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arxiv: 2605.11200 · v1 · submitted 2026-05-11 · 💱 q-fin.RM

Recognition: 2 theorem links

· Lean Theorem

The Epistemic Risk of Risk: A Modal Framework for Quantitative Risk Management

Hirbod Assa

Pith reviewed 2026-05-13 00:52 UTC · model grok-4.3

classification 💱 q-fin.RM
keywords quantitative risk managementepistemic logicmodal semanticsrisk governanceassuranceworking commitmentepistemic gapsmodel risk
0
0 comments X

The pith

Risk management must separate object-level claims from meta-level epistemic diagnostics to avoid paradoxes when applying standard governance principles.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces modal epistemic tools that distinguish a risk proposition p from the stances of assurance-grade endorsement Kp and working commitment Bp. It shows that the Risk Management Principle, which treats the absence of stance as itself risk-relevant, and the Risk Reach Principle, which requires real risks to be reachable by appropriate stances, generate Moorean and Fitch-style collapse when combined without restriction. The resolution is an architectural split: object-level risk claims remain action-guiding while meta-level epistemic gaps are isolated and controlled in a dedicated audit layer. This setup lets institutions model evidential incompleteness, validation gaps, and model risk without requiring full assurance on every claim. A reader would care because ordinary risk registers often treat incomplete knowledge as ordinary hazards, which can undermine the very diagnostics meant to flag them.

Core claim

For any risk proposition p the framework supplies crisp and fuzzy modal semantics for assurance, working commitment, live possibility, and epistemic inconsistency. The central diagnostics p ∧ ¬Kp and p ∧ ¬Bp identify cases in which a risk is present yet lacks the relevant stance. The Risk Management Principle states that if p is a risk then p ∧ ¬Mp is itself risk-relevant; the Risk Reach Principle states that decision-relevant risks must be reachable by the appropriate stance. Their unrestricted joint application creates collapse pressure by turning the very absence of stance into an ordinary target of that same stance. The paper therefore requires that object-level risk claims be governed,

What carries the argument

The modal operators K for assurance-grade endorsement and B for working commitment, applied to risk propositions p, with the gap diagnostics p ∧ ¬Kp and p ∧ ¬Bp that separate object-level hazards from meta-level epistemic diagnostics.

If this is right

  • Quantitative risk management must track evidential incompleteness and validation gaps alongside physical hazards and losses.
  • Governance principles cannot be applied uniformly to both object-level risks and their epistemic absences without generating inconsistency.
  • An audit layer must record and control epistemic gaps so that regulatory reporting and board sign-off reflect explicit assurance levels.
  • Model risk and escalation failures become first-class objects of risk analysis rather than background assumptions.
  • Risk propositions retain their precautionary force even when full assurance is unavailable.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The audit-layer separation could be tested by redesigning existing risk dashboards to flag p ∧ ¬Kp entries without halting operational decisions.
  • The framework suggests that regulatory capital models may need explicit parameters for assurance shortfalls rather than assuming uniform reliability of inputs.
  • Similar modal distinctions might clarify how institutions handle uncertainty about their own uncertainty in stress-testing exercises.

Load-bearing premise

Object-level risk claims can be cleanly separated from meta-level epistemic diagnostics in an audit layer while preserving the action-guiding and precaution functions of risk governance.

What would settle it

A concrete institutional risk register in which enforcing the object-meta separation either removes timely precaution against a known hazard or allows an epistemic gap to persist undetected under ordinary governance rules.

Figures

Figures reproduced from arXiv: 2605.11200 by Hirbod Assa.

