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The Great Mental Models Volume 3: Systems and Mathematics
The Great Mental Models: Volume 3 covers essential models from mathematics and systems.
In part one, you'll learn mental models from systems, helping you see unexpected connections and avoid costly mistakes. You'll discover how these concepts govern the behaviors and interactions in your life. Part one covers topics such as how to:
- Identify the right feedback loops to adjust for behavior change (your own and others')
- Leverage bottlenecks to supercharge your innovative capabilities
- Scale up businesses and other endeavors without damaging their longevity
- Reduce risk and preventing disaster by knowing when to incorporate a margin of safety
- Construct reliable algorithms in your mind for predictable success to get the results you want every time
In part two, you'll learn mental models from mathematics that reveal logical patterns in the world. This isn't your high school math class. Part two covers topics such as how to:
- Reap exponential gains by investing in knowledge, relationships, and experiences that compound
- Utilize the surprising power of sample sizes to reshape your perspective and open your mind
- Embrace randomness to become less predictable and more creative
- Identify the fundamental components of systems that lead to failure if neglected, so you can focus your energy where it matters most
In part one, you'll learn mental models from systems, helping you see unexpected connections and avoid costly mistakes. You'll discover how these concepts govern the behaviors and interactions in your life. Part one covers topics such as how to:
- Identify the right feedback loops to adjust for behavior change (your own and others')
- Leverage bottlenecks to supercharge your innovative capabilities
- Scale up businesses and other endeavors without damaging their longevity
- Reduce risk and preventing disaster by knowing when to incorporate a margin of safety
- Construct reliable algorithms in your mind for predictable success to get the results you want every time
In part two, you'll learn mental models from mathematics that reveal logical patterns in the world. This isn't your high school math class. Part two covers topics such as how to:
- Reap exponential gains by investing in knowledge, relationships, and experiences that compound
- Utilize the surprising power of sample sizes to reshape your perspective and open your mind
- Embrace randomness to become less predictable and more creative
- Identify the fundamental components of systems that lead to failure if neglected, so you can focus your energy where it matters most
373 pages, Hardcover
Published September 14, 2021
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Displaying 1 - 30 of 40 reviews
September 12, 2021
Each entry in these series is stronger than the last.
This is my favorite entry in the series so far.
This is my favorite entry in the series so far.
October 2, 2023
Originally published at myreadinglife.com.
I've been listening to the Knowledge Project podcast for a number of years now. It is put out by an organization called Farnam Street. As part of their mission they have published a series of books called The Great Mental Models. I've most recently read the third volume in the series. Each volume covers a few areas that it focuses on. For volume 3, these are systems and mathematics.
The book is divided into two section (systems and mathematics, naturally). Each chapter delves into a particular aspect with examples for how it is applied as a model. These are written in clear, easy-to-understand prose.
While I liked this volume, I feel like I didn't really learn much new. As a result, I don't rated as highly. But I highly recommend this volume and the previous two for building up a set of models for how to look at and interact with the world. These might be particularly helpful to teenagers.
My rating: 3/5
I've been listening to the Knowledge Project podcast for a number of years now. It is put out by an organization called Farnam Street. As part of their mission they have published a series of books called The Great Mental Models. I've most recently read the third volume in the series. Each volume covers a few areas that it focuses on. For volume 3, these are systems and mathematics.
The book is divided into two section (systems and mathematics, naturally). Each chapter delves into a particular aspect with examples for how it is applied as a model. These are written in clear, easy-to-understand prose.
While I liked this volume, I feel like I didn't really learn much new. As a result, I don't rated as highly. But I highly recommend this volume and the previous two for building up a set of models for how to look at and interact with the world. These might be particularly helpful to teenagers.
My rating: 3/5
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March 5, 2022Integrating mental models from systems and mathematics can help you overcome blind spots in your thinking. Challenge your perspective on the world by reflecting on it through the lens of systems theory. Models from mathematics can also help you become more tolerant and enhance your creative capabilities. By integrating models from these disciplines as much as possible, you’ll be sure to sharpen your problem-solving and decision-making skills.
Actionable advice:
Put your mental models into practice.
The first step in learning is to expose yourself to new information. But if you want to benefit from the knowledge in any practical way, you also need to put the learned concepts to the test. Every week, pick one mental model, and start looking at your life in that context. What do you see? What appears new or different? Write down your observations. By taking the time to reflect on your experiences through each set of insights, you’ll be able to apply that wisdom more easily.
