2018
Very timely
Excellent book that lays out a very good argument about how we are getting detached from the systems that are running out world due to their increasing complexity . The book is well written and suggests some solutions for the problem it describes.
Excellent book that lays out a very good argument about how we are getting detached from the systems that are running out world due to their increasing complexity . The book is well written and suggests some solutions for the problem it describes.
October 15, 2018
This book is kind of all over the place without ever really getting anywhere. It keeps coming back to the central theme (technology is becoming too complicated for us to handle) without really going anywhere from there.
October 20, 2018
“The ghost of the old system continues to haunt the new,” “The system always kicks back,” and the Unawareness Theorem: “If you’re not aware that you have a problem, how can you call for help?” Gall’s rules
March 13, 2020
This book isn't a very long read, but I was very impressed with the message. He does a great job explaining complexity in systems. A good read for any technologist who wants to deal with the challenges in their technical world.
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