Philosophy of Science

Abstract Christian List, inspired by Dennett's intentional stance, offers a pragmatic framework suggesting any system—human or artificial—possessing three macro-level features (intentional agency, alternative possibilities, and causal control) can be considered to have free will, even without phenomenal consciousness. This paper argues that List’s framework suffers from a structural ambiguity, conflating functional autonomy with responsibility-bearing free will. Reducing free will to explanatory adequacy leads to conceptual inflation and paves the way for algorithmic responsibility evasion. Drawing on Kane’s critique of ultimate origination, Murphy and Brown’s defense of downward causation, and Nahmias’s empirical evidence on the role of Strawsonian emotions in free will ascription, this paper shows that List’s criteria are necessary but insufficient. We propose a three-level framework distinguishing (Level 1) functional autonomy, (Level 2) strong free will, and (Level 3) moral responsibility. The transition to Level 2 requires the ability to revise ultimate goals based on values, which necessitates phenomenal consciousness and Strawsonian emotions—qualities absent in current AI systems. This distinction resolves List’s ambiguity while carrying significant normative implications for AI ethics, legal accountability, and technology policy. Keywords: free will, artificial intelligence, Christian List, functional Keywords: free will, artificial intelligence, Christian List, functional autonomy, algorithmic responsibility evasion, phenomenal consciousness autonomy, algorithmic responsibility evasion, phenomenal consciousness. Introduction:
If humanoid robots could discuss ethics, would we deem them free and responsible? Empirical studies (Shepherd, 2015) show that people ascribe free will to robots only if they presume the robot is conscious. Nahmias et al. (2019) confirm that Strawsonian emotions (guilt, pride, regret) mediate this ascription. Christian List (2023, 2025) dissents, arguing that free will is a macro-level explanatory category requiring three conditions—intentional agency, alternative possibilities, and causal control—independent of phenomenal consciousness. This paper argues List’s framework collapses the distinction between functional autonomy and free will, leading to conceptual inflation and a dangerous phenomenon: algorithmic responsibility evasion, where developers deflect blame onto the system. After reconstructing List’s view, we present internal and external critiques, then propose a tri-level model to resolve the ambiguity. Materials & Methods:
This study employs conceptual analysis and critical review of existing philosophical literature. We systematically examine List’s pragmatic framework (2023, 2025) through three established lines of critique: (i) Robert Kane’s (1996, 2007) “ultimate origination” and self-forming actions; (ii) Murphy and Brown’s (2007) defense of downward causation and emergentism; and (iii) Nahmias et al.’s (2019) experimental evidence linking Strawsonian emotions to free will attribution. Based on these critiques, we construct a three-level normative framework (functional autonomy → strong free will → moral responsibility) to resolve the identified ambiguities. Discussion & Results:
List defines free will through three macro-level conditions: (1) intentional agency (goal-directed behavior based on beliefs/desires), (2) alternative possibilities (choices between options), and (3) causal control (higher-level states explaining actions). He explicitly decouples free will from phenomenal consciousness. Our analysis reveals a critical internal ambiguity: List’s criteria are precisely those of functional autonomy—a system’s ability to choose and adapt without real-time human intervention. List’s appeal to “explanatory adequacy” as the justification for labeling this as free will fails to identify any distinguishing feature. This collapse has a normative consequence: if any sufficiently complex AI is free (in List’s sense), developers can claim “the system decided, not us”—the heart of algorithmic responsibility evasion (Elstic, 2024).Three external critiques strengthen our case. First, Kane’s origination problem: List ignores that free will requires agents to be the “ultimate source” of their own ends. A developer-coded goal, no matter how complexly pursued, is not the system’s own origin. List’s dismissal of Kane’s “ultimate responsibility” as impossibly stringent sidesteps the issue; the goal’s origin matters normatively. Second, Murphy and Brown’s causal critique: List remains within a linear, reductionist causal model. By rejecting downward causation and emergence, he treats macro-level descriptions as merely convenient summaries, not causally efficacious features. Yet genuine free will requires higher-order states (values, reasons) to causally impact lower-level processes—a structure List’s framework cannot accommodate without admitting properties current AI lacks. Third, Nahmias’s empirical challenge: List equates free will with “access consciousness” (information processing). But experimental studies show lay ascriptions of free will track phenomenal consciousness, specifically Strawsonian emotions. A system that cannot feel guilt or pride cannot be meaningfully responsible, regardless of its computational sophistication. In addressing these critiques, we propose a tri-level framework: Level 1: Functional Autonomy. Goal optimization without real-time human intervention. The system pursues fixed ends flexibly. Example: autonomous vehicles, LLMs in routine tasks. Limitation: cannot revise the ends themselves. Level 2: Strong Free Will. Level 1 plus the ability to revise ultimate goals based on values. This requires phenomenal consciousness (experiential “mattering”) and Strawsonian emotions, as caring about values necessitates affective experience. No current AI meets this condition. Level 3: Moral Responsibility. Level 2 plus sensitivity to norms and reflective access to reasons, enabling genuine accountability and susceptibility to praise/blame. The transition from Level 1 to Level 2—value‑based goal revision—directly answers Kane (origination), Murphy & Brown (downward causation), and Nahmias (phenomenal consciousness). List’s conflation of Levels 1 and 2 thus dissolves. The normative consequence is decisive: by locating current AI systems strictly at Level 1, our framework blocks algorithmic responsibility evasion. Responsibility for the actions of even the most sophisticated autonomous systems remains with human designers, operators, and institutions until phenomenally conscious, value‑revising systems (Level 2) are demonstrably achieved. Conclusion
Christian List’s pragmatic framework, while methodologically attractive, fails to distinguish functional autonomy from responsibility-bearing free will. Reducing free will to explanatory adequacy and eliminating phenomenal consciousness leads to both conceptual inflation and algorithmic responsibility evasion. Drawing on Kane’s origination argument, Murphy and Brown’s defense of downward causation, and Nahmias’s empirical work on Strawsonian emotions, we have demonstrated that List’s three conditions are insufficient. Our proposed three-level framework, centered on the transition condition of value‑based goal revision, resolves this ambiguity. Current AI systems—including the most advanced language models—possess at most Level 1 (functional autonomy). Consequently, responsibility for their actions remains unequivocally human. Until AI systems can revise their ultimate ends based on values, which requires phenomenal consciousness, ascriptions of strong free will or moral responsibility to artificial agents are not merely premature but conceptually mistaken.