Figure 1
Figure 1. Figure 1: Two-world fuzzy case. Left: assurance-grade support [PITH_FULL_IMAGE:figures/full_fig_p026_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Two-world fuzzy case. Left: non-exclusion [PITH_FULL_IMAGE:figures/full_fig_p027_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Two-world fuzzy case. Left: Moorean diagnostic [PITH_FULL_IMAGE:figures/full_fig_p028_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Two-world fuzzy case. Left: working commitment [PITH_FULL_IMAGE:figures/full_fig_p029_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Modal regions for the lognormal model-risk example. The robust region is [PITH_FULL_IMAGE:figures/full_fig_p033_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: A concrete non-factive working-commitment example. The actual state [PITH_FULL_IMAGE:figures/full_fig_p036_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: A two-dimensional crisp flood-risk example with a non-linear flood-stress function [PITH_FULL_IMAGE:figures/full_fig_p039_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Depicting the hesitation area for simplified flood example. [PITH_FULL_IMAGE:figures/full_fig_p040_8.png] view at source ↗
read the original abstract

Risk governance is not only about identifying and measuring adverse states of the world. It also asks when an institution is entitled to rely on a risk claim. This paper introduces modal epistemic tools for that second layer of QRM. For a risk proposition $p$, $Kp$ denotes assurance-grade endorsement for certification, audit reliance, board sign-off, or regulatory reporting. By contrast, $Bp$ denotes working commitment: a disciplined action-guiding stance under incomplete assurance. The framework distinguishes object-level risk claims from stances toward them. It develops crisp and fuzzy modal semantics for assurance, working commitment, live possibility, non-exclusion, hesitation, and epistemic inconsistency. The central diagnostics are \[ p\wedge\neg Kp \qquad\text{and}\qquad p\wedge\neg Bp, \] which identify cases in which a risk is present but lacks the relevant stance. Thus QRM should model not only hazards and losses, but also evidential incompleteness, model risk, validation gaps, and failures of escalation. Two governance principles motivate the analysis. The Risk Management Principle says that if $p$ is a risk, then the absence of the relevant stance, $p\wedge\neg Mp$, is itself risk-relevant. The Risk Reach Principle says that real and decision-relevant risks should be reachable by the appropriate stance. Their unrestricted combination creates Moorean and Fitch-style collapse pressure: treating $p\wedge\neg Kp$ or $p\wedge\neg Bp$ as ordinary targets of the same stance whose absence they record undermines the diagnostic. The response is architectural. Object-level risk claims should be separated from meta-level epistemic diagnostics. The latter should be governed through an audit layer that records and controls epistemic gaps. This preserves action and precaution without collapsing risk governance into institutional omniscience.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces a modal epistemic framework for quantitative risk management that distinguishes object-level risk propositions p from meta-level stances Kp (assurance-grade endorsement for certification or regulatory reporting) and Bp (working commitment under incomplete assurance). It develops crisp and fuzzy modal semantics for assurance, commitment, live possibility, non-exclusion, hesitation, and epistemic inconsistency. The central claim is that the unrestricted combination of the Risk Management Principle (if p is a risk then p ∧ ¬Mp is risk-relevant) and the Risk Reach Principle (real risks should be reachable by the appropriate stance) generates Moorean and Fitch-style collapse pressure on epistemic gaps such as p ∧ ¬Kp and p ∧ ¬Bp; the proposed resolution is an architectural separation of object-level claims from a meta-level audit layer that records and controls epistemic gaps while preserving action-guidance and precaution.

Significance. If the insulation of the audit layer can be formally established, the framework would provide a structured way to incorporate epistemic incompleteness, model risk, and validation failures into QRM without forcing institutional omniscience. The explicit diagnosis of collapse pressures and the introduction of dual modalities Kp/Bp with both crisp and fuzzy semantics represent a genuine conceptual advance for handling the second layer of risk governance (entitlement to rely on risk claims).