---
Construct reliable algorithms in your mind to improve your chance of success.
Let’s say someone asks you to press “enter” on your keyboard every minute, for eight hours a day. Doesn’t sound like a terribly exciting way to spend your time, does it? For most people, engaging in repetitive actions over and over again gets boring very quickly.
That’s why we codify machines to do tasks for us. To tell those machines what to do, like press a button every minute, we use one of the most important models in human civilization: the algorithm.
In fact, all systems – not just computers – need algorithms to function.
Here’s the key message: Construct reliable algorithms in your mind to improve your chance of success.
Algorithms are developed to produce a certain output in response to a given input. You can think of it as an if-then process that is consistently repeatable.
An algorithm can be simple, like a clear set of instructions for a recipe. You put the ingredients together, run them through a process, and, in the end, you get a cake. An algorithm can also be complicated, like a computer algorithm designed to predict future crime locations.
For the best chance of achieving a predictable outcome, all the parts of an algorithm need to be aligned toward the same goal. The question is, how do you know which inputs will result in the desired outputs?
Well, you can actually use “algorithmic thinking” to help you decide what inputs to feed into your system in the first place.
In the 1920s, Bayer, a German pharmaceutical company, exemplified the power of algorithmic thinking as it pursued a cure for major bacterial infections, including tuberculosis and E. coli. Until then, almost no antibacterial compounds had been discovered. So Bayer’s scientists decided they would test every single chemical compound against the most deadly bacteria.
During the research, thousands of mice died. But despite the negative results, the scientists at Bayer did not change their method. They continued to test every chemical, keeping careful records of each test. Finally, in 1932, the methodology paid off when Bayer created the world’s first broad-spectrum antibiotic.
This goes to show that as long as your algorithmic process is accurate, it will eventually produce results that will help you refine your inputs.
In other words, you don’t need to know the answers – you just need a good algorithm for finding them.
---
Expand your understanding of the world through the power of sampling.
Imagine you want to investigate the color of swan populations. If you go out to your neighborhood ponds to collect data, you might conclude that all swan populations are white. But if you were to expand your research sample and study a larger number of swans from across the country, you would discover that some are actually black.
When you want to get representative information about a population, you usually need to look at a sample – meaning a part of that population. But if that sample is not truly representative, you risk being misled.
Here’s the key message: Expand your understanding of the world through the power of sampling.
Sampling is a particularly common measure in scientific studies of people, especially statistics. In many societies, statistics often determine how resources are allocated. That’s what makes it so important for measures to be accurate.
Thinking about sample size shows how samples can counter some forms of bias. For example, if you move to a big city where you’re exposed to a large sample of diverse people, you may end up with fewer prejudices. Similarly, if you read books from across various disciplines, you may become more open-minded.
But gathering representative samples takes effort. In fact, sampling can reinforce bias if it’s done haphazardly.
The first factor to take into consideration is sample size. The higher the number of participants in a study, the lower the margin of error – and the more likely it is that the study accurately generalizes the whole population.
It’s important to acknowledge that one measurement isn’t enough. For example, most people tend to rely on anecdotes to get a sense of the world. But they forget that an anecdote is just a sample of one – so it can’t be a reliable representation.
In addition to being large, samples need to be random in order to be representative of a varied population. This means every subject within the population should have an equal chance of ending up in the sample. You can’t study the behavior of three-year-olds in California and then make universal deductions about children. Rather, you have to expand the variety of your sample.
The same applies in your personal life. Remember to scrutinize the quality of your samples, including your generalizations about the world. When your decisions affect others, ensure that you’re equipped with information that is truly representative of those people. This way, you’ll minimize risk and maximize reward.
Actionable advice:
Put your mental models into practice.
The first step in learning is to expose yourself to new information. But if you want to benefit from the knowledge in any practical way, you also need to put the learned concepts to the test. Every week, pick one mental model, and start looking at your life in that context. What do you see? What appears new or different? Write down your observations. By taking the time to reflect on your experiences through each set of insights, you’ll be able to apply that wisdom more easily.
---
Construct reliable algorithms in your mind to improve your chance of success.
Let’s say someone asks you to press “enter” on your keyboard every minute, for eight hours a day. Doesn’t sound like a terribly exciting way to spend your time, does it? For most people, engaging in repetitive actions over and over again gets boring very quickly.
