Appendix: Glossary of Key Terms

The Science of Luck: Statistical Thinking, Network Theory, Serendipity Engineering, Opportunity Recognition, and the Psychology of Chance

This glossary contains over 200 key terms introduced across the textbook. Entries are organized alphabetically. Each entry includes a definition and the chapter where the term first appears. When a term builds on a prior concept, cross-references are noted.

Terms marked with an asterisk (*) are foundational — understanding them is essential before tackling the more advanced concepts that follow.

A

Absorption (deep learning) — The process of immersing oneself deeply in a domain to build the pattern libraries that enable expert recognition of opportunities. Absorption is the preparation that makes prepared-mind luck possible; it differs from passive exposure in that it is intentional and sustained. First introduced: Chapter 29.

Act of God — A legal and colloquial term for events caused by natural forces beyond human control, such as floods, earthquakes, or lightning strikes. In luck science, the term is used to distinguish aleatory luck (genuinely uncontrollable randomness) from epistemic luck (uncertainty arising from limited information). First introduced: Chapter 1.

Agency ** — The capacity of an individual to act independently and make meaningful choices. In luck science, agency is not binary; it exists on a spectrum and is constrained by structural factors while still remaining real and consequential. The tension between agency and structure runs through the entire textbook. First introduced: Chapter 1.*

Aleatory luck — From the Latin alea (die, as in dice), aleatory luck refers to outcomes determined by genuinely random processes — coin flips, dice rolls, the precise angle of a wind gust. No amount of preparation or skill eliminates aleatory luck, though understanding its properties allows better decision-making around it. First introduced: Chapter 1.*

Algorithmic serendipity — The phenomenon by which recommendation algorithms on social media platforms surface unexpected content that turns out to be valuable or fortuitous for the user or creator. Unlike human serendipity, algorithmic serendipity is governed by engagement signals and is susceptible to filter bubbles. First introduced: Chapter 22.

Anchoring bias — A cognitive bias in which people rely too heavily on the first piece of information encountered (the "anchor") when making decisions, even when that information is arbitrary or irrelevant. Anchoring affects perceived probability, negotiation outcomes, and risk assessment. First introduced: Chapter 4.

Antifragility — A property of systems that not only withstand volatility and stress but actually improve because of it, coined by Nassim Nicholas Taleb. Antifragility is stronger than resilience (which merely survives shocks) and is presented as an advanced luck architecture goal. First introduced: Chapter 17.

Asymmetry of regret — The empirical finding that people tend to regret inactions (things they didn't do) more than actions in the long run, even though they feel action-regret more intensely in the short term. Understanding this asymmetry helps overcome opportunity avoidance caused by loss aversion. First introduced: Chapter 35.

Attention economy — The commercial ecosystem in which human attention is the scarce resource being competed for by platforms, advertisers, and content creators. In luck science, the attention economy is relevant because it determines which opportunities reach any given person's awareness. First introduced: Chapter 32.

Attribution error (fundamental) — The tendency to overattribute others' outcomes to their personal character or ability while underattributing structural and situational factors. In luck research, fundamental attribution error causes us to see lucky people as skilled and unlucky people as deficient. First introduced: Chapter 4.

Availability heuristic — A mental shortcut in which people judge the likelihood of an event by how easily an example comes to mind. Because dramatic failures and successes are more memorable, availability heuristic systematically distorts our sense of luck probabilities. First introduced: Chapter 4.

B

Barbell strategy — An investment and life-design strategy, developed by Nassim Nicholas Taleb, in which a person maintains a mix of very safe and very speculative bets while avoiding the vulnerable middle. Applied to luck design, it means having stable income sources alongside high-risk, high-upside experiments. First introduced: Chapter 37.

Base rate — The background frequency of an event in a reference population, independent of any specific individual or case. Base rates are systematically ignored by human intuition (see: base rate neglect), leading to poor probability assessments. First introduced: Chapter 6.*

Base rate neglect — The tendency to ignore or underweight base rate information when evaluating a specific case. For example, assuming a startup will succeed because the founder seems talented, while ignoring that 90% of startups fail. First introduced: Chapter 6.

Bayesian reasoning — A framework for updating beliefs in light of new evidence, formalized by Thomas Bayes in the 18th century. In intuitive form, Bayesian reasoning asks: given what I already knew, how much should this new evidence change my estimate? It contrasts with simple frequentist counting and is essential for good probability intuition. First introduced: Chapter 6.

Behavioral economics — A field combining psychology and economics that documents systematic ways human decision-making departs from the predictions of classical rational-actor models. Much of Part 3 of this textbook draws on behavioral economics research. First introduced: Chapter 10.

