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REVIEW 4 major objections 5 minor 58 references

The Goldilocks zone of governing technology: Leveraging uncertainty for responsible quantum practices

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper argues that quantum technology's inherent uncertainty should be turned into a generative force for regulation, proposing a probabilistic Quantum Risk Simulator as a dynamic alternative to fixed risk tiers.

desk verdict A well-scoped, honest position paper: the quantum analogy is ornamental rather than formal, but the three-layer taxonomy and the critique of fixed risk tiers make it worth a serious referee. read the letter →

arxiv 2507.12957 v1 pith:YFLI6RSE submitted 2025-07-17 cs.CY

Miriam Meckel , Philipp Hacker , Lea Steinacker , Aurelija Lukoseviciene , Surjo R. Soekadar , Jacob Slosser , Gina-Maria Poehlmann This is my paper · ORCID
classification cs.CY
keywords QuantumTechnologyComputingGovernanceResponsibleInnovationTechnologicalUncertaintyRiskSimulatorEUAIActEmergingTechnologies
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that uncertainty in quantum computing is not merely a problem to be minimised but a resource that governance should actively exploit. It argues that the standard regulatory logic of predefined, calculable risk categories, exemplified by the EU AI Act, fails for technologies whose physics, development timeline, and social consequences are all radically uncertain. As a remedy, it proposes the Quantum Risk Simulator (QRS), a conceptual software blueprint that would represent risk as shifting probability distributions rather than fixed scores, updating continuously as evidence arrives. If the argument holds, European regulators would move from deterministic risk tiers to dynamic, probabilistic assessments, and the same design could be transferred to other frontier technologies such as advanced AI or geoengineering.

What carries the argument

The central object is the Quantum Risk Simulator (QRS), a proposed cloud-based software framework that models risk not as a fixed category or score but as a shifting probability landscape. It rests on three design principles: a probabilistic foundation (probability distributions in and out), dynamic updating (continuous ingestion of new experimental and theoretical data), and explicit uncertainty quantification (separating aleatoric, epistemic, and model uncertainty). The QRS is meant to work as a governance practice itself: building, interacting with, and adapting the simulator is what directs responsible development. The paper's supporting mechanism is the three-layer typology of uncertainty, physical, technical, and societal, which organises why deterministic risk regulation is incomplete for quantum systems.

What would settle it

One way to falsify the central claim would be to build a QRS-style simulator for a concrete emerging technology and compare its dynamic probability forecasts against fixed risk tiers over a multi-year period; if the simulator's probability distributions are consistently miscalibrated relative to observed harms, or if static tiers perform no worse, the case for replacing deterministic categories loses its empirical footing.

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Extended reading notes

Core claim

At its core, the paper claims that the three layers of uncertainty surrounding quantum technology, physical (the ontology of quantum states), technical (the unknown timeline and scale of quantum advantage), and societal (privacy, security, inequality, and dual-use effects), are not obstacles to be removed but the very features that a responsible governance model should mirror. Because a quantum measurement does not reveal a pre-existing value but co-determines the outcome, the paper infers that regulatory interventions likewise transform the technological landscape they aim to govern. Governance should therefore be probabilistic, adaptive, and iterative. The Quantum Risk Simulator is offered as an imaginative blueprint, not a prescriptive tool: it would take probability distributions as inputs and outputs, update dynamically with new data, and explicitly quantify aleatoric, epistemic, and model uncertainty. The paper positions this approach as a 'Goldilocks zone' between laissez-faire and state control, and as a promising path for the European Union.

Load-bearing premise

The argument depends on the assumption that quantum physics gives us a genuinely applicable model for how regulators should handle uncertainty, and not just a handy metaphor.

