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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
Break new ground in problem-solving and decision-making by learning from AI
A well-paid executive feels trapped in her very respected but unsatisfying job. A startup founder has paying customers, but knows that unless he ‘fires’ them and pivots the business, his startup won’t make it. A senior government planner is tasked with undoing the nation’s reliance on outdated infrastructure.
These are all examples of individuals stuck in a Local Maximum; we’ve reached a peak, but not the one that fulfills the highest potential. In order to move up in our pursuits, we must first move back down - a realization which can lead to frustration, decision-making paralysis and lost opportunity.
In How to Move Up When the Only Way Is Down: Lessons from Artificial Intelligence for Overcoming Your Local Maximum, Judah Taub draws from his perspective guiding early stage AI startups, his years serving in military intelligence, and various experiences leading innovation throughout his career. With his off-the-beaten path perspective, Judah shares insights into how humans can achieve better decision-making by learning how AI overcomes local maximums.
What tech engineers already know is that with the rise of AI, we’ve developed new ways of addressing these limitations. These techniques, employed to save billions of dollars for global giants like Amazon and Google, are equally applicable to each of us.
To show how, Judah shares a variety of real world examples, involving Olympic high jumpers, the transition of Ethiopian immigrants from gas station attendants to high tech engineers, the evolution of playing cards into Nintendo, the development of ChatGPT, the link between wildfires and hedge fund managers - and much more.
Explore:
- How to anticipate and identify Local Maximums
- How to overcome psychological Local Maximum blocks and biases
- How to build skills and apply strategies to succeed in complex decision-making
- How Local Maximum thinking can help overcome major global challenges
The book is equipped to benefit anyone facing complex decisions, or obstacles to their personal or professional goals. How to Move Up When the Only Way is Down is designed to transform readers’ decision-making by recognizing Local Maximums and skill building based on lessons from AI.
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Seitenzahl: 296
Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
Dedication
Foreword
Introduction: The Local Maximum Trap: A Universal Challenge
How to Navigate This Book
Chapter 1: The Highest Mountain
Local Maximum: What It Is and Why It Matters
A Prime Example: The Delivery Route
Chapter 2: Assessing the Terrain vs. Climbing
Just Do It: The Power of X
Dusting Off an Old Invention
Testing, Testing: A/B/X
How Much Is Night Vision Worth?
R&D: Scouts vs. Climbers
Don’t Wait for Necessity to Be the Mother of Invention
Chapter 3: Training to Overcome the Valley of Death
Teach a Man to Code
Shorten the Valley of Death
How Much Greener Is the Other Side?
Building Bridges
The Judo Push
Chapter 4: Agility to Navigate the Unexpected
When They Go Deep, We Go Wide
Balancing Muscle and Agility
Skin in the Game
Survival of the Fittest
Chapter 5: The Mountain Within – The Psychology of a Local Maximum
Overcoming Confirmation Bias
Trapped by the Mental Output Gap
Maybe the Opposite Is Right
Reality Check
Chapter 6: Time: The Fourth Dimension
Time Dictates the Size of Your Map
Finding Time and Timing What Matters
2030 Goals vs. 2050 Goals
Utilizing Time Limitations
Why Now?
Chapter 7: Global Maximum Equilibrium vs. Self-Interest
Optimizing for the Group
Darwin’s Warning: The Kevins Among Us
Learn from the Ant
We Approach, but with Caution
This Is Not a Joke: A Cinematic Prisoner’s Dilemma
Chapter 8: Dangerous Mountains
Uneven Terrain: Volatility
The Single Ascent: No Way Down
The “Impossible” Summit: Steepest Peaks
Higher Ground and Lower Ground
Chapter 9: Using Local Maximum to Your Advantage
Viruses, Dictators, and Spies
Fair Play or Global Supremacy?
Chapter 10: Local Maximum’s Effect on Global Challenges
1. Education: Adaptive vs. Fixed
2. Globalization: The Costs of Standardization
3. Governance: Democratic Rule is a Local Maximum
4. Healthcare: Reactive vs. Preventative
5. Tech Disruption: The Future Relies on Confronting Local Maximums
Conclusion: Our Own Mountains
Notes
Introduction
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 7
Chapter 8
Chapter 9
Chapter 10
A Conversation with ChatGPT
Looking Ahead, the Limits of Local Maximum Thinking
Acknowledgments
About the Author
Index
End User License Agreement
Guide
Cover
Table of Contents
Title Page
Copyright
Dedication
Foreword
Begin Reading
Conclusion: Our Own Mountains
Notes
A Conversation with ChatGPT
Acknowledgments
About the Author
Index
End User License Agreement
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HOW TO MOVE UP WHEN THE ONLY WAY IS DOWN
LESSONS FROM ARTIFICIAL INTELLIGENCE FOR OVERCOMING YOUR LOCAL MAXIMUM
JUDAH TAUB
Copyright © 2025 by Judah Taub. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permission.
