24. Empirical Data Sources and Annotated Bibliography

24.1 Provenance and Cryptographic Integrity of the 13 Empirical Datasets

All 13 CSV files in data/*.csv were retrieved from public scientific repositories—the Rdatasets archive (github.com/vincentarelbundock/Rdatasets, commit 1dcc2bf5f955cc1224a3e1307256e1fe86b68dae), CRAN packages (fitdistrplus, nycflights13, nycflights23), NOAA HURDAT2, AEMO Victoria, and Open Data Toronto.

Their exact 64-character SHA-256 digests are recorded in data/SHA256SUMS and verified at runtime by stochology-cli before any laboratory or gate executes:

24.2 Annotated Bibliography by Thematic Pillar

I. Epistemology, Induction, and Severe Testing

• Bernoulli, Jacob (1713). Ars Conjectandi. Basel: Thurnisiorum. — Introduces ars conjectandi sive stochastice, the art of aiming conjectures under uncertainty.
• Peirce, Charles Sanders (1878). “Deduction, Induction, and Hypothesis.” Popular Science Monthly, 13, 470–482. — Separates truth-preserving deduction from ampliative induction and abduction.
• Popper, Karl (1959). The Logic of Scientific Discovery. London: Hutchinson. — Formulates falsificationism and propensity probability.
• Lakatos, Imre (1970). “Falsification and the Methodology of Scientific Research Programmes.” In Criticism and the Growth of Knowledge, Cambridge University Press. — Distinguishes progressive problemshifts (predicting novel testable facts) from degenerative patches.
• Mayo, Deborah G. (2018). Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars. Cambridge University Press. — Establishes the Severity Principle ($\Phi_3$).
• Simmons, Joseph P., Leif D. Nelson & Uri Simonsohn (2011). “False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant.” Psychological Science, 22(11), 1359–1366.
• Breznau, Nate et al. (2022). “Observing Many Researchers Using the Same Data and Hypothesis Reveals a Hidden Universe of Uncertainty.” Proceedings of the National Academy of Sciences (PNAS), 119(44), e2203150119.

II. Bridge Mechanics, Missingness, Performativity, and Severe Uncertainty

• Rubin, Donald B. (1976). “Inference and Missing Data.” Biometrika, 63(3), 581–592. — Formalizes MCAR, MAR, and MNAR.
• Meng, Xiao-Li (2018). “Statistical Paradises and Paradoxes in Big Data (I): Law of Large Populations, Big Data Paradox, and the 2016 US Presidential Election.” Annals of Applied Statistics, 12(2), 685–726. — Derives Meng’s Fundamental Identity ($\bar{Y}_n - \bar{Y}_N = \rho_{R,Y}\sqrt{\frac{N-n}{n}}\sigma_Y$).
• Manski, Charles F. (1990). “Nonparametric Bounds on Treatment Effects.” American Economic Review, 80(2), 319–323. — Establishes worst-case identification bounds under MNAR.
• Lee, David S. (2009). “Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects.” Review of Economic Studies, 76(3), 1071–1102. — Derives sharp quantile-trimming bounds under monotonic differential attrition (ManskiBounds::lee_trimming_bounds).
• Perdomo, Juan C., Tijana Zrnic, Celestine Mendler-Dünner & Moritz Hardt (2020). “Performative Prediction.” Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119:7599–7609. — Formalizes Performative Risk, Performative Stability ($\theta_{\mathrm{PS}}$), Performative Optimality ($\theta_{\mathrm{PO}}$), and RRM contraction.
• Izzo, Zachary, Lexing Ying & James Zou (2021). “How to Learn when Data Reacts to Your Model: Performative Gradient Descent.” Proceedings of the 38th International Conference on Machine Learning (ICML), PMLR 139:4631–4640. — Introduces PerfGD converging to the Stackelberg optimum $\theta_{\mathrm{PO}}$.
• Kennedy, Marc C. & Anthony O’Hagan (2001). “Bayesian Calibration of Computer Models.” Journal of the Royal Statistical Society: Series B, 63(3), 425–464. — Introduces Gaussian Process structural discrepancy $\delta(x) \sim \mathcal{GP}(m(x), k(x, x’))$.
• Brynjarsdóttir, Jenný & Anthony O’Hagan (2014). “Learning About Physical Parameters: The Importance of Model Discrepancy.” Inverse Problems, 30(11), 114007. — Demonstrates boundary-anchored GP kernels for physical parameter identifiability.
• Ben-Haim, Yakov (2006). Info-Gap Decision Theory: Decisions Under Severe Uncertainty (2nd ed.). Academic Press.
• Sniedovich, Moshe (2012). “Black Swans, New Nostradamuses, Voodoo Decision Theories, and the Science of Decision Making in the Face of Severe Uncertainty.” International Transactions in Operational Research, 19(1–2), 253–281. — Proves the local nature of Info-Gap and the necessity of Wald’s global Maximin safeguard across “No-Man’s Land.”

