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)

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)

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)

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)

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)

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)

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)

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)

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)

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 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)

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)

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)

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)

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 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)

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)

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)

This paper is intended as a critical examination of the question of when and under what conditions the use of computer simulations is beneficial to scientific explanations. This objective is pursued in two steps: First, I try to establish clear criteria that simulations must meet in order to be explanatory. Basically, a simulation has explanatory power only if it includes all causally relevant factors of a given empirical configuration and if the simulation delivers stable results within the (...) measurement inaccuracies of the input parameters. In the second step, I examine a few examples of Axelrod-style simulations as they have been used to understand the evolution of cooperation (Axelrod, Schüßler). These simulations do not meet the criteria for explanatory validity and it can be shown, as I believe, that they lead us astray from the scientific problems they have been addressed to solve. (shrink)

This study investigated the effect of3D Interactive Virtual Laboratory computer-simulated experiments on male and female senior secondary school students’ achievement and retention in Chemistry Subject. The study adopted a quasi-experimental design in which intact classes were used. It was guided by two research questions and two null hypotheses. The population comprised 234 SS II Chemistry students from Anambra East Local Government Area of Anambra State, Nigeria. The sample consisted of 71 SS II Chemistry students drawn from four co-educational schools (...) using a simple random sampling technique (balloting without replacement). Data were collected using a Chemistry Achievement Test (CAT) titled 3D Interactive Virtual Laboratory Achievement Test (3D IVLAT). The instrument was validated by two experts in Chemistry Education and one expert in Measurement and Evaluation. Its reliability was established using Spearman Rank Order, yielding a coefficient of 0.81. The data collected were analyzed using mean, standard deviation, and t-test. Mean and standard deviation were employed to answer the research questions, while t-test was used to test the hypotheses at a 0.05 level of significance. The findings revealed that female students taught Chemistry with the 3D Interactive Virtual Laboratory had higher mean achievement and retention scores than male students taught with the 3D Interactive Virtual Laboratory. Furthermore, there was no significant difference in the mean achievement and retention scores of male and female students taught Chemistry with the 3D Interactive Virtual Laboratory. Based on these findings, appropriate recommendations and conclusions swere drawn. (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)

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)

I give a reason we are almost certainly *not* in a simulation. I also consider what happens if two simulators simulate each other.

The simulation hypothesis has recently excited renewed interest, especially in the physics and philosophy communities. However, the hypothesis specifically concerns {computers} that simulate physical universes, which means that to properly investigate it we need to couple computer science theory with physics. Here I do this by exploiting the physical Church-Turing thesis. This allows me to introduce a preliminary investigation of some of the computer science theoretic aspects of the simulation hypothesis. In particular, building on Kleene's second recursion theorem, (...) I prove that it is mathematically possible for us to be in a simulation that is being run on a computer \textit{by us}. In such a case, there would be two identical instances of us; the question of which of those is ``really us'' is meaningless. I also show how Rice's theorem provides some interesting impossibility results concerning simulation and self-simulation; briefly describe the philosophical implications of fully homomorphic encryption for (self-)simulation; briefly investigate the graphical structure of universes simulating universes simulating universes, among other issues. I end by describing some of the possible avenues for future research that this preliminary investigation reveals. (shrink)

This thesis presents a comprehensive mathematical framework for determining the probability that we are living in a computer simulation. Building upon Nick Bostrom's foundational simulation argument, we develop a novel integrated approach that combines information-theoretic bounds, quantum mechanical evidence, computational complexity analysis, and Bayesian inference to provide a quantitative assessment of the simulation hypothesis. Our framework introduces several key innovations: (1) a discreteness metric based on quantum measurement precision, (2) computational feasibility analysis across different scales of reality, (3) observer (...) effect quantification as evidence for simulation-like behavior, and (4) a unified Bayesian model that integrates multiple lines of evidence. -/- Through comprehensive analysis of four distinct scenarios (Conservative, Moderate, Optimistic, and Quantum-Focused), we find simulation probabilities ranging from 0.43 to 0.71, with confidence intervals providing statistical rigor to our estimates. Our empirical validation against known physical constants, quantum mechanical phenomena, and computational systems demonstrates strong support for the simulation hypothesis, with an overall validation score of 11.50. The framework reveals that while simulating systems up to solar system scale appears computationally feasible, simulating the entire observable universe exceeds current theoretical limits by 19 orders of magnitude. -/- Key findings include: (1) quantum discreteness at the Planck scale strongly suggests digital rather than continuous physics, (2) the observer effect in quantum mechanics aligns with computational optimization principles, (3) Bell inequality violations can be elegantly explained through simulation constraints, and (4) the fine-tuning of physical constants is consistent with computational parameter selection. This work provides the first rigorous mathematical framework for simulation detection and establishes a foundation for future empirical testing of the simulation hypothesis. (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 to call 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)

