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Robin Hood Maths: Take Control of the Algorithms That Run Your Life
Big companies are using maths to take from the poor and give to the rich. Maths professor Noah Giansiracusa shows how you can beat the system using simple hacks anyone can learn.
'See what the algorithms are trying to hide from you, and even better, take back your own decision-making power.' Jordan Ellenberg, author of How Not to Be Wrong
In today’s data-driven world, maths is a weapon wielded by banks, insurance companies, tech firms, and government agencies. These organizations use sophisticated algorithms to calculate odds, make predictions, uncover patterns, manage risk, and optimize actions. And they treat you as another number to crunch along the way.
Robin Hood Maths explains the mathematical methods these companies and agencies use to manipulate and profit off of you. It’s easy to assume these algorithms are too complex to even understand, let alone use for yourself. But maths professor Noah Giansiracusa makes the compelling case that anyone can use these same methods without any special training.
He offers simple hacks and streamlined formulas for beating the number crunchers at their own
- MAKE BETTER INVESTMENTS
- TAKE CONTROL OF YOUR SOCIAL MEDIA
- HANDLE RISK RATIONALLY
With Professor Giansiracusa as your guide, you’ll learn how to reclaim agency over the decisions you make every day. In a society designed to take from the poor and give to the rich, maths has the potential to be a powerful democratizing force. Robin Hood Maths gives you the tools you need to think for yourself, act in your own best interest, and thrive.
'See what the algorithms are trying to hide from you, and even better, take back your own decision-making power.' Jordan Ellenberg, author of How Not to Be Wrong
In today’s data-driven world, maths is a weapon wielded by banks, insurance companies, tech firms, and government agencies. These organizations use sophisticated algorithms to calculate odds, make predictions, uncover patterns, manage risk, and optimize actions. And they treat you as another number to crunch along the way.
Robin Hood Maths explains the mathematical methods these companies and agencies use to manipulate and profit off of you. It’s easy to assume these algorithms are too complex to even understand, let alone use for yourself. But maths professor Noah Giansiracusa makes the compelling case that anyone can use these same methods without any special training.
He offers simple hacks and streamlined formulas for beating the number crunchers at their own
- MAKE BETTER INVESTMENTS
- TAKE CONTROL OF YOUR SOCIAL MEDIA
- HANDLE RISK RATIONALLY
With Professor Giansiracusa as your guide, you’ll learn how to reclaim agency over the decisions you make every day. In a society designed to take from the poor and give to the rich, maths has the potential to be a powerful democratizing force. Robin Hood Maths gives you the tools you need to think for yourself, act in your own best interest, and thrive.
240 pages, Kindle Edition
Published August 7, 2025
44 people are currently reading
483 people want to read
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Displaying 1 - 25 of 25 reviews
September 29, 2025
This book is about probability and statistics. The author provides good examples and offers some tools to improve decision making and everyday estimation. In the final part he touches on social media and the algorithms that dominate there. It gives some useful insights, though nothing groundbreaking. All in all, it is a decent book, but the title feels a bit like clickbait;)
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November 11, 2025In "Robin Hood Math: Take Control of the Algorithms That Run Your Life" by Noah Giansiracusa, the author offers a rare invitation to peer behind the curtain of modern technology and see the equations quietly steering our daily lives. From the moment we wake up and scroll through our phones to the instant we check our bank balances or read the news, we are surrounded by mathematical formulas deciding what we see, how we are ranked, and even how we are judged. These algorithms are presented to us as objective, scientific, and fair - yet they reflect human choices, biases, and priorities. Giansiracusa’s book seeks to reclaim this hidden power for ordinary people. Rather than viewing math as an abstract or intimidating subject, he shows how understanding a few simple principles can give us agency in a world increasingly ruled by algorithms. The mission of "Robin Hood Math" is to 'steal' mathematical power from Big Tech and give it back to the public.
