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Error

Error denotes the discrepancy between a represented state—whether in belief, judgment, measurement, or action—and the objective reality it purports to reflect, constituting a failure of correspondence to truth or correctness. In philosophical inquiry, particularly epistemology, error arises as a cognitive shortfall, such as through perceptual misinterpretation or inattentiveness to sensory data, undermining the pursuit of knowledge.^[1] Within empirical sciences, errors manifest distinctly as random variations, which introduce unpredictable fluctuations around the true value due to uncontrollable factors like environmental noise, and systematic biases, which consistently shift measurements in one direction owing to flawed instruments, procedures, or assumptions.^[2]^[3] These distinctions enable targeted mitigation: random errors diminish with repeated sampling and statistical averaging, while systematic ones demand calibration or methodological overhaul to restore alignment with causal realities. Errors thus serve as diagnostic signals, revealing underlying causal mechanisms of deviation and compelling rigorous validation against empirical benchmarks over mere theoretical consistency.^[4] Cognitively, errors propagate through flawed first-principles reasoning when foundational assumptions harbor inaccuracies, amplifying distortions in derived conclusions, as seen in cases where unexamined premises lead to erroneous chains of inference.^[5] In broader human endeavors, persistent errors—whether in policy, engineering, or discourse—often trace to unaddressed systematic influences like institutional incentives or selective data interpretation, underscoring the necessity of causal scrutiny to discern genuine patterns from artifactual ones.^[6]

Behavioral and Cognitive Errors

Human Mistakes and Gaffes

Human mistakes and gaffes constitute unintended deviations from intended actions or judgments, stemming from limitations in perception, attention, memory, or execution. Psychological frameworks, such as James Reason's model, distinguish slips—observable failures in performing a planned action correctly, often due to attentional slips—and lapses—internal failures involving memory retrieval or omission of steps, despite intact intentions.^[7]^[8] These errors arise from inherent cognitive constraints, including finite working memory capacity (typically holding 7±2 items) and vulnerability to divided attention, rendering flawless performance improbable under routine demands.^[9] Empirical evidence links specific causal factors to heightened error propensity. Fatigue, induced by extended wakefulness or irregular schedules, degrades vigilance and executive function; studies in high-reliability domains like aviation show error rates doubling after 17 hours of continuous activity, comparable to blood alcohol levels of 0.05%.^[10] Overconfidence exacerbates this by inflating perceived competence, leading to rule violations or overlooked cues; quantitative analyses in decision-making tasks reveal overconfidence correlating with 20-30% higher error incidence in complex judgments.^[11] Such factors manifest in gaffes ranging from verbal flubs—public figures mangling facts under pressure—to procedural oversights in daily operations. Notable historical instances highlight these dynamics without implying isolated culpability. In 1864, during the Siege of Petersburg, Union General John Sedgwick dismissed enemy fire with the quip, "They couldn't hit an elephant at this distance," only to be felled by a bullet seconds later, reflecting a momentary lapse in threat assessment amid battlefield fatigue.^[12] Similarly, Swedish King Gustavus Adolphus's 1632 decision to charge recklessly at Lützen, disregarding advisory caution, stemmed from overreliance on personal intuition, contributing to his fatal wounding despite tactical acumen elsewhere.^[12] Investigations into mishaps consistently portray errors as symptoms of broader systemic vulnerabilities rather than mere personal failings. National Transportation Safety Board (NTSB) reviews of aviation accidents attribute primary human causation to over 70% of cases but routinely uncover upstream contributors like deficient crew resource management or scheduling pressures that amplify cognitive loads.^[13] NASA analyses echo this, noting that while individual slips occur predictably due to fallible neurobiology, resilient systems mitigate inevitability through redundancy and error-tolerant protocols, prioritizing causal realism over punitive attributions.^[14]^[15]