Belief updating — The process of revising a probability estimate or belief in response to new evidence. Good belief updaters are neither dogmatic (refusing to update) nor credulous (updating too dramatically on weak evidence). First introduced: Chapter 6.

Black swan event — A term coined by Nassim Nicholas Taleb for a rare, unpredictable, and massively consequential event that falls outside the realm of normal expectations and is only explained in retrospect. Black swan events represent extreme aleatory luck. First introduced: Chapter 3.

Blind serendipity — One of Christian Busch's three types of serendipity: a completely chance encounter or discovery for which no special preparation or mindset was necessary — a pure gift of circumstance. First introduced: Chapter 24.

Bonding social capital — Social capital built among people who are similar to each other (same background, interests, profession). Bonding capital creates strong trust and emotional support within groups but limits access to novel information. Contrasted with bridging capital. First introduced: Chapter 21.

Bourdieu's capitals — Pierre Bourdieu's framework identifying four forms of capital that determine social position: economic capital (money and assets), social capital (networks and relationships), cultural capital (education, taste, and disposational knowledge), and symbolic capital (prestige and recognition). First introduced: Chapter 18.

Bridging social capital — Social capital built across different groups, communities, or professional domains. Bridging capital provides access to novel information, diverse opportunities, and weak ties that bonding capital does not. First introduced: Chapter 21.

C

Career capital — The skills, reputation, relationships, and track record that make a person an attractive collaborator, employee, or partner. Career capital is a key determinant of career luck because it determines which opportunities are offered and which are recognizable. First introduced: Chapter 38.

Causation vs. correlation — A foundational distinction in research: two variables can move together (correlation) without one causing the other. Many pop-luck claims confuse correlation with causation (e.g., "lucky people smile more" may not mean smiling causes luck). First introduced: Chapter 6.

Central limit theorem — A fundamental theorem in probability: regardless of the underlying distribution, the average of a sufficiently large number of independent samples will approximate a normal distribution. This underlies why averages are so powerful for reasoning about populations. First introduced: Chapter 7.

Clustering coefficient — A network measure indicating how interconnected a node's neighbors are with each other. High clustering creates dense bonding-capital groups; low clustering within a neighborhood can signal access to structural holes. First introduced: Chapter 20.

Cognitive bias — A systematic pattern of deviation from rational judgment in decision-making, typically arising from heuristics or mental shortcuts. Cognitive biases are the primary reason humans misread luck and probability. First introduced: Chapter 4.

Compound luck — The phenomenon by which early luck advantages create conditions for further luck advantages, causing outcomes to diverge dramatically over time from initially small differences in starting conditions. First introduced: Chapter 18.

Conditional probability — The probability of an event given that another event has already occurred, written P(A|B). Conditional probability is fundamental to understanding how context changes likelihood estimates. The Monty Hall problem is the most famous illustration. First introduced: Chapter 6.*

Confidence interval — A range of values within which the true value of a measured quantity is likely to fall, given the sample data. A 95% confidence interval, for example, means that 95% of intervals calculated this way will contain the true value. First introduced: Chapter 7.

Confirmation bias — The tendency to search for, interpret, and remember information in a way that confirms one's pre-existing beliefs. Confirmation bias causes people to see evidence of their luck beliefs everywhere, regardless of actual luck dynamics. First introduced: Chapter 4.

Constitutive luck — Luck with respect to who you are — including your innate talents, disposition, genetic endowments, and deep character traits. Constitutive luck is ethically profound because it suggests that even the effort you exert may be partly explained by your luck. First introduced: Chapter 1.*

Counterfactual thinking — Mental simulation of alternative outcomes ("what if...?" or "if only...?"). Upward counterfactual thinking (imaging better outcomes) can motivate improvement; downward counterfactual thinking (imagining worse outcomes) can generate gratitude and resilience. First introduced: Chapter 17.

Cumulative advantage — See: Matthew Effect. First introduced: Chapter 18.

Curiosity (as luck strategy) — The disposition to actively seek out novel information, experiences, and ideas across domain boundaries. Research shows that curiosity increases information intake, cross-domain connection-making, and the probability of serendipitous discovery. First introduced: Chapter 26.

D

Decision fatigue — The deterioration of decision quality after a prolonged period of decision-making, caused by the depletion of executive function resources. Decision fatigue is relevant to luck because fatigued attention misses opportunities that a fresh mind would catch. First introduced: Chapter 32.

Degrees of separation — The number of relationship links between two individuals in a social network. Stanley Milgram's original research suggested an average of six degrees of separation between any two people in the United States, later refined and formalized by Watts and Strogatz. First introduced: Chapter 20.