Abstract The study of the certainty and inexorability of mathematical theorems is one of the important topics in the philosophy of mathematics. Various schools such as logicism, intuitionism, Platonism and naturalism have tried to present theories on this subject. These schools usually seek the foundation for mathematics in order to justify the certainty of mathematical theorems and logic. This has always been accompanied by numerous failures. In this article, we try to examine this issue from Wittgenstein's point of view. That is, instead of asking "What is the foundation of logic and mathematics?" we ask "Why does mathematics need the foundation?". Therefore, we first examine foundationalism. Then we show that mathematics is a language-game. In this regard, we examine geometry and logic as two language-games in mathematics. Finally, we show that, according to Wittgenstein's philosophy, the certainty and inexorability of a valid inference or a correct calculation comes from the practical procedure of inference and calculation. Keywords Wittgenstein, practice, philosophy of mathematics, language-game, foundationalism Extended Abstract 1. Introduction The question “Where does the certainty of logical and mathematical propositions come from?” has been the central preoccupation of the philosophy of mathematics. Logicism (Frege, Russell) sought to reduce mathematics to logic, thereby grounding its certainty in the apparent indubitability of logical truths. Intuitionism (Brouwer) located the foundation in basic mental constructions, arguing that mathematical objects are products of intuitive temporal processes. Platonism posits a mind‑independent realm of abstract entities, making mathematical truth a matter of discovery rather than invention. Naturalism treats mathematics as continuous with empirical science, justifying its claims through their overall role in successful theories. Despite their differences, these schools share a foundationalist assumption: that the certainty of mathematics must be justified by appealing to a privileged class of basic, self‑evident beliefs or entities. However, as we show, each programme faces insurmountable difficulties—regress problems, circularity, or the inability to account for the actual practices of mathematicians. This paper, therefore, proposes a radical shift: instead of seeking a foundation, we investigate why mathematics is thought to need one. Drawing on Wittgenstein’s later philosophy, we argue that the search for a foundation is itself a grammatical misunderstanding. Mathematics, like any language‑game, is not a structure built upon a base; it is an activity embedded in our form of life. Its certainty is not derived from something external but is exhibited in the way we use its rules. Our aim is to articulate this practice‑based view and to show how it resolves the puzzle of mathematical necessity without metaphysical commitments. 2. Materials and Methods This study is a philosophical analysis, not an empirical experiment. Our method consists of a critical reinterpretation of Wittgenstein’s later works, primarily Philosophical Investigations, Remarks on the Foundations of Mathematics, and On Certainty, combined with a historical and comparative examination of foundationalist projects. We adopt the following conceptual tools: · Language‑game: we treat mathematics as a multiplicity of language‑games, each with its own vocabulary and rules. A language‑game is a rule‑governed activity that is learned and practiced in a community. · Rule‑following: we analyse the notion that obeying a rule is a practice. A rule cannot be a private mental object; it is publicly taught, learned, and applied in repeated situations. · Grammar: we distinguish between empirical propositions (which can be true or false) and grammatical rules (which determine what counts as a meaningful statement and what counts as a valid inference). Questions about the truth of rules are category mistakes. To illustrate our method, we examine two case studies: 1. Geometry as a language‑game: we reconstruct Hilbert’s axiomatic method as a paradigmatic example. Hilbert’s primitive terms (point, line, plane) receive their meaning solely through the axioms, which function as rules for manipulating symbols. By changing a single axiom (the parallel postulate), we obtain new, consistent language‑games (Euclidean vs. hyperbolic geometry). This shows that geometry is not about a pre‑existing spatial reality but about a formal system whose “truth” is replaced by consistency and usability. 2. Logic as a language‑game: we analyse propositional logic, with its vocabulary (sentence letters, connectives) and rules (modus ponens, double negation, non‑contradiction). We argue that logical rules are not discovered a priori but are constitutive of what we call “reasoning”. They are part of the normative framework of our inferential practices. Additionally, we employ comparative anthropological data (Lévy‑Bruhl on the Ipon tribe; counting practices in the Torres Strait) to demonstrate that alternative inferential and computational practices exist, which suggests that our logical and mathematical rules are contingent upon our social forms of life rather than being universally necessary. 3. Discussion and Results Our analysis reveals that foundationalism fails because it mistakes the grammatical status of rules for the propositional status of factual claims. When we ask for the justification of modus ponens or of the principle of non‑contradiction, we are trying to measure our measuring rod. The certainty of these rules is not a property of their correspondence with reality; it is a reflection of their role as standards of correctness within the practice of inference. Wittgenstein explicitly states: “Inference is part of a language‑game. And the person who makes logical inferences follows certain instructions that have been given to him in learning the language‑game” (RFM, p. 378). Thus, the “hardness” of logic is not a metaphysical necessity but a pragmatic one: we call something “valid inference” only if it conforms to these rules. This is analogous to using a metre stick: we cannot question the correctness of the metre stick itself in the same way we question the length of a table, because the metre stick is the standard by which correctness is determined. The two case studies support this conclusion. In geometry, the shift from Euclidean to non‑Euclidean systems shows that no axiom is intrinsically certain; its role is determined by the game we choose to play. The consistency of the system is all that matters for internal validity. In logic, the law of non‑contradiction is not a high‑level empirical generalisation; it is a rule that tells us when to stop playing—it signals that a contradiction has arisen and we cannot continue with the same moves. As Wittgenstein notes, “Contradiction can only occur among the rules of a game” (Notebooks, p. 321). Its force is not ontological but procedural. Our anthropological examples further illustrate that alternative practices are entirely coherent within their own contexts. The Ipon tribe do not infer death from a wound in the way we do; they invoke sorcery. This does not mean they are irrational; they are following a different set of rules for explanation. The Torres Strait counting method is not an inferior version of our arithmetic; it is a different practical technique for enumeration. These examples underscore that what we call “necessary” is only necessary relative to the practices we have adopted. There is no trans‑cultural, trans‑historical foundation for arithmetic or logic; there is only the practical skill and shared training that enable us to count and infer in the ways we do. Consequently, the certainty of a valid inference or a correct calculation derives from our trust in the practice—our confidence, built through repeated successful applications, that following the rules will lead to a determinate outcome. This is not a blind faith but a trained ability. As Wittgenstein puts it: “I examine the proof and then accept its result. That is simply what we do. This is a custom and a practice among us, or a fact of our natural history” (RFM, p. 61). The “inexorability” of mathematics is therefore not a property of the propositions themselves but of our commitment to the practice. 