BORN FOR THIS by Chris Guillebeau

• Many people really are stuck in soul-crushing jobs, with no escape route in sight. If you find yourself trapped for the foreseeable future, you have two obvious options: settle or make a trade-off.
• This book will also challenge many popular beliefs about the way we live and work. As you’ll see, some of these conventional assumptions about what a dream career should look like are misguided or simply wrong.
• Even if you receive a regular paycheck and have no intention of ever starting a business, it’s important to understand that you are still essentially self-employed. No one will look out for your interests as much as you will, so you should make active decisions and take responsibility for your own success as much as possible.
• There may be a few superhumans out there who know from age five exactly what they want to do when they grow up, and what form it will take. For the rest of us, it’s almost never that simple.
• Simply put, the process of discovery unfolds a bit differently for most people.
• You can learn something from any job, of course, but most of the time we learn as much about what we don’t want as what we do.
• Finding the work you were meant to do is rarely a linear journey.
• The Path to Lottery Winnings Decisions: make the right ones Luck: increase it wherever possible
• in the vast majority of jobs, specialization is hugely overrated.
• Put simply, here’s what we’re looking for:
• Something that makes us happy (joy)
• Something that’s financially viable (money)
• Something that maximizes our unique skills (flow)
• The reality is that there are some career fantasies you’ll never achieve no matter how hard you try…so whatever those are for you, they shouldn’t be your goals.
• It’s hard to be truly happy if you don’t fundamentally enjoy how you spend most of your time.
• Generally speaking, we want our work to “spark joy.” If you’re not sure whether your current work sparks joy, it probably doesn’t.
• It doesn’t matter how late you can sleep in the morning or how fun your co-workers are if you have to spend eight hours a day doing something you despise.
• The point is, working conditions are a huge part of finding that ideal combination of joy, money, and flow. You can’t just find the best possible work. You also have to find (or create) the working conditions that best suit your personality and preferences.
• Just remember: there’s more than one path, but the goal is to find the best one. You want work that meets the three requirements of joy, money, and flow. The closer you come to your ideal intersection of these three qualities, the happier—and more successful—you’ll be.
• Learn to evaluate risks, make better choices, and create a series of backup plans that will allow you to take the right kinds of chances.
• With a few exceptions, no matter how good a gambler you are, the people who run casinos are better—that is, better at separating you from your money.
• We play the game of life, especially the part about career planning, a lot like a roulette wheel. We make decisions based on intuition, and we tend to make the same mistakes over and over.
• It’s when the odds of success are between very unlikely and close to a sure thing that matters get complicated.
• Because we tend to make decisions based on fear or perceived scarcity, we sometimes feel pushed into a less-than-ideal course of action.
• The more we can make rational decisions based on the information currently available to us, the better we’ll become at assessing risk.
• With games of perfect information, everything you could possibly want to know about the game is reflected on the scorecard.
• Backup plans don’t make us wimpy; they actually allow us to take on more risk.
• In programming, good coders try to create fail-safe options in case something doesn’t go as planned.
• You can apply this kind of “if this, then that” thinking to your career planning.
• The next time you take on a potentially risky gambit or endeavor, sit down and sketch out your own “if-then” equation. Remember, there can always be a backup plan. If plan A fails, you have 25 letters left.
• Have more than one source of income. Even if you don’t want to be an entrepreneur or real estate mogul, having regular income from more than one paycheck is often the simplest and best way to reduce risk.
• There’s an old proverb about happiness: if your income exceeds your expenses, you’ll be happy, no matter how much money you make. By the same token, if your expenses exceed your income, you’ll be unhappy, also regardless of how much cash is coming in.
• When your income increases, you tend to spend more—and that’s not necessarily a bad thing. The key is to make sure you don’t spend more than what’s coming in.
• Because relationships are always your greatest asset, take time to regularly evaluate how you can be a better friend and colleague.
• How do you become luckier? An old idea suggests that luck is predictable, and to have more of it, you should take more chances. Let’s modify this a bit: to be luckier, take better chances. Remember, it’s not just a numbers game. It’s about managing your risk by picking the right numbers in the first place.
• Done is better than perfect.
• The point is that breaking out of prison, whether a real one or the career equivalent surrounded by cubicle walls, will force you to think differently and use a varied set of skills and tools.
• Most universities do not award degrees in escapology, and even if your prison is a corporate one, no one will hand you a key to freedom. Just as in prison, you’ll need to make one yourself.
• In working with thousands of people who’ve successfully broken out of prison, I’ve noticed that they are best positioned for success when they focus not just on improving their skills but on improving the right skills.
• They also tend to be acutely aware of two important facts. One is that everyone’s an expert at something.
• Often, the “something” has nothing to do with what you went to school for or even what you’ve been doing for however many years you’ve been working away at a job.
• Everyone has another skill or fountain of knowledge, perhaps hidden away or currently unused, that can be uncovered and developed in pursuit of a different (and more profitable) career goal.
• The second important fact is that if you’re good at one thing, you’re probably good at something else.
• Even though your formal qualifications may not be the most relevant in the search for freedom, you’ve probably gained skills along the way that can be repositioned or redeployed.
• Qualifications show that you can follow directions (good job!). But merely following directions rarely leads to freedom.
• Today, it’s easier than ever to self-learn the skills you need to get ahead in your career.
• Lesson: there’s often more than one way to do a job or obtain the qualifications you need.
• So before worrying about upgrading your skills and adding new ones (more on this in a bit), it pays to understand what your existing skills are.
• Make a list of things you do well.
• Even if your skills aren’t quite as specialized, the key is to make sure you have a simple inventory of them.
• Write down at least one thing you hate doing and aren’t good at.
• Just as breaking out of jail requires us to know our skills to execute a successful prison break, it’s also important to be in touch with our weaknesses.
• Your greatest weakness will probably never become a strength, especially if it’s not something you care about improving.
• Decide for yourself when “D-Day” will be, and do everything you can to work toward it.
• When most people think about “improving their skills,” they think about things like getting better at spreadsheets or practicing irregular verbs in another language. But for the most part, these things won’t help you make big advances in your career.
• If the goal is to break free of the job you hate and move into the job you dream of, you want to make rapid advancement in the right kinds of skills.
• There are two broad categories of these. In your specific field, there are technical skills that relate directly to the work you are hired to do.
• We’ll call these “hard skills”—they aren’t things that most people will learn, but they’re important to what you do.
• Other skills are more universal, or at least widely applicable. We’ll call these “soft skills” because they are abilities that help you no matter what you do in life and work.
• Improving soft skills will make you a better employee, a more attractive job candidate, and a more confident spokesperson for yourself in general. There’s no good reason not to improve them, at least for most of us.
• Improve your writing and speaking ability.
• To improve your writing, remember that all writing is essentially persuasive. Make sure your writing contains a call to action. Ask yourself, “What do I want people to do after reading this?”
• Another hallmark of good writing is simply to be engaging.
• Be succinct and try to keep it interesting, no matter the subject.
• The key is to learn to be more comfortable and natural when speaking in front of others.
• No matter what type of work you do, being able to craft a cogent argument is key.
• The lessons for both writing and speaking are: be persuasive, be interesting, be confident, and get other people on your side.
• Learn to negotiate.
• The art of negotiation is about finding win-win solutions to any problem in or out of the workplace.
• To improve your negotiation skills, consider the classic advice from the poker table. It’s not just about playing well; it’s about knowing what table to play in the first place. Clearly understand what you hope to achieve, as well as what the other party hopes to achieve. Play your cards wisely and save your best bet for when it feels right.
• Improve your ability to follow through and follow up.
• Successful people, no matter their field, are good at following through and following up.
• It’s easy to come up with ideas. Making ideas come to life is where the real value is.
• Writing things down is one of the most basic ways to improve your follow-through and follow-up skills. It’s nearly impossible to remember all the things you’re supposed to, and the mere act of trying to recall everything with total precision can drain your energy. But don’t just write down your action items; you should also give yourself a deadline for actually doing them. Follow-up is useless without the follow-through.
• Become comfortable with useful technology.
• Those who will thrive in the future, in other words, are those who will have the skills to use technology to make their lives better and more productive.
• When escaping from prison—or any job you don’t love—improving your “soft skills” increases your value in the post-prison job market and helps you break into the work you were born to do.
• DON’T JUST BE GOOD—BE SO GOOD THEY CAN’T IGNORE YOU
• To sum up:
• Professionals with marginal skills: undesired.
• Professionals with strong hard skills but poor social skills: needed on a short-term basis, but not always valued over the long run.
• Professionals with strong hard skills and strong social skills: indispensable.
• To be so good they can’t ignore you, focus your efforts on improving soft skills.
• As much as possible, ignore sunk costs.
• Fun as it might be to storm out of the conference room, it’s generally smarter to take your time and plan your escape a bit more deliberately. If you have the choice, use your prison sentence to plan for a better future by upgrading your skills using the strategies you’ve read about in this chapter; then use those skills to tunnel yourself out to freedom.
• “Everyone’s an expert at something” is a good principle to keep in mind, and often the “something” comes as a surprise. But even if you know what your skills are, a skill is only as valuable as the extent to which people will pay you to use it.
• Here’s the core principle: when you’re not sure what your “thing” is—when you don’t know quite where to look to find that job or career that brings you joy, flow, and a good income—the people you talk to every day can help you find it.
• Solving problems of daily life is usually the easiest and most successful approach.
• You can’t go wrong by helping people solve universal, everyday problems like losing weight, getting stronger, saving money, feeling better about themselves, or anything along those lines.
• Solving specific, measurable problems is much better than attempting to create huge behavior change.
• To avoid getting off track, always ask, “Why should people care about this?”
• List five problems you’ve been able to solve for someone.
• As I’ve traveled and met with groups all over the world, I’ve never stopped being amazed by all the different business ideas hatched and new careers created simply by finding ways to be helpful.
• Always do what feels most authentic to you.
• Remember, the whole point is learning what you’re good at that other people want to pay for.
• No matter what type of work you currently do—whether you’re an entrepreneur, a self-employed consultant, or an employee who’s looking for a way to bring in extra cash on the side—if you’re struggling to figure out what it is that you do well and that others will also pay you for, consider giving the 100 Person Project a try.
• People are not always able to clearly express their needs. If you can learn to pay attention to the unstated agenda and the unspoken needs, you’ll build stronger relationships.
• Whether you’re an entrepreneur or an employee, your goal is to meet needs and provide solutions. The more you focus on activities related to this goal, the more successful you’ll be.
• “Develop the habit of being a humble expert. Be interested more in how other people do things than in telling them how you are doing it. Your work will speak for itself.
• Of course luck plays a part in it, but every human being has genius-level talent. There are no chosen ones.
• Remember:
• Joy: what you love to do
• Money: what pays the bills
• Flow: what you’re really good at
• Your first step is to eliminate ideas that don’t bring you joy when you think of them. This should usually be the initial decision-making criterion, because life is short and you certainly don’t want to do something you don’t like.
• You should next eliminate ideas that don’t have real potential to produce income.
• Finally, you should eliminate ideas that you aren’t particularly good at, or where your skills aren’t unique.
• Sometimes you need to think like a CEO, but other times you should think like the janitor, the guy who works throughout the building, knows everyone, and keeps his eyes on the pulse of the business at all times.
• By choosing only five life goals, you’ll be far more invested in achieving them.
• If you’re having trouble finding your unique combination of joy, money, and flow, take a few minutes to consider what you’d like your resumé to look like several years from now. We’ll call this your “resumé from the future.”
• Generally speaking, everyone should have some kind of side hustle.