III. The Five Transversal Lenses and the Almost-Perfect Machine

• Blahut, Richard E. (1972). “Computation of Channel Capacity and Rate-Distortion Functions.” IEEE Transactions on Information Theory, 18(4), 460–473; & Arimoto, Suguru (1972). “An Algorithm for Computing the Capacity of Arbitrary Discrete Memoryless Channels.” IEEE Transactions on Information Theory, 18(1), 14–20.
• Raginsky, Maxim (2016). “Strong Data Processing Inequalities and $\Phi$-Sobolev Inequalities for Discrete Channels.” IEEE Transactions on Information Theory, 62(6), 3355–3389.
• Peters, Ole (2019). “The Ergodicity Problem in Economics.” Nature Physics, 15(12), 1216–1221.
• Kelly, John L. Jr. (1956). “A New Interpretation of Information Rate.” Bell System Technical Journal, 35(4), 917–926.
• Hill, Bruce M. (1975). “A Simple General Approach to Inference About the Tail of a Distribution.” Annals of Statistics, 3(5), 1163–1174.
• Dekkers, Arnold L. M., John H. J. Einmahl & Laurens de Haan (1989). “A Moment Estimator for the Index of an Extreme-Value Distribution.” Annals of Statistics, 17(4), 1833–1855.
• Pickands, James (1975). “Statistical Inference Using Extreme Order Statistics.” Annals of Statistics, 3(1), 119–131; & Balkema, A. A. & Laurens de Haan (1974). “Residual Life Time at Great Age.” Annals of Probability, 2(5), 792–804.
• Embrechts, Paul, Claudia Klüppelberg & Thomas Mikosch (1997). Modelling Extremal Events for Insurance and Finance. Berlin: Springer.
• Pearl, Judea (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press.
• Peters, Jonas, Peter Bühlmann & Nicolai Meinshausen (2016). “Causal Inference by Using Invariant Prediction: Identification and Confidence Intervals.” Journal of the Royal Statistical Society: Series B, 78(5), 947–1012.
• Chow, Gregory C. (1960). “Tests of Equality Between Sets of Coefficients in Two Linear Regressions.” Econometrica, 28(3), 591–605.
• Cinelli, Carlos & Chad Hazlett (2020). “Making Sense of Sensitivity: Extending Omitted Variable Bias.” Journal of the Royal Statistical Society: Series B, 82(1), 39–67.
• Richardson, David S. (2000). “Skill and Relative Economic Value of the ECMWF Ensemble Prediction System.” Quarterly Journal of the Royal Meteorological Society, 126(563), 649–667.
• Dimitriadis, Timo, Tilmann Gneiting & Alexander I. Jordan (2021). “Stable Reliability Diagrams for Probabilistic Classifiers.” Proceedings of the National Academy of Sciences (PNAS), 118(8), e2016191118. — Establishes the isotonic CORP-Murphy decomposition via Pool-Adjacent-Violators (PAV).
• Barron, Andrew R. (1993). “Universal Approximation Bounds for Superpositions of a Sigmoidal Function.” IEEE Transactions on Information Theory, 39(3), 930–945.
• Telgarsky, Matus (2016). “Benefits of Depth in Neural Networks.” Proceedings of the 29th Conference on Learning Theory (COLT), PMLR 49:1517–1539.

