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.

LYING by Sam Harris, Annaka Harris

• Among the many paradoxes of human life, this is perhaps the most peculiar and consequential: We often behave in ways that are guaranteed to make us unhappy
• Lying is the royal road to chaos.
• Deception can take many forms, but not all acts of deception are lies. Even the most ethical among us regularly struggle to keep appearances and reality apart.
• The boundary between lying and deception is often vague. It is even possible to deceive with the truth.
• To lie is to intentionally mislead others when they expect honest communication.
• People lie so that others will form beliefs that are not true. The more consequential the beliefs—that is, the more a person’s well-being demands a correct understanding of the world or of other people’s opinions—the more consequential the lie.
• To speak truthfully is to accurately represent one’s beliefs. But candor offers no assurance that one’s beliefs about the world are true.
• The intent to communicate honestly is the measure of truthfulness.
• Many of us lie to our friends and family members to spare their feelings.
• The liar often imagines that he does no harm so long as his lies go undetected. But the one lied to rarely shares this view. The moment we consider our dishonesty from the perspective of those we lie to, we recognize that we would feel betrayed if the roles were reversed.
• The opportunity to deceive others is ever present and often tempting, and each instance of deception casts us onto some of the steepest ethical terrain we ever cross.
• At least one study suggests that 10 percent of communication between spouses is deceptive.
• Lying is ubiquitous, and yet even liars rate their deceptive interactions as less pleasant than truthful ones.
• Once one commits to telling the truth, one begins to notice how unusual it is to meet someone who shares this commitment.
• Honest people are a refuge: You know they mean what they say; you know they will not say one thing to your face and another behind your back; you know they will tell you when they think you have failed—and for this reason their praise cannot be mistaken for mere flattery.
• Honesty is a gift we can give to others. It is also a source of power and an engine of simplicity. Knowing that we will attempt to tell the truth, whatever the circumstances, leaves us with little to prepare for. Knowing that we told the truth in the past leaves us with nothing to keep track of. We can simply be ourselves in every moment.
• In committing to being honest with everyone, we commit to avoiding a wide range of long-term problems, but at the cost of occasional short-term discomfort.
• Lying is the lifeblood of addiction. If we have no recourse to lies, our lives can unravel only so far without others’ noticing.
• Telling the truth can also reveal ways in which we want to grow but haven’t.
• Ethical transgressions are generally divided into two categories: the bad things we do (acts of commission) and the good things we fail to do (acts of omission). We tend to judge the former far more harshly.
• Doing something requires energy, and most morally salient actions are associated with conscious intent.
• Failing to do something can arise purely by circumstance and requires energy to rectify.
• Sincerity, authenticity, integrity, mutual understanding—these and other sources of moral wealth are destroyed the moment we deliberately misrepresent our beliefs, whether or not our lies are ever discovered.
• By lying, we deny our friends access to reality9—and their resulting ignorance often harms them in ways we did not anticipate.
• False encouragement is a kind of theft: It steals time, energy, and motivation that a person could put toward some other purpose.
• When we presume to lie for the benefit of others, we have decided that we are the best judges of how much they should understand about their own lives—about how they appear, their reputations, or their prospects in the world.
• When we pretend not to know the truth, we must also pretend not to be motivated by it.
• The opportunity to say something useful to the people we love soon disappears, never to return.
• Failures of personal integrity, once revealed, are rarely forgotten.
• Yes, it can be unpleasant to be told that we have wasted time, or that we are not performing as well as we imagined, but if the criticism is valid, it is precisely what we most need to hear to find our way in the world.
• Sparing others disappointment and embarrassment is a great kindness. And if we have a history of being honest, our praise and encouragement will actually mean something.
• A commitment to honesty does not necessarily require that we disclose facts about ourselves that we would prefer to keep private.
• To agree to keep a secret is to assume a burden. At a minimum, one must remember what one is not supposed to talk about. This can be difficult and lead to clumsy attempts at deception.
• Nevertheless, I still find that a willingness to be honest—especially about things that one might be expected to conceal—often leads to much more gratifying exchanges with other human beings.
• One of the worst things about breaking the law is that it puts you at odds with an indeterminate number of other people. This is among the many corrosive effects of unjust laws: They tempt peaceful and (otherwise) honest people to lie so as to avoid being punished for behavior that is ethically blameless.
• One of the greatest problems for the liar is that he must keep track of his lies.
• Psychopaths can assume the burden of mental accounting without any obvious distress. That is no accident: They are psychopaths.
• Lies beget other lies.
• When you tell the truth, you have nothing to keep track of. The world itself becomes your memory, and if questions arise, you can always point others back to it.
• A commitment to the truth is naturally purifying of error.
• Integrity consists of many things, but it generally requires us to avoid behavior that readily leads to shame or remorse.
• To lie is to erect a boundary between the truth we are living and the perception others have of us.