Deliberate practice — A structured, effortful form of practice with specific goals, immediate feedback, and progressive challenge, studied extensively by K. Anders Ericsson. Deliberate practice builds the expert pattern libraries that underlie prepared-mind luck. First introduced: Chapter 29.

Diversification (luck portfolio) — The strategy of spreading luck bets across multiple uncorrelated domains, so that failures in one area do not eliminate opportunity entirely. Applied from investment theory to life design. First introduced: Chapter 37.

Domain expertise — Deep, structured knowledge within a specific field, built through sustained practice and study. Domain expertise enables pattern recognition that makes "lucky" insights possible for experts but not novices. First introduced: Chapter 27.

E

Effect size — A measure of the practical magnitude of a research finding, independent of statistical significance. A statistically significant effect can be tiny and practically irrelevant; effect size communicates how much difference a variable actually makes. First introduced: Appendix B.

Ego depletion — A phenomenon in which self-control and decision quality decline after a period of sustained self-regulation. Related to decision fatigue; both affect attention quality and thus opportunity recognition. First introduced: Chapter 32.

Emerging technology — Technologies in early or growth phases of adoption, where opportunity windows are wide before mainstream adoption closes them. Understanding technology S-curves allows strategic positioning for technology luck. First introduced: Chapter 33.

Entrepreneur of information — Ronald Burt's term for the individual who bridges structural holes in a network, brokering information between otherwise disconnected groups. This position confers significant opportunity advantage. First introduced: Chapter 21.

Entrepreneurial alertness — Israel Kirzner's term for the perceptual capacity that allows entrepreneurs to notice opportunities before others do. Alertness is distinct from search; it is a receptive orientation rather than an active hunt. First introduced: Chapter 30.

Epistemic luck — Luck with respect to knowledge — being in the right epistemic position to have true beliefs. A person has epistemic luck if they believe something true, but their belief-forming process was unreliable (e.g., they guessed correctly). First introduced: Chapter 1.*

Expected value ** — The probability-weighted average of all possible outcomes of a decision. Expected value is calculated by multiplying each outcome's value by its probability and summing across all outcomes. Good decision-makers optimize expected value rather than hoping for best-case scenarios. First introduced: Chapter 10.*

Expertise paradox — The phenomenon by which deep expertise in a domain can, beyond a certain point, blind an expert to possibilities that lie outside established category structures — the very categories that make expertise efficient can make it brittle. First introduced: Chapter 29.

Explore-exploit tension — The fundamental tradeoff between exploring new options (to discover better possibilities) and exploiting known good options (to extract maximum value). Career luck often depends on calibrating this tension well across time. First introduced: Chapter 38.

F

False consensus effect — The tendency to overestimate how widely one's own opinions, behaviors, and experiences are shared by others. In luck science, it causes people to assume their personal luck experience represents the norm. First introduced: Chapter 4.

Fast thinking (System 1) — Daniel Kahneman's term for automatic, intuitive, pattern-matching thought. System 1 is fast and often right, but prone to predictable biases, especially in probabilistic reasoning. First introduced: Chapter 27.

Filter bubble — A phenomenon described by Eli Pariser in which personalized algorithmic feeds create an information environment that reinforces existing views by excluding dissenting information. Filter bubbles reduce epistemic luck by narrowing information intake. First introduced: Chapter 22.

First-mover advantage — The competitive benefit gained by being the first actor to enter a new market, technology, or domain. First-mover advantage is a form of technology luck, though it does not guarantee lasting success. First introduced: Chapter 33.

Flow state — Mihaly Csikszentmihalyi's concept of complete, effortless absorption in a challenging activity. Flow states are associated with peak performance and are a byproduct of high curiosity and domain engagement. First introduced: Chapter 26.

Fortune's Wheel (Rota Fortunae) — A pre-modern metaphor for fate, depicting a wheel turned by the goddess Fortuna (or Fortune), raising some people to prosperity while casting others down. The image captures the arbitrariness and cyclicality of worldly luck. First introduced: Chapter 5.

Frequentist probability — An interpretation of probability as the long-run frequency of outcomes over many repeated trials. Contrasted with Bayesian probability, which allows probability statements about one-time events. First introduced: Chapter 6.

G

Gatekeeper — A person or institution with the power to control access to an opportunity, resource, or network. Gatekeepers are a central feature of opportunity structures; understanding how they operate and what they value is part of luck engineering. First introduced: Chapter 23.

Generational luck — The luck of being born into a particular historical cohort with access to specific technological, economic, and cultural conditions. The Baby Boom generation's access to postwar prosperity and the Millennial generation's relationship with digital platforms are both forms of generational luck. First introduced: Chapter 31.

