The notion of Simulative Reasoning in the study of propositional attitudes within Artificial Intelligence (AI) is strongly related to the Simulation Theory of mental ascription in Philosophy. Roughly speaking, when an AI system engages in Simulative Reasoning about a target agent, it reasons with that agent’s beliefs as temporary hypotheses of its own, thereby coming to conclusions about what the agent might conclude or might have concluded. The contrast is with non-simulative meta-reasoning, where the (...) AI system reasons within a detailed theory about the agent’s (conjectured) reasoning acts. The motive within AI for preferring Simulative Reasoning is that it is more convenient and efficient, because of a simplification of the representations and reasoning processes. The chapter discusses this advantage in detail. It also sketches the use of Simulative Reasoning in an AI natural language processing system, ATT-Meta, that is currently being implemented. This system is directed at the understanding of propositional attitude reports. In ATT-Meta, Simulative Reasoning is yoked to a somewhat independent set of ideas about how attitude reports should be treated. Central here are the claims that (a) speakers often employ commonsense (and largely metaphorical) models of mind in describing agents’ attitudes, (b) the listener accordingly needs often to reason within the terms of such models, rather than on the basis of any objectively justifiable characterization of the mind, and (c) the commonsense models filter the suggestions that Simulative Reasoning comes up with concerning target agents’ reasoning conclusions. There is a yet tighter connection between the commonsense models and the Simulative Reasoning. It turns out that Simulative Reasoning can be rationally reconstructed in terms of a more general type of reasoning about the possibly-counterfactual “world” that the target agent believes in, together with an assumption that that agent has a faithful representation of the world. In the ATT-Meta approach, the reasoner adopts that assumption when it views the target agent through a particular commonsense model (called IDEAS-AS-MODELS). (shrink)

Astrophysics faces methodological challenges as a result of being a predominantly observation-based science without access to traditional experiments. In light of these challenges, astrophysicists frequently rely on computer simulations. Using collisional ring galaxies as a case study, I argue that computer simulations play three roles in reasoning in astrophysics: (1) hypothesis testing, (2) exploring possibility space, and (3) amplifying observations.

Alvin Goldman's early work in action theory and theory of knowledge was a major influence on my own thinking and writing about emotions. For that reason and others, it was a very happy moment in my professional life when I learned, in 1988, that in his presidential address to the Society for Philosophy and Psychology Goldman endorsed and defended the “simulation” theory I had put forward in a 1986 article. I discovered afterward that we share a strong conviction that empirical (...) evidence is relevant to a full assessment of the theory. We both find the burgeoning evidence from cognitive neuroscience to be of particular interest, I believe, in part because it makes possible a major departure for the philosophy of mind: turning its attention from " the neural basis of mental states, " to. (shrink)

Computer simulations are widely used for surrogative reasoning in scientific research. They also play a crucial role in engineering, more specifically in the design of new robotic systems, yet the nature of this role has been little discussed so far in the philosophy of technology literature. The main claim made in this article is that the notion of surrogative reasoning is central to understanding how computer simulations can serve the purpose of designing new robots. More specifically, it is (...) argued that computer simulations can support two forms of surrogative reasoning, which are called model-oriented and prediction-oriented, whose inferential structure is reconstructed to some extent. And it is argued that, when computer simulations are used to design new robots, they are distinctively used in the model-oriented way. By unravelling the structure of the computer simulation-supported methods adopted in robotic design, this article may contribute to a finer-grained understanding of the epistemic processes involved in technological research. (shrink)

Reasoning patterns found in Galileo’s treatise on machines, On Mechanics, are compared with patterns identified in case studies of scientifically trained experts thinking aloud, and many similarities are found. At one level the primary patterns identified are ordered analogy sequences and special diagrammatic techniques to support them. At a deeper level I develop constructs to describe patterns that can support embodied, imagistic, mental simulations as a central underlying process. Additionally, a larger hypothesized pattern of ‘progressive imagistic generalization’—Galileo’s development of (...) a model or mechanism that becomes more and more general with each machine while still being imagistically projectable into many machines—provides a way to think about his progress toward a modern explanatory model of torque. By unpacking his arguments, we gain an appreciation of his skillful ability to foster imagistic processes underlying scientific thinking. (shrink)

