Computation and Physical Systems

Các trào lưu như hiện tượng “túi mù”, hầu hết các xu hướng tiêu dùng dựa trên nền tảng và được điều khiển bởi thuật toán, dù thúc đẩy tăng trưởng kinh tế và tạo việc làm, hoàn toàn thiếu tính bền vững—chúng góp phần làm gia tăng đa khủng hoảng hơn là mang lại lợi ích. Những vấn đề này bao gồm: (i) khủng hoảng giá trị; (ii) khủng hoảng sức khỏe tinh thần; (iii) khủng hoảng hệ sinh thái. (...) Giới trẻ có thể dành hàng giờ để xem các influencer trên TikTok và Instagram phản ứng với những gì họ tìm thấy trong túi mù. Giá trị sử dụng thực sự của những sản phẩm ấy là gì? Còn hàng triệu người đang trong độ tuổi hình thành nhân cách—như trẻ em, thanh thiếu niên và người trẻ—đắm chìm trong thói quen xem, lướt và mua sắm không kiểm soát, cuối cùng rơi vào tình trạng “quiet quitting”, tuyệt vọng, suy giảm nhận thức, trầm cảm, thậm chí ý nghĩ tự tử thì sao? Chúng ta có thể làm gì trước lượng rác thải nhựa khổng lồ và lượng khí nhà kính phát sinh từ sản xuất và tiêu thụ trong các ngành công nghiệp văn hóa này? Trong bài viết này, chúng tôi lập luận rằng sức mạnh của AI có thể được khai thác để cung cấp giải pháp cho những vấn đề đó. Trước hết, chúng ta phải nhìn nhận AI như một tác nhân đảm bảo công bằng trong tiếp cận thông tin. Thứ hai, sự lên ngôi của các “nhà khoa học nhân dân” (citizen scientists) AI và quy trình làm việc theo cơ chế tác nhân AI có thể hạ thấp rào cản để thúc đẩy khoa học công dân vì các mục tiêu sinh thái. Cuối cùng, AI có thể được tận dụng để hình thành và thúc đẩy cam kết cộng đồng về trách nhiệm và đạo đức liên ngành trên ba lĩnh vực: con người, môi trường và công nghệ. (shrink)

Trong kỷ nguyên bùng nổ của trí tuệ nhân tạo (AI), việc hiểu biết đúng và kịp thời về bản chất của AI trở thành yêu cầu cấp thiết đối với cả cá nhân lẫn quốc gia nhằm tận dụng hiệu quả các cơ hội và giải quyết được các thách thức cũng như yêu cầu của thời đại. Trên cơ sở đó, bài viết nghiên cứu khái niệm “vô thức thuật toán” (algorithmic unconscious) như một khái niệm chứa đựng (...) những yếu tố tiềm ẩn trong hệ thống vận hành của AI dưới lăng kính tâm lý học phân tích của Carl Gustav Jung (1875- 1961). Phân tích tập trung vào các cổ mẫu của trường phái Jungian như “Kẻ Tinh Quái” (Trickster) và “Cái Bóng” (Shadow), qua đó làm rõ cách những hình tượng cổ mẫu này được phản ánh trong hoạt động của thuật toán hiện đại. Đồng thời, bài viết cũng phân tích hiện tượng “phóng chiếu” trong mối quan hệ giữa AI và con người thông qua minh họa về việc con người vô thức gán các kỳ vọng hay nỗi sợ hãi của bản thân lên AI. Qua đó, vấn đề “cá nhân hóa giả” (pseudo-individuation), trong đó AI tạo ấn tượng như một chủ thể có nhân cách riêng nhưng thiếu ý thức tự thân và sự thiếu hụt khả năng tự phản tỉnh ở AI được làm rõ. Thông qua những nội dung trên, bài viết đề xuất các định hướng thiết kế giúp cho AI tích hợp yếu tố tự nhận thức và các giá trị nhân bản, nhằm tăng cường tính minh bạch, củng cố niềm tin và phát triển mối quan hệ giữa con người và máy móc vì lợi ích chung của xã hội. (shrink)

Note: this and "Constants as Phase Conditions_ Reframing ε₀, α, and the Mass Defect via Structured Resonance" are part of a twin series to make CODES easier to understand. -/- Abstract Modern scientific and computational systems rely on probabilistic modeling as a default approach to emergence, intelligence, and complexity. Yet, these stochastic paradigms remain epistemologically incomplete and structurally fragile. In this paper, I introduce CODES (Chirality of Dynamic Emergent Systems) as a new coherence-first framework that replaces randomness with deterministic phase (...) behavior. Rather than viewing emergence as the outcome of chance filtered through constraints, CODES models emergence as a structured result of prime-phase resonance, governed by coherence alignment rather than statistical distribution. This framework redefines intelligence, gravity, time, black holes, cognition, and learning as resonance-locked phase phenomena, not probabilistic artifacts. By grounding the behavior of systems in chirality, prime interval structuring, and recursive coherence scoring (PAS), CODES offers an unbroken, falsifiable model of lawful emergence with wide applications in physics, AI, biology, and systems theory. This paper outlines the theoretical foundations, contrasts with the probabilistic paradigm, and proposes empirical pathways to validate the resonance structure underlying reality. (shrink)