Errors in Language and Linguistics

Linguistic errors manifest in speech production as systematic deviations from intended utterances, including malapropisms, spoonerisms, and grammatical slips, which corpus analyses reveal occur non-randomly and adhere to phonological and syntactic constraints. Malapropisms entail substituting a target word with a phonologically similar but semantically incongruent one, such as "pineapple of politeness" for "pinnacle of politeness," with frequency in corpora modulated by neighborhood density—denser phonological neighborhoods elevate error rates due to heightened lexical competition. Spoonerisms feature transposition of initial sounds or syllables across words, as in "tease my ears" for "ease my tears," reflecting inadvertent swaps during phonological encoding that preserve overall prosodic structure. Grammatical slips, like transient subject-verb agreement errors (e.g., "the team are winning" instead of "is winning"), arise from parallel activation of syntactic alternatives in sentence planning. These errors originate in modular stages of speech production, where brain processing involves selecting lemmas, assembling phonemes, and coordinating articulation; neurolinguistic models, such as the Directions Into Velocities of Articulators (DIVA), demonstrate that disruptions in feedforward and feedback loops—particularly in basal ganglia and cerebellar circuits—precipitate slips through unresolved competitions in neural representations. Empirical neurolinguistic data indicate that monitoring mechanisms, reliant on prefrontal and auditory regions, detect and repair many errors pre-articulation, yet residual slips persist when processing overloads exceed capacity, as quantified in self-repair rates averaging 10-20% of detected anomalies in conversational corpora. In bilingual contexts, error rates amplify due to frequency lags in less dominant languages; Spanish-English bilinguals, for instance, generated 2.15 phonological errors per non-overlapping tongue twister trial versus 1.01 for English monolinguals, a disparity persisting across dominance levels and attributable to sparser practice rather than cross-linguistic interference.^[16] Contrary to views framing all linguistic variations as neutral evolution, uncorrected errors introduce causal noise that propagates miscommunication, with studies linking speech production inaccuracies to elevated misunderstanding risks—residual errors in children, for example, correlate with 1.5-2 times higher odds of peer rejection and academic underperformance compared to typical speakers. Workplace analyses further quantify that language slips contribute to 15-25% of interpersonal breakdowns, impairing task efficiency and escalating frustration via iterative misinterpretations, as listeners infer unintended meanings from ambiguous or erroneous forms. Such patterns underscore that while adaptive changes occur via deliberate innovation, error-driven drifts erode precision, fostering systemic communication failures absent rigorous correction mechanisms.

Scientific and Technical Errors

Measurement and Experimental Errors

Measurement errors in scientific experiments are broadly classified into random and systematic types. Random errors arise from unpredictable fluctuations in the measurement process, such as thermal noise or minor variations in operator technique, and are characterized by their stochastic nature, often following a Gaussian distribution with mean zero.^[17] ^[4] These errors can be quantified using the standard deviation \sigma of repeated measurements and reduced through averaging multiple trials, as the standard error of the mean scales as \sigma / \sqrt{N} for N independent observations.^[18] Systematic errors, in contrast, introduce consistent biases due to flaws in instrumentation, calibration inaccuracies, or environmental factors like uncompensated temperature drifts, shifting all measurements in a predictable direction without averaging them away.^[17] ^[3] Error propagation formulas enable estimation of uncertainty in derived quantities. For independent random errors in addition or subtraction, such as z = x + y, the combined standard deviation is \sigma_z = \sqrt{\sigma_x^2 + \sigma_y^2}; for multiplication or division, z = x y, it approximates \frac{\sigma_z}{z} = \sqrt{\left(\frac{\sigma_x}{x}\right)^2 + \left(\frac{\sigma_y}{y}\right)^2} when relative errors are small.^[18] ^[19] Systematic errors propagate directly through the functional relationship, often requiring separate identification and correction, as they do not average out.^[17] The foundations of modern error analysis trace to Carl Friedrich Gauss's 1809 work Theoria Motus Corporum Coelestium, where he formalized the method of least squares to minimize the sum of squared residuals, assuming errors follow a normal distribution for optimal parameter estimation in astronomical data.^[20] ^[21] This approach underpins statistical inference in experimental physics and beyond, enabling quantification of uncertainty via chi-squared tests and confidence intervals. Contemporary methods, including Monte Carlo simulations, address complex nonlinear propagations intractable analytically by resampling input distributions to empirically derive output uncertainties, as implemented in frameworks like the NIST Uncertainty Machine.^[22] ^[23] In practice, systematic biases in experimental design and reporting—such as unacknowledged confounding variables or selective data exclusion—frequently undermine reproducibility, with meta-analyses revealing that low statistical power and flexible analyses inflate false positives.^[24] ^[25] John Ioannidis's 2005 analysis demonstrated that for studies with modest power (e.g., 20% chance of detecting true effects) and positive bias (e.g., 50% false positive rate among non-significant results), over 80% of significant findings may be false, a pattern corroborated across fields despite peer review.^[24] Academic incentives favoring novel results over rigorous error characterization exacerbate these issues, as evidenced by replication rates below 50% in large-scale efforts, highlighting the need for causal identification of error sources beyond surface-level statistics.^[26]