• Forget about updating your resumé; most dream jobs are found or created through nontraditional methods.
• Real winners won’t hesitate to walk away from an unsuccessful venture.
• The principle: you don’t have to quit your job to start something on the side, and it doesn’t have to take over your life unless you want it to.
• Whether you want to be a full-time business owner or not, everyone should have some income that independently arrives in their bank account, preferably on a recurring basis.
• life?” If you want an outcome different from the one your current path is leading to, somehow you’ll have to find the time. Being too busy may be the new social currency, but the real winners find time to do what matters to them.
• Sell something.
• What can you sell? Look at what other people are buying.
• Provide a consulting service.
• Become a middleman.
• The key to making money as an affiliate lies in leveraging at least one of two market forces: either some kind of technological advantage (better search engine results, for example) or “authority” in the minds of people interested in purchasing something through your referrals.
• Join the sharing economy as a provider.
• Trends and specific services may come and go, but the “sharing economy”—platforms and services that allow ordinary people to rent out things they own when not in use—is here to stay.
• Become a digital landlord.
• If you’re pursuing an entrepreneurial solution instead of just hopping on board an existing service, don’t base the price of your product or service on how much time it takes you to provide it; price it based on the value it offers.
• What matters most is how your customers’ or clients’ lives are improved. Think about that value when choosing a price.
• Go above and beyond to make sure people are satisfied.
• Opportunities are always around us, and when one gold rush door closes, another opens. Walk through the open door!
• TO FIND A GOLD RUSH, DON’T THINK DIFFERENT, THINK BORING
• You need a helpful idea.
• Again, being helpful is the highest value.
• Remember, always strive to meet needs and solve problems. Also, when you find a possible gold rush, strike quickly.
• Begin your experiment even when you aren’t sure what will happen. If it works, you can always improve it. If it doesn’t work, well, you haven’t invested much time and can easily move on.
• The reality is that you don’t usually know which projects will take off and which won’t.
• It may sound obvious, but when choosing between different ideas you’re equally attracted to, choose the idea with more income potential.
• Pricing based on value is always good, but you probably shouldn’t charge a fortune for something you create very quickly.
• once in a while, set aside a dedicated block of hours, typically the majority of a working day, to spend specifically on brainstorming things that can be done to improve or add to your current cash flow.
• But remember the point: side hustles generate disproportionate amounts of satisfaction for relatively little investment of time or effort. They allow you to keep doing what you do best at your day job, while exploring other passions and ambitions during off-hours.
• The bottom line is that a side hustle can be a low-risk, low-commitment way to test the waters for something bigger—and earn some extra money at the same time.
• There’s a classic business principle called “first-mover advantage.” It means that when all other variables are equal, the company or organization that enters a market first will be the default leader. According to this theory, it’s not necessarily impossible to topple a first mover, but the first mover will have a built-in head start.
• Find convergence between what you value and what other people will pay for.
• Start quickly and with a low level of investment.
• Growing a side hustle into a business is actually a lot simpler than it sounds. With any product or service, there are only two ways to grow:
• 1. Sell more to existing customers
• 2. Sell to more customers
• If you don’t invest in yourself, who will?
• If you decide you’re ready to be an investor in You, Inc., there are four areas you need to consider.
• 1. Skills. Improve both the technical aspects of your work (just as Heath set out to learn about the new technology in videography) and your soft skills such as writing, reading, negotiating, and more (see Chapter 4 for more advice on this).
• 2. Connections. Commit to networking and meeting everyone you can (like the debut author who gave Heath his first gig).
• 3. Experiments. Try new things and expose yourself to new places, people, and ideas (as Heath did on his year-long cross-country tour).
• 4. Opportunities. Say yes to business deals and opportunities (Heath said yes to almost every opportunity, even if they weren’t 100 percent related to what he eventually wanted to do).
• “Horizontal progress means copying things that work—going from 1 to n. It’s easy to imagine because we already know what it looks like. Vertical progress means doing new things—going from 0 to 1. It’s harder to imagine because it requires doing something that nobody else has ever done.
• As scary as going out on your own can be, try to resist the temptation to trade one semi-secure situation for another by copying an existing idea or simply joining up with an existing business. Instead, create your own security by going Zero-to-One and building your own small (or not so small) empire from the ground up.
• There’s one surefire way to make a decision if you’re on the fence: you quit your job when your business is making you enough money to live on—not when it’s merely promising and has potential, but when it’s actually providing enough income to pay your bills, even if it’s less than what you make in the job. Until then, resist the urge to quit unless you feel absolutely confident that your budding empire is on track for greater things.
• People tend to assume that starting a successful company requires a ton of skills or experience with technology. But these days that couldn’t be further from the case.
• Remember that the whole point of going into business on your own is so you don’t have to be a slave to anyone else, and in this case “anyone else” also includes technology.
• You don’t need to master every innovation that comes around; you just need to use technology to accomplish your goals.
• Instead of trying to be everywhere, do a good job staying present and engaged with your fans on a couple of networks, and leave it at that.
• Make sure you have a way to get paid.
• I’ve mentioned this elsewhere in the book, but it bears repeating here: you can’t make money if you don’t have a way to get paid.
• You can accept money for your goods or services just by setting up a free account with PayPal, Square, or several other systems.
• The most important thing to remember is to make it as easy as possible for the person who’s sending you money.
• Here’s a crazy idea: it doesn’t matter what you think about any particular payment method but rather what your customers prefer.
• You need some kind of website.
• There are a number of misconceptions about business school, the biggest of which is that earning an MBA will help you learn how to start a business. If you want to be a middle manager in a big corporation, an MBA can be a good choice. The letters stand for “master of business administration,” because what you learn is how to run someone else’s business.
• Let’s get a depressing fact out of the way. Most advice about getting the job of your dreams is highly misleading and even damaging.
• As with most things in life, when trying to find and land your dream job, it’s helpful to consider the other party’s perspective.
• There’s a popular principle from sociology called “the strength of weak ties.” The short version of this principle is that our acquaintances can open more doors to more people, and thus introduce us to more opportunities, than our friends can. This is because we tend to travel in the same circles as our friends, but people we know only casually (“weak ties”) tend to have much different networks of friends and other acquaintances.
• Success isn’t found completely in persistence; it’s found in working hard and smart. Try, try again, sure—but try again in a strategic manner.
• What matters is figuring out who your people are and where they hang out.
• A good writer seeks to build connection with the reader, and it starts with considering that person on the other side of the screen or page.
• WORKING SMARTER DOESN’T MEAN NOT WORKING HARD
• even if you’re earning a steady paycheck, you are essentially self-employed in terms of being responsible for your own career. Therefore, you should continually build your skills and look out for yourself. This is important for two big reasons: first, to safeguard your current position, and second, as a means of advancement.
• When your team, company, or business simply can’t function without you, you’ll have the best bargaining chip in the world when you come to your boss asking him to let you design your lottery-winning role.
• To be indispensable, be the busy person who gets things done—and keeps other things on track for the rest of the group.
• It’s hard to remove unproductive work completely, as there will always be a certain amount of unavoidable slippage in our daily tasks and meetings. Most unproductive work, however, is simply the result of a common bad habit of people taking on work to make themselves look good or enhance their stature without adding any real value.
• Economists refer to activities that seek to transfer wealth without increasing value as “rent-seeking.”
• Don’t just be a hard worker; find a way to boost profits or otherwise draw a direct connection between your efforts and the greater success of the organization.
• No matter your industry or employer, ask these questions about the business and consider what you can do to support positive change:
• Will people still want our products and services in five years?
• How can our business continue to be relevant in changing times?
• What can we do to build for the future?
• You want to be reliable and indispensable. The real heroes, though, are able to see the bigger picture and come up with solutions that are truly good for everyone involved.
• Be sure you can do everything in your current portfolio with excellence before raising your hand to volunteer for a ton of other stuff.
• A sabbatical can be a great way to reboot and recharge, so you come back to work energized and raring to go. It can also be a great opportunity to reflect on what’s not working for you in your current role, gain some perspective, and maybe do some experimentation to figure out what changes you can make to turn your current job into the work you were born to do.
• Almost without exception, those who succeed in the new economy share four specific characteristics: a body of work (product), a group of fans (audience), a means of sharing the body of work (platform), and a way of getting paid for their work (money).
• ALWAYS FOCUS ON MAKING SOMETHING THAT MATTERS
• If you get a few big things right, in other words, you can get a lot of small things wrong.
• Remember, there’s no set formula for what a career should look like, and we’re all making it up as we go along.
• The point is that remaining in paralysis is often worse than making any actionable choice. Like it or not, by refusing to make a choice, you’ve already made a choice to do nothing. And doing something—or several things, even if they turn out to be the wrong thing or things—is almost always better than doing nothing at all. Even when you feel paralyzed and indecisive, you must find a way to take action and move forward.
• commitment.” A barrier is something that discourages certain behavior, either positive or negative.
• A pre-commitment is the logical extension of a barrier. With pre-commitment, you create the conditions in advance to lead to your desired behavior or outcome.
• Sometimes the oddest pairings end up being the most lucrative business ideas
• “Never give up” is bad advice. Real winners won’t hesitate to walk away from an unsuccessful venture. Master the art of moving on by learning when to quit and when to keep going.
• The real secret is that selective quitting is a powerful practice—you just need to learn when to give up and when to keep going.
• When the Stakes Are Low, Make Changes or Give Up Quickly
• Don’t waste time on small things, and when the stakes are low, make changes right away.
• if you want to be successful, you can’t live your life out of fear.
• Ignore “Sunk Costs” as Much as Possible
• When the stakes are high and you need to choose whether to give up on any project or course of action, ask yourself these two basic questions:
• 1. Is it working?
• 2. Do you still enjoy it?
• GIVE UP THE NEED FOR PETTY CONTROL OF USELESS THINGS.
• You don’t need to be copied on every email or informed of every decision. If the right things are happening, don’t interrupt the flow.
• If you try to do everything, inevitably you’ll fall behind.
• Just as regular deposits in your savings account or retirement plan will grow over time, so too will regular investments in your relationship bank accounts.
• Contrary to popular belief, if you want to win, you shouldn’t always just keep going. You should regroup and try something totally different.
• “Winners never quit, and quitters never win” is a lie. To win, sometimes you need to find a new game to play.
• There’s more than one possible path. Use the Joy-Money-Flow model to find the best one.
• Craft backup plans. They will allow you to take more risks and make better choices.
• Make a commitment to resign your job every year.
• Improving “soft skills” can increase your value no matter what kind of career you have.
• Stop storing things in your head.
• Choose your own job title.
• Hack your job to create the best possible working conditions.
• Create a “side hustle” even if you never plan to work on your own full-time.
• Don’t fear commitment.
• If something isn’t working, give up.

The Early History of Smalltalk by Alan Kay

The Early History of Smalltalk

• Most ideas come from previous ideas.
•  Programming languages can be categorized in a number of ways: imperative, applicative, logic-based, problem-oriented, etc. But they all seem to be either an "agglutination of features" or a "crystallization of style".
• It is probably not an accident that the agglutinative languages all seem to have been instigated by committees, and the crystallization languages by a single person.
• Smalltalk's design--and existence--is due to the insight that everything we can describe can be represented by the recursive composition of a single kind of behavioral building block that hides its combination of state and process inside itself and can be dealt with only though the exchange of messages.
• Programming is at heart a practical art in which real things are build, and a real implementation thus has to exist.
• In fact many if not most languages are in use today not because they have any real merits but because of their existence on one or more machines, their ability to be bootstrapped, etc.