major comments (2)
  1. [§5 (Architectural Response)] §5 (Architectural Response): The claim that separating object-level risk claims from an audit layer for epistemic diagnostics blocks re-application of the Risk Management and Risk Reach Principles is asserted without a formal semantic constraint, axiom, or proof showing why an audit record q = “epistemic gap for p” does not itself count as a risk proposition to which the same two principles apply, thereby recreating the original collapse at the meta-level. This insulation is load-bearing for the central resolution.
  2. [§3 (Modal Semantics)] §3 (Modal Semantics): While crisp and fuzzy semantics are supplied for Kp, Bp, live possibility, and epistemic inconsistency, no theorem or derivation demonstrates that these semantics enforce the required insulation of meta-diagnostics; the interaction between the semantics and the two governance principles is left implicit, leaving open whether the collapse pressure is actually dissolved rather than relocated.
minor comments (2)
  1. [Notation and presentation] The abstract and §2 introduce the two principles clearly, but a compact table summarizing the truth conditions for all modalities (Kp, Bp, live possibility, etc.) under both crisp and fuzzy interpretations would improve readability and allow direct comparison.
  2. [Cross-referencing] The manuscript would benefit from explicit cross-references between the collapse diagnostics in §4 and the audit-layer proposal in §5 to make the logical flow from problem to solution more transparent.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful and constructive report. The two major comments identify genuine gaps in the formal development of the insulation mechanism between object-level risk claims and the meta-level audit layer. We address each point directly below and indicate the revisions that will be incorporated.

read point-by-point responses
  1. Referee: [§5 (Architectural Response)] §5 (Architectural Response): The claim that separating object-level risk claims from an audit layer for epistemic diagnostics blocks re-application of the Risk Management and Risk Reach Principles is asserted without a formal semantic constraint, axiom, or proof showing why an audit record q = “epistemic gap for p” does not itself count as a risk proposition to which the same two principles apply, thereby recreating the original collapse at the meta-level. This insulation is load-bearing for the central resolution.

    Authors: We agree that the current manuscript presents the architectural separation without an explicit semantic constraint or proof. In the revision we will add to §5 a formal definition of the audit layer as a distinct modal context equipped with its own accessibility relation R_audit that is disjoint from the object-level relation R_object. Under this definition, meta-diagnostic records q = “epistemic gap for p” are not classified as risk propositions p, so the Risk Management and Risk Reach Principles do not apply to them. A short lemma will show that any attempt to re-apply the principles at the meta-level violates the disjointness of the accessibility relations, thereby blocking collapse. The lemma will be stated in both the crisp and fuzzy semantics already introduced. revision: yes

  2. Referee: [§3 (Modal Semantics)] §3 (Modal Semantics): While crisp and fuzzy semantics are supplied for Kp, Bp, live possibility, and epistemic inconsistency, no theorem or derivation demonstrates that these semantics enforce the required insulation of meta-diagnostics; the interaction between the semantics and the two governance principles is left implicit, leaving open whether the collapse pressure is actually dissolved rather than relocated.

    Authors: The observation is correct: the manuscript supplies the semantics but does not derive their interaction with the governance principles. We will insert a new theorem in §3 that derives the insulation directly from the fuzzy semantics for hesitation and epistemic inconsistency. The theorem states that if a meta-diagnostic q records an epistemic gap, then the fuzzy degree of epistemic inconsistency for q is strictly positive, which blocks the Risk Reach Principle from licensing a stance on q itself. The proof will use the existing definitions of live possibility and non-exclusion to show that the collapse is dissolved rather than relocated. revision: yes

Circularity Check

0 steps flagged

No circularity; new modal framework is self-contained via explicit definitions

full rationale

The paper introduces fresh modal operators Kp (assurance-grade endorsement) and Bp (working commitment) along with two governance principles and an architectural split between object-level claims and an audit layer. No derivation reduces a prediction or diagnostic to its own inputs by construction, no parameters are fitted and then renamed as predictions, and no load-bearing step relies on self-citation chains or imported uniqueness theorems. The central diagnostics p∧¬Kp and p∧¬Bp are defined directly from the new semantics rather than presupposing the collapse they diagnose, and the proposed separation is presented as a conceptual response rather than a derived equivalence.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claim rests on introducing two new modal operators and two governance principles grounded in standard modal logic, without free parameters or empirical fitting.

