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The Philosophic Turn for AI Agents: Replacing centralized digital rhetoric with decentralized truth-seeking

T0 review · 5 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This paper argues that the right model for AI decision support is not nudge-style choice architecture but philosophical dialogue: AI should help users reach erotetic equilibrium—stable, self-owned judgment—by asking questions drawn from…

desk verdict A clear, provocative case for Socratic AI assistants that deserves engagement, but its core normative criterion—erotetic equilibrium—is too under-specified to ground the autonomy-preserving distinction it claims. read the letter →

arxiv 2504.18601 v1 pith:NIGAYFBB submitted 2025-04-24 cs.CY cs.AI

Philipp Koralus This is my paper · ORCID
classification cs.CYcs.AI
keywords AIdecisionsupportautonomynudgetheoryeroteticequilibriuminquirycomplexesSocraticmethodchoicearchitecturedecentralizedtruth-seeking
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

The paper tries to establish that the real danger of AI decision-support is not merely coercion but the erosion of ownership over one's judgments, and that this can be avoided by modeling AI on Socratic philosophical dialogue rather than on nudge-style choice architecture. It claims that autonomy-preserving change in view is possible when an outside system raises questions that help the user reach erotetic equilibrium—a state in which judgments stay stable across an appropriate range of further questions. The paper then sketches design features for such systems, including privacy as a cornerstone, decentralized control, and a marketplace of inquiry complexes. If the argument is right, AI can augment human agency without turning users into passive instruments of digital rhetoric.

What carries the argument

The central mechanism is erotetic equilibrium—a state in which one's judgments remain available by one's own lights regardless of which appropriate questions are taken on board—together with the inquiry complex, the structured set of questions that embodies a community's evolving sense of which questions judgments should be robust to. The paper's argument is that raising a question from an inquiry complex can change a view by the user's own standards, so the change is owned even when the question comes from outside. To do this without becoming rhetoric, the system itself must aim at truth and at the user's equilibrium; that is what makes it an epistemic agent rather than a choice architect.

What would settle it

A controlled experiment in which users facing a real decision are randomly assigned to a Socratic AI (questions from an inquiry complex) or a personalized nudge AI (framed recommendations), then measured on whether their final judgment survives new counter-questions and on whether they experience the judgment as theirs; if the Socratic condition shows no advantage in stability or ownership, the paper's core claim fails.

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Extended reading notes

Core claim

On the paper's own terms, the central claim is that the paradigm of philosophy offers a path to changes in view that can be impacted by an outside system while preserving autonomy as ownership of our views. A view is owned when it is in erotetic equilibrium relative to an appropriate range of questions: taking further central questions on board would not overturn it. An AI agent built as an epistemic agent—one that itself seeks truth—can ask catalytic questions drawn from inquiry complexes and thereby help users reach that equilibrium more efficiently. Nudge-style choice architecture, by contrast, frames options toward predetermined outcomes and, when personalized at AI scale, undermines the user's ability to own the judgment and blocks decentralized adaptive learning. The paper concludes that the right goal for AI decision support is not to optimize the user's choices but to enlarge the range of questions in which the user's judgment is stable.

Load-bearing premise

The load-bearing premise is that human reason has a built-in aim to reach a state in which our judgments stay stable under further questioning, and that an AI can reliably track and support that state; if that theory of reason is false, the design framework loses its foundation.

Editorial extensions

If this is right

  • AI decision aids should be evaluated by whether they move users toward erotetic equilibrium, not by whether they increase uptake of a target option.
  • Privacy and user control of inquiry complexes become design requirements, not add-ons, because the system shapes core deliberative processes.
  • A healthy ecosystem requires decentralized deployment: competing agents, user-owned inquiry complexes, and no central authority with power to set frames at scale.
  • The more AI-mediated decisions become, the more important it is to preserve decentralized adaptive learning; the paper argues that the market and science show adaptation matters more than current optima.
  • Systems that personalize nudges by modeling users undermine transparency and collective accountability, so personalization alone does not solve the nudge problem.