Errors in Biology and Medicine

In biological systems, errors arise primarily during DNA replication, where polymerases incorporate incorrect nucleotides at rates of approximately 10^{-4} to 10^{-5} per base pair prior to proofreading.^[27] Proofreading mechanisms, involving 3' to 5' exonuclease activity, enhance fidelity by excising mismatched bases, reducing the error rate to about 10^{-7} per base pair, while post-replication mismatch repair further lowers it to roughly 10^{-10} per base pair.^[28] These corrections reflect an evolutionary balance between replication speed—necessary for rapid cell division and organismal growth—and accuracy, as higher fidelity demands slower polymerization or additional energy costs for error detection, potentially compromising competitive fitness in resource-limited environments.^[29] In humans, the germline mutation rate, incorporating escaped replication errors across multiple cell divisions, averages 1.2 to 2.5 × 10^{-8} per nucleotide site per generation, yielding 60 to 100 de novo mutations per diploid genome.^[30]^[31] Such genetic errors manifest as point mutations, insertions, deletions, or chromosomal abnormalities, with somatic mutations accumulating in non-reproductive tissues due to similar replication infidelity compounded by environmental mutagens like UV radiation or chemicals.^[32] Quantitatively, DNA polymerase base selectivity alone discriminates against mismatches by 10^5 to 10^6 fold, but residual errors drive evolutionary variation and pathology; for instance, uncorrected mutations contribute to cancer via oncogene activation or tumor suppressor inactivation, with error rates rising in aging cells due to declining repair efficiency.^[33] From a causal standpoint, these rates persist because absolute error elimination would impose prohibitive metabolic costs, as biophysical models show fidelity gains beyond current levels yield diminishing returns relative to speed constraints in enzymatic kinetics.^[34] Medical errors, distinct from inherent biological variability, encompass unintended deviations in diagnosis, treatment, or prevention that harm patients, often termed iatrogenic events. The 1999 Institute of Medicine report estimated 44,000 to 98,000 annual U.S. hospital deaths from such errors, primarily systemic lapses rather than individual negligence.^[35] More recent analyses, drawing from adverse event databases, indicate over 250,000 annual deaths, positioning medical errors as a leading cause, with diagnostic failures accounting for 371,000 deaths and 424,000 permanent disabilities yearly across care settings.^[36] Iatrogenic mortality trended downward from 1999 to 2015 but rose 17% by 2020, correlating with procedural complexities and regional disparities in non-metropolitan areas.^[37] Diagnostic errors, comprising missed, delayed, or wrong assessments, stem causally from failures in data synthesis, inadequate clinical reasoning, or communication breakdowns, affecting 12% of ambulatory encounters and contributing to 40% of malpractice claims.^[38] Empirical data highlight testing errors (e.g., overlooked results) and patient assessment flaws (e.g., biased heuristics) as predominant, exacerbated by high workload and fragmented care systems rather than isolated incompetence.^[39] In hospitals, protocol deviations—such as medication mismanagement or surgical mishaps—amplify risks, with equipment malfunctions and human factors like fatigue underlying 18% of fatal adverse events reported in 2023.^[40] Unlike biological errors tuned by selection pressures, medical inaccuracies persist due to misaligned incentives and incomplete feedback loops, underscoring the need for causal interventions targeting root processes over blame attribution.^[41]

Errors in Quantum Computing and Emerging Technologies

In quantum computing, errors arise primarily from qubit decoherence, where quantum superpositions collapse due to interactions with the environment, and from imperfections in quantum gate operations, which introduce unintended phase shifts or bit flips. Decoherence times for superconducting qubits, a common platform, typically range from microseconds to milliseconds, limiting circuit depths to hundreds of gates before fidelity drops below 99%. Gate error rates in state-of-the-art systems hover around 0.1% to 1% per two-qubit operation, far exceeding the thresholds needed for fault-tolerant computation.^[42]^[43]^[44] Quantum error correction addresses these issues by encoding a logical qubit across multiple physical qubits using codes like the surface code, which detects errors via syndrome measurements without violating the no-cloning theorem—that prohibits perfect copying of unknown quantum states, necessitating redundant entangled encodings rather than backups. The theorem imposes fundamental limits, as direct state replication for error mitigation is impossible, forcing reliance on probabilistic correction that scales exponentially with code distance. In a 2023 experiment, Google demonstrated error suppression in a surface code logical qubit on its Sycamore processor, where scaling from distance-3 to distance-5 reduced logical error rates by encoding in 49 physical qubits, achieving modest improvement over uncorrected ensembles. By December 2024, Google reported operation below the surface code threshold on a 105-qubit Willow chip, with logical error rates decreasing exponentially as code size increased, verifying suppression for the first time in a programmable superconducting system.^[42]^[45]^[46] Empirical progress toward fault tolerance continued into 2025, with Microsoft and Quantinuum achieving logical qubits in 2024 with error rates 800 times lower than physical qubits using trapped-ion systems, and Microsoft reporting a 1,000-fold reduction to approximately 10^{-6} per cycle via novel 4D codes in mid-2025, though these remain small-scale demonstrations requiring thousands of physical qubits for practical utility. Such advances validate threshold theorems predicting fault tolerance if physical error rates fall below ~1%, but overhyped claims of quantum supremacy—such as Google's 2019 Sycamore sampling task—often overlook that uncorrected noise precludes scalable advantage, with critics noting persistent leakage and crosstalk errors undermine assertions without verified, below-threshold scaling across full algorithms. Verifiable thresholds demand logical error rates under 10^{-10} per cycle for million-qubit systems, a target unmet amid debates on whether inherent noise models permit exponential suppression without prohibitive overhead.^[47]^[48]^[49]

