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)

An introduction to three papers in a special issue.

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 paper examines how accepting the simulation hypothesis as a serious philosophical proposition forces a fundamental reconsideration of epistemological certainty. While previous work has focused on the probability of living in a simulation or the nature of consciousness within simulations, we demonstrate that the mere possibility of simulated reality creates a unique crisis for knowledge hierarchies that differs fundamentally from traditional sceptical arguments. Unlike Cartesian doubt, which preserves the notion of an objective reality while questioning our access to (...) it, the simulation hypothesis suggests reality itself might be programmatically mutable. We argue this undermines traditional distinctions between scientific and religious epistemology, creates an insoluble verification paradox for scientific methodology, and requires a radical reimagining of knowledge and truth. While we suggest that complete epistemological scepticism might be avoided through appeal to necessary logical primitives, we conclude that accepting the possibility of simulated reality requires a fundamental reconstruction of how we understand knowledge, scientific practice, and the relationship between competing explanatory frameworks. (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)

Preston Greene (2020) argues that we should not conduct simulation investigations because of the risk that we might be terminated if our world is a simulation designed to research various counterfactuals about the world of the simulators. In response, we propose a sequence of arguments, most of which have the form of an "even if” response to anyone unmoved by our previous arguments. It runs thus: (i) if simulation is possible, then simulators are as likely to care (...) about simulating simulations as they are likely to care about simulating basement (i.e. non-simulated) worlds. But (ii) even if simulations are interested only in simulating basement worlds the discovery that we are in a simulation will have little or no impact on the evolution of ordinary events. But (iii) even if discovering that we are in a simulation impacts the evolution of ordinary events, the effects of seeming to do so could also happen in a basement world, and might be the subject of interesting counterfactuals in the basement world Finally, (iv) there is little reason to think that there is a catastrophic effect from successful simulation probes, and no argument from the precautionary principle can be used to leverage the negligible credence one ought have in this. Thus, if we do develop a simulation probe, then let’s do it. (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)

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)

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)

To study climate change, scientists employ computer models, which approximate target systems with various levels of skill. Given the imperfection of climate models, how do scientists use simulations to generate knowledge about the causes of observed climate change? Addressing a similar question in the context of biological modelling, Levins (1966) proposed an account grounded in robustness analysis. Recent philosophical discussions dispute the confirmatory power of robustness, raising the question of how the results of computer modelling studies contribute to the body (...) of evidence supporting hypotheses about climate change. Expanding on Staley’s (2004) distinction between evidential strength and security, and Lloyd’s (2015) argument connecting variety-of-evidence inferences and robustness analysis, I address this question with respect to recent challenges to the epistemology robustness analysis. Applying this epistemology to case studies of climate change, I argue that, despite imperfections in climate models, and epistemic constraints on variety-of-evidence reasoning and robustness analysis, this framework accounts for the strength and security of evidence supporting climatological inferences, including the finding that global warming is occurring and its primary causes are anthropogenic. (shrink)

The formal and empirical-generative perspectives of computation are demonstrated to be inadequate to secure the goals of simulation in the social sciences. Simulation does not resemble formal demonstrations or generative mechanisms that deductively explain how certain models are sufficient to generate emergent macrostructures of interest. The description of scientific practice implies additional epistemic conceptions of scientific knowledge. Three kinds of knowledge that account for a comprehensive description of the discipline were identified: formal, empirical and intentional knowledge. The use (...) of formal conceptions of computation for describing simulation is refuted; the roles of programming languages according to intentional accounts of computation are identified; and the roles of iconographic programming languages and aesthetic machines in simulation are characterized. The roles that simulation and intentional decision making may be able to play in a participative information society are also discussed. (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)

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)

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)

