Conventional accounts of epistemic opacity, particularly those that stem from the definitive work of Paul Humphreys, typically point to limitations on the part of epistemic agents to account for the distinct ways in which systems, such as computational methods and devices, are opaque. They point, for example, to the lack of technical skill on the part of an agent, the failure to meet standards of best practice, or even the nature of an agent as reasons why epistemically relevant elements of (...) a process may be inaccessible. In this paper I argue that there are certain instances of epistemic opacity— particularly in computational methods such a computer simulations and machine learning processes—that do not arise from, are not responsive to, and are therefore not explained by the epistemic limitations of an agent. I call these instances agent-neutral and agent-independent instances of epistemic opacity respectively. As a result, I also argue that conventional accounts of epistemic opacity offer a limited understanding of the full spectrum of kinds and sources of epistemic opacity, particularly of the kind found in computational methods. In particular, as I will show below, the limitations of these accounts are reflected in the way they fail to provide satisfactory explanations when faced with certain instances of opacity. (shrink)

Each of our theories of mental representation provides some insight into how the mind works. However, these insights often seem incompatible, as the debates between symbolic, dynamical, emergentist, sub-symbolic, and grounded approaches to cognition attest. Mental representations—whatever they are—must share many features with each of our theories of representation, and yet there are few hypotheses about how a synthesis could be possible. Here, I develop a theory of the underpinnings of symbolic cognition that shows how sub-symbolic dynamics may give rise (...) to higher-level cognitive representations of structures, systems of knowledge, and algorithmic processes. This theory implements a version of conceptual role semantics by positing an internal universal representation language in which learners may create mental models to capture dynamics they observe in the world. The theory formalizes one account of how truly novel conceptual content may arise, allowing us to explain how even elementary logical and computational operations may be learned from a more primitive basis. I provide an implementation that learns to represent a variety of structures, including logic, number, kinship trees, regular languages, context-free languages, domains of theories like magnetism, dominance hierarchies, list structures, quantification, and computational primitives like repetition, reversal, and recursion. This account is based on simple discrete dynamical processes that could be implemented in a variety of different physical or biological systems. In particular, I describe how the required dynamics can be directly implemented in a connectionist framework. The resulting theory provides an “assembly language” for cognition, where high-level theories of symbolic computation can be implemented in simple dynamics that themselves could be encoded in biologically plausible systems. (shrink)

While scientific inquiry crucially relies on the extraction of patterns from data, we still have a far from perfect understanding of the metaphysics of patterns—and, in particular, of what makes a pattern real. In this paper we derive a criterion of real-patternhood from the notion of conditional Kolmogorov complexity. The resulting account belongs to the philosophical tradition, initiated by Dennett :27–51, 1991), that links real-patternhood to data compressibility, but is simpler and formally more perspicuous than other proposals previously defended in (...) the literature. It also successfully enforces a non-redundancy principle, suggested by Ladyman and Ross, that aims to exclude from real-patternhood those patterns that can be ignored without loss of information about the target dataset, and which their own account fails to enforce. (shrink)

The concept of time is examined using the second law of thermodynamics that was recently formulated as an equation of motion. According to the statistical notion of increasing entropy, flows of energy diminish differences between energy densities that form space. The flow of energy is identified with the flow of time. The non-Euclidean energy landscape, i.e. the curved space–time, is in evolution when energy is flowing down along gradients and levelling the density differences. The flows along the steepest descents, i.e. (...) geodesics are obtained from the principle of least action for mechanics, electrodynamics and quantum mechanics. The arrow of time, associated with the expansion of the Universe, identifies with grand dispersal of energy when high-energy densities transform by various mechanisms to lower densities in energy and eventually to ever-diluting electromagnetic radiation. Likewise, time in a quantum system takes an increment forwards in the detection-associated dissipative transformation when the stationary-state system begins to evolve pictured as the wave function collapse. The energy dispersal is understood to underlie causality so that an energy gradient is a cause and the resulting energy flow is an effect. The account on causality by the concepts of physics does not imply determinism; on the contrary, evolution of space–time as a causal chain of events is non-deterministic. (shrink)

I argue for patternism, a new answer to the question of when some objects compose a whole. None of the standard principles of composition comfortably capture our natural judgments, such as that my cat exists and my table exists, but there is nothing wholly composed of them. Patternism holds, very roughly, that some things compose a whole whenever together they form a “real pattern”. Plausibly we are inclined to acknowledge the existence of my cat and my table but not of (...) their fusion, because the first two have a kind of internal organizational coherence that their putative fusion lacks. Kolmogorov complexity theory supplies the needed rigorous sense of “internal organizational coherence”. (shrink)