Mathematical and Computational Errors

Numerical Analysis Errors

In numerical analysis, errors primarily manifest as truncation and round-off, stemming from approximations inherent to computational methods and the finite precision of digital arithmetic. Truncation errors arise from terminating infinite processes, such as summing a finite number of terms in a series expansion, while round-off errors result from representing real numbers with limited digits in floating-point formats. These errors can accumulate, amplifying inaccuracies in solutions to differential equations, linear systems, and optimizations, necessitating rigorous bounds like those from perturbation theory to ensure verifiable precision. Truncation errors in series approximations, exemplified by Taylor expansions, quantify the discrepancy between a function and its polynomial proxy. For a function f(x) expanded around a, the nth-order Taylor polynomial P_n(x) incurs a remainder R_n(x) = f(x) - P_n(x), bounded by the Lagrange form |R_n(x)| \leq \frac{M |x - a|^{n+1}}{(n+1)!}, where M bounds the (n+1)th derivative on the interval. This bound enables error estimation; for instance, approximating \sin(x) near 0 with the first few terms yields truncation errors decreasing factorially with n, but higher derivatives grow for functions like \exp(x), demanding careful order selection. Empirical validation via benchmark functions, such as integrating Gaussian quadratures, confirms these bounds, revealing that naive truncation without remainder checks can deviate by orders of magnitude in stiff problems.^[50] Round-off errors emerge in floating-point arithmetic, governed by standards like IEEE 754, where numbers are represented as \pm m \times 2^e with mantissa precision limited to about 53 bits for doubles, yielding machine epsilon \epsilon \approx 2.22 \times 10^{-16}. Each operation introduces relative error bounded by \epsilon/2, but accumulation in iterations—such as Gaussian elimination—can magnify this via loss of significance. Catastrophic cancellation exemplifies severe degradation: subtracting nearly equal quantities, like $1 - \cos(\theta) for small \theta, discards leading digits, reducing effective precision from 16 to potentially 0 decimal places; reformulation as $2 \sin^2(\theta/2) avoids this by preserving magnitude. In practice, benchmarks on ill-conditioned matrices, like the Hilbert matrix with condition number exceeding $10^{13} for n=10, demonstrate solutions erroneous by factors of the condition number \kappa(A) = \|A\| \|A^{-1}\|, where small input perturbations \delta x / |x| \approx 10^{-16} yield output errors \delta y / |y| \approx \kappa \cdot 10^{-16}.^[51]^[52] James Wilkinson's 1963 analysis in Rounding Errors in Algebraic Processes formalized backward error analysis, showing that algorithms like LU decomposition compute exact solutions to nearby perturbed problems, with perturbations bounded by n \epsilon times growth factors, rather than forward error propagation. This shifted focus from worst-case forward bounds to stable backward perturbations, enabling reliable software like LAPACK, which uses condition estimates to flag ill-posed cases. Real-world simulations underscore these risks: a 2023 study of atmospheric modeling revealed catastrophic cancellation in finite-difference schemes causing unphysical energy cascades, mimicking turbulence artifacts until higher precision or stabilized schemes intervened; similarly, eigenvalue computations on nearly defective matrices in quantum chemistry simulations have propagated round-off into spurious states, validated against exact analytic limits. Assumptions of infinite precision in unverified code thus fail empirically, as benchmark tests on platforms like NIST's Stajano suite expose deviations up to 100% in long recursions without error monitoring.^[53]^[54]^[55]

Errors in Cybernetics and Control Systems

In cybernetics, as formalized by Norbert Wiener in his 1948 work Cybernetics: Or Control and Communication in the Animal and the Machine, errors manifest as deviations in feedback loops that govern system behavior, where the error signal—defined as the difference between a desired setpoint and the actual output—drives corrective actions to maintain stability and goal-directed adaptation.^[56]^[57] This foundational concept applies to engineered control systems, such as servomechanisms, where persistent or amplified errors can lead to instability, underscoring the causal role of feedback in error correction rather than mere disturbance suppression.^[58] Control theory distinguishes between transient errors, which occur during dynamic responses (e.g., overshoot or oscillations approaching equilibrium), and steady-state errors, representing the residual offset after transients decay.^[59] In proportional-integral-derivative (PID) controllers, the proportional term addresses transient errors by scaling output proportionally to the error magnitude, while the integral term accumulates past errors to eliminate steady-state offsets for step inputs in type-1 systems, though excessive integral action risks windup and instability.^[60] Empirical tuning of PID parameters, as in industrial automation, demonstrates that integral gains below 0.1 can leave steady-state errors exceeding 5% in velocity-ramp disturbances, while derivative terms damp transients but amplify noise sensitivity.^[61] Stability analysis via the Nyquist criterion evaluates feedback loop encirclements of the critical point (-1,0) in the complex plane to predict error amplification; systems with right-half-plane poles require counterclockwise encirclements equal to unstable poles for closed-loop stability, preventing divergent error growth.^[62] This first-principles graphical method reveals causal vulnerabilities in high-gain loops, where phase lags exceed 180 degrees near unity gain, leading to oscillations—evident in servo motors where gain margins below 6 dB correlate with 20-30% overshoot in empirical tests.^[63] The Therac-25 radiation therapy machine incidents between June 1985 and January 1987 exemplify control system errors, where software race conditions and inadequate error handling in feedback routines caused beam misconfigurations, delivering overdoses up to 100 times intended levels and resulting in three deaths and three severe injuries across six documented cases.^[64] These failures stemmed from unhandled transient errors in operator-interface synchronization, bypassing hardware interlocks and amplifying setpoint deviations without integral correction, highlighting how flawed feedback logic overrides causal safeguards.^[65] Robust autonomous feedback in cybernetic systems often outperforms human oversight by enabling sub-millisecond error corrections unattainable by operators, as delays in human intervention—typically 100-500 ms—exacerbate transients in high-speed loops like flight controls.^[66] Over-reliance on oversight introduces cognitive lags and inconsistent error detection, whereas closed-loop designs with Nyquist-verified margins foster resilience, as validated in unmanned aerial vehicle swarms where feedback autonomy reduced error propagation by 40-60% compared to piloted variants in fault-injection simulations.^[67]

