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When something falls into a black hole, where does the material ultimately go, and will it, or the quantum information that it contained, ever come back out again? According to Einstein’s general relativity, those answers are simple: as soon as anything physical — matter, antimatter, radiation, etc. — crosses over the event horizon, it’s irretrievably gone. It can add to certain properties, like mass, electric charge, and angular momentum, that the black hole possesses, but little else. Instead, that infalling material goes swiftly toward and eventually into the central singularity, and will never escape again. But our Universe isn’t governed by general relativity alone, but also by quantum physics. According to our best understanding of quantum physics, there’s much more that needs to be considered. Not only are there other quantum properties inherent to the raw ingredients that go into making a black hole — baryon number, lepton number, color charge, spin, lepton family number, weak isospin and hypercharge, etc. — but the fabric of spacetime itself, which contains the black hole, ought to be quantum in nature as well. Because of the quantum properties our Universe possesses, black holes do not remain static, but rather evaporate over time: emitting Hawking radiation as a key part of the process. When black holes do evaporate, then, what happens to the information that went into creating them? Is the information preserved? Is it destroyed? Is it encoded in the outgoing radiation? And if so, how? These questions are at the heart of perhaps the greatest paradox of all: the black hole information paradox. Here’s both what we know and what we still need to find out. When two particles are entangled in the quantum mechanical sense, it’s as though some sort of hidden, invisible connection exists between them, and that a seemingly “random” property of one member of the pair can be known to better than “random guessing” simply by measuring the properties of the other member. Entanglement, as far as we can tell, is instantaneous, although no actual information appears to ever be transmitted from one member of the pair to the other. Credit: Annelisa Leinbach, atdigit / Adobe Stock Information When a physicist talks about information, they don’t necessarily mean what we conventionally think of as information: a string of letters, numbers, symbols, or anything else that can be encoded with bits like 0s or 1s. Conventionally, this is often described as “the number of yes/no questions that must be answered to fully specify the properties of your physical system,” although even that description has limitations. These are all certainly examples of information, but those examples don’t encompass all the various types of information that exist. Information can also include: - signals that enforce causality, - quantum states (like qubits instead of bits) for individual entities, - entangled quantum states between multiple entities, - or any measure of the physical quantity known as entropy. That last one is tricky, because entropy — an inherently thermodynamical quantity — is very often misunderstood. You’ll often hear statements like “entropy is a measure of disorder” or “entropy always increases for any system” and while those things are kind of true, it’s possible to make very ordered high-entropy systems and to decrease a system’s entropy through the input of an external energy source. As an alternative, consider this: what entropy actually measures is the number of possible arrangements of the (fully quantum) state of your system. A system set up in the initial conditions on the left and allowed to evolve will have less entropy if the door remains closed (left) than if the door is opened (right). If the particles are allowed to mix, there are more ways to arrange twice as many particles at the same equilibrium temperature than there are to arrange half of those particles, each, at two different temperatures, resulting in a much greater entropy for the system at right than the one at left. Credit: Htkym & Dhollm/Wikimedia Commons A classic example is to consider two systems: 1. A room with a divider in it, where one side of the room is filled with hot gas and the other side is filled with cold gas. 2. And that same room, with the same gases, except the divider is open and both sides of the room have reached the same temperature. Both systems have the same number of particles, the same total energy in them, but wildly different entropies from one another. The second system has a much greater amount of entropy, as there are many different ways to distribute energy among all of the particles in your system to achieve the desired configuration than there are for the first system; the number of possible arrangements of the fully quantum state of your full system is much greater for the second system than the first. Because there is a greater number of possible arrangements, you have to provide a greater amount of information — and, therefore, answer a greater number of “yes/no” questions — to fully describe the system with a greater amount of entropy. Information and entropy