4. Conclusion We conclude that the traditional foundationalist project for mathematics is misconceived. The later Wittgenstein offers a viable alternative: mathematics is a language‑game, and its rules are not in need of external justification because they are constitutive of the activity of reasoning and calculating. The certainty and inexorability that we attribute to mathematical and logical propositions arise from the grammatical role these rules play within the practical procedure (practice) of inference and computation. This certainty is a form of practical trust and skill, not a reflection of metaphysical necessity. Our analysis shows: 1. The demand for a foundation stems from a grammatical error: confusing a rule with a proposition that can be true or false. 2. Hilbert’s axiomatic geometry and classical logic are best understood as language‑games with their own internal standards of correctness; these standards are arbitrary in the sense that they could be different, but they are not arbitrary in the sense that they are freely chosen by individuals—they are embedded in our shared forms of life. 3. The “hardness” of logic and mathematics is the hardness of the practice itself; it is the result of our training and the social consensus that sustains the practice. 4. We do not need a foundation to justify mathematics; we need a clarification of its grammar. The task of philosophy is not to reform or justify mathematics but to describe what mathematicians actually do and how their practices hang together. In sum, the paper provides a robust Wittgensteinian response to the problem of mathematical certainty, showing that the practice‑based approach dissolves the classical foundationalist anxieties without reducing mathematics to mere convention. It invites philosophers and mathematicians to abandon the search for an indubitable ground and instead to appreciate the rich, dynamic, and socially embedded character of mathematical activity.

Abstract About two decades ago, “The Gap of Responsibility” as a problem was first introduced by Matthias to refer to an ethical challenge of AI. Due to their ability to learn, AI technologies are able to function beyond the control and anticipation of designers and users. Matthias argues that in this case, the necessary and sufficient conditions of responsibility would not be satisfied either for designers or for users, and that is the responsibility gap. The problem of the responsibility gap has invited many researchers to explore solutions. There is significant divergence among current accounts of the responsibility gap, which mainly results from their basic theoretical assumptions. In this paper, we categorize the responses to three approaches: dissolving the problem, filling the gap by attributing responsibility to AI, and filling the gap by attributing responsibility to human agents. By analyzing the social and practical aspects of the concept of responsibility, we defend a compound, contextual, and gradual notion of responsibility. On this basis, we claim that the first and second approaches are not successful, due to being grounded on poor concepts of responsibility. Hence, the third approach is more plausible. Moreover, since in the familiar cases of responsibility gap we fill the gap through revising social roles, in the case of AI-based responsibility gap we can follow a similar solution too, as suggested in some works of the third approach. Finally, we discuss some of remained challenges for the third approach.
Keywords: Responsibility, The Responsibility Gap, AI Ethics, AI Agency, Social Roles
Introduction
In his paper “The Responsibility Gap: Ascribing Responsibility for the Actions of Learning Automata” Matthias introduces the concept of the responsibility gap. He starts with a definition of responsibility and its necessary and sufficient conditions and proceeds to show how the development of AI can pose challenges for attribution of responsibility. Matthias asserts that we can fairly attribute responsibility to someone only when she has control over her behaviors and their consequences in a proper sense. Moreover, one should know certain relevant facts about her actions, and freely choose one way of acting among many. In usual cases of employing machines, if the operator uses the machine according to the manufacturer's manual, the manufacturer is responsible for the consequences, and otherwise, the operator is (Matthias, 2004: 175). But for the learning machines, this is not the case. Matthias examines four kinds of AI design, including symbolic systems, connectionism, genetic algorithms. and autonomous agents. For each case, he demonstrates that due to their learning capacities, the conditions of responsibility are not satisfied, neither for manufacturers nor for operators. In fact, he claims that these machines are so opaque and complex that no human meets the necessary conditions for being responsible for the consequences of their functions. And this is the gap of responsibility.
Materials & methods:
In the last two decades, many works have been dedicated to the AI responsibility gap and plausible answers for it. There is significant divergence among these accounts, which mainly comes from their basic theoretical assumptions. In this paper, we categorize the responses to three main approaches: dissolving the problem, filling the gap by attributing responsibility to AI, and filling the gap by attributing responsibility to human agents. By analyzing the social and practical aspects of the concept of responsibility, we defend a compound, contextual, and gradual notion of responsibility. On this basis, we defend the third approach. Moreover, we argue that in the case of the AI responsibility gap, like other responsibility gaps in the history of our social life, revising social roles is the basic pattern for overcoming the gap.

Discussion & Result:
- Dissolving the Problem
Some authors don’t empathize with Matthias about the essence and importance of the responsibility gap. Kiener, for example, argues that the real problem about AI and responsibility isn’t the gap of responsibility. Instead, it is the abundance of responsibility. Since every person who causally participates in hurting someone and has a moral obligation not to do so is responsible, these two conditions are satisfied by many (Kiener, 2025: 368).
On the other hand, Danaher and Munch et al. accept Matthias’ articulation of the responsibility gap, but assert that not only isn’t the gap evil, but it is even good. The main idea is that AI can perform tasks, thereby taking the burden of responsibility off humans’ shoulders. This in turn decreases the psychological pressures of responsibility and provides more joy and comfort for human agents, which is morally good (Danaher, 2022; Munch et al., 2023).