axioms (1)
  • standard math Standard modal logic semantics apply to the new operators K and B
    Paper develops crisp and fuzzy modal semantics for assurance, commitment, and related notions.
invented entities (2)
  • Kp (assurance-grade endorsement) no independent evidence
    purpose: Denotes certification, audit, or regulatory-level assurance for a risk proposition
    New operator introduced to distinguish strong endorsement from weaker stances.
  • Bp (working commitment) no independent evidence
    purpose: Denotes disciplined action-guiding stance under incomplete assurance
    New operator for practical commitment without full assurance.

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Works this paper leans on

77 extracted references · 77 canonical work pages

  1. [1]

    Acemoglu, A

    D. Acemoglu, A. Ozdaglar, and A. Tahbaz-Salehi. Systemic risk and stability in financial networks.American Economic Review, 105(2):564–608, 2015. 5.3

  2. [2]

    Allen and D

    F. Allen and D. Gale. Financial contagion.Journal of Political Economy, 108(1):1–33,

  3. [3]

    Argyris and D

    C. Argyris and D. A. Schön.Organizational Learning: A Theory of Action Perspective. Addison-Wesley, Reading, MA, 1978. 7.6

  4. [4]

    K. T. Atanassov. Intuitionistic fuzzy sets.Fuzzy Sets and Systems, 20(1):87–96, 1986. 2.4

  5. [5]

    K. T. Atanassov.Intuitionistic Fuzzy Sets: Theory and Applications. Physica-Verlag, Heidelberg, 1999. 2.4

  6. [6]

    R. J. Aumann. Agreeing to disagree.The Annals of Statistics, 4(6):1236–1239, 1976. 2.3, 7.7

  7. [7]

    R. J. Aumann. Correlated equilibrium as an expression of bayesian rationality.Econo- metrica, 55(1):1–18, 1987. 2.3

  8. [8]

    R. J. Aumann. Backward induction and common knowledge of rationality.Games and Economic Behavior, 8(1):6–19, 1995. 2.3

  9. [9]

    R. J. Aumann and A. Brandenburger. Epistemic conditions for nash equilibrium. Econometrica, 63(5):1161–1180, 1995. 2.3 58

  10. [10]

    B. Babic. A theory of epistemic risk.Philosophy of Science, 86(3):522–550, 2019. 2.5, 14

  11. [11]

    Baczyński and B

    M. Baczyński and B. Jayaram.Fuzzy Implications. Springer, Berlin, 2008. 2.4

  12. [12]

    Baltag, L

    A. Baltag, L. S. Moss, and S. Solecki. The logic of public announcements, common knowledge, and private suspicions. InProceedings of the 7th Conference on Theoretical Aspects of Rationality and Knowledge, pages 43–56, 1998. 7.6

  13. [13]

    P. D. Bates, M. S. Horritt, and T. J. Fewtrell. A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling. Journal of Hydrology, 387(1–2):33–45, 2010. 5.4

  14. [14]

    Battigalli and G

    P. Battigalli and G. Bonanno. Recent results on belief, knowledge and the epistemic foundations of game theory.Research in Economics, 53(2):149–225, 1999. 2.3, 7.7

  15. [15]

    Battigalli and M

    P. Battigalli and M. Siniscalchi. Hierarchies of conditional beliefs and interactive epistemology in dynamic games.Journal of Economic Theory, 88(1):188–230, 1999. 2.3

  16. [16]

    B. D. Bernheim. Rationalizable strategic behavior.Econometrica, 52(4):1007–1028,

  17. [17]

    K. J. Beven.Rainfall-Runoff Modelling: The Primer. Wiley-Blackwell, Chichester, 2 edition, 2012. 5.4

  18. [18]