The term conceptual simulation refers to a type of everyday reasoning strategy commonly called “what if” reasoning. It has been suggested in a number of contexts that this type of reasoning plays an important role in scientific discovery; however, little direct evidence exists to support this claim. This article proposes that conceptual simulation is likely to be used in situations of informational uncertainty, and may be used to help scientists resolve that uncertainty. We conducted two studies to (...) investigate the relationship between conceptual simulation and informational uncertainty. Study 1 was an in vivo study of expert scientists; the results suggest that scientists do use conceptual simulation in situations of informational uncertainty, and that they use conceptual simulation to make inferences from their data using the analogical reasoning process of alignment by similarity detection. Study 2 experimentally manipulated experts' level of uncertainty and provides further support for the hypothesis that conceptual simulation is more likely to be used in situations of informational uncertainty. Finally, we discuss the relationship between conceptual simulation and other types of reasoning using qualitative mental models. (shrink)

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)

Nick Bostrom’s ‘Simulation Argument’ purports to show that, unless we are confident that advanced ‘posthuman’ civilizations are either extremely rare or extremely rarely interested in running simulations of their own ancestors, we should assign significant credence to the hypothesis that we are simulated. I argue that Bostrom does not succeed in grounding this constraint on credence. I first show that the Simulation Argument requires a curious form of selective scepticism, for it presupposes that we possess good evidence for claims about (...) the physical limits of computation and yet lack good evidence for claims about our own physical constitution. I then show that two ways of modifying the argument so as to remove the need for this presupposition fail to preserve the original conclusion. Finally, I argue that, while there are unusual circumstances in which Bostrom’s selective scepticism might be reasonable, we do not currently find ourselves in such circumstances. There is no good reason to uphold the selective scepticism the Simulation Argument presupposes. There is thus no good reason to believe its conclusion. (shrink)

I present a new argument that we are much more likely to be living in a computer simulation than in the ground-level of reality. (Similar arguments can be marshalled for the view that we are more likely to be Boltzmann brains than ordinary people, but I focus on the case of simulations.) I explain how this argument overcomes some objections to Bostrom’s classic argument for the same conclusion. I also consider to what extent the argument depends upon an internalist conception (...) of evidence, and I refute the common line of thought that finding many simulations being run—or running them ourselves—must increase the odds that we are in a simulation. GPI Working Paper No. 16-2021. (shrink)

Simulation as an epistemic tool between theory and practice: A Comparison of the Relationship between Theory and Simulation in Science and in Folk Psychology In this paper I explore the concept of simulation that is employed by proponents of the so-called simulation theory within the debate about the nature and scientific status of folk psychology. According to simulation theory, folk psychology is not a sort of theory that postulates theoretical entities (mental states and processes) and general laws, but a practice (...) whereby we put ourselves into others’ shoes and simulate their situation from our own perspective. On the basis of this sort of simulation, we supposedly know how we would act or think or feel, and then expect the same of others. A closer look at the concept of simulation reveals some problems with this view, but also helps to clarify the insight motivating simulation theory. Specifically, I defend the thesis that the analogy to simulations in science shows us how theoretical elements in folk psychology can be complemented by (i.e. not replaced by) the central idea of simulation theory – namely that our own cognitive habits and dispositions provide us with a resource that is distinct from propositional knowledge in folk psychology. I also discuss the idea that our use of simulations during cognitive development enables us to imitate the people around us and thereby to become more similar to them, which in turn makes simulation an increasingly effective epistemic strategy. Insofar as theoretical elements – such as the distinctions, relations, and entities referred to in folk psychological discourse – play a role in imitative learning, they are causally embedded in our cognitive development, so we have good reason to regard them as being among the real causes of our behavior. (shrink)

It is often claimed that scientists can obtain new knowledge about nature by running computer simulations. How is this possible? I answer this question by arguing that computer simulations are arguments. This view parallels Norton’s argument view about thought experiments. I show that computer simulations can be reconstructed as arguments that fully capture the epistemic power of the simulations. Assuming the extended mind hypothesis, I furthermore argue that running the computer simulation is to execute the reconstructing argument. I discuss some (...) objections and reject the view that computer simulations produce knowledge because they are experiments. I conclude by comparing thought experiments and computer simulations, assuming that both are arguments. (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)