Gilovich-Tversky hot hand study — The landmark 1985 study by Thomas Gilovich, Robert Vallone, and Amos Tversky demonstrating that the "hot hand" in basketball is a cognitive illusion — that apparent streaks are consistent with chance. (See Appendix A for full treatment and the Miller-Sanjurjo 2018 reanalysis.) First introduced: Chapter 4.

Granovetter's weak ties — See: Weak ties. Named for sociologist Mark Granovetter whose 1973 paper "The Strength of Weak Ties" established the foundational insight. First introduced: Chapter 19.

Great Gatsby Curve — The empirical relationship, documented by economist Miles Corak, between income inequality and intergenerational mobility: higher inequality correlates with lower social mobility. Named by Alan Krueger with reference to F. Scott Fitzgerald's novel. First introduced: Chapter 18.

H

Halo effect — A cognitive bias in which a single positive attribute of a person or product creates an overall favorable impression across unrelated attributes. In luck research, the halo effect causes people to attribute a lucky person's success to skill across the board. First introduced: Chapter 4.

Hedging — A risk management strategy that involves making bets that partially offset each other, reducing variance. In life design, hedging means maintaining multiple paths that protect against total failure of any single strategy. First introduced: Chapter 37.

Hindsight bias — The tendency to see past events as having been predictable after the fact. Hindsight bias makes lucky outcomes appear inevitable in retrospect, obscuring the role of chance and distorting lessons drawn from success. First introduced: Chapter 4.

Hook (serendipity) — Christian Busch's term for the element of an encounter or piece of communication that creates the possibility of an unexpected, valuable connection — a comment, question, or disclosure that invites serendipitous response. First introduced: Chapter 24.

Hot hand fallacy — The mistaken belief that a person who has recently experienced success is more likely to continue succeeding, based on the pattern-seeking tendency of human cognition. Research shows that in most domains, recent performance beyond actual skill level is not predictive. First introduced: Chapter 4.

Hub (network) — A highly connected node in a network that serves as a central routing point for information and relationships. Hubs are rare but disproportionately powerful in determining the flow of opportunities through a network. First introduced: Chapter 20.

I

Illusory correlation — The tendency to perceive a relationship between two variables when no such relationship exists, or when the relationship is much weaker than perceived. Classic examples include believing black cats cause bad luck or that lucky charms improve outcomes. First introduced: Chapter 4.

Information asymmetry — A situation in which one party to a transaction or relationship has more or better information than another. Structural holes in networks create information asymmetry that translates into opportunity advantage. First introduced: Chapter 21.

Information foraging — A framework, adapted from animal foraging theory, that models how people search for and gather information. High-curiosity individuals forage more widely and are more likely to encounter serendipitous discoveries. First introduced: Chapter 26.

Inspection paradox — A probability puzzle: when you arrive at a bus stop, you will on average wait longer than the average gap between buses, because longer gaps are more likely to catch any given arriving passenger. The inspection paradox illustrates how sampling bias distorts intuition. First introduced: Chapter 11.

Institutional luck — The luck created or destroyed by the design of organizations and institutions — the rules, processes, and cultures that determine which people and ideas get noticed, funded, or promoted. Dr. Yuki Tanaka's primary research focus. First introduced: Chapter 40.

Intersectionality — A framework developed by Kimberlé Crenshaw for understanding how multiple social identities (race, class, gender, disability status, etc.) overlap and interact to create compound forms of privilege or disadvantage — and thus compound luck effects. First introduced: Chapter 18.

J

Judgment heuristic — See: Heuristic. Mental shortcuts used to make judgments quickly. In probability reasoning, heuristics are often accurate but produce systematic errors in specific contexts. First introduced: Chapter 4.

K

Kahneman's dual-process theory — Daniel Kahneman's framework dividing cognition into System 1 (fast, automatic, intuitive) and System 2 (slow, deliberate, analytical). Both systems have roles in luck perception and decision-making; System 1 creates biases, System 2 corrects them (at a cost). First introduced: Chapter 27.

Kirzner's entrepreneurial alertness — See: Entrepreneurial alertness. First introduced: Chapter 30.

L

Law of large numbers — A theorem in probability stating that as the number of trials of a random process increases, the observed average outcome will converge toward the expected (theoretical) value. The law underlies why casinos always win eventually and why n=7 personal experiments are unreliable. First introduced: Chapter 7.*

Learned helplessness — A condition developed by Martin Seligman in animal experiments, in which exposure to uncontrollable negative events leads to passivity even when control becomes available. In luck science, learned helplessness is a consequence of repeated bad luck that extinguishes opportunity-seeking behavior. First introduced: Chapter 13.