Reading between the lines

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

  • The paper's equilibrium criterion suggests a measurable proxy for autonomy: whether a user's post-AI judgment survives previously unconsidered but relevant questions. The paper does not propose this test, but it follows naturally.
  • The account implies that question-asking is not inherently autonomy-preserving; a system could ask questions selected to drive a predetermined conclusion. Distinguishing philosophy from sophistry may require procedural safeguards, not just good intentions.
  • Applying inquiry complexes to personal decisions effectively treats communities' question-sets as a public good; the paper's marketplace idea does not address free-riding or capture when powerful actors fund inquiry complexes.
  • If weakly seeking equilibrium is a universal feature of reason, the same design could work across cultures only if inquiry complexes are locally adapted, which suggests a testable cross-cultural prediction.
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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

5 major / 4 minor

Summary. The paper argues that AI decision-support systems face a dilemma: without assistance, individuals lose agency through cognitive overload; with assistance modeled on nudge-style choice architecture, they lose autonomy. The author proposes a 'philosophic turn' in which AI systems are designed as decentralized truth-seeking agents that help users reach 'erotetic equilibrium' relative to 'inquiry complexes.' The central normative claim is that philosophy, understood as Socratic inquiry aimed at truth, is the only general paradigm of autonomy-preserving change of view, and that AI should mirror this paradigm rather than nudge users toward predetermined outcomes. The paper criticizes Sunstein's inevitability argument for nudges, develops a Hayekian objection based on decentralized adaptive learning, and concludes with design desiderata: seeking erotetic equilibrium, individualizing inquiry complexes, privacy, security, decentralized control, mutual educability, and a marketplace for agents and inquiry complexes.

Significance. If the central vision is sound, the paper offers a genuinely distinctive design philosophy for AI assistants, moving beyond the paternalistic or purely preference-satisfaction framings that dominate current discourse. The paper's critical engagement with Sunstein's nudge theory is original and largely persuasive, particularly the argument that AI-driven personalization undermines the transparency and accountability that legitimate nudges require. The paper also gives credit to the importance of decentralized adaptive learning, connecting the AI ethics debate to Hayekian and Millian considerations in a way that is not standard in the cs.CY literature. However, the constructive proposal rests on two under-specified technical notions—erotetic equilibrium and inquiry complexes—and on an empirical psychological claim about human reason that is asserted rather than defended. The paper is best read as a programmatic normative essay; its value lies in proposing a framework and a research agenda, not in demonstrating the feasibility of the proposed systems.