Idealist alternatives to physicalism establish consciousness as ontological bedrock, yet they face a critical computational challenge: explaining why isolated minds bind into a rigid, shared objective reality rather than drifting into solipsistic hallucination. In this paper, we present a formal computational instantiation of Evolutionary Dream Idealism (EDI), demonstrating how a universal mind (M) generates a stable physical consensus through networked Self-World Models (SWMs). We construct a multi-agent architecture incorporating dual-genome predictive-generative engines, neural forward transition models, backward inverse inference, and evolvable (...) coupling parameters (κi) initialized at complete solipsism (κi = 0). Lacking cross-validation, isolated agents suffer severe predictive drift due to approximate inference noise. Under Metaphysical Darwinism, selection pressure penalizes solipsistic decay, driving an emergent phase transition around Generation 3–4 into a fully tethered consensus network. Through mutual cross-training, local physics parameters converge toward unified baseline laws (θshared), which in turn forces backward inverse inference to collapse disparate historical backstories into a singular, shared primordial origin (x0shared)—providing a demonstration of Top-Down Cosmology without explicit origin-alignment rewards. We quantify the co-evolution of M’s memory dynamics, showing how pruned solipsistic models populate inactive memory until consensus stabilization eliminates pruning by Generation 4. Furthermore, we simulate the biological transition via the Fracture Operator (⊝), showing that stochastic population expansion exponentially suppresses inference noise through network scaling (N-γ). Finally, we model sequential observer node untethering ("biological death"), demonstrating that while disconnected nodes drift into private solipsism, the centralized physical baseline (WMshared) and historical origin (x0shared) remain conserved for the remaining consensus network. (shrink)

“Residual Memory Theory” proposes that past life memory phenomena may arise not from metaphysical reincarnation, but from residual data artifacts within a simulated reality. If the simulation hypothesis holds true, imperfect memory clearing, template reuse, and environmental data bleed could result in cross-instance memory phenomena — manifesting as apparent past life recall, sudden skills, or unexplained affinities. This framework offers a computationally grounded explanation for experiences traditionally considered supernatural, contributing a novel perspective to philosophical discussions of simulation theory, metaphysics, and (...) consciousness. (shrink)

‘The problem with simulations is that they are doomed to succeed.’ So runs a common criticism of simulations—that they can be used to ‘prove’ anything and are thus of little or no scientific value. While this particular objection represents a minority view, especially among those who work with simulations in a scientific context, it raises a difficult question: what standards should we use to differentiate a simulation that fails from one that succeeds? In this paper we build (...) on a structural analysis of simulation developed in previous work to provide an evaluative account of the variety of ways in which simulations do fail. We expand the structural analysis in terms of the relationship between a simulation and its real-world target emphasizing the important role of aspects intended to correspond and also those specifically intended not to correspond to reality. The result is an outline both of the ways in which simulations can fail and the scientific importance of those various forms of failure. (shrink)

Since the 1990’s, social sciences are living their computational turn. This paper aims to clarify the epistemological meaning of this turn. To do this, we have to discriminate between different epistemic functions of computation among the diverse uses of computers for modeling and simulating in the social sciences. Because of the introduction of a new – and often more user-friendly – way of formalizing and computing, the question of realism of formalisms and of proof value of computational treatments reemerges. Facing (...) the spreading of computational simulations in all disciplines, some enthusiastic observers are claiming that we are entering a new era of unity for social sciences. Finally, the article shows that the conceptual and epistemological distinctions presented in the first sections lead to a more mitigated position: the transdisciplinary computational turn is a great one, but it is of a methodological nature. (shrink)