That’s why we codify machines to do tasks for us. To tell those machines what to do, like press a button every minute, we use one of the most important models in human civilization: the algorithm.
In fact, all systems – not just computers – need algorithms to function.
Here’s the key message: Construct reliable algorithms in your mind to improve your chance of success.
Algorithms are developed to produce a certain output in response to a given input. You can think of it as an if-then process that is consistently repeatable.
An algorithm can be simple, like a clear set of instructions for a recipe. You put the ingredients together, run them through a process, and, in the end, you get a cake. An algorithm can also be complicated, like a computer algorithm designed to predict future crime locations.
For the best chance of achieving a predictable outcome, all the parts of an algorithm need to be aligned toward the same goal. The question is, how do you know which inputs will result in the desired outputs?
Well, you can actually use “algorithmic thinking” to help you decide what inputs to feed into your system in the first place.
In the 1920s, Bayer, a German pharmaceutical company, exemplified the power of algorithmic thinking as it pursued a cure for major bacterial infections, including tuberculosis and E. coli. Until then, almost no antibacterial compounds had been discovered. So Bayer’s scientists decided they would test every single chemical compound against the most deadly bacteria.
During the research, thousands of mice died. But despite the negative results, the scientists at Bayer did not change their method. They continued to test every chemical, keeping careful records of each test. Finally, in 1932, the methodology paid off when Bayer created the world’s first broad-spectrum antibiotic.
This goes to show that as long as your algorithmic process is accurate, it will eventually produce results that will help you refine your inputs.
In other words, you don’t need to know the answers – you just need a good algorithm for finding them.
---
Expand your understanding of the world through the power of sampling.
Imagine you want to investigate the color of swan populations. If you go out to your neighborhood ponds to collect data, you might conclude that all swan populations are white. But if you were to expand your research sample and study a larger number of swans from across the country, you would discover that some are actually black.
When you want to get representative information about a population, you usually need to look at a sample – meaning a part of that population. But if that sample is not truly representative, you risk being misled.
Here’s the key message: Expand your understanding of the world through the power of sampling.
Sampling is a particularly common measure in scientific studies of people, especially statistics. In many societies, statistics often determine how resources are allocated. That’s what makes it so important for measures to be accurate.
Thinking about sample size shows how samples can counter some forms of bias. For example, if you move to a big city where you’re exposed to a large sample of diverse people, you may end up with fewer prejudices. Similarly, if you read books from across various disciplines, you may become more open-minded.
But gathering representative samples takes effort. In fact, sampling can reinforce bias if it’s done haphazardly.
The first factor to take into consideration is sample size. The higher the number of participants in a study, the lower the margin of error – and the more likely it is that the study accurately generalizes the whole population.
It’s important to acknowledge that one measurement isn’t enough. For example, most people tend to rely on anecdotes to get a sense of the world. But they forget that an anecdote is just a sample of one – so it can’t be a reliable representation.
In addition to being large, samples need to be random in order to be representative of a varied population. This means every subject within the population should have an equal chance of ending up in the sample. You can’t study the behavior of three-year-olds in California and then make universal deductions about children. Rather, you have to expand the variety of your sample.
The same applies in your personal life. Remember to scrutinize the quality of your samples, including your generalizations about the world. When your decisions affect others, ensure that you’re equipped with information that is truly representative of those people. This way, you’ll minimize risk and maximize reward.
July 18, 2022
Ideas that are now unthinkable were once considered moral or at least neutral. Then the feedback loop changed as people began to respond in less positive ways. Smith gives the example of infanticide, unthinkable in most countries today. But before the advent of accessible birth control, it was an accepted part of life in many countries. The ancient Greeks had no qualms about leaving sickly or otherwise unwanted babies to face the elements, lest they be a burden on their families. It still occurs in cultures without access to birth control or abortion.6 Smith writes, “We constantly hear men saying, ‘It’s commonly done,’ apparently thinking that this a sufficient excuse for something that is in itself the most unjust and unreasonable conduct.”
-----
Systems are rarely static. They are continuously adjusting toward equilibrium, but they rarely stay in balance for long. In our lives we often act like we can reach an equilibrium: once we get into a relationship, we’ll be happy; once we move, we’ll be productive; once X thing happens, we’ll be in Y state. But things are always in flux. We don’t reach a certain steady state and then stay there forever. The endless adjustments are our lives.
-----
Over 50,000 Japanese companies are more than a century old, with nearly 4,000 dating back over 200 years.