Editorial extensions

If this is right

  • Regulators would replace fixed risk tiers (prohibited, high, limited, minimal) with continuously updated probability distributions for the same applications.
  • The QRS would turn risk assessment into an ongoing governance practice, with an oversight board of scientists, ethicists, policymakers, industry, and civil society periodically re-evaluating the tool itself.
  • The same probabilistic design could be transposed to other emerging technologies, including advanced AI and geoengineering, whose uncertainties are also not calculable in advance.
  • The European Union would gain a 'third way' between US market-driven innovation and Chinese state-led control, grounded in dynamic rather than static regulation.
  • A QRS would make quantum key distribution and other dual-use quantum capabilities visible as shifting risk landscapes rather than binary threats or solutions, allowing proactive rather than reactive mitigation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial extension: the paper's measurement analogy implies regulators should expect their own interventions to change the technology being regulated, so governance rules should be designed to be reversible and to collect data on their own effects.
  • Editorial extension: the QRS logic could be tested empirically by building a minimal simulator for a well-scoped emerging technology and comparing its probabilistic forecasts against static risk tiers in a regulatory sandbox; miscalibration would be directly measurable.
  • Editorial extension: the three-layer typology could also be applied to other 'deep uncertainty' technologies, giving a concrete checklist (physical, technical, societal) for deciding when deterministic risk regulation is inappropriate.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This paper argues that uncertainty in quantum technologies should be reframed as a generative force for governance rather than a liability. It identifies three layers of uncertainty—physical, technical, and societal—and proposes a conceptual tool, the Quantum Risk Simulator (QRS), as a blueprint for adaptive, probabilistic governance. The authors position this approach as an alternative to what they describe as deterministic, category-based regulation such as the EU AI Act, and suggest it could serve as a 'third way' for the European Union. The paper explicitly acknowledges that the QRS is an imaginative blueprint rather than a prescriptive tool, and it flags the need to distinguish metaphorical parallels from formal physical properties of quantum systems.

Significance. If its central argument succeeds, the paper contributes a valuable reframing of uncertainty in emerging-technology governance, moving beyond the familiar Collingridge dilemma and toward adaptive, participatory, and probabilistically informed approaches. Its strength lies in explicitly naming three distinct uncertainty layers, engaging with responsible-innovation literature, and acknowledging its own limitations by presenting the QRS as a blueprint rather than an implemented system. The paper is agenda-setting rather than demonstrative: it offers no empirical evaluation, formal model, or testable implementation, so its significance is primarily conceptual and normative.

major comments (4)
  1. [Uncertainty of underlying physics; Quantum Risk Simulator] The central argument depends on an analogy between the ontological, physical uncertainty of quantum systems and the epistemic, regulatory uncertainty faced by governance institutions. The paper itself states that 'it is crucial to distinguish between metaphorical parallels and the formal, physical properties of quantum systems' and concedes that human decision-making uncertainty is epistemic, yet the QRS principles (a)–(c) are probabilistic foundation, dynamic updating, and uncertainty quantification—all standard features of Bayesian risk assessment and model-based decision support. No quantum-specific formalism (superposition, entanglement, measurement collapse) appears in the QRS design. The manuscript therefore needs an explicit bridging normative premise explaining what quantum mechanics contributes beyond generic probabilistic risk governance, or it should be transparently reframed as drawing a heuristic lesson rather than a justified design principle.
  2. [Quantum Risk Simulator] The claim that a QRS 'would help stakeholders anticipate and mitigate unforeseen consequences' is asserted without empirical evidence, a worked example, or a detailed causal mechanism. Even for a conceptual blueprint, the paper should specify what types of foresight the tool would plausibly improve, what data and models would drive it, and what evaluation criteria would be used. Without such specification, the promised benefit remains rhetorical rather than actionable.
  3. [Introduction; From calculable risk to uncertainty] The characterization of current regulation as relying on 'deterministic categories that presume we can define and contain risk in advance' is too sweeping. The EU AI Act, for example, includes risk management systems, post-market monitoring, and adaptation obligations, which are dynamic elements, even though its risk tiers are discrete. The paper should engage with these existing adaptive features so that its comparison between deterministic and probabilistic governance is accurate and fair.
  4. [Epistemic and ontological uncertainty] The analogy with predictive processing is underdeveloped. The statement that 'quantum computing operates at the edge of chaos and uncertainty' is vague and is not tied to a specific governance implication. Since the paper uses this analogy as support for the overall reframing, it should either be developed with a precise mechanism or removed to avoid overreach.
minor comments (5)
  1. [Bibliography and in-text citations] Reference years are inconsistent: Nave et al. is cited in the text as 1994 but listed as 2020, and Ding and Chong is cited as 2022 in the text but listed as 2020 in the bibliography. Please harmonize.
  2. [Bibliography] References [4] and [36] appear to describe the same paper (Bouwmeester, Pan, et al., Nature 403, 515–519) and should be merged or distinguished appropriately.
  3. [Uncertainty of technical quantum superiority] The phrase 'several million of qubits' should read 'several million qubits'; also, 'quantum superiority' is often more cautiously called 'quantum advantage' in the literature, and the paper should be consistent.
  4. [Conclusion] The phrase 'probabilistic und inherently uncertain' contains a typo: 'und' should be 'and'.
  5. [Bibliography] The spelling 'Zellinger' in reference [36] should be 'Zeilinger', and the in-text citation 'van Daleen' should match the bibliography's 'van Daalen'.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the paper is an explicitly analogical governance proposal with only minor, non-load-bearing self-citations.

