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SUR 1.2

2025, SUR - The Structural Uncertainty Relation (v1.2 - updated 15. September 2025)

https://doi.org/10.5281/ZENODO.17127462

Abstract

The Structural Uncertainty Relation (SUR) proposes that uncertainty in predicting tipping points is not a flaw of measurement but a structural property of nature. It states that the more stable a system is, the less precisely its critical threshold can be determined. This principle is tested across nuclear decay and cosmological data, covering 18 orders of magnitude in timescales, and shown to hold universally. SUR provides a unifying explanation for why wide uncertainty ranges persist across domains, offering a lawlike constraint on the predictability of complex systems.

References (26)

  1. Case Study: Nuclear Decay 4.1 System definition Order parameter (δS): Fraction of undecayed nuclei N(t)/N 0 .
  2. Driver (u): Time t. Threshold (σ): Characteristic decay point, defined by half-life T 1/2 (δS = 0.5) or mean life τ=T 1/2 /ln 2 (δS = 1/e).
  3. Stability (ρ): ρ=τ=T 1/2 /ln 2. Uncertainty in scale (Δρ): Derived from published uncertainty in T 1/2 : Δρ=ΔT 1/2 /ln 2. Uncertainty in threshold (Δσ): Relative uncertainty of half-life: Δσ=ΔT 1/2 /T 1/2 . SUR product: Δρ Δσ, with the same dimension as ρ.
  4. F-18 ENSDF/NuDat3 entry: https://www.nndc.bnl.gov/nudat3/checkENSDFDatasets.jsp?nucleus=18F Adopted data (BNL, ENSDF PDF): https://www.nndc.bnl.gov/ensnds/18/F/adopted.pdf Mn-54 ENSDF/NuDat3: https://www.nndc.bnl.gov/nudat3/checkENSDFDatasets.jsp?nucleus=54Mn
  5. Na-22 ENSDF/NuDat3: https://www.nndc.bnl.gov/nudat3/checkENSDFDatasets.jsp?nucleus=22Na
  6. Co-60 ENSDF Adopted data (BNL, PDF): https://www.nndc.bnl.gov/ensnds/60/Co/adopted.pdf IAEA recommended half-lives: https://www-nds.iaea.org/xgamma_standards/halflives1.htm Ba-133 ENSDF/NuDat3: https://www.nndc.bnl.gov/nudat3/checkENSDFDatasets.jsp?nucleus=133Ba
  7. Ra-226 ND2007 conference proceedings: Evaluation of decay data of radium-226 and its daughters (Chisté et al., 2007). PDF: https://nd2007.edpsciences.org/articles/ndata/pdf/2007/01/ndata07122.pdf ENSDF/NuDat3 entry: https://www.nndc.bnl.gov/nudat3/checkENSDFDatasets.jsp?nucleus=226Ra 5.2 Evaluated data sources Hubble time/ Age of the Universe (Planck 2018, base-ΛCDM)
  8. Planck Collaboration (N. Aghanim et al.). Planck 2018 results. VI. Cosmological parameters. Astronomy & Astrophysics 641, A6 (2020). DOI: https://doi.org/10.1051/0004-6361/201833910
  9. Hubble time (SH0ES, local distance ladder)
  10. Riess, A. G., et al. A Comprehensive Measurement of the Local Value of the Hubble Constant with 1 km/s/Mpc Uncertainty from the Hubble Space Telescope and the SH0ES Team. Astrophysical Journal Letters 934, L7 (2022). DOI: https://doi.org/10.3847/2041-8213/ac5c5b
  11. DESI Collaboration. DESI 2024 VI: Cosmological constraints from the full-shape clustering measurements of the DESI DR2. (arXiv:2404.03002). ArXiv: https://arxiv.org/abs/2404.03002
  12. Connelly, J. N., et al. The Absolute Chronology and Thermal Processing of Solids in the Solar Protoplanetary Disk. Science 338, 651-655 (2012). DOI: https://doi.org/10.1126/science.1226919
  13. Case Study: Financial Markets 7.1 System definition Order parameter (δS): Logarithmic price ln p(t) of a stock index (e.g., S&P 500, Shanghai Composite, IBOVESPA).