Simulation has emerged as an increasingly popular account of folk psychological (FP) talents at mind-reading: predicting and explaining human mental states. Where its rival (the theory-theory) postulates that these abilities are explained by mastery of laws describing the connections between beliefs, desires, and action, simulation theory proposes that we mind-read by "putting ourselves in another's shoes." This paper concerns connectionist architecture and the debate between simulation theory (ST) and the theory-theory (TT). It is only natural to associate TT with classical (...) architectures where rule governed operations apply to explicit propositional representations. On the other hand, ST would seem better tuned to procedurally oriented non-symbolic structures found in connectionist models. This paper explores the possible alignment between ST and connectionist architecture. Joe Cruz argues that connectionist models with distributed non-symbolic representations are particularly well suited to simulation theory. The purported linkage between connectionist architecture and simulation theory is criticized in this paper. The conclusion is that there are reasons for thinking that connectionist forms of representation are the enemy of both TT and ST. So the contribution of connectionism may be to suggest the need for an alternative to both views. (shrink)

Mindreading (or folk psychology, Theory of Mind, mentalizing) is the capacity to represent and reason about others’ mental states. The Simulation Theory (ST) is one of the main approaches to mindreading. ST draws on the common-sense idea that we represent and reason about others’ mental states by putting ourselves in their shoes. More precisely, we typically arrive at representing others’ mental states by simulating their mental states in our own mind. This entry offers a detailed analysis of ST, considers theoretical (...) arguments and empirical data in favour of and against it, discusses its philosophical implications, and illustrates some alternatives to it. (shrink)

This article provides a survey of some of the reasons why computational approaches have become a permanent addition to the set of scientific methods. The reasons for this require us to represent the relation between theories and their applications in a different way than do the traditional logical accounts extant in the philosophical literature. A working definition of computer simulations is provided and some properties of simulations are explored by considering an example from quantum chemistry.

A program for the simulation of rational social normative trust, predictive `trust,' and predictive reliance between agents will be introduced. It offers a tool for social scientists or a trust component for multi-agent simulations/multi-agent systems, which need to include trust between agents to guide the decisions about the course of action. It is based on an analysis of rational social normative trust (RSNTR) (revised version of M. Tuomela 2002), which is presented and briefly argued. For collective agents, belief conditions for (...) collective agency should be added. For the various forms of trust agents must have (at least) subjectively rational reasons to believe that the conditions of the trust account are fulfilled. A list of such reasons (of varied weights), e.g., given by empirical research, can manually be built into a parameter file or be generated by a calling program in a fixed format. From this list of reasons the program randomly generates a belief base for the agents of the artificial society. Reasons can be chained together so that one set of reasons satisfies several belief conditions. The program checks if the conditions are fulfilled for the artificial agents' social normative trust/predictive `trust'/`predictive reliance' in another agent that he will perform an action X. Each outcome is logged to a result file. In conclusion we discuss various aspects of the application of a trust component of the suggested kind in empirical research, social simulation, and multi-agent systems. (shrink)

Although scientific models and simulations differ in numerous ways, they are similar in so far as they are posing essentially philosophical problems about the nature of representation. This collection is designed to bring together some of the best work on the nature of representation being done by both established senior philosophers of science and younger researchers. Most of the pieces, while appealing to existing traditions of scientific representation, explore new types of questions, such as: how understanding can be developed within (...) computational science; how the format of representations matters for their use, be it for the purpose of research or education; how the concepts of emergence and supervenience can be further analyzed by taking into account computational science; or how the emphasis upon tractability--a particularly important issue in computational science--sheds new light on the philosophical analysis of scientific reasoning. (shrink)

ArgumentA number of theorists have proposed simulation theories of empathy. A review of these theories shows that, despite the fact that one version of the simulation theory can avoid a number of problems associated with such approaches, there are further reasons to doubt whether simulation actually explains empathy. A high-level simulation account of empathy, distinguished from the simulation theory of mindreading, can avoid problems associated with low-level (neural) simulationist accounts; but it fails to adequately address two other problems: the diversity (...) problem and the starting problem. It is argued that a narrative approach to empathy obviates all these problems and offers a more parsimonious account. (shrink)

Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might. Following H. Simon's notion of satisficing, the authors have proposed a family of algorithms based on a simple psychological mechanism: one-reason decision making. These fast and frugal algorithms violate fundamental tenets of classical rationality: They neither look up nor integrate all information. By computer simulation, the (...) authors held a competition between the satisficing "Take The Best" algorithm and various "rational" inference procedures. The Take The Best algorithm matched or outperformed all competitors in inferential speed and accuracy. This result is an existence proof that cognitive mechanisms capable of successful performance in the real world do not need to satisfy the classical norms of rational inference. (shrink)

Reasons are given to justify the claim that computer simulations and computational science constitute a distinctively new set of scientific methods and that these methods introduce new issues in the philosophy of science. These issues are both epistemological and methodological in kind.

Modeling and computer simulations, we claim, should be considered core philosophical methods. More precisely, we will defend two theses. First, philosophers should use simulations for many of the same reasons we currently use thought experiments. In fact, simulations are superior to thought experiments in achieving some philosophical goals. Second, devising and coding computational models instill good philosophical habits of mind. Throughout the paper, we respond to the often implicit objection that computer modeling is “not philosophical.”.

Simulations can only simulate knowledge.

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

Is it impossible to imaginatively simulate what it’s like to be someone with a different gender experience – to understand them empathically? Or is it simply difficult, a challenge requiring effort and dedication? I first distinguish three different sorts of obstacle to empathic understanding that are sometimes discussed: Missing Ingredient problems, Awkward Combination Problems, and Inappropriate Background Problems. I then argue that, although all three should be taken seriously, there is no clear reason to think that any of them are (...) both genuinely intractable and also significant impediments to the kind of empathic understanding we might want. To that extent, the challenge to interpersonal understanding here may be more a matter of skill and work than a hard boundary. (shrink)

Computer models and simulations have provided enormous benefits to researchers in the natural and social sciences, as well as many areas of philosophy. However, to date, there has been little attempt to use computer models in the development and evaluation of metaphysical theories. This is a shame, as there are good reasons for believing that metaphysics could benefit just as much from this practice as other disciplines. In this paper I assess the possibilities and limitations of using computer models in (...) metaphysics. I outline the way in which different kinds of model could be useful for different areas of metaphysics, and I illustrate in more detail how agent-based models specifically could be used to model two well-known theories of laws: David Lewis’s "Best System Account" and David Armstrong's "Nomic Necessitation" view. Some logically possible processes cannot be simulated on a standard computing device. I finish by assessing how much of a threat this is to the prospect of metaphysical modeling in general. (shrink)

Teaching the nuances of ethical decision making is particularly challenging in fully online, asynchronous courses where real-time discussion is not an option. Digital simulations, in the context of an integrative online ethics course, can offer applied learning and assessment experiences. However, scholarship on the impact of digital simulations for teaching ethical decision making is limited. The purpose of this study is to explore whether digital simulation used as an assessment for ethical reasoning and complex decision making is effective in (...) helping business students heighten their awareness of values in tension and discern ethical paths to resolution. We conduct an exploratory analysis of two ethics simulations used in teaching nine graduate business ethics classes offered in an asynchronous, online format. We discuss the results in terms of the Markkula Center for Applied Ethics’ five-step ethical decision making framework and outline implications for teaching and future research on business ethics simulations. (shrink)

Interest in thought experiments (TEs) derives from the paradox: “How can findings that carry conviction result from a new experiment conducted entirely within the head?” Historical studies have established the importance of TEs in science but have proposed disparate hypotheses concerning the source of knowledge in TEs, ranging from empiricist to rationalist accounts. This article analyzes TEs in think‐aloud protocols of scientifically trained experts to examine more fine‐grained information about their use. Some TEs appear powerful enough to discredit an existing (...) theory—a disconfirmatory purpose. In addition, confirmatory and generative purposes were identified for other TEs. One can also use details in transcript data, including imagery reports and gestures, to provide evidence for a central role played by imagistic simulations in many TEs, and to suggest that these simulations can generate new knowledge using several sources, including the “extended application” of perceptual motor schemas, implicit prior knowledge, and spatial reasoning operations, in contrast to formal arguments. These sources suggest what it means for TEs to be grounded in embodied processes that can begin to explain the paradox above. This leads to a rationalistic view of TEs as using productive internal reasoning, but the view also acknowledges the historical role that experience with the world can play in forming certain schemas used in TEs. Understanding such processes could help provide a foundation for developing a larger model of scientific investigation processes grounded on imagistic simulation (Clement, 2008). (shrink)

