The ArgumentIn its reconstruction of scientific practice, philosophy of science has traditionally placed scientific theories in a central role, and has reduced the problem of mediating between theories and the world to formal considerations. Many applications of scientific theories, however, involve complex mathematical models whose constitutive equations are analytically unsolvable. The study of these applications often consists in developing representations of the underlying physics on a computer, and using the techniques of computer simulation in order to learn about the behavior (...) of these systems. In many instances, these computer simulations are not simple number-crunching techniques. They involve a complex chain of inferences that serve to transform theoretical structures into specific concrete knowledge of physical systems. In this paper I argue that this process of transformation has its own epistemology. I also argue that this kind of epistemology is unfamiliar to most philosophy of science, which has traditionally concerned itself with the justification of theories, not with their application. Finally, I urge that the nature of this epistemology suggests that the end results of some simulations do not bear a simple, straightforward relation to the theories from which they stem. (shrink)

Large scale experiments at CERN’s Large Hadron Collider rely heavily on computer simulations, a fact that has recently caught philosophers’ attention. CSs obviously require appropriate modeling, and it is a common assumption among philosophers that the relevant models can be ordered into hierarchical structures. Focusing on LHC’s ATLAS experiment, we will establish three central results here: with some distinct modifications, individual components of ATLAS’ overall simulation infrastructure can be ordered into hierarchical structures. Hence, to a good degree of approximation, (...) hierarchical accounts remain valid at least as descriptive accounts of initial modeling steps. In order to perform the epistemic function Winsberg Model-based reasoning in scientific discovery. Kluwer Academic/plenum Publishers, New York, pp 255–269, 1999) assigns to models in simulation—generate knowledge through a sequence of skillful but non-deductive transformations—ATLAS’ simulation models have to be considered part of a network rather than a hierarchy, in turn making the associated simulation modeling messy rather than motley. Deriving knowledge-claims from this ‘mess’ requires two sources of justification: holistic validation :253–262, 2010; in Carrier M, Nordmann A Science in the context of application. Springer, Berlin, pp 115–130, 2011), and model coherence. As it turns out, the degree of model coherence sets HEP apart from other messy, simulation-intensive disciplines such as climate science, and the reasons for this are to be sought in the historical, empirical and theoretical foundations of the respective discipline. (shrink)

With the author(s)’ decision to opt for Open Choice the copyright of the article changed on 26 May 2021 to ©The Author(s) 2021 and the article is forthwith distributed under a Creative Commons Attribution.

ArgumentWe continue our analysis of modeling practices that focus more on qualitative understanding of system behavior than the attempt to provide sharp forecasts. The argument here is built around three episodes: the ambitious work of the Princeton Meteorological Project; the seemingly simple models of convection in weather systems by Edward Lorenz at MIT; and then finally analysis of the dripping faucet by Robert Shaw and the Dynamical Systems Collective at UC Santa Cruz. Using the Princeton Meteorological Project as an argumentative (...) foil for the later chaos work of Lorenz and Shaw, we first show how the epistemological interest of modeling came to shift fromissuingpredictions toprobingthe very meaning and limits of prediction. The second step of our argument shows that what may be seen in one context of use as a modeling technology that is error ridden, imprecise, or inadequate, may be parsed completely differently in another context. This argument about technology and practice, we argue, feeds through to epistemological conceptions of error. Far from being something that can be defined in the absolute, the notion of error is shown to be contextually plastic. (shrink)

Allan Franklin has identified a number of strategies that scientists use to build confidence in experimental results. This paper shows that Franklin's strategies have direct analogues in the context of computer simulation and then suggests that one of his strategies—the so-called 'Sherlock Holmes' strategy—deserves a privileged place within the epistemologies of experiment and simulation. In particular, it is argued that while the successful application of even several of Franklin's other strategies (or their analogues in simulation) may not be sufficient for (...) justified belief in results, the successful application of a slightly elaborated version of the Sherlock Holmes strategy is sufficient. (shrink)