He begins by examining one of the most familiar and misunderstood examples of algorithmic influence: rankings. College rankings, he explains, are a perfect demonstration of how arbitrary mathematical weighting can shape perception. When Brandeis University plummeted sixteen places in the 'U.S. News & World Report' rankings in 2023, nothing about the school itself had changed. What changed was the formula - the invisible 'recipe' determining which factors mattered most. Suddenly, smaller class sizes carried less weight, and other variables gained importance. The result? A massive reshuffling that made some schools appear to have improved overnight while others seemed to decline. Giansiracusa argues that this illusion of objectivity is everywhere. Weighted sums - formulas that assign importance to different variables - underlie not just college rankings but also credit scores, hiring algorithms, and social media feeds. Once you understand that these formulas are human creations, you realize they can be adjusted, challenged, or even personalized. Instead of letting others define what matters, individuals can make their own weighted decisions. Choosing between universities, jobs, or even potential partners can become a rational process built on your own values - whether you prioritize cost, creativity, reputation, or happiness. The takeaway is empowering: math doesn’t dictate truth; it encodes priorities.
From there, Giansiracusa moves into another foundational concept: the power of averages. He illustrates this through the carnival guessing game of estimating how many marbles are in a jar. No single person’s guess is perfect, but the average of many guesses often lands remarkably close to the truth. This is the principle behind polling, weather forecasting, and even market prediction: collective intelligence works better than isolated expertise. However, he cautions that not all averages are created equal. A 'weighted average' - one that gives more influence to reliable sources - produces even better results. This is how poll aggregators like FiveThirtyEight or financial forecasters combine multiple inputs to form a consensus. Yet, if every input shares the same bias, the average becomes misleading. That’s why diversity of thought and independence are essential, whether in data, investment portfolios, or public opinion.
The discussion naturally leads to the concept of expected value - another cornerstone of decision-making. Expected value weighs outcomes by their probabilities to determine what is worth pursuing in the long run. Casinos, insurers, and investors all use this logic. But as Giansiracusa warns, numbers can hide moral and emotional realities. The downfall of crypto magnate Sam Bankman-Fried serves as a cautionary tale: he treated life as one long expected-value calculation, assuming that short-term losses would always be offset by eventual gains. That cold rationality ignored real-world limits like trust, reputation, and ethics. The lesson is clear: math can guide decision-making, but it cannot replace judgment.
To build more nuanced thinking, Giansiracusa introduces Bayesian reasoning - a mathematical framework for updating beliefs as new evidence appears. Named after Thomas Bayes, this idea suggests that our understanding of the world should evolve continuously, not remain static. We all begin with 'priors' - initial assumptions - but as new information emerges, those beliefs should be recalibrated. Using relatable examples, such as interpreting COVID test results, Giansiracusa demonstrates how failing to adjust our confidence levels leads to flawed conclusions. Bayesian reasoning not only refines personal decision-making but also reveals how algorithms 'learn.' Every time a platform like YouTube or TikTok recommends new content, it’s applying Bayesian logic - adjusting its internal model based on your past behavior. By grasping this principle, we can better understand and even anticipate how the algorithms that surround us adapt over time.
With these mathematical tools in place, Giansiracusa turns to the digital platforms that dominate modern life. Social media, he explains, is essentially a vast experiment in weighted sums. Each post competes in a hidden scoring contest: how likely you are to like, share, comment, or rewatch it is multiplied by the importance the platform assigns to those actions. The sum of those scores determines what appears at the top of your feed. This invisible math engine drives engagement - and manipulation. On Facebook, shares outweigh comments, and comments outweigh likes, while the 'angry' reaction historically carried disproportionate weight, inadvertently promoting outrage and conflict. TikTok’s algorithm adds another layer by tracking how long you watch each video, rewarding anything that keeps your attention longer - even confusion. X, formerly Twitter, values replies above all else, turning argument threads into engagement goldmines.
Understanding this system gives users a surprising amount of control. You may not be able to alter the formula, but you can influence its inputs - your own behavior. Every second you linger, every replay, and every click trains the algorithm. In effect, you are feeding it. The cure, Giansiracusa suggests, is mindful engagement: starve the content you dislike by scrolling past quickly, and interact deliberately with the content you value. Treat every click as a vote for what kind of internet you want to inhabit. Algorithms may feel opaque, but their foundations - probabilities, weighted sums, and expected values - are accessible. Once you see the levers, you can start pulling them.