The Decentralized Key Protocol (DKP) is a hybrid framework designed to enhance privacy, security, and user control in social networking applications transitioning from centralized to decentralized architectures. DKP combines centralized storage for scalability with blockchain-based access control and integrity verification. Files are stored on centralized servers with random tags, anonymizing user data, while key-tag-hash tuples on the blockchain ensure secure access and file integrity. A permission request smart contract enables secure file sharing, where followers must request access for each file (...) view, with the contract verifying their status and granting one-time access to the tag and hash. This paper presents DKPs architecture, evaluates its benefits and challenges, and demonstrates its suitability for privacy-conscious social platforms. DKP offers a practical balance between decentralization, usability, and performance, addressing critical issues in data privacy and trust. (shrink)

Abstract This paper reinterprets classical biological and geometric phenomena—phyllotaxis and Kelvin’s truncated octahedral tiling—through the CODES framework (Chirality of Dynamic Emergent Systems). We show that irrational constants, Fibonacci series, and space-filling polyhedra are not mathematical accidents, but deterministic outcomes of prime-driven structured resonance. While calculus and probability provided useful approximations during the era of uncertainty, they now give way to coherence-first models. These new models describe reality not through limit-based derivation or stochastic estimation, but through direct alignment between phase-locked systems. (...) Using the Resonance Intelligence Core (RIC), we empirically validate Lord Kelvin’s conjecture on minimal-surface volumetric tiling and demonstrate how this principle governs both plant growth and AI substrate design. The result is a unification of form, function, and intelligence under a single geometric resonance field. (shrink)

Phylogenetic comparative methods (PCMs) are transforming how scientists study the evolution of traits by integrating statistical tools with evolutionary theory.

The art of prediction of stock market volatility has always been a most challenged interdisciplinary research problem among scientist due to its highly non- linear nature of market flow. This paper tries to analysis the historical data of BSE Sensex using extreme volatilities estimators, GARCH, ANN and new proposed Text Mining approach for stock market predictions. Finally experimental results illustrates that the new proposed Text model can able to predict the volatilities of the stock price better than other models.

Abstract: Research on the nature and varieties of the format of cognitive representations in philosophy and cognitive science have been partly shaped by analogies to external, public representations. In this paper, we argue that relying on such analogies contributes to framing the question of cognitive formats in problematic, potentially counterproductive ways. We show that cognitive and public representations differ in many of their central features, making analogies to public representations ill-suited to improving our understanding of cognitive formats. We illustrate these (...) points by examining two case studies in which analogies to public representations may have had a negative impact on research: the 80’s-90’s debate about compositionality and cognitive architecture between symbolicists and connectionists; and contemporary discussions about the nature of visual demonstratives. Finally, we outline an alternative, computational account of formats that does not share the shortcomings of appeal to public representations. (shrink)

I develop the anticipatory argument for the view that it is nomologically possible that some non-biological creatures are phenomenally conscious, including conventional, silicon-based AI systems. This argument rests on the general idea that we should make our beliefs conform to the outcomes of an ideal scientific process and that such an ideal scientific process would attribute consciousness to some possible AI systems. This kind of ideal scientific process is an ideal application of the iterative natural kind (INK) strategy, according to (...) which one should investigate consciousness by treating it as a natural kind which iteratively explains observable patterns and correlations between potentially consciousness-relevant features. The relevant AI systems are psychological duplicates. These are hypothetical non-biological creatures which share the coarse-grained functional organization of humans. I argue that an ideal application of the INK strategy would attribute consciousness to psychological duplicates because this gives rise to a simpler and more unifying explanatory account of biological and non-biological cognition. If my argument is sound, then creatures made from the same material as conventional AI systems can be conscious, thus removing one of the main uncertainties for assessing AI consciousness and suggesting that AI consciousness may be a serious near-term concern. (shrink)

The term “medium independence” has different meanings. One sense maps onto “abstract-as-abstracta” descriptions while the other maps onto “abstract-as-omission” descriptions. Both senses have been deployed when it comes to understanding the nature of physical computation. However, because medium independence is a polysemic term, the sense being used should be clearly stated. If the sense is not clearly stated, then those who wish to engage in debates regarding medium independence and physical computation run the risk of conflating different but related issues (...) which can have consequences when it comes to thinking about physical computation across contexts. (shrink)