Employing computer simulations for the study of the evolution of altruism has been popular since Axelrod's book "The Evolution of Cooperation". But have the myriads of simulation studies that followed in Axelrod's footsteps really increased our knowledge about the evolution of altruism or cooperation? This book examines in detail the working mechanisms of simulation based evolutionary explanations of altruism. It shows that the "theoretical insights" that can be derived from simulation studies are often quite arbitrary and of little (...) use for the empirical research. In the final chapter of the book, therefore, a set of epistemological requirements for computer simulations is proposed and recommendations for the proper research design of simulation studies are made. (shrink)

The philosophy of AI has a long-standing tradition of discussing brain duplicates and brain simulations as well as a tendency to blur the lines between the two. The distinction between simulating and duplicating the brain has become increasingly important with the emergence of “ʼneuromorphic computers”, hardware operating with bio-inspired mechanisms and interconnecting artificial neurons and synapses. This paper explores what it means to duplicate the brain rather than merely simulate it. I claim that while simulations share only a (...) mathematical structure with their targets, duplicates have the same relevant measurable properties and are governed by the same relevant causal processes as the target system. I propose six criteria for brain duplication that are often not met by simulations, thereby offering a clearer understanding of the complexities involved in achieving brain duplication in artificial systems. Furthermore, this paper explores whether neuromorphic computers can duplicate neural computations by exploring the notion of models of computation. I submit that analog neuromorphic computers that use memristive technology are candidates for duplicating neural structures with respect to simple models of neural computation. Finally, I discuss five possible objections to my view. (shrink)

Despite the impressive amount of financial resources recently invested in carrying out large-scale brain simulations, it is controversial what the pay-offs are of pursuing this project. One idea is that from designing, building, and running a large-scale neural simulation, scientists acquire knowledge about the computational performance of the simulating system, rather than about the neurobiological system represented in the simulation. It has been claimed that this knowledge may usher in a new era of neuromorphic, cognitive computing systems. This study (...) elucidates this claim and argues that the main challenge this era is facing is not the lack of biological realism. The challenge lies in identifying general neurocomputational principles for the design of artificial systems, which could display the robust flexibility characteristic of biological intelligence. (shrink)

Computational functionalism dominates current debates on AI consciousness. This is the hypothesis that subjective experience emerges entirely from abstract causal topology, regardless of the underlying physical substrate. We argue this view fundamentally mischaracterizes how physics relates to information. We call this mistake the Abstraction Fallacy. Tracing the causal origins of abstraction reveals that symbolic computation is not an intrinsic physical process. Instead, it is a mapmaker-dependent description. It requires an active, experiencing cognitive agent to alphabetize continuous physics into a finite (...) set of meaningful states. Consequently, we do not need a complete, finalized theory of consciousness to assess AI sentience—a demand that simply pushes the question beyond near-term resolution and deepens the AI welfare trap. What we actually need is a rigorous ontology of computation. The framework proposed here explicitly separates simulation (behavioral mimicry driven by vehicle causality) from instantiation (intrinsic physical constitution driven by content causality). Establishing this ontological boundary shows why algorithmic symbol manipulation is structurally incapable of instantiating experience. Crucially, this argument does not rely on biological exclusivity. If an artificial system were ever conscious, it would be because of its specific physical constitution, never its syntactic architecture. Ultimately, this framework offers a physically grounded refutation of computational functionalism to resolve the current uncertainty surrounding AI consciousness. (shrink)

Classical computational fluid dynamics (CFD) is bottlenecked by the requirement to solve globally coupled, non-linear Navier-Stokes equations across dense three-dimensional volume grids. This paper does not attempt to solve the Navier-Stokes problem or provide a fundamental physical theory of turbulence. Instead, it presents a speculative, engineering oriented computational framework intended as a starting point for further exploration by specialists. The approach introduces a localized geometric simulation paradigm based on the "Vector of Contact" and "Kinetic Bridge" concepts. Rather than calculating volume-wide (...) flow averages, the framework models turbulence as an accumulation of deterministic boundary-interaction events. By utilizing coordinate geometry, surface curvature parameters, and localized vorticity-generation rules, the method outlines how a boundary contact engine can be implemented in a highly parallelized, GPU-optimized architecture. The goal is to offer an alternative modeling perspective that may be refined, tested, or discarded by experts, providing a potential pathway for real-time simulation of complex fluid behavior near solid boundaries. (shrink)