(Draft of Feb 2023, see upcoming issue for Chalmers' reply) In Reality+: Virtual Worlds and the Problems of Philosophy, David Chalmers argues, among other things, that: if we are living in a full-scale simulation, we would still enjoy broad swathes of knowledge about non-psychological entities, such as atoms and shrubs; and, our lives might still be deeply meaningful. Chalmers views these claims as at least weakly connected: The former claim helps forestall a concern that if objects in the (...) class='Hi'>simulation are not genuine (and so not knowable), then life in the simulation is illusory and therefore, not as valuable as a non-simulated life. Taking up these questions, I argue that in general, the value of social knowledge for a meaningful life dramatically swamps the value of non-social knowledge for a meaningful life. Along the way, I propose a non-additive model of the meaningfulness of life, according to which the overall effect of some potential contributor of value to a life depends in part on what is already in a life. One upshot is that the vindication of non-social knowledge, absent a correlative vindication of social knowledge, contributes either not at all or scarcely at all to the claim that our lives in the simulation might be deeply meaningful. This is so even though the vindication of non-social knowledge does forestall the concern that in the simulation, our lives might be wholly meaningless. (shrink)

The aim of this paper is to discuss the “Framework for M&S with Agents” (FMSA) proposed by Zeigler et al. [2000, 2009] in regard to the diverse epistemological aims of agent simulations in social sciences. We first show that there surely are great similitudes, hence that the aim to emulate a universal “automated modeler agent” opens new ways of interactions between these two domains of M&S with agents. E.g., it can be shown that the multi-level conception at the core of (...) the FMSA is similar in both contexts: notions of “levels of system specifi cation”, “behavior of models”, “simulator”and “endomorphic agents” can be partially translated in the terms linked to the “denotational hierarchy” (DH) recently introduced in a multi-level centered epistemology of M&S. Second, we suggest considering the question of “credibility” of agent M&S in social sciences when we do not try to emulate but only to simulate target systems. Whereas a stringent and standardized treatment of the heterogeneous internal relations (in the DH) between systems of formalisms is the key problem and the essential challenge in the scope of Agent M&S driven engineering, it is urgent too to address the problem of the external relations (and of the external validity, hence of the epistemic power and credibility) of such levels of formalisms in the specific domains of agent M&S in social sciences, especially when we intend to introduce the concepts of activity tracking. (shrink)

Currently, the widely used notion of activity is increasingly present in computer science. However, because this notion is used in specific contexts, it becomes vague. Here, the notion of activity is scrutinized in various contexts and, accordingly, put in perspective. It is discussed through four scientific disciplines: computer science, biology, economics, and epistemology. The definition of activity usually used in simulation is extended to new qualitative and quantitative definitions. In computer science, biology and economics disciplines, the new (...) class='Hi'>simulation activity definition is first applied critically. Then, activity is discussed generally. In epistemology, activity is discussed, in a prospective way, as a possible framework in models of human beliefs and knowledge. (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)

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)

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)

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)

The classical theory of computation does not represent an adequate model of reality for simulation in the social sciences. The aim of this paper is to construct a methodological perspective that is able to conciliate the formal and empirical logic of program verification in computer science, with the interpretative and multiparadigmatic logic of the social sciences. We attempt to evaluate whether social simulation implies an additional perspective about the way one can understand the concepts of program and computation. (...) We demonstrate that the logic of social simulation implies at least two distinct types of program verifications that reflect an epistemological distinction in the kind of knowledge one can have about programs. Computer programs seem to possess a causal capability (Fetzer, 1999) and an intentional capability that scientific theories seem not to possess. This distinction is associated with two types of program verification, which we call empirical and intentional verification. We demonstrate, by this means, that computational phenomena are also intentional phenomena, and that such is particularly manifest in agent-based social simulation. Ascertaining the credibility of results in social simulation requires a focus on the identification of a new category of knowledge we can have about computer programs. This knowledge should be considered an outcome of an experimental exercise, albeit not empirical, acquired within a context of limited consensus. The perspective of intentional computation seems to be the only one possible to reflect the multiparadigmatic character of social science in terms of agent-based computational social science. We contribute, additionally, to the clarification of several questions that are found in the methodological perspectives of the discipline, such as the computational nature, the logic of program scalability, and the multiparadigmatic character of agent-based simulation in the social sciences. (shrink)