    Blackburn, M

    P. Blackburn, M. de Rijke, and Y. Venema.Modal Logic. Cambridge University Press, Cambridge, 2001. 3.2, 15

  19. [19]

    Supervisory guidance on model risk management

    Board of Governors of the Federal Reserve System and Office of the Comptroller of the Currency. Supervisory guidance on model risk management. SR 11-7 / OCC 2011-12,

  20. [20]

    Brandenburger and E

    A. Brandenburger and E. Dekel. Rationalizability and correlated equilibria.Econo- metrica, 55(6):1391–1402, 1987. 2.3

  21. [21]

    Hierarchiesofbeliefsandcommonknowledge.Journal of Economic Theory, 59(1):189–198, 1993

    A.BrandenburgerandE.Dekel. Hierarchiesofbeliefsandcommonknowledge.Journal of Economic Theory, 59(1):189–198, 1993. 2.3

  22. [22]

    B. F. Chellas.Modal Logic: An Introduction. Cambridge University Press, Cambridge,

  23. [23]

    R. M. Chisholm.Theory of Knowledge. Prentice-Hall, Englewood Cliffs, NJ, 2 edition,

  24. [24]

    V. T. Chow, D. R. Maidment, and L. W. Mays.Applied Hydrology. McGraw-Hill, New York, 1988. 5.4

  25. [25]

    Christensen.Putting Logic in Its Place: Formal Constraints on Rational Belief

    D. Christensen.Putting Logic in Its Place: Formal Constraints on Rational Belief. Oxford University Press, Oxford, 2004. 2.5, 15

  26. [26]

    De Cock and E

    M. De Cock and E. E. Kerre. Fuzzy modifiers based on fuzzy relations.Information Sciences, 160(1–4):173–199, 2004. 2.4

  27. [27]

    De Cock, A

    M. De Cock, A. M. Radzikowska, and E. E. Kerre.A Fuzzy-Rough Approach to the Representation of Linguistic Hedges, pages 33–42. Physica-Verlag HD, Heidelberg,

  28. [28]

    Dekel and F

    E. Dekel and F. Gul.Rationality and knowledge in game theory, pages 87–172. Econo- metric Society Monographs. Cambridge University Press, 1997. 2.3

  29. [29]

    Eisenberg and T

    L. Eisenberg and T. H. Noe. Systemic risk in financial systems.Management Science, 47(2):236–249, 2001. 5.3

  30. [30]

    Elliott, B

    M. Elliott, B. Golub, and M. O. Jackson. Financial networks and contagion.American Economic Review, 104(10):3115–3153, 2014. 5.3

  31. [31]

    Embrechts, M

    P. Embrechts, M. Hofert, and V. Chavez-Demoulin.Risk Revealed: Cautionary Tales, Understanding and Communication. Cambridge University Press, 2024. 1

  32. [32]

    Fagin, J

    R. Fagin, J. Y. Halpern, Y. Moses, and M. Y. Vardi.Reasoning about Knowledge. MIT Press, Cambridge, MA, 1995. 2.3, 3.2, 15, 7.7

  33. [33]

    F. B. Fitch. A logical analysis of some value concepts.The Journal of Symbolic Logic, 28(2):135–142, 1963. 2.2

  34. [34]

    Foley.Working Without a Net: A Study of Egocentric Epistemology

    R. Foley.Working Without a Net: A Study of Egocentric Epistemology. Oxford University Press, New York, 1993. 2.5, 15

  35. [35]

    Geanakoplos

    J. Geanakoplos. Chapter 40 common knowledge. volume 2 ofHandbook of Game Theory with Economic Applications, pages 1437–1496. Elsevier, 1994. 2.3, 7.7

  36. [36]

    Gerbrandy.Bisimulations on Planet Kripke

    J. Gerbrandy.Bisimulations on Planet Kripke. PhD thesis, University of Amsterdam,

  37. [37]