  14. Driver (u): Calendar time t. Threshold (σ): Critical transition time t c of a market crash, defined by LPPL calibration (20/80 or 10/90 percentile intervals) or by a large drawdown. Stability (ρ): Persistence of volatility, measured as the half-life of shocks in a GARCH(1,1) process: ρ=ln2/-ln(α+β) [days] where α and β are the ARCH and GARCH parameters. Uncertainty in scale (Δρ): Uncertainty of the half-life, derived from the published standard errors or t-statistics of α and β (Delta method). Uncertainty in threshold (Δσ): Relative uncertainty of the critical time t c , defined as Δσ=Δt c /t window , where Δt c is the published LPPL forecast interval and t window is the calibration span. SUR product: Δρ Δσ [days] ⋅ 7.2 Evaluated data sources S&P 500 (2010-2019)
  15. Ceballos, F. (2020). Modeling Conditional Volatility in R. Journal for Economic Educators, 20(1). PDF: https://libjournals.mtsu.edu/index.php/jfee/article/download/1763/1173/4860
  16. Shanghai Composite (SSEC, 2008-2009)
  17. Jiang, Z.-Q., Zhou, W.-X., & Sornette, D. (2009). Bubble diagnosis and prediction of the 2005- 2007 and 2008-2009 Chinese stock market bubbles. arXiv:0909.1007. PDF: https://arxiv.org/pdf/0909.1007
  18. Shenzhen Composite (SZSC, 2008-2009)
  19. Jiang, Z.-Q., Zhou, W.-X., & Sornette, D. (2009). Bubble diagnosis and prediction of the 2005- 2007 and 2008-2009 Chinese stock market bubbles. arXiv:0909.1007. PDF: https://arxiv.org/pdf/0909.1007
  20. IBOVESPA (Brazil, 2008-2009)
  21. Financial Bubble Experiment (2009). The Financial Bubble Experiment: Advanced Diagnostics and Forecasts of Bubble Terminations. ETH Zürich. arXiv:0911.1844. PDF: https://arxiv.org/pdf/0911.1844
  22. Shanghai Composite (SHCOMP), GARCH parameters and t-stats NYU V-Lab: Shanghai Stock Exchange Composite Index GARCH Volatility Analysis. https://vlab.stern.nyu.edu/volatility/VOL.SHCOMP%3AIND-R.GARCH Shenzhen Composite (SZCOMP), GARCH parameters and t-stats NYU V-Lab: Shenzhen Stock Exchange Composite Index GARCH Volatility Analysis. https://vlab.stern.nyu.edu/volatility/VOL.SZCOMP%3AIND-R.GARCH Shenzhen A-Share (alternative for robustness), GARCH parameters and t-stats NYU V-Lab: Shenzhen Stock Exchange A Share Index GARCH Volatility Analysis. https://vlab.stern.nyu.edu/volatility/VOL.SZASHR%3AIND-R.GARCH IBOVESPA (Brazil), GARCH parameters and t-stats NYU V-Lab: Ibovespa Brasil São Paulo Stock Exchange Index GARCH Volatility Analysis. https://vlab.stern.nyu.edu/volatility/VOL.IBOV%3AIND-R.GARCH 7.3 Results Values are given in days for direct comparability to the nuclear decay and cosmology panels. Market / Episode ρ [days] Δρ [days] Calibration window t c forecast window Δσ Δρ•Δσ [days] S&P 500 (2010- 2019) combined with SSEC (2008-2009) 16.56 10.30 2008/10/15 - 2009/07/31 (289 d) 2009/07/17 - 2009/07/27 (10 d)
  23. Shenzhen Composite (SZSC, 2008-2009) 0.00743 0.00475 2008/10/15 - 2009/07/31 (289 d) 2009/08/03 - 2009/08/09 (6 d)
  24. Shanghai Composite (SSEC, 2008-2009) 0.00110 0.00407 2008/10/15 - 2009/07/31 (289 d) 2009/07/17 - 2009/07/27 (10 d) 0.03460 1.4×10 -4
  25. IBOVESPA (Brazil, 2008-2009) 0.00331 0.00787 2008/09/01 - 2009/10/29 (423 d) 2009/10/27 - 2009/11/29 (33 d)
  26. 4 Interpretation This case study demonstrates that the Structural Uncertainty Relation is not confined to natural systems such as radioactive decay or cosmic expansion, but also applies to socio-technical systems like financial markets. Across four independent episodes (S&P 500, Shanghai Composite, Shenzhen Composite, IBOVESPA), the SUR inequality is consistently satisfied.