Modern semiotics is a branch of logics that formally defines symbol-based communication. In recent years, the semiotic classification of signs has been invoked to support the notion that symbols are uniquely human. Here we show that alarm-calls such as those used by African vervet monkeys (Cercopithecus aethiops), logically satisfy the semiotic definition of symbol. We also show that the acquisition of vocal symbols in vervet monkeys can be successfully simulated by a computer program based on minimal semiotic (...) and neurobiological constraints. The simulations indicate that learning depends on the tutor-predator ratio, and that apprentice-generated auditory mistakes in vocal symbol interpretation have little effect on the learning rates of apprentices (up to 80% of mistakes are tolerated). In contrast, just 10% of apprentice-generated visual mistakes in predator identification will prevent any vocal symbol to be correctly associated with a predator call in a stable manner. Tutor unreliability was also deleterious to vocal symbol learning: a mere 5% of “lying” tutors were able to completely disrupt symbol learning, invariably leading to the acquisition of incorrect associations by apprentices. Our investigation corroborates the existence of vocal symbols in a non-human species, and indicates that symbolic competence emerges spontaneously from classical associative learning mechanisms when the conditioned stimuli are self-generated, arbitrary and socially efficacious. We propose that more exclusive properties of human language, such as syntax, may derive from the evolution of higher-order domains for neural association, more removed from both the sensory input and the motor output, able to support the gradual complexification of grammatical categories into syntax. (shrink)

This paper proposes an extensionalist analysis of computer simulations (CSs). It puts the emphasis not on languages nor on models, but on symbols, on their extensions, and on their various ways of referring. It shows that chains of reference of symbols in CSs are multiple and of different kinds. As they are distinct and diverse, these chains enable different kinds of remoteness of reference and different kinds of validation for CSs. Although some methodological papers have already underlined the role (...) of these various relationships of reference in CSs and of cross-validations, this diversity is still overlooked in the epistemological literature on CSs. As a consequence, a particular outcome of this analytical view is an ability to classify existing epistemological theses on CSs according to what their authors choose to select and put at the forefront: either the extensions of symbols, or the symbol-types, or the symbol-tokens, or the internal denotational hierarchies of the CS or the reference of these hierarchies to external denotational hierarchies. Through the adoption of this extensionalist view, it also becomes possible to explain more precisely the reasons why some complete reduction of CSs to classical epistemic paradigms such as “experiment” or “theoretical argument” remains doubtful. On this last point, in particular, this paper is in agreement with what many epistemologists already have acknowledged. (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)

Today, artificial intelligence, especially machine learning, is structurally dependent on human participation. Technologies such as Deep Learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical (...) critique, I differentiate five types of “media technologies of capture” in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term “cybernetic AI”. This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control and discrimination of users. (shrink)

The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial, natural sciences, and philosophy. The question is, what at this stage of the development the inspiration from nature, specifically its computational models (...) such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach humanlevel intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature. (shrink)

A recent evolution of computer simulations has led to the emergence of complex computer simulations. In particular, the need to formalize composite objects (those objects that are composed of other objects) has led to what the author suggests calling pluriformalizations, i.e. formalizations that are based on distinct sub-models which are expressed in a variety of heterogeneous symbolic languages. With the help of four case-studies, he shows that such pluriformalizations enable to formalize distinctly but simultaneously either different aspects or (...) different parts or different scales of the same object or system. From an epistemological standpoint, he suggests that this kind of computer-aided complex formalization of composite objects renew the traditional relations between computer simulations, theoretical models and operational models. (shrink)

In computer science, Artificial Intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Deep Learning is a new field of research. One of the branches of Artificial Intelligence Science deals with the creation of theories (...) and algorithms that allow the machine to learn by simulating neurons in the human body. Most in-depth learning research focuses on finding high-level methods. The strippers analyze a large data set using linear and nonlinear transformations. The method of deep learning is used in the detection of several diseases including blood cell diseases and their classification using the radiography of blood cells to help decision makers to know the type of blood cell and its associated diseases and the results will be presented in detail and discussed. This thesis is using python language and deep learning to detect blood cell diseases and their classifications. The proposed deep learning model was trained, validated and the tested. The accuracy of proposed model was 98.00%. (shrink)