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Library of Congress Cataloging-in-Publication Data:
Names: Taub, Judah, author.
Title: How to move up when the only way is down : lessons from artificial intelligence for overcoming your local maximum / Judah Taub.
Description: Hoboken, New Jersey : Wiley, [2025] | Includes index.
Identifiers: LCCN 2024020702 (print) | LCCN 2024020703 (ebook) | ISBN 9781394278091 (hardback) | ISBN 9781394278084 (adobe pdf) | ISBN 9781394278077 (epub)
Subjects: LCSH: Decision making. | Artificial intelligence.
Classification: LCC HD30.23 .T38 2025 (print) | LCC HD30.23 (ebook) | DDC 658.4/03—dc23/eng/20240603
LC record available at https://lccn.loc.gov/2024020702
LC ebook record available at https://lccn.loc.gov/2024020703
Cover Design: Paul McCarthy
Cover Art: © Getty Images | Da-Kuk
To Aviad, Itamar, Lavi, and Meital, the lights of my life.
Foreword
Consider intelligence.
What do we mean by it, what forms does it take, where is it headed?
As we look to the future, the past speaks volumes. It is a story of ever-increasing intelligence. A story not devoid of challenge, strife, and opportunity.
The first age of intelligence – the age of biological intelligence – well predates humanity. Brimming with thought processes of many types and degrees, it is noteworthy that nature, too, is characterized by social organization and of rival parties advancing competing goals. Characteristic of this period is that intelligence at the organism (or collective) level is bound by its biological limits. These limits also govern the rate at which intelligence increases.
The second age of intelligence is the age of humanity and technological augmentation. As humans, we have transcended our biological limits. We have learned to harness technology to augment our capabilities and enhance our individual and collective intelligence at a snowballing rate. Our increased capabilities allow us, in turn, to further advance and evolve technology itself at an ever-accelerating rate. Thus far, our ever-more-capable technologies are developed in our service, with limited agency of their own.
We are now at the precipice of the third age of intelligence – the age of Artificial Intelligence (AI). In this epoch we will face radically new and diverse forms of intelligence. And for the first time, these intelligent agents will have evolving goals of their own. These intelligences can and will self-improve – unshackled by their corporal underpinnings. This is not a future of silicon alone; this is a future of rewriting biology as well. Most importantly, it is a future of nonhuman intelligence, far surpassing our own.
As I write these words, I am making my way to MIT, where I lead the FundamentalAI group within the MIT Futuretech project. On a daily basis we consider and push at these fundamental limits of AI, limits of both performance and safety. We are working toward ushering in this third epoch, such that when, not if AI exceeds human capabilities across any and all tasks, it does so in the service of humanity.
It is against this backdrop, of improving agents of artificial intelligence, that I find fascinating Judah’s question of “What I, an intelligence bounded individual – and agent of natural augmented intelligence if you will – can learn and very practically adopt in improving myself by drawing on AI’s mechanisms of improvement?”
In the quest for artificial intelligence, it has long been the case that our understanding of learning itself, the process by which we imbue intelligence with capabilities and skills, has looked inward to how humans and other organisms learn.
In building AI systems our understanding is being afforded newfound tools; as we can pry at the very inner workings of these systems as they learn and perform, we can change them and the way we teach them and assess the effects on their performance.
While our understanding of artificial intelligence learning and improvement remains remarkably opaque (even though we’re the ones building these machines), we are already beginning to gain incredible insights.
Tapping into these insights, How to Move Up When the Only Way Is Down inverts the traditional question “What insights of human intelligence can we apply to improve AI?” and instead asks “What insights from AI improvement can we apply to improve ourselves?”
Core to the notion of improving is the concept of reaching the best possible solution, of reaching a Global Maximum, and avoiding the plethora of suboptimal solutions – Local Maxima. As alluded to by the title, Judah deeply engages and adopts this long-known concept in the fields of mathematics and computer science as a framework: explicitly, a framework of how to deal with the challenge of Local Maxima, where one reaches a peak where any further progress involves an initial regression.