In the book’s final section, Giansiracusa moves beyond individual algorithms to challenge the power structures of Big Tech itself. Companies like Amazon and Google have weaponized math to control visibility and profit. On Amazon, 'Featured' search results often favor sellers who pay for placement or belong to Amazon’s in-house brands, while higher-quality or lower-priced options are buried deeper. Users can reclaim some autonomy simply by changing default filters to 'average customer review' or 'lowest price.' Similarly, Google’s search engine increasingly prioritizes advertisers over genuine relevance. Yet with a few simple tricks - using minus signs to exclude words, 'site:' to focus on credible domains, or 'before:' to access pre-AI results - anyone can cut through the digital fog. Giansiracusa also proposes larger policy fixes, such as taxing targeted advertising to fund independent journalism and reduce invasive tracking. His vision is one where mathematical transparency and ethical regulation level the playing field between tech giants and everyday users.
Ultimately, "Robin Hood Math: Take Control of the Algorithms That Run Your Life" is not a book about numbers; it’s a manifesto for digital empowerment. Giansiracusa redefines math as a civic skill - something every citizen should use to question, interpret, and shape the systems governing their lives. Weighted sums remind us that priorities can be changed. Averages remind us that collective insight is powerful. Bayesian reasoning teaches us to stay adaptable. And algorithmic awareness gives us the tools to reclaim autonomy from systems built to predict and influence us.
In conclusion, Giansiracusa’s "Robin Hood Math" fulfills its title’s promise by redistributing mathematical power from corporations back to individuals. He shows that the same formulas Silicon Valley uses to manipulate behavior can be wielded by citizens to resist manipulation, make informed decisions, and live more intentionally. The takeaway is hopeful: math, often seen as cold or abstract, is actually a source of freedom when understood properly. In a world saturated with data, the ability to interpret and question numbers becomes an act of resistance. With curiosity, skepticism, and a bit of arithmetic, we can reclaim our place as decision-makers in the digital age - not just as data points in someone else’s equation, but as the ones writing our own.
He begins by examining one of the most familiar and misunderstood examples of algorithmic influence: rankings. College rankings, he explains, are a perfect demonstration of how arbitrary mathematical weighting can shape perception. When Brandeis University plummeted sixteen places in the 'U.S. News & World Report' rankings in 2023, nothing about the school itself had changed. What changed was the formula - the invisible 'recipe' determining which factors mattered most. Suddenly, smaller class sizes carried less weight, and other variables gained importance. The result? A massive reshuffling that made some schools appear to have improved overnight while others seemed to decline. Giansiracusa argues that this illusion of objectivity is everywhere. Weighted sums - formulas that assign importance to different variables - underlie not just college rankings but also credit scores, hiring algorithms, and social media feeds. Once you understand that these formulas are human creations, you realize they can be adjusted, challenged, or even personalized. Instead of letting others define what matters, individuals can make their own weighted decisions. Choosing between universities, jobs, or even potential partners can become a rational process built on your own values - whether you prioritize cost, creativity, reputation, or happiness. The takeaway is empowering: math doesn’t dictate truth; it encodes priorities.
From there, Giansiracusa moves into another foundational concept: the power of averages. He illustrates this through the carnival guessing game of estimating how many marbles are in a jar. No single person’s guess is perfect, but the average of many guesses often lands remarkably close to the truth. This is the principle behind polling, weather forecasting, and even market prediction: collective intelligence works better than isolated expertise. However, he cautions that not all averages are created equal. A 'weighted average' - one that gives more influence to reliable sources - produces even better results. This is how poll aggregators like FiveThirtyEight or financial forecasters combine multiple inputs to form a consensus. Yet, if every input shares the same bias, the average becomes misleading. That’s why diversity of thought and independence are essential, whether in data, investment portfolios, or public opinion.