IV. Cluster Invariants, Fairness, Funnel, and Drift Governance

• Hurlbert, Stuart H. (1984). “Pseudoreplication and the Design of Ecological Field Experiments.” Ecological Monographs, 54(2), 187–211.
• Moulton, Brent R. (1986). “Random Group Effects and the Precision of Regression Estimates.” Journal of Econometrics, 32(3), 385–397.
• Chouldechova, Alexandra (2017). “Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments.” Big Data, 5(2), 153–163.
• Kleinberg, Jon, Sendhil Mullainathan & Manish Raghavan (2017). “Inherent Trade-Offs in the Fair Determination of Risk Scores.” Proceedings of the 8th Innovations in Theoretical Computer Science Conference (ITCS), LIPIcs 67:43:1–43:23.
• Gigerenzer, Gerd & Ulrich Hoffrage (1995). “How to Improve Bayesian Reasoning Without Instruction: Frequency Formats.” Psychological Review, 102(4), 684–704.
• Talts, Sean, Michael Betancourt, Daniel Simpson, Aki Vehtari & Andrew Gelman (2018). “Validating Bayesian Inference Algorithms with Simulation-Based Calibration.” arXiv:1804.06788.
• Grünwald, Peter, Rianne de Heide & Wouter M. Koolen (2024). “Safe Testing.” Journal of the Royal Statistical Society: Series B, 86(5), 1091–1128; & Waudby-Smith, Ian & Aaditya Ramdas (2024). “Estimating Means of Bounded Random Variables by Betting.” JRSS: Series B, 86(1), 1–27.
• Lei, Lihua & Emmanuel J. Candès (2021). “Conformal Inference of Counterfactuals and Individual Treatment Effects.” Journal of the Royal Statistical Society: Series B, 83(5), 911–938.
• Gibbs, Isaac & Emmanuel Candès (2021). “Adaptive Conformal Inference Under Distribution Shift.” Advances in Neural Information Processing Systems (NeurIPS), 34, 1660–1672.
• Lipton, Zachary C., Yu-Xiang Wang & Alexander Smola (2018). “Detecting and Correcting for Label Shift with Black Box Predictors.” Proceedings of ICML, PMLR 80:3122–3130.

V. Relational Ontology, Emergence, and Complex Systems

• Nāgārjuna (c. 150 CE / trans. Jay L. Garfield, 1995). The Fundamental Wisdom of the Middle Way: Nāgārjuna’s Mūlamadhyamakakārikā. Oxford University Press.
• Rovelli, Carlo (1996). “Relational Quantum Mechanics.” International Journal of Theoretical Physics, 35(8), 1637–1678.
• Ladyman, James & Don Ross (2007). Every Thing Must Go: Metaphysics Naturalized. Oxford University Press.
• Prigogine, Ilya & René Lefever (1968). “Symmetry Breaking Instabilities in Dissipative Systems. II.” Journal of Chemical Physics, 48(4), 1695–1700.
• Schnakenberg, Jürgen (1976). “Network Theory of Microscopic and Macroscopic Behavior of Master Equation Systems.” Reviews of Modern Physics, 48(4), 571–585.
• Prigogine, Ilya & Isabelle Stengers (1984). Order Out of Chaos: Man’s New Dialogue with Nature. Bantam Books.
• Maturana, Humberto R. & Francisco J. Varela (1980). Autopoiesis and Cognition: The Realization of the Living. D. Reidel.
• Anderson, Philip W. (1972). “More Is Different.” Science, 177(4047), 393–396.
• Schelling, Thomas C. (1971). “Dynamic Models of Segregation.” Journal of Mathematical Sociology, 1(2), 143–186.
• Elias, Norbert (1939 / 2000). The Civilizing Process: Sociogenetic and Psychogenetic Investigations. Blackwell.
• Lewontin, Richard C. (2000). The Triple Helix: Gene, Organism, and Environment. Harvard University Press.
• Watson, Andrew J. & James E. Lovelock (1983). “Biological Homeostasis of the Global Environment: The Parable of Daisyworld.” Tellus B, 35(4), 284–289.
• Mirowski, Philip (1989). More Heat than Light: Economics as Social Physics, Physics as Nature’s Economics. Cambridge University Press.