Abstract This chapter is interested in the epistemology of algorithms. As I intend to approach the topic, this is an issue about epistemic justification. Current approaches to justification emphasize the transparency of algorithms, which entails elucidating their internal mechanisms –such as functions and variables– and demonstrating how (or that) these produce outputs. Thus, the mode of justification through transparency is contingent on what can be shown about the algorithm and, in this sense, is internal to the algorithm. In contrast, I (...) advocate for an externalist epistemology of algorithms that I term computational reliabilism (CR). While I have previously introduced and examined CR in the field of computer simulations ([42, 53, 4]), this chapter extends this reliabilist epistemology to encompass a broader spectrum of algorithms utilized in various scientific disciplines, with a particular emphasis on machine learning applications. At its core, CR posits that an algorithm’s output is justified if it is produced by a reliable algorithm. A reliable algorithm is one that has been specified, coded, used, and maintained utilizing reliability indicators. These reliability indicators stem from formal methods, algorithmic metrics, expert competencies, cultures of research, and other scientific endeavors. The primary aim of this chapter is to delineate the foundations of CR, explicate its operational mechanisms, and outline its potential as an externalist epistemology of algorithms. (shrink)

This paper defends the naïve thesis that the method of experiment has per se an epistemic superiority over the method of computer simulation, a view that has been rejected by some philosophers writing about simulation, and whose grounds have been hard to pin down by its defenders. I further argue that this superiority does not come from the experiment’s object being materially similar to the target in the world that the investigator is trying to learn about, as (...) both sides of dispute over the epistemic superiority thesis have assumed. The superiority depends on features of the question and on a property of natural kinds that has been mistaken for material similarity. Seeing this requires holding other things equal in the comparison of the two methods, thereby exposing that, under the conditions that will be specified, the simulation is necessarily epistemically one step behind the corresponding experiment. Practical constraints like feasibility and morality mean that scientists do not often face an other-things- equal comparison when they choose between experiment and simulation. Nevertheless, I argue, awareness of this superiority and of the general distinction between experiment and simulation is important for maintaining motivation to seek answers to new questions. (shrink)

Critical in the computationalist account of the mind is the phenomenon called computational or computer simulation of human thinking, which is used to establish the theses that human thinking is a computational process and that computing machines are thinking systems. Accordingly, if human thinking can be simulated computationally then human thinking is a computational process; and if human thinking is a computational process then its computational simulation is itself a thinking process. This paper shows that the said (...) phenomenon—the computational simulation of human thinking—is ill-conceived, and that, as a consequence, the theses that it intends to establish are problematic. It is argued that what is simulated computationally is not human thinking as such but merely its behavioral manifestations; and that a computational simulation of these behavioral manifestations does not necessarily establish that human thinking is computational, as it is logically possible for a non-computational system to exhibit behaviors that lend themselves to a computational simulation. (shrink)

Now that complex Agent-Based Models and computer simulations spread over economics and social sciences - as in most sciences of complex systems -, epistemological puzzles (re)emerge. We introduce new epistemological tools so as to show to what precise extent each author is right when he focuses on some empirical, instrumental or conceptual significance of his model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity, (...) section 2 gives conceptual tools to explain the rationale of the diverse epistemological positions presented in section 1. Finally, we claim that a careful attention to the real multiplicity of denotational powers of symbols at stake and then to the implicit routes of references operated by models and computer simulations is necessary to determine, in each case, the proper epistemic status and credibility of a given model and/or simulation. (shrink)

In this paper we shed new light on the Argument from Disagreement by putting it to test in a computer simulation. According to this argument widespread and persistent disagreement on ethical issues indicates that our moral opinions are not influenced by any moral facts, either because no such facts exist or because they are epistemically inaccessible or inefficacious for some other reason. Our simulation shows that if our moral opinions were influenced at least a little bit by (...) moral facts, we would quickly have reached consensus, even if our moral opinions were affected by factors such as false authorities, external political shifts, and random processes. Therefore, since no such consensus has been reached, the simulation gives us increased reason to take seriously the Argument from Disagreement. Our conclusion is however not conclusive; the simulation also indicates what assumptions one has to make in order to reject the Argument from Disagreement. The simulation algorithm we use builds on the work of Hegselmann and Krause (J Artif Soc Social Simul 5(3); 2002, J Artif Soc Social Simul 9(3), 2006). (shrink)

In this paper, we bring together research on complex problem solving with that on motivational psychology about goal setting. Complex problems require motivational effort because of their inherent difficulties. Goal Setting Theory has shown with simple tasks that high, specific performance goals lead to better performance outcome than do-your-best goals. However, in complex tasks, learning goals have proven more effective than performance goals. Based on the Zurich Resource Model, so-called motto-goals should activate a person’s resources through positive affect. It (...) was found that motto-goals are effective with unpleasant duties. Therefore, we tested the hypothesis that motto-goals outperform learning and performance goals in the case of complex problems. A total of N = 123 subject participated in the experiment. In dependence of their goal condition, subjects developed a personal motto, learning, or performance goal. This goal was adapted for the computer-simulated complex scenario Tailorshop, where subjects worked as managers in a small fictional company. Other than expected, there was no main effect of goal condition for the management performance. An unexpected gender effect revealed better performance for men than women, pointing to a potential stereotype threat. As hypothesized, motto goals led to higher positive and lower negative affect than the other two goal types. Even though positive affect decreased and negative affect increased in all three groups during Tailorshop completion, participants with motto goals reported the lowest rates of negative affect. Exploratory analyses investigated the role of affect in complex problem solving via mediational analyses and the influence of goal type on perceived goal attainment. (shrink)

Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have s...