The discussion naturally leads to the concept of expected value - another cornerstone of decision-making. Expected value weighs outcomes by their probabilities to determine what is worth pursuing in the long run. Casinos, insurers, and investors all use this logic. But as Giansiracusa warns, numbers can hide moral and emotional realities. The downfall of crypto magnate Sam Bankman-Fried serves as a cautionary tale: he treated life as one long expected-value calculation, assuming that short-term losses would always be offset by eventual gains. That cold rationality ignored real-world limits like trust, reputation, and ethics. The lesson is clear: math can guide decision-making, but it cannot replace judgment.
To build more nuanced thinking, Giansiracusa introduces Bayesian reasoning - a mathematical framework for updating beliefs as new evidence appears. Named after Thomas Bayes, this idea suggests that our understanding of the world should evolve continuously, not remain static. We all begin with 'priors' - initial assumptions - but as new information emerges, those beliefs should be recalibrated. Using relatable examples, such as interpreting COVID test results, Giansiracusa demonstrates how failing to adjust our confidence levels leads to flawed conclusions. Bayesian reasoning not only refines personal decision-making but also reveals how algorithms 'learn.' Every time a platform like YouTube or TikTok recommends new content, it’s applying Bayesian logic - adjusting its internal model based on your past behavior. By grasping this principle, we can better understand and even anticipate how the algorithms that surround us adapt over time.
With these mathematical tools in place, Giansiracusa turns to the digital platforms that dominate modern life. Social media, he explains, is essentially a vast experiment in weighted sums. Each post competes in a hidden scoring contest: how likely you are to like, share, comment, or rewatch it is multiplied by the importance the platform assigns to those actions. The sum of those scores determines what appears at the top of your feed. This invisible math engine drives engagement - and manipulation. On Facebook, shares outweigh comments, and comments outweigh likes, while the 'angry' reaction historically carried disproportionate weight, inadvertently promoting outrage and conflict. TikTok’s algorithm adds another layer by tracking how long you watch each video, rewarding anything that keeps your attention longer - even confusion. X, formerly Twitter, values replies above all else, turning argument threads into engagement goldmines.
Understanding this system gives users a surprising amount of control. You may not be able to alter the formula, but you can influence its inputs - your own behavior. Every second you linger, every replay, and every click trains the algorithm. In effect, you are feeding it. The cure, Giansiracusa suggests, is mindful engagement: starve the content you dislike by scrolling past quickly, and interact deliberately with the content you value. Treat every click as a vote for what kind of internet you want to inhabit. Algorithms may feel opaque, but their foundations - probabilities, weighted sums, and expected values - are accessible. Once you see the levers, you can start pulling them.
In the book’s final section, Giansiracusa moves beyond individual algorithms to challenge the power structures of Big Tech itself. Companies like Amazon and Google have weaponized math to control visibility and profit. On Amazon, 'Featured' search results often favor sellers who pay for placement or belong to Amazon’s in-house brands, while higher-quality or lower-priced options are buried deeper. Users can reclaim some autonomy simply by changing default filters to 'average customer review' or 'lowest price.' Similarly, Google’s search engine increasingly prioritizes advertisers over genuine relevance. Yet with a few simple tricks - using minus signs to exclude words, 'site:' to focus on credible domains, or 'before:' to access pre-AI results - anyone can cut through the digital fog. Giansiracusa also proposes larger policy fixes, such as taxing targeted advertising to fund independent journalism and reduce invasive tracking. His vision is one where mathematical transparency and ethical regulation level the playing field between tech giants and everyday users.
Ultimately, "Robin Hood Math: Take Control of the Algorithms That Run Your Life" is not a book about numbers; it’s a manifesto for digital empowerment. Giansiracusa redefines math as a civic skill - something every citizen should use to question, interpret, and shape the systems governing their lives. Weighted sums remind us that priorities can be changed. Averages remind us that collective insight is powerful. Bayesian reasoning teaches us to stay adaptable. And algorithmic awareness gives us the tools to reclaim autonomy from systems built to predict and influence us.
In conclusion, Giansiracusa’s "Robin Hood Math" fulfills its title’s promise by redistributing mathematical power from corporations back to individuals. He shows that the same formulas Silicon Valley uses to manipulate behavior can be wielded by citizens to resist manipulation, make informed decisions, and live more intentionally. The takeaway is hopeful: math, often seen as cold or abstract, is actually a source of freedom when understood properly. In a world saturated with data, the ability to interpret and question numbers becomes an act of resistance. With curiosity, skepticism, and a bit of arithmetic, we can reclaim our place as decision-makers in the digital age - not just as data points in someone else’s equation, but as the ones writing our own.