VI. Physical, Analog, and Wave-Interference Computing

• Horowitz, Mark (2014). “1.1 Computing’s Energy Problem (and What We Can Do About It).” 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC), pp. 10–14. — Quantifies the 45nm CMOS energy hierarchy ($640\text{ pJ}$ off-chip DRAM read vs. $0.9\text{ pJ}$ FP32 add $= 711.11\times$; $6400\times$ vs. $0.1\text{ pJ}$ Int32 add).
• Shumailov, Ilia, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross Anderson & Yarin Gal (2024). “AI Models Collapse When Trained on Recursively Generated Data.” Nature, 631, 755–759. — Proves generational variance collapse and tail loss under recursive synthetic training.
• Villalobos, Pablo et al. (2024). “Will We Run Out of Data? Limits of LLM Scaling Based on Human-Generated Data.” Proceedings of the 41st International Conference on Machine Learning (ICML) / Epoch AI.
• Hasani, Ramin, Mathias Lechner, Alexander Amini, Daniela Rus & Radu Grosu (2021). “Liquid Time-Constant Networks.” Proceedings of the AAAI Conference on Artificial Intelligence, 35(9), 7657–7666.
• Sitzmann, Vincent, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell & Gordon Wetzstein (2020). “Implicit Neural Representations with Periodic Activation Functions (SIREN).” Advances in Neural Information Processing Systems (NeurIPS), 33, 7462–7473. — Establishes $\sin(\omega_0 Wx + b)$ networks with closed-form infinitely differentiable wave derivatives ($\nabla^2 u + k^2 u = s(x)$).
• Hughes, Tyler W., Ian A. D. Williamson, Momchil Minkov & Shanhui Fan (2019). “Wave Physics as an Analog Recurrent Neural Network.” Science Advances, 5(12), eaay6946. — Demonstrates direct correspondence between the damped nonlinear wave PDE ($\partial_t^2 u + \gamma \partial_t u = c^2(x,u)\partial_x^2 u + f$) and a Recurrent Neural Network.
• Xu, Zhihao, Tiankuang Zhou, Muzhou Ma, ChenChen Deng, Qionghai Dai & Lu Fang (2023). “Large-Scale Photonic Chiplet Taichi / ACCEL All-Analog Photoelectronic Chip for High-Speed Vision.” Nature, 623, 48–55 (ACCEL, 2023) & Science, 384(6692), 202–209 (Taichi, 2024).
• Zuo, Peng, Zhong Sun et al. (2025). “High-Precision Analog Computing via Closed-Loop Low-Precision RRAM Inversion and Bit-Sliced Iterative Refinement (HP-INV).” Nature Electronics, October 2025. — Demonstrates 24-bit FP32-equivalent accuracy ($< 10^{-7}$ relative error) on physical 3-bit RRAM chips via closed-loop LP-INV preconditioning and bit-sliced HP-MVM.
• Zheng, Jieyu & Markus Meister (2024). “The Unbearable Slowness of Being: Why Do We Live at 10 Bits/s?” Neuron, 113(2), 192–204. — Quantifies the $1.6\text{ Gb/s} \to 10\text{ bit/s}$ ($1.6 \times 10^8 : 1$) human sensory information-compression funnel.
• Smirnova, Lena, Brian S. Caffo, David H. Gracias et al. (2023). “Organoid Intelligence (OI): The New Frontier in Biocomputing and Intelligence-in-a-Dish.” Frontiers in Science, 1, 1017235.
• Raul, Gibram (2026). Computação Analógica, Aprendizado por Interferência de Ondas e Ancoragem Neuro-Simbólica na Superação da Barreira de von Neumann.