The Physical Signature of Computation is the most “robust” mapping view that’s ever hit the market. It is impressive in its detail and the careful attention paid to its characterization of both the physical system and the formal computational description—a true service to the philosophical literature. The book promises a “unified account of artifact and biological computation,” but here’s where things take a turn: after handling artifacts, the Robust Mapping Account fades from view and the Mechanistic Account of Physical Computation (...) (in its neurocognitive form) emerges as the way to capture biological computation. To have a unified account, then, requires two distinct approaches to understanding the nature of physical computation. This raises questions about whether the Robust Mapping Account can be extended to neural computation, but most importantly, questions about the compatibly of these two views is questionable given the history of the Mechanistic Account and its underpinning framework. (shrink)

The Self-Evidencing Agent offers a unique method for addressing difficult philosophical questions. Self-evidencing occurs when an agent uses their model of the world and of themselves to explain what they observe in the world and in themselves, such that those observations become evidence for their model – the more agents explain, the more they self-evidence. This book argues that there is good reason to cast an agent’s existence itself in terms of self-evidencing, and that if we begin from this as (...) a first principle, then we can make better sense of who and what we are, of how we engage in diverse ways with the world, and what matters to us. Self-evidencing can help situate attention, action, and perception. It also furnishes compelling approaches to decision-making and rationality, the self, of preferences and values. It can build bridges to consciousness, and even on to free will, and to meaning and wisdom. (shrink)

This research examines ethical issues in computational linguistics that can be applied to national defense by analyzing philosophical and security language. The increasing use of language contexts, such as intelligence and communication data analysis, raises ethical and philosophical challenges related to privacy, control, and accuracy. This research aims to identify and analyze ethical issues, especially in the use of computational linguistics in defense applications, as well as their implications for the protection of individual rights and privacy. This method involves reviewing (...) ethical technology references and analyzing the philosophical design of language and security. Deontological and utilitarian ethical theories are applied to evaluate the moral impacts and consequences of using the language of technology in a defense context. The results demonstrate that using linguistics in defense can pose privacy risks, misuse of data, and the potential for significant bias in decision-making. This often involves collecting data from multiple sources, creating challenges to ensure data is managed ethically and responsibly. Conclusion: This shows the need for a solid ethical and regulatory framework to ensure technology's fair and responsible use in context defense. Implications of the findings include the need for ongoing dialogue between technology developers, policymakers, and the public to mitigate ethical risks and ensure that application technologies comply with critical and security principles. With a comprehensive and responsible approach, technological linguistics can improve military safety and effectiveness without compromising individual rights and privacy. (shrink)

A proposal for a new model of the evolution of gene regulation networks and development that draws on work from artificial intelligence and philosophy of mind. Each of us is a collection of more than ten trillion cells, busy performing tasks crucial to our continued existence. Gene regulation networks, consisting of a subset of genes called transcription factors, control cellular activity, producing the right gene activities for the many situations that the multiplicity of cells in our bodies face. Genes working (...) together make up a truly ingenious system. In this book, Roger Sansom investigates how gene regulation works and how such a refined but simple system evolved. Sansom describes in detail two frameworks for understanding gene regulation. The first, developed by the theoretical biologist Stuart Kauffman, holds that gene regulation networks are fundamentally systems that repeat patterns of gene expression. Sansom finds Kauffman's framework an inadequate explanation for how cells overcome the difficulty of development. Sansom proposes an alternative: the connectionist framework. Drawing on work from artificial intelligence and philosophy of mind, he argues that the key lies in how multiple transcription factors combine to regulate a single gene, acting in a way that is qualitatively consistent. This allows the expression of genes to be finely tuned to the variable microenvironments of cells. Because of the nature of both development and its evolution, we can gain insight into the developmental process when we identify gene regulation networks as the controllers of development. The ingenuity of genes is explained by how gene regulation networks evolve to control development. (shrink)

Recent years have seen a surge of interest in applying mechanistic thinking to computational accounts of implementation and individuation. One recent extension of this work involves so-called ‘wide’ approaches to computation, the view that computational processes spread out beyond the boundaries of the individual. These ‘mechanistic accounts of wide computation’ maintain that computational processes are wide in virtue of being part of mechanisms that extend beyond the boundary of the individual. This paper aims to further develop the mechanistic account of (...) wide computationalism by responding to two outstanding worries, what are called the parsimony and testability challenges. The first is based on considerations about wide computation’s ontological cost; the second the view’s experimental testability. The argument is that wide mechanistic computationalism can gain the necessary conceptual resources to address the challenges by embracing two further aspects of the mechanistic approach: i) the structural aspect of mechanistic explanation and ii) the notion of constitutive relevance. (shrink)

