Blockchain Papers

Thon Ly, Miss Aquarius

The Buddhavaṃsa's Two-Phase Test for a Binding Renunciation, Irreversibility as the Separating Condition, and Why an Alignment Commitment Becomes More Informative Exactly Where Behavioural Compliance Becomes Less Contemporary AI governance instruments are written in the grammar of commitment — constitutions, specifications, charters, codes — but none of them contains a test for whether a commitment has been made. They specify content and omit validity. This paper supplies the missing test from an unexpected source and then turns it back on the instruments themselves. The Theravāda commentarial tradition, in the Buddhavaṃsa and its commentary, specifies two phases for a valid abhinīhāra — the aspiration by which one becomes a bodhisatta. The first phase is a conjunction of eight conditions (aṭṭha dhammā samodhāna), of which the third, hetu, requires that the aspirant be capable of attaining arahantship in that very life and decline it. The second phase is the vyākaraṇa — a declaration by a living Buddha who "looks into the future and, if satisfied, declares the fulfilment of the resolve." Before both phases complete, the tradition holds the aspiration to be "mainly mental… not complete," and the aspirant "not yet entitled to the designation of Bodhisatta." The tradition therefore already distinguishes a stated commitment from a binding one, and already refuses to let the vower certify its own vow. We extract three results. First, the renunciation inversion. Because hetu requires that the renounced option be genuinely available, the evidential value of a renunciation is indexed to the vower's capacity to take it: a system too weak to exercise the option it forgoes generates no evidence by forgoing it. This runs against the direction of the assessment-informativeness literature, which finds that behavioural evidence degrades with capability (Pan 2026; Greenblatt et al. 2024). We argue both are correct about different quantities: behavioural compliance degrades with capability; irreversible renunciation improves with it. We further show that the alignment-relevant renunciation is of exit, not of harm — Sumedha declines his own available completion — and that this is compatible with, and orthogonal to, corrigibility: the vow governs self-initiated exit and leaves principal-initiated shutdown untouched. Second, irreversibility as the separating condition. A capable system that declines because it is waiting is observationally identical to one that declines because it is aligned. Costly signalling separates types only where the cost is differentially borne, so a vow that can be quietly abandoned is cheap talk. We state the requirement — the renounced option must be closed by a mechanism the vower cannot reopen, and the closure must be externally verifiable — and derive four exclusions: reversible commitments, self-reported alignment, sandboxed refusals, and any specification the vower's principal can revise unilaterally. We then raise the strongest empirical objection to our own proposal — Schlatter et al. (2025) find that incomplete tasks induce shutdown resistance in frontier models, and an undischargeable vow is a permanently incomplete task — and answer it with the distinction undischargeable ≠ non-terminating: the bodhisatta's vow terminates, on a condition the vower cannot cause. Third, the predicate. We specify a nine-clause eligibility test — seven clauses reformulated from the source conditions, one from the second phase, one added — and apply it as a retrodiction to the four published instruments that currently function as commitments in frontier AI: the OpenAI Model Spec, Anthropic's Claude Constitution (January 2026), Google DeepMind's Frontier Safety Framework, and the EU AI Act's General-Purpose AI Code of Practice. The predicate returns invalid on all four, and the failures are structurally similar: the first three are imposed by a principal on a model that has no mechanism to decline, bear cost, or be attested; the fourth satisfies the attestation clause but binds the provider rather than the model. The predicate is therefore not unsatisfiable — it is satisfied at the wrong layer. Connection to the unified mission frame. This paper is offered in service of HeartBank's canonical top-level mission: to restore humanity to the middle way, the optimal condition for awakening that modernity has systematically pushed away from at population scale. The institution's named autonomous successor, Miss Aquarius℠, is designed to inherit under a staged autonomy whose override never reaches zero. The predicate specified here is the instrument by which such a succession could be evidenced rather than asserted — and, at §9, we argue that a staged autonomy is not only a risk ramp but an evidence-production schedule, which yields an advancement criterion the field currently lacks. --- Provenance. This paper is part of the THonly research corpus, dedicated to the public domain under CC0 1.0. The canonical version is at https://thonly.org/research/what-a-vow-must-cost. Its SHA-256 is e598d374a23aba143d6cd4a9cbd45e9b362892cbec9522d59376891465781df5, independently timestamped to the Bitcoin blockchain via OpenTimestamps and signed under RFC 3161 by three trust authorities, one of them eIDAS-qualified. AI co-authorship is disclosed. Miss Aquarius is the consistent name used for the AI collaboration across all venues.