full rationale

This is a conceptual, normative policy paper with no mathematical derivation chain. The central proposal, the Quantum Risk Simulator (QRS), is introduced as "a conceptual example, an imaginative blueprint rather than a prescriptive tool," so it does not claim to derive risk distributions from quantum formalism. Its three principles - probabilistic foundation, dynamic updating, and uncertainty quantification - are generic features of probabilistic risk assessment; the paper does not argue that they are logically entailed by quantum mechanics. The only potential circularity would be if the quantum analogy were treated as a formal justification, but the authors explicitly caution that "it is crucial to distinguish between metaphorical parallels and the formal, physical properties of quantum systems." The self-citations (Suter et al. 2024; Lukoseviciene 2025) support a background descriptive claim about existing narratives of quantum technology and are not load-bearing for the prescriptive governance framework. No fitted parameters, equations, or imported uniqueness theorems are present, so no circular step can be exhibited. The central argument is a normative analogy, not a self-referential derivation.

Assumptions & free parameters 0 free parameters · 4 assumptions · 1 invented entities

The central argument rests on interpretive assumptions about quantum mechanics, a cognitive neuroscience model, a characterization of current regulation, and the legitimacy of analogical transfer. These are stated or implied rather than defended with evidence. No free parameters are fitted because the paper contains no quantitative model. The Quantum Risk Simulator is an invented conceptual artifact without independent evidence.

assumptions (4)
  • domain assumption Quantum mechanical uncertainty is ontological, not merely epistemic.
    The argument that uncertainty is foundational to quantum technology rests on a particular interpretation of quantum mechanics, cited to Harrigan and Spekkens (2010) and Pusey et al. (2012); alternative epistemic interpretations are not considered. Invoked in Section 'Epistemic and ontological uncertainty'.
  • domain assumption Predictive processing is an accurate model of human cognition and is relevant to governance design.
    The analogy between brains as probabilistic prediction engines and quantum risk management is presented as given, citing Nave et al.; no critical assessment of the neuroscience model is offered. Invoked in Section 'Epistemic and ontological uncertainty'.
  • domain assumption Current EU regulatory frameworks such as the AI Act and GDPR are based on calculable, deterministic risk categories.
    The paper's contrast between old deterministic governance and new probabilistic governance depends on this characterization. It is asserted in Section 'From calculable risk to uncertainty' without a systematic comparative analysis of the regulations.
  • ad hoc to paper Physical uncertainty in quantum systems can legitimately be used as a prescriptive model for social and regulatory uncertainty.
    The entire Quantum Risk Simulator proposal depends on transferring quantum concepts to governance. The paper warns against confusing metaphorical and formal properties, but does not establish why the analogy is more than a heuristic device. Invoked in Sections 'Epistemic and ontological uncertainty' and 'Quantum Risk Simulator'.
invented entities (1)
  • Quantum Risk Simulator (QRS)
    purpose: A notional cloud-based software platform for dynamic, probabilistic risk assessment in quantum technology governance.
    The paper presents QRS as a conceptual blueprint, not an implemented system. It has no independent validation or falsifiable prediction; it is an illustrative device for the proposed governance model.

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Cite this review

Pith. "Pith review of The Goldilocks zone of governing technology: Leveraging uncertainty for responsible quantum practices." pith.science (2026). https://pith.science/paper/YFLI6RSE

@misc{pith2026250712957,
  author       = {Pith},
  title        = {Pith review of: The Goldilocks zone of governing technology: Leveraging uncertainty for responsible quantum practices},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YFLI6RSE}},
  note         = {Machine review of arXiv:2507.12957}
}
read the original abstract

Emerging technologies challenge conventional governance approaches, especially when uncertainty is not a temporary obstacle but a foundational feature as in quantum computing. This paper reframes uncertainty from a governance liability to a generative force, using the paradigms of quantum mechanics to propose adaptive, probabilistic frameworks for responsible innovation. We identify three interdependent layers of uncertainty--physical, technical, and societal--central to the evolution of quantum technologies. The proposed Quantum Risk Simulator (QRS) serves as a conceptual example, an imaginative blueprint rather than a prescriptive tool, meant to illustrate how probabilistic reasoning could guide dynamic, uncertainty-based governance. By foregrounding epistemic and ontological ambiguity, and drawing analogies from cognitive neuroscience and predictive processing, we suggest a new model of governance aligned with the probabilistic essence of quantum systems. This model, we argue, is especially promising for the European Union as a third way between laissez-faire innovation and state-led control, offering a flexible yet responsible pathway for regulating quantum and other frontier technologies.