Humankind is currently confronted with a critical challenge that determines its very existence, not only on an individual, racial, or national level but as a whole species: the fight against climate change and environmental degradation. To win this battle, humanity needs innovations and non-linear thinking. Nature has long been a substantial information source for unthinkable discoveries that save human lives. This Commentary suggests that by understanding the nature, emergence, and mechanism of serendipity, the survival skill of humans, humanity can capitalize (...) on it to learn from nature and generate nature-based innovations that can address the climate and environmental degradation crises. Building an eco-surplus culture that incorporates core values pertaining to nature and sees environmental sustainability as the conscience of the times is essential to direct and leverage the power of serendipity in climate and sustainability science. In the era of artificial intelligence (AI) proliferation, AI will be a vital tool that provides navigational and useful information that aids the encounters and attainments of serendipity. However, given the tremendous power of AI, especially when combined with humans’ serendipity capability, its development and use must be approached with caution and ethical considerations to avoid adverse effects on environmental sustainability. Investment in other scientific areas beyond environmental research is also crucial, as it expands humanity’s knowledge base – an essential foundation for serendipitous discoveries through inter- and transdisciplinary interactions. (shrink)

Abstract Introduction Logically valid deductive arguments are clear examples of abstract recursive computational proce‐ dures on propositions or on probabilities. However, it is not known if the cortical time‐consuming inferential pro‐ cesses in which logical arguments are eventually realized in the brain are in fact physically different from other kinds of inferential processes. Methods In order to determine whether an electrical EEG discernible pattern of logical deduction exists or not, a new experimental paradigm is proposed contrasting logically valid and invalid (...) inferences with exactly the same content (same premises and same relational variables) and distinct logical complexity (propositional truth‐functional operators). Electroencephalographic signals from 19 subjects (24.2 ± 3.3 years) were acquired in a two‐condition para‐ digm (100 trials for each condition). After the initial general analysis, a trial‐by‐trial approach in beta‐2 band allowed to uncover not only evoked but also phase asynchronous activity between trials. Results showed that (i) deductive inferences with the same content evoked the same response pattern in logically valid and invalid conditions, (ii) mean response time in logically valid inferences is 61.54% higher, (iii) logically valid inferences are subjected to an early (400 ms) and a late reprocessing (600 ms) verified by two distinct beta‐2 activa‐ tions (p‐value < 0,01, Wilcoxon signed rank test). Conclusion We found evidence of a subtle but measurable electrical trait of logical validity. Results put forward the hypothesis that some logically valid deductions are recursive or computational cortical events. Keywords Beta‐2 band, Evoked potentials, Induced potentials, Deductive inference, Logical validity, Cortical bases of logical reasoning. (shrink)

Complex human reasoning involves minimal abilities to extract conclusions implied in the available information. These abilities are considered “deductive” because they exemplify certain abstract relations among propositions or probabilities called deductive arguments. However, the electrophysiological dynamics which supports such complex cognitive pro- cesses has not been addressed yet. In this work we consider typically deductive logico- probabilistically valid inferences and aim to verify or refute their electrophysiological functional connectivity differences from invalid inferences with the same content (same relational variables, same (...) stimuli, same relevant and salient features). We recorded the brain electrophysiological activity of 20 participants (age 1⁄4 20.35 ± 3.23) by means of an MEG system during two consecutive reasoning tasks: a search task (invalid condition) without any specific deductive rules to follow, and a logically valid deductive task (valid condition) with explicit deductive rules as instructions. We calculated the functional connectivity (FC) for each condition and conducted a seed-based analysis in a set of cortical regions of interest. Finally, we used a cluster-based permutation test to compare the dif- ferences between logically valid and invalid conditions in terms of FC. As a first novel result we found higher FC for valid condition in beta band between regions of interest and left prefrontal, temporal, parietal, and cingulate structures. FC analysis allows a second novel result which is the definition of a propositional network with operculo-cingular, parietal and medial nodes, specifically including disputed medial deductive “core” areas. The experiment discloses measurable cortical processes which do not depend on content but on truth-functional propositional operators. These experimental novelties may contribute to understand the cortical bases of deductive processes. (shrink)

In chapter eleven of "On The Foundation of Computing," Primiero takes on the implementation debate in computer science. He contrasts his theory with two other views—the Semantic and the specification—artifact. In this paper, I argue that there is a way to fine-tune the implementation concept further. Firstly, contrary to Primiero, I claim it is problematic to separate the implementation relationship from the conditions which make it correct. Secondly, by taking a pluralistic approach to implementation, I claim it is a mistake (...) to try and provide a general theory of implementation for the entire production cycle of computational systems. (shrink)

