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Here’s a refined version of what you're looking for: a chapter-by-chapter gist of Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart by Ian Ayres, structured into bullet-form theme summaries with a couple of notable quotes interwoven to capture the essence of each chapter.
Super Crunchers: Chapter-by-Chapter Gist
Chapter 1: Who's Doing Your Thinking for You?
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Gist: The rise of collaborative filtering—systems like Amazon, Netflix, and eHarmony—illustrates how collective data, not individual choice, is shaping decisions across entertainment, hiring, and beyond (Bookey, Wikipedia).
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Quote: “Because of super crunching, firms sometimes may be able to make more accurate predictions about how you’ll behave than you could ever make yourself.” (Blinkist)
Chapter 2: In Vino Veritas – Predicting Wine Quality
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Gist: Economist Orley Ashenfelter developed a regression formula using weather data (rainfall, temperature) to predict Bordeaux wine quality—offering a data-driven alternative to expert tasting (12min, Wikipedia).
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Quote: Achieving precision: “Wine quality = 12.145 + 0.00117 winter rainfall + 0.0614 average growing season temperature – 0.00386 harvest rainfall.” (12min)
Chapter 3: Regression — Who’s Doing Your Thinking for You?
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Gist: Regression analysis unlocks hidden patterns in big datasets—used in contexts like eHarmony’s match algorithms and major statistical breakthroughs across domains (12min, Wikipedia).
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Example: eHarmony’s model—29 traits used to predict romantic compatibility by analyzing thousands of successful marriages (12min).
Chapter 4: Randomized Trials — Creating Your Own Data
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Gist: Randomized controlled trials (RCTs) are the gold standard of causation inference, extending beyond medicine to business and policy. Examples include Capital One testing teaser rates and experiments like HUD’s “Moving to Opportunity” (12min).
Chapter 5: Experts vs Equations — Are Humans Becoming Obsolete?
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Gist: Statistical models consistently outperform human experts. Historical studies, including Meehl’s meta-analysis, show that simple equations often yield more accurate predictions than seasoned professionals (12min, Wikipedia).
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Example: Kasparov’s loss to IBM’s Deep Blue highlights how computational access to vast data outmatches human intuition (12min).
Chapter 6: They Shoot Turkeys, Don’t They?
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Gist: Neural networks, like those developed by Epagogix, predict film success from scripts—demonstrating how even creative industries can benefit from super crunching (12min, Wikipedia).
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Quote: Successful forecasts of 6 out of 9 films based solely on script analysis—“twice as good as traditional Hollywood experts.” (12min)
Additional Themes & Insights from Other Chapters
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Digital Revolution & Data Commodification (e.g., Chapter 6 from Bookey): Data has become a commodity, fueling industries and creative prediction tools like Epagogix, while raising ethical concerns around privacy and standardization (Bookey).
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Balancing Numbers and Humanity: Ayres stresses embracing statistical literacy—“instead of sticking your head in the sands of innumeracy… fill your head with the basic tools of Super Crunching” (jdupuis.blogspot.com).
Summary at a Glance
| Chapter / Theme | Core Message |
|---|---|
| Collaborative Filtering | Data trumps intuition in shaping choices |
| Wine Quality Predictive Model | Weather-regression beats tasting expertise |
| Regression in Decision-Making | Statistical patterns outdo personal judgment |
| Randomized Trials (RCTs) | Rigorous, causal testing beyond anecdotes |
| Equations vs Experts | Algorithms frequently outperform professionals |
| Algorithms in Creativity | Even art (e.g., film scripts) can be forecasted |
| Ethical & Human Considerations | Statistical literacy is essential—but must include nuance and privacy awareness |
Key Takeaways:
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Super crunching—the use of massive datasets + statistical tools—is transforming industries from dating to medicine, from sports to entertainment.
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Machines often surpass human experts when predicting outcomes—but human judgment still matters in framing questions, interpreting data, and managing ethics.
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Embracing statistical thinking isn’t about replacing humans—it’s about combining intuition with evidence.
If you'd like fleshed-out quotes from more chapters or deeper dive into any section, feel free to ask!
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Here are some standout top quotes from Super Crunchers by Ian Ayres, followed by a punchy tagline that captures the essence of the book:
Top Quotes
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“My nephew Marty has a T-shirt that says on the front: ‘There are 10 types of people in the world…’ If you read these words and are trying to think of what the ten types are, you’ve already typed yourself. The back of the shirt reads: ‘Those that understand binary, and those that don’t.’”
A playful way to underscore the importance of numerical literacy. (Goodreads) -
“What caused a law review article to be cited more or less. Fred and I collected citation information… Our central statistical formula had more than fifty variables… Articles with shorter titles and fewer footnotes were cited significantly more… the regression formula predicted that citations per page peak for articles that were a whopping fifty-three pages long.”
A fascinating dive into how even seemingly trivial factors can influence academic impact. (Goodreads) -
“Instead of a Luddite rejection of this powerful new technology, it is better to become a knowledgeable participant in the revolution. Instead of sticking your head in the sands of innumeracy, I recommend filling your head with the basic tools of Super Crunching.”
A compelling call to arms for embracing statistical understanding rather than rejecting it. (Goodreads, scienceblogs.com) -
“When statistics conflict with expert opinion, bet on statistics…. Businesses, consumers, and governments are waking up to the power of analyzing enormous tracts of information.”
Emphasizes that data-driven insights often outpace intuition—even from seasoned professionals. (Tertulia) -
“All of these collaborative filters are examples of what James Surowiecki called ‘The Wisdom of Crowds.’”
Highlights how recommendation systems (like those used by Netflix, Amazon) harness group intelligence for smart predictions. (Bookey) -
“The very resistance of some studios creates more of an opportunity for outsiders to come in and see if optimized scripts really do sell more tickets.”
Shows how traditional barriers sometimes open doors for innovation through data. (Bookey)
Suggested Tagline
“Thinking-by-Numbers: The New Smart.”
This tagline mirrors the title's rhythm and essence, neatly capturing the core idea: in today's world, smart decision-making increasingly depends on data and analytics rather than purely on intuition.
Let me know if you'd like more quotes, or a refined version of the tagline for different contexts!
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