• Though OOP came from many motivations, two were central. The large scale one was to find a better module scheme for complex systems involving hiding of details, and the small scale one was to find a more flexible version of assignment, and then to try to eliminate it altogether.
• New ideas go through stages of acceptance, both from within and without. From within, the sequence moves from "barely seeing" a pattern several times, then noting it but not perceiving its "cosmic" significance, then using it operationally in several areas, then comes a "gran rotation" in which the pattern become the center of a new way of thinking, and finally, it turns into the same kind of inflexible religion that it originally broke away from. From without, as Schopenhauer noted, the new idea is first denounced as the work of the insane, in a few years it is considered obvious and mundane, and finally the original denouncers will claim to have invented it.
• The basic principle of recursive design is to make the parts have the same power as the whole.
• Of course, philosophy is about opinion and engineering is about deeds, with science the happy medium somewhere in between.
• An extensional system seemed to be called for in which the end-users would do most of the tailoring (and even some of the direct construction) of their tools.
• One of the basic insights I had gotten from Seymour was that you didn't have to do a lot to make a computer an "object for thought" for children, but what you did had to be done well and be able to apply deeply.
• Point of view is worth 80 IQ points.
• Any tools for children should have great thinking patterns and deep beauty "built-in".
• The best way to predict the future is to invent it. Don't worry about what all those other people might do, this is the century in which almost any clear vision can be made!
• As I mentioned previously, it was annoying that the surface beauty of LISP was marred by some of its key parts having to be introduced as "special forms" rather than as its supposed universal building block of functions. The actual beauty of LISP came more from the promise of its metastructures than its actual model.
• When we turn to the various languages for specifying computations we find many to be general and a few to be parsimonious. For example, we can define universal machine languages in just a few instructions that can specify anything that can be computed. But most of these we would not call beautiful, in part because the amount and kind of code that has to be written to do anything interesting is so contrived and turgid. A simple and small system that can do interesting things also needs a "high slope"--that is a good match between the degree of interestingness and the level of complexity needed to express it.
• You just do it and it's done.
• Main ideas of Smalltalk
• Everything is an object.
• Objects communicate by sending and receiving messages (in terms of objects).
• Objects have their own memory (in terms of objects).
• Every object is an instance of a class (which must be an object).
• The class holds the shared behavior for its instances (in the form of objects in a program list).
• To eval a program list, control is passed to the first object and the remainder is treated as its message.
• Of course, the whole idea of Smalltalk (and OOP in general) is to define everything intentionally.
• It is unfortunate that much of what is called "object-oriented programming" today is simply old style programming with fancier constructs. Many programs are loaded with "assignment-style" operations now done by more expensive attached procedures.
• Where does the special efficiency of object-oriented design come from? This is a good question given that it can be viewed as a slightly different way to apply procedures to data-structures. Part of the effect comes from a much clearer way to represent a complex system. Here, the constraints are as useful as the generalities. Four techniques used together--persistent state, polymorphism, instantiation, and methods-as-goals for the object--account for much of the power. None of these require an "object-oriented" languages to be employed--ALGOL 68 can almost be turned to this style--on OOPL merely focuses the designer's mind in a particular fruitful direction.
• Doing encapsulation right is a commitment not just to abstraction of state, but to eliminate state oriented metaphors from programming.
• Perhaps the most important principle--again derived from operating system architectures--is that when you give someone a structure, rarely do you want them to have unlimited privileges with it.
• In other words, human programmers aren't Turing machines--and the less their programming systems require Turing machine techniques the better.
• You should teach a programming language holistically from working examples of serious programs.
• In part, what we were seeing was the "hacker phenomenon", that, for any given pursuit, a particular 5% of the population will jump into it naturally, while the 80% or so who can learn it in time do not find it at all natural.
• It helps greatly to have some powerful ideas under one's belt to better acquire more powerful ideas.
• It is hard to claim success if only some of the children are successful--and if a maximum effort of both children and teachers was required to get the success to happen. Real pedagogy has to work in much less idealistic settings and be considerably more robust.
• Unfortunately, inheritance--though an incredibly powerful technique--has turned out to be very difficult for novices (and even professional) to deal with.
• The problem is not to get the kids to do stuff--they love to do, even when they are not sure exactly what they are doing. [...] What is difficult is to determine what ideas to put forth and how deeply they should penetrate at a given child's development level.
• When, in what order and depth, and how should the powerful ideas be taught?
• I have met hundreds of programmers in the last 30 years and can see no discernible influence of programming on their general ability to think well or to take an enlightened stance on human knowledge. If anything, the opposite is true. Expert knowledge often remains rooted in the environments in which it was first learned--and most metaphorical extensions result in misleading analogies.
• Tools provide a path, a context, and almost an excuse for developing enlightenment, but no tool ever contained it or can dispense it.
• Cesare Pavese observed: to know the world we must construct it. In other words, we make not just to have, but to know. But the having can happen without most of the knowing taking place.
• Our society has lowered its aims so far that it is happy with "increases in [test] scores" without daring to inquire whether any important threshold has been crossed.
• At the liberal arts level we would expect that connections between each of the fluencies would form truly powerful metaphors for considering ideas in the light of others.
• The reason that many of us want children to understand computing deeply and fluently is that like literature, mathematics, science, music and art, it carries special ways of thinking about situations that in contrast with other knowledge and other ways of thinking critically boost our ability to understand our world.
• Strong paradigms like LISP and Smalltalk are so compelling that they eat their young: when you look at an application in either of these two systems, they resemble the systems themselves, not a new idea.
• Hardware is really just software crystallized early. It is there to make program schemes run as efficiently as possible. But far too often the hardware has been presented as a given and it is up to software designers to make it appear reasonable. This has caused low-level techniques and excessive optimization to hold back progress in program design.
• Systems programmers are high priests of a low cult.
• One way to think about progress in software is that a lot of it has been about finding ways to late-bind, then waging campaigns to convince manufactures to build the ideas into hardware.
• Most machines till have no support for dynamic allocation and garbage collection and so forth. In short, most hardware designs today are just re-optimizations of moribund architectures.
• From the late-binding perspective, OOP can be viewed as a comprehensive technique for late-binding as many things as possible: the mix of state and process in a set of behaviors, where they are located, what they are called, when and why they are invoked, which hardware is used, etc., and more subtle, the strategies used in the OOP scheme itself.
• Again, the whole point of OOP is not to have to worry about what is inside an object.
• Objects made on different machines and with different languages should be able to talk to each other--and will have to in the future.
• A twentieth century problem is that technology has become too "easy". When it was hard to do anything whether good or bad, enough time was taken so that the result was usually good. Now we can make things almost trivially, especially in software, but most of the designs are trivial as well. This is inverse vandalism: the making of things because you can.

How to Become a Great Boss

• Great bosses are always great leaders. But great leaders, can be frightful bosses, terrible managers.
• The great boss makes people believe in themselves and feel special, selected, anointed. The great boss makes people feel good.
• The Great Boss Simple Success Formula
• Only hire top-notch, excellent people.
• Put the right people in the right job. Weed out the wrong people.
• Tell the people what needs to be done.
• Tell the people why it is needed.
• Leave the job up to the people you've chosen to do it.
• Train the people.
• Listen to the people.
• Remove frustration and barriers that fetter the people.
• Inspect progress.
• Say "Thank you" publicly and privately.
• Companies do what the boss does.
• Great bosses position the organization to succeed, not with politics, but with posture and presence.
• Because the company does what the boss does, the boss better perform, or the company won't.
• It is the customer's money that funds paychecks, bonuses, health insurance, taxes, and everything else. Because it is the customer who pays the employees, then the employees--all employees, including the boss--work for the customer. Therefore, every single job in the company must be designed to get or keep customers. Without exception!
• If there is a job that does not directly get or keep a customer, that job is redundant and should eliminated or outsourced.
• A responsibility of the great boss is to teach the employees how to get and keep customers.
• The customer is the real boss. And the dissatisfied customer fires employees every day.
• One of the biggest macro problems is that environments change and companies do not.
• Great companies and great bosses are constantly training, teaching, improving, and growing their employees.
• If an employee can't or won't generate a positive return on all the investments made in the employee, then the employee must go.
• The careful boss listens and observes before making any decisions about people.
• Mediocrity is an insidious disease that saps the vitality, innovation, and energy of any organization.
• Once mediocrity infects an organization, it is extremely difficult to cure.
• Mediocrity starts when weak managers hire even weaker employees.
• Tolerating mediocrity is management malpractice.
• The cost of a mishire goes up as responsibility level of the hired person goes up.
• To reduce mishiring costs, hire slowly and with care.
• The fact is that no matter how careful the hiring process, how glittering the recommendations, or how rich the resume, you will never really know if you have hired correctly until the person has been on the job awhile.
• If you have made a hiring mistake, fix the mistake fast.
• Treat people the way you would wish to be treated. People understand reality. Treat people with dignity, and even the most difficult of circumstances goes better.
• Getting good people into an organization, and keeping mediocre people out, is absolutely critical to success.
• The right ability plus the right attitude adds up to an A player. A players are winners. They are smart, savvy, and get the job done. They are motivated and hardworking. A players have a nose for the goal line, and they go for it.
• Only hire A players or people with A potential. Never hire a C or D player.
• You can groom an A- player to an A. You can make a B+ player an A. But you can never make a C player a B or an A. Never.
• A players usually cost more, but they deliver more.
• A players are often more difficult to manage because they have lots of energy and move fast and don't wait for the organization to catch up. A players need to be challenged, so the great boss gives them challenges.
• Ability plus attitude: the more of each the better.
• When the boss understands the root cause of an employees performance problem, he or she can begin an action plan to remedy the situation.
• Great bosses learn from mistakes.
• Have principles. Live them. Teach them. Keep them.
• Solid principles are to a boss as a compass is to a sailor.
• The great boss remembers his or her roots and remembers who helped along the way. Never forget that your success was not earned by you alone.
• The hiring moment is the time for employer and employee to clarify employment issues and conditions.
• The great boss eliminates future problems on the day of hiring. Be exceptionally clear on compensation, benefits, work product, hours, company culture, and behavior. Be certain the new employee understands with concomitant clarity.
• If you are delegating without without clear direction or without providing appropriate training, you are not delegating, you are relegating--relegating the employee to error making and misperformance.
• Give the task, job, or project to the least senior (possibly the least paid) person who can do the job properly.
• Don't let employees delegate to you the decisions they are responsible for making.
• If you hire an employee to do a job, train the employee properly, and let the person do the job.
• Don't meddle with how someone is doing his or her job.
• Delegation is about trusting the experts expertise.
• Good people in lean companies are busy.
• The great boss gets what he or she inspects, not what he or she expects.
• When in a meeting with an employee, or employees, pay attention.
• Employees know when you are not paying attention.
• Listen to what everyone says. Everyone has experience.
• The great boss makes sure everyone keeps every promise.
• The cost of broken promises is insidious and enormous.
• Successful organizations keep their promises.
• Your employees must know that they can freely tell you what you have to hear, not what you want to hear.
• One goal of the great boss is to teach people how to think for themselves, to stand by themselves.
• The great boss is not afraid to not know everything, or to not know something.
• Challenging good and able people to perform is sometimes as simple as asking a question.
• Seven common words ("I don't know. What do you think?")--and the courage, self-assurance, and modesty to use them--make for uncommon wisdom.
• The great boss encourages food-based mini-celebrations.
• Smart shooters don't shoot from the hip. Smart bosses don't shoot from the lip.
• Heed what you say. Heed how you say it. Your words carry weight; speak with discretion.
• To an employee, a boss's whisper is like a lion's roar.
• Surprise bonuses are most appreciated and long remembered.