Legal, Policy, and Economic Errors

Errors in Law

In legal proceedings, errors are categorized into those of fact, law, and discretion, each subject to distinct standards of appellate review to ensure procedural rigor and empirical accuracy in outcomes. Errors of fact occur when trial courts misapprehend evidence or witness testimony, reviewed under a "clear error" standard where appellate courts defer unless the finding lacks evidentiary support or is plainly wrong. Errors of law involve misinterpretation or misapplication of statutes and precedents, scrutinized de novo for independent assessment without deference to the lower court. Errors of discretion arise in rulings like evidentiary admissions or sentencing, overturned only for abuse—defined as decisions so unreasonable as to evince arbitrariness or failure to consider pertinent factors. These distinctions prioritize causal fidelity to evidence over subjective equity, as appellate intervention corrects deviations that undermine factual truth or legal consistency.^[68]^[69] Prejudicial errors, those materially affecting case outcomes, contrast with harmless ones under doctrines requiring demonstration of substantial rights impairment for reversal; harmless errors—such as minor procedural lapses without outcome influence—are disregarded to avoid inefficient relitigation. In U.S. federal appeals, reversal rates quantify prejudicial error prevalence, with fewer than 9% of civil and criminal appeals reversed in 2015, and criminal cases at 6.6% from 2017–2018, reflecting rigorous filtering but underscoring persistent causal links to miscarriages where errors like flawed evidentiary rulings evade correction. The U.S. Supreme Court has reversed on procedural due process grounds in cases exemplifying these, such as Glossip v. Oklahoma (2025), remanding for retrial due to inadequate process in penalty-phase evidence handling, emphasizing empirical prejudice over nominal compliance.^[70]^[71]^[72]^[73] Wrongful convictions empirically manifest these errors' consequences, with DNA exonerations revealing fact errors from eyewitness misidentification in 63% of cases and false confessions in 29%, often compounded by prosecutorial withholding of exculpatory evidence—causally driving convictions absent guilt. The Innocence Project's database, tracking over 375 DNA exonerations since 1989, attributes such miscarriages to individual procedural failures like inadequate cross-examination or forensic misapplication, rather than excusing them via systemic narratives; while confirmation bias in investigations contributes, data stress accountability for actors ignoring contradictory evidence. Appellate data indicate these errors persist despite low reversal rates, as harmless doctrine may overlook cumulative prejudice, with Black exonerees seven times more likely in murder cases due to biased identification practices rooted in cross-racial inaccuracy, not inherent institutional favoritism but empirically verifiable procedural lapses.^[74]^[75]^[76]^[77]

Governmental Policy Errors

Governmental policy errors arise when state interventions deviate from their intended goals due to flaws in design, execution, or adaptation to human behavior, often exacerbated by centralized planning's limitations in aggregating dispersed information and incentives. These errors include implementation gaps, where bureaucratic inertia or misaligned incentives prevent effective rollout, and unintended consequences, where policies alter behavior in counterproductive ways, such as distorting markets or encouraging dependency. Econometric analyses, including the Lucas critique, underscore how policies relying on static historical models fail as rational agents adjust expectations, rendering fine-tuning ineffective; for instance, attempts to exploit short-run trade-offs like the Phillips curve between inflation and unemployment collapsed in the 1970s stagflation era as expectations shifted.^[78] A prominent example is the U.S. War on Poverty, launched in 1964 under President Lyndon B. Johnson with the explicit aim of eliminating poverty through expanded welfare programs. Despite cumulative federal spending on means-tested welfare exceeding $22 trillion in inflation-adjusted 2012 dollars from 1965 to 2012, the official poverty rate only fell from 19% in 1964 to 11.1% by 1973 before stagnating around 11-15% through 2023, failing to achieve eradication amid rising dependency and family structure breakdowns correlated with program expansions.^[79]^[80] This outcome reflects causal failures in assuming transfers alone address root causes like work disincentives, with public choice theory highlighting how entrenched bureaucracies resist reform despite evidence of inefficacy.^[81] Regulatory policies frequently succumb to capture and rent-seeking, where interest groups influence rules to extract favors, imposing deadweight losses on the economy. Empirical studies document regulatory capture in agencies like the early Interstate Commerce Commission, which set freight rates favoring railroads over shippers, stifling competition and raising consumer costs.^[82] Rent-seeking amplifies this, as lobbying for subsidies or barriers diverts resources from productive uses; research estimates such activities depress U.S. productivity growth by encouraging zero-sum competition over innovation, with market mechanisms providing superior error correction through price signals and exit options absent in persistent government programs.^[83]^[84] Unintended effects, evidenced in econometric work on policies like minimum wage hikes, show elevated youth unemployment without commensurate poverty reduction, as labor demand responds elastically to price floors.^[85]