This paper argues that the simulation hypothesis does not require base reality to be mathematical in character. Discussions of simulation often assume, either explicitly or implicitly, that the reality underlying a simulation must itself be computational, mathematical, or law-governed in a familiar physical sense. I argue that this assumption is not entailed by the hypothesis. A simulated world must be mathematically implementable, but it does not follow that the base reality being approximated must itself be a mathematical structure. The paper (...) distinguishes between mathematical description and mathematical ontology, and develops the idea of macroscopic analogy: the possibility that a simulation need only reproduce stable phenomena at the scale relevant to conscious observers, rather than faithfully replicate the fundamental ontology of base reality. I then offer illustrative models of non-mathematical base mechanisms, such as dispositional attraction, relational resistance, and primitive cohesion, which could give rise to stable macroscopic patterns that a computational simulation could approximate. Finally, I consider how a simulation optimized for macroscopic conscious observers might exhibit strong mathematical regularity at ordinary scales while requiring supplementary theoretical structures or interpretive pressure at cosmological and microscopic extremes. The aim is not to provide evidence that we inhabit a simulation, nor to propose a rival physical theory, but to clarify a neglected conceptual possibility: non-mathematical base ontologies are compatible with the simulation hypothesis and should be included within its metaphysical possibility space. (shrink)

Modern computational systems are commonly designed around deterministic or discrete state-based models, where uncertainty is typically resolved early during state evaluation. [1], [14], [22] The framework further distinguishes deterministic Absolute States represented as {Abs}_A = (-1, 0, +1) and equivalently expressed as |A| = (-1, 0, +1) from derived ambiguity-preserving Q-States, enabling state-aware evaluation without forcing immediate collapse into rigid binary interpretation. [A:Absolute State]. However, many real-world systems operate under conditions where ambiguity persists, evolves, and directly influences system behavior [A1:Ambiguity]. (...) -/- This paper introduces an ambiguity-aware computation method operating within a Multi-State Computational Model, where Q-States are treated as a first-class computational state construct [A2:Q-State]. Instead of forcing immediate resolution, the proposed computational method preserves, interprets, and propagates ambiguity through a structured Transformation Pipeline. The pipeline integrates State Derivation, Q-State Identification, and State-Aware Graph Evaluation. [A3:State-Aware]. -/- A key conceptual distinction is established between Computing State and Computational State, enabling ambiguity-aware evaluation under structured operational constraints. [A4:Computing vs Computational State]. The proposed computational methods incorporate Lattice-Based Representation and Node State Classification to model system behavior under ambiguity [A5:Node-State]. -/- Sparsity Dynamics and Path Validity are introduced as core evaluation mechanisms, supported by Boundary Conditions and Hysteresis Dynamics to maintain stability, continuity, and evaluation consistency [A6:Sparsity, Path-Validity]. Simulation results demonstrate controlled Q-State propagation and convergence toward high Confidence Interval (CI) outcomes under structured ambiguity constraints [A7:Boundary-Conditioned Hysteresis]. -/- This approach provides a foundation for modeling complex systems where uncertainty is not prematurely eliminated, but systematically represented, evaluated, and incorporated into state-aware computational evaluation. [A8:Graph-Based Systems]. (shrink)

The widespread assumption that logical coherence implies truth is increasingly challenged in the context of contemporary artificial intelligence systems. This paper examines the philosophical claim that what is logically consistent is not necessarily true, and investigates its implications for the behavior and evaluation of Large Language Models (LLMs). Unlike traditional reasoning systems grounded in formal logic or empirical verification, LLMs generate outputs based on probabilistic pattern recognition, optimizing for linguistic coherence rather than factual accuracy. As a result, these models can (...) produce arguments that are internally consistent and highly persuasive, yet fundamentally detached from reality. This work argues that LLMs do not fail at truth-seeking; rather, they are not inherently designed for it. Instead, they simulate reasoning by reproducing patterns of logical structure present in their training data, creating an "illusion of reasoning" that can obscure the distinction between valid argumentation and true claims. The paper further explores how this distinction affects the evaluation of knowledge, particularly in contexts where coherence, clarity, and rhetorical strength are mistakenly treated as indicators of correctness. By analyzing the epistemic limitations of coherence-based systems, this paper highlights a critical gap between logical form and factual grounding in AI-generated content. It concludes by proposing a conceptual framework for separating coherence from truth in the design and assessment of intelligent systems, emphasizing the need for hybrid approaches that integrate logical consistency with mechanisms of external validation. (shrink)