• Be mentally tough. Be emotionally tough. Make the tough decisions. You can care and be tough. You can be tough and nice at the same time.
• Bullies, tyrants, autocrats, ranters, and ravers are weak. Their authority is a function of job position, not personal character.
• To belittle someone is to be little. Don't belittle; be big.
• The great boss cares only about the quality of the idea, not the source of the idea.
• Listen. Consider. Decide. Then do what you think is best.
• People with honor don't need an honor code. People without honor won't heed an honor code.
• If someone falsifies expenses, fire that person.
• Having to check expense reports means you have the wrong people.
• Being lucky is an outcome of thinking, research, listening, preparation, and taking reasoned chances.
• Being lucky is a function of doing things: not just talking about, but actually picking up the shovel to start digging, or picking up the pen to start writing, or picking up the sales literature to start selling.
• The great boss is friendly, but not a friend.
• The great boss does not quit and does not let the organization quit. They may lose, but they don't quit.
• Every boss is measured on the combined output of his or her people.
• The great boss understands his or her ascent is a function of the output and contribution of good and able employees. The great boss also knows that a de-motivated, demoralized, disorganized workforce will pull him down. The great boss appreciates and pays close attention to this crucial source of energy.
• The great boss, although lifted by his employees, never looks down on them. To do so is to soar no more.
• Spend your supervisory time with your best people.
• Taking personal accountability is such an increasingly rare phenomena that it is a point of difference.
• The boss who takes responsibility stands out.
• Employees respect a boss who takes responsibility and gives credit. Employees doubly respects a boss who takes responsibility to protect someone else and who is overly generous with credit.
• The great boss takes public responsibility when he or she errs or when the team makes a mistake. The great boss also gives public credit to the employee for any success.
• Teach or train something to someone everyday.
• Teaching and training is part of the continuous grooming that improves the employee and strengthens the company.
• Teaching generates a high return on a low investment.
• The bigger the "policy and procedures" manual the duller the company.
• Innovation and entrepreneurship go down as the number of policies goes up.
• The best policy is to get the job done, and get the job done well.
• Weak bosses hide behind policies. Great bosses are leery of policy.
• Great bosses don't make policy; they make performance possible.
• Abundant policies are a warning signal that the company is hiring weak people, people who can't think for themselves.
• The great boss protects his or her good people.
• Quirky bosses break the stereotyped mold of the buttoned-up executive. They signal to their organization that it is okay to be different; that it's okay to be nonconforming and nontraditional.
• People want energetic, vigorous, go-getting bosses.

Oblique Strategies

• Abandon normal instruments
• Accept advice
• Accretion
• A line has two sides
• Allow an easement (an easement is the abandonment of a stricture)
• Always first steps
• Always give yourself credit for having more than personality
• Are there sections? Consider transitions
• Ask people to work against their better judgement
• Ask your body
• Assemble some of the elements in a group and treat the group
• A very small object. Its center
• Balance the consistency principle with the inconsistency principle
• Be dirty
• Be extravagant
• Be less critical more often
• [blank white card]
• Breathe more deeply
• Bridges -build -burn
• Cascades
• Change instrument roles
• Change nothing and continue with immaculate consistency
• Children -speaking -singing
• Cluster analysis
• Consider different fading systems
• Consult other sources -promising -unpromising
• Convert a melodic element into a rhythmic element
• Courage!
• Cut a vital connection
• Decorate, decorate
• Define an area as `safe' and use it as an anchor
• Destroy -nothing -the most important thing
• Discard an axiom
• Disciplined self-indulgence
• Disconnect from desire
• Discover the recipes you are using and abandon them
• Distorting time
• Do nothing for as long as possible
• Don't be afraid of things because they're easy to do
• Don't be frightened of cliches
• Don't be frightened to display your talents
• Don't break the silence
• Don't stress one thing more than another
• Do something boring
• Do the washing up
• Do the words need changing?
• Do we need holes?
• Emphasize differences
• Emphasize repetitions
• Emphasize the flaws
• Faced with a choice, do both
• Feed the recording back out of the medium
• Fill every beat with something
• From nothing to more than nothing
• Get your neck massaged
• Ghost echoes
• Give the game away
• Give way to your worst impulse
• Go outside. Shut the door.
• Go slowly all the way round the outside
• Go to an extreme, move back to a more comfortable place
• Honor thy error as a hidden intention
• How would you have done it?
• Humanize something free of error
• Idiot glee (?)
• Imagine the piece as a set of disconnected events
• Infinitesimal gradations
• Intentions -nobility of -humility of -credibility of
• In total darkness, or in a very large room, very quietly
• Into the impossible
• Is it finished?
• Is the intonation correct?
• Is there something missing?
• It is quite possible (after all)
• Just carry on
• Left channel, right channel, centre channel
• Listen to the quiet voice
• Look at the order in which you do things
• Look closely at the most embarrassing details and amplify them
• Lost in useless territory
• Lowest common denominator
• Make a blank valuable by putting it in an exquisite frame
• Make an exhaustive list of everything you might do and do the last thing on the list
• Make a sudden, destructive unpredictable action; incorporate
• Mechanicalize something idiosyncratic
• Mute and continue
• Not building a wall but making a brick
• Once the search is in progress, something will be found
• Only a part, not the whole
• Only one element of each kind
• (Organic) machinery
• Overtly resist change
• Put in earplugs
• Question the heroic approach
• Remember those quiet evenings
• Remove ambiguities and convert to specifics
• Remove specifics and convert to ambiguities
• Repetition is a form of change
• Retrace your steps
• Revaluation (a warm feeling)
• Reverse
• Short circuit (If eating peas improves virility, shovel them into your pants)
• Simple subtraction
• Simply a matter of work
• Spectrum analysis
• State the problem in words as clearly as possible
• Take a break
• Take away the elements in order of apparent non-importance
• Tape your mouth
• The inconsistency principle
• The most important thing is the thing most easily forgotten
• The tape is now the music
• Think of the radio
• Tidy up
• Towards the insignificant
• Trust in the you of now
• Turn it upside down
• Twist the spine
• Use an old idea
• Use an unacceptable color
• Use fewer notes
• Use filters
• Use `unqualified' people
• Water
• What are the sections sections of? Imagine a caterpillar moving
• What are you really thinking about just now?
• What is the reality of the situation?
• What mistakes did you make last time?
• What wouldn't you do?
• What would your closest friend do?
• Work at a different speed
• Would anybody want it?
• You are an engineer
• You can only make one dot at a time
• You don't have to be ashamed of using your own ideas

THINKING IN SYSTEMS: A PRIMER by Donella H. Meadows, Diana Wright

• Systems, big or small, can behave in similar ways, and understanding those ways is perhaps our best hope for making lasting change on many levels.
• Once we see the relationship between structure and behavior, we can begin to understand how systems work, what makes them produce poor results, and how to shift them into better behavior patterns.
• A system is a set of things—people, cells, molecules, or whatever—interconnected in such a way that they produce their own pattern of behavior over time.
• The system, to a large extent, causes its own behavior!
• Every person we encounter, every organization, every animal, garden, tree, and forest is a complex system.
• The behavior of a system cannot be known just by knowing the elements of which the system is made.
• A system isn’t just any old collection of things. A system* is an interconnected set of elements that is coherently organized in a way that achieves something. If you look at that definition closely for a minute, you can see that a system must consist of three kinds of things: elements, interconnections, and a function or purpose.
• Systems can be embedded in systems, which are embedded in yet other systems.
• A system is more than the sum of its parts. It may exhibit adaptive, dynamic, goal-seeking, self-preserving, and sometimes evolutionary behavior.
• Many of the interconnections in systems operate through the flow of information. Information holds systems together and plays a great role in determining how they operate.
• A system’s function or purpose is not necessarily spoken, written, or expressed explicitly, except through the operation of the system.
• The best way to deduce the system’s purpose is to watch for a while to see how the system behaves.
• An important function of almost every system is to ensure its own perpetuation.
• Changing elements usually has the least effect on the system.
• The least obvious part of the system, its function or purpose, is often the most crucial determinant of the system’s behavior.
• A stock is the memory of the history of changing flows within the system.
• If you understand the dynamics of stocks and flows—their behavior over time—you understand a good deal about the behavior of complex systems.
• All models, whether mental models or mathematical models, are simplifications of the real world.
• A stock can be increased by decreasing its outflow rate as well as by increasing its inflow rate.
• A stock takes time to change, because flows take time to flow. That’s a vital point, a key to understanding why systems behave as they do. Stocks usually change slowly.
• Stocks generally change slowly, even when the flows into or out of them change suddenly. Therefore, stocks act as delays or buffers or shock absorbers in systems.
• Stocks allow inflows and outflows to be decoupled and to be independent and temporarily out of balance with each other.
• Human beings have invented hundreds of stock-maintaining mechanisms to make inflows and outflows independent and stable.
• Most individual and institutional decisions are designed to regulate the levels in stocks. If inventories rise too high, then prices are cut or advertising budgets are increased, so that sales will go up and inventories will fall.
• Systems thinkers see the world as a collection of stocks along with the mechanisms for regulating the levels in the stocks by manipulating flows. That means system thinkers see the world as a collection of “feedback processes.”
• When a stock grows by leaps and bounds or declines swiftly or is held within a certain range no matter what else is going on around it, it is likely that there is a control mechanism at work. In other words, if you see a behavior that persists over time, there is likely a mechanism creating that consistent behavior. That mechanism operates through a feedback loop. It is the consistent behavior pattern over a long period of time that is the first hint of the existence of a feedback loop.
• A feedback loop is formed when changes in a stock affect the flows into or out of that same stock. A feedback loop can be quite simple and direct.
• Not all systems have feedback loops. Some systems are relatively simple open-ended chains of stocks and flows. The chain may be affected by outside factors, but the levels of the chain’s stocks don’t affect its flows.
• A feedback loop is a closed chain of causal connections from a stock, through a set of decisions or rules or physical laws or actions that are dependent on the level of the stock, and back again through a flow to change the stock.
• Remember—all system diagrams are simplifications of the real world.
• Balancing feedback loops are goal-seeking or stability-seeking.
• A balancing feedback loop opposes whatever direction of change is imposed on the system.
• Balancing feedback loops are equilibrating or goal-seeking structures in systems and are both sources of stability and sources of resistance to change.
• The second kind of feedback loop is amplifying, reinforcing, self-multiplying, snowballing—a vicious or virtuous circle that can cause healthy growth or runaway destruction. It is called a reinforcing feedback loop,
• A reinforcing feedback loop enhances whatever direction of change is imposed on it.
• Reinforcing loops are found wherever a system element has the ability to reproduce itself or to grow as a constant fraction of itself.
• Reinforcing feedback loops are self-enhancing, leading to exponential growth or to runaway collapses over time. They are found whenever a stock has the capacity to reinforce or reproduce itself.
• Because we bump into reinforcing loops so often, it is handy to know this shortcut: The time it takes for an exponentially growing stock to double in size, the “doubling time,” equals approximately 70 divided by the growth rate (expressed as a percentage).
• One good way to learn something new is through specific examples rather than abstractions and generalities,
• The information delivered by a feedback loop can only affect future behavior; it can’t deliver the information, and so can’t have an impact fast enough to correct behavior that drove the current feedback.
• The information delivered by a feedback loop—even nonphysical feedback—can only affect future behavior; it can’t deliver a signal fast enough to correct behavior that drove the current feedback. Even nonphysical information takes time to feedback into the system.
• The specific principle you can deduce from this simple system is that you must remember in thermostat-like systems to take into account whatever draining or filling processes are going on. If you don’t, you won’t achieve the target level of your stock.