The notion of an ideal reasoner has several uses in epistemology. Often, ideal reasoners are used as a parameter of (maximum) rationality for finite reasoners (e.g. humans). However, the notion of an ideal reasoner is normally construed in such a high degree of idealization (e.g. infinite/unbounded memory) that this use is unadvised. In this dissertation, I investigate the conditions under which an ideal reasoner may be used as a parameter of rationality for finite reasoners. In addition, I present and (...) justify the research program of computational epistemology, which investigates the parameter of maximum rationality for finite reasoners using computer simulations. (shrink)

There is much disagreement about how extensive a role theoretical mind-reading, behavior-reading, and simulation each have and need to have in our knowing and understanding other minds, and how each method is implemented in the brain, but less discussion of the epistemological question what it is about the products of these methods that makes them count as knowledge or understanding. This question has become especially salient recently as some have the intuition that mirror neurons can bring understanding of another's (...) action despite involving no higher-order processing, whereas most epistemologists writing about understanding think that it requires reflective access to one's grounds, which is closer to the intuitions of other commenters on mirror neurons. I offer a definition of what it is that makes something understanding that is compelling independently of the context of cognition of other minds, and use it to show two things: 1) that theoretical mind-reading and simulation bring understanding in virtue of the same epistemic feature, and 2) why the kind of motor representation without propositional attitudes that is done by mirror neurons is sufficient for action understanding. I further suggest that more attention should be paid to the potential disadvantages of a simulative method of knowing. Though it can be more efficient in some cases, it can also bring vulnerability, wear and tear on one's personal equipment, and unintended mimicry. (shrink)

I show that some of the most initially attractive routes of refuting epistemological solipsism face serious obstacles. I also argue that for creatures like ourselves, solipsism is a genuine form of external world skepticism. I suggest that together these claims suggest the following morals: No proposed solution to external world skepticism can succeed which does not also solve the problem of epistemological solipsism. And, more tentatively: In assessing proposed solutions to external world skepticism, epistemologists should explicitly consider whether those solutions (...) extend to knowledge of other minds. Finally, and also tentatively: epistemological solipsism warrants more philosophical attention than it currently enjoys. (shrink)

The debates on the scientificity of social sciences in general, and sociology in particular, are recurring. From the original methodenstreitat the end the 19th Century to the contemporary controversy on the legitimacy of “regional epistemologies”, a same set of interrogations reappears. Are social sciences really scientific? And if so, are they sciences like other sciences? How should we conceive “research programs” Lakatos (1978) or “research traditions” for Laudan (1977) able to produce advancement of knowledge in the field of social and (...) human phenomena? Is the progress of knowledge in social sciences similar to the one generally observed in natural sciences? Is it possible to evaluate the relative merits of each one of these research programs? -/- These debates are important vectors of social and intellectual polarization. The historical divide between the positivist and the hermeneutics poles precedes the structure of the contemporary debate around the epistemic space of social sciences, It is not only a question of renewing the opposition between a monist view of sciences (e.g. McIntyre, 1996) and a dualistic one (e.g. Geertz, 1973) or even a trialist view of sciences (e.g. Lepenies, 1985). It is also a question of asserting dichotomies transformed into framework (including when it is a question of exceeding them): nature-culture, nomothetic-idiographic, models-narrative, structure-history, cause-reason, explanation-comprehension. -/- In this short introduction, we provide, in section 2, a first overview of this epistemological debate in social science. Section 3 proposes a different standpoint on the same questions, by introducing both ontological and methodological aspects in this basic epistemological debate. Namely, following (Hollis, 1994) oppositions of the explanation - understanding, causes - meaning, etc., types discussed in the firsts ection are comparatively examined together with oppositions of the structure - action, holism - individualism types. This allows us to discus show multi-agent design, by integrating various dimensions and standpoints in the same framework (Phan, Amblard, 2007, Chapters 1, 5, 14) can help us to shift these boundaries, and to bypass these oppositions. As model building and ontology design are at the core of this process (Phan, Amblard, 2007, Chapter 12), Section4 discusses various issues of the art of modelling, starting both from economists’ and sociologists’ current standpoints. (shrink)