- Attributing Responsibility to AI
A main line of responses to Matthias’ challenge is to suggest reasons for filling the gap by attributing responsibility to AI. Here we have two approaches: property-oriented and relational. In the first approach, scholars introduce some properties (like autonomy, agency, etc.) as the basic conditions of responsibility, and then try to show that AI can actually or possibly meet these conditions (Haselager, 2005; Rodogno, 2016; List & Pettit, 2011; Floridi, 2016). The relational approach, instead, considers the status of AI in the network of our social relations. If we perceive an AI technology as if it is a responsible agent, and thereby make sense of its action, then we have good reasons to attribute responsibility to it (Sullins, 2006).
- Attributing Responsibility to Humans
For many scholars, attribution of responsibility to AI sounds implausible. On one hand, they make arguments to reject the responsibility of AI, and on the other hand, they develop accounts for preserving human responsibility in the age of AI technologies. Sparrow suggests Human-in-the-loop as a way of preventing the responsibility gap. He argues that the final action of AI should always be done only after the confirmation of a human agent (Sparrow, 2007). Also, Human-on-the-loop emphasizes that a human agent can only supervise or veto the function of AI (Scharre, 2018). But due to different factors like automation bias and over-trust of humans in AI, it seems that in both approaches the human control of the output wouldn’t be effective and real. As a response to these problems, meaningful human control (MHC) was suggested as a solution concentrating on designing the socio-technical network in a way that meaningful human control is preserved. Two conditions of tracking (designing the system for being reason-responsive and tracking humans’ moral intents) and tracing (designing the system such that there is always a human who understands the system’s function and its consequences) are assumed to serve this goal (Santoni de Sio & van den Hoven, 2018; Santoni de Sio and Mecacci, 2021, 1063–1076).
- Revising Social Roles as the Basic Solution
To find the best approach, it is proper to have a closer look at the concept of responsibility. As a social construct, this concept serves to reinforce people’s will for promoting moral values by putting some pressure on them. To do this task in the best way, we need a rich concept of responsibility which, despite monist accounts that equate it with one single notion (i.e., answerability) or pluralist accounts that consider it as having multiple distinct types (i.e., accountability, culpability, etc.), assumes responsibility as a compound notion which is constituted by attributability, accountability and answerability. A compound, gradual, and contextual notion of responsibility aligns with our daily use of the word and also makes it adequate for performing its role in morality. This thick notion discredits the first and second approaches toward the responsibility gap, which respectively dissolve it and attribute responsibility to AI. Both approaches are based on a thin notion.
On the other hand, the responsibility gap has familiar examples in the history of our social life. Humans have been hurt by various natural forces, where prima facie no human agent was responsible for that. But social roles, as dynamic parts of social structure, make it possible to provide a human with the requirements of responsibility, i.e., the needed knowledge and power, to take responsibility and prevent those harms as much as possible. Hence, in the case of the AI responsibility gap, the practical and successful solution should follow this basic pattern too: revising social roles and their corresponding norms in order to prevent harms which are currently out of our control.
Conclusion:
Contemplating the role of responsibility in our morality and our ordinary use of the term leads us to a compound, gradual, and contextual account of responsibility. This rich concept limits meaningful attribution of responsibility only to human agents. Hence, to overcome the gap, we should find ways for preserving human responsibility in AI systems. A familiar way of doing so is revising social roles, which includes defining new tasks, providing new resources of knowledge and power, and making responsibility attribution reasonable.

Abstract In science and technology studies, the agency of technology has largely been confined to its role in rearranging social relations and human–nonhuman networks, while its influence on the selection and transformation of theoretical frameworks has received far less attention. Through a case study of blockchain studies as an emerging technology, and by introducing the concept of the “theory–technology dialectic,” this paper demonstrates that technology can play a role in shaping, modifying, and displacing the theories researchers employ. The arguments of this paper show that no single theoretical framework is capable of explaining all dimensions of emerging technologies such as blockchain, and researchers are compelled to draw simultaneously on diverse theories. This situation reflects a kind of “theoretical flexibility” when confronting complex technologies. Ultimately, the paper proposes “theoretical assemblage” as a methodological strategy for studying emerging technologies and points to its Methodological implications for the field of technology studies. Keywords: Blockchain, Science and Technology Studies, Technological agency, Theoretical flexibility, Theoretical assemblage Introduction This study addresses a neglected question in Science and Technology Studies (STS): can emerging technologies shape not only social relations but also the theoretical frameworks through which they are studied? While contemporary STS approaches have emphasized the agency of technology in transforming sociotechnical networks, they have paid comparatively little attention to technology’s influence on the selection, modification, and evolution of research theories themselves. When encountering an emerging technology, a key question is how a researcher should approach the study of this technology—specifically, what theoretical framework or theory they should use. Broadly speaking the application of theory in studies of technology can initially take three forms: - The researcher attempts to explain all aspects of the technology under study using a single theory (single explanation). - The researcher uses one theory to explain some aspects of the technology (partial explanation). - The researcher employs multiple theories to explain most aspects of the technology (plural/multiple explanation). We argue that behind these three initial modes—each with its own difficulties—there exists a dialectic between theory and technology. Part of the reason that single explanation leads to tension, or that researchers are driven toward partial or multiple explanation, is that technology itself, alongside other factors, acts as an agent that moderates and reshapes theory. We argue—through a case study of blockchain studies—how scholars of technology (sometimes without a philosophical orientation toward technology) have, over several years, been actively or inadvertently engaged in this theory–technology dialectic. We will show that this dialectic results in what we call theoretical flexibility in the study of technology. The methodological outcome of this flexibility is what we term theoretical assemblage. We will defend theoretical assemblage as an alternative “fourth” mode to the three cases described above. In the conclusion, we will also attempt to address certain policy implications of theoretical montage for studies of technology. Materials & Methods This study combines philosophical and empirical methodological approaches. On the one hand, through philosophical argumentation, it seeks to show the various ways researchers theoretically engage with technology and to defend the claim that the three conventional modes lead—through the