ArgumentThis paper is the first part of a two-part examination of computer modeling practice and philosophy. It discusses electrical engineer Jay Forrester's work on Industrial Dynamics, later called System Dynamics. Forrester developed Industrial Dynamics after being recruited to the newly-established School of Industrial Management at the Massachusetts Institute of Technology (MIT), which had been seeking a novel pedagogical program for management for five years before Forrester's arrival. We argue that Industrial Dynamics should be regarded in light of this institutional context. (...) Unlike economics, as well as operations research and management science (twin fields which were also gaining traction at that time), Industrial Dynamics was not meant to be applied by mathematicians and technical specialists in consultation with managers, but by managers themselves. This concern shifted the emphasis in modeling from the specialist act of analysis of a crucial problem to the managerial act of choosing which problems were crucial and developing effective policies around them. (shrink)

Computers have transformed science and help to extend the boundaries of human knowledge. However, does the validation and diffusion of results of computational inquiries and computer simulations call for a novel epistemological analysis? I discuss how the notion of novelty should be cashed out to investigate this issue meaningfully and argue that a consequentialist framework similar to the one used by Goldman to develop social epistemologySocial epistemology can be helpful at this point. I highlight computational, mathematical, representational, and (...) social stages on which the validity of simulation-based belief-generating processes hinges, and emphasize that their epistemic impact depends on the scientific practices that scientists adopt at these different stages. I further argue that epistemologists cannot ignore these partially novel issues and conclude that the epistemology of computational inquiries needs to go beyond that of models and scientific representations and has cognitive, social, and in the present case computational, dimensions. (shrink)

"Der Aufsatz plädiert dafür, die Geschichte der wissenschaftlichen Computersimulation auf eine spezifisch medienhistorische Weise zu untersuchen. Nach einigen Vorschlägen zur Charakterisierung der Besonderheiten von Computersimulationen werden zwei Beispiele interpretiert (Management-Simulationen der 1960er und verkehrstechnische bzw. epidemiologische Simulationen der 1990er). Daraus leiten sich Fragen nach dem veränderten Status wissenschaftlichen Wissens, nach der Genese wissenschaftstheoretischer Konzepte und nach wissenschaftskritischen Optionen ab. The paper suggests to analyze the history of scientific computer simulations with respect to the history of media. After presenting some (...) ideas concerning the peculiarities of computer simulation, two examples (management simulations of the 1960s; traffic-related and epistemological simulations of the 1990s) are interpreted. From them, further questions concerning the status of scientific knowledge, the genesis of epistemological concepts and their critique are derived. ". (shrink)

An introduction to three papers in a special issue.

The paper suggests to analyze the history of scientific computer simulations with respect to the history of media. After presenting some ideas concerning the peculiarities of computer simulation, two examples (management simulations of the 1960s; traffic-related and epistemological simulations of the 1990s) are interpreted. From them, further questions concerning the status of scientific knowledge, the genesis of epistemological concepts and their critique are derived.

Artificial intelligence has often been seen as an attempt to reduce the natural mind to informational processes and, consequently, to naturalize philosophy. The many criticisms that were addressed to the so-called “old-fashioned AI” do not concern this attempt itself, but the methods it used, especially the reduction of the mind to a symbolic level of abstraction, which has often appeared to be inadequate to capture the richness of our mental activity. As a consequence, there were many efforts to evacuate the (...) semantical models in favor of elementary physiological mechanisms simulated by information processes. However, these views, and the subsequent criticisms against artificial intelligence that they contain, miss the very nature of artificial intelligence, which is not reducible to a “science of the nature”, but which directly impacts our culture. More precisely, they lead to evacuate the role of the semantic information. In other words, they tend to throw the baby out with the bath-water. This paper tries to revisit the epistemology of artificial intelligence in the light of the opposition between the “sciences of nature” and the “sciences of culture”, which has been introduced by German neo-Kantian philosophers. It then shows how this epistemological view opens on the many contemporary applications of artificial intelligence that have already transformed—and will continue to transform—all our cultural activities and our world. Lastly, it places those perspectives in the context of the philosophy of information and more particularly it emphasizes the role played by the notions of context and level of abstraction in artificial intelligence. (shrink)