We develop arithmetical measure theory along the lines of Lutz [10]. This yields the same notion of measure 0 set as considered before by Martin-Löf, Schnorr, and others. We prove that the class of sets constructible by r.e.-constructors, a direct analogue of the classes Lutz devised his resource bounded measures for in [10], is not equal to RE, the class of r.e. sets, and we locate this class exactly in terms of the common recursion-theoretic reducibilities below K. We note that (...) the class of sets that bounded truth-table reduce to K has r.e.-measure 0, and show that this cannot be improved to truth-table. For Δ2-measure the borderline between measure zero and measure nonzero lies between weak truth-table reducibility and Turing reducibility to K. It follows that there exists a Martin-Löf random set that is tt-reducible to K, and that no such set is btt-reducible to K. In fact, by a result of Kautz, a much more general result holds. (shrink)

Complex systems research is becoming ever more important in both the natural and social sciences. It is commonly implied that there is such a thing as a complex system, different examples of which are studied across many disciplines. However, there is no concise definition of a complex system, let alone a definition on which all scientists agree. We review various attempts to characterize a complex system, and consider a core set of features that are widely associated with complex systems in (...) the literature and by those in the field. We argue that some of these features are neither necessary nor sufficient for complexity, and that some of them are too vague or confused to be of any analytical use. In order to bring mathematical rigour to the issue we then review some standard measures of complexity from the scientific literature, and offer a taxonomy for them, before arguing that the one that best captures the qualitative notion of the order produced by complex systems is that of the Statistical Complexity. Finally, we offer our own list of necessary conditions as a characterization of complexity. These conditions are qualitative and may not be jointly sufficient for complexity. We close with some suggestions for future work. (shrink)

Two decades ago, Rolf Landauer (1991) argued that “information is physical” and ought to have a role in the scientific analysis of reality comparable to that of matter, energy, space and time. This would also help to bridge the gap between biology and mathematics and physics. Although it can be argued that we are living in the ‘golden age’ of biology, both because of the great challenges posed by medicine and the environment and the significant advances that have been made—especially (...) in genetics and molecular and cell biology—we feel that information as an essential aspect of life has been neglected, or at least misunderstood. We therefore summon Maxwell’s Demon and its distant relative the ratchet, and apply these to biology. (shrink)

The abstract basis of modern computation is the formal description of a finite state machine, the Universal Turing Machine, based on manipulation of integers and logic symbols. In this contribution to the discourse on the computer-brain analogy, we discuss the extent to which analog computing, as performed by the mammalian brain, is like and unlike the digital computing of Universal Turing Machines. We begin with ordinary reality being a permanent dialog between continuous and discontinuous worlds. So it is with computing, (...) which can be analog or digital, and is often mixed. The theory behind computers is essentially digital, but efficient simulations of phenomena can be performed by analog devices; indeed, any physical calculation requires implementation in the physical world and is therefore analog to some extent, despite being based on abstract logic and arithmetic. The mammalian brain, comprised of neuronal networks, functions as an analog device and has given rise to artificial neural networks that are implemented as digital algorithms but function as analog models would. Analog constructs compute with the implementation of a variety of feedback and feedforward loops. In contrast, digital algorithms allow the implementation of recursive processes that enable them to generate unparalleled emergent properties. We briefly illustrate how the cortical organization of neurons can integrate signals and make predictions analogically. While we conclude that brains are not digital computers, we speculate on the recent implementation of human writing in the brain as a possible digital path that slowly evolves the brain into a genuine (slow) Turing machine. (shrink)

Complexity is a relatively new field of study that is still heavily influenced by philosophy. However, with the advent of modern computing, it has become easier to conduct thorough investigations of complex systems using computational simulations. Despite significant progress, there remain certain characteristics of complex systems that are difficult to comprehend. To better understand these features, information can be applied using simple models of complex systems. The concepts of Shannon's information theory, Kolgomorov complexity, and logical depth are helpful in this (...) regard. Computational mathematical models based on recursive processes, recursive functions, and information in functions offer new insights into complexity by exploring its connection with irreversibility and the loss of information. Through these models, it is possible to see complexity as a transformation of input information through algorithmic computation, with time, logical depth, and information loss serving as key factors in understanding the process. (shrink)