• It is simply astonishing how people destroy their marriages, careers, and reputations by saying one thing and doing another.
• Vulnerability comes in pretending to be someone you are not.
• Big lies have led many people to reflexively distrust those in positions of authority.
• We seem to be predisposed to remember statements as true even after they have been disconfirmed.
• The need for state secrets is obvious. However, the need for governments to lie to their own people seems to me to be virtually nonexistent.
• I suspect that the telling of necessary lies will be rare for anyone but a spy—assuming we grant that espionage is ethically defensible in today’s world.
• The ethics of war and espionage are the ethics of emergency—and are, therefore, necessarily limited in scope.
• Most forms of private vice and public evil are kindled and sustained by lies.
• Lying is, almost by definition, a refusal to cooperate with others.
• Lies are the social equivalent of toxic waste: Everyone is potentially harmed by their spread.
• Ultimately, we all die, and the only question is, what have you done between the time you’re born and the time you die?

HOW WE LEARN by Benedict Carey

• To say it another way, the collective findings of modern learning science provide much more than a recipe for how to learn more efficiently. They describe a way of life.
• The brain is not like a muscle, at least not in any straightforward sense. It is something else altogether, sensitive to mood, to timing, to circadian rhythms, as well as to location, environment. It registers far more than we’re conscious of and often adds previously unnoticed details when revisiting a memory or learned fact. It works hard at night, during sleep, searching for hidden links and deeper significance in the day’s events. It has a strong preference for meaning over randomness, and finds nonsense offensive. It doesn’t take orders so well, either, as we all know—forgetting precious facts needed for an exam while somehow remembering entire scenes from The Godfather or the lineup of the 1986 Boston Red Sox.
• If the brain is a learning machine, then it’s an eccentric one. And it performs best when its quirks are exploited.
• Yet we work more effectively, scientists have found, when we continually alter our study routines and abandon any “dedicated space” in favor of varied locations.
• Sticking to one learning ritual, in other words, slows us down.
• Studies find that the brain picks up patterns more efficiently when presented with a mixed bag of related tasks than when it’s force-fed just one, no matter the age of the student or the subject area, whether Italian phrases or chemical bonds.
• Games are the best learning tool.
• The brain has modules, specialized components that divide the labor.
• Before wading into brain biology, I want to say a word about metaphors. They are imprecise, practically by definition. They obscure as much as they reveal. And they’re often self-serving,* crafted to serve some pet purpose—in the way that the “chemical imbalance” theory of depression supports the use of antidepressant medication. (No one knows what causes depression or why the drugs have the effects they do.)
• The cells that link to form these networks are called neurons. A neuron is essentially a biological switch. It receives signals from one side and—when it “flips” or fires—sends a signal out the other, to the neurons to which it’s linked.
• Pretesting is most helpful when people get prompt feedback
• Remember: These apparently simple attempts to communicate what you’ve learned, to yourself or others, are not merely a form of self-testing, in the conventional sense, but studying—the high-octane kind, 20 to 30 percent more powerful than if you continued sitting on your butt, staring at that outline. Better yet, those exercises will dispel the fluency illusion. They’ll expose what you don’t know, where you’re confused, what you’ve forgotten—and fast.
• An insight problem, by definition, is one that requires a person to shift his or her perspective and view the problem in a novel way.
• Self-testing is one of the strongest study techniques there is.
• Verbatim copying adds very little to the depth of your learning, and the same goes for looking over highlighted text or formulas.
• Deliberate interruption is not the same as quitting.
• Making your memory work a little harder—by self-quizzing, for example, or spacing out study time—sharpens the imprint of what you know, and exposes fluency’s effects.
• Focusing on one skill at a time—a musical scale, free throws, the quadratic formula—leads quickly to noticeable, tangible improvement. But over time, such focused practice actually limits our development of each skill. Mixing or “interleaving” multiple skills in a practice session, by contrast, sharpens our grasp of all of them.

DISCIPLINE EQUALS FREEDOM: FIELD MANUAL by Jocko Willink

• The shortcut is a lie. The hack doesn’t get you there.
• THERE IS NO EASY WAY.
• Where does discipline come from? This is a simple answer. Discipline comes from within. Discipline is an internal force.
• What you are looking for, what you need, is SELF-DISCIPLINE. Self-discipline, as the very term implies, comes from the SELF. YOU. It comes when you make a decision to be disciplined.
• Were do you start? You start right HERE. When do you start? You start right NOW. You initiate action. You GO.
• YOU HAVE TO DO IT. And you have to do it now. So stop thinking about it. Stop dreaming about it. Stop researching every aspect of it and reading all about it and debating the pros and cons of it … Start doing it. Take that first step and Make It Happen. GET AFTER IT. HERE and NOW.
• People, even those people you have put up on a pedestal, are going to be faulted, weak, egomaniacal, condescending.