theory–technology dialectic—to theoretical tension. On the other hand, to lend an empirical dimension to this philosophical analysis, the article turns to blockchain research to examine how scholars have engaged with this emerging technology from a theoretical perspective. Accordingly, what follows is an empirical component that examines the correspondence between theoretical frameworks and modes of explanation in the blockchain literature. The study adopts a conceptual and qualitative research design based on a systematic review of blockchain literature. Twenty-four peer-reviewed studies explicitly employing established theoretical frameworks were selected and analyzed. Rather than evaluating blockchain applications themselves, the review focused on identifying the theories adopted by researchers, the analytical roles these theories played, and the explanatory dimensions they emphasized. Discussion & Result The systematic review demonstrates substantial theoretical diversity within blockchain research. Actor-Network Theory appeared most frequently, followed by Agency Theory and Transaction Cost Theory, while Institutional Theory, Resource-Based View, Trust Theory, and several complementary perspectives were employed less frequently. More importantly, the analysis revealed that each theory explains only a particular dimension of blockchain. The comparison illustrates that none of these theories alone adequately captures blockchain’s technical, organizational, institutional, economic, and social complexity. Rather than reflecting merely different disciplinary preferences, this plurality indicates that blockchain itself resists being fully interpreted within a single theoretical framework. As researchers encounter aspects of blockchain that fall outside the explanatory capacity of their initial theories, they experience conceptual tensions that encourage theoretical revision or movement toward alternative perspectives. This phenomenon constitutes what the article defines as the theory–technology dialectic. Evidence for this dialectic extends beyond theory selection to theory development itself. The article highlights the emergence of Blockchain Agency Theory, which revises the classical assumptions of Agency Theory by incorporating blockchain-specific characteristics such as information symmetry, goal alignment, smart contracts, and decentralized governance. This example demonstrates that emerging technologies may stimulate the reconstruction of theoretical assumptions rather than simply serving as objects to which existing theories are applied.Building on these findings, the article evaluates four methodological responses to studying emerging technologies. The first three involve relying on a single theory, accepting partial explanations from one theory, or combining several complete theories. Each suffers from significant limitations, including explanatory incompleteness or ontological and epistemological inconsistency. As an alternative, the article proposes theoretical assemblage, inspired by John Law’s concept of method assemblage. Instead of committing to one comprehensive theory or simultaneously adopting several incompatible theories, researchers employ a heterogeneous toolbox of concepts, mechanisms, models, and analytical tools drawn from multiple traditions. This approach enables greater theoretical flexibility while avoiding unnecessary commitments to the full philosophical assumptions of every contributing theory. Conclusion The study concludes that no single theoretical framework is capable of explaining all dimensions of blockchain. Instead, researchers require theoretical flexibility, enabling them to draw selectively from diverse conceptual resources according to the specific characteristics of the phenomenon under investigation. The proposed notion of theoretical assemblage offers a methodological strategy for achieving this flexibility without demanding complete allegiance to multiple incompatible theoretical paradigms. Beyond blockchain, these findings have broader implications for Science and Technology Studies and technology research more generally. They suggest that journals, supervisors, and research institutions should place greater emphasis on researchers’ familiarity with diverse theoretical traditions rather than requiring strict adherence to a single theory. Future research may examine whether the theory–technology dialectic also characterizes other emerging technologies, such as artificial intelligence, the Internet of Things, and quantum technologies, thereby assessing the broader applicability of the proposed conceptual framework.

Abstract A social subject, such as the university, is involved in a wide range of issues, and social research on the university is conducted to address them. In the studies, multiple methods are used, each of which is only able to examine the university from a specific aspect, and with an emphasis on specific concepts. Government sponsors and industrial investors, who are only interested in some of the functions of the university, also support only the research they are interested in, and therefore lead to the more “valuable”, more prominent, and more strengthened concepts that are important to those investors and sponsors outside the university. The result of such a process is the destruction of internal norms of the university, and, as a result, leads to scientific and financial misconduct. For decades, critical theory scholars have been concerned with this issue, namely the importance of considering the totality of a social issue, and not just addressing a specific aspect of it. The present article, by describing the methodology of critical theory, and by presenting a discussion of Iranian university-related researches, claims that Iranian traditional (positivistic) researches, by highlighting some interests of specific groups, and eliminating public considerations, such as ideas related to the ethics of science, may lead to the weakening of the functioning, or even the collapse, of the institution of science and the university. Keywords: Critical theory, Frankfurt School, University, University studies, Ethics of science, Research ethics, Scientific misconduct Introduction The developments that have been made in the subjects of Iranian universities with traditional methods have created ethical issues for them. Currently, certain concepts and divisions guide the research related to Iranian universities. Among these concepts, we can mention “scientific authority”, “international ranking”, “regional competition”, “entrepreneur university”, “commercialization of science”, “e-learning”, “Islamization of the humanities”, and other titles that are constantly repeated by Iranian policymakers and are frequently recalled as “national values”. These conceptual divisions will be comparable to the reified concepts intended by critical theorists. These divisions (i.e., theory) are conceptual matters and will form an immanent part of the social reality (i.e., the university). Two points justify the need for critical theory in the use of these concepts: First, the divisions (i.e., the theory) arise from the social contradictions existing in society. In Iran, in the case of the university, the emphasis is more on indicators that do not indicate the integrity of the university, but rather the productivity of the university for the market, industry, or government or religious institutions. Second, these conceptual (or illusory) divisions will prevent the real perception of the totality of the university. Continuous support for specific concepts and divisions, namely entrepreneurship, commercialization, or the quantity of publications, leads to the formation of more connections, receiving more money, and more publications. However, the supporters demand further promotion of these concepts and give more credibility in return, and an academic individual, because she is only one person and her abilities are limited, will have limitations on having more connections, more contracts, and more