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

The justification of induction is of central significance for cross-cultural social epistemology. Different ‘epistemological cultures’ do not only differ in their beliefs, but also in their belief-forming methods and evaluation standards. For an objective comparison of different methods and standards, one needs (meta-)induction over past successes. A notorious obstacle to the problem of justifying induction lies in the fact that the success of object-inductive prediction methods (i.e., methods applied at the level of events) can neither be shown to be (...) universally reliable (Hume's insight) nor to be universally optimal. My proposal towards a solution of the problem of induction is meta-induction. The meta-inductivist applies the principle of induction to all competing prediction methods that are accessible to her. By means of mathematical analysis and computer simulations of prediction games I show that there exist meta-inductive prediction strategies whose success is universally optimal among all accessible prediction strategies, modulo a small short-run loss. The proposed justification of meta-induction is mathematically analytical. It implies, however, an a posteriori justification of object-induction based on the experiences in our world. In the final section I draw conclusions about the significance of meta-induction for the social spread of knowledge and the cultural evolution of cognition, and I relate my results to other simulation results which utilize meta-inductive learning mechanisms. (shrink)

The present paper introduces a simple framework for modeling the relationship between environmental states, perceptual states, and action. The framework represents situations where an agent’s perceptual state forms the basis for choosing an action, and what action the agent performs determines the agent’s payoff, as a function of the environmental conditions in which the action is performed. The framework is used as the basis for a simulation study of the sorts of correspondence between perceptual and environmental states that are important (...) for successful navigation of the world. Some of the results are surprising and conflict with long held views about the kind of perception-to-environment correspondence that is important for knowledge of the world. The results also raise doubts concerning the view that our perceptual states provide a basis for knowledge of the real structure of the external world. (shrink)

In the second half of the twentieth-century, the traditional problem of other minds was re-focused on special problems with propositional attitudes and how we attribute them to others. How do ordinary people, with no education in scientific psychology, understand and ascribe such complex, unobservable states? In different terminology, how do they go about "interpreting" their peers?

Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science , but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the (...) class='Hi'>epistemology and semantics of models and theories, and the relation between experimentation and theorising, and submit that simulations demand a fundamentally new philosophy of science in many respects. The aim of this paper is to critically evaluate these claims. Our conclusion will be sober. We argue that these claims are overblown and that simulations, far from demanding a new metaphysics, epistemology, semantics and methodology, raise few if any new philosophical problems. The philosophical problems that do come up in connection with simulations are not specific to simulations and most of them are variants of problems that have been discussed in other contexts before. (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)

Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science, but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology (...) and semantics of models and theories, and the relation between experimentation and theorising, and submit that simulations demand a fundamentally new philosophy of science in many respects. The aim of this paper is to critically evaluate these claims. Our conclusion will be sober. We argue that these claims are overblown and that simulations, far from demanding a new metaphysics, epistemology, semantics and methodology, raise few if any new philosophical problems. The philosophical problems that do come up in connection with simulations are not specific to simulations and most of them are variants of problems that have been discussed in other contexts before. (shrink)

Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science, but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology (...) and semantics of models and theories, and the relation between experimentation and theorising, and submit that simulations demand a fundamentally new philosophy of science in many respects. The aim of this paper is to critically evaluate these claims. Our conclusion will be sober. We argue that these claims are overblown and that simulations, far from demanding a new metaphysics, epistemology, semantics and methodology, raise few if any new philosophical problems. The philosophical problems that do come up in connection with simulations are not specific to simulations and most of them are variants of problems that have been discussed in other contexts before. (shrink)

Whereas experiments and computer simulations seem very different at first view because the former, but not the latter, involve interactions with material properties, we argue that this difference is not so important with respect to validation, as far as epistemologyEpistemology is concerned. Major differences remain nevertheless from the methodological point of view. We present and defend this distinction between epistemology and methodology. We illustrate this distinction and related claims by comparing how experiments and simulations are validated in (...) evolutionary studies, a domain in which both experiments in the lab and computer simulations are relatively new but mutually reinforcing. (shrink)