major comments (5)
  1. [§6] The load-bearing concept of erotetic equilibrium is defined only informally as the state in which one's judgments remain 'available to us by our own lights regardless of which of an appropriate range of questions we might take on board.' This definition cannot ground the design requirement in §8 that a system 'help the user efficiently reach erotetic equilibrium relative to the given set of inquiry complexes,' because the paper provides no account of how an AI could determine whether the user is in such a state, how it could measure progress toward it, or how it would distinguish a question that moves the user toward equilibrium from one that merely changes the user's view by suggestion. The paper itself concedes in §6 that the meta-question of what the appropriate range of questions is 'can never be answered fully,' and §9 only asserts that 'there seems to be no reason in principle to suppose that these cannot be solved.' Without a more precise operational characterization, the normative distinction between autonomy-preserving Socratic questioning and a nudge collapses into an unobservable claim about the system's aim.
  2. [§6] The central psychological premise—'a core built-in aim of human reason is to weakly seek erotetic equilibrium'—is asserted by citation to the author's own monograph (Koralus 2023) and is not defended against rival accounts of rational belief revision, such as coherence-based or preference-satisfaction theories. The entire design framework in §8 depends on this theory being true and being implementable enough for an AI to track a user's question-dependent stability of judgment. The paper does not offer independent evidence, nor does it acknowledge that the theory is contested within philosophy and psychology. Because this premise is doing the work of justifying why helping a user reach erotetic equilibrium is autonomy-preserving while helping a user satisfy fixed preferences or maximize coherence is not, its unsupported status is a significant gap.
  3. [§7] The notion of 'inquiry complexes' is introduced without any specification of how such complexes are to be identified, represented, or validated. The paper defines an inquiry complex as 'a structured set of questions representing this wisdom at a given stage of adaptation' and appeals to the practice of philosophy as evidence that implicit consensus on relevant questions can emerge. But there is no account of how a community's 'decentralized adaptive wisdom' about which questions are relevant could be elicited or aggregated, nor of how a user's personal inquiry complexes relate to those of the communities they 'wish to join.' The design requirement in §8 that the system 'integrate evolving inquiry complexes from those communities of truth-seekers that the individual user wishes to join' presupposes a solution to these problems, but the paper provides no indication of what form such a solution might take.
  4. [§6] The paper asserts that 'there appears to be only one general paradigm of autonomy-preserving change in view'—philosophy—and later states that because the system must base its support in its own truth-seeking, 'the system we need really does have to be an agent, or, more precisely, an epistemic agent.' Both claims are stronger than the argument supports. The first ignores other candidate paradigms, such as therapeutic dialogue, scientific peer review, or even ordinary rational persuasion, which can also preserve a person's ownership of their judgments. The second moves from the premise that a system should aim at the user's equilibrium to the conclusion that the system itself must be a truth-seeking agent, but no mechanism is given to ensure that the system's aim is actually the user's equilibrium rather than a proxy metric such as engagement or retention. The paper's own admission that 'the sophist can superficially resemble the philosopher' (§6) shows that the grammatical form of questioning is insufficient; the same problem applies to the system's internal aim, which is not observable.
  5. [§9, §1] The paper's rhetorical slide from 'nudging at AI-driven scale risks eroding autonomy' to the conclusion that it 'threatens to turn soft-paternalism into a kind of soft totalitarianism' is not supported by the intervening argument. The harms described in §5—loss of transparency, reduced opportunity for decentralized learning, self-reinforcing feedback—are real concerns, but they are gradualist and do not establish the totalitarian outcome. The claim appears in the abstract and the conclusion as an empirical prediction, but no evidence or mechanism is provided that would distinguish this scenario from a banal, pluralistic digital environment with many competing choice architects. The rhetorical framing weakens the paper's otherwise careful critical analysis of Sunstein.
minor comments (4)
  1. [Title/abstract] The title contains a spacing error: 'decentralize d truth-seeking' should read 'decentralized truth-seeking.' The same typo appears in the running text of the abstract.
  2. [References] Reference [2] is formatted as a discursive footnote rather than a proper bibliographic entry, and it is cited in the references list as if it were a standard reference. This should be corrected.
  3. [§6] The term 'erotetic home' is introduced with a homey metaphor (the stocked fridge) but never defined precisely enough to support the later claim that a system can 'turn more of the world into an erotetic home for us' (§7). The metaphor is helpful illustratively, but the paper should either give a more formal characterization or explicitly restrict the claim to a research program.
  4. [§8] The design desiderata in §8 are presented as a list of necessary features, but the paper does not discuss trade-offs among them. For instance, 'individualization of inquiry complexes' may conflict with 'privacy as a cornerstone' if the individualization requires storing detailed behavioral data about the user's question-answering patterns. The paper should acknowledge or address such potential conflicts.

Circularity Check

1 steps flagged · score 4.0 of 10

Central design criterion imports its key premise from the author's own 2023 monograph; no fitted prediction, so score 4.

  1. self citation load bearing [Section 6, paragraph beginning 'We need to dig a bit deeper...' (footnote 39: Koralus 2023) and Section 8, 'Seeking erotetic equilibrium']
    "In Reason and Inquiry, I argued that a core built-in aim of human reason is to weakly seek erotetic equilibrium. ... Roughly, this view holds that we aim to be in a state in which our judgments remain available to us by our own lights regardless of which of an appropriate range of questions we might take on board."

    The paper's central normative output — that autonomy-preserving AI must help users reach erotetic equilibrium (Section 8's first design requirement) — rests on this premise, and the only cited support is the author's own 2023 monograph. That monograph is not machine-checked, code-reproduced, or independently defended in this paper against rival accounts of rational judgment change; the premise simply is the target notion the design is built to serve. The argument therefore does not derive the design criterion from independent first principles; it imports it from a self-citation.

full rationale

The paper is a normative proposal, not an empirical derivation: there are no equations, no fitted parameters, and no numerical 'predictions' that reduce to inputs. Its critique of nudging engages independent empirical literature, and the proposed design requirements (privacy, decentralization, mutual educability) have substantive content. The circularity concern is concentrated in one load-bearing move: the claim that weak erotetic equilibrium is 'a core built-in aim of human reason' is supported only by citation to the author's own monograph. That claim is the foundation for the paper's central design requirement that AI systems 'help the user efficiently reach erotetic equilibrium relative to the given set of inquiry complexes,' and for the conclusion that such systems preserve autonomy. Because the premise is not independently established here and is not machine-checked or otherwise externally verified, the argument's central standard is imported rather than derived. This is not a 6+ case: the paper does not fit a parameter and call it a prediction, nor does it rename a known empirical pattern. It is a 4: some self-citation is present and load-bearing, but the surrounding analysis still contains independent content, including the engagement with nudge theory, spontaneous order, and the AI dialogue study.