Stock Market and Financial Errors

Stock market errors manifest as systematic deviations in asset pricing from fundamental values, often arising from information asymmetries where market participants possess incomplete or uneven knowledge of underlying economic realities, leading to inefficient allocations. These errors are exacerbated by behavioral tendencies such as herding, where investors mimic collective actions rather than independent assessments, resulting in amplified volatility and mispricings quantifiable through residuals in the Capital Asset Pricing Model (CAPM), which deviate from expected beta-market return relationships due to non-fundamental factors.^[86] Empirical analyses of return distributions reveal persistent anomalies, including herding toward high-order systematic risks, where cross-sectional dispersions in CAPM factor sensitivities narrow during market stress, indicating non-rational convergence rather than efficient pricing.^[87] A prominent example of pricing misestimation occurred during Black Monday on October 19, 1987, when the Dow Jones Industrial Average plummeted 22.6%—the largest single-day percentage decline in history—triggering global sell-offs totaling over $1.7 trillion in U.S. market value. Causal factors included overvaluation from prior bull market excesses, coupled with automated program trading strategies like portfolio insurance that mechanically sold futures as prices fell, creating feedback loops of liquidity evaporation and panic amplification independent of new fundamental information.^[88] This event underscored precursors to modern flash crashes, where thin order books and synchronized algorithmic responses magnify transient errors into widespread dislocations. Algorithmic trading errors represent a growing class of technical failures in high-frequency environments, as seen in the Knight Capital incident on August 1, 2012, where a software deployment glitch activated dormant code, unleashing erroneous buy orders across 148 stocks and incurring a $440 million loss in 45 minutes—nearly wiping out the firm's equity. Similarly, the May 6, 2010 Flash Crash saw the Dow Jones drop nearly 1,000 points (9%) intraday before partial recovery, initiated by a large E-Mini S&P 500 sell order executed via an algorithm that failed to adapt to market liquidity, interacting with high-frequency traders to exacerbate a temporary imbalance worth $1 trillion in market cap evaporation.^[89]^[90] Such incidents highlight causal vulnerabilities in code-dependent systems, where untested updates or order routing flaws propagate errors faster than human oversight can intervene. Risk assessment errors stem from models underestimating fat-tail distributions in returns, where empirical data show heavier tails than Gaussian assumptions, with extreme events occurring far more frequently—e.g., Korean stock returns exhibiting kurtosis exceeding 10, invalidating normal-distribution-based Value-at-Risk (VaR) metrics used by institutions. Post-2008 analyses reveal how regulatory safety nets, intended to mitigate systemic risks, inadvertently amplify moral hazard by signaling implicit guarantees against failure, encouraging leveraged positions in tail-risk assets as banks perceive bailouts as probable rescues rather than market discipline. This dynamic, evident in the crisis's buildup where "too big to fail" expectations fueled subprime exposures, critiques overregulation for distorting incentives without addressing root informational and behavioral asymmetries.^[91]^[92]^[93]

Errors in Collectibles and Material Production

Philatelic Errors

Philatelic errors encompass unintended deviations in the production of postage stamps, arising primarily from faults in printing, perforation, or paper handling processes. These mistakes, distinct from deliberate design variations, create rarities valued for their scarcity and verifiable documentation through expert certification. Common categories include printing errors—such as inverted centers, where a multicolored element appears upside down relative to the frame; color omissions, where a printing plate run is skipped; and misregistrations, where colors fail to align properly—and perforation errors, like imperforate stamps lacking separations or those with blind or partial perforations.^[94]^[95] A prominent example is the 1918 United States 24-cent airmail stamp featuring the inverted Curtiss JN-4 "Jenny" biplane, resulting from a plate misalignment during intaglio printing that inverted the central vignette on one sheet of 100 stamps before the error was noticed. Only a fraction of that sheet survives in private hands, with individual examples routinely fetching high sums at auction; one mint specimen sold for $2,006,000 at a Siegel auction in November 2023, establishing a record for a single U.S. stamp due to its pristine centering and gum condition.^[96]^[97] Other notable printing errors include the 1954 Sweden 30 öre with an inverted frame, caused by incorrect plate orientation, and China's 1968 "Whole Country is Red" series, where excessive red ink obscured other colors due to a color application fault, leading to withdrawn printings and elevated values based on surviving quantities.^[98] Causal mechanisms in these errors trace to mechanical lapses in traditional printing techniques, such as lithographic or intaglio presses, where foreign particles on plates, air bubbles in ink, or operator errors in plate alignment or sequencing produce defects like albinos (blank impressions from ink failure) or double impressions from repeated strikes. Perforation anomalies often stem from gummed sheets shifting during the guillotine or roulette process, while paper errors involve unintended use of wrong stock or watermarks from misfed supplies. Pre-automation eras, reliant on manual oversight, amplified such incidents compared to modern digital controls, though exact frequencies remain sparsely quantified in philatelic literature, with societies like the American Philatelic Society emphasizing certification to distinguish true errors from post-production freaks.^[99]^[100]^[101] Market valuation of philatelic errors hinges on empirical scarcity dynamics, condition grading, and provenance, rather than aesthetic appeal alone, as auction records demonstrate premiums for verified rarities—e.g., the Inverted Jenny's value derives from its documented print run of exactly 100, with attrition reducing supply over time. Collectors and investors assess these through catalogs like Scott or Gibbons, which catalog errors only after rigorous verification, ensuring prices reflect supply constraints; for instance, imperforate pairs from routine issues can command 10-100 times normal values if proven as production artifacts, not trimmed fakes. This truth-seeking approach prioritizes auction data and expert authentication over anecdotal narratives, underscoring errors as quantifiable anomalies in mass production rather than serendipitous art.^[102]^[103]