Form follows function, but it does not follow from function. Form is not derivable from the latter. To realize a desired technical function, a form must first be found that is able to realize it at all. Secondly, the question arises as to whether an envisaged form realizes the function in an appropriate way. Functions are multiply realizable—various different forms can bear the very same function. One needs to find a form of a technical artifact that realizes an envisaged function (...) sufficiently efficient, robust, or whatever criteria might be imposed. This paper scrutinizes biomimetics as one way to find a good solution to the realization problem. Drawing on an approach from the philosophy of simulations, it reconstructs the biomimetic relation as being mediated by a theoretical model. It is shown that the robustness of the functioning system is usually reached in different ways in biological and in technological systems, which explains differences in morphogenetic mechanisms or principles found in these fields. This reconstruction helps to understand problems with robustness in synthetic biology that occur when technical design principles are implemented in a biological system. The mimetic relation between the biological and the technical realm is found to be asymmetric. (shrink)

As the editors of the series, New Literary Theory, proclaim in the preface of the book, the purpose of the series is to make more room in literary theory for playful and accessible approaches to li...

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 (...) class='Hi'>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)

In the Western tradition, at least since the 14th century, the philosophy of knowledge has been built around the idea of knowledge as a representation [Boulnois 1999]. The question of the evaluation of knowledge refers at the same time (1) to the object represented (which one does one represent?), (2) to the process of knowledge formation, in particular with the role of the knowing subject (which one does one represent and how does one represent it?), and finally (3) to the (...) relationship between the representation and the represented object. Criteria of evaluation such as “validity”, “adequacy” or “truth”, as mentioned in chapter 4, make sense only with respect to these three dimensions. An evaluation can thus (1) depend on the ontological nature of the object of knowledge, (2) relate to the relationship between subject and object—including the structures (cognitive, social) which organize this relationship, or (3) relate to the relation of similarity between the object and its representation as well. The relevant criteria of evaluation thus depend on the points of view adopted on these questions. As there are indeed a plurality of points of view in this field, the goal of this appendix is to summarize, as briefly as possible, the various positions adopted by the philosophers and to refer to the relevant texts of reference for more information. The first section introduces useful discussions about the philosophy of theoretical knowledge and general epistemology, from a quasi-historical perspective. Section two discusses the intermediary but central notion of models. Section three, more exploratory, intro-duces an approach to simulation as “concrete experiment”. It suggests that such a frequent claim in the literature, when precisely evaluated, can, to some extent, renew both the representational and the linguistic views on simulation. (shrink)

This book constitutes the revised versions of the invited and selected papers from the Second Epistemological Perspectives on Simulation Workshop, EPOS 2006, which was held in Brescia, Italy, during October 5-6, 2006. The 11 papers presented together with 2 invited papers were carefully reviewed and selected from 35 submissions. The topics addressed were epistemological and methodological contents, such as the relevance of empirical foundations for agent-based simulations, the role of theory, the concepts and meaning of emergence, the trade-off between (...) simplification and complexification of models. (shrink)

Computer simulations are widely used within the area of building science research. Building science research deals with the physical phenomena that affect buildings, including heat and mass transfer, lighting and acoustic transmission. This wide usage of computer simulations, however, is characterized by a divergence in thought on the composition of an epistemological framework that may provide guidance for their deployment in research. This paper undertakes a fundamental review of the epistemology of computer simulations within the context (...) of the philosophy of science. Thereafter, it reviews the epistemological framework within which computer simulations are used in practice within the area of building science research. A comparison between the insights obtained from the realms of theory and practice is made, which then interrogates the adequacy of the epistemological approaches that have been employed in previously published simulation-based research. These insights may help in informing a normative composition of an adequate epistemological framework within which computer simulation-based building science research may be conducted. (shrink)

Astrophysics is often cast as an observational science, devoid of traditional experiments, along with astronomy and cosmology. Yet, a thriving field of experimental research exists called laboratory astrophysics. How should we make sense of this apparent tension? I argue that approaching the epistemology of astrophysics by attending to the production of empirical data and the aims of the research better illuminates both the successes and challenges of empirical research in astrophysics than evaluating the epistemology of astrophysics according to (...) the presence or absence of experiments. (shrink)

We discuss three philosophically interesting epistemic peculiarities of black hole astrophysics: (1) issues concerning whether and in what sense black holes do exist; (2) how to best approach multiplicity of available definitions of black holes; (3) short (i.e., accessible within an individual human lifespan) dynamical timescales present in many of the recent, as well as prospective, observations involving black holes. In each case we argue that the prospects for our epistemic situation are optimistic.