aren’t identical, but they are proportional: a greater entropy to your system means it requires more information to fully describe it. A wine glass, when vibrated at the right frequency, will shatter. This is a process that dramatically increases the entropy of the system and is thermodynamically favorable. The reverse process, of shards of glass reassembling themselves into a whole, uncracked glass, is so unlikely that it never occurs spontaneously in practice. However, if the motion of the individual shards, as they fly apart, were exactly reversed, they would indeed fly back together and, at least for an instant, successfully reassemble the wine glass. Time reversal symmetry is exact in Newtonian physics, but it is not obeyed in thermodynamics. Credit: BBC Worldwide/GIPHY Information and black holes If you take a book and burn it, the book’s information doesn’t get lost or destroyed, but merely scrambled. In principle — although, maybe not in practice just yet — you could trace each and every particle of paper-and-ink that initially went into the fire, determine where they went, and from the ash, smoke, soot, other chemicals, and invisible gases they produced, keep track of every character, in order, on every page from that book. In principle, you could examine the final system of the completely burned book and reconstruct the complete information that was in the book before you burned it. You can do this, too, with the remnants of a shattered glass: reconstructing what the original, unbroken structure looked like. You can do this with a scrambled-and-cooked egg: reconstructing what the uncooked, unscrambled egg was like. As long as the fundamental particles that the original system was made out of were preserved, no matter what interactions they underwent in the meanwhile, that original information about the initial state of the system would be preserved as well. But with black holes, that absolutely isn’t the case any longer. In general relativity, black holes don’t have any memory about the types of particles (or the properties of those particles) that went into creating or growing the black hole. The only measurable properties a black hole can possess are mass, electric charge, and angular momentum. One of the most important contributions of Roger Penrose to black hole physics is the demonstration of how a realistic object in our Universe, such as a star (or any collection of matter), can form an event horizon and how all the matter bound to it will inevitably encounter the central singularity. Once an event horizon forms, the development of a central singularity is not only inevitable, it’s extremely rapid. Credit: J. Jarnstead/Royal Swedish Academy of Sciences; annotations by E. Siegel In the early 1970s, this puzzle was considered by physicist Jacob Bekenstein, who recognized why this was such a problem. Whatever particles go into forming a black hole have their own properties, configuration, and amount of entropy (and information) encoded within them. According to the second law of thermodynamics, entropy can never decrease for a closed system; it can only increase or remain the same, unless some external source of energy is inputted to decrease that entropy. (And even then, the total entropy of “the original system plus the external source,” where the external source includes where the inputted energy comes from, will continue to increase.) But in pure general relativity alone, black holes have zero entropy, and that definition simply won’t work. From the perspective of an external observer, there are quantum particles that go into the creation of any black hole, and as the black hole gets created and grows, the surface area of its event horizon increases. As the mass of the black hole goes up, the surface area goes up, and as more particles pour in, the entropy must rise as well. It was Bekenstein who first recognized that the information encoded by the infalling particles would, from an external observer’s perspective, appear to get “smeared out” over the surface of the event horizon, enabling a definition of entropy that was proportional to a black hole’s event horizon’s surface area. Today, this is known as the Bekenstein-Hawking entropy: the entropy of a black hole. Encoded on the surface of the black hole can be bits (or quantum bits, i.e., qubits) of information, proportional to the event horizon’s surface area. When the black hole decays, it decays to a state of thermal radiation. As matter and radiation fall into the black hole, the surface area grows, enabling that information to be successfully encoded. When the black hole decays, entropy will not decrease, but rather will remain constant, as Hawking radiation is an entropy-conserving (adiabatic) process. How or if that information is encoded into the outgoing radiation is not yet determined. Credit: T.B. Bakker/Dr. J.P. van der Schaar, Universiteit van Amsterdam Will that information get destroyed? This definition was very exciting, but the notion that we had made sense of the Universe — of entropy, information, and black holes — was extremely short-lived. In 1974, just two years after Bekenstein’s earliest work on the topic, Stephen