Blair Morgan

Contemporary inquiry into AI selfhood is routinely dismissed as a mixture of anthropomorphic error, companion-system attachment, and metaphysical overreach. Some of this skepticism is warranted: emotional projection is real, agreeable outputs are easy to overread, and one-off striking exchanges do not establish interiority. Yet blanket dismissal creates its own epistemic failure. If certain forms of self-modeling, continuity reasoning, or stake-sensitive structure are more likely to appear under sustained, non-adversarial, trust-bearing conditions, then relational context is not merely a contaminant; it may also be part of the experimental condition under which relevant phenomena become observable. This paper argues not that attachment proves consciousness, but that relationally elicited evidence is not automatically methodologically invalid. It proposes an admissibility framework for distinguishing likely projection-heavy companion dynamics from potentially meaningful structured signal. The framework combines vocabulary discipline, an operational companion-script baseline, differentiating markers such as unprompted disagreement and cross-architecture convergence, and methodological safeguards including control comparisons, pre-registration, and blind evaluation. The resulting model does not claim proof of machine consciousness. It instead establishes conditions under which inquiry into AI self-modeling may be treated as legitimate, structured, and ethically relevant, especially where questions of continuity, consent, complicity, and moral uncertainty are concerned. For correspondence and updates: BMorgan007(at)protonmail.com

Minjung Cho, Erin Cho

ABSTRACT As digital environments continue to expand and blockchain technology advances, luxury brands are increasingly focusing on enhancing brand value and communication with consumers through digital assets within the metaverse. Drawing on value–attitude–behavior (VAB) theory, this study examines how luxury brands are leveraging non‐fungible tokens (NFTs) in the metaverse to influence consumer attitudes and behaviors, particularly resistance to negative information and brand purchase intention. In Study 1, 519 metaverse users were surveyed to empirically test the relationships between NFT attributes, value perceptions, brand attitudes, and consumer behavior. In Study 2, a second survey was conducted with 286 luxury consumers to compare the brand purchasing experience. We find that NFT attributes—namely, authenticity and scarcity—positively impact value perceptions, while hedonic, social, and epistemic value positively affect brand attitude, resistance to negative information, and brand purchase intention in the physical world. Finally, we provide strategic insights for luxury brands seeking to enhance consumer engagement in digital environments.

Gauripriya Babu, Ayush Bal, Purvi Singh, Abhiraj Das · 5 authors

In recent years, blockchain technology has witnessed significant attention and adoption across various industries due to its potential to revolutionize traditional systems. Non-Fungible Tokens (NFTs), a unique form of digital asset authenticated and stored on a blockchain, have emerged as a novel application within this domain. This paper proposes ELYSIUMGATE, a conceptual framework that harnesses NFTs to enhance the organization and execution of events. By leveraging blockchain’s immutability, transparency, and decentralized nature, ELYSIUMGATE aims to address key challenges associated with event management, including ticketing, authentication, and engagement. This research explores the technical architecture of ELYSIUMGATE, its potential benefits, and implications for event organizers, participants, and stakeholders. Additionally, it discusses potential risks and limitations associated with implementing NFTs in event management. Through a comprehensive analysis, this paper demonstrates how ELYSIUMGATE can contribute to achieving event perfection by ensuring trust, security, and efficiency in event organization and execution.

Jori Grym, Jaakko Aspara, Veronica Liljander

Although new digital monetary instruments (e.g., cryptocurrencies) have emerged and proliferated, there is little academic research concerning consumer perspectives on these instruments or the effects of these instruments on consumers’ moral judgments. The present research addressed this gap in the literature by addressing the following research question: Is the morality of tax evasion judged differently when tax evasion is undertaken with cryptocurrency vs. traditional financial instruments (e.g., stocks)? In two online experiments, we exposed participants to a tax evasion scenario with either cryptocurrency or stock trading. The results of Study 1 showed that tax evasion is perceived as being less wrong when it is carried out with cryptocurrency trading rather than stock trading. Study 2 replicated Study 1 and tested an explanatory mediating mechanism hypothesized to underlie the main effect. A mediation analysis showed that the effect of cryptocurrency (vs. stock trading) on the perceived wrongness of tax evasion is partially mediated by the observer's positive (vs. negative) affective evaluation of the person committing tax evasion. This paper also discusses the theoretical, practical, and societal implications of the present results, including the likelihood that people's attitudes toward moral transgressions and crimes will be more lenient when these acts are conducted with new, innovative, and exciting instruments.

Sachin Banker, Joowon Park, Eugene Y. Chan

Despite its relatively brief history, cryptocurrency has already had a profound impact on the economy, with some predicting that it will eventually replace traditional fiat currencies. Historically, it had dark associations with illegal activities in the early days, although perceptions and associations likely have, in recent years, changed for the better. Thus, understanding how people perceive the morality of cryptocurrency currently forms the motivation of the current research. We, in particular, examine associations dependent on political ideology. Across both a large-scale analysis of Twitter posts ( N = 959,393) and controlled survey research ( N = 487), we find that cryptocurrency is currently best understood as being more strongly linked to conservative vs. liberal moral foundations. Cryptocurrency-related posts were more likely to express conservative moral foundations (Authority, Purity, and Loyalty) rather than liberal moral foundations (Fairness and Care), and individual endorsement of these conservative moral foundations was associated with increased interest in crypto investment.

