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 concepts so as to show to what extent authors are right when they focus on some empirical, instrumental or conceptual significance of their model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity obtained through (...) a simulation, section 2 gives the possibility to understand more precisely - and then to justify - the diversity of the epistemological positions presented in section 1. Our final claim is that careful attention to the multiplicity of the denotational powers of symbols at stake in complex models and computer simulations is necessary to determine, in each case, their proper epistemic status and credibility. (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)
This paper proposes an extensionalist analysis of computer simulations (CSs). It puts the emphasis not on languages nor on models, but on symbols, on their extensions, and on their various ways of referring. It shows that chains of reference of symbols in CSs are multiple and of different kinds. As they are distinct and diverse, these chains enable different kinds of remoteness of reference and different kinds of validation for CSs. Although some methodological papers have already underlined the role (...) of these various relationships of reference in CSs and of cross-validations, this diversity is still overlooked in the epistemological literature on CSs. As a consequence, a particular outcome of this analytical view is an ability to classify existing epistemological theses on CSs according to what their authors choose to select and put at the forefront: either the extensions of symbols, or the symbol-types, or the symbol-tokens, or the internal denotational hierarchies of the CS or the reference of these hierarchies to external denotational hierarchies. Through the adoption of this extensionalist view, it also becomes possible to explain more precisely the reasons why some complete reduction of CSs to classical epistemic paradigms such as “experiment” or “theoretical argument” remains doubtful. On this last point, in particular, this paper is in agreement with what many epistemologists already have acknowledged. (shrink)
The 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) and 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)
According to the Argument from Disagreement (AD) widespread and persistent disagreement on ethical issues indicates that our moral opinions are not influenced by moral facts, either because there are no such facts or because there are such facts but they fail to influence our moral opinions. In an innovative paper, Gustafsson and Peterson (Synthese, published online 16 October, 2010) study the argument by means of computer simulation of opinion dynamics, relying on the well-known model of Hegselmann and Krause (J Artif (...) Soc Soc Simul 5(3):1–33, 2002; J Artif Soc Soc Simul 9(3):1–28, 2006). Their simulations indicate that if our moral opinions were influenced at least slightly by moral facts, we would quickly have reached consensus, even if our moral opinions were also affected by additional factors such as false authorities, external political shifts and random processes. Gustafsson and Peterson conclude that since no such consensus has been reached in real life, the simulation gives us increased reason to take seriously the AD. Our main claim in this paper is that these results are not as robust as Gustafsson and Peterson seem to think they are. If we run similar simulations in the alternative Laputa simulation environment developed by Angere and Olsson (Angere, Synthese, forthcoming and Olsson, Episteme 8(2):127–143, 2011) considerably less support for the AD is forthcoming. (shrink)
As scientists begin to study increasingly complex questions, many have turned to computer simulation to assist in their inquiry. This methodology has been challenged by both analytic modelers and experimentalists. A primary objection of analytic modelers is that simulations are simply too complicated to perform model verification. From the experimentalist perspective it is that there is no means to demonstrate the reality of simulation. The aim of this paper is to consider objections from both of these perspectives, and to (...) argue that a proper understanding and application of robustness analysis is able to resolve them. ‡The author would like to thank Cristina Bicchieri, Michelle Foa, Paul Humphreys and Michael Weisberg for their helpful comments and suggestions. †To contact the author, please write to: Department of Philosophy, University of Pennsylvania, 433 Logan Hall, 249 S. 36th Street, Philadelphia, PA, 19104-6304; e-mail: rmuldoon@sas.upenn.edu. (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)
As brightly shown by Mainzer [24], the science of complexity has many distinct origins in many disciplines. Those various origins has led to “an interdisciplinary methodology to explain the emergence of certain macroscopic phenomena via the nonlinear interactions of microscopic elements” (ibid.). This paper suggests that the parallel and strong expansion of modeling and simulation - especially after the Second World War and the subsequent development of computers - is a rationale which also can be counted as an explanation of (...) this emergence. With the benefit of hindsight, one can find three periods in the methodologies of modeling in the empirical sciences: 1st the simple modeling of the simple, 2nd the simple modeling of the complex, 3rd the complex modeling and simulation of the complex. Our main thesis is that the current spreading (since the 90’s) of complex computer simulations of systems of models (where a simulation is no more a step by step calculus of a unique logico-mathematical model) is another promising dimension of the science of complexity. Following this claim, we propose to distinguish three different types of computer simulations in the context of complex systems’ modeling. Finally, we show that these types of simulations lead to three different types of weak emergence, too. (shrink)
Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the "model-building era" (J. Niehans) that dominated the theoretical activities of the sciences for a long time is about to be succeeded or at least lastingly supplemented by the "simulation era". But what exactly are models? What is a simulation and what is the difference and the relation between a model and a simulation? (...) These are some of the questions addressed in this article. I maintain that the most significant feature of a simulation is that it allows scientists to imitate one process by another process. "Process" here refers solely to a temporal sequence of states of a system. Given the observation that processes are dealt with by all sorts of scientists, it is apparent that simulations prove to be a powerful interdisciplinarily acknowledged tool. Accordingly, simulations are best suited to investigate the various research strategies in different sciences more carefully. To this end, I focus on the function of simulations in the research process. Finally, a somewhat detailed case-study from nuclear physics is presented which, in my view, illustrates elements of a typical simulation in physics. (shrink)
Thought experiments have a mysterious way of informing us about the world, apparently without examining it, yet with a great degree of certainty. It is tempting to try to explain this capacity by making use of the idea that in thought experiments, the mind somehow simulates the processes about which it reaches conclusions. Here, I test this idea. I argue that when they predict the outcomes of hypothetical physical situations, thought experiments cannot simulate physical processes. They use mental models, which (...) should not be confused with process-driven simulations. A convincing case can be made that thought experiments about hypothetical mental processes are mental simulations. Concerning moral thought experiments, I argue that construing them as simulations of mental processes favours certain moral theories over others. The scope of mental simulation in thought experiments is primarily limited by the constraint of relevant similarity on source and target processes: on one hand, this constraint disqualifies thought from simulating external natural processes; on the other hand, it is a source of epistemic bias in moral thought experiments. In view of these results, I conclude that thought experiments and mental simulations cannot be assimilated as means of acquiring knowledge. (shrink)
Scientists of many disciplines use theoretical models to explain and predict the dynamics of the world. They often have to rely on digital computer simulations to draw predictions fromthe model. But to deliver phenomenologically adequate results, simulations deviate from the assumptions of the theoretical model. Therefore the role of simulations in scientific explanation demands itself an explanation. This paper analyzes the relation between real-world system, theoretical model, and simulation. It is argued that simulations do not explain (...) processes in the real world directly. The way in which simulations help explaining real-world processes is conceived as indirect, mediated by the theoretical model. Simulacra are characterized further, and turn out to be a priori measurable. This gives a clue to a better understanding of the epistemic role of computer simulations in scientific research. (shrink)
Whereas computer simulations involve no direct physical interaction between the machine they are run on and the physical systems they are used to investigate, they are often used as experiments and yield data about these systems. It is commonly argued that they do so because they are implemented on physical machines. We claim that physicality is not necessary for their representational and predictive capacities and that the explanation of why computer simulations generate desired information about their target system (...) is only to be found in the detailed analysis of their semantic levels. We provide such an analysis and we determine the actual consequences of physical implementation for simulations. (shrink)
Mathematical models are a well established tool in most natural sciences. Although models have been neglected by the philosophy of science for a long time, their epistemological status as a link between theory and reality is now fairly well understood. However, regarding the epistemological status of mathematical models in the social sciences, there still exists a considerable unclarity. In my paper I argue that this results from specific challenges that mathematical models and especially computer simulations face in the social (...) sciences. The most important difference between the social sciences and the natural sciences with respect to modeling is that in the social sciences powerful and well confirmed background theories (like Newtonian mechanics, quantum mechanics or the theory of relativity in physics) do not exist in the social sciences. Therefore, an epistemology of models that is formed on the role model of physics may not be appropriate for the social sciences. I discuss the challenges that modeling faces in the social sciences and point out their epistemological consequences. The most important consequences are that greater emphasis must be placed on empirical validation than on theoretical validation and that the relevance of purely theoretical simulations is strongly limited. (shrink)
‘The problem with simulations is that they are doomed to succeed.’ So runs a common criticism of simulations—that they can be used to ‘prove’ anything and are thus of little or no scientific value. While this particular objection represents a minority view, especially among those who work with simulations in a scientific context, it raises a difficult question: what standards should we use to differentiate a simulation that fails from one that succeeds? In this paper we build (...) on a structural analysis of simulation developed in previous work to provide an evaluative account of the variety of ways in which simulations do fail. We expand the structural analysis in terms of the relationship between a simulation and its real-world target emphasizing the important role of aspects intended to correspond and also those specifically intended not to correspond to reality. The result is an outline both of the ways in which simulations can fail and the scientific importance of those various forms of failure. (shrink)