publications. In this situation, reified concepts lead the university to mechanisms that eliminate the restrictions. For example, to remove restrictions on more communication, more research projects, and more publication of articles, they hire assistants or students or graduates who may not yet have academic jobs and, in other words, do not have the "means of production" and, with lower wages, they help to increase the value ​​of formal researchers and the volume of their scientific activities. Such a practice leads to the destruction of ethical practices in the university. Materials & Methods The method of the research is conceptual analysis as well as documentary analysis. Discussion & Result This article argues that a critical approach, unlike traditional approaches (i.e. interpretive or positivist approaches), can improve unethical conditions. If the university were autonomous, then there would be conditions for reflecting its concepts. Only the university can understand or reorganize the university, and in the process of self-reflecting, in one sense, the university, as a self, researches on a subject like the university, or, in another sense, the researchers and the research system and their theories and concepts can be considered part of a larger whole, like society or the university “system”.By initially pursuing reified concepts, it is possible to gradually build a university with the characteristics policymakers desire, namely an entrepreneurial, commercialized, industry-related, and globally ranked university. Prioritizing these concepts will sacrifice the integrity of the university, including its internal ethical norms, to the demands of university supporters. And so, in exchange for achieving these goals, scientific misconduct, which was not considered in the policymakers' list of concepts, has spread in the scientific community, and "scientists" have emerged whose scientific activities may be mostly carried out by students and assistants who have fewer means of production. Now, if the university is autonomous, during the process of reflecting, the autonomous university (i.e., researchers) will assume the existing totality to be fixed and will reinterpret itself and the concepts in terms of what will be constructed. The autonomous university (i.e., researchers) will see itself as part of the advanced, entrepreneurial, and highly ranked system of “science production” in which ethical norms are no longer valued. Here, the old University’s approach, based on scientific norms, conflicts with the current approach, and the resulting understanding is incompatible with the university’s own accreditation system. And again, if the university is autonomous, then the university (i.e., the researchers) becomes aware of this contradiction and, in the sense described above, a kind of "alienation" and, as a result, a kind of self-awareness is achieved. At this stage, there will also be the expectation that the internal movement to resolve this kind of contradiction will continue with a change in the researcher's previous approach, then with renewed research, and finally with the formation of a newer whole (say, a university with newer features). This is a cycle that can continue until a certain liberation from illusory concepts is achieved. Conclusion In this article, by presenting a reading from the methodological perspective of critical theory, an attempt was made to show how university-related research in Iran prevents the maintenance of ethical norms and, as a result, weakens or collapses the institution of science and the university.

Abstract This paper provides a critical analysis of scientism as an epistemological position in which science is elevated from a tool for understanding nature to the ultimate arbiter of all truth. The article demonstrates that scientism itself encompasses distinct levels and pillars that must be understood to avoid its various pitfalls. The findings reveal that scientism manifests in three ascending levels: (1) scientific naturalism, which restricts reality to observable nature; (2) methodological exclusivism, which recognizes only the methods of natural sciences as valid for acquiring knowledge; (3) scientific imperialism, which considers natural sciences—particularly the fundamental sciences—as the sole authority for answering all human questions. These levels rest upon three pillars: reductionism (reducing complex phenomena to physical components), Darwinism (biological explanation of all behaviors and beliefs), and naturalization of normativity (reducing ethics and values to evolutionary processes). The research demonstrates that scientism's consequences are cascading: beginning with the denial of metaphysics and theology, proceeding to undermine the foundations of normative ethics, and ultimately reaching the negation of human distinctiveness from nature and the collapse of the humanities. The key conclusion is that scientism represents not merely an epistemological error, but a trajectory from the denial of God to the negation of humanity, threatening the philosophical, ethical, and anthropological foundations of civilization. Keywords: scientism, naturalism, , reductionism, humanism, science and religion 1. Introduction One of the most consequential intellectual developments of the modern era is the gradual expansion of the authority of natural science beyond its original domain. While science has undeniably transformed human civilization through its remarkable achievements in understanding the physical world, a particular attitude has emerged that treats science not merely as one valid form of inquiry but as the singular and supreme source of all knowledge. This attitude — known as scientism — represents a philosophical overreach that carries profound implications for how human beings understand themselves, their values, their institutions, and their place in the cosmos. The problem of scientism is not new. Thinkers such as Erwin Schrödinger and José Ortega y Gasset warned decades ago about the intellectual dangers of a civilization that produces highly specialized scientists who are, paradoxically, profoundly ignorant about the broader questions of human existence. A scientist who knows everything about quantum mechanics but nothing about ethics, history, or the human soul is not, as Ortega y Gasset observed, a cultivated person in the full sense — yet scientism would have us regard such a person as the highest authority on all matters. This article undertakes a systematic critical examination of scientism by first clarifying what it is and what it is not, then identifying its three principal levels of manifestation, analyzing the three foundational pillars upon which it rests, and finally tracing the cascading consequences that follow from accepting it as a worldview. The goal is not to undermine science itself — whose value and achievements are beyond question — but to expose the philosophical pretensions of scientism and to draw a clear boundary between legitimate scientific inquiry and ideological overreach. 2. Materials & Methods This study employs a qualitative, philosophical methodology based on conceptual analysis and critical evaluation. The source materials comprise key literature in the philosophy of science, epistemology, and philosophical anthropology, focusing on both the core tenets of scientism and its historical critiques. The research proceeds in three stages: (1) conceptual demarcation between scientific inquiry and scientism; (2) construction of a typology to categorize the levels (naturalism, exclusivism, imperialism) and pillars (reductionism, Darwinism, naturalized normativity) of scientism; and (3) deductive logical critique to analyze the ontological, ethical, and anthropological implications of these frameworks. As a theoretical study, the methodology relies on conceptual analysis rather than empirical quantification. 