Table of Contents
Can You Actually Outsmart the Algorithms Controlling Your Daily Life?
Reclaim your digital agency from Big Tech. We break down the math behind rankings, credit scores, and social feeds—and teach you how to make them work for you. Ready to stop being a datapoint and start calling the shots? Scroll down to build your personal toolkit for digital independence and learn exactly how to retrain the systems trying to define you.
Genres
Science, Technology and the Future, Personal Development, Education
Learn how to game Big Tech.
Robin Hood Math (2025) is your backstage pass to the hidden math running rankings, credit scores, and the feeds you didn’t choose. The good news is, by familiarizing yourself with these formulas, you can gain a street-smart toolkit that will help you build your own rankings, average predictions the right way, update beliefs, and “train” your social algorithms. It’s a practical playbook to take back a little power – both online and off.
Your day starts with a number on your phone screen – 7:00 a.m. – and before you’ve even gotten out of bed, more numbers are already shaping your world. TikTok’s algorithm picks what videos to show you first. Social media tallies your worth in likes and followers. And your drive to the grocery store quietly churns out gigabytes of data about how you brake, what you listen to, and where you go. By the time you’re scrolling job ads, you’re already knee-deep in a sea of rankings, ratings, and hidden formulas deciding which listings you see and how you measure up.
And it doesn’t stop there. Workplaces track keystrokes, banks reduce your dreams to a credit score, and even classrooms and hospitals slot you into metrics designed more for efficiency than for humanity. It’s enough to make anyone feel less like a person and more like a datapoint. But here’s the good news: the math behind all this isn’t just for the tech giants and hedge funds. With a little know-how, you can use those same ideas to push back, make smarter choices, and reclaim a bit of agency in a number-obsessed world. That’s the spirit of this summary – taking the power of math out of the hands of the few and sharing it with the rest of us.
Rankings, recipes, and the illusion of objectivity
On a late September afternoon in 2023, Brandeis University sent out an email that no school ever wants to write. Their ranking in the US News & World Report’s Best Colleges list had slipped – and not by a couple of spots, either. They’d fallen 16 places. From number 44 to number 60. It was a gut-punch. Parents fretted, alumni groaned, and students asked themselves: “Did my school suddenly get worse?”
But in reality, Brandeis hadn’t changed at all. What had changed was the recipe – the secret weighting system that US News uses to cook up its annual list. That recipe is as good a place as any to get into the concept of weighted sums, which is something you’ll find being put to use in many of the algorithms that affect your life.
For those sought-after college rankings, it works like this: the magazine picks common factors – such as graduation rates, faculty pay, alumni donations, peer assessment, and class sizes – and then decides how much each factor counts. That second step – the weighting – is crucial. For decades, the single most heavily weighted factor has been “peer assessment,” a survey of how other college administrators feel about a school. It carries 20 percent of the total weight, meaning it matters more than things like student debt, retention rates, or test scores.
In 2023, US News made the biggest adjustment in its 40-year history. It shuffled weights around and some factors like alumni donations and class-size were de-prioritized. As a result, some schools climbed while others sank. For Brandeis in particular, losing the class-size factor hurt most of all.
What should stand out here is that rankings like these don’t reveal any hidden, objective truths. The different weights being placed on different factors are choices that people are making based on their own set of subjective priorities. And when priorities are baked into formulas like these, gamesmanship follows. Many schools have been caught boosting their numbers. Some have paid incoming students to retake the SAT, hoping for higher averages, others have admitted to inflating data. Columbia made headlines when it was revealed it falsified faculty credentials and spending numbers.
But on the bright side, this also means that you have the power to make your own weighted ranking. Imagine you’re choosing between Tufts, UCLA, and Georgetown. Your “factors” might be weather, student-faculty ratio, politics, and basketball. If sunshine has the most weight with you, UCLA will top the list. If political life matters more, Georgetown wins. If classroom size matters most, then Tufts will rise to the top. Same schools, different weights, totally different rankings.
The same kind of math drives credit scores, loan approvals, job candidate metrics, as well as the algorithms curating your social media feed. But you don’t need fancy software to make weighted sums work for you. Just choose the things you value, give them weight, and see where that takes you. There’s a recipe behind every ranking, so use the one that appeals to your specific taste.
The value of the average
Have you ever tried the old carnival game of guessing how many marbles are in a jar? Turns out there‘s a secret to this game, but you can’t do it alone. The winning formula is something called a weighted average.
In the last section, we looked at weighted sums. This method is a little different. It takes into account the fact that some people are going to over estimate and others are bound to under estimate. But if you take 100 guesses and then average them out, something magical happens – you’ll get a number that is astonishingly close to the number of marbles in the jar.