Linking social capital — Social capital that connects individuals to those with different levels of power or institutional authority — connecting across vertical hierarchies. Linking capital is what makes mentor and sponsor relationships especially powerful. First introduced: Chapter 23.

Long tail — Chris Anderson's concept describing how, in digital markets with low distribution costs, a large number of niche products can collectively outsell a small number of hits. The long tail creates niche luck opportunities for creators who serve specific communities. First introduced: Chapter 22.

Longitudinal study — A research design in which the same participants are observed over an extended period of time, enabling researchers to track changes, identify causes, and distinguish short-term from long-term effects. First introduced: Appendix B.

Locus of control — Julian Rotter's construct measuring the degree to which people believe their outcomes are determined by their own actions (internal locus) versus external forces such as luck, fate, or powerful others (external locus). First introduced: Chapter 13.*

Loss aversion — The psychological principle, established by Kahneman and Tversky, that losses loom approximately twice as large psychologically as equivalent gains. Loss aversion causes people to avoid positive expected-value opportunities when framed as risks. First introduced: Chapter 15.*

Luck audit — A structured self-assessment framework for evaluating the current state of one's luck architecture across seven life domains: network, skills, mindset, opportunity surface, attention, timing, and environmental conditions. First introduced: Chapter 36.*

Luck egalitarianism — A political-philosophical position holding that inequalities arising from unchosen luck are unjust and should be corrected by society, while inequalities arising from genuine choices may be acceptable. Associated with philosophers G.A. Cohen and Ronald Dworkin. First introduced: Chapter 39.

Luck-skill continuum — Michael Mauboussin's framework for positioning activities on a spectrum from pure luck (roulette) to pure skill (chess) based on the extent to which outcomes reflect individual ability vs. chance factors. First introduced: Chapter 2.

Luck surface area — See: Opportunity surface. First introduced: Chapter 25.

M

Matthew Effect — The sociological principle, named by Robert Merton from the Gospel of Matthew, that those who already have advantages tend to accumulate further advantages: "to those who have, more will be given." First introduced: Chapter 18.

Medici Effect — Frans Johansson's term for the explosion of innovation that occurs at the intersection of different disciplines, domains, and cultures. Named for the Medici family's Renaissance Florence, which funded the confluence of diverse thinkers. Curiosity is the primary mechanism for creating Medici Effect conditions. First introduced: Chapter 26.

Mental model — A cognitive representation of how something works, used to reason about it and make predictions. Strong mental models (e.g., of probability, network dynamics, or cognitive bias) are themselves luck-enabling tools. First introduced: Chapter 1.

Meritocracy — The belief or system in which rewards (wealth, status, opportunity) are distributed based on individual merit (talent + effort). Luck science critiques meritocracy by revealing how structural and constitutive luck shape who can demonstrate merit in the first place. First introduced: Chapter 2.

Meta-analysis — A statistical method for combining results across multiple independent studies to produce more reliable estimates of an effect. Meta-analyses sit at the top of the evidence hierarchy. First introduced: Appendix B.

Monte Carlo simulation — A computational technique that uses repeated random sampling to estimate the probability of different outcomes in a complex system. Monte Carlo methods are used extensively in Chapter simulations. First introduced: Chapter 6.

Monty Hall problem — A famous probability puzzle: given three doors, one hiding a prize, you choose one. The host (who knows where the prize is) opens one of the other doors to reveal nothing. Should you switch? Yes — switching doubles your probability of winning. First introduced: Chapter 11.

Moral luck — The philosophical problem, raised by Thomas Nagel and Bernard Williams, of how we can hold people morally responsible for outcomes that were significantly determined by factors outside their control. First introduced: Chapter 39.

N

Narrative fallacy — Nassim Nicholas Taleb's term for the tendency to construct retrospective stories that make the past appear more ordered, predictable, and intentional than it actually was. Narrative fallacy is a major source of survivorship bias in success stories. First introduced: Chapter 9.

Network diversity — The extent to which a person's network spans different industries, backgrounds, geographies, and social groups. Network diversity correlates with access to novel information and more varied luck opportunities. First introduced: Chapter 19.

Network effect — A phenomenon in which a product or service becomes more valuable as more people use it. Network effects drive winner-take-all dynamics in digital platforms and create concentrated luck for early participants. First introduced: Chapter 22.

Negativity bias — The tendency for negative events, emotions, and information to exert a stronger influence on cognition than equivalent positive ones. Negativity bias causes people to notice bad luck more than good luck, distorting their luck self-assessments. First introduced: Chapter 16.