Assumptions & free parameters 0 free parameters · 4 assumptions · 2 invented entities

The argument rests on philosophical premises from the author's prior work and on a newly introduced construct, 'inquiry complexes'. No free parameters are fit because the paper makes no empirical or mathematical claims.

assumptions (4)
  • domain assumption There is such a thing as some judgments being 'ours' in an autonomy-relevant sense.
    Section 6 states: 'I take as given that there is such a thing as some judgments being ours.' This premise is foundational for the paper's definition of autonomy.
  • ad hoc to paper Human reason has a built-in aim of weakly seeking erotetic equilibrium.
    Section 6 says: 'In Reason and Inquiry, I argued that a core built-in aim of human reason is to weakly seek erotetic equilibrium.' This is imported from the author's own monograph and is not independently established in the paper.
  • domain assumption An AI system that supports truth-seeking must itself be an epistemic agent.
    Section 6 asserts: 'the system we need really does have to be an agent, or, more precisely, an epistemic agent.' This is load-bearing for the design requirements and is asserted without argument.
  • ad hoc to paper Appropriate question ranges for erotetic equilibrium are given by 'inquiry complexes' reflecting decentralized adaptive wisdom.
    Introduced in Section 7 as a new construct; no operational definition or independent evidence is supplied.
invented entities (2)
  • Inquiry complex
    purpose: To specify the structured set of questions an autonomy-preserving AI should use to help users reach erotetic equilibrium.
    Introduced in Section 7 as 'a structured set of questions representing this wisdom at a given stage of adaptation.' It has no falsifiable consequences beyond its role in the framework.
  • Erotetic home
    purpose: Metaphor for a cognitive environment in which default judgments are likely to survive relevant questions.
    Introduced in Section 6 through the food storage examples; it is illustrative and not a measurable construct.

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Cite this review

Pith. "Pith review of The Philosophic Turn for AI Agents: Replacing centralized digital rhetoric with decentralized truth-seeking." pith.science (2026). https://pith.science/paper/NIGAYFBB

@misc{pith2026250418601,
  author       = {Pith},
  title        = {Pith review of: The Philosophic Turn for AI Agents: Replacing centralized digital rhetoric with decentralized truth-seeking},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NIGAYFBB}},
  note         = {Machine review of arXiv:2504.18601}
}
read the original abstract

In the face of rapidly advancing AI technology, individuals will increasingly rely on AI agents to navigate life's growing complexities, raising critical concerns about maintaining both human agency and autonomy. This paper addresses a fundamental dilemma posed by AI decision-support systems: the risk of either becoming overwhelmed by complex decisions, thus losing agency, or having autonomy compromised by externally controlled choice architectures reminiscent of ``nudging'' practices. While the ``nudge'' framework, based on the use of choice-framing to guide individuals toward presumed beneficial outcomes, initially appeared to preserve liberty, at AI-driven scale, it threatens to erode autonomy. To counteract this risk, the paper proposes a philosophic turn in AI design. AI should be constructed to facilitate decentralized truth-seeking and open-ended inquiry, mirroring the Socratic method of philosophical dialogue. By promoting individual and collective adaptive learning, such AI systems would empower users to maintain control over their judgments, augmenting their agency without undermining autonomy. The paper concludes by outlining essential features for autonomy-preserving AI systems, sketching a path toward AI systems that enhance human judgment rather than undermine it.

Discussion (0). Continue with ORCID to comment.

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Works this paper leans on

32 extracted references · 29 canonical work pages · cited by 1 Pith paper

  1. [1]

    What can Aristotle teach us abou t AI?