The God Experiment – Let there be Light -/- The question “What is real?” can be traced back to the shadows in Plato’s cave. Two thousand years later, Rene Descartes lacked knowledge about arguing against an evil´ deceiver feeding us the illusion of sensation. Descartes’ epistemological concept later led to various theories of what our sensory experiences actually are. The concept of ”illusionism”, proposing that even the very conscious experience we have – our qualia – is an illusion, is not (...) only a red-pill scenario found in the 1999 science fiction movie ”The Matrix” but is also a philosophical concept promoted by modern tinkers, most prominently by Daniel Dennett. He describes his argument against qualia as materialistic and scientific. Reflection upon a possible simulation and our perceived reality was beautifully visualized in “The Matrix”, bringing the old ideas of Descartes to coffee houses around the world. Irish philosopher Bishop Berkeley was the father of what has later been coined as “subjective idealism”, basically stating that “what you perceive is real” (e.g., ”The Matrix” is real because its population perceives it). Berkeley then argued against Isaac Newton’s absolutism of space, time, and motion in 1721, ultimately leading to Ernst Mach and Albert Einstein’s respective views. Several neuroscientists have rejected Dennetts’ perspective on the illusion of consciousness, and idealism is often dismissed as the notion that people want to pick and choose the tenets of reality. Even Einstein ended his life on a philosophical note, pondering the very foundations of reality. With the advent of quantum technologies based on the control of individual fundamental particles, the question of whether our universe is a simulation isn’t just intriguing. Our ever-advancing understanding of fundamental physical processes will likely lead us to build quantum computers utilizing quantum effects for simulating nature quantum-mechanically in all complexity, as famously envisioned by Richard Feynman. Finding an answer to the simulation question will potentially alter our very definition and understanding of life, reshape theories on the evolution and fate of the universe, and impact theology. No direct observations provide evidence in favor or against the simulation hypothesis, and experiments are needed to verify or refute it. In this paper, we outline several constraints on the limits of computability and predictability in/of the universe, which we then use to design experiments allowing for first conclusions as to whether we participate in a simulation chain. We elaborate on how the currently understood laws of physics in both complete and small-scale universe simulations prevent us from making predictions relating to the future states of a universe, as well as how every physically accurate simulation will increase in complexity and exhaust computational resources as global thermodynamic entropy increases. Eventually, in a simulation in which the computer simulating a universe is governed by the same physical laws as the simulation and is smaller than the universe it simulates, the exhaustion of computational resources will halt all simulations down the simulation chain unless an external programmer intervenes or isn’t limited by the simulation’s physical laws, which we may be able to observe. By creating a simulation chain and observing the evolution of simulation behavior throughout the hierarchy taking stock of statistical relevance, as well as comparing various least complex simulations under computability and predictability constraints, we can gain insight into whether our universe is part of a simulation chain. -/- Index Terms—Simulation, simulation hypothesis, quantum computing, universe, life, intelligence. (shrink)

The terms ‘verification’ and ‘validation’ are widely used in science, both in the natural and the social sciences. They are extensively used in simulation, often associated with the need to evaluate models in different stages of the simulation development process. Frequently, terminological ambiguities arise when researchers conflate, along the simulation development process, the technical meanings of both terms with other meanings found in the philosophy of science and the social sciences. This article considers the problem of verification (...) and validation in social science simulation along five perspectives: The reasons to address terminological issues in simulation; the meaning of the terms in the philosophical sense of the problem of “truth”; the observation that some debates about these terms in simulation are inadvertently more terminological than epistemological; the meaning of the terms in the technical context of the simulation development process; and finally, a comprehensive outline of the relation between terminology used in simulation, different types of models used in the development process and different epistemological perspectives. (shrink)