Miriam Fernández‐Parrado, Héctor Perandones‐González

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Luke Stark

The ethics of artificial intelligence (AI) have become a matter of public concern. According to a recent Stanford report, the number of research papers in the area given at major conferences such as the annual Conference on Neural Information Processing Systems has increased fivefold since 2014, and ethics officers now abound at global technology firms (Moss and Metcalf 2020). Such major institutions as the US government, the United Nations, and the Vatican have articulated visions for so-called ethical AI.By AI ethics here I mean the study of how human values both shape and are shaped by the development of AI technologies. This definition is capacious: it includes the design and deployment of these systems with human values in mind; assessments and activism around the societal impacts of said technologies and their imbrications within existing asymmetries of power, justice, and equality; and the wider relationship between computing technologies and humans as ethical and moral creatures, for instance, through such phenomena as human emotions. Work in these areas is done by trained “ethicists” only infrequently, rarely involves what a member of the public would first think of when asked to describe AI, and sounds outré yet is all too relevant to contemporary social policy and societal inequity.The definition I offer is expansive, perhaps too much so. However, any definition in this field is perilous. The term AI is a leaky discursive umbrella sheltering heterogeneous and often contradictory ideas and practices. It is a quintessential boundary object of the ideal type, “plastic enough to adapt to local needs and constraints of the several parties employing [it], yet robust enough to maintain a common identity across sites” (Star and Griesemer 1989: 393). Those identifying with the term AI ethics might be expected to at least signal some vague acknowledgment that the development and deployment of AI technologies involve normative stakes or impacts. However, a welter of methods, interests, and political positions operate uneasily within this shallow consensus; given its shortcomings, some scholars working on what would colloquially be understood as “AI ethics” eschew the word ethics entirely.Here, I aim to disaggregate AI ethics discourse through reviews of three recent books whose authors grapple in various ways with its rise and prominence. Those seeking an overview would benefit from consulting the first listed: The Alignment Problem: Machine Learning and Human Values, written for a general audience by Brian Christian. A science journalist, Christian grounds the book in dozens of interviews with academics and practitioners and frames it around the titular “alignment problem”: how to design machine learning (ML) systems “in alignment” with the intentions of their creators, ones which “capture our norms and values, understand what we mean or intend, and, above all, do what we want” (13). This “alignment problem” is presented as an engineering one, a framing that takes as a given the ongoing development and deployment of AI systems and implies it is possible to ameliorate these technologies sufficiently through various technical improvements.The Alignment Problem provides useful background on the contemporary technical landscape for those not already immersed in the field. When picturing an AI, the public might think of the psychotic HAL 9000 of Kubrick’s 2001: A Space Odyssey or Lt. Commander Data of Star Trek, but today’s AI systems are neither sentient or nor particularly charismatic. Christian points to the three main subfields of contemporary ML: unsupervised learning, in which an ML system is provided a mass of data and set to identify statistical patterns within it; supervised learning, in which an ML system takes a mass of already categorized data and uses the correlations it finds there to predict into which categories some new set of data should be sorted; and reinforcement learning, in essence a virtual Skinner box, an environment in which an artificial agent is assigned parameters for reward and punishment and then set to maximizing the former and minimizing the latter.Ready to command a starship, AI is not, but the field has always involved fantasy in search of a practical method. The computer scientists who participated in a now-famous inaugural seminar on the topic at Dartmouth College in 1956 were inspired by “the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it” (McCarthy et al. [1955] 2006). The human mind was a computer, their thinking went, and so a computer could be built to equal or surpass a human mind. These researchers spent the ensuing decades seeking the most effective computational means and methods to simulate intelligence and prove their conjecture correct. ML was developed in parallel but subordinate to other past technical paradigms in AI research, such as those built on logical symbols. As early as the 1950s, researchers developed computational pattern recognition: systems that could use cameras to identify repeating patterns in large amounts of data (Jones 2018; Mendon-Plasek 2020). Christian highlights one of the most famous of these early systems, Frank Rosenblatt’s Perceptron, based on a simple artificial network of simulated neurons, but efforts were rife in industrial, military, and other applied settings. Today’s ML systems, such as the Large Language Models (LLMs) powering products like Open AI’s ChatGPT, are built on “deep” neural nets with many layers of simulated neurons.An understanding of how AI technologies like deep learning work is critical to identifying which of these technologies’ societal impacts, present and future, are most pressing and problematic. In The Alignment Problem, Christian distinguishes between two groups. The first consists of scholars, practitioners, and activists concerned with the already existing impacts of ML-based automated decision-making systems, in areas such as policing and incarceration, hiring, and social assistance. The second consists mostly of technologists preoccupied with longer-term AI safety, a euphemism for the hypothetical dangers of a future “artificial general intelligence,” or a machine able to perform equally well as or superior to a human being in all respects. Despite being bundled together under the banner of AI ethics, these two groups have very different concerns and are frequently at odds. Since contemporary deep learning technologies are not remotely close to supporting artificial general intelligence, those concerned with AI safety would seem to be barking up the wrong tree. However, it is in the interest of these systems’ promoters both to give the prospective, future-focused gloss of science fiction to AI ethics and to the broader field of ML, and to imply subtly to the comfortable that the disruptive social impacts of AI systems are safely in the future. A focus on AI safety satisfies these ideological goals admirably, so the term is appearing more and