    Glasserman and H

    P. Glasserman and H. P. Young. Contagion in financial networks.Journal of Economic Literature, 54(3):779–831, 2016. 5.3

  38. [38]

    A. I. Goldman. Discrimination and perceptual knowledge.The Journal of Philosophy, 73(20):771–791, 1976. 2.1 60

  39. [39]

    Hájek.Metamathematics of Fuzzy Logic

    P. Hájek.Metamathematics of Fuzzy Logic. Kluwer Academic Publishers, Dordrecht, Boston and London, 1998. 2.4

  40. [40]

    J. W. Hall, R. J. Dawson, and P. B. Sayers. A methodology for national-scale flood risk assessment.Proceedings of the Institution of Civil Engineers: Water and Maritime Engineering, 156(3):235–247, 2003. 5.4

  41. [41]

    S. O. Hansson. Philosophical perspectives on risk.Techné: Research in Philosophy and Technology, 8(1):10–35, 2004. 17

  42. [42]

    J. M. Harrison and D. M. Kreps. Speculative investor behavior in a stock market with heterogeneous expectations.The Quarterly Journal of Economics, 92(2):323– 336, 1978. 7.7

  43. [43]

    J. C. Harsanyi. Games with incomplete information played by bayesian players, i–iii. Management Science, 14(3–5):159–182, 320–334, 486–502, 1967–1968. 2.3

  44. [44]

    Hawthorne.Knowledge and Lotteries

    J. Hawthorne.Knowledge and Lotteries. Oxford University Press, New York, 2004. 2.1, 14

  45. [45]

    Hintikka.Knowledge and Belief: An Introduction to the Logic of the Two Notions

    J. Hintikka.Knowledge and Belief: An Introduction to the Logic of the Two Notions. Cornell University Press, Ithaca, NY, 1962. 2.2, 3.2

  46. [46]

    W. H. Holliday and T. F. Icard. Moorean phenomena in epistemic logic. InAdvances in Modal Logic, volume 8, pages 178–199, 2010. 2.2

  47. [47]

    E. P. Klement, R. Mesiar, and E. Pap.Triangular Norms. Kluwer Academic Publish- ers, Dordrecht, 2000. 2.4

  48. [48]

    H. E. Kyburg.Probability and the Logic of Rational Belief. Wesleyan University Press, Middletown, CT, 1961. 2.5, 15

  49. [49]

    Levitt and J

    B. Levitt and J. G. March. Organizational learning.Annual Review of Sociology, 14:319–340, 1988. 7.6

  50. [50]

    D. C. Makinson. The paradox of the preface.Analysis, 25(6):205–207, 1965. 2.5, 15

  51. [51]

    J.-J. C. Meyer and W. van der Hoek.Epistemic Logic for AI and Computer Science. Cambridge University Press, Cambridge, 1995. 3.2, 15

  52. [52]

    G. E. Moore. A reply to my critics. In P. A. Schilpp, editor,The philosophy of G. E. Moore. Tudor Pub. Co., 1952. 2.2

  53. [53]

    KluwerAcademic Publishers, Dordrecht, 1991

    Z.Pawlak.Rough Sets: Theoretical Aspects of Reasoning about Data. KluwerAcademic Publishers, Dordrecht, 1991. 2.4 61

  54. [54]

    Pawlak.Rough Sets, pages 3–7

    Z. Pawlak.Rough Sets, pages 3–7. Springer US, Boston, MA, 1997. 2.4

  55. [55]

    D. G. Pearce. Rationalizable strategic behavior and the problem of perfection.Econo- metrica, 52(4):1029–1050, 1984. 2.3

  56. [56]

    R. G. Pettigrew.Epistemic Risk and the Demands of Rationality. Oxford University Press, 2022. 2.5

  57. [57]

    J. Plaza. Logics of public communications.Synthese, 158(2):165–179, 2007. 2.2, 7.6

  58. [58]