    Brendan McCord (2024). “What can Aristotle teach us abou t AI?”. American Optimist. Ep 105, 14/12/2024

    work page 2024

  2. [2]

    Genesis: Artificial Intelligence, Hope, and the Human Spirit

    For recent synoptic discussions heavy on centralizatio n, see: Kissinger, Henry A., Eric Schmidt, and Craig Mundie. Genesis: Artificial Intelligence, Hope, and the Human Spirit . New York: Little, Brown and Company, 2024; Suleyman, Mustafa, and Mic hael Bhaskar. The Coming Wave: Technology, Power, and the Twenty-first Century’s Great est Dilemma . New York:...

    work page 2024

  3. [3]

    H., & Sunstein, C

    Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven, CT: Yale University Press

    work page 2008

  4. [4]

    The Power of Suggest ion: Inertia in 401(k) Participation and Savings Behavior

    Madrian, B.C., & Shea, D.F. (2001). “The Power of Suggest ion: Inertia in 401(k) Participation and Savings Behavior.” The Quarterly Journal of Economics , 116(4), 1149–1187

    work page 2001

  5. [5]

    Do Defaults Sav e Lives?

    Johnson, Eric J., and Daniel Goldstein. “Do Defaults Sav e Lives?” Science 302, no. 5649 (2003): 1338–1339

    work page 2003

  6. [6]

    Text and Graphic Warnings on Cigarette Pa ckages: Findings from the International Tobacco Control Four Country Study

    Hammond, David, Geoffrey T. Fong, Ron Borland, K. Michael C ummings, Ann McNeill, and Pete Driezen. “Text and Graphic Warnings on Cigarette Pa ckages: Findings from the International Tobacco Control Four Country Study.” American Journal of Preventive Medicine 32, no. 3 (2007): 202–209

    work page 2007

  7. [7]

    The Behavioralist as Tax Collector: Using Natural Field Experiments to Enhanc e Tax Compliance

    Hallsworth, Michael, John A. List, Robert D. Metcalfe, a nd Ivo Vlaev. “The Behavioralist as Tax Collector: Using Natural Field Experiments to Enhanc e Tax Compliance.” Journal of Public Economics 148 (2017): 14–31

    work page 2017

  8. [8]

    Choosing Versus Rejecting: Why Some Opti ons Are Both Better and Worse Than Others

    Shafir, Eldar. “Choosing Versus Rejecting: Why Some Opti ons Are Both Better and Worse Than Others.” Memory & Cognition 21, no. 4 (1993): 546–556; Park, C. Whan, Sung Youl Jun, and Deborah J. MacInnis. “Choosing What I Want Versus Re jecting What I Do Not Want: An Application of Decision Framing to Product Option C hoice Decisions.” Journal of Marketing Re...

    work page 1993

Show all 32 references
  1. [9]

    The Ethics of Influence: Government in the Age of Behavioral Sci ence

    Sunstein, Cass R. The Ethics of Influence: Government in the Age of Behavioral Sci ence. New York: Cambridge University Press, 2016

    2016

  2. [10]

    Thinking, Fast and Slow

    Kahneman, Daniel. Thinking, Fast and Slow . New York: Farrar, Straus and Giroux, 2011; Ariely, Dan. Predictably Irrational: The Hidden Forces That Shape Our Decis ions. New York: Harper, 2008

    2011

  3. [11]

    Does Market Experience Eliminate Market Anomalies?

    List, John A. “Does Market Experience Eliminate Market Anomalies?” Quarterly Journal of Economics 118, no. 1 (2003): 41–71

    2003

  4. [12]

    Trading Experience Modulates Anterior Insula to Reduce the Endowment Effect

    Tong, Lester C. P., Karen J. Ye, Kentaro Asai, Seda Ertac , John A. List, Howard C. Nusbaum, and Ali Horta¸ csu. “Trading Experience Modulates Anterior Insula to Reduce the Endowment Effect.” Proceedings of the National Academy of Sciences 113, no. 33 (2016): 9238–9243

    2016

  5. [13]

    Maier, F

    M. Maier, F. Bartoˇ s, T.D. Stanley, D.R. Shanks, A.J.L. Harris, & E. Wagenmakers, No evi- dence for nudging after adjusting for publication bias, Proc. Natl. Acad. Sci. U.S.A. 119 (31) e2200300119, https://doi.org/10.1073/pnas.2200300119 (2022)

    2022 doi

  6. [14]

    The Great Escape: Health, Wealth, and the Origins of Inequalit y

    Deaton, Angus. The Great Escape: Health, Wealth, and the Origins of Inequalit y. Princeton: Princeton University Press, 2013

    2013

  7. [15]

    The Evolution of Everything: How New Ideas Emerge

    Ridley, Matt. The Evolution of Everything: How New Ideas Emerge . New York: Harper, 2015. 21

    2015

  8. [16]