In an attempt to determine the epistemic status of computer simulation results, philosophers of science have recently explored the similarities and differences between computer simulations and experiments. One question that arises is whether and, if so, when, simulation results constitute novel empirical data. It is often supposed that computer simulation results could never be empirical or novel because simulations never interact with their targets, and cannot go beyond their programming. This paper argues against this (...) position by examining whether, and under what conditions, the features of empiricality and novelty could be displayed by computer simulation data. I show that, to the extent that certain familiar measurement results have these features, so can some computer simulation results. (shrink)

While Thomas Kuhn's theory of scientific revolutions does not specifically deal with validation, the validation of simulations can be related in various ways to Kuhn's theory: 1) Computer simulations are sometimes depicted as located between experiments and theoretical reasoning, thus potentially blurring the line between theory and empirical research. Does this require a new kind of research logic that is different from the classical paradigm which clearly distinguishes between theory and empirical observation? I argue that this is not the (...) case. 2) Another typical feature of computer simulations is their being ``motley'' (Winsberg 2003) with respect to the various premises that enter into simulations. A possible consequence is that in case of failure it can become difficult to tell which of the premises is to blame. Could this issue be understood as fostering Kuhn's mild relativism with respect to theory choice? I argue that there is no need to worry about relativism with respect to computer simulations, in particular. 3) The field of social simulations, in particular, still lacks a common understanding concerning the requirements of empirical validation of simulations. Does this mean that social simulations are still in a pre-scientific state in the sense of Kuhn? My conclusion is that despite ongoing efforts to promote quality standards in this field, lack of proper validation is still a problem of many published simulation studies and that, at least large parts of social simulations must be considered as pre-scientific. (shrink)

A series of representations must be semantics-driven if the members of that series are to combine into a single thought: where semantics is not operative, there is at most a series of disjoint representations that add up to nothing true or false, and therefore do not constitute a thought at all. A consequence is that there is necessarily a gulf between simulating thought, on the one hand, and actually thinking, on the other. A related point is that a popular doctrine (...) - the so-called 'computational theory of mind' (CTM) - is based on a confusion. CTM is the view that thought-processes consist in 'computations', where a computation is defined as a 'form-driven' operation on symbols. The expression 'form-driven operation' is ambiguous, as it may refer either to syntax-driven operations or to morphology-driven operations. Syntax-driven operations presuppose the existence of operations that are driven by semantic and extra-semantic knowledge. So CTM is false if the terms 'computation' and 'form-driven operation' are taken to refer to syntax-driven operations. Thus, if CTM is to work, those expressions must be taken to refer to morphology-driven operations. CTM therefore fails, given that an operation must be semantics-driven if it is to qualify as a thought. CTM therefore fails on each possible disambiguation of the expressions 'formal operation' and 'computation,' and it is therefore false. (shrink)

Simulation models of the Reiterated Prisoner's Dilemma have been popular for studying the evolution of cooperation since more than 30 years now. However, there have been practically no successful instances of empirical application of any of these models. At the same time this lack of empirical testing and confirmation has almost entirely been ignored by the modelers community. In this paper, I examine some of the typical narratives and standard arguments with which these models are justified by their authors (...) despite the lack of empirical validation. I find that most of the narratives and arguments are not at all compelling. None the less they seem to serve an important function in keeping the simulation business running despite its empirical shortcomings. (shrink)

The paper analyses in some depth the distinction by Paul Humphreys between "epistemic opacity" —which I refer to as "weak epistemic opacity" here— and "essential epistemic opacity", and defends the idea that epistemic opacity in general can be made sense as coming in degrees. The idea of degrees of epistemic opacity is then exploited to show, in the context of computer simulations, the tight relation between the concept of epistemic opacity and actual scientific (modelling and simulation) practices. As (...) a consequence, interesting questions arise in connection with the role of agents dealing with epistemically opaque processes such as computer simulations. (shrink)

This book addresses key conceptual issues relating to the modern scientific and engineering use of computer simulations. It analyses a broad set of questions, from the nature of computer simulations to their epistemological power, including the many scientific, social and ethics implications of using computer simulations. The book is written in an easily accessible narrative, one that weaves together philosophical questions and scientific technicalities. It will thus appeal equally to all academic scientists, engineers, and researchers in industry (...) interested in questions related to the general practice of computer simulations. (shrink)