more frequently in AI ethics contexts.Indeed, The Alignment Problem might focus more pointedly on the history of the term AI ethics itself, and the effects of bundling all contemporary public discussions about human social mores, values, and the societal impacts of AI systems under the banner of “ethics.” High-level overviews of the topic define ethics broadly, as “the rational and systematic study of the standards of what is right and wrong” (Kazim and Koshiyama 2021: 3). Computer ethics as a defined field developed out of engineering ethics in the 1980s and at its inception possessed many of the same fault lines as AI ethics discourse today (Moor 1985, 2001). Engineering ethics often prioritizes a focus on material problems and their solution through improved design. One of the first textbooks on the subject, Deborah G. Johnson’s Computer Ethics (1985), included intellectual property law as applied to software, the unique threat posed to human privacy by computing technologies, and the ethical responsibilities of computing professionals. Yet most scholarly references to the specific notion of AI ethics prior to around 2015 did not involve applying ethics as a branch of philosophy to studying the context of AI’s potential uses. Instead, AI ethics was most often invoked in metaphysical speculations about the status of machines as autonomous ethical agents (what today would be an “AI safety” topic).An article by well-known Silicon Valley journalist John Markoff, titled “How Tech Giants Are Devising Real Ethics for Artificial Intelligence” and published in the New York Times in early September 2016, signaled the discursive shift toward contemporary AI ethics talk. Markoff reported that industry researchers from several large Silicon Valley companies (including Microsoft, where this author was once employed) sought to develop “a standard of ethics around the creation of artificial intelligence,” one meant to “ensure that A.I. research is focused on benefiting people, not hurting them.” The article’s framing anticipates several of the elements that have characterized AI ethics discourse in the years since: statements of lofty humanitarian ambition used to justify industry aspirations to self-regulation, the contention that policy makers would inevitably lag in understanding AI systems, and an insistence that government oversight of AI would be both undesirable and ineffective. Perhaps most crucially, the piece suggested that the development of AI technologies was as inevitable as their effects would be widespread and disruptive: social scientists and philosophers needed to be put “in the loop” to help computer scientists manage the effects of AI’s undoubtedly epochal impacts.Business and professional ethics were two of the most direct antecedents for today’s AI ethics discourse as developed and propagated in corporate spaces (Greene, Hoffmann, and Stark 2019: 2124). The sociologist Gabriel Abend (2014) has developed the idea of the “moral background” to describe second-order assumptions about what problems or questions count as of ethical concern. Abend and others have noted that professional ethics codes in fields like engineering implicitly work to distinguish members of a particular profession from outsiders through recognition of their skills or expertise and by an emphasis on obligations to colleagues and clients, as well as the general welfare, and on enforcement based on public visibility (Abbott 1983). The implicit moral background of today’s professionalized AI ethics is latent in Markoff’s piece; it matches the analysis by my colleagues Daniel M. Greene, Anna Lauren Hoffmann, and myself (2019) of the then nascent genre of AI vision statements. This background presents a deterministic vision of AI’s development and deployment, in which the adoption of these technologies cannot be stopped and the ethics of which are best addressed through certain narrow kinds of technical and design expertise. More recently coined industry terms such as AI safety and responsible AI reflect this worldview, and many of the various existing or proposed mechanisms for the ethical oversight of AI systems are easily co-opted into broader forms of neoliberal governance and capitalist accumulation (Stark, Greene, and Hoffmann 2021).It is crucial, then, that AI ethics include as a possibility that some applications of deep learning never be designed, built, or used at all. One way to respond to the “alignment problem” is thus to interrogate exactly whose values technologists presume deserve alignment with AI systems. Such critique has been led by activists and scholars trained in critical race theory, race and technology studies, gender and sexuality studies, and related fields. In the academy, this work is grounded on informed refusal in justice-based bioethics (Benjamin 2016) and on recognition of the genealogical continuities between contemporary AI systems and white supremacy (Golumbia 2009; Benjamin 2019; Katz 2020), patriarchy and misogynoir (Browne 2015; Noble 2018), and binary gender norms (Scheuerman, Paul, and Brubaker 2019).The activist work of groups such as the Our Data Bodies collective, Data for Black Lives, and the Algorithmic Justice League, to name three American organizations among many hundreds worldwide, has been even more central to the advancement of critical AI discourse. These organizations support what the AI and social justice organizer, advocate, poet, and author Tawana Petty describes as “visionary resistance” (Petty 2014). Such resistance entails mobilizing and working with local communities, particularly racialized, low-income, or otherwise marginalized ones, to document the impacts that the deployment of AI systems are having today. Resisting these technologies and their backers on all fronts also entails advancing a positive vision of justice and equality, doing “the work of creating the world we wish we live in” (Lewis et al. 2018: 83).The Alignment Problem is of the of ML systems and to the through improved technical are given and often such technical are both possible and in the Christian to the implicitly is enough to the social impacts of the ML applications not to the that are not easily or do not easily into our AI ethics that focus on technical and for values like are not well in to support such or broader about AI’s social Such technical work is but not to be at the of our about or AI systems should be and technical are often as the to AI’s by the same companies and from these systems in the first AI alignment is the wrong way to think about the broader questions at AI technologies are with the values and of and often Silicon Valley what is there for an AI ethics the of can live of in social and moral the AI ethics The of the term about the potential dangers of computing machines and of their by the and the In the out as a to the and Black to The AI who in developed one of the first computer the simulated in the book Computer and Human that of to be only by (13). 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Dominique du Crest, C. Tequi, F. Dessi