​I use network models to simulate social learning situations in which the dominant group ignores or devalues testimony from the marginalized group. I find that the marginalized group ends up with several epistemic advantages due to testimonial ignoration and devaluation. The results provide one possible explanation for a key claim of standpoint epistemology, the inversion thesis, by casting it as a consequence of another key claim of the theory, the unidirectional failure of testimonial reciprocity. Moreover, the results complicate the (...) understanding and application of previously discovered network epistemology effects, notably the Zollman effect (Zollman 2007, 2010). (shrink)

This article will rework the classical question ‘Can a machine think?’ into a more specific problem: ‘Can a machine think anything new?’ It will consider traditional computational tasks such as prediction and decision-making, so as to investigate whether the instrumentality of these operations can be understood in terms of the creation of novel thought. By addressing philosophical and technoscientific attempts to mechanise thought on the one hand, and the philosophical and cultural critique of these attempts on the other, I will (...) argue that computation’s epistemic productions should be assessed vis-à-vis the logico-mathematical specificity of formal axiomatic systems. Such an assessment requires us to conceive automated modes of thought in such a way as to supersede the hope that machines might replicate human cognitive faculties, and to thereby acknowledge a form of onto-epistemological autonomy in automated ‘thinking’ processes. This involves moving beyond the view that machines might merely simulate humans. Machine thought should be seen as dramatically alien to human thought, and to the dimension of lived experience upon which the latter is predicated. Having stepped outside the simulative paradigm, the question ‘Can a machine think anything new?’ can then be reformulated. One should ask whether novel behaviour in computing might come not from the breaking of mechanical rules, but from following them: from doing what computers do already, and not what we might think they should be doing if we wanted them to imitate us. (shrink)

Studying evidence law as part of naturalized epistemology means using the tools and results of the sciences to evaluate evidence rules based on the accuracy of the verdicts they are likely to produce. In this chapter, we introduce the approach and address skeptical concerns about the value of systematic empirical research for evidence scholarship, focusing, in particular, on worries about the external validity of jury simulation studies. Finally, turning to applications, we consider possible reforms regarding eyewitness identifications and (...) character evidence. (shrink)

When solving a complex problem in a group, should group members always choose the best available solution that they are aware of? In this paper, I build simulation models to show that, perhaps surprisingly, a group of agents who individually randomly follow a better available solution than their own can end up outperforming a group of agents who individually always follow the best available solution. This result has implications for the feminist philosophy of science and social epistemology.

Bayesian models can be related to cognitive processes in a variety of ways that can be usefully understood in terms of Marr's distinction among three levels of explanation: computational, algorithmic and implementation. In this note, we discuss how an integrated probabilistic account of the different levels of explanation in cognitive science is resulting, at least for the current research practice, in a sort of unpredicted epistemological shift with respect to Marr's original proposal.

We study imagination as reality-oriented mental simulation (ROMS): the activity of simulating nonactual scenarios in one’s mind, to investigate what would happen if they were realized. Three connected questions concerning ROMS are: What is the logic, if there is one, of such an activity? How can we gain new knowledge via it? What is voluntary in it and what is not? We address them by building a list of core features of imagination as ROMS, drawing on research in cognitive (...) psychology and the philosophy of mind. We then provide a logic of imagination as ROMS which models such features, combining techniques from epistemic logic, action logic, and subject matter semantics. Our logic comprises a modal propositional language with non-monotonic imagination operators, a formal semantics, and an axiomatization. (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)

The problem of infinite regress presents a profound challenge in epistemology and philosophy, questioning the possibility of achieving foundational knowledge amidst an endless chain of justifications. This paper introduces a set of four axioms designed to directly address and resolve the problem of infinite regress, ensuring theoretical rigor and applicability across diverse scenarios, including simulated or illusory realities. By focusing on Direct Address, Intellectual Rigor in All Realities, Avoiding Pragmatic Dismissals, and Theoretical Consistency, these axioms provide a structured framework (...) for evaluating philosophical theories aimed at overcoming this complex issue. The paper explores current theories that align with these axioms, evaluates popular theories that do not, and argues for the critical role of these axioms in guiding future philosophical inquiry and resolution of infinite regress. (shrink)