July 31, 2025
Thank you to Riverhead Books for the #gifted finished copy of Robin Hood Math by Noah Giansiracusa, publishing August 5!
As a words person (hi, comms-nerd here!), I’ve never naturally gravitated toward numbers. But this one caught my eye as a way to stretch myself, and I’m so glad I picked it up.
Robin Hood Math is a smart, surprisingly approachable read about how formulas shape the world around us, from investments and insurance to social media and personal decision-making. Giansiracusa breaks down how the rich and powerful use math and algorithms to tilt the playing field in their favor, and how the rest of us can flip the equation and take back some control.
I found it super engaging, eye-opening, and far from dry. Whether he’s walking through the logic of rational risk or explaining how data is bought and sold, Giansiracusa makes the numbers feel empowering, not overwhelming.
This is a great read for anyone curious about how to navigate our algorithm-driven world with more agency and intention. Highly recommend!
As a words person (hi, comms-nerd here!), I’ve never naturally gravitated toward numbers. But this one caught my eye as a way to stretch myself, and I’m so glad I picked it up.
Robin Hood Math is a smart, surprisingly approachable read about how formulas shape the world around us, from investments and insurance to social media and personal decision-making. Giansiracusa breaks down how the rich and powerful use math and algorithms to tilt the playing field in their favor, and how the rest of us can flip the equation and take back some control.
I found it super engaging, eye-opening, and far from dry. Whether he’s walking through the logic of rational risk or explaining how data is bought and sold, Giansiracusa makes the numbers feel empowering, not overwhelming.
This is a great read for anyone curious about how to navigate our algorithm-driven world with more agency and intention. Highly recommend!
November 11, 2025
BLUF (Bottom Line Up Front)
Use the same weighted sums, averages, and probabilities that steer modern algorithms to serve your values—and train your feeds by starving low-quality signals so the system learns to send you only the good stuff.
Executive Summary
In order to appreciate this read, you have to get comfortable with a few key terms:
Weighted Sums: Combine multiple factors, each with a chosen weight, into one score.
Averages: Smooth noisy data by aggregating across sources or time.
Probability: Quantify uncertainty to compare options and risks.
Bayesian Updating: Start with a prior belief, add evidence, update rationally so one loud datapoint doesn’t hijack judgment.
Algorithmic Scoring: The machinery behind credit ratings, hiring filters, rankings, and social feeds.
Value Weighting: Decide what matters to you and assign heavier weights to those criteria.
Becoming Robin Hood: Expose the hidden “recipe,” rewrite it to reflect your goals, and withhold attention from junk so algorithms adapt to your curated signals.
Review
I read this concept as an instructional designer who lives in the land of metrics: it’s a 4-star idea that rewards anyone with a basic interest in probability and stats. What clicks for me is the agency: pick the inputs, set the weights, and refuse to feed low-signal content—especially on social platforms that obsess about user data.
For online higher ed, keep it simple: weight reflection, transfer, and peer teaching higher than raw clicks; let students see the formula; and encourage them to “starve” low-value behaviors (mindless page views) while amplifying high-value ones (evidence-backed posts, worked examples, retrieval practice). The math is humble, the payoff is compounding attention toward quality.
Similar Reads
Weapons of Math Destruction by Cathy O’Neil — why opaque scoring systems can entrench unfairness.
Naked Statistics by Charles Wheelan — friendly intuition for the stats you actually use.
Algorithms to Live By by Brian Christian and Tom Griffiths — practical heuristics from computer science for everyday choices.
Authorship Note: This review was co-authored using a time-saving GPT I built to help structure and refine my thoughts.
Use the same weighted sums, averages, and probabilities that steer modern algorithms to serve your values—and train your feeds by starving low-quality signals so the system learns to send you only the good stuff.