Simulation, if used as a way of becoming aware of other people’s mental states, is the joint exercise of imagination and attribution. If A simulates B, then (i) A attributes to B the mental state in which A finds herself at the end of a process in which (ii) A has imagined being in B’s situation. Although necessary, imagination and attribution are not sufficient for simulation: the latter occurs only if (iii) the imagination process grounds or justifies the attribution. Depending (...) on the notion of justification we use to make sense of the idea that an episode of imagining serves as a reason for attributing a mental state, the shape of the debate and the options it offers look very different. Reconfiguring the discussion in this way, we claim, shifts the focus of the simulation vs. theory-theory debate to a question located in epistemology. (shrink)

In a seminal book, Alvin I. Goldman outlines a theory for how to evaluate social practices with respect to their “veritistic value”, i.e., their tendency to promote the acquisition of true beliefs in society. In the same work, Goldman raises a number of serious worries for his account. Two of them concern the possibility of determining the veritistic value of a practice in a concrete case because we often don't know what beliefs are actually true, and even if we did, (...) the task of determining the veritistic value would be computationally extremely difficult. Neither problem is specific to Goldman's theory and both can be expected to arise for just about any account of veritistic value. It is argued here that the first problem does not pose a serious threat to large classes of interesting practices. The bulk of the paper is devoted to the computational problem, which, it is submitted, can be addressed in promising terms by means of computer simulation. In an attempt to add vividness to this proposal, an up-and-running simulation environment is presented and put to some preliminary tests. (shrink)

In the Western tradition, at least since the 14th century, the philosophy of knowledge has been built around the idea of knowledge as a representation [BOU 99]. The question of the evaluation of knowledge refers at the same time (1) to the object represented (which one does one represent?), (2) to the process of knowledge formation, in particular with the role of the knowing subject (which one does one represent and how does one represent it?), and finally (3) to the (...) relationship between the representation and the represented object. Criteria of evaluation such as “validity”, “adequacy” or “truth”, as mentioned in [AMB 07], make sense only with respect to these three dimensions. An evaluation can thus (1) depend on the ontological nature of the object of knowledge, (2) relate to the relationship between subject and object—including the structures (cognitive, social) which organize this relationship, or (3) relate to the relation of similarity between the object and its representation as well. The relevant criteria of evaluation thus depend on the points of view adopted on these questions. As there are indeed a plurality of points of view in this field, the goal of this appendix is to summarize, as briefly as possible, the various positions adopted by the philosophers and to refer to the relevant texts of reference for more information (for a first outline [CHA 76, SCH 01]). The first section 1 introduces useful discussions about the philosophy of theoretical knowledge and general epistemology, from a quasi-historical perspective. Section two 2 discusses the intermediary but central notion of models. Section three 3, more exploratory, introduces an approach to simulation as “concrete experiment”. It suggests that such a frequent claim in the literature, when precisely evaluated, can, to some extent, renew both the representational and the linguistic views on simulation. (shrink)

In a seminal book, Alvin I. Goldman outlines a theory for how to evaluate social practices with respect to their , i.e., their tendency to promote the acquisition of true beliefs (and impede the acquisition of false beliefs) in society. In the same work, Goldman raises a number of serious worries for his account. Two of them concern the possibility of determining the veritistic value of a practice in a concrete case because (1) we often don't know what beliefs are (...) actually true, and (2) even if we did, the task of determining the veritistic value would be computationally extremely difficult. Neither problem is specific to Goldman's theory and both can be expected to arise for just about any account of veritistic value. It is argued here that the first problem does not pose a serious threat to large classes of interesting practices. The bulk of the paper is devoted to the computational problem, which, it is submitted, can be addressed in promising terms by means of computer simulation. In an attempt to add vividness to this proposal, an up-and-running simulation environment (Laputa) is presented and put to some preliminary tests. (shrink)