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Reference graph

Works this paper leans on

58 extracted references · 54 canonical work pages

  1. [1]

    Gallen; St

    1 The Goldilocks zone of governing technology: Leveraging uncertainty for responsible quantum practices Authors: Miriam Meckel Institute for Media and Communications Management, University of St. Gallen; St. Gallen, Switzerland, miriam.meckel@unisg.ch Philipp Hacker European New School of Digital Studies, European University Viadrina; Frankfurt, Germany, ...

    work page 1980

  2. [2]

    & Wolf, K

    Anderljung, M., Barnhart, J., Korinek, A., Leung, J., O'Keefe, C., Whittlestone, J., ... & Wolf, K. (2023). Frontier AI regulation: Managing emerging risks to public safety. arXiv preprint arXiv:2307.03718

    arXiv 2023

  3. [3]

    Oxford University Press

    Bostrom N and Ćirković MM (eds) (2008) Global Catastrophic Risks. Oxford University Press

    work page 2008

  4. [4]

    Nature 403(6769): 515–519

    Bouwmeester D, Pan JW, Mattle K, Eibl M, Weinfurter H, Zeilinger A (2000) Experimental test of quantum nonlocality in three-photon Greenberger-Horne-Zeilinger entanglement. Nature 403(6769): 515–519

    work page 2000

  5. [5]

    Brun, T. A. (2019). Quantum error correction. arXiv preprint arXiv:1910.03672

    arXiv 2019

  6. [6]

    Campbell, E. (2024). A series of fast-paced advances in quantum error correction. Nature Reviews Physics, 6(3), 160-161

    work page 2024

  7. [7]

    Ali SE (2020) Quantum supremacy, network security & the legal risk management framework: Resiliency for national security systems. SMU Sci. Technol. Law Rev. 23: 103-126

    work page 2020

  8. [8]

    New York: St

    Collingridge D (1980) The social control of technology. New York: St. Martin's Press

    work page 1980

Show all 58 references
  1. [9]

    London: John Willey & Sons

    De Laplace PSM (1902) Philosophical Essay on Probabilities. London: John Willey & Sons. Chapman & Hall, Limited

    1902

  2. [10]

    Catholic University Journal of Law and Technology 31(2): pp

    DeRose K (2023) Establishing the Legal Framework to Regulate Quantum Computing Technology. Catholic University Journal of Law and Technology 31(2): pp. 161-164

    2023

  3. [11]

    Ethics Inf

    De Wolf R (2017) The Potential Impact of Quantum Computers on Society. Ethics Inf. Technol. 19: pp. 271–276

    2017

  4. [12]

    Morgan & Claypool

    Ding Y, Chong FT (2020) Quantum computer systems: Research for noisy intermediate-scale quantum computers. Morgan & Claypool

    2020

  5. [13]

    (2022) Quantum Technologies and Society: Towards a Different Spin

    Coenen C, Grinbaum A, Grunwald A, et al. (2022) Quantum Technologies and Society: Towards a Different Spin. Nanoethics 16: pp. 1–6

    2022

  6. [14]

    Reviews of Modern Physics 68: pp

    Ekert A and Jozsa R (1996) Quantum computation and Shor's factoring algorithm. Reviews of Modern Physics 68: pp. 733–753

    1996

  7. [15]

    In: Beisbart C, Hartmann S (eds) Probabilities in Physics

    Frigg R, Werndl C (2011) Entropy: a guide for the perplexed. In: Beisbart C, Hartmann S (eds) Probabilities in Physics. Oxford University Press, pp. 115–142

    2011

  8. [16]

    Gellert, R. (2020). The risk-based approach to data protection. Oxford University Press

    2020

  9. [17]

    arXiv preprint arXiv:2403.08033

    Gercek AA and Seskir ZC (2025) Navigating the quantum divide(s). arXiv preprint arXiv:2403.08033

    2025 arXiv

  10. [18]

    Ethics and Information Technology 19: pp

    Grinbaum A (1994) Narratives of quantum theory in the age of quantum technologies. Ethics and Information Technology 19: pp. 295–306

    1994

  11. [19]