This problem has applications in many fields of endeavor, professional and personal. It has also become a critical dilemma at the global level with implications across healthcare, global trade, the environment, and many other critical domains.
With a background in investing in technological innovation and in dealing with sensitive defense and intelligence issues, Judah is able to draw on a wide range of compelling examples to show how new AI-generated understandings can contribute to our own thinking on the most urgent and thorny issues. In doing so, he makes complex new ideas both digestible and easy to understand, and provides a valuable toolbox for moving up, even when the only way seems to be down.
More broadly, this book provides a model for a question that will increasingly accompany us in the years to come: Are lessons even transferable between artificial intelligence and human intelligence in addressing the challenges that we face? After all, unlike in the artificial case, I don’t get to swap my computational hardware and am not afforded the parallelism of virtually limitless experiences or attempts.
How to Move Up When the Only Way Is Down suggests persuasively that lessons from AI are transferable with concrete practical implications. And so, while it may be the case that we are ushering in intelligence surpassing our own well before fully understanding either, it heartens me to consider that in so doing we may be finding ways to better ourselves and our society.
Enjoy the ride.
Dr. Jonathan (“Jonny”) Rosenfeld
Co-Founder and CTO at Somite.ai
Head of FundamentalAI group at MIT FutureTech
Introduction: The Local Maximum Trap: A Universal Challenge
John’s earliest memory is of playing with his father’s stethoscope. He’s always dreamed of following in his dad’s footsteps and becoming a surgeon. Most of his college classes have been pre-med, but, by the end of sophomore year, he’s felt a pull toward biomedical engineering. His academic advisor has been encouraging him to specialize (his father has, too), and it’s time to declare a major. John’s not positive which career path will lead to the best outcome. His dad’s surgical residency has led to a very nice life for the family, but he wonders if he can make a greater impact on people’s lives from the engineering side. Part of the dilemma is that he’s still learning and gathering information. He’s not sure he has enough to go on to decide, but the registrar needs an answer.
Lucy has been playing the dating game for 15 years, and she’s eager to get married and start a family. She’s dated over 80 guys; three of which were serious. She wonders, is she being too picky, or was one of those three the “one”? Should she stay in the dating pool and keep looking for her dream partner, or should she let go of some of the “must haves” in lieu of what “will do”?
Fred is a top-level marketing executive with a high salary and lifestyle expenses to match. From the outside, his life seems enviable. He’s been on a steady career trajectory for 15 years, gathering prestige and properties along the way. But when he thinks about how he really wants to spend his time, marketing is not it. The problem is his salary is so high, he’ll have to take a severe loss to go in a different direction. He’s trapped in the proverbial golden handcuffs.
John, Lucy, and Fred are each stuck in a Local Maximum. In nearly every field of human endeavor and facet of decision-making, in which we aim to go as high as possible, a Local Maximum is a point from which we can only go down. Crucially, we may not be at the highest point. There are higher peaks around us, but we find ourselves trapped on our own peak within our own Local Maximum, with significant costs and implications, but without the necessary tools, or even the language, to describe them.
Though humans may lack the nomenclature to articulate their predicaments (beyond the word, “stuck”), there are several cutting-edge industries in which the challenge of Local Maximum is well-known. And because of the enormous costs it can incur, it has been given careful attention. These include fusing alloys, telecom routing, weather forecasting, mobile advertising, oil and gas mining, molecular modeling, aerodynamics, cryptography, and many more. They are encountered by the brightest coders and engineers at tech giants like Google and Amazon.1 Professionals within these arenas readily acknowledge that getting stuck with suboptimal solutions is one of the biggest issues they face. Critically, the challenge of a Local Maximum is not exclusive to the tech industry, to computer scientists, or to programmers. The challenge exists in ways both large and small, personally and professionally, for everyone, as evidenced by John, Lucy, and Fred.