Scientists and engineers seek to understand how real-world systems work and could work better. Any modeling method devised for such purposes must simplify reality. Ideally, however, the modeling method should be flexible as well as logically rigorous; it should permit model simplifications to be appropriately tailored for the specific purpose at hand. Flexibility and logical rigor have been the two key goals motivating the development of Agent-based Computational Economics (ACE), a completely agent-based modeling method characterized by seven specific modeling principles. (...) This perspective provides an overview of ACE, a brief history of its development, and its role within a broader spectrum of experiment-based modeling methods. (shrink)

The paper develops a positive account of the representational capacity of cognitive systems: simple, associationist learning mechanisms and an architecture that supports bootstrapping are sufficient conditions for symbol tool use. In terms of the debates within the philosophy of mind, this paper offers a plausibility account of representation externalism, an alternative to the reductive, computational/representational models of intentionality that still play a leading role in the field. Although the central theme here is representation, methodologically this view complements embodied, (...) enactivist approaches to studying cognition. (shrink)

Simulation models of the Reiterated Prisoner's Dilemma have been popular for studying the evolution of cooperation since more than 30 years now. However, there have been practically no successful instances of empirical application of any of these models. At the same time this lack of empirical testing and confirmation has almost entirely been ignored by the modelers community. In this paper, I examine some of the typical narratives and standard arguments with which these models are justified by their authors (...) despite the lack of empirical validation. I find that most of the narratives and arguments are not at all compelling. None the less they seem to serve an important function in keeping the simulation business running despite its empirical shortcomings. (shrink)

Abstract: In the future, it will be possible to create advance simulations of ancestor in computers. Superintelligent AI could make these simulations very similar to the real past by creating a simulation of all of humanity. Such a simulation would use all available data about the past, including internet archives, DNA samples, advanced nanotech-based archeology, human memories, as well as text, photos and videos. This means that currently living people will be recreated in such a simulation, and (...) in some sense, “resurrected”. Such “resurrectional simulation” could be deliberately created just for this goal: to return to life all people who have ever lived. The main technical problem of such simulation will be uncertainty about the past, which increases exponentially for more remote times. Such problem could be partly addressed by “acausal trade” between different branches of the multiverse, which will create slightly different versions of the simulation using a quantum randomness generator. Such trade will result in resurrection of all possible people (including those who existed in other branches). Ethical problems of such a resurrectional simulation include: a) possible resurrection of some people against their will; b) such simulation may create additional suffering; с) such simulation could be used by hostile AI to return people to life and then torture them. In this work, I explore preliminary ideas about how to address these problems. (shrink)

The credibility of digital computer simulations has always been a problem. Today, through the debate on verification and validation, it has become a key issue. I will review the existing theses on that question. I will show that, due to the role of epistemological beliefs in science, no general agreement can be found on this matter. Hence, the complexity of the construction of sciences must be acknowledged. I illustrate these claims with a recent historical example. Finally I temperate this (...) diversity by insisting on recent trends in environmental sciences and in industrial sciences. (shrink)

A new computational methodology for executing calculations with infinite and infinitesimal quantities is described in this paper. It is based on the principle ‘The part is less than the whole’ introduced by Ancient Greeks and applied to all numbers (finite, infinite, and infinitesimal) and to all sets and processes (finite and infinite). It is shown that it becomes possible to write down finite, infinite, and infinitesimal numbers by a finite number of symbols as particular cases of a unique framework. The (...) new methodology has allowed us to introduce the Infinity Computer working with such numbers (its simulator has already been realized). Examples dealing with divergent series, infinite sets, and limits are given. (shrink)

I discuss here the definition of computer simulations, and more specifically the views of Humphreys, who considers that an object is simulated when a computer provides a solution to a computational model, which in turn represents the object of interest. I argue that Humphreys's concepts are not able to analyse fully successfully a case of contemporary simulation in physics, which is more complex than the examples considered so far in the philosophical literature. I therefore modify Humphreys's definition (...) of simulation. I allow for several successive layers of computational models, and I discuss the relations that exist between these models, the computer, and the object under study. An aim of my proposal is to clarify the distinction between computational models and numerical methods, and to better understand the representational and the computational functions of models in simulations. (shrink)

This paper explores the epistemological challenges in using computer simulations for two distinct goals: explanation via hypothesis-testing and prediction. It argues that each goal requires different strategies for justifying inferences drawn from simulation results due to different practical and conceptual constraints. The paper identifies unique and shared strategies researchers employ to increase confidence in their inferences for each goal. For explanation via hypothesis-testing, researchers need to address the underdetermination, interpretability, and attribution challenges. In prediction, the emphasis is on (...) the model's ability to generalize across multiple domains. Shared strategies researchers employ to increase confidence in inferences are empirical corroboration of theoretical assumptions and adequacy of computational operationalizations, and this paper argues that these are necessary for explanation via hypothesis-testing but not for prediction. This paper emphasizes the need for a nuanced approach to the epistemology of computer simulation, given the diverse applications of computer simulation in scientific research. Understanding these differences is crucial for both researchers and philosophers of science, as it helps develop appropriate methodologies and criteria for assessing the trustworthiness of computer simulation. (shrink)