Executive Summary
In order to appreciate this read, you have to get comfortable with a few key terms:
Weighted Sums: Combine multiple factors, each with a chosen weight, into one score.
Averages: Smooth noisy data by aggregating across sources or time.
Probability: Quantify uncertainty to compare options and risks.
Bayesian Updating: Start with a prior belief, add evidence, update rationally so one loud datapoint doesn’t hijack judgment.
Algorithmic Scoring: The machinery behind credit ratings, hiring filters, rankings, and social feeds.
Value Weighting: Decide what matters to you and assign heavier weights to those criteria.
Becoming Robin Hood: Expose the hidden “recipe,” rewrite it to reflect your goals, and withhold attention from junk so algorithms adapt to your curated signals.
Review
I read this concept as an instructional designer who lives in the land of metrics: it’s a 4-star idea that rewards anyone with a basic interest in probability and stats. What clicks for me is the agency: pick the inputs, set the weights, and refuse to feed low-signal content—especially on social platforms that obsess about user data.
For online higher ed, keep it simple: weight reflection, transfer, and peer teaching higher than raw clicks; let students see the formula; and encourage them to “starve” low-value behaviors (mindless page views) while amplifying high-value ones (evidence-backed posts, worked examples, retrieval practice). The math is humble, the payoff is compounding attention toward quality.
Similar Reads
Weapons of Math Destruction by Cathy O’Neil — why opaque scoring systems can entrench unfairness.
Naked Statistics by Charles Wheelan — friendly intuition for the stats you actually use.
Algorithms to Live By by Brian Christian and Tom Griffiths — practical heuristics from computer science for everyday choices.
Authorship Note: This review was co-authored using a time-saving GPT I built to help structure and refine my thoughts.
September 8, 2025
I thought this book was super fun! As a data geek I enjoyed how it started by talking about creating your own ranking systems and how easy it is to go about one. Going to college? Ranking your vacation destination? Turn to weighted averages and see what it says!
I especially enjoyed focusing on how to change your algos. I am guilty of engaging in content that gets me fired up, so I thought it was good to see how to lay off of that, even if it wasn’t all new info.
Thank you Penguin and Net Galley for an advanced copy of this book.
I especially enjoyed focusing on how to change your algos. I am guilty of engaging in content that gets me fired up, so I thought it was good to see how to lay off of that, even if it wasn’t all new info.
Thank you Penguin and Net Galley for an advanced copy of this book.
October 27, 2025
An informative, approachable book about the math behind algorithms and how to be aware enough to change them. It is the prescient issue of the current time.
Some of it was a little in the weeds but I followed as best I could. The last couple chapters made it all worth it in the end, describing how predatory the algorithms are. I quite liked the junk food example.
Say you were tired after a particularly hard day at work and proceeded to stop for fast food on your way home. Now, the next day, instead of being a couple minutes out of your way, it moves to the end of your block. Succumb again and it's more or less in your kitchen.
This is what feeds do to us, from innocuous cute pictures to ragebait to slop to whatever, by chance, grabs our attention.
It also goes into the details of how websites get paid from ads (and why Google is pages of ads with few results these days). It really does feel a bit like the wizard behind the curtain.
Some of his solutions, such as making bigger companies responsible for the tangible effects they cause, seem reasonable to me, even if I don't think it'll ever reach groundswell support. The constricting chains of ethical behavior are off and have been of all the big tech companies for a while.
Just another reminder: Less phone, less algorithmic content before there are more safeguards in place to enforce and invite ethical behavior.
Some of it was a little in the weeds but I followed as best I could. The last couple chapters made it all worth it in the end, describing how predatory the algorithms are. I quite liked the junk food example.
Say you were tired after a particularly hard day at work and proceeded to stop for fast food on your way home. Now, the next day, instead of being a couple minutes out of your way, it moves to the end of your block. Succumb again and it's more or less in your kitchen.
This is what feeds do to us, from innocuous cute pictures to ragebait to slop to whatever, by chance, grabs our attention.
It also goes into the details of how websites get paid from ads (and why Google is pages of ads with few results these days). It really does feel a bit like the wizard behind the curtain.