The development of synthetic biology calls for accurate understanding of the critical functions that allow construction and operation of a living cell. Besides coding for ubiquitous structures, minimal genomes encode a wealth of functions that dissipate energy in an unanticipated way. Analysis of these functions shows that they are meant to manage information under conditions when discrimination of substrates in a noisy background is preferred over a simple recognition process. We show here that many of these functions, including transporters and (...) the ribosome construction machinery, behave as would behave a material implementation of the informationmanaging agent theorized by Maxwell almost 150 years ago and commonly known as Maxwell’s demon (MxD). A core gene set encoding these functions belongs to the minimal genome required to allow the construction of an autonomous cell. These MxDs allow the cell to perform computations in an energy-efficient way that is vastly better than our contemporary computers. (shrink)

To construct a synthetic cell we need to understand the rules that permit life. A central idea in modern biology is that in addition to the four entities making reality, matter, energy, space and time, a fifth one, information, plays a central role. As a consequence of this central importance of the management of information, the bacterial cell is organised as a Turing machine, where the machine, with its compartments defining an inside and an outside and its metabolism, reads and (...) expresses the genetic program carried by the genome. This highly abstract organisation is implemented using concrete objects and dynamics, and this is at the cost of repeated incompatibilities (frustration), which need to be sorted out by appropriate «patches». After describing the organisation of the genome into the paleome (sustaining and propagating life) and the cenome (permitting life in context), we describe some chemical hurdles that the cell as to cope with, ending with the specific case of the methionine salvage pathway. (shrink)

This paper investigates the seeming incompatibility of reductionism and non-reductionism in the context of complexity sciences. I review algorithmic information theory for this purpose. I offer two physical metaphors to form a better understanding of algorithmic complexity, and I briefly discuss its advantages, shortcomings and applications. Then, I revisit the non-reductionist approaches in philosophy of mind which are often arguments from ignorance to counter physicalism. A new approach called mild non-reductionism is proposed which reconciliates the necessities of acknowledging irreducibility found (...) in complex systems, and maintaining physicalism. (shrink)

Ordinal polytheism is motivated by the cosmological and design arguments. It is also motivated by Leibnizian–Lewisian modal realism. Just as there are many universes, so there are many gods. Gods are necessary concrete grounds of universes. The god-universe relation is one-to-one. Ordinal polytheism argues for a hierarchy of ranks of ever more perfect gods, one rank for every ordinal number. Since there are no maximally perfect gods, ordinal polytheism avoids many of the familiar problems of monotheism. It links theology with (...) counterpart theory, mathematics and computer science. And it entails that the system of universes has an attractive axiological structure. (shrink)

In this article, I will discuss possible differences between ecosystems and organisms on the basis of their intrinsic complexity. As the concept of complexity still remains highly debated, I propose here a practical and original way to measure the complexity of an ecosystem or an organism. For this purpose, I suggest using the concept of logical depth (LD) in a specific manner, in order to take into account the difficulty as well as the time needed to generate the studied object. (...) I illustrate this method with fully controlled Daisyworld simulations (i.e., simulations based on the Gaia hypothesis) that have often been proposed to mimic living systems. The method consists of the following sequential stages: (1) identification of the shortest program able to numerically model the studied system (also called the Kolmogorov–Solomonoff complexity); (2) running the program, once if there are no stochastic components in the system, several times if stochastic components are there; and (3) computing the time needed to generate the system with LD complexity. This measure is supposed to estimate the system complexity. It appears in this study that LD estimations fit well with the intuition we have of complex systems, with higher complexities being found for more realistic Daisyworlds. With such a method of capturing the time needed to build a system, we expect to detect in future studies any quantified differences between complex ecosystems and organisms. (shrink)

One general principle in the construction of classification schemes is that of grouping phenomena to be classified according to their shared origin in evolution or history (phylogenesis). In general schemes, this idea has been applied by several classificationists in identifying a series of integrative levels, each originated from the previous ones, and using them as the main classes. In special schemes, common origin is a key principle in many domains: examples are given from the classification of climates, of organisms, and (...) of musical instruments. Experience from these domains, however, suggests that using common origin alone, as done in cladistic taxonomy, can produce weird results, like having birds as a subclass of reptiles; while the most satisfying classifications use a well balanced mix of common origin and similarity. It is discussed how this could be applied to the development of a general classification of phenomena in an emergentist perspective, and how the resulting classification tree could be structured. Charles Bennett's notion of logical depth appears to be a promising conceptual tool for this purpose. (shrink)

University of Copenhagen University of Copenhagen University of Copenhagen Blegdamsvej 17 Njalsgade 80 Njalsgade 80 DK-2100 Copenhagen Ø DK 2300 Copenhagen S DK-2300 Copenhagen S Denmark.