• People ask me, “How do I get tougher?” BE TOUGHER.
• You have control over your mind. You just have to assert it.
• Whatever problems or stress you are experiencing, detach from them. Stress is generally caused by what you can’t control.
• It is never finished. You always have more to do.
• Discipline starts with waking up early. It really does.
• Thought is what wins—the MIND is what wins—knowledge is what wins. And you gain knowledge by asking questions.
• Which questions should you ask? Simple: Question everything. Don’t accept anything as truth. QUESTION IT ALL.
• When working with other people and dynamic situations and relationships and deals, a person, especially a leader, must compromise.
• The people who are successful decide they are going to be successful. They make that choice.
• You are never too old to decide where you are going to focus your efforts and push to make the most out of every situation.
• Take the risk, take the gamble, take the first step. Take action. And don’t let another day slip by.
• Those donuts aren’t food. THEY ARE POISON.
• Unless you have gone an extended period of time without food, you don’t need to eat. And you definitely don’t need to eat that poison. YOU DON’T NEED TO EAT. You don’t even know what hungry is. Humans can go thirty days without food. You can make it.
• We are the product of our mistakes.
• The most important thing to learn is that we have so much to learn.
• The only thing valuable in regret is the lesson you learned. The knowledge you gained.
• HESITATION IS THE ENEMY.
• Do not hesitate. Do not wait. Go forward: And win.
• Sometimes, bad things happen to good people. I don’t know why. Life is not fair. That is the reality.
• Lead. Step up. Be the one who people look to. Absorb the impact—and the negativity. Draw fire—yes: Draw fire.
• When things are going bad: Don’t get all bummed out, don’t get startled, don’t get frustrated. No. Just look at the issue and say: “Good.”
• Accept reality, but focus on the solution. Take that issue, take that setback, take that problem, and turn it into something good. Go forward.
• Death is part of life, like the contrast between the darkness and the light. Without death, there is no life.
• Don’t worry about motivation. Motivation is fickle. It comes and goes. It is unreliable and when you are counting on motivation to get your goals accomplished—you will likely fall short.
• Don’t count on motivation. Count on Discipline.
• You know what you have to do. So: MAKE YOURSELF DO IT.
• These notions that you can “be whatever you want to be as long as you want it bad enough” are not true. They are fairy tales. We all have limitations.
• We are defeated one tiny, seemingly insignificant surrender at a time that chips away at who we should really be.
• You have to BE VIGILANT. You have to be ON GUARD. You have to HOLD THE LINE on the seemingly insignificant little things— things that shouldn’t matter—but that do.
• Step aggressively toward your fear—that is the step into bravery.
• We are scared of what we don’t know, and there is but one way to confront that fear: Step. GO. And that simple action, this simple attitude answers so many questions.
• No matter what is happening—no matter how hard the fight is. As long as you keep fighting—you win. Only surrender is defeat.
• Ignore and outperform.
• Do not surrender any ground. EVER.
• Get up and go. Do it quickly, without thought. Do not reason with weakness. You cannot. You must only take action. Get up and GO.
• In order to improve, we need stress. We need to push the body and the mind in order to get better.
• This will be hard at first, but it will become normal. And once you are accustomed to it, early rising is guaranteed to make your day better.  So GET AFTER IT.
• When you are on the path you want to stay on the path. Unfortunately, the opposite is also true. Once you step off the path, you tend to stray far.
• Discipline begets discipline. Will propagates MORE WILL.
• Sleep is a necessity. Humans need sleep. Failure to get enough sleep has serious side effects.
• Go to bed earlier.
• Going to bed at 10 p.m. and waking up at 5 a.m. gets you a solid seven hours.
• The world is yours when you are up before the enemy.
• People constantly ask me for the secret of getting up early. I tell them it is simple: SET YOUR ALARM CLOCK AND GET OUT OF BED WHEN IT GOES OFF. That’s it. Is it easy? No.
• The key to getting to sleep early is GETTING UP EARLY.
• Get up early every day. If you need extra sleep, take a power nap.
• Power naps. They are real. If you are feeling tired they can be a lifesaver. And if you are feeling tired due to lack of sleep, they can be very powerful.
• Warning: Be careful about letting your six- to eight-minute nap turn into a two-hour slumber. If you do this, you will have trouble falling asleep at night, which leads to trouble waking up in the morning. That means there is a higher chance you will fall off the early morning schedule.
• Exercise doesn’t need to be some complex, multi-level, multi-dimensional, scientifically proven methodology. But it does need to be SOMETHING.
• Maintain the routine. Maintain the discipline.
• Having a gym in your home eliminates all kinds of excuses. There is nothing more convenient than having your gym collocated with your domicile.
• Everyone should train in martial arts, just as everyone should eat.
• There are three broad forms of martial arts: grappling, striking, and weapons.