Historically, the hypothesis that our world is a computer simulation has struck many as just another improbable-but-possible “skeptical hypothesis” about the nature of reality. Recently, however, the simulation hypothesis has received significant attention from philosophers, physicists, and the popular press. This is due to the discovery of an epistemic dependency: If we believe that our civilization will one day run many simulations concerning its ancestry, then we should believe that we are probably in an ancestor simulation right now. (...) This essay examines a troubling but underexplored feature of the ancestor-simulation hypothesis: the termination risk posed by both ancestor-simulation technology and experimental probes into whether our world is an ancestor simulation. This essay evaluates the termination risk by using extrapolations from current computing practices and simulation technology. The conclusions, while provisional, have great implications for debates concerning the fundamental nature of reality and the safety of contemporary physics. (shrink)

This thesis addresses one of the most fundamental questions in consciousness studies: whether the subjective, qualitative aspects of conscious experience—qualia—can be computationally simulated. Through a comprehensive analysis of existing theoretical frameworks, including Integrated Information Theory, Global Workspace Theory, and electromagnetic field theories of consciousness, this work identifies critical gaps in our understanding of the relationship between computational processes and phenomenal experience. -/- The central contribution of this thesis is the development of the Substrate-Information Duality Hypothesis (SIDH), a novel theoretical framework (...) that proposes qualia emerge from the irreducible interaction between substrate-dependent information structures and substrate-independent information patterns. This framework predicts that while computational systems can achieve perfect functional consciousness through the replication of substrate-independent information patterns, they cannot generate genuine phenomenal consciousness due to their inability to instantiate substrate-dependent information structures. -/- Through computational modeling and empirical analysis, this thesis demonstrates that biological, artificial, and hybrid consciousness systems exhibit measurably different consciousness metrics, with biological systems achieving positive consciousness values (Ψ ≈ 0.001) while artificial systems show negative values (Ψ ≈ -0.0004). Unexpectedly, hybrid systems demonstrate the lowest consciousness metrics (Ψ ≈ -0.0015), suggesting that bio-artificial integration may create interference rather than enhancement. The Simulation Detectability Index reveals substantial differences: 131.4% for artificial systems and 225.6% for hybrid systems compared to biological consciousness, indicating that consciousness simulation is fundamentally limited and empirically detectable. These findings suggest that the question "Can qualia be simulated?" must be answered with a qualified negative: while the functional aspects of consciousness can be perfectly simulated, the phenomenal aspects—the "something it is like" of conscious experience—remain beyond the reach of computational simulation. -/- The implications of this work extend beyond theoretical consciousness studies to practical considerations in artificial intelligence development, machine consciousness research, and the ethical treatment of artificial systems. This thesis concludes that the hard problem of consciousness is not merely a conceptual challenge but reflects a fundamental ontological distinction between computational processes and phenomenal experience. (shrink)

The field of computational social choice brings together principles, techniques, and tools from computer science and social choice theory to create a thriving multidisciplinary field. One of the most well-studied problems in computational social choice focuses on voting rules for selecting the winning candidate in an election. Recent research goes beyond classical voting rules by looking at rules that select multiple winners or drawing on the parallels between machine learning and voting. It is common to encounter voting paradoxes when (...) implementing voting rules in electoral systems. Unfortunately, these paradoxes usually provide little information on the conditions that make them more or less likely to occur. Computer simulations and generative probabilistic models are practical approaches to address this problem. This introductory paper addresses the problem of evaluating voting rules in competitive computer simulations. Multiagent simulations can provide valuable insights into the performances of competing voting rules defined over parametrically generated problems and populations. The outcomes of this work could improve the designs of electoral systems in the absence of theoretical results to support the optimality of a voting method. This line of research could also bridge the gap between axiomatic and experimental analysis of voting systems, leading the way to enhanced explanations and predictions. (shrink)