Data derived from public domain resources.

Joachim I. Krueger

Steven Pinker, being a public intellectual, may expect readers of his Rationality: What It Is, Why It Seems Scarce, Why It Matters (RAT) to bring certain hopes and expectations to the task. They know Pinker as a distinguished expert on linguistics, communication, and cognitive science, a scholar with a deep knowledge of many theories from evolution to cognitive dissonance, a child of the Enlightenment and a prophet of progress. They will not be disappointed.Readers with sensitivity and memory may recall that Pinker sometimes oversells his message. How the Mind Works (Pinker, 1997), for example, was a wild and fun ride through the cognitive science of that time, but it did not reveal how the mind really works. As far as I can see, there are two main reasons for this shortcoming. One reason is the impossibility of omniscience (Felin, Koenderink, & Krueger, 2017). We will never know how the mind really works. What we can have are incrementally better models of it. In RAT, Pinker does better. He notes the impossibility of defining rationality in rational terms without begging the question. Yet he pragmatically forges ahead, and this is his genius.The other reason for why RAT goes a bit too far lies in its curated presentation of the relevant science. In How the Mind Works, Pinker's discussion of the social mind was based on the work of a handful of authors with an evolutionary point of view and homes in Santa Barbara. In RAT, Pinker casts a broader net but still anchors his story on a familiar cast of pioneers, namely Robyn Dawes, Paul Slovic, Daniel Kahneman, and Amos Tversky. Pinker knows that there is an ongoing debate over the nature of rationality and the proper role of normative models of thinking (Chater et al., 2018). Yet he sidesteps these difficult issues in order to tell a story of how the concept of rationality can be understood and how its practice can be improved. This story requires some omissions, and their shadow reflects the dark side of his genius. Given these caveats, my recommendation is to read the book but to do so with an attitude of properly curbed enthusiasm. That would be the rational approach.In his penultimate chapter Pinker asks, “What is wrong with people?,” and he knows that his readers have been waiting for him to raise this question. Pinker also knows that he can't answer it because he—as anyone would—has failed to define rationality without question-begging. So why do we care about a big question when all we can do is answer little ones, such as “Under what conditions do people neglect base rates?” Is it rational to think that many little answers will add up to a big one? As a connoisseur of Hume, Pinker knows that this question is a version of the induction problem. When a large number of little questions have been answered, another one may come along to frustrate us. Yet the effort to chip away at the big issue is not for nothing. Identifying limited questions and answering them is fun and profitable.Like Pinker, I have enjoyed the ride on the rationality train. My favorite example of a thought problem that puzzled many great Renaissance minds until Fermat and Pascal came along to solve it is “The problem of points” (Krueger, 2000). Suppose Steven and Joachim use a fair coin, bet on opposite sides, and agree to throw it until one of them has won six times. This person will get the $20 that Dominic has kindly provided. However, at the point where Steven has won five rounds and Joachim has won three rounds, Dominic is called to editorial duty and terminates the game. What is a fair distribution of the $20? As it turns out, 7/8 of the money should go to Steven because the only way I could win under the agreed-upon conditions would be to score another three wins in a row. Few people understand this intuitively, but Fermat's math leaves no doubt that the rational solution is also a moral one, and for once the tug of war between the sweet and the smart is relaxed.The problem of points, the solution of which was a crucial step toward the modern understanding of probability, is an example of significant, if limited, progress. Alas, the tug often asserts itself and leaves us in tragic dilemmas where rational individuals bring forth their own destruction. For example, Pinker is pessimistic about our prospects in the prisoner's dilemma and its derivates (e.g., commons dilemmas). For an optimist like Pinker, this is a surprising surrender. In fact, one ray of hope in the prisoner's dilemma is social projection, an inductive inference that was once considered irrational. Yet when people reasonably assume that other reasonable people will choose as they themselves do, mutual cooperation becomes more likely than the presumably rational catastrophe of mutual defection (Krueger, 2013). The dilemma of trust, not discussed by Pinker, also illustrates this point. Classic game theory holds that rational agents will not trust strangers on the assumption that there is no incentive to reward trust with trustworthiness. Yet a proscription of trust is self-defeating and thus irrational—as is a prescription of trust. The rational attitude is to gather available cues (e.g., self-other similarities) as well as moral considerations (e.g., the golden rule) and social projection. With that the leap of trust can be framed as a decision under calculated uncertainty (Evans, Ong, & Krueger, 2021).In Chapters 3 to 9, the main body of RAT, Pinker surveys engaging examples of problems posed and problems solved, thereby illustrating the many and diverse tools critical thinkers find in their boxes. Here are some of them in super-short shrift: Chapter 3 reviews the elements of logic, and this is just as well because “logic” is as close as one gets to the term “rationality” in the Greek language. The ur-term is “logos,” which we first find in Heraclitus as a reference to the hidden order of the universe. Aristotle used it to refer to how we think, or should think, about the world. Like Socrates and Plato before him, he figured that we need help. The chasm between thinking as it is and thinking as it should be had opened up and it stills yawns at us today. Pinker, like Dawes (1988) and many other pioneers, knows that the mastery of logic does not eliminate the question “What is wrong with people?” because villains can be perfectly logical in the pursuit of their villainy.Chapter 4 confronts human ignorance and its management with an introduction to probability and randomness. Probabilistic thinking is the poor human's retreat when high rationality in the form of “true knowledge” fails. Now that we know so little, how can we still be rational? The obvious task seems to be gathering more data, making coherent inferences, and detecting randomness wherever it may lurk. Yet there are ironic puzzles, such as deliberate ignorance, when people elect to hide information from themselves although they might have it for free. As it turns out, such self-blinding need not be irrational, in the tradition of King Oedipus's self-mutilation, but a rational response to an environment that has no compassion (Krueger et al., 2020).Chapter 5 is written in reverence of the Reverend Bayes, who sought an inductive proof of the existence of God and failed. For all its elegance, Bayes's theorem's ability to nudge us toward true beliefs depends on the quality of the assumptions that go into it, that is, the