Numismatic Errors

Numismatic errors encompass minting defects in coins that occur during production at official mints, primarily due to anomalies in planchet preparation, die fabrication, or the striking process itself. These errors deviate from standard manufacturing tolerances, which are engineered to minimize variations through high-precision machinery operating at pressures exceeding 60 tons and speeds of hundreds of strikes per minute. While most minor imperfections fall within acceptable limits and are destroyed during quality control, significant errors that evade detection enter circulation or are preserved, deriving value from their scarcity and verifiable authenticity rather than intentional design. The economic incentive for collectors to identify and authenticate genuine errors stems from market premiums, often amplified by third-party grading that employs forensic techniques to distinguish them from post-mint alterations or counterfeits.^[104] Die errors arise from imperfections in the engraving or hubbing of coin dies, leading to anomalies transferred to multiple coins until the die is replaced. Common subtypes include doubled dies, where misalignment during hubbing causes design elements to appear duplicated, as in the 1955 Doubled Die Obverse Lincoln Cent produced at the Philadelphia Mint; this variety resulted from a single hubbing misalignment, with original production estimates of 20,000 to 40,000 pieces, though survival rates in uncirculated condition remain low due to circulation wear and early recognition by collectors. Cracked or chipped dies produce raised lines or breaks on coins, often from repetitive high-pressure impacts—dies typically endure 100,000 to 500,000 strikes before wear exceeds tolerances—creating irregular features like die breaks or cud errors where portions of the die erode. These errors' rarity is evidenced by mint culling practices, where visual inspections reject dies showing cracks beyond 0.1 mm depth, yet occasional escapes occur due to production volumes exceeding billions annually for denominations like U.S. cents.^[105]^[106]^[107] Striking errors manifest during the coining press operation, when planchets fail to align properly with dies under immense force. Off-center strikes, for instance, result from planchet misalignment in the collar, producing incomplete designs shifted by 10% or more of the coin's diameter, with severity correlating to the degree of offset—errors beyond 50% offset are exceptionally rare as they often jam machinery. Broadstrikes occur sans collar confinement, yielding expanded, irregular flans without reeding, caused by mechanical failures in automated feeders handling up to 500 planchets per minute. These mishaps trace to tolerances in press calibration, where vibrations or lubrication inconsistencies allow slippage, but mint protocols like die inspections and sampling reduce incidence to fractions of a percent, per quality metrics from facilities like the U.S. Mint.^[108]^[109] Planchet defects precede striking, stemming from flaws in metal blank fabrication, such as improper alloy mixing or cutting errors from coiled strips. Laminated planchets, for example, exhibit peeling surfaces due to impurities like oxides in the copper-zinc alloy, failing cohesion under rolling pressures of 20-30 tons per inch; clipped planchets arise from shears misaligning by millimeters, yielding crescent-shaped misses. Wrong-planchet errors, rarer still, involve blanks of incorrect denomination or metal—e.g., a cent struck on dime stock—arising from segregated storage failures in high-volume blanking lines producing millions daily. Empirical rarity data from grading services indicate such errors comprise under 0.01% of submissions, as mint annealing and cleaning stages filter most defects, with survivors commanding premiums based on forensic confirmation of mint-era composition via spectrometry. Authentication relies on market-vetted methods like those of PCGS and NGC, which cross-verify via microscopy for die flow lines, specific gravity tests for alloy purity, and databases excluding fabricated "errors" common in post-mint scams, ensuring only causally mint-originated anomalies hold value.^[110]^[111]^[112]