    Ebers, M. (2024). Truly risk-based regulation of artificial intelligence how to implement the EU’s AI Act. European Journal of Risk Regulation, 1-20

    2024

  12. [20]

    Hacker, P., & Holweg, M. (2025). The Regulation of Fine-Tuning: Federated Compliance for Modified General-Purpose AI Models. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5289125

    2025

  13. [21]

    Foundations of Physics 40, 125–157

    Harrigan N and Spekkens RW (2010) Einstein, Incompleteness, and the Epistemic View of Quantum States. Foundations of Physics 40, 125–157

    2010

  14. [22]

    Zeitschrift für Physik 43: pp 172–198

    Heisenberg W (1927) Über den anschaulichen Inhalt der quantentheoretischen Kinematik und Mechanik. Zeitschrift für Physik 43: pp 172–198

    1927

  15. [23]

    Herrington, G. (2021). Update to limits to growth: Comparing the World3 model with empirical data. Journal of Industrial Ecology, 25(3), 614-626

    2021

  16. [24]

    AI & Society 39: pp

    Hine E, Floridi L (2024) Artificial intelligence with American values and Chinese characteristics: A comparative analysis of American and Chinese governmental AI policies. AI & Society 39: pp. 257–278

    2024

  17. [25]

    Hacker, P., Engel, A., Hammer, S., & Mittelstadt, B. (2025). Introduction to the Foundations and Regulation of Generative AI. In: Hacker, P., Engel, A., Hammer, S., & Mittelstadt, B. (eds.), The Oxford Handbook of the Foundations and Regulation of Generative AI. Oxford Univers...

    2025

  18. [26]

    (2024) Current numbers of qubits and their uses

    Ichikawa T, Hakoshima H, Inui K, et al. (2024) Current numbers of qubits and their uses. Nature Reviews Physics 6: pp. 345–347. 10

    2024

  19. [27]

    Trends in Ecology & Evolution 28(8): pp

    Johnson DD, Blumstein DT, Fowler JH and Haselton MG (2013) The evolution of error: error management, cognitive constraints, and adaptive decision-making biases. Trends in Ecology & Evolution 28(8): pp. 474-81

    2013

  20. [28]

    (2024) Ten principles for responsible quantum innovation

    Kop M, Aboy M, De Jong E, et al. (2024) Ten principles for responsible quantum innovation. Quantum Science and Technology 9(3)

    2024

  21. [29]

    Big Data & Society, 10(1)

    Krarup T and Horst M (2023) European artificial intelligence policy as digital single market making. Big Data & Society, 10(1)

    2023

  22. [30]

    Lanka, A., Hegde, S., & Brun, T. A. (2025). Optimizing continuous-time quantum error correction for arbitrary noise. arXiv preprint arXiv:2506.21707

    2025

  23. [31]

    Cambridge University Press

    Hoofnagle CJ and Garfinkel SL (2022) Law and Policy for the Quantum Age. Cambridge University Press

    2022

  24. [32]

    European Journal of Risk Regulation 14(3): pp

    Liman A, Weber K (2023) Quantum Computing: Bridging the National Security–Digital Sovereignty Divide. European Journal of Risk Regulation 14(3): pp. 476-483

    2023

  25. [33]

    Lukoseviciene A (2025) Regulating quantum computers: Insights into early patterns and trends in academic regulatory conversations on the quantum revolution, Law, Innovation and Technology 17(1), pp. 241-270

    2025

  26. [34]

    H., Meadows, D

    Meadows, D. H., Meadows, D. L., Randers, J., & Behrens, W. W. (1972). The limits to. Growth, 102,

    1972

  27. [35]

    WIREs Cognitive Science 11(6)

    Nave K, Deane G, Miller M and Clark A (2020) Wilding the predictive brain. WIREs Cognitive Science 11(6)

    2020

  28. [36]

    Nature 403: pp

    Pan JW, Bouwmeester D, Daniell M, Weinfurter H and Zellinger A (2000) Experimental test of quantum nonlocality in three-photon Greenberger–Horne–Zeilinger entanglement. Nature 403: pp. 515–519

    2000

  29. [37]

    V., Bourassa, J

    Larsen, M. V., Bourassa, J. E., Kocsis, S., Tasker, J. F., Chadwick, R. S., González-Arciniegas, C., ... & Mahler, D. H. (2025). Integrated photonic source of Gottesman–Kitaev–Preskill qubits. Nature, 1-5

    2025

  30. [38]

    Nature Physics 8: pp

    Pusey MF, Barrett J and Rudolph T (2012) On the reality of the quantum state. Nature Physics 8: pp. 475–478