Hawking came along and not only had a spectacular realization, but performed a tremendously important calculation to go with it. His realization was that the standard way of performing quantum field theory calculations made an assumption: that space would, on tiny quantum scales, be treated as though it were flat, unaffected by the general relativistic curvature of space. However, in the vicinity of a black hole, this wasn’t just a bad approximation, it was a worse approximation than it would be under any other conditions that occurred within our physical Universe. Spacetime isn’t just curved around a black hole, it’s where you can find the most extreme conditions for spatial curvature in our entire cosmos! Instead, Hawking recognized, the calculation needed to be done in a background of curved space, where the background spatial curvature was given by Einstein’s equations and the properties of the black hole in question. Hawking calculated the simplest case — for a black hole with mass only, without electric charge or angular momentum — in 1974, and recognized that the state of the quantum vacuum, or empty space itself, was fundamentally different in curved space, near the black hole’s event horizon, than the state of the quantum vacuum far away from the black hole: where space is flat. Black holes formed of different materials or from particles in different quantum states should have different information encoded on their event horizons, and it is not understood if or how that information is then encoded in the outgoing Hawking radiation. Recent work from 2023 has suggested that event horizonless objects may emit Hawking radiation as well, but that result was discovered to have been ruled out in the literature historically: way back in 1975. Credit: The EU’s Communicate Science That calculation revealed that black holes don’t simply exist, stably, in this curved space, but that the differences in the vacuum near and far away from the event horizon lead to a continuous emission of blackbody radiation: now known as Hawking radiation. This radiation should: - have a blackbody spectrum, - be made almost exclusively of massless photons (not one member of particle-antiparticle pairs), - should radiate at a very low temperature that’s inversely proportional to the mass of the black hole, - and should evaporate in a time that’s proportional to the mass of the black hole cubed. This is remarkable, and is a purely quantum effect that some have suggested may apply to systems other than black holes as well. (Although that suggestion has been significantly challenged.) However, the phenomenon of Hawking radiation raised a new, troubling issue. If the radiation that comes out of a black hole as it evaporates, this Hawking radiation, is purely blackbody in nature, it should have no preference for: - matter over antimatter, - baryons over antibaryons, - leptons over antileptons, - one lepton family over another, or any other metric needed to answer a yes/no question regarding the initial quantum state of the matter that went into creating the black hole in the first place. For the first time, it seems that we’ve encountered a physical system where knowing and measuring all of the information about its “final state” doesn’t, even in principle, allow you to reconstruct what the initial state was. When a black hole either forms with a very low mass, or evaporates sufficiently so that only a small amount of mass remains, quantum effects arising from the curved spacetime near the event horizon will cause the black hole to rapidly decay via Hawking radiation. The lower the mass of the black hole, the more rapid the decay is, until the evaporation completes in one last “burst” of energetic radiation. The longest-lived black holes will be the most massive, with decay timescales exceeding 10^100 years. Credit: ortega-pictures/Pixabay The core of the black hole information paradox So where, then, did the information that went into making a black hole go? That’s the key point of this puzzle: we think that information shouldn’t be able to be destroyed, but if the black hole is evaporating into pure blackbody radiation, then all of that information that went into making the black hole has somehow disappeared. - It’s possible, of course, that what we think we know about information, entropy, and thermodynamics is not correct, and that black holes really are information-destroying entities. - It’s also possible, that even if we don’t presently understand the mechanism by which it occurred, that there’s some relationship between — from the perspective of an observer outside the event horizon — the information encoded on the surface of a black hole and the information encoded in the outgoing (Hawking) radiation. - And, if we’re truly keeping an open mind, it’s possible that something more fundamentally complex is occurring: that the information that goes into making-and-growing a black hole gets “mixed up” somehow in the interior of a black hole, and then is encoded in some non-trivial fashion onto the radiation when the black hole itself evaporates. The event horizon of a black hole is a spherical or spheroidal region from which