Philosophical and Theoretical Perspectives

Epistemological and Causal Views of Error

In epistemology, error functions as a falsification mechanism essential for distinguishing scientific knowledge from pseudoscience, as theorized by Karl Popper in Logik der Forschung (1934). Popper argued that theories gain scientific credibility through their vulnerability to empirical refutation: a hypothesis is scientific insofar as it prohibits certain outcomes, and observed errors—discrepancies between predicted and actual results—provide the grounds for rejecting it.^[113] This process underscores error's role in knowledge acquisition, where failed predictions, such as the anomalous perihelion precession of Mercury under Newtonian mechanics (resolved by general relativity in 1915), compel theoretical refinement rather than ad hoc adjustments.^[113] By prioritizing bold conjectures susceptible to error, Popper's framework frames scientific progress as error-driven elimination, contrasting with inductivist accumulation of confirmations that risks entrenching falsehoods.^[114] Causal realism further elucidates error through the lens of underlying causal structures, positing causation as an objective feature of reality rather than a mere epistemic construct. Errors manifest as mismatches between hypothesized causal chains and observed effects, detectable via interventions that isolate variables and reveal hidden dependencies or absences.^[115] For instance, in experimental settings, anticipated causal outcomes failing to occur—due to unaccounted confounders—signal errors in the posited mechanism, prompting reconstruction of the causal model to align with empirical regularities. This view emphasizes error's necessity in mapping real-world causal dependencies, as deterministic assumptions overlook how incomplete causal knowledge generates predictable deviations, fostering iterative refinement toward accurate representations.^[116] Bayesian models formalize error correction as probabilistic belief revision, where agents update credences upon evidence contradicting priors. Bayes' theorem dictates that posterior probabilities incorporate likelihoods of data under rival hypotheses, reducing confidence in erroneous beliefs when evidence lowers their predictive success.^[117] Errors thus trigger downward revisions, as in cases where initial high credence in a model yields low-likelihood observations, necessitating recalibration to avoid persistent inaccuracies. Critiquing purely deterministic epistemologies, which imply errors as mere informational deficits eliminable by perfect knowledge, stochastic analyses reveal errors' adaptive utility: simulations of learning systems, such as stochastic gradient descent algorithms, show that injected randomness—mimicking irreducible error—avoids convergence traps and enhances long-term accuracy by exploring broader parameter spaces.^[118] Empirical studies confirm this, with noisy perturbations in optimization yielding superior generalization compared to error-free deterministic paths.^[119]

Moral Error Theory

Moral error theory posits that all moral judgments are systematically false because they presuppose the existence of objective moral values or facts that do not obtain in reality.^[120] This metaethical position, advanced by J.L. Mackie in his 1977 book Ethics: Inventing Right and Wrong, holds that moral claims aim to describe mind-independent properties but fail due to their metaphysical implausibility.^[121] Mackie argued that if objective moral values existed, they would be metaphysically "queer"—intrinsically prescriptive entities capable of motivating action independently of desires, unlike any observable natural properties.^[122] He supplemented this with the argument from relativity, observing that persistent cross-cultural moral disagreements suggest invention rather than discovery of values, undermining claims to universality.^[120] The theory's cognitivist semantics implies that moral statements are truth-apt but erroneous, akin to assertions about non-existent entities like witches.^[123] Empirical support draws from linguistics, where moral language often carries presuppositions of objectivity, such as in constructions implying binding imperatives (e.g., "torture is wrong" presupposes a non-contingent reason for aversion), which fail if no such reasons exist.^[124] From a causal realist perspective, moral intuitions arise from evolutionary heuristics adapted for social coordination, not detection of transcendent truths; these mechanisms, shaped by kin selection and reciprocal altruism over millennia, explain belief in morals without requiring their ontology, as evidenced by debunking arguments linking ethical variance to adaptive pressures rather than veridical perception.^[125]^[126] Debates on implications center on the "now what" question: if moral error theory holds, should moral discourse be abolished or reconceived? Abolitionists, like Jonas Olson, advocate eliminating moral concepts to avoid perpetuating falsehoods, arguing that retention risks cognitive dissonance without epistemic gain.^[127] In contrast, fictionalists such as Richard Joyce propose treating morals as useful fictions, preserving social functions like coordination without belief in their truth, though critics contend this undermines genuine motivation.^[128] Recent literature, including 2010s-2020s analyses, highlights tensions: abolitionism aligns with strict error but faces practicality challenges, while fictionalism risks insincerity, yet both reject realist impositions prevalent in academic and media narratives despite evidence of institutional biases favoring normative frameworks over skeptical ones. Error theory thus privileges individual liberty by dissolving purported binding duties, attributing ethical "errors" to heuristic misfires rather than violations of cosmic law, and cautioning against coercive policies justified by unverified moral absolutes.^[129]

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9 Moral Error Theory, and Then What? - Oxford Academic
Moral abolitionism, ie the view that in the wake of the realization that morality involves systematic error it should be abolished, is considered and rejected.
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The Second Revolution of Moral Fictionalism | Ergo an Open Access ...
Mar 31, 2023 · According to Richard Joyce's revolutionary moral fictionalism, error theorists should pretend to believe moral propositions in order to keep the benefits moral ...
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[PDF] Evolution and Moral Realism
Feb 26, 2016 · Evolutionary accounts of mor- ality have often been recruited in support of error theory: moral language is truth-apt, but substantive moral ...