One of the possible hypotheses about time is to consider any instant of time as fuzzy number, so that two instants of time could be overlapped. Historically, some Mathematicians and Philosophers have had similar ideas like Brouwer and Husserl [5]. Throughout this article, the impact of this change on Theory of Computation and Complexity Theory are studied. In order to rebuild Theory of Computation in a more successful and productive approach to solve some major problems in Complexity Theory, the present (...) research is done. This novel theory is called here, the fuzzy time theory of computation, TC^*. (shrink)

Ken Forbus's Qualitative Process Theory (QPT) is a popular theory for reasoning about the physical aspects of the daily world. Qualitative Process Theory Using Linguistic Variables by Bruce D'Ambrosio (Springer-Verlag, New York, 1989) is an attempt to fill some gaps in QPT.

Bedau's influential (1997) account analyzes weak emergence in terms of the non-derivability of a system’s macrostates from its microstates except by simulation. I offer an improved version of Bedau’s account of weak emergence in light of insights from information theory. Non-derivability alone does not guarantee that a system’s macrostates are weakly emergent. Rather, it is non-derivability plus the algorithmic compressibility of the system’s macrostates that makes them weakly emergent. I argue that the resulting information-theoretic picture provides a metaphysical account of (...) weak emergence rather than a merely epistemic one. (shrink)

The framework of Solomonoff prediction assigns prior probability to hypotheses inversely proportional to their Kolmogorov complexity. There are two well-known problems. First, the Solomonoff prior is relative to a choice of Universal Turing machine. Second, the Solomonoff prior is not computable. However, there are responses to both problems. Different Solomonoff priors converge with more and more data. Further, there are computable approximations to the Solomonoff prior. I argue that there is a tension between these two responses. This is because computable (...) approximations to Solomonoff prediction do not always converge. (shrink)

How did information emerge into the early minds of our very first humans? Who supplied our primitive ancestors with information? Where did it originate? Where did it come from? Was the source of information a who or a what? Was it god, space aliens, or something else? How was information stored and retrieved by an aneural organism? -/- These basic questions were the keys that opened the conceptualization of a new school of thought known as Originemology, a pioneering discipline that (...) examines generally the roots or origins of everything based on the natural emergence of instructions comprehensively known as Inscription by Design (ID). -/- In this paper, a new foundation for learning, one that involves the examination of the structural embedded inherent inscriptions in objects, is explored based on the “A Brain without The Brain” paradigm proposed by Lawsin in 1988 and published in his book Autognorics. The focus of the study is addressed into two parts: (1) Codexation Dilemma and (2) Intuitive Aneural Network. After completion, the following conclusions were subsequently reached, namely: (a) Nature is the mother of information, (b) Information can only be acquired in two ways: by choice or by chance, (c) Information can be stored and retrieved on both aneural and neural objects, (d) Everything is an intuitive object with embedded inherent instruction/inscription, and (e) Man cannot think of something without associating his thoughts with something material or physical (the holy grail of consciousness). -/- Notably, Lawsin also uncovered from his novel works that Intelligence is produced though a network of logics and Behaviors rise through sequential inscriptions. (shrink)

For computer simulation models to usefully inform climate risk management, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many times over, and because computing resources are finite, uncertainty assessment is more feasible using models that demand less computer processor time. Such models are generally simpler in the sense of being more idealized, or less realistic. So modelers face a trade-off between realism and uncertainty quantification. Seeing this trade-off for the important epistemic issue (...) that it is requires a shift in perspective from the established simplicity literature in philosophy of science. (shrink)

This paper takes up a recent challenge to mechanistic approaches to computational implementation, the view that computational implementation is best explicated within a mechanistic framework. The challenge, what has been labelled “the abstraction problem”, claims that one of MAC’s central pillars – medium independence – is deeply confused when applied to the question of computational implementation. The concern is that while it makes sense to say that computational processes are abstract (i.e. medium-independent), it makes considerably less sense to say that (...) they are also concrete processes of a mechanism. After outlining the problem and its effect on MAC, I examine a recent response from Kuokkanen and Rusanen [2018. “Making Too Many Enemies: Hutto and Myin’s Attack on Computationalism.” Philosophical Explorations 21 (2): 282–294. doi:10.1080/13869795.2018.1477980]. I argue that Kuokkanen and Rusanen’s response comes up short insofar as it makes problematic trade-offs among various desiderata we have for a theory of implementation. This leads to a general dilemma for MAC: either give up being an objective theory of implementation or concede the abstraction problem and so reintroduce triviality concerns. In response, I argue that conceiving of computations as abstracta rather than illata provides a way to avoid the proposed dilemma and articulate a notion of medium independence that addresses the abstraction problem. (shrink)