Why are long-lived companies more common in Japan than the rest of the world? It’s impossible to know for certain. But most of the oldest companies have something in common: the way they scale. Or rather, the way they don’t scale.
Long-lived Japanese companies tend to be small. They’re owned and run by relatives and people with close relationships. They usually have fewer than a hundred employees and trade within a small area inside Japan. Durable, loyal customer relationships are integral to their business models. Also, they are driven by a strong internal philosophy that goes beyond their products and services, enabling them to adapt to changing times.
-----
Diminishing Returns and Societal Collapse
Why do complex societies, like the Roman Empire, collapse? One theory, advanced by Joseph A. Tainter in The Collapse of Complex Societies, is that it comes down to diminishing returns. As societies grow and develop, they become more complex and require more and more “energy flow” to stay intact.1 With increasingly advanced networks between individuals, “more hierarchical controls are created to regulate these networks, more information is processed, there is more centralization of information flow, there is increasing need to support specialists not directly involved in resource production, and the like.” More complex societies extract an exponentially higher amount of energy from individuals just to stay intact than simple ones. At a certain point, the cost may exceed the benefits individuals derive from being part of that society. When this happens, it may begin to disintegrate.2 Being complex no longer carries benefits, and it makes sense to return to a simpler level of organization.
-----
Similarly, we can forget that the past was as random as the future will be. In hindsight, history can seem ordered and logical. When we open a history book, we see structured narratives. Events have a beginning, a middle, and an end. It only seems this way in retrospect. Not only are past events random, so is the information we have about them. Historical documents survive at random, and it’s also up to chance whether a particular researcher comes across them or even how they interpret them.
-----
Systems are rarely static. They are continuously adjusting toward equilibrium, but they rarely stay in balance for long. In our lives we often act like we can reach an equilibrium: once we get into a relationship, we’ll be happy; once we move, we’ll be productive; once X thing happens, we’ll be in Y state. But things are always in flux. We don’t reach a certain steady state and then stay there forever. The endless adjustments are our lives.
-----
Over 50,000 Japanese companies are more than a century old, with nearly 4,000 dating back over 200 years.
Why are long-lived companies more common in Japan than the rest of the world? It’s impossible to know for certain. But most of the oldest companies have something in common: the way they scale. Or rather, the way they don’t scale.
Long-lived Japanese companies tend to be small. They’re owned and run by relatives and people with close relationships. They usually have fewer than a hundred employees and trade within a small area inside Japan. Durable, loyal customer relationships are integral to their business models. Also, they are driven by a strong internal philosophy that goes beyond their products and services, enabling them to adapt to changing times.
-----
Diminishing Returns and Societal Collapse
Why do complex societies, like the Roman Empire, collapse? One theory, advanced by Joseph A. Tainter in The Collapse of Complex Societies, is that it comes down to diminishing returns. As societies grow and develop, they become more complex and require more and more “energy flow” to stay intact.1 With increasingly advanced networks between individuals, “more hierarchical controls are created to regulate these networks, more information is processed, there is more centralization of information flow, there is increasing need to support specialists not directly involved in resource production, and the like.” More complex societies extract an exponentially higher amount of energy from individuals just to stay intact than simple ones. At a certain point, the cost may exceed the benefits individuals derive from being part of that society. When this happens, it may begin to disintegrate.2 Being complex no longer carries benefits, and it makes sense to return to a simpler level of organization.
-----
Similarly, we can forget that the past was as random as the future will be. In hindsight, history can seem ordered and logical. When we open a history book, we see structured narratives. Events have a beginning, a middle, and an end. It only seems this way in retrospect. Not only are past events random, so is the information we have about them. Historical documents survive at random, and it’s also up to chance whether a particular researcher comes across them or even how they interpret them.
This entire review has been hidden because of spoilers.
November 14, 2021
Mental models are a representation of how something works. The Great Mental Models series is meant to provide public models from various fields of discipline to help us make better decisions in our lives. Systems and maths provide a nice group of mental models for everyday life. Here are a few models from the book that really resonated with me.