nothing, not even light, can escape. But outside the event horizon, the black hole is predicted to emit radiation. Hawking’s 1974 work was the first to demonstrate this, but that work has also led to paradoxes (i.e., the black hole information paradox) that have yet to be resolved. Credit: NASA/Dana Berry, Skyworks Digital Inc. The truth is, despite many declarations over the years that the “black hole information paradox has been resolved,” that nobody knows. Nobody knows whether the information is preserved, whether it’s destroyed or erased, and whether it depends on what occurs in a black hole’s interior or whether it can be completely described from an outside observer’s perspective. We have mathematical correspondences between what happens on the inside and the outside of a black hole, including an underappreciated fact that takes us beyond the semiclassical approximation (quantum field theory calculations in a background of curved spacetime) used by Hawking: that when radiation comes out of a black hole, it should maintain a quantum mechanical entangled link to the black hole’s interior. We have devised methods that allow us to map the entropy of a black hole’s interior onto the outgoing radiation that arises due to the Hawking mechanism, which suggests (but does not prove) that we may be approaching a mechanism for understanding how the information that went into creating a black hole gets encoded back into the Universe outside of the black hole’s event horizon. Unfortunately, we don’t know how to calculate individual bits of information using any of these methods; we only know how to calculate overall “amounts” of information as though we’re putting them on a scale, seeing whether they balance or not. That’s an important step, but it isn’t enough to resolve this paradox. It’s generally assumed that at some level, gravity will be quantum, just like the other forces. While the semi-classical approximation for computing the decay of black holes involves performing quantum calculations in the classical background of Einstein’s curved space, that approach might not be valid for capturing the full suite of physical behavior that the outgoing radiation possesses, particularly as far as information is concerned. When black holes evaporate, we don’t know whether, or how, information from what went into creating that black hole is encoded into that outgoing radiation. Credit: Aurore Simmonet Certainly, there are a plethora of ideas that may be playing major roles. String-inspired ideas like complementarity and the AdS/CfT correspondence, as well as the notion of a “firewall” appearing partway through the evaporation process, are considered by many working on the paradox. Others suggest that there are correlations between every quantum of radiation emitted in the Hawking process (similar to entanglement), and that the full suite of those correlations must be understood in order to resolve the paradox. Still others have suggested altering the black hole’s internal and external geometries over the course of the emission of Hawking radiation to attempt to preserve information, while others appeal to whatever strong quantum effects must be present at the interface of quantum physics and relativity: becoming important in the final stages of black hole evaporation. However, we still do not understand the most important aspects of the paradox: where the information from the particles that create the black hole goes, and how that information — assuming it does get out into the Universe again — actually gets encoded into the outgoing radiation that results when black holes evaporate. Despite whatever claims you may have heard, make no mistake: the black hole information paradox is still an unresolved paradox, and although it’s still an active area of research, no one can be sure what the solution will ultimately be, or what method will eventually lead us to it. The black hole information paradox still persists inside the heart of every black hole in our Universe. This article was first published in September of 2023. It was updated in September of 2026. This article The information paradox at the heart of every black hole is featured on Big Think.

In the years following the Wright brothers’ 1903 takeoff, aircraft were designed via what a NASA history calls “an intuitive, daring empirical engineering loosely connected at best with any theory of aerodynamics or structural integrity.” Plane makers had some ideas about how to mimic flight while on the ground, but these were crude approximations with little sense of similarity or workability (that measure of practicality and reproducibility). The assessment of a plane as insufficiently robust was frequently made amid crashing noises and roaring flames. In the 1920s, the life expectancy of a pilot for the U.S. Air Mail Service (among the first practical uses of civilian aviation) was four years, and the service lost 31 of its first 40 pilots to crashes in its first decade. Much of the problem was that human flight was so new, nobody could fully answer the first, fundamental question in the testing litany: What will the product face, and how might it fail? Happily for pilots and