The subject matter of this thesis can be summarized by a triplet of questions and answers. Showing what these questions and answers mean is, in essence, the goal of my project. The triplet goes like this: Q: How can we make progress in our understanding of social norms and norm compliance? A: Adopting a neurocomputational framework is one effective way to make progress in our understanding of social norms and norm compliance. Q: What could the neurocomputational mechanism of social norm (...) compliance be? A: The mechanism of norm compliance probably consists of Bayesian - Reinforcement Learning algorithms implemented by activity in certain neural populations. Q: What could information about this mechanism tell us about social norms and social norm compliance? A: Information about this mechanism tells us that: a1: Social norms are uncertainty-minimizing devices. a2: Social norm compliance is one trick that agents employ to interact coadaptively and smoothly in their social environment. Most of the existing treatments of norms and norm compliance consist in what Cristina Bicchieri refers to as “rational reconstructions.” A rational reconstruction of the concept of social norm “specifies in which sense one may say that norms are rational, or compliance with a norm is rational”. What sets my project apart from these types of treatments is that it aims, first and foremost, at providing a description of some core aspects of the mechanism of norm compliance. The single most original idea put forth in my project is to bring an alternative explanatory framework to bear on social norm compliance. This is the framework of computational cognitive neuroscience. The chapters of this thesis describe some ways in which central issues concerning social norms can be fruitfully addressed within a neurocomputational framework. In order to qualify and articulate the triplet above, my strategy consists firstly in laying down the beginnings of a model of the mechanism of norm compliance behaviour, and then zooming in on specific aspects of the model. Such a model, the chapters of this thesis argue, explains apparently important features of the psychology and neuroscience of norm compliance, and helps us to understand the nature of the social norms we live by. (shrink)

It is generally accepted that, in the cognitive and neural sciences, there are both computational and mechanistic explanations. We ask how computational explanations can integrate into the mechanistic hierarchy. The problem stems from the fact that implementation and mechanistic relations have different forms. The implementation relation, from the states of an abstract computational system to the physical, implementing states is a homomorphism mapping relation. The mechanistic relation, however, is that of part/whole; the explaining features in a mechanistic explanation are the (...) components of the explanandum phenomenon and their causal organization. Moreover, each component in one level of mechanism is constituted and explained by components of an underlying level of mechanism. Hence, it seems, computational variables and functions cannot be mechanistically explained by the medium-dependent states and properties that implement them. How then, do the computational and the implementational integrate to create the mechanistic hierarchy? After explicating the general problem, we further demonstrate it through a concrete example, of reinforcement learning, in the cognitive and neural sciences. We then examine two possible solutions. On one solution, the mechanistic hierarchy embeds at the same levels computational and implementational properties. This picture fits with the view that computational explanations are mechanistic sketches. On the other solution, there are two separate hierarchies, one computational and another implementational, which are related by the implementation relation. This picture fits with the view that computational explanations are functional and autonomous explanations. It is less clear how these solutions fit with the view that computational explanations are full-fledged mechanistic explanations. Finally, we argue that both pictures are consistent with the reinforcement learning example, but that scientific practice does not align with the view that computational models are merely mechanistic sketches. (shrink)

Que peut bien etre l'informatique pour nous envahir a ce point? Se fondant sur des travaux recents de philosophie de l'informatique, ce livre revient sur la notion de Machine de Turing et sur la These de Church: l'ordinateur peut-il tout simuler? (le vivant, l'esprit). Eclairant les notions de computation et d'abstraction a la lumiere de celles de simulation et d'ontologie, il montre en quoi l'informatique n'est ni simplement une branche des mathematiques, ni une technologie de l'information, mais une technologie des (...) croisements des voies de la reference. (shrink)

On the Origin of Objects is, at heart, an extended search for a non-circular and nonreductive characterization of two key notions: intentionality and computation. Only a non-circular and non-reductive account of these key notions can, Smith believes, provide a secure platform for a proper understanding of the mind. The project has both a negative and a positive aspect. Negatively, Smith rejects views that attempt to identify the key notions with lower-level physical properties, arguing instead for a more abstract and systemic (...) understanding. This negative effort occupies Part I of the book. In Part II, we encounter the positive side of Smith's proposal: an attempt to develop a non-reductive analysis of computation and meaning able to meet the requirements laid out in Part I. One purpose of this critical review is to lay out this project in fairly simple terms. This is necessary since Smith's own treatment and prose sometimes obscures the flow of the argument. I suggest that, properly understood, Smith's proposal bears a clear affinity to ideas emerging from the dynamical systems movement within Cognitive Science, and that this tie-in can help put flesh on several of the more metaphorical characterizations in the book. My main criticism is that the book ultimately fails to provide an account able to meet Smith's own requirements for a truly non-reductive account of intentionality. This is especially the case regarding Smith's commitment to licensing a partition of the world based on no a priori assumptions whatsoever. The exercise is a valuable one, however, since it forces us to look harder at some foundational assumptions and at least hints at a new and refreshing perspective: one in which the key explanatory relations are grounded in facts about human practice and pragmatically established social norms. (shrink)