1. Bottlenecks (the specific process of a workflow that limited that amount of output from an overall workflow)
2. Algorithms (clear set of rules that process inputs and produce expected results in a logical and repeatable way)
3. Critical Mass (an incremental point that leads to a drastic change in the system state—for the better or for the worse)
4. Law of Diminishing Returns (threshold where the effort and reward relationship becomes non-linear)
5. Compounding (daily reinvestment of resources can lead to large growth on a long time scale)
6. Surface Area (considering the number of dependencies or assumptions something has.)
I really enjoyed the wide practical use these models have to offer in the personal, professional, and spiritual aspects of my life.
my rating - overall Score: 4.0/5.0
- quality of writing (4/5)
- quality of the content (5/5)
- impact on my perspective (4/5)
- personal resonance (4/5)
- rereading potential (3/5)
1. Bottlenecks (the specific process of a workflow that limited that amount of output from an overall workflow)
2. Algorithms (clear set of rules that process inputs and produce expected results in a logical and repeatable way)
3. Critical Mass (an incremental point that leads to a drastic change in the system state—for the better or for the worse)
4. Law of Diminishing Returns (threshold where the effort and reward relationship becomes non-linear)
5. Compounding (daily reinvestment of resources can lead to large growth on a long time scale)
6. Surface Area (considering the number of dependencies or assumptions something has.)
I really enjoyed the wide practical use these models have to offer in the personal, professional, and spiritual aspects of my life.
my rating - overall Score: 4.0/5.0
- quality of writing (4/5)
- quality of the content (5/5)
- impact on my perspective (4/5)
- personal resonance (4/5)
- rereading potential (3/5)
December 29, 2021
As a fan of Farnam Street, and the first couple volumes of The Great Mental Models series, I was definitely looking forward to reading Volume 3. I have a degree in Mathematics, and it has been my favorite school subject for as long as I can remember. This started likely due to the fact that I was quick with my arithmetic and mental math skills in elementary school, and that progressed all the way to college courses I took on logic that has helped my way of thinking post-graduate. This book showcases mental models not only in mathematics, but also in systems, and it is a book, like the other volumes, that does a great job in explaining concepts without the repetitious problems of normal schooling. Following up this book, I am planning to take one model per week from all the volumes and focus on using it in my day-to-day activities to establish my own latticework of mental models, instead of only reading the concepts and moving on. This is a book that high school math teachers should read because some of the concepts, like compound interest, should be taught in schools as it is something that can help everyone in many avenues of life. Already looking forward to the next volume of The Great Mental Models series.
January 16, 2022
I love their presentation of the ideas once again just as in Vol. 1 & 2! But I can’t help it but notice how little of an effort was and is yet again put into teaching the applying of these models in reality! For me, I have decided the informing part is their job and the application part is my job! So I can’t hate them for paying too little attention to the application part of these models!
I definitely recommend this book to anyone out there who is looking to challenge and upgrade their own thinking built upon principles and models that stand the test of time and that best represent reality.
A 5 because I believe they excelled at doing their job of informing thoroughly! I am however struggling with the application part of the models! The good news is that I have a draft version of framework that I can further build on! The challenging part is that I am dependent on the framework itself. I have to write things out to think…and I have to navigate through the models in the framework to find which applies best in or to my x situation/context. The fact that the process itself is challenging is just another good news! Because I wouldn’t bother do all this otherwise!
Lastly, the last & final model: Global maxima and local maxima was my favorite model of this book!
I definitely recommend this book to anyone out there who is looking to challenge and upgrade their own thinking built upon principles and models that stand the test of time and that best represent reality.
A 5 because I believe they excelled at doing their job of informing thoroughly! I am however struggling with the application part of the models! The good news is that I have a draft version of framework that I can further build on! The challenging part is that I am dependent on the framework itself. I have to write things out to think…and I have to navigate through the models in the framework to find which applies best in or to my x situation/context. The fact that the process itself is challenging is just another good news! Because I wouldn’t bother do all this otherwise!
Lastly, the last & final model: Global maxima and local maxima was my favorite model of this book!
October 20, 2023
The third volume of the series explores the topics of mathematics and system thinking - the latter has been on my radar for quite some time (check out my review of Donella Meadows' Primer to System Thinking).
The concept of system thinking isn’t well known, nor taught at school, which gets me thinking, why not? Awareness of systems, their behaviours (like feedback loops or bottlenecks) and our ability to notice them in the real world help us with understanding the world around us. The book does a great job of explaining systems.
The second part of the book is dedicated to mathematics, where timeless aspects are presented to us using real-world situations. Distribution, compounding, sampling or randomness, among others, are presented using practical examples.
Using system thinking and mathematics helps us with understanding of the world and as a side effect it improves our decision making process.