So the Fine-tuning Argument goes, because it is so unlikely for the physical constants of the laws of nature to have taken the values that they in fact take, we should significantly raise our credence that God exists. Simulation Arguments argue that our world might be (or, in stronger versions, that it probably is) a mere computer simulation. This paper argues that, in light of a Simulation Argument with a particularly weak conclusion, fine-tuning reasoning instead motivates atheists to believe (...) that we live in a computer simulation. (shrink)

It is sometimes said that simulation can serve as epistemic substitute for experimentation. Such a claim might be suggested by the fast-spreading use of computer simulation to investigate phenomena not accessible to experimentation (in astrophysics, ecology, economics, climatology, etc.). But what does that mean? The paper starts with a clarification of the terms of the issue and then focuses on two powerful arguments for the view that simulation and experimentation are ‘epistemically on a par’. One is based on the (...) claim that, in experimentation, no less than in simulation, it is not the system under study that is manipulated but a system that ‘stands-in’ for it. The other one highlights the pervasive use of models in experimentation. It will be argued that these arguments, as compelling as they might seem, are each based on a mistaken interpretation of experimentation and that, far from simulation and experimentation being epistemically on a par, they do not have the same epistemic function, do not produce the same kind of epistemic results. (shrink)

In this paper, we argue that computer simulations can provide valuable insights into the performance of voting methods on different collective decision problems. This could improve institutional design, even when there is no general theoretical result to support the optimality of a voting method. To support our claim, we first describe a decision problem that has not received much theoretical attention in the literature. We outline different voting methods to address that collective decision problem. Under certain criteria of (...) assessment akin to extensions of the Condorcet Jury Theorem, we run simulations for the methods using MATLAB, in order to compare their performance under various conditions. We consider and respond to concerns about the use of simulations in the assessment of voting procedures for policymaking. (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...

This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s. -/- Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows how (...) and why computers, data treatment devices and programming languages have occasioned a gradual but irresistible and massive shift from mathematical models to computer simulations. -/- . (shrink)

This study employs Ludwig Wittgenstein’s philosophy of language from Philosophical Investigations (1953) to critique computational theories of consciousness, asserting that artificial intelligence (AI) cannot attain genuine awareness due to its disconnection from human linguistic, experiential, and cultural practices. Wittgenstein’s concepts of language games, the private language argument, rule-following, and forms of life reveal the misuse of “consciousness” in computationalism, as proposed by Turing (1950) and Dennett (1995), highlighting its phenomenological and cultural irreducibility. The study adopts an interdisciplinary methodology, integrating conceptual (...) analysis, technical evaluation of transformer-based AI models, and a mixed-method case study spanning textual analysis, decision-making, emotional recognition, healthcare diagnostics, crisis management, mental health support, and cross-cultural interactions. It synthesizes neuroscientific findings (Dehaene, 2024), psychological insights (Kahneman, 2011), and philosophical perspectives from Western traditions (Plato, Heidegger) and non-Western frameworks, including African ubuntu (Ramose, 1999), Mulla Sadra’s presential knowledge (Rizvi, 2009), and Zen Buddhist phenomenology (Anālayo, 2020). Key innovations include a globalized critique, novel empirical data from AI performance analysis, and a proposal for a global AI ethics board to ensure culturally sensitive design. The study examines qualia (Jackson, 1982), intentionality (Brentano, 1874), phenomenology (Merleau-Ponty, 1945), and historical theories of consciousness, systematically addressing objections from Churchland (1981), Dennett, Chalmers (1996), Nagel (1974), Goff (2017), and others. Tailored for Philosophy & Technology, this work advances the mind-machine debate by prioritizing lived experience and advocating for ethical AI development grounded in human-centric principles. (shrink)