In mathematical terms, we’re accounting for things like bias, variance, and covariance. But the intuition is simple: a crowd of independent, reasonably good guesses beats almost any lone expert.
But a weighted average takes it a step further. It recognizes that some people are consistently sharper in their guesswork, so you give their voice more weight. This is the logic behind financial forecasters, sports analysts, weather models, as well as election forecasts. They combine different sources and give more weight to the ones with proven track records.
Beware, though. If the polls all share the same blind spot, the average inherits it. That’s what happened in 2016 when certain voter groups were underestimated in the same direction across the board. Independence matters just as much as accuracy. It’s the same reason investors diversify. If your stocks all move together, you’re fragile. If they wobble in different rhythms, you’re better off.
This leads us to the cautionary tale of expected value, which is like the first cousin of averaging. Rather than weighing votes or guesses, expected value weighs possible outcomes by their probabilities.
If a coin flip pays three dollars for heads and one for tails, the expected value is two dollars per flip. That’s what insurers, casinos, and lotteries use – not to win every time, but to win in the long run. The trouble comes when the averages hide the extremes. A lottery’s expected value might look tempting because of a giant jackpot, but the actual odds of you hitting it are microscopic. Insurance, meanwhile, looks like a bad deal on average, yet it protects you from financial catastrophe.
But expected value can run amok. Example A is the case of crypto catastrophe Sam Bankman-Fried. He treated life as one giant expected-value calculation, placing massive bets on the idea that the averages would bail him out. But life isn’t infinite coin flips. If you risk everything too often, ruin catches up – whether financially, legally, personally, or in the case of Bankman-Fried, all three combined.
Expected value can be a trusty compass, but you need to remember that not all consequences can be reduced to numbers. Sometimes the weight of trust and reputation outweighs the math. Once you start thinking this way, then you’re ready for a tool that updates beliefs as new evidence comes in. That tool is known as Bayesian reasoning.
Adjusting the dial on belief
Named after the eighteenth century statistician, Thomas Bayes, Bayesian reasoning helps us absorb new information. Your dial gets set based on your gut, your knowledge, or your hunches. But that dial needs to be adjusted when new information arrives. The question is: how much?
Let’s run a scenario: Maybe you were around 100 percent certain that a positive result from a COVID test meant that you had the virus. But then you learn that the test is only 80 percent accurate and also shows false positives 5 percent of the time. Bayes would point out that since the positives come not just from the sick but also from a slice of the healthy group, your certainty level should go down to around 64 percent.
Is your brain struggling with that math? Well, here’s a friendly shortcut in the form of a simple equation: A – B + C.
A is the number that represents your prior certainty. Zero is near certainty – close to 100 percent. One is 50 percent certainty. Two is 25 percent. Three is 10 percent. Six is 1 percent, – or one in a hundred. Ten is 0.1 percent – or one in a thousand. And 20 is one in a million. Pick one of these numbers as your initial ballpark figure.
Next, consider which of those numbers best represents the baseline probability of the situation you’re looking at. Make that number B. Then, for C, choose the number that you think best represents the probability given the circumstances.
For example, let’s say your friend says she’s a savvy investor, but you’re a little skeptical, so your belief starts at around fifty-fifty. But you give her a test: from ten stocks, pick the one that’ll rise most in a month. Sure enough, she nails the test. So the question is, how much do you believe in her now?
If we plug in some ballpark numbers we would have A=1 for 50 percent, B=3 for the possibility of a 1-in-10 random hit, and C=2 for a 25 percent success rate if she’s genuinely skilled. The result of A − B + C = 0, which maps to “near certainty.”
Bayesian reasoning helps to explain why two people can watch the same debate and walk away with different impressions: they started with different priors, so the same evidence nudged their dials in different ways. It also helps us to understand how algorithms update their own “beliefs” – especially the ones curating our feeds.
Algorithms, engagement, and the math of your feed
Now’s the point where everything we’ve touched upon so far comes together. It happens when you open your favorite social media app. As your feed loads, it’s like the gears of a slot machine going into motion. But it’s not a game of chance. Each post that someone makes is competing in an invisible math contest.
And it all comes down to the first concept we looked at: weighted sums. More precisely, Facebook, TikTok, and X – the platform formerly known as Twitter – all run on a similar algorithm. It multiplies the chance you’ll like, comment, or rewatch by the weight assigned to that action. Then they add it all up and rank the results. That’s it. A weighted sum decides what you see.
Those weights can make all the difference, and just like the schools involved in the US News rankings, once you know the formula you can start gaming the system. For example, among the most viral posts to TikTok in late 2022 was an image of a young woman looking into a mirror, with the message: “Imagine how good your life would be if you had a 26yo nursing assistant by your side, now replace S with N.” Millions rewatched, puzzled, commented, and tagged their bewildered friends. The algorithm didn’t care that it was indecipherable nonsense. People engaged with it and those signals caused the clip to go viral.