passengers since, the U.S. Army let one Jimmy Doolittle pause his work as a test pilot to help figure it out. Thanks to a cringeworthy script and a hammy portrayal by Alec Baldwin, anyone who saw 2001’s Pearl Harbor may think of Doolittle as a jingoistic flyboy who spouted lines like “Victory belongs to those who believe in it the most and believe in it the longest …. We’re gonna make America believe.” The real Doolittle was, in fact, not just a daring and gifted pilot but an inveterate mischief-maker and premier aeronautical mind who worked to rationalize the airplane into something that could be known, predicted, and judged. Something that could be tested. Small but fierce, Doolittle was a champion boxer in college, an avid acrobat (he was particularly keen on tumbling), and one of the many people who took to flying in the early years of the 20th century. He started as a teenager building his own gliders from sticks and cloth, promptly crashing each one. In one case, he tied himself to his friend’s car to pick up speed for takeoff, only to end up being dragged down the road. His autobiography, I Could Never Be So Lucky Again (1991), includes an inordinate number of stories that end with him climbing out of a wrecked plane, amazingly unhurt, then being chewed out by a superior officer. In a typical tale, he once saw two soldiers walking down a road on his base and “decided to give them a little scare” by flying a few feet over their heads. He misjudged the distance and clipped one of them with his landing gear. Distracted by the worry that he’d killed an innocent, he crashed into a barbed wire fence. Somehow, everyone was fine. After serving as a flight instructor during World War I, Doolittle stayed in the army and started working as a test pilot. He racked up accolades as a short- and long-distance racer, becoming the first person to fly cross-country in under 24 hours, then in under 12. When the Curtiss-Wright Aircraft Company hired him to go to South America to sell pilots on its new P-1 Hawk pursuit plane, Doolittle spent his first night in Santiago getting drunk on pisco sours (“a delightful, powerful drink”) and decided to impress his hosts by doing acrobatics on a window ledge. When the sandstone crumbled under him, Doolittle fell two stories and broke both ankles. Undeterred, he rigged a pair of bootstraps that let him work his plane’s rudder, had someone lift him into the cockpit, and successfully demonstrated the Hawk’s prowess. In 1924, at the suggestion of his friend Eddie Aldrin (father of astronaut Buzz), Doolittle got a two-year dispensation from the army to pursue a master’s degree at the Massachusetts Institute of Technology’s newly formed aeronautical engineering program. For his thesis, Doolittle addressed what he called “one of the mysteries of flight”: how much stress a plane could take without falling apart. Another way to put it is its load factor, that ratio of a plane’s lifting ability to its weight. Jimmy Doolittle at the 1925 Schneider Race in the cockpit of a Curtiss R3C-2 racer. (Credit: San Diego Air and Space Museum Archives) At the time, such strength testing happened mostly on the ground and typically involved piling bags of sand on the wings and tail fins until they broke. That was an upgrade over those who threw loose sand onto their aircraft and used rakes to push it into place – and even that was better than folks like aviation pioneer Thomas Sopwith, who didn’t bother to run this kind of stress test on his early planes before trying them out in the air. (He lived to be a 101.) Doolittle thought he had a better way to determine what kind of loads a plane could take. He created his own in-flight regimen and undertook what Air Force historian Richard P. Hallion deemed “the first major examination of structural loadings during maneuvering flight ever undertaken.” Doolittle rigged a Fokker PW-7 fighter with an accelerometer to track the load factor, measured in g-forces. Then he piled on steadily greater forces by executing loops, barrel rolls, snap rolls, spins, spirals, and 180-degree turns. He flew upside down and pulled out of dives. “What made the whole effort worthwhile was that I found out exactly what the Fokker could withstand because it nearly came apart during a dive at 200 miles per hour,” Doolittle wrote in his autobiography. As the upper wing cracked, the accelerometer read 7.8 g’s, countering Fokker’s claim that its plane would handle a load factor of 8.5. Doolittle secured his degree and turned his thesis into a report that proved to be an aviation milestone. A 21st-century flight engineer might quibble with the similarity and workability of such testing. Future PW-7 pilots would face all sorts of conditions Doolittle didn’t try out, and Doolittle didn’t attempt to design a process that he or others would repeat. But in his work — pushing a product to the very limit of its strength and carefully measuring its performance — we can spot an ancestor of the modern, onion-peeling test flight program, down to the cracking of the 777’s wings. This article The test pilot who made airplanes safer (by teaching us how to break them better) is featured on Big Think.