Anthony Brueckner, in a recent article, proffers ‘a new way of thinking about Bostrom's Simulation Argument’ . His comments, however, misconstrue the argument; and some words of explanation are in order.The Simulation Argument purports to show, given some plausible assumptions, that at least one of three propositions is true . Roughly stated, these propositions are: almost all civilizations at our current level of development go extinct before reaching technological maturity; there is a strong convergence among technologically mature civilizations such that (...) almost all of them lose interest in creating ancestor-simulations; almost all people with our sorts of experiences live in computer simulations. I also argue that conditional on you should assign a very high credence to the proposition that you live in a computer simulation. However, pace Brueckner, I do not argue that we should believe that we are in simulation. 1 In fact, I believe that we are probably not simulated. The Simulation Argument purports to show only that, as well as , at least one of – is true; but it does not tell us which one.Brueckner also writes: " It is worth noting that one reason why Bostrom thinks that the number of Sims [computer-generated minds with experiences similar to those typical of normal, embodied humans living in a Sim-free early 21 st century world] will vastly outstrip the number of humans is that Sims ‘will run their … ". (shrink)

Church's thesis, that all reasonable definitions of “computability” are equivalent, is not usually thought of in terms of computability by acontinuouscomputer, of which the general-purpose analog computer (GPAC) is a prototype. Here we prove, under a hypothesis of determinism, that the analytic outputs of aC∞GPAC are computable by a digital computer.In [POE, Theorems 5, 6, 7, and 8], Pour-El obtained some related results. (The proof there of Theorem 7 depends on her Theorem 2, for which the proof in [POE] is (...) incorrect, but for which a correct proof is given in [LIR]. Also, the proof in [POE] of Theorem 8 depends on the unproved assertion that a solution of an algebraic differential equation must be analytic on an open subset of its domain. However, this assertion was later proved in [BRR].) As in [POE], we reduce the problem to a problem about solutions of certain systems of algebraic differential equations (ADE's). If such a system is nonsingular (i.e. if the “separant” does not vanish along the given solution), then the argument is very easy (see [VSD] for an even simpler situation), so that the essential difficulties arise fromsingularsystems. Our main tools in handling these difficulties are drawn from the excellent (and difficult) paper [DEL] by Denef and Lipshitz. The author especially wants to thank Leonard Lipshitz for his kind help in the preparation of the present paper.We emphasize here that our proof of the simulation result applies only to the GPAC as described below. The GPAC's form a natural subclass of the class of all analog computers, and are based on certain idealized components (“black boxes”), mostly associated with the technology of past decades. One can easily envisage other kinds of black boxes of an input-output character that would lead to different kinds of analog computers. (For example, one could incorporate delays, or spatial integrators in addition to the present temporal integrators, etc.) Whether digital simulation is possible for these “extended” analog computers poses a rich and challenging set of research questions. (shrink)

The paper deals with an intellectual and historical approach to the changing meanings of the term “model” in life sciences. The author 1st tries to understand how modeling has gradually spread over life sciences then he particularly focus on the birth of mathematical modeling in this field. This quite new practice offers new insights on the old debate concerning the mathematization of life sciences. Nowadays, through computers, mathematics not only analyze or quantify but model things: what does it mean? The (...) question turns out to be dramatic as far as digital simulation is concerned. That is the reason why he choosed to study a particular case: the history of the individual plant mathematical modeling. On this case, one may discern various epistemological standpoints that caused various reactions to the emergence of computer simulation, from the 50s to the 90s. The author shows that philosophical views often play a role in the history of sciences, especially in the choice of supposed proper mathematical formalisms. This will indicate that contemporary discourses tend to echo each other. That is the reason why he feels authorized to address the Foucault’s concept—épistémè—to denote these convergences between the scientific and the philosophical discourses. Finally, it is suggested that this épistémè gradually is changing because one can currently observe the emergence of a “graphical” thought through these simulation experiments, which tends to replace a more functionalist thought. (shrink)

This article aims to study the simulation and design of automobile automatic clutch under mechatronics. A new control strategy for the automatic clutch of the electromagnetic transmission is proposed. The clutch mechanism model, clutch drive model, clutch system model, and internal combustion engine model are constructed. The fuzzy logic control performance of the automatic clutch was verified in different operating modes, including starting on flat roads and mountain roads. The method provides a reasonable reference for the design of an automatic (...) clutch of electromagnetic continuously variable transmission. (shrink)

Cognitive Science, Volume 46, Issue 1, January 2022.