Some of his solutions, such as making bigger companies responsible for the tangible effects they cause, seem reasonable to me, even if I don't think it'll ever reach groundswell support. The constricting chains of ethical behavior are off and have been of all the big tech companies for a while.
Just another reminder: Less phone, less algorithmic content before there are more safeguards in place to enforce and invite ethical behavior.
April 30, 2025
What a fun and helpful read! If you’ve paid attention to many of the social media and online search/buying scandals, you’ll be familiar with what Giansiracusa details in this book. If you’ve thought about better ways to spend time on (or avoid) social media apps, many of the recommendations will feel like common sense to you. But you will learn more about how to shop better, scroll better and be less anxious by what’s being “fed” to you because of some choices and actions you’ve made. Similarly, you’ll learn more about finances and how to interpret other people’s analyses—and do your own perhaps to calm some medical scares. Polling may make sense. Risk assessments will make more sense. And the author teaches you how to do a lot of this on your own if you want.
Very helpful suggestions in each chapter come after example stories and a breakdown of what’s happening “behind the scenes.” While the stories are illustrative, many are long—which you can skim if you want to accelerate to the gist of the chapters—and some concepts/points in the argument are repetitive. The repetition isn’t all bad as most of us need repetition for lessons to sink in.
While this book describes the state of the art “today,” tech-related scenarios will change as companies continue to adapt their algorithms to altered priorities and regulations. This book, however, will give you some ways to look for the changes, take stock of the changes and adapt your usages and decision-making as well.
I’m appreciative of the publisher providing an advanced copy.
Very helpful suggestions in each chapter come after example stories and a breakdown of what’s happening “behind the scenes.” While the stories are illustrative, many are long—which you can skim if you want to accelerate to the gist of the chapters—and some concepts/points in the argument are repetitive. The repetition isn’t all bad as most of us need repetition for lessons to sink in.
While this book describes the state of the art “today,” tech-related scenarios will change as companies continue to adapt their algorithms to altered priorities and regulations. This book, however, will give you some ways to look for the changes, take stock of the changes and adapt your usages and decision-making as well.
I’m appreciative of the publisher providing an advanced copy.
Read
August 30, 2025This isn’t the book I expected based on the blurb and marketing. It is, in short, a book on real-world applied mathematics, such as using weighted sums to prepare your own personalized rankings for things you care about, or using Bayesian reasoning to figure out how certain you should be of your opinions. The last couple of chapters deal with social media and other online algorithms ¬– what I expected the bulk of the book would be about when I picked it up – but I found the solutions he gives to be somewhat milquetoast. When it comes to social media, he recommends “engage with content you want to see more of, and don’t engage with content you don’t want to see more of.” Y’know, the sort of recommendation that anyone who’s done even a cursory search into social media algorithms would be able to figure out. With the enshittification of the internet more broadly, he recommends collective action, specifically a tax on personalized ads. To which I say: great idea, and more power to him in ever getting something like that passed. It’s not that I thought this book was bad – it was fine for what it was – I was just expecting more from it than I got.
October 1, 2025
This book's subtitle is a publisher's attempt to fool readers into thinking they are reading a book about working around modern-tech algorithms. That is approximately 15% of the book with almost none of the analysis being that insightful except for the author teaching me how to make Amazon's search results (changed from "featured" to "Average Customer Rating") and Google's search results (switch to the tab labeled "web") not completely suck. It has been nice to be able to use Google as it used to be -- i.e. a search engine designed to find websites.
So, what is this book about? It is a primer for statistical analysis and Bayesian reasoning. If you already have a basic grasp of these items, you can probably skip this book altogether. But I found it to be a good refresher as I had not studied any of these concepts in two decades -- and hadn't read about them in about a decade. I also found the author's prose to be solid with the book flowing well. And he used cogent real-life examples for his didactic sections, which made the book easier to enjoy. So, for me, the book worked, but it may not work for others more advance in math.
3.75 stars
So, what is this book about? It is a primer for statistical analysis and Bayesian reasoning. If you already have a basic grasp of these items, you can probably skip this book altogether. But I found it to be a good refresher as I had not studied any of these concepts in two decades -- and hadn't read about them in about a decade. I also found the author's prose to be solid with the book flowing well. And he used cogent real-life examples for his didactic sections, which made the book easier to enjoy. So, for me, the book worked, but it may not work for others more advance in math.