But there are some differences between the major platforms. On Facebook, shares carry more weight than comments, comments carry more weight than likes, and for years the “angry” emoji carried an outsized amount of weight. That meant arguing with a conspiracy post gave it more fuel than ignoring it.
TikTok adds another layer: playtime. This is why a lot of videos have “wait for it” captions or contain obscure mysteries that get users to loop the videos over and over, trying to figure it out. Replays and heated comment threads also act like rocket fuel on TikTok.
On X, it’s the same skeleton, but here replies far outweigh likes, and digging deep into a comment thread is engagement gold. Meanwhile, reports, mutes, and “not interested” signals will drag a post down and act as a negative weight.
Knowing all this provides some power. While you can’t change the weights, you can control your probabilities. Every time you linger, rewatch, or reply, you feed the machine. The best advice is: if you want less junk, starve it. Quick scrolls, no comments, no replays.
So treat your clicks and views like votes. Every second you spend is a ballot for more of the same. If you want more puppies, upvote more puppies. The algorithm may feel like a black box, but it runs on expected value – math you can game back. And this brings us to the finale: math not just as a lens, but as a tool to stand up to the tech titans themselves.
Taking control from the tech titans
Over the years, giants like Amazon and Google have solidified their control over what you see and what you buy. But while math is what built Big Tech – it’s also what can empower everyone else.
Over at Amazon, there’s a very reliable pattern at play whenever you search for something. It’s the default “Featured” setting at the top of the results. This gives precedence to companies that pay Amazon for a top spot in their search results, as well as Amazon’s own house brands.
So the next time you’re searching Amazon, sort by “average customer review” or “lowest price,” and be willing to scroll. The real bargains and better quality often sit far below the prefab “Featured” section.
If you really want to take matters more into your own hands, there are browser plug-ins, like the one that tracks the ISBN numbers for books on Amazon and then points you toward local libraries and bookstores.
As for Google, this is another company that has shifted from serving searchers to serving advertisers. Getting to the best results requires some real digging through the layers of ads and junk that blanket the top results.
One solution is to get more deliberate with your search terminology. You can use the minus sign to exclude words from your search. Let’s say you’re looking for a square-shaped pair of trousers, but aren’t interested in a certain cartoon character. In that case, try searching for “square pants -SpongeBob”. And if you want your results to be from the pre-AI slop era, add a date like “before:2022-4-1” to your search.
Then there are site: or filetype: commands, which can also help you to filter through the muck. Getting advice about vaccines on the internet is like entering a minefield, so typing something like “COVID vaccine site:cdc.gov” will allow you to focus your results on a specific site. Likewise, if you’re looking for a pdf document, add “filetype:pdf” to your search for better results.
But the bigger battle is about how ads function on the internet. We need to fix the auction formula on Google so that credible brands aren’t funding misinformation.
Google doesn’t want to mess with ads because they’re thin gruel to begin with. At Instagram scale, an hour of user attention is still mere pennies. But this is why platforms are so insidious, addictive, and invasive in their practices. They’re desperate to maximize compulsive usage and track your data.
But if we put a progressive tax on targeted ad revenue, it would hit the giants hardest and discourage invasive micro-targeting at the same time. The proceeds could fund nonprofit newsrooms, public-interest platforms, even universal basic income if automation hits jobs hard.
But here’s a final, hopeful note to end on. Let’s use math for good. Let’s use the same tools that power Silicon Valley to empower ordinary people. Let’s teach people how to train their feeds, shop smart, support transparency, and back public-interest fixes. Used wisely, math can level the digital playing field for everyone.
Conclusion
The main takeaway of this summary to Robin Hood Math by Noah Giansiracusa is that much of modern life runs on a few simple mathematical formulas, including weighted sums, averages, and probability. These are powering influential rankings, election and weather forecasts, as well as the social media feeds that keep us scrolling.
But you can use those same tools to your advantage. You can choose what matters most to you in order to personalize your own rankings. You can blend predictions the smart way by averaging and giving the reliable ones more weight. And when uncertainty rattles you, reach for Bayes: start with a prior, add evidence, and update calmly so a scary single data point doesn’t hijack your belief.
These are all part of the feeds being served to you by social media, and you can train them by starving the junk you don’t want and being mindful to engage with what you really like. The same math helps you shop better on Amazon by changing the defaults and searching better on Google by cutting through the ad fog. All of this provides agency, because once you see the formulas, you can question them, tweak them, and make choices that put you back in charge.