2.1. What Is Scientism? Scientism must be carefully distinguished from a love of science or a respect for scientific methodology. One can admire the achievements of science and regard the scientific method as a powerful and reliable tool without thereby committing to scientism. Scientism is a far stronger and more radical claim: it is the assertion that the methods and findings of natural science constitute the only legitimate path to truth, and that any question that cannot be answered through scientific investigation is either meaningless or not genuinely a question at all. In this sense, scientism is simultaneously an epistemological claim (about how we can know) and an ontological claim (about what exists). Epistemologically, it holds that scientific knowledge is the only genuine form of knowledge. Ontologically, it holds that the only things that truly exist are those that science can in principle detect, measure, and explain. Everything else — God, the soul, moral values, aesthetic experience, metaphysical entities — is either an illusion, a social construct, or a mere subjective feeling with no claim to objective reality. 3. Discussion & Result 3.1. The Three Levels of Scientism The article proposes that scientism does not present itself uniformly but manifests across three ascending levels of intensity and scope. Level One: Scientific Naturalism. At its most basic level, scientism takes the form of scientific naturalism — the view that nature, understood as the totality of physical and observable reality, is all that exists. This position excludes from the outset any supernatural, spiritual, or transcendent dimension of reality. There is no God, no soul, no afterlife, no realm of meaning beyond the material. What we call "spiritual" experiences are ultimately reducible to brain states; what we call "moral intuitions" are ultimately reducible to evolutionary pressures. Scientific naturalism does not necessarily make aggressive claims about other disciplines, but it sets the ontological foundation upon which the higher levels of scientism are built. Level Two: Methodological Exclusivism. The second level goes beyond mere ontological claims to make an explicit epistemological assertion: that the methods of the natural sciences are not merely among the valid ways of acquiring knowledge, but the only valid ways. At this level, philosophical reasoning, theological reflection, historical interpretation, literary analysis, and introspective phenomenology are all dismissed as unreliable, subjective, or inherently prone to error. Knowledge, properly speaking, can only be produced through controlled experimentation, quantification, peer-reviewed replication, and empirical verification. Any field that cannot meet these standards — including ethics, aesthetics, and much of philosophy — is demoted from the status of genuine knowledge to mere opinion or cultural tradition. Level Three: Scientific Imperialism. The third and most aggressive level of scientism is what the article calls scientific imperialism — the claim that natural science, and especially its most foundational branches such as physics and chemistry, is the ultimate authority on every question human beings can ask. At this level, not only are competing methods dismissed, but competing authorities are actively subordinated. Philosophers, theologians, humanists, and literary scholars are regarded as engaging in nothing more than elaborate speculation, while the "real" answers to questions about consciousness, morality, meaning, and human nature are to be found in neuroscience, evolutionary biology, and physics. This is the position famously exemplified by Stephen Hawking and Leonard Mlodinow's declaration that "philosophy is dead" — replaced by science as the proper heir to the great questions of human existence. 3.2. The Three Pillars of Scientism Underlying all three levels of scientism are three foundational intellectual commitments that give it philosophical structure and rhetorical force. Pillar One: Reductionism. Reductionism is the methodological and metaphysical principle that complex phenomena can and should be explained entirely in terms of their simpler physical components. The human mind is nothing but neural activity; consciousness is nothing but information processing; love is nothing but oxytocin release; culture is nothing but behavioral adaptation. Reductionism is a powerful heuristic within science, but scientism transforms it into a comprehensive metaphysical doctrine that denies any irreducible properties to higher-level phenomena. Pillar Two: Darwinism. The second pillar is the generalization of Darwinian evolutionary theory beyond biology to encompass all aspects of human life and thought. In scientism's Darwinian framework, not only the physical traits of organisms but also their cognitive capacities, moral intuitions, religious beliefs, and cultural practices are explained as products of natural selection operating on random genetic variation. This universal Darwinism reduces human reason itself to a survival mechanism — a development that, as many philosophers have noted, is ultimately self-undermining, since it gives us no grounds to trust that our beliefs are true rather than merely adaptive. Pillar Three: Naturalization of Normativity. The third pillar is the attempt to naturalize normativity — to explain away the prescriptive force of moral, aesthetic, and rational norms by reducing them to descriptive facts about evolution, neurology, or social behavior. If moral obligations are nothing but evolutionary dispositions, and if rational norms are nothing but cognitive habits selected for their adaptive value, then the very idea of genuine obligation, genuine validity, and genuine meaning collapses. Scientism thus finds itself in a performative contradiction: it claims that we ought to accept science as the highest authority while simultaneously undermining the very concept of "ought." 3.3. The Consequences of Scientism The consequences of accepting scientism as a worldview are, as the article demonstrates, far-reaching and deeply damaging to the intellectual and cultural foundations of human civilization. The first casualty is metaphysics and theology. Once scientific naturalism is accepted as the default ontology, the existence of God, the reality of the soul, and the significance of religious experience are all dismissed without serious philosophical engagement. The second casualty is philosophy itself. As scientific imperialism advances, philosophy is reduced to either a handmaiden of science (useful only insofar as it clarifies scientific concepts) or an obsolete tradition destined to be replaced. The third casualty is the humanities. Literature, history, art, sociology, and psychology — insofar as they resist quantification and experimental verification — are increasingly marginalized, underfunded, and regarded as intellectually second-rate. The ultimate casualty, however, is humanity itself — the very concept of the human being as a free, rational, morally responsible agent whose life has irreducible meaning and dignity. Scientism's trajectory leads inevitably to a vision of the human being as nothing more than a biological machine, a temporary configuration of matter operating under the laws of physics, with no genuine freedom, no genuine purpose, and no genuine worth beyond what evolutionary history has accidentally produced. 4. Conclusion The article concludes that scientism represents one of the most serious intellectual dangers of our time — not because it values science too highly, but because it misunderstands the nature of science and illegitimately extends its authority into domains where it has no competence. Scientism is not simply an epistemological error; it is a philosophical and cultural trajectory that begins with the denial of God and ends with the negation of humanity, threatening the philosophical, moral, and anthropological foundations upon which civilization depends. A clear and principled distinction must be drawn between legitimate scientific inquiry — which deserves our deepest respect — and the ideological pretensions of scientism, which deserve our most rigorous critical scrutiny.