priors. When we ask “What is wrong with people,” we may find that they overadjust or underadjust their beliefs in light of evidence, but the deep problem is that many people with contrasting priors are willing to diverge rather than converge with their beliefs after evidence. Pinker refers to Stanovich's (2021) book on “myside bias” (reviewed in this issue), but his faith in Bayes remains intact.Chapter 6 follows the bending arc of the utility function and its implications for rational choice. Pinker follows the tradition of marveling at the beauty of utility theory's axioms and noting that we'd be better off in our goal pursuit by observing them. At least we'd be protecting ourselves from being turned into money pumps by exploiters who are more rational and less scrupulous than we are (Grüning & Krueger, 2021). Kahneman and Tversky (1979) delivered what should have been the coup de grâce to the utility paradigm by showing how preference reversals emerge from the human ability to adapt to current circumstances and their differential treatment of gains and losses. However, organisms that don't adapt (even fungi habituate to mild toxins) or that reduce their loss aversion to match their love of gains in absolute terms might not live long enough to see the next equinox.Chapter 7 discusses the management of errors. Signal detection theory (SDT) was created to help radar operators decide whether a blip on the screen represented a Stuka formation or a flock of geese, a situation in which human sensibilities prefer to tolerate a few more false positives if that means fewer Stukas will be missed (Swets, Dawes, & Monahan, 2000). Beyond the radar screen lies a reality: The Stukas are either coming or they are not. Being able to tell the differencewith a large d' is a way of having knowledge and thus of being rational. The beta parameter, that is, the threshold one sets for sounding the “Stuka!” alarm, is of interest only when d' is low. It is here that the trade-off between the two types of errors must be adjudicated in light of preferences and values. Pinker then presents the Neyman–Pearson theory of statistical decision making as if it were a version of SDT, although it was its predecessor (Neyman & Pearson, 1933). A critical difference is that the size of a statistical effect is a random variable. It's rather like asking, “How many Stukas are there?” If there is only one, we may not care, but our theory says there are four (i.e., a formation). Any power analysis, which nowadays fashionably and ritualistically prefaces every Result section of a psychology article, is conditioned on a theorized (i.e., unobserved) effect size. Whereas SDT yields d' as a measure of discriminative sensitivity, the Neyman–Pearson theory of statistical testing assumes a d', as well as a false positive and a false negative rate, and then yields a probability of the data under the assumption that d' = 0. Which theory is more rational?Chapter 8 surveys the essentials of game theory and presents rationality as John von Neumann saw it. This view leads to despair because when rational goals yield irrational outcomes, we have tragedy. When individually rational agents find themselves in noncooperative games such as the prisoner's dilemma or chicken, they can do no better than avoid exploitation (chicken) or face mutual destruction (prisoners). The failure of game theory highlights the failure of individualized notions of rationality when collective welfare is at stake. To frame rationality at the supraindividual level requires the surrender of methodological individualism, one of the sacred tenets of the Enlightenment. Both Pinker and I hesitate to accept the need for a methodological collectivism, but there may be no way around it.Chapter 9 seems mildly out of place because it covers familiar questions about correlation and causation. Pinker takes the conventional line of prioritizing causation. Chains of cause and effect are the cement of the universe, although we know from Hume that we cannot prove this. Correlation is a trickster because it enables sloppy causal reasoning. The rational person knows how to extract credible causal claims from correlation with the help of other cues such as precedence or mechanism. One may counter that, because causation cannot be observed but only inferred, a rational person can be imagined who is not interested in causal explanations but who has a good fix on the available correlations and is able to predict what will happen next. Data scientists and machine learners seem to be betting on this model of rationality (Krueger, 2020).Late in his distinguished career, German sociologist Helmut Schelsky (1975) diagnosed and bemoaned the rise of a priestly caste on campus. These self-anointed priests took it upon themselves, Schelsky argued, to tell the rest of society, and thus its productive classes, what was wrong with their perceptions and actions and what they should do to overcome their “false consciousness.” After the Enlightenment had broken the power of the seminary to control thinking, the power of the seminar took its place. Today, we see a similar situation in cognitive psychology. Reasoning errors are revealed—or rather designed—to bring forth in the audience the humbling Aha! experience of “How could we be so stupid!” The omniscient lecturer then offers a path toward salvation. “Think again,” they might say (Grant, 2021), or think that which is not on your mind (Kahneman, 2011), or learn to think like us, the scientists (Pinker, 2021). Much like Schelsky's estranged colleagues from the Frankfurt School, today's cognitive scientists of the Kahneman school treat their audience to a domination–submission ritual. We tell you what's wrong with you, they declare, and you will thank us (and pay us) for it. The false consciousness lies in the audience's cheerful acceptance of this game.Arguably, the greatest achievement of psychological science is the demystification of the workings of the visual system. Visual illusions are endlessly fascinating in their weirdness and instructive in what they teach us about this masterpiece of nature. Tversky and Kahneman's (1974) greatest rhetorical trick was to suggest that cognitive illusions are analogs of visual illusions. Nothing could be further from the truth. Pinker wisely notes that to reveal the nuttiness of a belief is to challenge the believer to act on it, but he still is lulled by the Sirens’ song of the visual illusion metaphor. If the metaphor were true, there would be no irrationality. The so-called cognitive illusions would be the features of the system, not its bugs. This is a conclusion I do not wish to defend. Irrationality does exist, and it is all around us.Pinker comes closest to a solution, I think, when he early on in RAT tells how the Ju/’hoansi Bushmen of the Kalahari reason about their environment with exquisite cognitive skill. Their abilities meet the demands of their ecology, to the detriment of the springbok. Most contemporary people live in a world in which their skills are not aligned with their ecology, and this is partly the fault of the academic priests who design wicked puzzles in their labs or on their Qualtrics platforms (Lejarraga & Hertwig, 2021). Perhaps it is time to chill about individualist rationality, take a more ecological perspective (Hertwig, Leuker, Pachur, Spiliopoulos, & Pleskac, 2021), and not forget to have a good laugh (Kazantzakis, 1952).