• A stock-maintaining balancing feedback loop must have its goal set appropriately to compensate for draining or inflowing processes that affect that stock. Otherwise, the feedback process will fall short of or exceed the target for the stock.
• Dominance is an important concept in systems thinking. When one loop dominates another, it has a stronger impact on behavior. Because systems often have several competing feedback loops operating simultaneously, those loops that dominate the system will determine the behavior.
• Complex behaviors of systems often arise as the relative strengths of feedback loops shift, causing first one loop and then another to dominate behavior.
• System dynamics models explore possible futures and ask “what if” questions.
• Model utility depends not on whether its driving scenarios are realistic (since no one can know that for sure), but on whether it responds with a realistic pattern of behavior.
• Physical capital is drained by depreciation—obsolescence and wearing out.
• Systems with similar feedback structures produce similar dynamic behaviors.
• A delay in a balancing feedback loop makes a system likely to oscillate.
• Delays are pervasive in systems, and they are strong determinants of behavior. Changing the length of a delay may (or may not, depending on the type of delay and the relative lengths of other delays) make a large change in the behavior of a system.
• Economies are extremely complex systems; they are full of balancing feedback loops with delays, and they are inherently oscillatory.
• But any real physical entity is always surrounded by and exchanging things with its environment.
• Therefore, any physical, growing system is going to run into some kind of constraint, sooner or later. That constraint will take the form of a balancing loop that in some way shifts the dominance of the reinforcing loop driving the growth behavior, either by strengthening the outflow or by weakening the inflow.
• In physical, exponentially growing systems, there must be at least one reinforcing loop driving the growth and at least one balancing loop constraining the growth, because no physical system can grow forever in a finite environment.
• Profit is income minus cost.
• A quantity growing exponentially toward a constraint or limit reaches that limit in a surprisingly short time.
• Nonrenewable resources are stock-limited. The entire stock is available at once, and can be extracted at any rate (limited mainly by extraction capital). But since the stock is not renewed, the faster the extraction rate, the shorter the lifetime of the resource.
• Renewable resources are flowlimited. They can support extraction or harvest indefinitely, but only at a finite flow rate equal to their regeneration rate. If they are extracted faster than they regenerate, they may eventually be driven below a critical threshold and become, for all practical purposes, nonrenewable.
• If pushed too far, systems may well fall apart or exhibit heretofore unobserved behavior. But, by and large, they manage quite well.
• Placing a system in a straitjacket of constancy can cause fragility to evolve.
• Resilience is a measure of a system’s ability to survive and persist within a variable environment. The opposite of resilience is brittleness or rigidity.
• There are always limits to resilience.
• Systems need to be managed not only for productivity or stability, they also need to be managed for resilience—the ability to recover from perturbation, the ability to restore or repair themselves.
• Awareness of resilience enables one to see many ways to preserve or enhance a system’s own restorative powers.
• This capacity of a system to make its own structure more complex is called self-organization
• Self-organization produces heterogeneity and unpredictability. It is likely come up with whole new structures, whole new ways of doing things. It requires freedom and experimentation, and a certain amount of disorder. These conditions that encourage self-organization often can be scary for individuals and threatening to power structures.
• Systems often have the property of self-organization—the ability to structure themselves, to create new structure, to learn, diversify, and complexify. Even complex forms of self-organization may arise from relatively simple organizing rules—or may not.
• Science knows now that self-organizing systems can arise from simple rules.
• Complex systems can evolve from simple systems only if there are stable intermediate forms.
• When a subsystem’s goals dominate at the expense of the total system’s goals, the resulting behavior is called suboptimization.
• Hierarchical systems evolve from the bottom up. The purpose of the upper layers of the hierarchy is to serve the purposes of the lower layers.
• Everything we think we know about the world is a model.
• Our models usually have a strong congruence with the world.
• We can improve our understanding, but we can’t make it perfect.
• Everything we think we know about the world is a model. Our models do have a strong congruence with the world. Our models fall far short of representing the real world fully.
• The behavior of a system is its performance over time—its growth, stagnation, decline, oscillation, randomness, or evolution.
• When a systems thinker encounters a problem, the first thing he or she does is look for data, time graphs, the history of the system. That’s because long term behavior provides clues to the underlying system structure. And structure is the key to understanding not just what is happening, but why.
• The structure of a system is its interlocking stocks, flows, and feedback loops.
• System structure is the source of system behavior. System behavior reveals itself as a series of events over time.
• A linear relationship between two elements in a system can be drawn on a graph with a straight line. It’s a relationship with constant proportions.
• A nonlinear relationship is one in which the cause does not produce a proportional effect. The relationship between cause and effect can only be drawn with curves or wiggles, not with a straight line.
• Nonlinearities are important not only because they confound our expectations about the relationship between action and response. They are even more important because they change the relative strengths of feedback loops. They can flip a system from one mode of behavior to another.
• Many relationships in systems are nonlinear. Their relative strengths shift in disproportionate amounts as the stocks in the system shift. Nonlinearities in feedback systems produce shifting dominance of loops and many complexities in system behavior.
• Everything, as they say, is connected to everything else, and not neatly. There is no clearly determinable boundary between the sea and the land, between sociology and anthropology, between an automobile’s exhaust and your nose. There are only boundaries of word, thought, perception, and social agreement—artificial, mental-model boundaries.
• The greatest complexities arise exactly at boundaries.
• Everything physical comes from somewhere, everything goes somewhere, everything keeps moving.
• The lesson of boundaries is hard even for systems thinkers to get. There is no single, legitimate boundary to draw around a system. We have to invent boundaries for clarity and sanity; and boundaries can produce problems when we forget that we’ve artificially created them.
• There are no separate systems. The world is a continuum. Where to draw a boundary around a system depends on the purpose of the discussion—the questions we want to ask.
• The right boundary for thinking about a problem rarely coincides with the boundary of an academic discipline, or with a political boundary.
• It’s a great art to remember that boundaries are of our own making, and that they can and should be reconsidered for each new discussion, problem, or purpose.
• Systems surprise us because our minds like to think about single causes neatly producing single effects. We like to think about one or at most a few things at a time. And we don’t like, especially when our own plans and desires are involved, to think about limits. But we live in a world in which many causes routinely come together to produce many effects. Multiple inputs produce multiple outputs, and virtually all of the inputs, and therefore outputs, are limited.
• At any given time, the input that is most important to a system is the one that is most limiting.
• Insight comes not only from recognizing which factor is limiting, but from seeing that growth itself depletes or enhances limits and therefore changes what is limiting.
• Any physical entity with multiple inputs and outputs—a population, a production process, an economy—is surrounded by layers of limits.
• Any physical entity with multiple inputs and outputs is surrounded by layers of limits.
• No physical entity can grow forever.
• There always will be limits to growth. They can be self-imposed. If they aren’t, they will be system-imposed.
• Delays are ubiquitous in systems.
• Changing the length of a delay may utterly change behavior.
• Delays determine how fast systems can react, how accurately they hit their targets, and how timely is the information passed around a system.
• When there are long delays in feedback loops, some sort of foresight is essential. To act only when a problem becomes obvious is to miss an important opportunity to solve the problem.
• Bounded rationality means that people make quite reasonable decisions based on the information they have. But they don’t have perfect information, especially about more distant parts of the system.
• We are not omniscient, rational optimizers, says Simon. Rather, we are blundering “satisficers,” attempting to meet (satisfy) our needs well enough (sufficiently) before moving on to the next decision.11 We do our best to further our own nearby interests in a rational way, but we can take into account only what we know. We don’t know what others are planning to do, until they do it. We rarely see the full range of possibilities before us.
• We often don’t foresee (or choose to ignore) the impacts of our actions on the whole system. So instead of finding a long term optimum, we discover within our limited purview a choice we can live with for now, and we stick to it, changing our behavior only when forced to.
• We misperceive risk, assuming that some things are much more dangerous than they really are and others much less. We live in an exaggerated present—we pay too much attention to recent experience and too little attention to the past, focusing on current events rather than long term behavior. We discount the future at rates that make no economic or ecological sense. We don’t give all incoming signals their appropriate weights. We don’t let in at all news we don’t like, or information that doesn’t fit our mental models. Which is to say, we don’t even make decisions that optimize our own individual good, much less the good of the system as a whole.
• If you become a manager, you probably will stop seeing labor as a deserving partner in production, and start seeing it as a cost to be minimized.
• Seeing how individual decisions are rational within the bounds of the information available does not provide an excuse for narrow-minded behavior.
• Change comes first from stepping outside the limited information that can be seen from any single place in the system and getting an overview. From a wider perspective, information flows, goals, incentives, and disincentives can be restructured so that separate, bounded, rational actions do add up to results that everyone desires.
• It’s amazing how quickly and easily behavior changes can come, with even slight enlargement of bounded rationality, by providing better, more complete, timelier information.
• The bounded rationality of each actor in a system may not lead to decisions that further the welfare of the system as a whole.
• The world is nonlinear. Trying to make it linear for our mathematical or administrative convenience is not usually a good idea even when feasible, and it is rarely feasible.
• Balancing loops stabilize systems; behavior patterns persist.
• Policy resistance comes from the bounded rationalities of the actors in a system, each with his or her (or “its” in the case of an institution) own goals.
• One way to deal with policy resistance is to try to overpower it. If you wield enough power and can keep wielding it, the power approach can work, at the cost of monumental resentment and the possibility of explosive consequences if the power is ever let up.
• The most effective way of dealing with policy resistance is to find a way of aligning the various goals of the subsystems, usually by providing an overarching goal that allows all actors to break out of their bounded rationality. If everyone can work harmoniously toward the same outcome (if all feedback loops are serving the same goal), the results can be amazing.
• Harmonization of goals in a system is not always possible, but it’s an worth looking for. It can be found only by letting go of more narrow goals and considering the long term welfare of the entire system.
• The trap called the tragedy of the commons comes about when there is escalation, or just simple growth, in a commonly shared, erodable environment.
• The tragedy of the commons arises from missing (or too long delayed) feedback from the resource to the growth of the users of that resource.
• To be effective, regulation must be enforced by policing and penalties.
• Some systems not only resist policy and stay in a normal bad state, they keep getting worse. One name for this archetype is “drift to low performance.”
• There are two antidotes to eroding goals. One is to keep standards absolute, regardless of performance. Another is to make goals sensitive to the best performances of the past, instead of the worst.
• Allowing performance standards to be influenced by past performance, especially if there is a negative bias in perceiving past performance, sets up a reinforcing feedback loop of eroding goals that sets a system drifting toward low performance.
• Keep performance standards absolute. Even better, let standards be enhanced by the best actual performances instead of being discouraged by the worst. Use the same structure to set up a drift toward high performance!
• Escalation, being a reinforcing feedback loop, builds exponentially. Therefore, it can carry a competition to extremes faster than anyone would believe possible. If nothing is done to break the loop, the process usually ends with one or both of the competitors breaking down.
• When the state of one stock is determined by trying to surpass the state of another stock—and vice versa—then there is a reinforcing feedback loop carrying the system into an arms race, a wealth race, a smear campaign, escalating loudness, escalating violence. The escalation is exponential and can lead to extremes surprisingly quickly. If nothing is done, the spiral will be stopped by someone’s collapse—because exponential growth cannot go on forever.
• The best way out of this trap is to avoid getting in it. If caught in an escalating system, one can refuse to compete (unilaterally disarm), thereby interrupting the reinforcing loop. Or one can negotiate a new system with balancing loops to control the escalation.