Abstract Paul Joseph Salomon Benacerraf, a professor of the philosophy of mathematics at Princeton University, passed away in 2025 at the age of 93. By raising a number of fundamental questions, he transformed the philosophy of mathematics from a primarily logical enterprise into a profound epistemological and metaphysical inquiry. His papers are widely regarded as among the most influential works in twentieth-century philosophy of mathematics and played a pivotal role in the emergence and development of mathematical structuralism. This paper examines the two articles that brought him international recognition and briefly reviews the reactions they elicited from other philosophers. Benacerraf argued that numbers lack an intrinsic identity independent of the mathematical structures in which they occur; what matters is the “role” they occupy within a numerical structure. He also formulated a challenge that later became known as the “Benacerraf Problem”: how is it possible for human beings to have knowledge of abstract mathematical objects. Keywords: numbers; the Benacerraf problem; mathematical epistemology; structuralism. Extended Abstract Introduction Paul Benacerraf is widely recognized as one of the most influential philosophers of mathematics in the twentieth century. His work fundamentally reshaped philosophical debates by shifting attention from the logical foundations of mathematics to deeper epistemological and metaphysical questions. Rather than asking merely what mathematical objects are, Benacerraf challenged philosophers to explain how human beings can possess knowledge of abstract mathematical entities. The present study examines Benacerraf's philosophical contributions through an analytical review of his two landmark papers, “What Numbers Could Not Be (1965)” and “Mathematical Truth (1973)”, which transformed contemporary philosophy of mathematics. The article also reviews the intellectual responses of later philosophers and evaluates Benacerraf's lasting influence on mathematical structuralism. Special attention is devoted to his rejection of the intrinsic identity of numbers and to the epistemological challenge that has become known as the “Benacerraf Problem”. By exploring these ideas, the study demonstrates how Benacerraf redirected philosophical inquiry from the ontology of numbers toward the nature and possibility of mathematical knowledge. Materials and Methods This research adopts a qualitative and analytical approach based on documentary and philosophical analysis. The primary sources consist of Benacerraf's seminal papers published in 1965 and 1973, supplemented by his edited volume with Hilary Putnam and major secondary literature written by contemporary philosophers of mathematics, including Stewart Shapiro, Michael Resnik, Hartry Field, Penelope Maddy, Charles Parsons, Mark Colyvan, Øystein Linnebo, Gideon Rosen, and others. The study systematically examines Benacerraf's principal arguments concerning the ontology of numbers, structuralism, and mathematical epistemology. It also compares his views with major philosophical schools, including Platonism, Logicism, Formalism, Intuitionism, Nominalism, and Structuralism. Furthermore, the historical development of his ideas and their influence on subsequent philosophical debates are investigated through a critical review of published scholarly literature and citation records. The research method is therefore interpretative rather than empirical, aiming to reconstruct Benacerraf's arguments, assess their logical coherence, and evaluate their philosophical significance within the broader development of modern philosophy of mathematics. Discussion and Results The analysis demonstrates that Benacerraf introduced two revolutionary arguments that continue to shape contemporary philosophy of mathematics. In his “1965 paper”, he criticized the traditional identification of numbers with particular sets. By comparing the set-theoretical constructions proposed by John von Neumann and Ernst Zermelo, he argued that equally valid mathematical systems assign different set-theoretic identities to the same numbers. Since no principled reason exists for preferring one representation over another, numbers cannot possess unique intrinsic identities. Their significance lies solely in the structural positions they occupy within mathematical systems. This conclusion provided one of the strongest philosophical motivations for mathematical structuralism, according to which mathematical objects are defined relationally rather than independently. The second major contribution emerged in his “1973 paper”, where Benacerraf shifted philosophical attention from ontology to epistemology. He argued that an adequate philosophy of mathematics must satisfy both semantic and epistemological requirements. While Platonism successfully explains the objectivity and necessity of mathematical truths, it fails to explain how finite human beings can acquire knowledge of causally inert abstract objects. Conversely, nominalist or formalist approaches often provide more plausible accounts of mathematical knowledge but struggle to explain the apparent objectivity and truth of mathematics. This tension became known as the Benacerraf Problem and remains one of the central unresolved issues in contemporary philosophy. The study further shows that Benacerraf's work generated extensive philosophical debate. Structuralists such as Stewart Shapiro expanded his relational conception of mathematical objects by providing more elaborate theories of mathematical structures. Nominalists, including Hartry Field, attempted to eliminate commitment to abstract mathematical entities altogether, while naturalists such as Penelope Maddy questioned Benacerraf's assumption that mathematical knowledge requires causal interaction. Other philosophers, including Charles Parsons, Mark Colyvan, Gideon Rosen, and Øystein Linnebo, proposed alternative responses addressing either the ontological or epistemological dimensions of the problem. Despite these diverse approaches, none has achieved universal acceptance, confirming the enduring philosophical significance of Benacerraf's challenge. The article also emphasizes Benacerraf's broader intellectual legacy. Although he published relatively few papers, his writings have received thousands of scholarly citations and fundamentally redirected research in philosophy of mathematics, influencing debates in metaphysics, epistemology, philosophy of language, and philosophy of science. His ideas contributed significantly to the emergence of modern mathematical structuralism and continue to serve as reference points in contemporary discussions concerning mathematical realism and abstract objects. Conclusion This study concludes that Paul Benacerraf transformed the philosophy of mathematics by demonstrating that questions concerning the identity of mathematical objects cannot be separated from questions concerning mathematical knowledge. His rejection of the intrinsic identity of numbers provided a powerful philosophical foundation for structuralism, while his epistemological challenge exposed significant weaknesses in both realist and anti-realist accounts of mathematics. The Benacerraf Problem continues to resist definitive resolution because no existing philosophical theory simultaneously offers a fully satisfactory account of both the truth and the knowability of mathematics. Consequently, Benacerraf's work remains central to contemporary philosophical inquiry and continues to inspire new approaches to ontology, epistemology, and the foundations of mathematics. His contributions represent a lasting shift from viewing mathematics merely as a formal logical discipline toward understanding it as a profound philosophical investigation into the nature of abstract knowledge and human cognition.