There is an ongoing debate on whether or to what degree computer simulations can be likened to experiments. Many philosophers are sceptical whether a strict separation between the two categories is possible and deny that the materiality of experiments makes a difference (Morrison 2009, Parker 2009, Winsberg 2010). Some also like to describe computer simulations as a “third way” between experimental and theoretical research (Rohrlich 1990, Axelrod 2003, Kueppers/Lenhard 2005). In this article I defend the view that (...) class='Hi'>computer simulations are not experiments but that they are tools for evaluating the consequences of theories and theoretical assumptions. In order to do so the (alleged) similarities and differences between simulations and experiments are examined. It is found that three fundamental differences between simulations and experiments remain: 1) Only experiments can generate new empirical data. 2) Only Experiments can operate directly on the target system. 3) Experiments alone can be employed for testing fundamental hypotheses. As a consequence, experiments enjoy a distinct epistemic role in science that cannot completely be superseded by computer simulations. This finding in connection with a discussion of border cases such as hybrid methods that combine measurement with simulation shows that computer simulations can clearly be distinguished from empirical methods. It is important to understand that computer simulations are not experiments, because otherwise there is a danger of systematically underestimating the need for empirical validation of simulations. (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 ti...

Quantum computing is of high interest because it promises to perform at least some kinds of computations much faster than classical computers. Arute et al. 2019 (informally, “the Google Quantum Team”) report the results of experiments that purport to demonstrate “quantum supremacy” – the claim that the performance of some quantum computers is better than that of classical computers on some problems. Do these results close the debate over quantum supremacy? We argue that they do not. In the following, we (...) provide an overview of the Google Quantum Team’s experiments, then identify some open questions in the quest to demonstrate quantum supremacy. (shrink)

This paper is devoted to some generalizations of explanatory potential of connectionist approaches to theoretical problems of the philosophy of mind. Are considered both strong, and weaknesses of neural network models. Connectionism has close methodological ties with modern neurosciences and neurophilosophy. And this fact strengthens its positions, in terms of empirical naturalistic approaches. However, at the same time this direction inherits weaknesses of computational approach, and in this case all system of anticomputational critical arguments becomes applicable to the connectionst models (...) of mind. The last developments in the field of deep learning gave rich empirical material for cognitive sciences. Multilayered networks, mathematical models of associative dynamics of learning, self-organizing neuronets and all that allow to explain the principles of human conceptual organizing and after this to emulate these processes in computer systems. At all engineering achievements of this technology there is a traditional criticism from representatives of cognitive psychology who cannot accept a thesis about learning ability of a neuronet on the basis of redistribution of scales. Process of learning of natural intelligence, according to cognitive models, happens due to attraction of knowledge broadcast in a symbolical form (mental representations, concepts) at the expense of the systems of output knowledge expressed in the propositional contents. Some philosophical aspects of «neural metaphor» in modern cognitive sciences create the problem field which demands comprehensive understanding, the first step towards which is taken in this work. (shrink)

In this paper we investigate with a case study from chemistry under what conditions a simulation can serve as a surrogate for an experiment. The case-study concerns a simulation of H2-formation in outer space. We find that in this case the simulation can act as a surrogate for an experiment, because there exists comprehensive theoretical background knowledge in form of quantum mechanics about the range of phenomena to which the investigated process belongs and because any particular modelling (...) assumptions as can be justified. If these requirements are met then direct empirical validation may even be dispensable. We conjecture that this is not the case in the absence of comprehensive theoretical background knowledge. (shrink)

This article aims to develop a new account of scientific explanation for computer simulations. To this end, two questions are answered: what is the explanatory relation for computer simulations? And what kind of epistemic gain should be expected? For several reasons tailored to the benefits and needs of computer simulations, these questions are better answered within the unificationist model of scientific explanation. Unlike previous efforts in the literature, I submit that the explanatory relation is between the (...) class='Hi'>simulation model and the results of the simulation. I also argue that our epistemic gain goes beyond the unificationist account, encompassing a practical dimension as well. (shrink)