• Success to the successful is a well-known concept in the field of ecology, where it is called “the competitive exclusion principle.” This principle says that two different species cannot live in exactly the same ecological niche, competing for exactly the same resources. Because the two species are different, one will necessarily reproduce faster, or be able to use the resource more efficiently than the other. It will win a larger share of the resource, which will give it the ability to multiply more and keep winning. It will not only dominate the niche, it will drive the losing competitor to extinction. That will happen not by direct confrontation usually, but by appropriating all the resource, leaving none for the weaker competitor.
• The trap of success to the successful does its greatest damage in the many ways it works to make the rich richer and the poor poorer.
• Species and companies sometimes escape competitive exclusion by diversifying. A species can learn or evolve to exploit new resources. A company can create a new product or service that does not directly compete with existing ones.
• The success-to the-successful loop can be kept under control by putting into place feedback loops that keep any competitor from taking over entirely.
• The most obvious way out of the success-to the-successful archetype is by periodically “leveling the playing field.”
• If the winners of a competition are systematically rewarded with the means to win again, a reinforcing feedback loop is created by which, if it is allowed to proceed uninhibited, the winners eventually take all, while the losers are eliminated.
• Diversification, which allows those who are losing the competition to get out of that game and start another one; strict limitation on the fraction of the pie any one winner may win (antitrust laws); policies that level the playing field, removing some of the advantage of the strongest players or increasing the advantage of the weakest; policies that devise rewards for success that do not bias the next round of competition.
• Addiction is finding a quick and dirty solution to the symptom of the problem, which prevents or distracts one from the harder and longer-term task of solving the real problem. Addictive policies are insidious, because they are so easy to sell, so simple to fall for.
• It’s worth going through the withdrawal to get back to an unaddicted state, but it is far preferable to avoid addiction in the first place.
• THE TRAP: SHIFTING THE BURDEN TO THE INTERVENOR Shifting the burden, dependence, and addiction arise when a solution to a systemic problem reduces (or disguises) the symptoms, but does nothing to solve the underlying problem. Whether it is a substance that dulls one’s perception or a policy that hides the underlying trouble, the drug of choice interferes with the actions that could solve the real problem. If the intervention designed to correct the problem causes the self-maintaining capacity of the original system to atrophy or erode, then a destructive reinforcing feedback loop is set in motion. The system deteriorates; more and more of the solution is then required. The system will become more and more dependent on the intervention and less and less able to maintain its own desired state. THE WAY OUT Again, the best way out of this trap is to avoid getting in. Beware of symptom-relieving or signal-denying policies or practices that don’t really address the problem. Take the focus off short-term relief and put it on long term restructuring.
• Wherever there are rules, there is likely to be rule beating. Rule beating means evasive action to get around the intent of a system’s rules—abiding by the letter, but not the spirit, of the law.
• Notice that rule beating produces the appearance of rules being followed.
• Rule beating is usually a response of the lower levels in a hierarchy to overrigid, deleterious, unworkable, or ill-defined rules from above. There are two generic responses to rule beating. One is to try to stamp out the self-organizing response by strengthening the rules or their enforcement—usually giving rise to still greater system distortion. That’s the way further into the trap. The way out of the trap, the opportunity, is to understand rule beating as useful feedback, and to revise, improve, rescind, or better explain the rules.
• THE TRAP: RULE BEATING Rules to govern a system can lead to rule beating—perverse behavior that gives the appearance of obeying the rules or achieving the goals, but that actually distorts the system. THE WAY OUT Design, or redesign, rules to release creativity not in the direction of beating the rules, but in the direction of achieving the purpose of the rules.
• Although there is every reason to want a thriving economy, there is no particular reason to want the GNP to go up. But governments around the world respond to a signal of faltering GNP by taking numerous actions to keep it growing. Many of those actions are simply wasteful, stimulating inefficient production of things no one particularly wants.
• Seeking the wrong goal, satisfying the wrong indicator, is a system characteristic almost opposite from rule beating. In rule beating, the system is out to evade an unpopular or badly designed rule, while giving the appearance of obeying it. In seeking the wrong goal, the system obediently follows the rule and produces its specified result—which is not necessarily what anyone actually wants.
• You have the problem of wrong goals when you find something stupid happening “because it’s the rule.” You have the problem of rule beating when you find something stupid happening because it’s the way around the rule. Both of these system perversions can be going on at the same time with regard to the same rule.
• THE TRAP: SEEKING THE WRONG GOAL System behavior is particularly sensitive to the goals of feedback loops. If the goals—the indicators of satisfaction of the rules—are defined inaccurately or incompletely, the system may obediently work to produce a result that is not really intended or wanted. THE WAY OUT Specify indicators and goals that reflect the real welfare of the system. Be especially careful not to confuse effort with result or you will end up with a system that is producing effort, not result.
• Leverage points are points of power.
• The world’s leaders are correctly fixated on economic growth as the answer to virtually all problems, but they’re pushing with all their might in the wrong direction.
• Leverage points frequently are not intuitive. Or if they are, we too often use them backward, systematically worsening whatever problems we are trying to solve.
• As systems become complex, their behavior can become surprising.
• You can often stabilize a system by increasing the capacity of a buffer.5 But if a buffer is too big, the system gets inflexible. It reacts too slowly.
• Delays in feedback loops are critical determinants of system behavior. They are common causes of oscillations.
• A complex system usually has numerous balancing feedback loops it can bring into play, so it can self-correct under different conditions and impacts.
• A balancing feedback loop is self-correcting; a reinforcing feedback loop is self-reinforcing. The more it works, the more it gains power to work some more, driving system behavior in one direction.
• Reinforcing feedback loops are sources of growth, explosion, erosion, and collapse in systems. A system with an unchecked reinforcing loop ultimately will destroy itself. That’s why there are so few of them. Usually a balancing loop will kick in sooner or later.
• Missing information flows is one of the most common causes of system malfunction. Adding or restoring information can be a powerful intervention, usually much easier and cheaper than rebuilding physical infrastructure.
• There is a systematic tendency on the part of human beings to avoid accountability for their own decisions. That’s why there are so many missing feedback loops—and why this kind of leverage point is so often popular with the masses, unpopular with the powers that be, and effective, if you can get the powers that be to permit it to happen (or go around them and make it happen anyway).
• Constitutions are the strongest examples of social rules. Physical laws such as the second law of thermodynamics are absolute rules, whether we understand them or not or like them or not. Laws, punishments, incentives, and informal social agreements are progressively weaker rules.
• Power over the rules is real power.
• The most stunning thing living systems and some social systems can do is to change themselves utterly by creating whole new structures and behaviors.
• The ability to self-organize is the strongest form of system resilience.
• A system that can evolve can survive almost any change, by changing itself.
• Magical leverage points are not easily accessible, even if we know where they are and which direction to push on them. There are no cheap tickets to mastery. You have to work hard at it, whether that means rigorously analyzing a system or rigorously casting off your own paradigms and throwing yourself into the humility of not-knowing.
• Social systems are the external manifestations of cultural thinking patterns and of profound human needs, emotions, strengths, and weaknesses. Changing them is not as simple as saying “now all change,” or of trusting that he who knows the good shall do the good.
• Self-organizing, nonlinear, feedback systems are inherently unpredictable. They are not controllable. They are understandable only in the most general way.
• Before you disturb the system in any way, watch how it behaves.
• Starting with the behavior of the system forces you to focus on facts, not theories.
• Remember, always, that everything you know, and everything everyone knows, is only a model. Get your model out there where it can be viewed. Invite others to challenge your assumptions and add their own.
• Thou shalt not distort, delay, or withhold information.
• You can make a system work better with surprising ease if you can give it more timely, more accurate, more complete information.
• Information is power. Anyone interested in power grasps that idea very quickly.
• Our information streams are composed primarily of language. Our mental models are mostly verbal. Honoring information means above all avoiding language pollution—making the cleanest possible use we can of language. Second, it means expanding our language so we can talk about complexity.
• In fact, we don’t talk about what we see; we see only what we can talk about.
• The first step in respecting language is keeping it as concrete, meaningful, and truthful as possible—part of the job of keeping information streams clear. The second step is to enlarge language to make it consistent with our enlarged understanding of systems.
• Our culture, obsessed with numbers, has given us the idea that what we can measure is more important than what we can’t measure.
• Pretending that something doesn’t exist if it’s hard to quantify leads to faulty models.
• Human beings have been endowed not only with the ability to count, but also with the ability to assess quality. Be a quality detector.
• Remember that hierarchies exist to serve the bottom layers, not the top. Don’t maximize parts of systems or subsystems while ignoring the whole.
• The thing to do, when you don’t know, is not to bluff and not to freeze, but to learn. The way you learn is by experiment—or, as Buckminster Fuller put it, by trial and error, error, error.
• Pretending you’re in control even when you aren’t is a recipe not only for mistakes, but for not learning from mistakes.
• No part of the human race is separate either from other human beings or from the global ecosystem.
• • A system is more than the sum of its parts.
• Many of the interconnections in systems operate through the flow of information.
• The least obvious part of the system, its function or purpose, is often the most crucial determinant of the system’s behavior.
• System structure is the source of system behavior. System behavior reveals itself as a series of events over time.
• A stock is the memory of the history of changing flows within the system.
• • If the sum of inflows exceeds the sum of outflows, the stock level will rise.
• • If the sum of outflows exceeds the sum of inflows, the stock level will fall.
• If the sum of outflows equals the sum of inflows, the stock level will not change — it will be held in dynamic equilibrium.
• • A stock can be increased by decreasing its outflow rate as well as by increasing its inflow rate.
• Stocks act as delays or buffers or shock absorbers in systems.
• Stocks allow inflows and outflows to be de-coupled and independent.
• A feedback loop is a closed chain of causal connections from a stock, through a set of decisions or rules or physical laws or actions that are dependent on the level of the stock, and back again through a flow to change the stock.
• Balancing feedback loops are equilibrating or goal-seeking structures in systems and are both sources of stability and sources of resistance to change.
• Reinforcing feedback loops are self-enhancing, leading to exponential growth or to runaway collapses over time.
• The information delivered by a feedback loop—even nonphysical feedback—can affect only future behavior; it can’t deliver a signal fast enough to correct behavior that drove the current feedback.
• A stock-maintaining balancing feedback loop must have its goal set appropriately to compensate for draining or inflowing processes that affect that stock. Otherwise, the feedback process will fall short of or exceed the target for the stock.
• Systems with similar feedback structures produce similar dynamic behaviors.
• Complex behaviors of systems often arise as the relative strengths of feedback loops shift, causing first one loop and then another to dominate behavior.
• • A delay in a balancing feedback loop makes a system likely to oscillate.
• Changing the length of a delay may make a large change in the behavior of a system.
• System dynamics models explore possible futures and ask “what if” questions.
• Model utility depends not on whether its driving scenarios are realistic (since no one can know that for sure), but on whether it responds with a realistic pattern of behavior.
• In physical, exponentially growing systems, there must be at least one reinforcing loop driving the growth and at least one balancing loop constraining the growth, because no system can grow forever in a finite environment.
• Nonrenewable resources are stock-limited.
• Renewable resources are flow-limited.
• There are always limits to resilience.
• Systems need to be managed not only for productivity or stability, they also need to be managed for resilience.
• Systems often have the property of self-organization—the ability to structure themselves, to create new structure, to learn, diversify, and complexify.
• Hierarchical systems evolve from the bottom up. The purpose of the upper layers of the hierarchy is to serve the purposes of the lower layers.
• Many relationships in systems are nonlinear.
• There are no separate systems. The world is a continuum. Where to draw a boundary around a system depends on the purpose of the discussion.