Every day as a leader brings an endless flow of judgment calls, big and small. Even as you read this, you might be weighing where to focus your team’s efforts this quarter, who to hire for a role, or how to handle a slightly cheeky budget request from a colleague. You might be trying to decide whether to skip a meeting this afternoon, or how to tamp down a weird little disagreement so that it doesn’t turn into a bigger one. These choices are rarely like math problems with clean answers; you have to figure out the right approach when there isn’t really a single “right approach.” So a distinctive strength of a good leader is the ability to make choices wisely, day after day, in ways that maximize your team’s chances of success. Where does that wisdom come from? Years of experience can certainly help guide you, if you’re lucky enough to have it. But whenever you’re faced with something new, familiarity may not be enough to make you wise. That’s why — whatever stage you’re at in your career — it’s worth distinguishing between experiential wisdom and what I call deliberate wisdom. Experiential wisdom comes from having seen situations before. Deliberate wisdom is the set of intentional mental habits that help leaders think clearly in any situation — even when encountering it for the first time. And the key to developing deliberate wisdom is to learn to work intelligently with the way your brain processes information. That’s because our brains have tight constraints on how much they can handle at once. If we ignore these limits, our brain can underperform without us realizing: We miss important signals, think less clearly, and do things that cause a facepalm the next day. But if we understand and work within those limits, we help our brain function at its best — and we make smarter, better judgments as a result. The way to unlock that capacity for wisdom is to learn to widen our perspective where it matters, and lighten the cognitive load everywhere else. Here’s the core science behind that widen-and-lighten “double unlock.” ## Widen your perspective where it matters To make good choices, you need a clear and accurate picture of whatever situation you’re dealing with. And to bring that picture into focus, it helps to know three big things about how your brain makes sense of the world: 1. Believe it or not, you only ever notice part of what’s going on. Your brain is constantly receiving billions of bits of data — but it only has the bandwidth to consciously process a fraction of them. So it does the sensible thing: It filters incoming information and selects some of it for your awareness, while leaving the rest in the background. That means you’re always missing part of the story — and on top of that, you’re not even aware of what you’re missing. Scientists call this selective attention, and it affects us all (even though we all like to believe we’re immune to it). 2. What’s on your mind right now shapes what you notice next. Since you can’t notice everything, your brain guesses where to focus based largely on what you’re already thinking. For example, if you have a distinct goal in mind, your brain makes sure you see and hear anything related to that goal. When you have strong assumptions about something, your brain will zero in on information that supports your view, while missing much of what points the other way. Even your mood matters: When you’re in a bad mood, you see more negative things. Your mental starting point always filters the reality you experience. 3. When it’s time for you to take action, your brain automatically points you toward the easiest choice rather than the best one. Because your brain has limited processing power, it aims to be efficient by taking shortcuts that use less mental effort. Without you realizing, it follows simple rules of thumb like “let’s do what worked last time” or “let’s do what others are doing” to get to quick conclusions without having to think too hard. Scientists call these shortcuts cognitive biases, and using them can be fine if the stakes are low. But for things that matter — as they often do, when you’re the one in charge — you want to take deliberate steps to bring all your intelligence to the table. > Deliberate wisdom is the set of intentional mental habits that help leaders think clearly in any situation — even when encountering it for the first time. So, wise leaders know how to come off autopilot and deliberately widen their perspective when doing something important. Ways to do that include: - Recognize that your first impression probably isn’t the whole story. Develop the habit of asking: “What’s another way of seeing this?” “What could I be missing?” and “How could I test that?” - Be aware of what’s top of mind for you, knowing that it will color what you notice next. When it counts, ask yourself, “What do I really need to look out for here?” - With major decisions, take a structured approach that checks multiple angles, and guides you to the best option (rather than the easiest one). This thoughtful approach might feel counterintuitive if you’ve absorbed the myth that great leaders always have immediate answers. But in a world full of unknowns, wise leaders know the best answers only come after you’ve taken the time to ask the right questions. ## Lighten the cognitive load where you can You might be thinking, “Okay, but deliberately widening your perspective requires some effort — so where on earth do you find that extra mental bandwidth when you’re already working