In my own life, the concept of Local Maximum has crept in through the back door and become a key factor in determining an initiative’s success or failure. As an intelligence officer in national security, I have been trained to think in new and innovative ways to reverse engineer my regiment’s way out of both common and uncommon military challenges: how to analyze the field, how to define its parameters, and how to tackle complex problems effectively and efficiently under duress. In my career first at a hedge fund and then as co-founder and managing partner of Hetz Ventures, an international investment fund focused on early stage Israeli start-ups led by hi-tech entrepreneurs, and as a board member of many of those companies, I have the opportunity to help founders identify and avoid Local Maximums every day. The CEOs of these companies are dealing with hundreds of oversight-related items, very few of which have to do with actual innovation, strategy, or implementation (or, not nearly as much as they would like). They are focused on the nuts and bolts of daily operations, so when we sit down to talk, they’re craving a high-level perspective as to whether their efforts, or the organization’s efforts, are driving them to the optimal outcome. They want to know: Are we climbing toward the highest point? In these two very different environments, my military colleagues and the start-up entrepreneurs are amongst the most talented, driven, educated, and thoughtful people in their fields. But it turns out, running as fast as you can toward the highest point is not always the best strategy.
How to Navigate This Book
For decades, since the advent of computer programming, talented programmers have used their best efforts to teach computers human logic. A classic example is – if A is bigger than B, and B is bigger than C, then A must be bigger than C. In recent years, though, computers have begun to develop a logic of their own. This book turns the tables and asks what we humans can now learn from how computers make decisions. Specifically, it focuses on the challenge of avoiding the Local Maximum trap. Now that the brightest minds of tech giants such as Amazon and Google have devoted years to addressing this challenge and saving billions of dollars and millions of work-hours, this book explores what computers can teach us about avoiding the Local Maximum trap in our own lives.
Rest assured this book does NOT require a background in computing, engineering, or math. In fact, as you will see, the major obstacles we face emerge from human psychology far more than from algorithms. At the end of each chapter, I have included a short section titled “A Little Byte of Data Science,” which you should feel fine to skip. Although they do not require knowledge of, or interest in, technology, they demonstrate how the ideas from each chapter are used in the tech world.
In Chapter 1, we explore the concept of Local Maximum through the metaphor of a combat paratrooper in training. He is dropped into the middle of the desert and given the task of climbing the tallest mountain. How does he decide which mountain to climb, and how does he know it is the tallest? Through his experience searching for a solution to a seemingly simple problem, we will see that Local Maximum is a challenge with far-reaching implications and applications.
In Chapter 2, we focus on understanding why Local Maximums can be so attractive and the dangers of the most common methods used by marketers, strategic planners, and others to assess potential courses of action. We will learn the extraordinary and simple power of tweaking our thinking and metrics by moving from A/B testing to A/B/X testing.
In Chapter 3, we ask how we can get off a Local Maximum when we find ourselves stranded on one. We will analyze “death valleys” – the seemingly insurmountable canyons between where we are and where we need to be. And we will encounter practical examples from fields as diverse as Military Intelligence to career choices of Ethiopians living in Israel to international Judo competitions.
In Chapter 4, we turn inward and understand how certain personal or organizational characteristics make us more susceptible to falling foul of a Local Maximum. We will consider how we can develop practices, such as balancing agility and muscle, to achieve our current targets while reducing the potential pain of hitting a Local Maximum.
In Chapter 5, we address the psychological dimension and contrast the Local Maximum mindset with a Global Maximum way of thinking. With lessons from the Israeli Air Force and the future of Healthcare 3.0, we’ll see that often our greatest mental strengths can also be our most forceful opponent.
In Chapter 6, we discover that time can both constrain and expand our realm of possible outcomes. We learn to view time as a variable rather than a fixed factor, and – like YouTube, drone units, and early-stage start-ups – utilize this knowledge to our advantage.
In Chapter 7, we dig down into our core values and consider how the degree to which we see ourselves, as individuals or as part of a collective, impacts our ability to overcome obstacles and reach higher ground. Charles Darwin, Robin Dunbar, and the Joker from Batman may believe our DNA limits our potential, but ants may suggest otherwise.
In Chapter 8, we consider not only the height of the mountain we are aiming to climb, but its unique shape. We will learn to recognize the topography of different types of dangerous mountains. With examples from hedge fund managers and public company CEOs, greatest of all-time (GOAT) athletes and major start-up busts, we will meet four mountain shapes to be wary of.
In Chapter 9, we pause to consider whether there may be situations in which a Local Maximum is not a bug, but actually something to aim for. Wildfires, viruses, and US sports leagues all offer unique insights to utilizing the tools from earlier chapters to our advantage.
Finally, in Chapter 10, we put the lessons we’ve learned from artificial intelligence to the test by examining five fundamental global challenges. We examine whether the tools we have acquired can provide a fresh look at education, globalization, governance, healthcare, and technological development.