This article offers an account and defence of constructionism, both as a metaphilosophical approach and as a philosophical methodology, with references to the so-called maker's knowledge tradition. Its main thesis is that Plato's “user's knowledge” tradition should be complemented, if not replaced, by a constructionist approach to philosophical problems in general and to knowledge in particular. Epistemic agents know something when they are able to build (reproduce, simulate, model, construct, etc.) that something and plug the obtained information into the correct (...) network of relations that account for it. Their epistemic expertise increases with the scope and depth of the questions that they are able to ask and answer. Thus, constructionism deprioritises mimetic, passive, and declarative knowledge that something is the case, in favour of poietic, interactive, and practical knowledge of something being the case. Metaphilosophically, constructionism suggests adding conceptual engineering to conceptual analysis as a fundamental method. (shrink)

We motivate a picture of social epistemology that sees forgetting as subject to epistemic evaluation. Using computer simulations of a simple agent-based model, we show that how agents forget can have as large an impact on group epistemic outcomes as how they share information. But, how we forget, unlike how we form beliefs, isn’t typically taken to be the sort of thing that can be epistemically rational or justified. We consider what we take to be the most promising argument (...) for this claim and find it lacking. We conclude that understanding how agents forget should be as central to social epistemology as understanding how agents form beliefs and share information with others. (shrink)

The problem of other minds has a distinguished philosophical history stretching back more than two hundred years. Taken at face value, it is an epistemological question: it concerns how we can have knowledge of, or at least justified belief in, the existence of minds other than our own. In recent decades, philosophers, psychologists, neuroscientists, anthropologists and primatologists have debated a related question: how we actually go about attributing mental states to others (regardless of whether we ever achieve knowledge or rational (...) justification in this domain). Until the mid-nineties, the latter debate – which sometimes goes under the name of the “mindreading” debate – was characterized by a fairly clear-cut opposition between two theoretical outlooks: “theory-theory” (TT) and “simulation theory” (ST). Theory-theorists typically argued that we attribute mental states to others on the basis of a “theory of mind” that is either constructed in early infancy and subsequently revised and modified (Gopnik 1996), or else is the result of maturation of innate mindreading “modules” (Baron-Cohen 1995). (shrink)

This essay develops an inferential account of model explanation, based on Mauricio Suárez’s inferential conception of scientific representation and Alisa Bokulich’s counterfactual account of model explanation. It is suggested that the fact that a scientific model can explain is essentially linked to how a modeler uses an established model to make various inferences about the target system on the basis of results derived from the model. The inference practice is understood as a two-step activity, with the first step involving making (...) counterfactual statements about the model itself and the second step involving making hypothetical statements transferring over claims derived from the model onto the target. To illustrate how this two-step activity proceeds, an agent-based simulation model is discussed. (shrink)

Coherence considerations play an important role in science and in everyday reasoning. However, it is unclear what exactly is meant by coherence of information and why we prefer more coherent information over less coherent information. To answer these questions, we first explore how to explicate the dazzling notion of ``coherence'' and how to measure the coherence of an information set. To do so, we critique prima facie plausible proposals that incorporate normative principles such as ``Agreement'' or ``Dependence'' and then argue (...) that the coherence of an information set is best understood as an indicator of the truth of the set under certain conditions. Using computer simulations, we then show that a new probabilistic measure of coherence that combines aspects of the two principles above, but without strictly satisfying either principle, performs particularly well in this regard. (shrink)