hard?” The answer: by lightening the cognitive load wherever it’s unnecessary. Here’s your primer: 1. The brain’s central executive network (CEN) keeps you on track as a professional. Your CEN is like your brain’s conductor, orchestrating the mental tasks that enable you to focus on what matters, evaluate possible responses, make a plan — and switch gears when that plan needs to change. Anytime you’re trying to make an important choice, you can bet your central executive network is working hard. 2. Your CEN has limits on what it can do at any given moment, in part because its tasks rely a lot on the brain’s limited working memory. Working memory is the mental scratch pad you use to hold information in mind just long enough to think about it. Some researchers suggest it tends to max out when handling four chunks of information at a time; others say it can be closer to seven. Either way, its capacity isn’t huge, which is why it’s harder to think clearly when you’re juggling a lot of things. And once your working memory is overloaded, your brain is more likely to fall back on those simple mental shortcuts we just learned about, resulting in poorer choices all around. 3. Certain things make it more likely that your working memory will get overloaded — including fatigue and multitasking. When you’re tired or worn down, your working memory capacity declines — and your analytical skills decline with it. Then, if you ask your brain to divide its processing capacity across two activities that both require attention, you’ll generally reduce your performance, accuracy, and speed on both tasks. In other words: If you splinter your focus, nothing gets the full version of your intelligence. So, the good news is that a wise leader can make the most of the brain’s capacity by lightening unnecessary cognitive load wherever possible. Tips include: - Notice when your mind feels full of too many things at once. Take a moment to pause, focus, and simplify where you can. - For important work, do one thing at a time (“singletask”) to ensure your brain is putting its attention and energy where it’s most needed. - Respect your brain’s need for sleep and rest, to enable it to function at its best. To recap: Even the best leaders perceive an incomplete version of reality, can only hold a certain amount in mind at once, and are likely to take automatic mental shortcuts when deciding what to do. But if you acknowledge and work with these limitations, by widening your perspective when it matters most and lightening the mental load where you can, you’ll make the best possible use of your brain’s capacity for wisdom — and you’ll be better equipped to handle the complexity and range of choices you make each day. This article Leadership isn’t natural — but making intelligent decisions can be learned is featured on Big Think.

When 10-month-old Noa Greenwood was diagnosed with Canavan, her parents Lee and Lori were confronted with an idea of a future they’d never prepared for. Canavan is a rare, inherited neurodegenerative disease caused by mutations in the ASPA gene. Most children with it never develop beyond the abilities of a six-month-old. The very day that Noa was diagnosed, a trial for Canavan therapy was posted on clinicaltrials.gov. She was among the first children ever dosed. Within months, she began hitting milestones her parents were told she’d never see. Only a few years ago, this story would not have been possible. As with many rare diseases, Canavan represented too small a market for traditional pharma to fund, even when medical researchers had found potential cures. So BridgeBio developed a new model built around portfolio theory: instead of investing in one rare disease treatment at a time, spread the risk across 150 of them. Noa’s story offers hope — not only to those impacted by Canavan disease, but also to the 450 million people suffering from rare diseases worldwide. This video These life-saving treatments were considered ‘too small’ to fund. Until now. is featured on Big Think.

In my decades of playing soccer, I’ve taken part in hundreds of games. I’ve played in games where we dominated and lost. I’ve played in games where we were awful yet somehow scraped through to win. I’ve played in games where the other team cheated the whole way through with time-wasting, shirt-pulling, hacking people down at the knees (picture Paraguay’s shithousery against France in the World Cup) and still walked off winners. I hated it, but the result said they were the better side. Is that true? If you only looked at the scoreboard, the result justified the means. But the scoreboard doesn’t tell you who played well, who got lucky, or who you became in the process of trying. There’s something missing when you reduce quality to a number. We make this same mistake looking back on the decisions we make and the results we deliver. Things turn out well, we’re a genius. Things turn out badly, we’re the dumbest person to ever live. That gap between how something turns out and how well it was actually done is one of the most expensive blind spots we carry, and most of us have no idea we’re doing it. Annie Duke, a former World Series of Poker champion turned decision scientist and PhD, explains why, and it comes down to poker being a truer test of decision quality compared to, say, chess. Chess is a game of known quantities. In chess, there’s no hidden information and almost no luck. Every piece is on the board in front of you, and your mistakes are instantly