Normal distribution — A symmetrical, bell-shaped probability distribution in which most outcomes cluster around the mean and extreme values become progressively rarer. Many naturally occurring quantities approximate normal distributions, though power-law distributions are more common in social and economic phenomena. First introduced: Chapter 7.

O

Open monitoring — A mindfulness practice involving non-directed, receptive awareness of whatever arises in consciousness. Open monitoring is contrasted with focused attention practices and is thought to enhance serendipitous discovery by broadening cognitive receptivity. First introduced: Chapter 24.

Opportunity cost — The value of the best alternative foregone when a choice is made. In luck science, opportunity cost is central to understanding the true price of status quo bias and loss aversion. First introduced: Chapter 15.

Opportunity recognition — The perceptual capacity to identify conditions in which value can be created — to see a possibility where others see nothing. Opportunity recognition is partially teachable through training attention, building pattern libraries, and reducing cognitive biases. First introduced: Chapter 30.

Opportunity surface — The total number and variety of contexts a person inhabits — physical, digital, professional, and social — across which lucky encounters can occur. A larger opportunity surface creates more surface area for luck. Synonymous with "luck surface area." First introduced: Chapter 25.*

Optimal stopping — A mathematical framework for determining the best moment to stop searching and commit to a choice. The classic result (the "37% rule" or secretary problem) states that you should observe and reject the first 37% of options before committing to the next option that beats all previous ones. First introduced: Chapter 10.

Outcome bias — The tendency to evaluate the quality of a decision based on its outcome rather than on the quality of the reasoning at the time of the decision. Outcome bias is especially damaging in luck-heavy domains where good decisions sometimes produce bad outcomes. First introduced: Chapter 10.

P

P-value — In statistical hypothesis testing, the probability of observing results at least as extreme as those obtained, if the null hypothesis were true. A low p-value (conventionally below 0.05) indicates that results are unlikely to have occurred by chance. First introduced: Appendix B.

Pattern recognition — The cognitive capacity to detect regularities, structures, and signals within noisy data. Expert pattern recognition is trained by deliberate practice and extensive domain experience; it is a primary mechanism of prepared-mind luck. First introduced: Chapter 27.

Placebo effect — The phenomenon in which a person experiences real measurable improvements after receiving an inert treatment, due to the expectation of improvement. The placebo effect demonstrates that positive expectation has genuine physiological effects. First introduced: Chapter 14.

Platform S-curve — See: Technology adoption S-curve. First introduced: Chapter 31.

Portfolio thinking (luck) — An approach to life design inspired by investment portfolio theory, in which the individual deliberately diversifies across multiple luck domains, manages correlations between bets, and maintains a mix of low-variance and high-upside opportunities. First introduced: Chapter 37.

Post-traumatic growth — The phenomenon, documented by Richard Tedeschi and Lawrence Calhoun, in which individuals experience significant positive psychological development as a result of struggling with highly challenging life circumstances. PTG is the scientific basis for understanding resilience as a luck asset. First introduced: Chapter 17.

Power law distribution — A probability distribution in which a small number of items account for a disproportionately large share of outcomes (e.g., 80% of effects from 20% of causes). Social media virality, wealth distribution, and city sizes follow power laws. Power laws create high variance in luck outcomes. First introduced: Chapter 22.

Prepared mind — The state of readiness, built through deep domain knowledge and sustained curiosity, that enables a person to recognize and act on a lucky break that would be invisible to an unprepared observer. Derived from Louis Pasteur's dictum: "Chance favors the prepared mind." First introduced: Chapter 29.

Prospect theory — The behavioral economics framework developed by Daniel Kahneman and Amos Tversky describing how people actually evaluate outcomes relative to a reference point (not in absolute terms) and weight losses more heavily than gains. Prospect theory explains loss aversion, risk-seeking in loss domains, and the endowment effect. First introduced: Chapter 15.*

Pseudo-serendipity — Christian Busch's term for situations in which a person set out to find one thing and, while not finding it, found something else of value. Contrasted with blind serendipity (pure luck) and sagacity serendipity (recognizing the value of an unexpected find). First introduced: Chapter 24.

Pygmalion effect — The phenomenon, demonstrated by Rosenthal and Jacobson (1968), in which higher expectations placed on individuals lead to higher performance. Named for the myth of the sculptor Pygmalion; demonstrates the power of positive expectation. First introduced: Chapter 14.

R

Randomness — The property of a process whose outcomes cannot be predicted with certainty based on prior information. True randomness (quantum events) is distinct from practical randomness (processes so complex that outcomes are unpredictable for all practical purposes). First introduced: Chapter 3.*

Rational expectations — The economic principle that agents form beliefs about the future based on all available information and make optimal decisions accordingly. Real human behavior systematically departs from rational expectations in ways documented by behavioral economics. First introduced: Chapter 10.