Advancements in computing, instrumentation, robotics, digital imaging, and simulation modeling have changed science into a technology-driven institution. Government, industry, and society increasingly exert their influence over science, raising questions of values and objectivity. These and other profound changes have led many to speculate that we are in the midst of an epochal break in scientific history. -/- This edited volume presents an in-depth examination of these issues from philosophical, historical, social, and cultural perspectives. It offers arguments both for and (...) against the epochal break thesis in light of historical antecedents. Contributors discuss topics such as: science as a continuing epistemological enterprise; the decline of the individual scientist and the rise of communities; the intertwining of scientific and technological needs; links to prior practices and ways of thinking; the alleged divide between mode-1 and mode-2 research methods; the commodification of university science; and the shift from the scientific to a technological enterprise. Additionally, they examine the epochal break thesis using specific examples, including the transition from laboratory to real world experiments; the increased reliance on computer imaging; how analog and digital technologies condition behaviors that shape the object and beholder; the cultural significance of humanoid robots; the erosion of scientific quality in experimentation; and the effect of computers on prediction at the expense of explanation. -/- Whether these events represent a historic break in scientific theory, practice, and methodology is disputed. What they do offer is an important occasion for philosophical analysis of the epistemic, institutional and moral questions affecting current and future scientific pursuits. (shrink)

Este artigo discute o uso de simulações de computador em Epistemologia(Epistemologia Computacional). O objetivo o artigo é fundamentar e discu-tir a ideia de uma Epistemologia Computacional, além de apresentar umexemplo de estudo nesse campo. Na Introdução, discuto as objeções maiscomuns aos métodos da Epistemologia Tradicional e à proposta de Quine deuma Epistemologia Naturalizada. Argumento que a Epistemologia Compu-tacional não está sujeita a nenhuma destas objeções. Na Seção 1, apresentouma revisão bibliográfica dos estudos em Epistemologia Computacional(tanto em Epistemologia individualista quanto em (...) Epistemologia Social) ediscuto a estrutura geral destes estudos. Na Seção 2, apresento alguns resul-tados de um estudo em Epistemologia Computacional que realizei em minha tese de doutorado. -/- ENGLISH: This paper is about the use of computer simulations in Epistemology (Com- putational Epistemology). The goal of the paper is to ground and discuss theidea of a Computational Epistemology, and to present an example of studyin this field. In the Introduction, I discuss the most common objections tothe methods of Traditional Epistemology and to Quine’s Naturalized Episte-mology. I argue that Computation Epistemology is not subject to any of the-se objections. In Section 1, I review the literature on Computational Episte-mology (both in individualistic Epistemology and in Social Epistemology)and discuss the general structure of these studies. In Section 2, I presentsome results of my PhD dissertation on Computational Epistemology. (shrink)

It is commonly held that our intuitive judgements about imaginary problem cases are justified a priori, if and when they are justified at all. In this paper I defend this view — ‘rationalism’ — against a recent objection by Timothy Williamson. I argue that his objection fails on multiple grounds, but the reasons why it fails are instructive. Williamson argues from a claim about the semantics of intuitive judgements, to a claim about their psychological underpinnings, to the denial of rationalism. (...) I argue that the psychological claim — that a capacity for mental simulation explains our intuitive judgements — does not, even if true, provide reasons to reject rationalism. (More generally, a simulation hypothesis, about any category of judgements, is very limited in its epistemological implications: it is pitched at a level of explanation that is insensitive to central epistemic distinctions.) I also argue that Williamson’s semantic claim — that intuitive judgements are judgements of counterfactuals — is mistaken; rather, I propose, they are a certain kind of metaphysical possibility judgement. Several other competing proposals are also examined and criticized. (shrink)

In this article, I argue that the artificial components of hybrid bionic systems do not play a direct explanatory role, i.e., in simulative terms, in the overall context of the systems in which they are embedded in. More precisely, I claim that the internal procedures determining the output of such artificial devices, replacing biological tissues and connected to other biological tissues, cannot be used to directly explain the corresponding mechanisms of the biological component(s) they substitute (and therefore cannot be used (...) to explain the local mechanisms determining an overall biological or cognitive function replicated by such bionic models). I ground this analysis on the use of the Minimal Cognitive Grid (MCG), a novel framework proposed in Lieto (Cognitive design for artificial minds, 2021) to rank the epistemological and explanatory status of biologically and cognitively inspired artificial systems. Despite the lack of such a direct mechanistic explanation from the artificial component, however, I also argue that the hybrid bionic systems can have an indirect explanatory role similar to the one played by some AI systems built by using an overall structural design approach (but including the partial adoption of functional components). In particular, the artificial replacement of part(s) of a biological system can provide i) a local functional account of that part(s) in the context of the overall functioning of the hybrid biological–artificial system and ii) global insights about the structural mechanisms of the biological elements connected to such artificial devices. (shrink)