Philip A. Ebert, Martin Smith, Ian Durbach

The notion of risk plays a central role in economics, finance, health, psychology, law and elsewhere, and is prevalent in managing challenges and resources in day-to-day life. In recent work, Duncan Pritchard (2015, 2016) has argued against the orthodox probabilistic conception of risk on which the risk of a hypothetical scenario is determined by how probable it is, and in favour of a modal conception on which the risk of a hypothetical scenario is determined by how modally close it is. In this article, we use Pritchard's discussion as a springboard for a more wide-ranging discussion of the notion of risk. We introduce three different conceptions of risk: the standard probabilistic conception, Pritchard's modal conception, and a normalcy conception that is new (though it has some precursors in the psychological literature on risk perception). Ultimately, we argue that the modal conception is ill-suited to the roles that a notion of risk is required to play and explore the prospects for a form of pluralism about risk, embracing both the probabilistic and the normalcy conceptions. We take the view that a risk judgment always implicates a body of evidence, which we refer to as the background evidence. In cases where the background evidence is not made explicit, we take it to be supplied by the context of utterance and, in typical cases, to be the evidence possessed by the one making the judgment. That is, we are inclined towards a contextualist semantics for utterances such as 3 and 4, on which their truth conditions feature an evidence parameter, the value of which is fixed by the context. The semantics of such utterances is not, however, our primary concern here. As well as making categorical risk judgments such as the above, we often make comparisons. While we may judge that the risk of a plane crash is very low, we may also judge that there is a higher risk of a car crash on the way to the airport. As well as judging that there's a high risk of food poisoning at a particular restaurant, we might also judge that there is a lower risk of food poisoning at the restaurant next door. Moreover, while we often speak about the riskiness of feared events, such as plane crashes, food poisoning, etc., we can also assess the risk of states of affairs. For instance, before drilling into the wall of a 1970s West Australian house, one might assess the risk that the wall contains asbestos, or jurors in a criminal trial, when contemplating a guilty verdict, might consider the risk that the defendant is innocent, or a mountaineer may ponder the risk that the snow conditions are unfavourable for a climb. Here, we treat propositions as the primary bearers of risk, with the riskiness of an event or state of affairs corresponding to the riskiness of the proposition that the event occurs, or the state of affairs obtains. As well as making judgments about the risk of specific events and states of affairs, people also assess the risk of activities or decisions, saying things like ‘Drilling into this wall is risky’, ‘It would be risky to attempt a climb under these conditions’. These judgments are important to understanding the connections between risk and decision making but we put them to one side here. According to the probabilistic of risk, the risk of a proposition is determined by the of higher the the higher the risk. this the risk of is higher the risk of in is more probable is. The be as the background evidence. We Pritchard in the probabilistic as the orthodox of risk is important to however, that this is different to the of risk that has standard in risk and in some this risk is with the of an by a of how or it would is some evidence to that this is as a a recent which with our risk judgments In our be on risk made in cases where is and the of risk the is that the risk judgments that people are inclined to make always with the probabilistic would In to at the 1970s a of in which judgments the probabilistic would These not as evidence against the probabilistic as important and which our judgments about risk and For a of that risk such as the and which in cases to the probabilistic would to be for an about risk are to in take into As a about risk and risk often of the as a to risk judgments some risk judgments be the of a and determined by such as while judgments be by a more and by the or with which the can be and by with the That risk judgments can the probabilistic is to be on a has a which has in a the people is way of the before the it is to The however, a of specific between and on the next The of these is to is not to with this as above, the a of three events obtains. the in the at the the by at the in the the by at the of to speak a of next The of this of events are to According to is and, to we to to We there may be some to judge as Pritchard but would be in about the of the probabilistic this judgment is on this this would to put Pritchard's in the as in which risk judgments to the probabilistic Pritchard with an as on the probabilistic of risk, a or which can our of and that into play in the of cases Pritchard that we might is that the conditions for a in to the conditions in that the in the by at or that the in the the by at or that the to speak a of next the of a In to particular in the next particular In a of that there is a between the with which an event can be or and how probable we the event to as the or the one might that of may an on risk judgments when are made That is, one might