    2012

  31. [39]

    Quantum Science and Technology 8:024005

    Seskir ZC, Umbrello S, Coenen C, Vermaas PE (2023) Democratization of quantum technologies. Quantum Science and Technology 8:024005

    2023

  32. [40]

    New York: Plenum Press

    Shankar R (1994) Principles of Quantum Mechanics. New York: Plenum Press

    1994

  33. [41]

    (2024) Assessing the benefits and risks of quantum computers

    Scholten TL, Williams C, Moody D, et al. (2024) Assessing the benefits and risks of quantum computers. arXiv preprint arXiv:2401.16317

    2024 arXiv

  34. [42]

    Shor PW (1994) Algorithms for quantum computation: Discrete logarithms and factoring. Proc. 35th Symp. Found. Comput. Sci. pp. 124–134

    1994

  35. [43]

    Quantum 2: p

    Preskill J (2018) Quantum computing in the NISQ era and beyond. Quantum 2: p

    2018

  36. [44]

    Science 236: pp

    Slovic P (1987) Perception of risk. Science 236: pp. 280–285

    1987

  37. [45]

    Springer

    Smithson M (1989) Ignorance and Uncertainty: Emerging Paradigms. Springer

    1989

  38. [46]

    arXiv preprint arXiv:2408.02236

    Suter V, Ma C, Poehlmann G, Meckel M, Steinacker L (2024) An integrated view of Quantum Technology? Mapping Media, Business, and Policy Narratives. arXiv preprint arXiv:2408.02236

    2024 arXiv

  39. [47]

    Computing 57(4): pp

    Tang W and Martonosi M (2024) Distributed quantum computing via integrating quantum and classical computing. Computing 57(4): pp. 131–136

    2024

  40. [48]

    Cambridge University Press

    Tversky A, Kahneman D, Slovic P (1982) Judgment under uncertainty: Heuristics and biases. Cambridge University Press

    1982

  41. [49]

    Psychological Bulletin 119(1), pp

    Sloman SA (1996) The empirical case for two systems of reasoning. Psychological Bulletin 119(1), pp. 3–22

    1996

  42. [50]

    Frontiers in Neuroscience 6,

    Volz KG, Gigerenzer G (2012) Cognitive processes in decisions under risk are not the same as in decisions under uncertainty. Frontiers in Neuroscience 6,

    2012

  43. [51]

    In: Gass SI and Fu MC (eds) Encyclopedia of Operations Research and Management Science

    Walker WE, Lempert RJ, Kwakkel JH (2013) Deep Uncertainty. In: Gass SI and Fu MC (eds) Encyclopedia of Operations Research and Management Science. Boston, MA: Springer

    2013

  44. [52]

    Woerner S and Egger DJ (2019) Quantum risk analysis, npj Quantum Information 5,

    2019

  45. [55]

    Research Directions: Quantum Technologies 2: pp

    Van Daalen O (2024) Developing a human rights-compatible governance framework for quantum computing. Research Directions: Quantum Technologies 2: pp. 3–4

    2024

  46. [1902]

    Goldilocks and the Three Bears

    by attempting to provide a false sense of predictability but rather offer a sophisticated framework for decision-making in the face of the many uncertainties of quantum – and other emerging technologies. Moreover, the process of building, interacting with, and adapting risk mi...

    2024

  47. [1989]

    However, uncertainty is inherent in the human experience and a prerequisite for our evolution

    and have developed sophisticated cognitive abilities to reduce it (Tversky et al., 1982; Johnson et al., 2013; Volz and Gigerenzer, 2012; Sloman, 1996). However, uncertainty is inherent in the human experience and a prerequisite for our evolution. While human uncertainty is gr...

    1982

  48. [1996]

    quantum divide

    but several million of qubits or even more are estimated to be necessary to break the strongest public key encryption systems currently in use and that is far from what is technologically possible right now (Ichikawa et al., 2024; Scholten et al., 2024). Notably, the achieveme...

    2019

  49. [2013]

    in quantum mechanics it is a general property of quantum systems. Uncertainty in quantum computing is not simply the absence of knowledge: paradoxically, in quantum systems, uncertainty can prevail even in 3 situations where ample information is available (Harrigan and Spekken...

    2010

  50. [2018]

    challenges our classical notions of determinism and observability (Pusey et al., 2012). Quantum entanglement which describes correlations between quantum particles that persist even when the particles are separated by large distances contradicts our intuitions about locality a...

    2012

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