Mathematical models are a well established tool in most natural sciences. Although models have been neglected by the philosophy of science for a long time, their epistemological status as a link between theory and reality is now fairly well understood. However, regarding the epistemological status of mathematical models in the social sciences, there still exists a considerable unclarity. In my paper I argue that this results from specific challenges that mathematical models and especially computer simulations face in the social (...) sciences. The most important difference between the social sciences and the natural sciences with respect to modeling is that in the social sciences powerful and well confirmed background theories (like Newtonian mechanics, quantum mechanics or the theory of relativity in physics) do not exist in the social sciences. Therefore, an epistemology of models that is formed on the role model of physics may not be appropriate for the social sciences. I discuss the challenges that modeling faces in the social sciences and point out their epistemological consequences. The most important consequences are that greater emphasis must be placed on empirical validation than on theoretical validation and that the relevance of purely theoretical simulations is strongly limited. (shrink)

The stream of human consciousness appears to be interruptible, in that we can experience a sensation of ‘returning to ourselves after an absence of content’ (e.g. sleep, anaesthesia, full-absorption meditation). Prima facie, such evidence poses a challenge to simple applications of theories of consciousness based on electromagnetic or neural activity in the brain, because some of this activity persists during periods of interruption. This paper elaborates one of several possible responses to the challenge. We build on a previous theory in (...) which EM field topology delineates complex macrophenomenal experience. We first apply an analytic lens to identify different ways in which field topologies might generate sensations of ‘absence of content’. We then test the feasibility of a particular phenomenological approach for assessing those field topologies, powered by the unusual lucidity about cessation pathways induced via certain meditation practices and psychedelic dosages. We build computational simulations of these cessation pathways and test them on eight interviewees with various levels of experience in cessation states. We conclude that this approach can generate usable insight, but requires further formalisation to fulfil its potential. The findings tentatively support our claims but the theory remains speculative at this stage. We set out empirical tests with 11 specific predictions, both for further phenomenological research and for brain manipulation research. Although they align neatly, our ontological and cessation theories can also be separated. The paper describes how our theories can be consistent with other perspectives on consciousness, such as variants of predictive processing or global network theory. (shrink)

Five years ago, on May 24, 2019, the computer program ‘bayesvl’ was officially published on R under the name “bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with ‘Stan’”. This program (or package) was developed by two founders of the SM3D Portal, Vuong Quan Hoang and La Viet Phuong, to improve the productivity of conducting social research. The package was designed with a pedagogical orientation, supporting users in familiarizing themselves with Bayesian statistical methods, MCMC (...) simulation, and visualization of technical diagnoses and results. (shrink)

Since the second half of the XXth century, researchers in cybernetics and AI, neural nets and connectionism, Artificial Life and new robotics have endeavoured to build different machines that could simulate functions of living organisms, such as adaptation and development, problem solving and learning. In this book these research programs are discussed, particularly as regard the epistemological issues of the behaviour modelling. One of the main novelty of this book consists of the fact that certain projects involving the building (...) of simulative machine models before the advent of cybernetics are been investigated for the first time, on the basis of little known, and sometimes completely forgotten or unpublished, texts and figures. These pre-cybernetics projects can be considered as steps toward the “discovery” of a modelling methodology that has been fully developed by those more recent research programs, and that shares some of their central goals and key methodological proposals. -/- More info in Springer link: http://www.springer.com/new+%26+forthcoming+titles+%28default%29/book/978-1-4020-0606-7 -/- This book is the English translation of La scoperta dell'artificiale, Dunod/Masson, Milan, 1998. (shrink)

Although formal thought disorder (FTD) has been for long a clinical label in the assessment of some psychiatric disorders, in particular of schizophrenia, it remains a source of controversy, mostly because it is hard to say what exactly the “formal” in FTD refers to. We see anomalous processing of terminological knowledge, a core construct of human knowledge in general, behind FTD symptoms and we approach this anomaly from a strictly formal perspective. More specifically, we present here a symbolic computational model (...) of storage in, and activation of, a human semantic network, or semantic memory, whose core element is logical form; this is normalized by description logic (DL), namely by CL, a DL-based language – Conception Language – designed to formalize conceptualization from the viewpoint of individual cognitive agency. In this model, disruptions in the rule-based implementation of the logical form account for the apparently semantic anomalies symptomatic of FTD, which are detected by means of a CL-based algorithmic assessment. (shrink)

Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact that the classical problem (...) of opacity in artificial neural networks (ANNs) explodes with the adoption of current Deep Learning techniques [LeCun, Bengio, Hinton, 2015]. In this paper we argue that the explanatory deficit of such techniques represents an important problem, that limits their adoption in the cognitive modelling and computational cognitive science arena. In particular we will show how the current attempts of providing explanations of the deep nets behaviour (see e.g. [Ritter et al. 2017] are not satisfactory. As a possibile way out to this problem, we present two different research strategies. The first strategy aims at dealing with the opacity problem by providing a more abstract interpretation of neural mechanisms and representations. This approach is adopted, for example, by the biologically inspired SPAUN architecture [Eliasmith et al., 2012] and by other proposals suggesting, for example, the interpretation of neural networks in terms of the Conceptual Spaces framework [Gärdenfors 2000, Lieto, Chella and Frixione, 2017]. All such proposals presuppose that the neural level of representation can be considered somehow irrelevant for attacking the problem of explanation [Lieto, Lebiere and Oltramari, 2017]. In our opinion, pursuing this research direction can still preserve the use of deep learning techniques in artificial cognitive models provided that novel and additional results in terms of “transparency” are obtained. The second strategy is somehow at odds with respect to the previous one and tries to address the explanatory issue by avoiding to directly solve the “opacity” problem. In this case, the idea is that one of resorting to pre-compiled plausible explanatory models of the word used in combination with deep-nets (see e.g. [Augello et al. 2017]). We argue that this research agenda, even if does not directly fits the explanatory needs of Computational Cognitive Science, can still be useful to provide results in the area of applied AI aiming at shedding light on the models of interaction between low level and high level tasks (e.g. between perceptual categorization and explanantion) in artificial systems. (shrink)

Counterfactual thinking involves the imagination of non-factual alternatives to reality. We investigated the spontaneous generation of both upward counterfactuals, which improve on reality, and downward counterfactuals, which worsen reality. All subjects gained $5 playing a computer-simulated blackjack game. However, this outcome was framed to be perceived as either a win, a neutral event, or a loss. "Loss" frames produced more upward and fewer downward counterfactuals than did either "win" or "neutral" frames, but the overall prevalence of counterfactual thinking did (...) not vary with outcome valence. In addition, subjects who expected to play the game again made more upward counterfactuals and were less satisfied with the outcome than were subjects who did not expect to play again. However, once subjects saw the cards from which they could have selected had they "hit" again (two winning cards and two losing cards), all subjects generated primarily upward counterfactuals and showed a corresponding decrease in satisfaction. These results implicate both cognitive and motivational factors in the generation of counterfactuals and tell us something about the functional value of counterfactual thinking: downward counterfactuals provide comfort; upward counterfactuals prepare one for the future. (shrink)

This study reflects on Harold Kincaid and Jule Zahle’s view that there is no necessary association between methodological individualism and agent-based models because the analysis of social phenomena in terms of the latter cannot always be regarded as an implementation of the former. Their view remains in contention with the standpoint of several philosophers of science and social scientists, including Chen and Di Iorio. Kincaid and Zahle’s main argument against such a standpoint is that agent-based simulation is compatible with (...) holistic explanations that are at odds with methodological individualism. The following study argues that Kincaid and Zahle’s conclusion remains untenable since it stems from inaccurate historical assumptions concerning the tradition of methodological individualism and the way the individualism-holism debate is understood within this tradition. (shrink)

In modern days, person-computer communication systems have gradually penetrated our lives. One of the crucial technologies in person-computer communication systems, Speech Emotion Recognition (SER) technology, permits machines to correctly recognize emotions and greater understand users' intent and human-computer interlinkage. The main objective of the SER is to improve the human-machine interface. It is also used to observe a person's psychological condition by lie detectors. Automatic Speech Emotion Recognition(SER) is vital in the person-computer interface, but SER has (...) challenges for accurate recognition. In this work to resolve the above problem, automatic Speech enhancement shows that deep learning techniques effectively eliminate background noise. Using Deep leaning models for four states were created: happy, sad, angry, and intoxicated. Recurrent Neural Network (RNN) algorithm used to reduce the possibility of over fitting by randomly omitting neurons in the hidden layers. The proposed RNN method could be implemented in personal assistant systems to give better and more appropriate state-based interactions between humans. In the simulation results shows Improving accuracy, Time complexity, Error rate is also reduced to using the proposed method. -/- . (shrink)

This paper attempts to answer a central question in unsupervised learning: what does it mean to “make sense” of a sensory sequence? In our formalization, making sense involves constructing a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the causal theory – objects, properties, and laws – must be integrated into a coherent whole. On our account, making sense of sensory input is (...) a type of program synthesis, but it is unsupervised program synthesis. Our second contribution is a computer implementation, the Apperception Engine, that was designed to satisfy the above requirements. Our system is able to produce interpretable human-readable causal theories from very small amounts of data, because of the strong inductive bias provided by the unity conditions. A causal theory produced by our system is able to predict future sensor readings, as well as retrodict earlier readings, and impute (fill in the blanks of) missing sensory readings, in any combination. In fact, it is able to do all three tasks simultaneously. We tested the engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction intelligence tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The Apperception Engine performs well in all these domains, significantly out-performing neural net baselines. We note in particular that in the sequence induction intelligence tests, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve intelligence tests, but a general-purpose system that was designed to make sense of any sensory sequence. (shrink)