How to Move Up When the Only Way Is Down: Lessons from Artificial Intelligence for Overcoming Your Local Maximum is a tool to observe problems in a new way, and it is a mechanism to monitor the long-term health of an individual, corporate, or societal trajectory. To harness the full impact of the concept, we will explore the numerous techniques, typically taken from the computing world, to recognize, solve for, and avoid a Local Maximum. Though we are not all computer programmers, we are Johns, Lucys, and Freds, looking for tools to help guide us in the hardest decisions we have to make.
Chapter 1The Highest Mountain
We have lost the battle, but we won the war.
– Pyrrhus of Epirus, Battle of Asculum (279 BCE)
Imagine you have just been dropped from a helicopter into the middle of the Negev Desert. Four other soldiers from your battalion have been dropped at different points and are nowhere in sight.
Your mission: Locate the highest peak within 10 square miles and be the first of the group to climb to the top. You have 12 hours to get there.
This is the toughest test in your combat paratrooper training so far. You’re well beyond communications satellite range, you have no navigational tools, and you’re surrounded by dusty sand dunes and jagged mountain ranges as far as the eye can see. In the vast desolation, the wind whistles as you get situated and visually zero in on what appears to be the tallest mountain in sight. Adjusting your pack, heavy with enough water and rations for the day, you start toward the base.
After three hours of battling heavy, dry sand, you reach the base of the mountain range, well ahead of the other guys. Preparing to begin the ascent, you’re aware of your buddy, Yoni, in the distance, gaining traction and proximity, but you’ve got the lead. Your blood is pumping with adrenaline, knowing Yoni always has an energetic reserve tank, and you have to widen the gap. Recalling that half the guys from last week’s exercise didn’t make it to the top of the mountain, and your friend Daniel was hospitalized overnight from exertion, you pick up speed. There’s no way you’re going to be in the 20% of aspiring troopers cut from the program. Not today. No way.
The climb entails steep switchbacks, navigating around and over boulders and slippery sand dunes, and ripping through spiky cactus trees. After three more hours of grueling effort, you pause briefly to dump the rocks out of your boots, refuel, rehydrate, and reassess.
The last time you spotted Yoni was an hour ago, when he abruptly veered inward off the “trail” and vanished out of sight, seemingly heading toward a smaller mountain. What does he know? Where did he go? You haven’t seen a glimpse or heard so much as a twig snap from the other three soldiers. Where could they be? You’re too close to the top to worry about them. They’ll figure it out. Or perhaps they won’t.
With renewed determination, you push upward, satisfied knowing you’re in the lead and making good time. At this pace, you calculate, you could conceivably summit within an hour. You tap into your own energy reserve tank and break into a run.
By late afternoon, you’ve reached the peak, physically and mentally exhausted. Taking in a long, deep breath of fresh mountain air, what the troops call the “victory inhale,” you ease your pack to the ground and sit down on a smooth, flat rock to remove your boots and rub your blisters. You executed this drill to perfection: at every decision point you made the right decision, successfully detouring around a few smaller mountains, keeping up pace, and then, using the last of your energy to race up the final incline.
Slowly, draining the last of the water from your canteen, you settle in to wait for Yoni and the others to catch up. Raising your eyes, you take in the magnificent clarity of the distant mountain range that surrounds the one you’re on – the one you’ve conquered, and with time to spare.
Just then, a heavy feeling lands in the pit of your stomach and slowly travels through your pulsing bloodstream up to your head. Doubt spreads through every rational thought as it dawns on you, with absolute certainty, something is terribly wrong. Where are the others? They should be here by now, or at least within earshot. Either they miscalculated, or you did.
Rising to walk to the edge of the very peak you climbed, you’re gob-smacked at what you see. There is another mountain, right in front of you, and it’s taller than the one you’re on. You’re on the wrong mountain.
You release a pained and violent wail into the open air. It taunts back in a cold and rippling echo.
Your mind races: “No. This is impossible. What the hell? How?”
You did everything right! You did not set out haphazardly. You made a plan, calculated the distance within the allotted time, conserved your energy and your rations, and made admirable advances despite the obstacles.
Peering down, you can just barely make out the shape of another climber at the bottom of the tallest mountain beginning the ascent. It’s that joker, Yoni! You are higher up than he is, but he is undeniably further ahead on the mission, because he is climbing the tallest peak. Every one of your steps up this mountain was a step further away from the peak you needed to reach. You got to the “top” and discovered that the only way forward is to go all the way back down.