ABSTRACT: How can we think about a universal ethics that could be adopted by any intelligent being, including the rising population of cyborgs, intelligent machines, intelligent algorithms or even potential extraterrestrial life? We generally give value to complex structures, to objects resulting from a long work, to systems with many elements and with many links finely adjusted. These include living beings, books, works of art or scientific theories. Intuitively, we want to keep, multiply, and share such structures, as well as (...) prevent their destruction. Such objects have value not because more information would simply mean more value. Instead, they have value because they require a long computational history, numerous interactions for their construction that we can assimilate to a computation, and they display what we call organized complexity. To propose the foundations of a universal ethics based on the intrinsic value of organized complexity, we first discuss conceptions of complexity, and argue that Charles Bennett’s logical depth is certainly a first approximation of what we are looking for. We then put forward three fundamental imperatives: to preserve, augment and recursively promote organized complexity. We show a broad range of applications with human, non-human and non-living examples. Finally, we discuss some specific issues of our framework such as the distribution of complexity, of managing copies and erasures, and how our universal ethics tackles classical ethical issues. In sum, we propose a clear, homogenous and consistent ethical foundation that can integrate many universal ethics desiderata. (shrink)

Although algorithmic information theory provides a measure of the information content of string of characters, problems of noise and noncomputability emerge. However, if pattern in a noisy string is recognized by reference to a set of similar strings, this article shows that a compressed algorithmic description of a noisy string is possible and illustrates this with some simple examples. The article also shows that algorithmic information theory can quantify the information in complex organized systems where pattern is nested within pattern.

We extend recent information-theoretic phase transition approaches to evolutionary and cognitive process via the Rate Distortion and Joint Asymptotic Equipartition Theorems, in the circumstance of interaction with a highly structured environment. This suggests that learning plateaus in cognitive systems and punctuated equilibria in evolutionary process are formally analogous, even though evolution is not cognitive. Extending arguments by Adami et al. (2000), we argue that 'adaptation' is the process by which a distorted genetic image of a coherently structured environment is imposed (...) upon a species. (shrink)

The Church-Turing thesis makes a bold claim about the theoretical limits to computation. It is based upon independent analyses of the general notion of an effective procedure proposed by Alan Turing and Alonzo Church in the 1930''s. As originally construed, the thesis applied only to the number theoretic functions; it amounted to the claim that there were no number theoretic functions which couldn''t be computed by a Turing machine but could be computed by means of some other kind of effective (...) procedure. Since that time, however, other interpretations of the thesis have appeared in the literature. In this paper I identify three domains of application which have been claimed for the thesis: (1) the number theoretic functions; (2) all functions; (3) mental and/or physical phenomena. Subsequently, I provide an analysis of our intuitive concept of a procedure which, unlike Turing''s, is based upon ordinary, everyday procedures such as recipes, directions and methods; I call them mundane procedures. I argue that mundane procedures can be said to be effective in the same sense in which Turing machine procedures can be said to be effective. I also argue that mundane procedures differ from Turing machine procedures in a fundamental way, viz., the former, but not the latter, generate causal processes. I apply my analysis to all three of the above mentioned interpretations of the Church-Turing thesis, arguing that the thesis is (i) clearly false under interpretation (3), (ii) false in at least some possible worlds (perhaps even in the actual world) under interpretation (2), and (iii) very much open to question under interpretation (1). (shrink)

Ranging from Alan Turing’s seminal 1936 paper to the latest work on Kolmogorov complexity and linear logic, this comprehensive new work clarifies the relationship between computability on the one hand and constructivity on the other. The authors argue that even though constructivists have largely shed Brouwer’s solipsistic attitude to logic, there remain points of disagreement to this day. Focusing on the growing pains computability experienced as it was forced to address the demands of rapidly expanding applications, the content maps the (...) developments following Turing’s ground-breaking linkage of computation and the machine, the resulting birth of complexity theory, the innovations of Kolmogorov complexity and resolving the dissonances between proof theoretical semantics and canonical proof feasibility. Finally, it explores one of the most fundamental questions concerning the interface between constructivity and computability: whether the theory of recursive functions is needed for a rigorous development of constructive mathematics. This volume contributes to the unity of science by overcoming disunities rather than offering an overarching framework. It posits that computability’s adoption of a classical, ontological point of view kept these imperatives separated. In studying the relationship between the two, it is a vital step forward in overcoming the disagreements and misunderstandings which stand in the way of a unifying view of logic. (shrink)