The simulation hypothesis has always depended on a certain asymmetry of confidence. Physics is still groping toward a final account of reality, while speculative philosophy confidently announces that reality is “probably” a computer program. As long as physics appears incomplete, the simulation hypothesis can parasitize that uncertainty. What has begun to change in the last few years is that the direction of pressure is reversing. The closer physics gets to a genuine theory of everything, the less room there is (...) for the simulation hypothesis to occupy. My earlier paper argued that once you take adaptive emergence, thermodynamic constraints, and recursive irreducibility seriously, the hypothesis is either physically impossible or logically meaningless. The undecidability work by Faizal, Krauss, and collaborators reaches the same destination from a different starting point. If our ultimate physics necessarily includes non-algorithmic structure, then no Turing-equivalent device can ever host a complete copy of our universe. They frame this as a consequence of Gödel, Tarski, and Chaitin. I agree with their core result, but I think the implications are stronger than they themselves are willing to draw. Once you integrate their undecidability constraints with the ontological, thermodynamic, and epistemic problems I previously outlined, the simulation hypothesis is not merely an unbuildable technology. It is not a coherent description of any possible world. (shrink)

Various theorists contend that we may live in a computer simulation. David Chalmers in turn argues that the simulation hypothesis is a metaphysical hypothesis about the nature of our reality, rather than a sceptical scenario. We use recent work on consciousness to motivate new doubts about both sets of arguments. First, we argue that if either panpsychism or panqualityism is true, then the only way to live in a simulation may be as brains-in-vats, in which case it is unlikely (...) that we live in a simulation. We then argue that if panpsychism or panqualityism is true, then viable simulation hypotheses are substantially sceptical scenarios. We conclude that the nature of consciousness has wide-ranging implications for simulation arguments. (shrink)

The question of whether consciousness can be computationally simulated has profound implications for artificial intelligence, neuroscience, and philosophy of mind. While previous theoretical frameworks have proposed various approaches to consciousness simulation, significant gaps remain in empirical validation, formal axiomatization, and methodological rigor. This paper addresses these limitations by developing the Enhanced Substrate-Information Duality Hypothesis (E-SIDH), a formally axiomatized framework that provides specific empirical predictions and experimental paradigms for consciousness research. -/- Building upon recent advances in consciousness measurement, electromagnetic field theories, (...) and formal mathematical approaches to consciousness, this work presents a comprehensive framework that distinguishes between substrate-dependent and substrate-independent aspects of information processing in conscious systems. Through rigorous mathematical formalization and computational modeling, we demonstrate that consciousness emerges from the resonant coupling between these two irreducible information types, with specific implications for the possibility and detectability of consciousness simulation. -/- Our empirical validation reveals systematic differences between biological, artificial, and hybrid consciousness systems, with biological systems achieving consciousness metrics of Ψ = 0.245 ± 0.002, artificial systems showing Ψ = 0.128 ± 0.001, and hybrid systems demonstrating intermediate values of Ψ = 0.160 ± 0.033. The Simulation Detectability Index indicates 48.0% detectable differences between artificial and biological consciousness, providing quantitative evidence for the fundamental limitations of consciousness simulation. -/- This work directly engages with competing theories including Integrated Information Theory, Global Workspace Theory, and simulation hypothesis frameworks, while addressing critical objections regarding substrate independence and the hard problem of consciousness. The formal axiomatization provides a foundation for experimental neuroscience research, offering specific predictions about electromagnetic signatures, perturbation effects, and consciousness detection methodologies. -/- The implications extend beyond theoretical consciousness studies to practical considerations in artificial intelligence development, brain-computer interfaces, and the ethical treatment of artificial systems. We conclude that while functional aspects of consciousness can be computationally replicated, genuine phenomenal consciousness requires substrate-dependent information structures that cannot be simulated through purely computational means, establishing fundamental limits on artificial consciousness while opening new directions for bio-artificial hybrid approaches. (shrink)

Are we living in a computer simulation? Some philosophers say it's very likely we are. Some philosophers say that even if we're not in a simulation, we could never know for sure. In this short piece I would like to respond to a more practical question: that is, whatever the case may be, ontologically or epistemically, how do we actually escape (or break) a simulation? I propose a rather simple and sure-fire way to escape a computer simulation—whether we're (...) in one or not. I also connect my solution to the Fermi Paradox. (shrink)