Hot-cold empathy gaps are a pervasive phenomena wherein one’s predictions about others tend to skew ‘in the direction’ of one’s own current visceral states. For instance, when one predicts how hungry someone else is, one’s prediction will tend to reflect one’s own current hunger state. These gaps also obtain intrapersonally, when one attempts to predict what one oneself would do at a different time. In this paper, we do three things: We draw on empirical evidence to argue that so-called hot-cold (...) empathy gaps arise when one projects one’s own current state into a simulation about another. Second, we argue that this process does not typically confer knowledge, even when the predictions it produces happen to be accurate. Third, we suggest that these results can be used to develop a challenge for L.A. Paul's view that authentic action sometimes requires subjective knowledge of one’s own values and how these values relate to relevant outcomes. We then sketch an alternative view of the epistemic grounds of authenticity, one on which authenticity requires a kind of understanding. The relevant form of understanding can be achieved by subjective knowledge but can also be achieved elsewise, such as through testimony from a close friend about what one values. (shrink)

In this paper we suggest a clarification in relation to the notions of computational and intrinsic emergence, by showing how the latter is deeply connected to the new Logical Openness Theory, an original extension of Gödel theorems to the model theory. The epistemological scenario we are going to make use of is that of the theory of vision, a particularly instructive one. In order to reach our goal we introduce a dynamic theory of relationship between the observer and the observed (...) which takes into account the co-adaptive processes as well as the ecological nature of the mind/world relations. In order to fulfill the requirements of complexity sciences, this theory assumes that the scientific activity has to be able to comprehend the adaptive dynamics between system and environment as well as the model and the context where it is applied. This leads to an adaptive epistemology based on logical openness which overcomes the thigh corners of both naïve objectivist conception and radical relativism temptations by stressing that the knowledge building is a process of continuous shifting from the “frozen” syntactical dimensions to the plurality choices which make possible for mind and world to meet each other. (shrink)

Does pre-voting group deliberation improve majority outcomes? To address this question, we develop a probabilistic model of opinion formation and deliberation. Two new jury theorems, one pre-deliberation and one post-deliberation, suggest that deliberation is beneficial. Successful deliberation mitigates three voting failures: (1) overcounting widespread evidence, (2) neglecting evidential inequality, and (3) neglecting evidential complementarity. Formal results and simulations confirm this. But we identify four systematic exceptions where deliberation reduces majority competence, always by increasing Failure 1. Our analysis recommends deliberation that (...) is 'participatory', 'neutral', but not necessarily 'equal', i.e., that involves substantive sharing, privileges no evidences, but might privilege some persons. (shrink)

The philosophy of measurement studies the conceptual, ontological, epistemic, and technological conditions that make measurement possible and reliable. A new wave of philosophical scholarship has emerged in the last decade that emphasizes the material and historical dimensions of measurement and the relationships between measurement and theoretical modeling. This essay surveys these developments and contrasts them with earlier work on the semantics of quantity terms and the representational character of measurement. The conclusions highlight four characteristics of the emerging research program in (...) philosophy of measurement: it is epistemological, coherentist, practice oriented, and model based. (shrink)

Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization that was developed to address just this sort of epistemic opacity. The visualization (...) is unusual in that it depicts the dynamics and structure of a computer model instead of that model’s target system, and because it is generated algorithmically. Using considerations from epistemology and aesthetics, we explore how this new kind of visualization increases scientific understanding of the content and function of computer models in systems biology to reduce epistemic opacity. (shrink)