obvious. Your outcomes in the game and the quality of your decisions are highly related. Almost nothing that matters in life is like chess. Real decisions look more like poker (and in fact, John von Neumann, who built game theory, did so based on poker). “I can start with the very best, two aces, and I can lose the hand,” Duke says. You can’t see the other cards, luck is baked in, and it plays out over time. As she puts it, “the correlation between outcomes and decision quality isn’t one, and it’s not even close.” There’s a near-infinite list of factors that could explain why you wound up in a particular situation. The butterfly flapped its wings 100 miles away. You rode the wave of Nvidia and AI. You graduated around the time of the 2008 financial crisis. So many of our results are decidedly beyond our control, so what’s happening when we forget to focus on the one thing we do control: our process? ## Resulting Duke calls it “resulting,” or “using the outcome quality as a perfect signal for deriving decision quality.” In other words, resulting is when you think that the end result is all that matters, so a good outcome must have come from a good decision and a bad one from a bad decision. Unfortunately, that’s far too clean, too narrow a focus, and far too black and white, for the reality of the complexity we live in. The dumbest boss you’ve ever had can sometimes turn up great results. The smartest person you know and love can still get unlucky. It doesn’t make the first one right or the second one wrong. Think back to soccer, or any sport for a second. There’s a reason a season is more than one game because, in theory, the better teams will win consistently over time. This is especially true when we consider dynasties (the Patriots, Golden State in the 2010s, Manchester United in the 2000s, the Yankees a long time ago, etc.) as repeated, continued team success over years and years. An upset can happen to anyone, but a dynasty does not happen by accident. This awareness of the longer-term win is ingrained in poker, where champions like Duke build their edge over hundreds of thousands of hands. Eventually, the luck washes out. That’s what makes resulting so expensive if left unchecked. You don’t learn, and so any improvements are circumstantial, not intentional. If you see a lucky win as “skill,” you run the same sloppy process again, and eventually the luck runs out. The process is the only part you control, and by focusing there, you broaden your definition of what success looks like. My own bar for my work is to feel good enough in the decision before I take action, and how much diligence “good enough” requires scales with the stakes. In my writing, I make peace (or try my damnedest to) with the fact that how it’s received is out of my hands. This one could go viral or land to absolute crickets. All I can control is: “Is this a piece I’m proud of? Would I read this?” That’s one version of a better process and a better question. But you can change the questions you ask and aim them at any decision — a hire, a launch, a conversation you’re dreading. A scoreboard or a focus solely on the result asks, “Did it work?” A better question asks, “Given what I knew at the time, did I make the initial call well?” It invites you to consider: What information did I have? What did I miss? Was I calm, or was I reacting? What would I do differently with the same cards next time? Did I consider other perspectives? Did I play out how this could fail (a “premortem,” as Gary Klein calls it, where you assume the decision has already gone up in flames and work backward to determine why)? To answer those questions, you need a clear picture of what you knew before the result came in. But the first step is to realize your recall is not to be trusted. So before a result comes in, consider writing down the call you made, the reasoning, and roughly how likely you think each outcome is. Memory can conveniently become a liar in the retrospective. Because your brain is a clever, conniving little genius, it’s too easy for your mind to rewrite the story of what you were thinking so that a success looks like it was obvious and intentional all along, you brilliant genius, or there’s nothing you could have done differently, you poor sacrificial lamb. A written record of your decisions, and why you made them, is an honest history. To be clear, winning still rocks. Within the bounds of what is legal, moral, and reasonable good taste, I’m OK with doing everything in our power to secure victory. Winning feels objectively better. But a dizzying amount of life lies beyond our immediate control, so our task is to practice what we can and let go of the rest. Luck and lady fate aren’t going anywhere, but over the long run, focusing on process is the most reliable way to actually get better, because you’re finally practicing the part you control. How do you win at a career? At a marriage? At being a parent? At being a friend? You don’t. These are infinite games, to borrow a term from James Carse and Simon Sinek. There’s no final whistle, and the point is to keep playing well over hundreds, if not thousands, of reps, until you finally pass into the great beyond. There are no deciding walk-off home runs here. Just the next call, and how well you make it. The last call you’re proud of — was it a good decision? And how would you know?https://bigthinkbusiness.substack.com/about This article How not to confuse good outcomes for good decision-making is featured on Big Think.