The epistemology of computer simulations has become a mainstream topic in the philosophy of technology. Within this large area, significant differences hold between the various types of models and simulation technologies. Agent-based and multi-agent systems simulations introduce a specific constraint on the types of agents and systems modelled. We argue that such difference is crucial and that simulation for the artificial sciences requires the formulation of its own specific epistemological principles. We present a minimally committed epistemology (...) which relies on the methodological principles of the Philosophy of Information and requires weak assumptions on the usability of the simulation and the controllability of the model. We use these principles to provide a new definition of simulation for the context of interest. (shrink)

The literature in agent-based social simulation suggests that a model is validated when it is shown to ‘successfully’, ‘adequately’ or ‘satisfactorily’ represent the target phenomenon. The notion of ‘successful’, ‘adequate’ or ‘satisfactory’ representation, however, is both underspecified and difficult to generalise, in part, because practitioners use a multiplicity of criteria to judge representation, some of which are not entirely dependent on the testing of a computational model during validation processes. This article argues that practitioners should address social epistemology to (...) achieve a deeper understanding of how warrants for belief in the adequacy of representation are produced. Two fundamental social processes for validation: interpretation and commensuration, are discussed to justify this claim. The analysis is advanced with a twofold aim. First, it shows that the conceptualisation of validation could greatly benefit from incorporating elements of social epistemology, for the criteria used to judge adequacy of representation are influenced by the social, cognitive and physical organisation of social simulation. Second, it evidences that standardisation tools such as protocols and frameworks fall short in accounting for key elements of social epistemology that affect different instances of validation processes. (shrink)

As climate policy decisions are decisions under uncertainty, being based on a range of future climate change scenarios, it becomes a crucial question how to set up this scenario range. Failing to comply with the precautionary principle, the scenario methodology widely used in the Third Assessment Report of the International Panel on Climate Change (IPCC) seems to violate international environmental law, in particular a provision of the United Nations Framework Convention on Climate Change. To place climate policy advice on a (...) sound methodological basis would imply that climate simulations which are based on complex climate models had, in stark contrast to their current hegemony, hardly an epistemic role to play in climate scenario analysis at all. Their main function might actually consist in ‘foreseeing future ozone-holes’. In order to argue for these theses, I explain first of all the plurality of climate models used in climate science by the failure to avoid the problem of underdetermination. As a consequence, climate simulation results have to be interpreted as modal sentences, stating what is possibly true of our climate system. This indicates that climate policy decisions are decisions under uncertainty. Two general methodological principles which may guide the construction of the scenario range are formulated and contrasted with each other: modal inductivism and modal falsificationism. I argue that modal inductivism, being the methodology implicitly underlying the third IPCC report, is severely flawed. Modal falsificationism, representing the sound alternative, would in turn require an overhaul of the IPCC practice. (shrink)

CHAPTER Simulation theory and mental concepts Alvin I. Goldman Rutgers University. Folk psychology and the TT-ST debate The study of folk psychology, ...

Following a distinction of the sciences into exact and inexact, based on the kinds of methods and reasoning processes involved in explanation and prediction, the authors briefly develop the peculiar method of the inexact branch, with special emphasis on the systematic use of experts in the formation of limited scientific generalizations, and on the use of simulated experiments.-- J. A. B.

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)

In this chapter I attempt to curb the pretensions of simulationism. I argue that it is, at best, an epistemological doctrine of limited scope. It may explain how we go about attributing beliefs and desires to others, and perhaps to ourselves, in some cases. But simulation cannot provide the fundamental basis of our conception of, or knowledge of, minded agency.