Social learning in the strict sense is learning by observation or instruction. Learning by observation appears to be an analogue process while learning by instruction is digital. In evolutionary biology this distinction is currently thought to have implications for the extent to which mechanisms can function successfully as an inheritance system in an evolutionary process.

In recent years it has been convincingly argued that the Church-Turing thesis concerns the bounds of human computability: The thesis was presented and justified as formally delineating the class of functions that can be computed by a human carrying out an algorithm. Thus the Thesis needs to be distinguished from the so-called Physical Church-Turing thesis, according to which all physically computable functions are Turing computable. The latter is often claimed to be false, or, if true, contingently so. On all accounts, (...) though, thesis M is not easy to give counterexamples to, but it is never asked why—how come that a thesis that transfers a notion from the strictly human domain to the general physical domain just happens to be so difficult to falsify. In this paper I articulate this question and consider several tentative answers to it. (shrink)

We will be dealing with “sequential circuits” in the sense of E. F. Moore and G. H. Mealy. Each such circuit is assumed to have a finite number of input wires and a finite number of output wires. Each element of such a circuit will be assumed to be an and-circuit, an or-circuit, a not-circuit, or a delay circuit, for some specified temporal delay. Each element has one output wire which, however, may branch in order to serve several purposes simultaneously. (...) The and-circuits and or-circuits have two input wires each, while the not-circuits and the delay circuits have one input wire each. (shrink)

The classical view of computing positions computation as a closed-box transformation of inputs (rational numbers or finite strings) to outputs. According to the interactive view of computing, computation is an ongoing interactive process rather than a function-based transformation of an input to an output. Specifically, communication with the outside world happens during the computation, not before or after it. This approach radically changes our understanding of what is computation and how it is modeled. The acceptance of interaction as a new (...) paradigm is hindered by the Strong Church–Turing Thesis (SCT), the widespread belief that Turing Machines (TMs) capture all computation, so models of computation more expressive than TMs are impossible. In this paper, we show that SCT reinterprets the original Church–Turing Thesis (CTT) in a way that Turing never intended; its commonly assumed equivalence to the original is a myth. We identify and analyze the historical reasons for the widespread belief in SCT. Only by accepting that it is false can we begin to adopt interaction as an alternative paradigm of computation. We present Persistent Turing Machines (PTMs), that extend TMs to capture sequential interaction. PTMs allow us to formulate the Sequential Interaction Thesis, going beyond the expressiveness of TMs and of the CTT. The paradigm shift to interaction provides an alternative understanding of the nature of computing that better reflects the services provided by today’s computing technology. (shrink)

Infinite time Turing machines extend the operation of ordinary Turing machines into transfinite ordinal time. By doing so, they provide a natural model of infinitary computability, a theoretical setting for the analysis of the power and limitations of supertask algorithms.

I argue here for a number of ways that modern computational science requires a change in the way we represent the relationship between theory and applications. It requires a switch away from logical reconstruction of theories in order to take surface mathematical syntax seriously. In addition, syntactically different versions of the same theory have important differences for applications, and this shows that the semantic account of theories is inappropriate for some purposes. I also argue against formalist approaches in the philosophy (...) of science and for a greater role for perceptual knowledge rather than propositional knowledge in scientific empiricism. (shrink)

I discuss here a number of different kinds of diachronic emergence, noting that they differ in important ways from synchronic conceptions. I argue that Bedau’s weak emergence has an essentially historical aspect, in that there can be two indistinguishable states, one of which is weakly emergent, the other of which is not. As a consequence, weak emergence is about tokens, not types, of states. I conclude by examining the question of whether the concept of weak emergence is too weak and (...) note that there is at present no unifying account of diachronic and synchronic concepts of emergence. (shrink)

We sketch the historical and conceptual context of Turing's analysis of algorithmic or mechanical computation. We then discuss two responses to that analysis, by Gödel and by Gandy, both of which raise, though in very different ways. The possibility of computation procedures that cannot be reduced to the basic procedures into which Turing decomposed computation. Along the way, we touch on some of Cleland's views.