This paper aims at integrating the work onanalogical reasoning in Cognitive Science into thelong trend of philosophical interest, in this century,in analogical reasoning as a basis for scientificmodeling. In the first part of the paper, threesimulations of analogical reasoning, proposed incognitive science, are presented: Gentner''s StructureMatching Engine, Mitchel''s and Hofstadter''s COPYCATand the Analogical Constraint Mapping Engine, proposedby Holyoak and Thagard. The differences andcontroversial points in these simulations arehighlighted in order to make explicit theirpresuppositions concerning the nature of (...) analogicalreasoning. In the last part, this debate in cognitivescience is applied to some traditional philosophicalaccounts of formal and material analogies as a basisfor scientific modeling, like Mary Hesse`s, and tomore recent ones, that already draw from the work inArtificial Intelligence, like that proposed byAronson, Harré and Way. (shrink)

This paper examines the response offered by Robert Gordon to the question how an interpreter can reach the correct content of others'psychological states. It exposes the main problems raised by Gordon's proposal, and provides a tentative solution that emphasizes the structuring role of counterfactual reasoning in embedding simulations and deriving facts that are holding across them.

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

In this paper I critically evaluate the Devil Simulation Argument for cognitive immoralism—the position that moral flaws with a work of art can be cognitively virtuous, and thereby artistically valuable. I focus on Matthew Kieran's version of the argument. Kieran holds that by simulating the attitudes of fictional devils we can come to gain important moral insights. In response, I argue that we have no reason to believe that we can effectively adopt immoral attitudes, that any successful narrative artworks ask (...) us to do so, or that it would be an effective means of moral instructions. (shrink)

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

One approach to validating simulation models is to formally compare model outputs with independent data. We consider such model validation from the point of view of Frequentist statistics. A range of estimates and tests of goodness of fit have been advanced. We review these approaches, and demonstrate that some of the tests suffer from difficulties in interpretation because they rely on the null hypothesisHypothesis that the model is similar to the observationsObservations. This reliance creates two unpleasant possibilities, namely, a model (...) could be spuriously validated when dataData are too few, or inappropriately rejected when data are too many. Finally, these tests do not allow a principled declaration of what a reasonable level of difference would be considering the purposes to which the model will be put. We consider equivalence tests, and demonstrate that they do not suffer from the previously identified shortcomings. We provide two case studies to illustrate the claims of the chapter. (shrink)

Increasing working memory (WM) capacity is often cited as a major influence on children's development and yet WM capacity is difficult to examine independently of long‐term knowledge. A computational model of children's nonword repetition (NWR) performance is presented that independently manipulates long‐term knowledge and WM capacity to determine the relative contributions of each in explaining the developmental data. The simulations show that (a) both mechanisms independently cause the same overall developmental changes in NWR performance, (b) increase in long‐term knowledge provides (...) the better fit to the child data, and (c) varying both long‐term knowledge and WM capacity adds no significant gains over varying long‐term knowledge alone. Given that increases in long‐term knowledge must occur during development, the results indicate that increases in WM capacity may not be required to explain developmental differences. An increase in WM capacity should only be cited as a mechanism of developmental change when there are clear empirical reasons for doing so. (shrink)

Both thought experiments and simulation experiments apparently belong to the family of experiments, though they are somewhat special members because they work without intervention into the natural world. Instead they explore hypothetical worlds. For this reason many have wondered whether referring to them as “experiments” is justified at all. While most authors are concerned with only one type of “imagined” experiment – either simulation or thought experiment – the present chapter hopes to gain new insight by considering what the two (...) types of experiment share, and what they do not. A close look reveals at least one fundamental methodological difference between thought and simulation experiments: while thought experiments are a cognitive process that employs intuition, simulation experiments rest on automated iterations of formal algorithms. It will be argued that this difference has important epistemological ramifications. (shrink)