Morrison points out many similarities between the roles of simulation models and other sorts of models in science. On the basis of these similarities she claims that running a simulation is epistemologically on a par with doing a traditional experiment and that the output of a simulation therefore counts as a measurement. I agree with her premises but reject the inference. The epistemological payoff of a traditional experiment is greater (or less) confidence in the fit between a model and a (...) target system. The source of this payoff is the existence of a causal interaction with the target system. A computer experiment, which does not go beyond the simulation system itself, lacks any such interaction. So computer experiments cannot confer any additional confidence in the fit (or lack thereof) between the simulation model and the target system. (shrink)

At least since Kuhn’s Structure, philosophers have studied the influence of social factors in science’s pursuit of truth and knowledge. More recently, formal models and computer simulations have allowed philosophers of science and social epistemologists to dig deeper into the detailed dynamics of scientific research and experimentation, and to develop very seemingly realistic models of the social organization of science. These models purport to be predictive of the optimal allocations of factors, such as diversity of methods used in science, (...) size of groups, and communication channels among researchers. In this paper we argue that the current research faces an empirical challenge. The challenge is to connect simulation models with data. We present possible scenarios about how the challenge may unfold. (shrink)

In this paper, we provide an analysis about the relationship between expert and pragmatic categories used to name and categorize social space, from the point of view of the social hierarchies that structure it. Taking the case of Chile, and applying an experimental simulation methodology, we seek to contribute to the epistemological debate about the representations that individuals elaborate about social space, from a cognitive and interactionist perspective of everyday life. We analyse in particular the scope of the concept of (...) social class as an element of expert description of social reality, but also as a categorization used by ordinary subjects. We argue that although classifications used by ordinary people to describe members of society are not consistent with the most common academic class schemes, they nevertheless share the same logic and ranking system, adding evaluative and normative elements to the cognitive distinction, in the social construction of reality. En este artículo, presentamos un análisis acerca de la relación entre categorías expertas y categorías pragmáticas usadas para nombrar y categorizar el espacio social, desde el punto de vista de las jerarquías sociales que lo estructuran. Tomando el caso de Chile y aplicando una metodología experimental de simulación, aportamos al debate epistemológico acerca de las representaciones que los sujetos sociales elaboran acerca del espacio social, desde una perspectiva cognitiva e interaccionista situada en la vida cotidiana de las personas. Analizamos en especial el alcance del concepto de clase social en cuanto elemento de descripción experta de la realidad social, contrastándolo con la categorización de la misma de parte de sujetos comunes y corrientes. Postulamos que si bien las clasificaciones que ocupan las personas comunes y corrientes para describir a los integrantes de la sociedad no se condicen con las clasificaciones académicas más comunes, comparten sin embargo las mismas lógicas clasificatorias de jerarquización y ordenamiento, agregándose a la distinción cognitiva elementos normativos que aportan a la construcción social de la realidad. (shrink)

Simulation Theory says we need not rely exclusively on prepositional knowledge of other minds in order to explain the actions of others. Seeking to know what you will do, I imagine myself in your situation, and see what decision I come up with. I argue that this conception of simulation naturally generalizes: various bits of our mental machine can be run‘off‐line’, fulfilling functions other than those they were made for. In particular, I suggest that visual imagery results when the visual (...) system is run off‐line. I briefly review the empirical evidence and consider the philosophical implications, particularly concerning the mode of mental representation in imagery. (shrink)

In this paper, I hope to show how a recent theory in the philosophy of mind concerning how we ‘read’ the minds of others – namely, Heal’s version of simulation theory – is consistent with the view that the kind of understanding we bring to bear on the irrational is different in kind from the way we understand one another in the course of everyday life. I shall attempt to show that Heal’s version of simulation theory (co-cognition) is to be (...) favoured over its rival ‘theory theories’ in the light of its accommodation of deeply irrational mental states. I claim that simulation theory preserves an asymmetry of psychological explanation which is not similarly preserved by a theory theory account, and I argue that this preservation of asymmetry is an advantage of Heal’s account. (shrink)

After showing how Deborah Mayo’s error-statistical philosophy of science might be applied to address important questions about the evidential status of computer simulation results, I argue that an error-statistical perspective offers an interesting new way of thinking about computer simulation models and has the potential to significantly improve the practice of simulation model evaluation. Though intended primarily as a contribution to the epistemology of simulation, the analysis also serves to fill in details of Mayo’s epistemology of experiment.