VII. Stochary-Conjectural Synthesis, Supply-Chain Physics, and Cybernetic Governance (VSM & PATE)

• Aristotle (c. 340 BCE). Nicomachean Ethics (1104a) & Rhetoric (1360b); Cicero (45 BCE). De Finibus Bonorum et Malorum (3.22); & Alexander of Aphrodisias (c. 200 CE). Quaestiones (2.16). — Classical foundation of the technai stochastikai and the distinction between internal telos (facere omnia quae possit ut conliniet) and exogenous skopos.
• Meadows, Donella H. (1999). Leverage Points: Places to Intervene in a System. The Sustainability Institute. — Establishes stabilizing buffers and system structure/paradigms as primary leverage points.
• Dierickx, Ingemar & Karel Cool (1989). “Asset Stock Accumulation and Sustainability of Competitive Advantage.” Management Science, 35(12), 1504–1511. — Formalizes the 5 laws of non-tradeable asset stock accumulation (time compression diseconomies, asset mass efficiency, interconnectedness, erosion, and causal ambiguity).
• Powell, Warren B. (2022). Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions. Wiley. — Establishes the 4 universal policy meta-classes (PFA, CFA, VFA, DLA).
• Friston, Karl et al. (2015). “Active Inference and Epistemic Value.” Cognitive Neuroscience, 6(4), 187–214; & Parr, Thomas, Giovanni Pezzulo & Karl J. Friston (2022). Active Inference: The Free Energy Principle in Mind, Brain, and Behavior. MIT Press. — Formalizes Expected Free Energy $G(\pi)$ unifying epistemic foraging and pragmatic control across a Markov Blanket.
• Little, John D. C. (1961). “A Proof for the Queuing Formula: $L = \lambda W$.” Operations Research, 9(3), 383–387; Kingman, J. F. C. (1961). “The Single Server Queue in Heavy Traffic.” Mathematical Proceedings of the Cambridge Philosophical Society, 57(4), 902–904; & Hopp, Wallace J. & Mark L. Spearman (2011). Factory Physics (3rd ed.). Waveland Press. — Derives the VUT utilization cliff and the triad of inventory, capacity, and time buffers.
• Sterman, John D. (1989). “Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic Decision Making Experiment.” Management Science, 35(3), 321–339; & Lee, Hau L., V. Padmanabhan & Seungjin Whang (1997). “Information Distortion in a Supply Chain: The Bullwhip Effect.” Management Science, 43(4), 546–558. — Proves how supply-line ($SL_t$) underweighting and structural forecast/batching mechanisms amplify variance upstream.
• Scarf, Herbert (1960). “The Optimality of $(S, s)$ Policies in the Dynamic Inventory Problem.” In Mathematical Methods in the Social Sciences, Stanford University Press; Clark, Andrew J. & Herbert Scarf (1960). “Optimal Policies for a Multi-Echelon Inventory Problem.” Management Science, 6(4), 475–490; & Graves, Stephen C. & Sean P. Willems (2000). “Optimizing Strategic Safety Stock Placement in Supply Chains.” Manufacturing & Service Operations Management, 2(1), 68–83. — Establishes $K$-convexity, echelon stock decomposition, and Guaranteed-Service Model (GSM) strategic decoupling points.
• Eppen, Gary D. (1979). “Effects of Centralization on Expected Costs in a Multi-Location Newsboy Problem.” Management Science, 25(5), 498–501; & Allon, Gad & Jan A. Van Mieghem (2010). “Global Dual Sourcing: Tailored Base-Surge Allocation to Near- and Offshore Production.” Management Science, 56(1), 110–124. — Formalizes the Square-Root Law of risk pooling (and its breakdown under correlated tails) and frequency-domain Tailored Base-Surge (TBS) dual sourcing.
• Simchi-Levi, David, William Schmidt & Yehua Wei (2014). “From Superstorms to Factory Fires: Managing Unpredictable Supply-Chain Disruptions.” Harvard Business Review, 92(1–2), 96–101; Simchi-Levi, David et al. (2015). Interfaces, 45(5), 375–390; & Ivanov, Dmitry (2020/2022). “Viable Supply Chain Model: Integrating Agility, Resilience and Sustainability Perspectives.” Annals of Operations Research, 319, 1411–1431. — Introduces $\mathrm{TTS}$, $\mathrm{TTR}$, $\mathrm{REI}$, the structural Ripple Effect, and the Viable Supply Chain (VSC).
• Rockafellar, R. Tyrrell & Stanislav Uryasev (2000). “Optimization of Conditional Value-at-Risk.” Journal of Risk, 2(3), 21–41; & Mohajerin Esfahani, Peyman & Daniel Kuhn (2018). “Data-Driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations.” Mathematical Programming, 171(1–2), 115–166. — Establishes convex $\beta$-CVaR and Wasserstein DRO (WDRO) certificates against the Optimizer’s Curse.
• Ashby, W. Ross (1956). An Introduction to Cybernetics. Chapman & Hall; Conant, Roger C. & W. Ross Ashby (1970). “Every Good Regulator of a System Must Be a Model of That System.” International Journal of Systems Science, 1(2), 89–97; & Beer, Stafford (1972/1981). Brain of the Firm (2nd ed.). Wiley. — Establishes the Law of Requisite Variety, the Good Regulator Theorem, and the Viable System Model (VSM S1–S5, S3*, and Algedonic Channel).
• Kahneman, Daniel, Olivier Sibony & Cass R. Sunstein (2021). Noise: A Flaw in Human Judgment. Little, Brown Spark. — Decomposes judgment noise into level, pattern, and occasion noise and establishes decision hygiene protocols.

VIII. Empirical Dataset Primary Sources

• LaLonde, Robert J. (1986). “Evaluating the Econometric Evaluations of Training Programs with Experimental Data.” American Economic Review, 76(4), 604–620.
• Dehejia, Rajeev H. & Sadek Wahba (1999). “Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs.” Journal of the American Statistical Association, 94(448), 1053–1062.
• Weinberg, Jonathan, Lawrence D. Brown & Jonathan R. Stroud (2007). “Bayesian Forecasting of an Inhomogeneous Poisson Process With Applications to Call Center Data.” Journal of the American Statistical Association, 102(480), 1185–1198.
• McNeil, Alexander J. (1997). “Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory.” ASTIN Bulletin, 27(1), 117–137.