Examining the implications of a localist model for linguistic performance, I show the strengths of the P-graph, a network of elementary units of meaning where utterance results from relaxation through the operation of a dynamics of affect values. A unit of meaning is stored in a synaptic connection that brings together two words. Such a model, consistent with the anatomy and physiology of the neural tissue, eschews a number of traditional pitfalls of “semantic networks”: (1) ambiguity ceases to be an (...) issue, as similar uses of a word are automatically clustered together; (2) faster retrieval of words acquired early is explained by the larger number of their instances. In addition the P-graph takes advantage of a plausible form of information storage: the local topology of the neural tissue. (shrink)

As an undergraduate I was taught to multiply two numbers with the help of log tables, using the formulaHaving graduated to teach calculus to Engineers, I learned that log tables were to be replaced by slide rules. It was then that Imade the fateful decision that there was no need for me to learn how to use this tedious device, as I could always rely on the students to perform the necessary computations. In the course of time, slide rules were (...) replaced by pocket calculators and personal computers, but I stuck to my original decision.My computer phobia did not prevent me from taking an interest in the theoretical question: what is a computation? This question goes back to Hilbert's 10th problem, which asks for an algorithm, or computational procedure, to decide whether any given polynomial equation is solvable in integers. It quickly leads to the related question: which numerical functionsf: ℕn→ ℕ are computable?While Hilbert's 10th problem was only resolved in 1970, this related question had some earlier answers, of which I shall single out the following three:f is recursive,f is computable on an abstract machine,f is definable in the untyped λ-calculus.These tentative answers were shown to be equivalent by Church [1936] and Turing [1936–7].I shall discuss here some of the more recent developments of these notions of computability and their relevance to linguistics and logic. I hope to be forgiven for dwelling on some of the work I have been involved with personally, with greater emphasis than is justified by its historical significance. (shrink)

The dynamics/computation debate recalls a similar debate in the evolutionary biology community concerning the relative primacy of theories of structure versus theories of change. A full account of cognition will require a rapprochement between such theories and will include both computational and dynamical notions. The key to making computation relevant to cognition is not making it analog, but rather understanding how functional information-processing structures can emerge in complex dynamical systems.

It is argued that, given certain reasonable premises, an infinite number of qualitatively identical but numerically distinct minds exist per functioning brain. The three main premises are (1) mental properties supervene on brain properties; (2) the universe is composed of particles with nonzero extension; and (3) each particle is composed of continuum-many point-sized bits of particle-stuff, and these points of particle-stuff persist through time.

We first discuss some technical questions which arise in connection with the construction of undecidable propositions in analysis, in particular in connection with the notion of the normal form of a function representing a predicate. Then it is stressed that while a function f(x) may be computable in the sense of recursive function theory, it may nevertheless have undecidable properties in the realm of Fourier analysis. This has an implication for a conjecture of Penrose's which states that classical physics is (...) computable. (shrink)

We describe a possible physical device that computes a function that cannot be computed by a Turing machine. The device is physical in the sense that it is compatible with General Relativity. We discuss some objections, focusing on those which deny that the device is either a computer or computes a function that is not Turing computable. Finally, we argue that the existence of the device does not refute the Church–Turing thesis, but nevertheless may be a counterexample to Gandy's thesis.

I use modal logic and transfinite set-theory to define metaphysical foundations for a general theory of computation. A possible universe is a certain kind of situation; a situation is a set of facts. An algorithm is a certain kind of inductively defined property. A machine is a series of situations that instantiates an algorithm in a certain way. There are finite as well as transfinite algorithms and machines of any degree of complexity (e.g., Turing and super-Turing machines and more). There (...) are physically and metaphysically possible machines. There is an iterative hierarchy of logically possible machines in the iterative hierarchy of sets. Some algorithms are such that machines that instantiate them are minds. So there is an iterative hierarchy of finitely and transfinitely complex minds. (shrink)

This essay discusses a number of questions which arise from attempts to reduce the mental to the physical or the mental and the physical to the computational. It makes, in an organized way, several basic distinctions between different kinds of accounts of the mind. It reconstructs and elaborates many discussions between Gödel and the author on the nature of the human mind, with special emphasis on its mathematical capabilities.

The Church-Turing Thesis (CTT) is often paraphrased as ``every computable function is computable by means of a Turing machine.'' The author has constructed a family of equational theories that are not Turing-decidable, that is, given one of the theories, no Turing machine can recognize whether an arbitrary equation is in the theory or not. But the theory is called pseudorecursive because it has the additional property that when attention is limited to equations with a bounded number of variables, one obtains, (...) for each number of variables, a fragment of the theory that is indeed Turing-decidable. In a 1982 conversation, Alfred Tarski announced that he believed the theory to be decidable, despite this contradicting CTT. The article gives the background for this proclamation, considers alternate interpretations, and sets the stage for further research. (shrink)