• Grappling uses leverage and holds to control or submit your opponent. Striking uses punches, kicks, knees, elbows, headbutts, and any other body parts to hit the opponent. Martial arts with weapons obviously utilize a variety of weapons, including sticks, knives, and, in the modern world, firearms.
• Perhaps the most critical form of self-defense is the mind. By being smart and aware, you can avoid situations that are likely to expose you to danger.
• If a person truly needs self-protection in a high-threat area, there is no substitute for the firearm.
• There is no choice but to be prepared.
• Most important, without proper training, possessing a firearm is useless, or even more dangerous to its owner than not having one.
• Learning how to shoot quickly and accurately while under stress is absolutely mandatory if one is going to own a firearm.
• Martial arts are not static. They evolve all the time. If you do not evolve with them, you will be left behind.
• If you are confronted by another person or a group of people, the best thing you can do is run away: avoid the conflict.
• The first goal of a beginner in jiu-jitsu is not to get the fight to the ground, but to get up off the ground and get away.
• Although there is a finite number of basic moves and positions, there is an infinite number of moves beyond the basics, and more are developed every day in this constantly evolving art. Due to this unending depth in jiu-jitsu, it is also the most cerebral of the martial arts.
• Muay Thai is also about pain and the ability to withstand pain.
• The physical grind of wrestling hardens the body and mind without mercy.
• In areas where there might not be any Brazilian jiu-jitsu schools, judo is the next best replacement.
• There is no reason to ever stop training and learning martial arts.
• If the martial art you are training in is easy, it isn’t likely doing you much good.
• Martial arts will make you better.
• Proximity is important. The more convenient it is to get to training, the more often you will be able train.
• Pay attention to your surroundings.
• If you are maintaining situational awareness, you should be very hard to surprise. If you sense something is going wrong or you sense a threat, proactively move away from it.
• If you do get surprised and you are caught in a bad situation: ACT.
• If you can run away from an assailant, do it. If you can’t run because they have a hold on you, attack them. Put all your training to use as quickly and as violently as possible. As soon as you can break free, do it and run.
• If shooting starts, get down.
• If you are carrying a firearm, use it to eliminate any immediate threat to your life or the life of someone else.
• Homeostasis is the tendency to move toward a state of balance.
• In order to use the fat in your body for energy, the body must have gone through its most readily available source of energy: glucose or sugar in the blood. Once that is depleted, the body begins to utilize fat for energy. You can deplete that sugar in the blood by exercising until it is gone, fasting until it is gone, or adjusting your carbohydrate intake.
• Sugar truly is addictive. It stimulates the same parts of the brain as heroin and cocaine. When you have it you want more of it. And you know this to be true.
• Stop eating sugar.
• When we eat grains, they are turned to sugar in our stomachs.
• Do not eat these:
• Grains
• Potatoes
• Refined salt
• Refined sugar
• Processed oils (margarine)
• Legumes
• Eating a paleo or caveman diet flips the Standard American Diet on its head from a macronutrient perspective. Instead of eating mostly carbohydrates with minimum fat, this diet consists of mostly fat, then protein, and finally minimal carbohydrates.
• Now, there are times during travel and work and life when the right foods simply are not available. In an airport or an office party or a restaurant where you are having a business meeting. My solution to that is very simple: Don’t eat. It’s called a fast, and it is actually very good for you.
• Newsflash: YOU DON’T HAVE TO EAT.
• In this age, much of the food around is actually trying to kill you. It is poison.
• YOU ARE NOT STARVING. Humans can survive thirty days without food. You can make it a few extra hours. You can actually make it a few days without any issue.
• fasting isn’t that hard and you will feel better at the end of it. Fasting will recalibrate what hunger is to you. You will realize that you aren’t actually hungry most of the time. You are just bored.
• Stretching is an important part of being physically fit. It improves range of motion, helps in recovery, and also prevents injuries.
• When warming up, go slow and go through the entire range of motion, even pushing a little bit past normal at the top and bottom of the exercise.
• Like anything else in health and fitness, stretching requires consistency, so figure out what movements are most beneficial for you.
• Injuries and illness will occur.
• My theory for overcoming injuries and illnesses is simple: DO WHAT YOU CAN.
• Take advantage of physical injuries and sickness by doing something you don’t normally have time for. In other words: GET AFTER IT.
• Before you start your workout, you need to get warm.
• Many jobs require travel. Travel can make working out difficult. BUT IT DOESN’T MAKE IT IMPOSSIBLE.
• So. When you are on the road. Don’t get lazy. Don’t get complacent. Don’t use the road as an excuse. Get creative. Get aggressive. Get it done. When you are on the road, STAY ON THE PATH.
• The muscle-up is a staple of my workouts, and it is a basic movement in gymnastics.
• The only thing that matters is that you actually do. SO: DO.