In this essay, I argue that an immortal existence could be desirable. Taking the accounts of Williams and Smuts under careful consideration, I agree with Fischer that an immortal existence could be gratifying. When Fischer argues that it is unfair for Williams to posit that an immortal life must have self-exhausting pleasures and, overall, a better experience than mortal life, he gets to the crux of the argument for immortality: as long as there are positive categorical desires for the individual, (...) then such life-affirming desires will provide an impetus to carry on. In moving past the Teiresias model of a phenomenon that retains memories while changing characters, I argue that a life of intellectual inquiry – which essentially alters the character of the individual while maintaining memories – offers an outward looking existence which provides internal pleasures. Accordingly, with the use of technology, computer simulations have the potential to provide pleasures and experiences that escape reality. In this sense, technology has the potential to supplement an immortal life. We cannot say whether there will be a pinnacle of such learning and pleasure which leads to decreasing returns, but it seems plausible that an immortal being who incorporates learning and pleasure that could potentially lead to innovation and discovery would seek to continue such intellectual inquiry and varied experiences until all learning potentials were exhausted. (shrink)

This paper concerns “human symbolic output,” or strings of characters produced by humans in our various symbolic systems; e.g., sentences in a natural language, mathematical propositions, and so on. One can form a set that consists of all of the strings of characters that have been produced by at least one human up to any given moment in human history. We argue that at any particular moment in human history, even at moments in the distant future, this set is finite. (...) But then, given fundamental results in recursion theory, the set will also be recursive, recursively enumerable, axiomatizable, and could be the output of a Turing machine. We then argue that it is impossible to produce a string of symbols that humans could possibly produce but no Turing machine could. Moreover, we show that any given string of symbols that we could produce could also be the output of a Turing machine. Our arguments have implications for Hilbert’s sixth problem and the possibility of axiomatizing particular sciences, they undermine at least two distinct arguments against the possibility of Artificial Intelligence, and they entail that expert systems that are the equals of human experts are possible, and so at least one of the goals of Artificial Intelligence can be realized, at least in principle. (shrink)

Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate–argument structure; (iv) logical operators; (v) inferential (...) promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy-of-mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures, including iconic and associative structures as well as deep-neural-network-like architectures. However, as computational/representational approaches to the mind continue to advance, classical compositional symbolic structures – that is, LoTs – only prove more flexible and well-supported over time. (shrink)

Although virtue ethics has repeatedly been proposed as a suitable framework for the development of artificial moral agents, it has been proven difficult to approach from a computational perspective. In this work, we present the first technical implementation of artificial virtuous agents in moral simulations. First, we review previous conceptual and technical work in artificial virtue ethics and describe a functionalistic path to AVAs based on dispositional virtues, bottom-up learning, and top-down eudaimonic reward. We then provide the details of (...) a technical implementation in a moral simulation based on a tragedy of the commons scenario. The experimental results show how the AVAs learn to tackle cooperation problems while exhibiting core features of their theoretical counterpart, including moral character, dispositional virtues, learning from experience, and the pursuit of eudaimonia. Ultimately, we argue that virtue ethics provides a compelling path toward morally excellent machines and that our work provides an important starting point for such endeavors. (shrink)

With the rapidly growing amounts of information, visualization is becoming increasingly important, as it allows users to easily explore and understand large amounts of information. However the field of information visualiza- tion currently lacks sufficient theoretical foundations. This article addresses foundational questions connecting information visualization with computing and philosophy studies. The idea of multiscale information granula- tion is described based on two fundamental concepts: information (structure) and computation (process). A new information processing paradigm of Granular Computing enables stepwise increase of (...) granulation/aggregation of information on different levels of resolution, which makes possible dynamical viewing of data. Information produced by Google Earth is an illustration of visualization based on clustering (granulation) of information on a succession of layers. Depending on level, specific emergent properties become visible as a result of different ways of aggregation of data/information. As information visualization ultimately aims at amplifying cognition, we discuss the process of simulation and emulation in relation to cognition, and in particular visual cognition. (shrink)

The so-called Baldwin Effect generally says how learning, as a form of ontogenetic adaptation, can influence the process of phylogenetic adaptation, or evolution. This idea has also been taken into computation in which evolution and learning are used as computational metaphors, including evolving neural networks. This paper presents a technique called evolving self-taught neural networks – neural networks that can teach themselves without external supervision or reward. The self-taught neural network is intrinsically motivated. Moreover, the self-taught neural network (...) is the product of the interplay between evolution and learning. We simulate a multi-agent system in which neural networks are used to control autonomous agents. These agents have to forage for resources and compete for their own survival. Experimental results show that the interaction between evolution and the ability to teach oneself in self-taught neural networks outperform evolution and self-teaching alone. More specifically, the emergence of an intelligent foraging strategy is also demonstrated through that interaction. Indications for future work on evolving neural networks are also presented. (shrink)