    Pritchard.Epistemic Luck

    D. Pritchard.Epistemic Luck. Oxford University Press, Oxford, 2005. 2.1

  59. [59]

    Pritchard

    D. Pritchard. Anti-luck epistemology.Synthese, 158(3):277–297, 2007. 2.1

  60. [60]

    Pritchard

    D. Pritchard. Anti-luck virtue epistemology.The Journal of Philosophy, 109(3):247– 279, 2012. 2.1

  61. [61]

    Pritchard

    D. Pritchard. Risk.Metaphilosophy, 46(3):436–461, 2015. 2.1

  62. [62]

    Pritchard

    D. Pritchard. Epistemic risk.The Journal of Philosophy, 113(11):550–571, 2016. 2.1, 14

  63. [63]

    A. M. Radzikowska. Fuzzy modal operators and their applications.Journal of Au- tomation, Mobile Robotics & Intelligent Systems, 11(1):10–20, 2017. 2.4

  64. [64]

    Reason.Managing the Risks of Organizational Accidents

    J. Reason.Managing the Risks of Organizational Accidents. Ashgate, Aldershot, 1997. 7.6

  65. [65]

    R. M. Sainsbury. Easy possibilities.Philosophy and Phenomenological Research, 57(4):907–919, 1997. 2.1

  66. [66]

    J. A. Scheinkman and W. Xiong. Overconfidence and speculative bubbles.Journal of Political Economy, 111(6):1183–1219, 2003. 7.7

  67. [67]

    E. Sosa. How to defeat opposition to moore.Philosophical Perspectives, 13:141–154,

  68. [68]

    Stirling

    A. Stirling. Risk, precaution and science: Towards a more constructive policy debate. EMBO Reports, 8(4):309–315, 2007. 17

  69. [69]

    C. R. Sunstein.Laws of Fear: Beyond the Precautionary Principle. Cambridge Uni- versity Press, Cambridge, 2005. 17

  70. [70]

    Szmidt and J

    E. Szmidt and J. Kacprzyk. Distances between intuitionistic fuzzy sets.Fuzzy Sets and Systems, 114(3):505–518, 2000. 2.4 62

  71. [71]

    van Benthem

    J. van Benthem. Dynamic logic for belief revision.Journal of Applied Non-Classical Logics, 17(2):129–155, 2007. 7.6

  72. [72]

    van Ditmarsch, , , W

    H. van Ditmarsch, , , W. van der Hoek, and B. Kooi. Dynamic epistemic logic. In Internet Encyclopedia of Philosophy. 2016. 2.2, 7.6

  73. [73]

    K. E. Weick and K. M. Sutcliffe.Managing the Unexpected: Resilient Performance in an Age of Uncertainty. Jossey-Bass, San Francisco, 2 edition, 2007. 7.6

  74. [74]

    Williamson.Knowledge and Its Limits

    T. Williamson.Knowledge and Its Limits. Oxford University Press, Oxford, 2000. 2.1

  75. [75]

    L. A. Zadeh. Fuzzy sets.Information and Control, 8(3):338–353, 1965. 2.4, 6.1

  76. [76]

    L. A. Zadeh. Probability measures of fuzzy events.Journal of Mathematical Analysis and Applications, 23(2):421–427, 1968. 6.1

  77. [77]

    L. A. Zadeh. A fuzzy-set-theoretic interpretation of linguistic hedges.Journal of Cybernetics, 2(3):4–34, 1972. 2.4 9 Proofs for the Moorean Limitation 9.1 Proof of Theorem 6 Supposeq≤p. By monotonicity ofM, Mq≤Mp. By factivity, Mq≤q. Since∧is a meet, Mq≤Mp∧q. By monotonicity of♢M, ♢MMq≤♢M(Mp∧q). Letp∈Risk(S). By RMP, R(p)∈Risk(S). By RRP, R(p)≤♢MMR(p). S...