3.75 stars
December 29, 2025
Well written and clearly presented topics including a basic intro to Bayesian statistics. For a book about the power of math in everyday thinking, I prefer How Not to Be Wrong by Jordan Ellenberg, which offers more breadth and depth in its presentation. However, I think Robin Hood is worth reading, especially to share with teenagers who might not appreciate the math behind many of the things they are exposed to daily.
September 8, 2025
It's one of the most interesting math books that I have ever read!
Easy to read. And practical!
The author told me some mind sets that can really be used in daily life!
Now I know that when I plan my skill set for the future, I have to have the Covarience in mind to prevent from the influence of AI.
Easy to read. And practical!
The author told me some mind sets that can really be used in daily life!
Now I know that when I plan my skill set for the future, I have to have the Covarience in mind to prevent from the influence of AI.
September 9, 2025
It was really interesting learning about some of the behind the scenes math concepts that relate to different parts of life. It’s a good book that doesn’t get heavy in the technical math. Though the author does try to describe how you yourself can use some of the formulas in your life, but I said no thanks. I’m not here to do math but I don’t mind learning about it 😂
September 16, 2025
This book is a must read for everyone. It is completely approachable and full of tips and advice on how to make decisions on every day needs in a way that makes sense for you. The individual you. It is written so well. To me, it should be mandatory reading for juniors and seniors in high school, just like balancing a checkbook or how to do laundry. And, with all the craziness with how data is interpreted these days, it truly gives you a roadmap for understanding what matters to you and how to avoid the overblown interpretations and biases in the news and social media.
November 24, 2025
Its a maths refresher for common folks, very 101 for mathematicians. The author begins with strong foundations, and well-connected thoughts and examples. Later on, especially Chapter 8, it loses clarity and sounds montonous. The author positions himself in a role of policymaker which sounded very one-sided. Overall, definitely worth reading and good to have kinda book!
September 1, 2025
The math is pretty simple with weighted sums/ averages, some predictions and beyes with decent examples including how social media ranking methods.
Didn’t need the last chapter on progressive tax recommendations 🤷♂️ but overall a good and very easy read.
Didn’t need the last chapter on progressive tax recommendations 🤷♂️ but overall a good and very easy read.
September 9, 2025
Everything in life comes down to numbers! Good reminder to pick up and park my hate for statistics. I was mostly interested in the parts of social media but my main takeaway is the ranking systems. Fun read
September 26, 2025
I found the title to be a bit misleading. Good perspective on uses for math in everyday life, but less so in regards to digital life. Avoid looking at or clicking on stuff as a way to shape your feed? sure, I knew that. I expected more insight given the word algorithms in the title.
October 31, 2025
This book isn't exactly easily digestible if you're not a math geek, and makes some bold assumptions about the reader's ability to find statistics and facts on their own. But overall a good read, especially since it was short.
September 24, 2025
A fun read. I especially enjoyed the chapter about how to hack the social media algorithms.
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October 6, 2025Not sure if this was a good book for me, when I'm so bad at math :)
November 22, 2025
Practical and readable for non-practitioner
November 26, 2025
Thank you, Noah Giansiracusa, for your contribution to the vital category of Books I Can Give My Dad for Christmas.
December 5, 2025
Interesting book about how to use math to overcome the constraints of the algorithms all around us. Nice complement to Weapons of Math Destruction.
Learned a lot. Felt the math was well explained for non-math people. Great examples. I enjoyed it.
Learned a lot. Felt the math was well explained for non-math people. Great examples. I enjoyed it.
December 21, 2025
Contains barely any new concepts, wasn‘t very intrigued by the writing style either.
November 28, 2025
Interesting book about math in our everyday, or almost everyday lives (depending on if you gamble a lot and use social media a lot). I think I would have enjoyed it more if I knew math better and liked it more, but even so, I think it's worth your time, so read it and remember it when you look at any rankings, or scroll on social media
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