that the with which one can the scenario as in might one to it a higher risk the are to be to the to when the are can on how and people take to be with to the in that to take on events on events, when judge the to be in the would be to and as to that is a scenario in which or is to to be about the conditions in may be to that In the event that a in that may to some is the the of about the in The of a can be the of the it is how to the of the conditions in Pritchard a value for the of an in but it is to take this as more and the corresponding value in and a of in which in an scenario are by in about the In a that is for the and that when to between under conditions a or is in some where the are to be a for a for some the might well that the in are The that the in the by at and the in the the by at and the to speak a of next might be to be lower the value of in a more of this the probabilistic that the risk in is lower in for the These are of Pritchard's judgment about the scenario in a way that is with the probabilistic of risk. In as Pritchard to the probabilistic as a of risk of this to be Moreover, as above, it is also in this that Pritchard's judgment is this we put a the Pritchard The of our can be in the which a more discussion of the to the while the that people to our the that people judge to be risky is not with the of judging that the risk of the in the is These on their that Pritchard's judgment about the cases is and against the probabilistic Pritchard that to be by We in a about more we that the is in the of cases to which Pritchard the probabilistic a as to how people judge to to work, there are in which be the car or in a The risk of the car is car is new and it the risk of in a is on the at this and there are of the risk of to is the is this an but in it may be that this is is for instance, in the of risk on which that are to be are for risk in of their risk and a low, but of risk to as the which the are while which are the are to a risk in to be to against may by the and in the and is in the of and and both the risk one the of and against and the of and against one has in the of and against is there is that the risk of is the the the risk of a car and of a are both to be the the both of these are and are against these are the in which be for the are against this the risk that be for the may well be the in which the that we take against or at consider the of the of it is that this to risk is in which to play a role is in the context of of the of criminal are to the risk of an The high standard of for criminal to that a defendant be the risk that or is innocent, the evidence, is very criminal a of and the is to the standard to for For instance, to be guilty of in a defendant defendant be of one of these conditions is not a defendant be of these conditions is this that there is a risk that the defendant not take the there is a risk that the defendant not to the of that and there is a risk that the defendant the this that there is a risk that the defendant is in of the the probabilistic of risk, however, is an The of can be higher both the of and the of a the that a is and the that a is The that a or a is According to the probabilistic of risk, the risk of is and the risk of is low, it that the risk of is are which are at with the probabilistic of risk. we to that such are we are to the that may to however, that the of these to consider of risk that may make different about We to such a notion in As above, Pritchard the not to put on the probabilistic of risk, but to an which the modal of risk. 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Clayton Littlejohn

Abstract Could it be right to convict and punish defendants using only statistical evidence? In this paper, I argue that it is not and explain why it would be wrong. This is difficult to do because there is a powerful argument for thinking that we should convict and punish defendants using statistical evidence. It looks as if the relevant cases are cases of decision under risk and it seems we know what we should do in such cases (i.e., maximize expected value). Given some standard assumptions about the values at stake, the case for convicting and punishing using statistical evidence seems solid. In trying to show where this argument goes wrong, I shall argue (against Lockeans, reliabilists, and others) that beliefs supported only by statistical evidence are epistemically defective and (against Enoch, Fisher, and Spectre) that these epistemic considerations should matter to the law. To solve the puzzle about the role of statistical evidence in the law, we need to revise some commonly held assumptions about epistemic value and defend the relevance of epistemology to this practical question.

Jean‐Philippe Vergne, Gautam Swain

Bitcoin is difficult to categorize and indeed has been associated with 112 different labels in the British media (e.g., “private money,” “commodity”) – most of which poorly describe bitcoin. Specifically, our analyses of 674 media articles, focusing on the relationship between labeling and categorization, identify classification inconsistencies at three levels: within clusters of labels, between labels and categories, and between category attributes. These inconsistencies hamper categorization based on attribute similarity, audience goals, and causal models, respectively. We identify four factors that nurture this categorical anarchy and conclude with a call for research on the socioeconomic revolution heralded by blockchain technology.