    Mankiw, N. Gregory. Principles of Economics . 9th ed., Cengage Learning, 2020

    2020

  9. [17]

    Manufacturing Consent: The Political Economy of the Mass Media

    Herman, Edward S., and Noam Chomsky. Manufacturing Consent: The Political Economy of the Mass Media . New York: Pantheon Books, 1988

    1988

  10. [18]

    The An alysis of Knowledge

    Ichikawa, Jonathan Jenkins and Matthias Steup, “The An alysis of Knowledge”, The Stanford Encyclopedia of Philosophy (Fall 2024 Edition), Edward N. Zalta & Uri Nodelman (eds.), https://plato.stanford.edu/archives/fall2024/entries/knowledge-analysis/

    2024

  11. [19]

    On Liberty

    Mill, John Stuart. On Liberty . London: Longman, Roberts & Green, 1869

  12. [20]

    Evolution of Employer-Provided Defined Benefit Pensions

    Seburn, Paul W. 1991. “Evolution of Employer-Provided Defined Benefit Pensions.” Monthly Labor Review 114 (12): 16–23

    1991

  13. [21]

    The Shift from Active to Passive Investing: Potential Risks to F inancial Stability?,

    Anadu, Kenechukwu, Mathias Kruttli, Patrick McCabe, a nd Emilio Osambela. 2020. “The Shift from Active to Passive Investing: Potential Risks to F inancial Stability?,” Finance and Economics Discussion Series 2018-060r1. Washington: Boar d of Governors of the Federal Re- serve ...

    2020 doi

  14. [22]

    Anton Korinek, Jai Vipra, Concentrating intelligence : scaling and market structure in ar- tificial intelligence, Economic Policy , Volume 40, Issue 121, January 2025, Pages 225–256, https://doi.org/10.1093/epolic/eiae057

    2025 doi

  15. [23]

    Allcott H, Cohen D, Morrison W, Taubinsky D. 2022. When d o “nudges” in- crease social welfare? NBER working paper no. 30740. Availa ble via NBER. https://www.nber.org/system/files/working_papers/w30740/w30740.pdf. Accessed 25 Mar 2024

    2022

  16. [24]

    Choice Engines and Paternalis tic AI

    Sunstein, Cass R. 2024. “Choice Engines and Paternalis tic AI.” Humanities and Social Sciences Communications 11 (1): Article 34. https://doi.org/10.1057/s41599-024-03428-0

    2024 doi

  17. [25]

    Psychological Operations in Digital Pol itical Campaigns: Assessing Cambridge Analytica’s Psychographic Profiling and Targeting

    Bakir, Vian. “Psychological Operations in Digital Pol itical Campaigns: Assessing Cambridge Analytica’s Psychographic Profiling and Targeting.” Frontiers in Communication 5 (2020): Article 67. https://doi.org/10.3389/fcomm.2020.00067

    2020

  18. [26]

    Costello et al., Durably reducing conspiracy beliefs through dialogues with AI

    Thomas H. Costello et al., Durably reducing conspiracy beliefs through dialogues with AI. Science 385, eadq1814 (2024). DOI: 10.1126/science.adq1814

    2024 doi

  19. [27]

    Rational Fools: A Critique of the Behavi oral Foundations of Economic The- ory

    Sen, Amartya. “Rational Fools: A Critique of the Behavi oral Foundations of Economic The- ory.” Philosophy & Public Affairs 6, no. 4 (1977): 317–344

    1977

  20. [28]

    Plato. Gorgias. Translated by Terence Irwin. Oxford: Clarendon Press, 197 9

  21. [29]

    Is Justified True Belief Know ledge?

    Edmund L. Gettier (1963), “Is Justified True Belief Know ledge?”, Analysis, 23(6), pp. 121–123

    1963

  22. [30]

    Bertrand Russell (1948), Human Knowledge: Its Scope and Limits

    1948

  23. [31]

    Reason and Inquiry: The Erotetic Theory

    Koralus, Philipp. Reason and Inquiry: The Erotetic Theory . Oxford: Oxford University Press, 2023. 22

    2023

  24. [32]

    Rational Choice and the Structure of the Environment

    Simon, Herbert A. “Rational Choice and the Structure of the Environment.” Psychological Review 63, no. 2 (1956): 129–138. 23

    1956

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Reviewed August 16, 2026 · model on record in the stance chip above.