• At any given time, the input that is most important to a system is the one that is most limiting.
• Any physical entity with multiple inputs and outputs is surrounded by layers of limits.
• There always will be limits to growth.
• A quantity growing exponentially toward a limit reaches that limit in a surprisingly short time.
• When there are long delays in feedback loops, some sort of foresight is essential.
• The bounded rationality of each actor in a system may not lead to decisions that further the welfare of the system as a whole.
• Everything we think we know about the world is a model.
• Our models do have a strong congruence with the world.
• Our models fall far short of representing the real world fully.

THE SIGNAL AND THE NOISE by Nate Silver

• Human judgment is intrinsically fallible.
• The original revolution in information technology came not with the microchip, but with the printing press.
• Hedgehogs are type A personalities who believe in Big Ideas—in governing principles about the world that behave as though they were physical laws and undergird virtually every interaction in society.
• Foxes, on the other hand, are scrappy creatures who believe in a plethora of little ideas and in taking a multitude of approaches toward a problem.
• Foxes, Tetlock found, are considerably better at forecasting than hedgehogs.
• Foxes sometimes have more trouble fitting into type A cultures like television, business, and politics. Their belief that many problems are hard to forecast—and that we should be explicit about accounting for these uncertainties—may be mistaken for a lack of self-confidence.
• But foxes happen to make much better predictions. They are quicker to recognize how noisy the data can be, and they are less inclined to chase false signals. They know more about what they don’t know.
• Too much information can be a bad thing in the hands of a hedgehog.
• Hedgehogs who have lots of information construct stories—stories that are neater and tidier than the real world, with protagonists and villains, winners and losers, climaxes and dénouements—and, usually, a happy ending for the home team.
• You can get lost in the narrative. Politics may be especially susceptible to poor predictions precisely because of its human elements: a good election engages our dramatic sensibilities.
• The FiveThirtyEight forecasting model started out pretty simple—basically, it took an average of polls but weighted them according to their past accuracy—then gradually became more intricate. But it abided by three broad principles, all of which are very fox-like.
• Principle 1: Think Probabilistically
• Our brains, wired to detect patterns, are always looking for a signal, when instead we should appreciate how noisy the data is.
• We have trouble distinguishing a 90 percent chance that the plane will land safely from a 99 percent chance or a 99.9999 percent chance, even though these imply vastly different things about whether we ought to book our ticket.
• With practice, our estimates can get better.
• What distinguished Tetlock’s hedgehogs is that they were too stubborn to learn from their mistakes. Acknowledging the real-world uncertainty in their forecasts would require them to acknowledge to the imperfections in their theories about how the world was supposed to behave—the last thing that an ideologue wants to do.
• Principle 2: Today’s Forecast Is the First Forecast of the Rest of Your Life
• Another misconception is that a good prediction shouldn’t change.
• Ultimately, the right attitude is that you should make the best forecast possible today—regardless of what you said last week, last month, or last year. Making a new forecast does not mean that the old forecast just disappears.
• Making the most of that limited information requires a willingness to update one’s forecast as newer and better information becomes available.
• It is the alternative—failing to change our forecast because we risk embarrassment by doing so—that reveals a lack of courage.
• Principle 3: Look for Consensus
• Every hedgehog fantasizes that they will make a daring, audacious, outside-the-box prediction—one that differs radically from the consensus view on a subject.
• Quite a lot of evidence suggests that aggregate or group forecasts are more accurate than individual ones, often somewhere between 15 and 20 percent more accurate depending on the discipline. That doesn’t necessarily mean the group forecasts are good. (We’ll explore this subject in more depth later in the book.) But it does mean that you can benefit from applying multiple perspectives toward a problem.
• The word objective is sometimes taken to be synonymous with quantitative, but it isn’t. Instead it means seeing beyond our personal biases and prejudices and toward the truth of a problem.
• Pure objectivity is desirable but unattainable in this world.
• Wherever there is human judgment there is the potential for bias.
• The way to become more objective is to recognize the influence that our assumptions play in our forecasts and to question ourselves about them.
• The goal, as in formulating any prediction, is to weed out the root cause:
• Olympic gymnasts peak in their teens; poets in their twenties; chess players in their thirties11; applied economists in their forties,12 and the average age of a Fortune 500 CEO is 55.
• To be sure, whenever human judgment is involved, it also introduces the potential for bias.
• most of us are still in a state of mental adolescence until about the age of twenty-four.
• Sanders has no formal definition of what a player’s mental toolbox should include, but over the course of our conversation, I identified five different intellectual and psychological abilities that he believes help to predict success at the major-league level.
• Preparedness and Work Ethic
• Concentration and Focus
• Competitiveness and Self-Confidence
• Stress Management and Humility
• Adaptiveness and Learning Ability
• These same habits, of course, are important in many human endeavors.
• The key to making a good forecast, as we observed in chapter 2, is not in limiting yourself to quantitative information. Rather, it’s having a good process for weighing the information appropriately. This is the essence of Beane’s philosophy: collect as much information as possible, but then be as rigorous and disciplined as possible when analyzing it.
• The litmus test for whether you are a competent forecaster is if more information makes your predictions better.
• Good innovators typically think very big and they think very small. New ideas are sometimes found in the most granular details of a problem where few others bother to look. And they are sometimes found when you are doing your most abstract and philosophical thinking, considering why the world is the way that it is and whether there might be an alternative to the dominant paradigm. Rarely can they be found in the temperate latitudes between these two spaces, where we spend 99 percent of our lives. The categorizations and approximations we make in the normal course of our lives are usually good enough to get by, but sometimes we let information that might give us a competitive advantage slip through the cracks.
• The key is to develop tools and habits so that you are more often looking for ideas and information in the right places—and in honing the skills required to harness them into W’s and L’s once you’ve found them.
• Given perfect knowledge of present conditions (“all positions of all items of which nature is composed”), and perfect knowledge of the laws that govern the universe (“all forces that set nature in motion”), we ought to be able to make perfect predictions (“the future just like the past would be present”). The movement of every particle in the universe should be as predictable as that of the balls on a billiard table. Human beings might not be up to the task, Laplace conceded. But if we were smart enough (and if we had fast enough computers) we could predict the weather and everything else—and we would find that nature itself is perfect.
• Probabilism was, at first, mostly an epistemological paradigm: it avowed that there were limits on man’s ability to come to grips with the universe. More recently, with the discovery of quantum mechanics, scientists and philosophers have asked whether the universe itself behaves probabilistically.
• Perfect predictions are impossible if the universe itself is random.
• The most reliable way to improve the accuracy of a weather forecast—getting one step closer to solving for the behavior of each molecule—is to reduce the size of the grid that you use to represent the atmosphere.
• Chaos theory applies to systems in which each of two properties hold: The systems are dynamic, meaning that the behavior of the system at one point in time influences its behavior in the future; And they are nonlinear, meaning they abide by exponential rather than additive relationships.
• The most basic tenet of chaos theory is that a small change in initial conditions—a butterfly flapping its wings in Brazil—can produce a large and unexpected divergence in outcomes—a tornado in Texas. This does not mean that the behavior of the system is random, as the term “chaos” might seem to imply. Nor is chaos theory some modern recitation of Murphy’s Law (“whatever can go wrong will go wrong”). It just means that certain types of systems are very hard to predict.
• Exponential operations, however, extract a lot more punishment when there are inaccuracies in our data.
• Chaos theory therefore most definitely applies to weather forecasting, making the forecasts highly vulnerable to inaccuracies in our data.
• Sometimes these inaccuracies arise as the result of human error. The more fundamental issue is that we can only observe our surroundings with a certain degree of precision. No thermometer is perfect, and if it’s off in even the third or the fourth decimal place, this can have a profound impact on the forecast.
• Humans can make the computer forecasts better or they can make them worse.
• They are too literal-minded, unable to recognize the pattern once its subjected to even the slightest degree of manipulation. Humans by contrast, out of pure evolutionary necessity, have very powerful visual cortexes.
• The statistical reality of accuracy isn’t necessarily the governing paradigm when it comes to commercial weather forecasting. It’s more the perception of accuracy that adds value in the eyes of the consumer.
• Calibration is difficult to achieve in many fields. It requires you to think probabilistically, something that most of us (including most “expert” forecasters) are not very good at. It really tends to punish overconfidence—a trait that most forecasters have in spades. It also requires a lot of data to evaluate fully—cases where forecasters have issued hundreds of predictions.
• If you compare the frequencies of earthquakes with their magnitudes, you’ll find that the number drops off exponentially as the magnitude increases. While there are very few catastrophic earthquakes, there are literally millions of smaller ones—about 1.3 million earthquakes measuring between magnitude 2.0 and magnitude 2.9 around the world every year.
• This pattern is characteristic of what is known as a power-law distribution, and it is the relationship that Richter and Gutenberg uncovered. Something that obeys this distribution has a highly useful property: you can forecast the number of large-scale events from the number of small-scale ones, or vice versa.
• What happens in systems with noisy data and underdeveloped theory—like earthquake prediction and parts of economics and political science—is a two-step process. First, people start to mistake the noise for a signal. Second, this noise pollutes journals, blogs, and news accounts with false alarms, undermining good science and setting back our ability to understand how the system really works.
• In statistics, the name given to the act of mistaking noise for a signal is overfitting.
• You’ve given me an overly specific solution to a general problem. This is overfitting, and it leads to worse predictions. The name overfitting comes from the way that statistical models are “fit” to match past observations.
• In almost all real-world applications, however, we have to work by induction, inferring the structure from the available evidence. You are most likely to overfit a model when the data is limited and noisy and when your understanding of the fundamental relationships is poor; both circumstances apply in earthquake forecasting.
• Overfitting represents a double whammy: it makes our model look better on paper but perform worse in the real world. Because of the latter trait, an overfit model eventually will get its comeuppance if and when it is used to make real predictions. Because of the former, it may look superficially more impressive until then, claiming to make very accurate and newsworthy predictions and to represent an advance over previously applied techniques.
• We may, without even realizing it, work backward to generate persuasive-sounding theories that rationalize them, and these will often fool our friends and colleagues as well as ourselves.
• The theory of complexity that the late physicist Per Bak and others developed is different from chaos theory, although the two are often lumped together. Instead, the theory suggests that very simple things can behave in strange and mysterious ways when they interact with one another.
• Complex systems seem to have this property, with large periods of apparent stasis marked by sudden and catastrophic failures. These processes may not literally be random, but they are so irreducibly complex (right down to the last grain of sand) that it just won’t be possible to predict them beyond a certain level.
• Indeed, economists have for a long time been much too confident in their ability to predict the direction of the economy.
• There is almost no chance19 that the economists have simply been unlucky; they fundamentally overstate the reliability of their predictions.
• Results like these are the rule; experts either aren’t very good at providing an honest description of the uncertainty in their forecasts, or they aren’t very interested in doing so.
• Getting feedback about how well our predictions have done is one way—perhaps the essential way—to improve them.
• As Hatzius sees it, economic forecasters face three fundamental challenges. First, it is very hard to determine cause and effect from economic statistics alone. Second, the economy is always changing, so explanations of economic behavior that hold in one business cycle may not apply to future ones. And third, as bad as their forecasts have been, the data that economists have to work with isn’t much good either.
• Most of you will have heard the maxim “correlation does not imply causation.” Just because two variables have a statistical relationship with each other does not mean that one is responsible for the other.
• Most statistical models are built on the notion that there are independent variables and dependent variables, inputs and outputs, and they can be kept pretty much separate from one another.39 When it comes to the economy, they are all lumped together in one hot mess.