As with previous volumes, I’m amazed by its quality both content-wise and looks-wise.
My Reviews of other volumes:
- Volume 1
- Volume 2
The concept of system thinking isn’t well known, nor taught at school, which gets me thinking, why not? Awareness of systems, their behaviours (like feedback loops or bottlenecks) and our ability to notice them in the real world help us with understanding the world around us. The book does a great job of explaining systems.
The second part of the book is dedicated to mathematics, where timeless aspects are presented to us using real-world situations. Distribution, compounding, sampling or randomness, among others, are presented using practical examples.
Using system thinking and mathematics helps us with understanding of the world and as a side effect it improves our decision making process.
As with previous volumes, I’m amazed by its quality both content-wise and looks-wise.
My Reviews of other volumes:
- Volume 1
- Volume 2
Currently reading
November 23, 2021"The more moving parts you have in something, the more possibilities there are."
1. FEEDBACK LOOPS
People are driven by incentives. We do certain behavior in order to gain something or avoid losing something.
A feedback loop is a stimulus creating a response which response is the initiator of the other stimulus, which in terms creates a loop. Think, conversation.
There are positive and negative feedback loops. A positive feedback loop amplifies the response with a stronger stimulus (e.g. anxiety going in viscious cycle). A negative feedback loop is two responses acting in turns in order to keep the stimulus steady (e,g. body temperature).
!!-In Prisoner's Dilemma, trust first (cooperate) and then mirror your opponent's last behavior. This sends the first feedback loop that you are willing to be trustworthy.
Complex systems often have many feedback loops, and it can be hard to appreciate how adjusting to feedback in one part of the system will affect the rest.
2. EQUILIBRIUM
3. BOTTLENECKS
!- A bottleneck is also the point that is most under strain. It can be the part that is most likely to break down or has the most impact if it does. In trying to improve the flow of your system, focusing on anything besides the bottleneck is a waste of time. You will just create more pressure on the bottleneck, further increasing how much it holds you back by generating more buildup.
If bottlenecks are unavoidable, we at least want them to be in a less disruptive place.
Liebig's law of the minimum refers to the idea that a plant's growth will be limited by the nutrient that is least available. Yield is thus constrained by resource limitation.
!- In the long run, bottlenecks can make us better off because they promote innovation.
4. SCALE
1. FEEDBACK LOOPS
People are driven by incentives. We do certain behavior in order to gain something or avoid losing something.
A feedback loop is a stimulus creating a response which response is the initiator of the other stimulus, which in terms creates a loop. Think, conversation.
There are positive and negative feedback loops. A positive feedback loop amplifies the response with a stronger stimulus (e.g. anxiety going in viscious cycle). A negative feedback loop is two responses acting in turns in order to keep the stimulus steady (e,g. body temperature).
!!-In Prisoner's Dilemma, trust first (cooperate) and then mirror your opponent's last behavior. This sends the first feedback loop that you are willing to be trustworthy.
Complex systems often have many feedback loops, and it can be hard to appreciate how adjusting to feedback in one part of the system will affect the rest.
2. EQUILIBRIUM
3. BOTTLENECKS
!- A bottleneck is also the point that is most under strain. It can be the part that is most likely to break down or has the most impact if it does. In trying to improve the flow of your system, focusing on anything besides the bottleneck is a waste of time. You will just create more pressure on the bottleneck, further increasing how much it holds you back by generating more buildup.
If bottlenecks are unavoidable, we at least want them to be in a less disruptive place.
Liebig's law of the minimum refers to the idea that a plant's growth will be limited by the nutrient that is least available. Yield is thus constrained by resource limitation.
!- In the long run, bottlenecks can make us better off because they promote innovation.
4. SCALE
February 9, 2024
This provide a number of thinking models that can be used as metaphors in thinking about life. The is mainly a book of reiteration as a lot of the material was familiar to me. It is a book I am going to revisit via passively listening to as an audio book until I internalize the models contained in this book and the books previously written. I religiously listen to the Farnam Street Podcast and I am amazed at Shane Parrish’s generosity in allowing authorship to fall to Beaubien. This is a text book for thinking about life.
January 15, 2022
The 3rd volume resonated with me almost as much as the first one.
If you're dealing with complex systems (and who does not these days!) - the models presented in this volume should be quite relevant for you.
This book series offers too much for a single pass, and as authors suggest in the end of each volume, the reader is expected to come back and revisit the models to build their own "latticework".