Regret minimization framework — Jeff Bezos's decision-making heuristic of imagining yourself at age 80 looking back: what decision would you regret less? The framework leverages the asymmetry of regret to overcome short-term loss aversion. First introduced: Chapter 35.

Regression to the mean — The statistical phenomenon by which extreme measured values in any sample tend to be followed by less extreme values upon re-measurement, purely because of measurement variation. Regression to the mean is not caused by any intervention; it is a mathematical necessity. First introduced: Chapter 8.*

Relative age effect — The phenomenon in which individuals born in the months just after a competitive age-group cutoff date are statistically overrepresented in elite sports, academic programs, and leadership roles, because their slight developmental advantage early in childhood compounds over time. An example of constitutive luck. First introduced: Chapter 18.

Replication crisis — An ongoing methodological crisis in psychology and other sciences, beginning around 2011, in which a significant proportion of published findings failed to replicate when retested. The replication crisis has important implications for which luck science findings can be trusted. First introduced: Appendix B.

Resilience — The capacity to recover from adversity, failure, or bad luck. In luck science, resilience is not passive recovery but an active skill set including cognitive reframing, social support mobilization, and behavioral flexibility. First introduced: Chapter 17.

Resultant luck — Luck with respect to how things turn out — the actual outcome of a risky action, independent of whether the action was wise. Two people can make identical decisions and experience different resultant luck. First introduced: Chapter 1.*

Risk tolerance — An individual's psychological and financial capacity to accept variance in outcomes. Risk tolerance interacts with expected value reasoning: the same bet can be optimal for one person and suboptimal for another depending on their risk situation. First introduced: Chapter 10.

S

Sagacity serendipity — Christian Busch's term for serendipity that requires preparation: an unexpected event occurs, but recognizing its value requires prior knowledge, pattern-matching, or openness. Corresponds to Pasteur's "prepared mind." First introduced: Chapter 24.

Sample size — The number of observations in a study or data set. Small samples produce unreliable estimates and are the primary cause of the "hot hand" illusion and other streak phenomena. First introduced: Chapter 7.

Selection bias — A type of sampling bias in which the sample studied is not representative of the population of interest. Survivorship bias is a specific form of selection bias. First introduced: Chapter 9.

Self-efficacy — Albert Bandura's term for a person's belief in their capacity to execute the behaviors necessary to achieve specific goals. High self-efficacy increases the tendency to attempt tasks and persist despite setbacks, making it a luck multiplier. First introduced: Chapter 13.

Self-fulfilling prophecy — A belief that causes itself to become true through the behavior it triggers. Positive expectations of success increase persistence and effort, which increase the probability of success; negative expectations produce the reverse. First introduced: Chapter 14.

Serendipity — A fortunate, unexpected discovery made while looking for something else or while not looking at all. The word was coined by Horace Walpole in 1754. Serendipity is distinguished from luck in that it specifically involves discovery and typically requires some preparation. First introduced: Chapter 24.*

Serendipity antenna — Christian Busch's metaphor for the receptive orientation that increases the probability of serendipitous encounters — a habit of openness, broad attention, and conversational investment that notices unexpected value in interactions. First introduced: Chapter 24.

Serendipity engineering — The practice of deliberately designing one's environment, behaviors, and social exposure to increase the probability and quality of fortunate, unexpected discoveries. Serendipity engineering treats luck as a partially controllable variable. First introduced: Chapter 24.

Slow thinking (System 2) — Daniel Kahneman's term for deliberate, effortful, analytical cognition. System 2 can override System 1 biases but is expensive in cognitive resources and is not deployed automatically. First introduced: Chapter 27.

Small-world network — A network topology, formalized by Duncan Watts and Steven Strogatz, characterized by high local clustering combined with short average path lengths between any two nodes. Small-world networks explain why six-degrees-of-separation phenomena arise even in very large social networks. First introduced: Chapter 20.

Social capital — The value embedded in social relationships and networks — the resources, information, trust, and cooperation that flow through connections between people. Robert Putnam, Pierre Bourdieu, and Ronald Burt have each developed influential accounts of social capital. First introduced: Chapter 21.*

Social mobility — The ability of individuals or families to move between different social positions over time. Social mobility is constrained by structural luck factors including family wealth, geography, education access, and network position. First introduced: Chapter 18.

Sponsor (career) — A person in a position of power who actively advocates for another person's advancement, uses their own reputation and influence on that person's behalf, and creates specific opportunities. Sponsors are distinct from mentors, who offer advice rather than active sponsorship. First introduced: Chapter 23.