This work responds to a criticism of effective complexity made by James McAllister, according to which such a notion is not an appropriate measure for information content. Roughly, effective complexity is focused on the regularities of the data rather than on the whole data, as opposed to algorithmic complexity. McAllister’s argument shows that, because the set of relevant regularities for a given object is not unique, one cannot assign unique values of effective complexity to considered expressions and, therefore, that algorithmic (...) complexity better serves as a measure of information than effective complexity. We accept that problem regarding uniqueness as McAllister presents it, but would not deny that if contexts could be defined appropriately, one could in principle find unique values of effective complexity. Considering this, effective complexity is informative not only regarding the entity being investigated but also regarding the context of investigation itself. Furthermore, we argue that effective complexity is an interesting epistemological concept that may be applied to better understand crucial issues related to context dependence such as theory choice and emergence. These applications are not available merely on the basis of algorithmic complexity. (shrink)

ABSTRACTLadyman and Ross’s account of the metaphysical component of ontic structural realism was associated with a unificationist view of the connection between fundamental physics and special sciences. The aim of the present article is to assess the sense of unification that is at issue in Ladyman and Ross’s definition of metaphysics. Given the ontic core of Ladyman and Ross’s version of structural realism, it should be assumed that the unifying endeavour is worthwhile only if the connective links that underpin unification (...) are metaphysically significant. Ladyman and Ross employed information-theoretic notions, e.g. ‘projectibility’, to account for the significance of real patterns, which underpin unification. I build upon McAllister’s engagements with the same topic, to argue that these notions fail to accomplish this objective. (shrink)

A short review of complexity research from the perspective of history and philosophy of biology is presented. Complexity and its emergence has scientific and metaphysical meanings. From its beginning, biology was a science of complex systems, but with the advent of electronic computing and the possibility of simulating mathematical models of complicated systems, new intuitions of complexity emerged, together with attempts to devise quantitative measures of complexity. But can we quantify the complex?

In this article, I study Richard’s paradox, and I consider several of its solutions. I then restate the paradox using Kolmogorov’s theory of complexity. Taking as a starting point Chaitin’s demonstration that Kolmogorov’s understanding of «complexity» is only relative, I put forth a new solution to the paradox.

A sketchy subquantum theory deeply influenced by Wheeler’s ideas (Am. J. Phys. 51:398–404, 1983) and by the de Broglie-Bohm interpretation (Goldstein in Stanford Encyclopedia of Philosophy, 2006) of quantum mechanics is further analyzed. In this theory a fundamental system is defined as a dual entity formed by bare matter and a methodological probabilistic classical Turing machine. The evolution of the system would be determined by three Darwinian informational regulating principles. Some progress in the derivation of the postulates of quantum mechanics (...) from these regulating principles is reported. The entanglement in a bipartite system is preliminarily considered. (shrink)

This article addresses the question of the mechanisms of the emergence of structure and meaning in the biological and physical sciences. It proceeds from an examination of the concept of intentionality and proposes a model of intentional behavior on the basis of results of computer simulations of structural and functional self-organization. Current attempts to endow intuitive aspects of meaningful complexity with operational content are analyzed and the metaphor of DNA as a computer program (the `genetic program') is critically examined in (...) relation to an alternative metaphor of DNA as data. It is argued that relatively simple networks of boolean automata can classify and recognize patterns of binary strings on the basis of non-programmed, self-generated criteria, but lack a capacity for self-observation and interpretation. To overcome this problem it is necessary to clarify the relationships between the goals and underlying mechanisms of a process and between a system and its environment. It will be shown that memory devices that record the histories of interactions are essential for models of conscious and unconscious intentional behavior and that the possibility of infinitely sophisticated - and therefore unprogrammable - machines cannot be avoided. It will be argued that the notion of infinite sophistication allows the ideas of self-organization and physical determinism to be reconciled. These models will be used to suggest how the voluntary aspect of decision-making in general can emerge out of functional self-organizing processes. The conclusion will introduce the notion of `underdetermination' of theories, which imposes an intrinsic limitation on models of complex natural systems - a limitation that, at the same time, may be precisely what makes possible mutual understanding and intersubjectivity. (shrink)