If you're dealing with complex systems (and who does not these days!) - the models presented in this volume should be quite relevant for you.
This book series offers too much for a single pass, and as authors suggest in the end of each volume, the reader is expected to come back and revisit the models to build their own "latticework".
March 5, 2022
Integrating mental models from systems and mathematics can help us overcome blind spots in our thinking. Challenge our perspective on the world by reflecting on it through the lens of systems theory. Models from mathematics can also help us become more tolerant and enhance our creative capabilities. By integrating models from these disciplines as much as possible, we‘ll be sure to sharpen our problem-solving and decision-making skills.
May 31, 2022
A continuation of the series, volume 3 looks at mental models in the area of systems and mathematics. Much like prior volumes in the series, each model is presented along with real-world examples of their application. This series continues to be required reading for anyone who's interested in the models by which the world operates. Reading these will make you better equipped to make important decisions.
July 26, 2022
Good addition to earlier volumes
Book-Level : Intermediate.
This volune adds good set of mental models on top of earlier volumes, so it's recommended, not restricted though, to read in sequence.
The book covers mental models from Science and Math concept, which are covered well, but i feel it does not have content appeal as good as earlier volumes, probably because of different area of topic. But nevertheless it could not have been covered better.
Book-Level : Intermediate.
This volune adds good set of mental models on top of earlier volumes, so it's recommended, not restricted though, to read in sequence.
The book covers mental models from Science and Math concept, which are covered well, but i feel it does not have content appeal as good as earlier volumes, probably because of different area of topic. But nevertheless it could not have been covered better.
October 24, 2021
Good read on mental models. Volume three from the Farnam Street. I thought this one was good, but not as impactful on terms of the actual models. I thought the first two books were more insightful. Still, there is some really good stuff here: compounding, margin of safety, randomness to name a few. For anyone who likes the Farnam Street work, this is another good read.
December 7, 2022
Book 3. I can't wait to pass down these books to my kids. (Though I am not sure whether they would want it! Hah!) I love the 3 volumes. After a certain point, it is not about how many mental models you have ... but knowing how to find there in all the different places in life and using them. Love some of the examples and stories in the books.
February 4, 2023
For all the chapters, the conclusion is provided that gives us the point and take away. Most of the content in every chapter seemed to be filler at least for me. Not straight to the point, the examples quoted backing each model are not impactful IMO, could have been better. I still could not get why the word Mathematics is used in the book title.
May 6, 2023
A collections models that meet the Lindy effect criteria of applicability of across time and topic. Gals Law, Global and Local Maxima, Churn, and Critical Mass are all common understandings. However, I better understand with the stories of their discovery and application. I have enjoyed this entire collection making it to my reread list too.
October 12, 2021
This is by far the strongest of the series so far and I really had some "aha" moments. Not all of the practical applications of concepts (i.e. mental models) are insightful or even accurate (IMHO) but the entire way to approach is is highly intuitively useful. Recommend.
December 5, 2021
A concise and easily accessible collection of the fundamental concepts in math and systems theory. The most valuable part is the application of these concepts in an interdisciplinary context and how you can use them for your life.
March 14, 2022
Another nice volume in the series, this time dealing with systems and mathematics. I felt the mathematics portion was lighter and can be more comprehensive. Continue to learn new historical facts narrated via the lens of mental models.
May 16, 2022
A really good book. I probably learned more from and liked this volume even better than the previous two in the series. The chapters on algorithms was especially interesting and thought provoking to me.
June 8, 2022
See the world differently and more clearly!!
Huge fan of all 3 books!! Systems and Math models are so interesting and I’m already finding them improving my decision making and life!!
Huge fan of all 3 books!! Systems and Math models are so interesting and I’m already finding them improving my decision making and life!!
January 28, 2022
It's not wrong, but some examples are too much of a stretch. It takes effort to suspend the outrage. Sure, who am I to talk - it’s not easy to come up with general examples, but still.
August 22, 2022
An amazing supplement to how to solve is by George Polya - my favorite book growing up.
September 19, 2022
10/10 .. reread it over and over again
September 26, 2022
A useful addition to the previous 2 volumes.
October 18, 2022
Una vez leído el primero, los demás volúmenes, aunque cambian los temas, resultan un poco repetitivos.
January 3, 2023
I continue to love the mental models audiobooks. Looking forward to vol 4.
January 12, 2023
Much better than volume 2.
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