You’re at a Local Maximum, and the worst part – worse than all the time and energy you’ve already wasted, and worse than all that still lies ahead – is it could have been avoided. Even though you tried your very best and calculated every step along the way, you lost.
The painful realization that we have been investing our time, resources, and efforts to reach the wrong goal is one that can haunt us in many of the most important aspects of our lives.
Local Maximum: What It Is and Why It Matters
A Local Maximum is a point on a field that is not the highest or the best, but it is a point from which we can only go down. It’s deceptive because it’s attractive. We are naturally pulled toward it and work very hard to get there, but once we arrive, we realize there is a higher or better option.
The terrain includes many maximum points from which a climber can only go down. There is one Global Maximum and many Local Maximums, some of which are very low down, but are nevertheless maximum points.
Most of us don’t spend our lives in army fatigues navigating desert landscapes. But the Local Maximum scenario is one that affects us all, and in the most important aspects of our lives. As individuals, as managers, or as leaders, we devote our days, our skills, and our resources to pursuing particular goals. Yet, we are often blind as to whether a better, perhaps vastly better, course of action is available. We are so busy exercising our muscles to improve the speed and efficiency of our climb. But how effective are we at recognizing whether we are climbing the right peak in the first place, or in getting off the wrong peak to course correct while there is still time?
Consider the following real-life scenarios:
The manager of an English football team at the bottom of the second division.
All the team players are average except for the star striker, who is responsible for most of the team’s goals. The fact that all the other players are centered around the star player seriously limits their play and their own development. In the long run, the team would be better off without the star player. In the short term, there is a price to be paid: the team will likely go down a division, and it could take years to recover.
The military needs to determine how to spend their budget.
Combat divisions need ammunition and motor vehicles, and they need to invest in intelligence to predict the type of warfare anticipated. How do you trade off building the military force (running up the mountain) while also balancing intelligence to make sure you are investing in the appropriate tools and training (heading in the right direction)?
The CEO of a successful start-up that has gained tremendous traction.
Out of the gate and on a shoestring budget, the CEO introduced an immediately popular and widely adopted freemium product, generally known to be the envy of his heavily backed competitors. However, she needs to raise more money to bring the product to a broader market. Some investors are advising her to prioritize short-term revenues, which means sacrificing part of her unique brand and potentially alienating her original community of supporters.
A senior government official charged with upgrading national infrastructure.
New 5G telecom technology promises major benefits throughout the country’s economy. While it is clear 6G and 7G technologies will arise in the future and may render the enormously expensive investments in 5G redundant before too long, voters are hungry for speedy results. How do you balance the huge potential without getting stuck with a huge “sunk cost”?
Local Maximum offers a simple framework to understand why some businesses plateau, why some people find themselves in jobs they can’t leave, and why we find ourselves trapped in situations that prevent us reaching our full potential in so many fields of life. Understanding this concept gives us the tools to ask:
What are the behaviors or decisions that lead us to a Local Maximum?
What can we do to steer ourselves away from these limiting Maximums before we get there?
And, if we do get there, what can we do to get unstuck?
A Prime Example: The Delivery Route
A classic example of the Local Maximum challenge is Amazon Prime and its complex system to manage deliveries. Consider how the system determines the most efficient route for the driver to deliver packages to hundreds of locations around a city. This may sound like a simple A to B mapping project, but finding the optimal solution is nearly impossible due to the sheer volume of options.
Think about it this way. Imagine you need to make 10 deliveries across the city in a day. How many possible optimal routes are there? (The answer is over 3 million!) Now, pretend you have to make 20 deliveries, that is 3×10^64 optional routes. (That’s more than the number of steps it would take to “walk” to the Sun!) In reality, Amazon has thousands of drivers, and each of them make hundreds of deliveries a day; the number of route options is simply too large for the mind to comprehend. More so – and this might come as a surprise – the number of route options is too large for even the fastest and best computer to comprehend. So, how do computer scientists overcome this? They turn the problem into mountains.
So, consider Amazon Prime as a mountain climber:
Amazon Prime delivers packages. Its profit relates directly to the speed of its deliveries. The more deliveries it can make in an hour, the more profit. The process of planning delivery routes is a mountain that must be climbed. To solve the task, the data scientist converts the deliveries into a topographic map: the better the delivery route, the higher the point it represents on the map. (Routes that are similar appear next to each other.) Next, the data scientist asks himself: How do I reach the route/peak of greatest efficiency and avoid the costs of adopting a route/peak that looks efficient, but that ignores faster, more cost-effective routes/peaks?