Statistical significance — A statement that a result is unlikely to have occurred by chance, given a specified probability threshold (conventionally p < 0.05). Statistical significance does not imply practical or real-world importance. First introduced: Appendix B.

Status quo bias — The tendency to prefer the current state of affairs and to require disproportionately strong evidence before accepting change. Status quo bias is a manifestation of loss aversion and causes missed opportunities. First introduced: Chapter 15.

Streaks — Consecutive sequences of similar outcomes. Most apparent streaks in human performance data are consistent with random processes; the perception of meaningful streaks is a product of the hot hand fallacy and pattern-seeking cognition. First introduced: Chapter 4.

Structural holes — Ronald Burt's term for the gaps between groups in a network — the absence of direct connections between two otherwise separate clusters. A person who bridges a structural hole has access to information from both sides and can act as an information entrepreneur. First introduced: Chapter 21.

Structural luck — Luck arising from the social, economic, and institutional structures into which a person is born or in which they operate, as opposed to individual action or character. Structural luck includes family wealth, neighborhood school quality, professional network access, and historical timing. First introduced: Chapter 18.

Survivorship bias — The logical error of drawing conclusions from the visible subset of entities that survived a selection process while ignoring the invisible majority that failed. Survivorship bias is endemic in success advice, where only successful people's stories are heard. First introduced: Chapter 9.*

T

Technology adoption S-curve — A model of how new technologies spread through a population: slow initial adoption (early adopters), rapid growth through the mainstream, and eventual saturation. Opportunity windows are widest during the early growth phase. First introduced: Chapter 31.

Third place — Ray Oldenburg's sociological concept of environments outside home (first place) and work (second place) — cafes, community centers, parks, clubs — where informal social interaction creates unexpected connections and serendipitous encounters. First introduced: Chapter 25.

Timing luck — The aspect of luck governed by when you enter a market, field, relationship, or platform. Timing is partially controllable through awareness of technology and social cycles, making it a partially engineerable form of luck. First introduced: Chapter 31.

U

Uncertainty — A state in which the probabilities of possible outcomes are not precisely known, as distinct from risk (where probabilities are known). True uncertainty requires judgment rather than calculation and is the normal condition in most life domains. First introduced: Chapter 3.

Upward counterfactual thinking — Mental simulation of how things could have gone better. While motivating, upward counterfactuals can also produce regret and reduced satisfaction; the direction (comparing up vs. down) profoundly affects mood. First introduced: Chapter 17.

Utility function — An economic representation of an individual's preferences, mapping outcomes to subjective value. Because of loss aversion and diminishing returns, utility functions are not linear in wealth or resources. First introduced: Chapter 10.

V

Viral coefficient — In epidemiology and network theory, the average number of new people that each affected person passes a "contagion" to (whether viral content, product, or behavior). A viral coefficient above 1 produces exponential growth; below 1, the spread dies. First introduced: Chapter 22.

Viral spread — The rapid propagation of content, information, or behavior through a network, driven by network effects and platform amplification mechanics. Understanding viral spread requires understanding both network structure and algorithmic distribution. First introduced: Chapter 3.

Vulnerability (luck context) — The quality of openness to uncertainty and the possibility of failure. In network and serendipity contexts, vulnerability — sharing ideas before they're complete, disclosing goals to acquaintances — is shown to increase the probability of receiving helpful, unexpected responses. First introduced: Chapter 24.

W

Weak ties — Mark Granovetter's term for relationships characterized by infrequent contact, limited emotional intensity, and low intimacy — acquaintances, former colleagues, friends-of-friends. Weak ties are the primary conduit through which novel information and job opportunities flow through social networks. First introduced: Chapter 19.*

Wald's problem — The World War II problem solved by Abraham Wald in which survivorship bias was applied to bullet-hole data: analysts were planning to reinforce the most-shot areas of returning planes, but Wald recognized they should reinforce the areas with no holes — because planes hit there didn't return. First introduced: Chapter 9.

Winner-take-all market — A market structure in which a small number of participants capture an outsized majority of total rewards, typically driven by network effects and low marginal distribution costs. Social media fame and many digital careers exhibit winner-take-all dynamics. First introduced: Chapter 22.

Z

Zero-sum thinking — The cognitive error of assuming that another person's gain necessarily comes at one's own expense. Zero-sum thinking limits cooperation, network-building, and the creation of new luck opportunities. In social capital terms, most networking is positive-sum because information is non-rivalrous. First introduced: Chapter 21.

See also: Appendix A (Key Studies Summary), Appendix B (Research Methods Primer), and Appendix E (FAQ) for additional context on many of these terms.