• A forecaster should almost never ignore data, especially when she is studying rare events like recessions or presidential elections, about which there isn’t very much data to begin with. Ignoring data is often a tip-off that the forecaster is overconfident, or is overfitting her model—that she is interested in showing off rather than trying to be accurate.
• The other rationale you’ll sometimes hear for throwing out data is that there has been some sort of fundamental shift in the problem you are trying to solve.
• The problem with this is that you never know when the next paradigm shift will occur,
• An economic model conditioned on the notion that nothing major will change is a useless one.
• The third major challenge for economic forecasters is that their raw data isn’t much good.
• Most economic data series are subject to revision, a process that can go on for months and even years after the statistics are first published. The revisions are sometimes enormous.
• Any illusion that economic forecasts were getting better ought to have been shattered by the terrible mistakes economists made in advance of the recent financial crisis.
• Statistical inferences are much stronger when backed up by theory or at least some deeper thinking about their root causes.
• If you’re looking for an economic forecast, the best place to turn is the average or aggregate prediction rather than that of any one economist.
• The aggregate forecast is made up of individual forecasts; if those improve, so will the group’s performance.
• When you have your name attached to a prediction, your incentives may change.
• The less reputation you have, the less you have to lose by taking a big risk when you make a prediction.
• Things like Google search traffic patterns, for instance, can serve as leading indicators for economic data series like unemployment.
• Danger lurks, in the economy and elsewhere, when we discourage forecasters from making a full and explicit account of the risks inherent in the world around us.
• Extrapolation is a very basic method of prediction—usually, much too basic. It simply involves the assumption that the current trend will continue indefinitely, into the future. Some of the best-known failures of prediction have resulted from applying this assumption too liberally.
• Extrapolation tends to cause its greatest problems in fields—including population growth and disease—where the quantity that you want to study is growing exponentially.
• Perhaps the bigger problem from a statistical standpoint, however, is that precise predictions aren’t really possible to begin with when you are extrapolating on an exponential scale.
• One of the most useful quantities for predicting disease spread is a variable called the basic reproduction number. Usually designated as R0, it measures the number of uninfected people that can expect to catch a disease from a single infected individual.
• In theory, any disease with an R0 greater than 1 will eventually spread to the entire population in the absence of vaccines or quarantines.
• In many cases involving predictions about human activity, the very act of prediction can alter the way that people behave.
• A case where a prediction can bring itself about is called a self-fulfilling prediction or a self-fulfilling prophecy.
• A self-canceling prediction is just the opposite: a case where a prediction tends to undermine itself.
• Needlessly complicated models may fit the noise in a problem rather than the signal, doing a poor job of replicating its underlying structure and causing predictions to be worse.
• Still, while simplicity can be a virtue for a model, a model should at least be sophisticatedly simple.
• If you can’t make a good prediction, it is very often harmful to pretend that you can.
• The philosophy of this book is that prediction is as much a means as an end. Prediction serves a very central role in hypothesis testing, for instance, and therefore in all of science.
• The key is in remembering that a model is a tool to help us understand the complexities of the universe, and never a substitute for the universe itself.
• Finding patterns is easy in any kind of data-rich environment; that’s what mediocre gamblers do. The key is in determining whether the patterns represent noise or signal.
• Bayes’s theorem is concerned with conditional probability. That is, it tells us the probability that a theory or hypothesis is true if some event has happened.
• Usually, however, we focus on the newest or most immediately available information, and the bigger picture gets lost.
• The idea behind Bayes’s theorem, however, is not that we update our probability estimates just once. Instead, we do so continuously as new evidence presents itself to us.
• Many scientific findings that are commonly accepted today would have been dismissed as hooey at one point.
• Absolutely nothing useful is realized when one person who holds that there is a 0 percent probability of something argues against another person who holds that the probability is 100 percent.
• Making predictions based on our beliefs is the best (and perhaps even the only) way to test ourselves.
• One property of Bayes’s theorem, in fact, is that our beliefs should converge toward one another—and toward the truth—as we are presented with more evidence over time.
• Technology is beneficial as a labor-saving device, but we should not expect machines to do our thinking for us.
• In accordance with Bayes’s theorem, prediction is fundamentally a type of information-processing activity—a matter of using new data to test our hypotheses about the objective world, with the goal of coming to truer and more accurate conceptions about it.
• In chess, we have both complete knowledge of the governing rules and perfect information—there are a finite number of chess pieces, and they’re right there in plain sight. But the game is still very difficult for us. Chess speaks to the constraints on our information-processing capabilities—and it might tell us something about the best strategies for making decisions despite them. The need for prediction arises not necessarily because the world itself is uncertain, but because understanding it fully is beyond our capacity.
• A heuristic approach to problem solving consists of employing rules of thumb when a deterministic solution to a problem is beyond our practical capacities.
• Heuristics are very useful things, but they necessarily produce biases and blind spots.
• Chess players learn through memory and experience where to concentrate their thinking.
• Elite chess players tend to be good at metacognition—thinking about the way they think—and correcting themselves if they don’t seem to be striking the right balance.
• The blind spots in our thinking are usually of our own making and they can grow worse as we age.
• Computers are very, very fast at making calculations. Moreover, they can be counted on to calculate faithfully—without getting tired or emotional or changing their mode of analysis in midstream. But this does not mean that computers produce perfect forecasts, or even necessarily good ones. The acronym GIGO (“garbage in, garbage out”) sums up this problem. If you give a computer bad data, or devise a foolish set of instructions for it to analyze, it won’t spin straw into gold. Meanwhile, computers are not very good at tasks that require creativity and imagination, like devising strategies or developing theories about the way the world works.
• The search results that Google returns, and the order in which they appear on the page, represent their prediction about which results you will find most useful.
• Google’s best-known statistical measurement of a Web site is PageRank,45 a score based on how many other Web pages link to the one you might be seeking out. But PageRank is just one of two hundred signals that Google uses46 to approximate the human evaluators’ judgment.
• Nevertheless, a commitment to testing ourselves—actually seeing how well our predictions work in the real world rather than in the comfort of a statistical model—is probably the best way to accelerate the learning process.
• In many ways, we are our greatest technological constraint. The slow and steady march of human evolution has fallen out of step with technological progress: evolution occurs on millennial time scales, whereas processing power doubles roughly every other year.
• We have to view technology as what it always has been—a tool for the betterment of the human condition. We should neither worship at the altar of technology nor be frightened by it. Nobody has yet designed, and perhaps no one ever will, a computer that thinks like a human being.49 But computers are themselves a reflection of human progress and human ingenuity: it is not really “artificial” intelligence if a human designed the artifice.
• The key thing about a learning curve is that it really is a curve: the progress we make at performing the task is not linear.
• Luck and skill are often portrayed as polar opposites. But the relationship is a little more complicated than that.
• More broadly, overconfidence is a huge problem in any field in which prediction is involved.
• In the United States, we live in a very results-oriented society. If someone is rich or famous or beautiful, we tend to think they deserve to be those things.
• As an empirical matter, however, success is determined by some combination of hard work, natural talent, and a person’s opportunities and environment—in other words, some combination of noise and signal.
• Across a number of disciplines, from macroeconomic forecasting to political polling, simply taking an average of everyone’s forecast rather than relying on just one has been found to reduce forecast error,14 often by about 15 or 20 percent.
• Very often, we fail to appreciate the limitations imposed by small sample sizes and mistake luck for skill when we look at how well someone’s predictions have done.
• Herding can also result from deeper psychological reasons. Most of the time when we are making a major life decision, we’re going to want some input from our family, neighbors, colleagues, and friends—and even from our competitors if they are willing to give it.
• There are asymmetries in the market: bubbles are easier to detect than to burst.
• Noisy data can obscure the signal, even when there is essentially no doubt that the signal exists.
• Scientists require a high burden of proof before they are willing to conclude that a hypothesis is incontrovertible.
• In formal usage, consensus is not synonymous with unanimity—nor with having achieved a simple majority. Instead, consensus connotes broad agreement after a process of deliberation, during which time most members of a group coalesce around a particular idea or alternative.
• The goal of any predictive model is to capture as much signal as possible and as little noise as possible. Striking the right balance is not always so easy, and our ability to do so will be dictated by the strength of the theory and the quality and quantity of the data.
• Although climatologists might think carefully about uncertainty, there is uncertainty about how much uncertainty there is. Problems like these are challenging for forecasters in any discipline.
• Uncertainty is an essential and nonnegotiable part of a forecast.
• The fundamental dilemma faced by climatologists is that global warming is a long-term problem that might require a near-term solution.
• Republicans have moved especially far away from the center,112 although Democrats have to some extent too.
• The dysfunctional state of the American political system is the best reason to be pessimistic about our country’s future. Our scientific and technological prowess is the best reason to be optimistic. We are an inventive people.
• In the field of intelligence analysis, the absence of signals can signify something important (the absence of radio transmissions from Japan’s carrier fleet signaled their move toward Hawaii) and the presence of too many signals can make it exceptionally challenging to discern meaning.
• When a possibility is unfamiliar to us, we do not even think about it. Instead we develop a sort of mind-blindness to it.
• [T]here are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns—there are things we do not know we don’t know.—Donald
• If we ask ourselves a question and can come up with an exact answer, that is a known known. If we ask ourselves a question and can’t come up with a very precise answer, that is a known unknown. An unknown unknown is when we haven’t really thought to ask the question in the first place.
• An unknown unknown is a contingency that we have not even considered.
• Good intelligence is still our first line of defense against terror attacks.
• Where our enemies will strike us is predictable: it’s where we least expect them to.
• Whatever range of abilities we have acquired, there will always be tasks sitting right at the edge of them. If we judge ourselves by what is hardest for us, we may take for granted those things that we do easily and routinely.
• Nature’s laws do not change very much. So long as the store of human knowledge continues to expand, as it has since Gutenberg’s printing press, we will slowly come to a better understanding of nature’s signals, if never all its secrets.
• There is no reason to conclude that the affairs of men are becoming more predictable. The opposite may well be true.
• Our brains process information by means of approximation.8 This is less an existential fact than a biological necessity: we perceive far more inputs than we can consciously consider, and we handle this problem by breaking them down into regularities and patterns.
• Our brains simplify and approximate just as much in everyday life. With experience, the simplifications and approximations will be a useful guide and will constitute our working knowledge.11 But they are not perfect, and we often do not realize how rough they are.
• There is nothing wrong with an approximation here and there.
• The problem comes when we mistake the approximation for the reality.
• Bayes’s theorem requires us to state—explicitly—how likely we believe an event is to occur before we begin to weigh the evidence. It calls this estimate a prior belief.
• the vast majority of the time, collective judgment will be better than ours alone.
• Information becomes knowledge only when it’s placed in context. Without it, we have no way to differentiate the signal from the noise, and our search for the truth might be swamped by false positives.
• To state your beliefs up front—to say “Here’s where I’m coming from”12—is a way to operate in good faith and to recognize that you perceive reality through a subjective filter.
• Bayes’s theorem says we should update our forecasts any time we are presented with new information.
• If our ideas are worthwhile, we ought to be willing to test them by establishing falsifiable hypotheses and subjecting them to a prediction.
• Most of the time, we do not appreciate how noisy the data is, and so our bias is to place too much weight on the newest data point.
• It’s more often with small, incremental, and sometimes even accidental steps that we make progress.
• Prediction is difficult for us for the same reason that it is so important: it is where objective and subjective reality intersect. Distinguishing the signal from the noise requires both scientific knowledge and self-knowledge: the serenity to accept the things we cannot predict, the courage to predict the things we can, and the wisdom to know the difference.
• But our bias is to think we are better at prediction than we really are.