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)
Whereas computer simulations involve no direct physical interaction between the machine they are run on and the physical systems they are used to investigate, they are often used as experiments and yield data about these systems. It is commonly argued that they do so because they are implemented on physical machines. We claim that physicality is not necessary for their representational and predictive capacities and that the explanation of why computer simulations generate desired information about their target system (...) is only to be found in the detailed analysis of their semantic levels. We provide such an analysis and we determine the actual consequences of physical implementation for simulations. (shrink)
A computer simulation runs a model generating a phenomenon under investigation. For the simulation to be explanatory, the model has to be explanatory. The model must be isomorphic to the natural system that realizes the phenomenon. This paper elaborates the method of assessing a simulation's explanatory power. Then it illustrates the method by applying it to two simulations in game theory. The first is Brian Skyrms's (1990) simulation of interactive deliberations. It is intended to explain the emergence of a (...) Nash equilibrium in a noncooperative game. The second is Skyrms's (2004) simulation of the evolution of cooperation. It is intended to explain cooperation in assurance games. The final section suggests ways of enhancing the explanatory power of these simulations. (shrink)
Computer simulations are usually considered to be non-explanatory because, when a simulation reveals that a property is instantiated in a system, it does not enable the exact identification of what it is that brings this property out (relevance requirement). Conversely, analytical deductions are widely considered to yield explanations and understanding. In this paper, I emphasize that explanations should satisfy the relevance requirement and argue that the more they do so, the more they have explanatory value. Finally, I show that (...) this emphasis on relevance has the unexpected consequence that simulations can sometimes be explanatory. (shrink)
This paper is intended as a critical examination of the question of when the use of computer simulations is beneficial to scientific explanations. This objective is pursued in two steps: First, I try to establish clear criteria that simulations must meet in order to be explanatory. Basically, a simulation has explanatory power only if it includes all causally relevant factors of a given empirical configuration and if the simulation delivers stable results within the measurement inaccuracies of the input (...) parameters. If a simulation is not explanatory, it can still be meaningful for exploratory purposes, but only under very restricted conditions. In the second step, I examine a few examples of Axelrod-style simulations as they have been used to understand the evolution of cooperation (Axelrod, Schüßler) and the evolution of the social contract (Skyrms). These simulations do not meet the criteria for explanatory validity and it can be shown, as I believe, that they lead us astray from the scientific problems they have been addressed to solve and at the same time bar our imagination against more conventional but still better approaches. (shrink)
Classically, the question of recognizing another subject is posed unilaterally, in terms of the observed behaviour of the other entity. Here, we propose an alternative, based on the emergent patterns of activity resulting from the interaction of both partners. We employ a minimalist device which forces the subjects to externalize their perceptual activity as trajectories which can be observed and recorded; the results show that subjects do identify the situation of perceptual crossing with their partner. The interpretation of the results (...) is guided by comparable evolutionary robotics simulations. There are two components to subjects' recognition capacities: distinguishing mobile and fixed entities; and behaving so as to interact with their partner rather than with a mobile lure. The “Other“ is characterized by the feature that there is sufficient regularity in the interactions to encourage the formation of anticipations; but sufficient indetermination that the actual behaviour is consistently surprising. Keywords: Recognition of other; perceptual crossing; evolutionary robotics. (shrink)
Using four examples of models and computer simulations from the history of psychology, I discuss some of the methodological aspects involved in their construction and use, and I illustrate how the existence of a model can demonstrate the viability of a hypothesis that had previously been deemed impossible on a priori grounds. This shows a new way in which scientists can learn from models that extends the analysis of Morgan (1999), who has identified the construction and manipulation of models (...) as those phases in which learning from models takes place. (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.
Some researchers claim that simulations of the emergence of communication in populations of autonomous agents provide empirical support for 'use' theories of meaning. I argue that this claim faces at least two major challenges. First, the empirical adequacy of such simulations must be justified, or the inference from simulation results to real-world linguistic behavior must be dropped; and second, the proffered simulations are in fact compatible with all of the competing theories of meaning surveyed, suggesting that theories (...) of meaning are not the kinds of theories for which simulations provide evidence. To conclude, I consider what impact this has on the project of developing a naturalized theory of language. (shrink)
Systems of logico-probabilistic (LP) reasoning characterize inference from conditional assertions that express high conditional probabilities. In this paper we investigate four prominent LP systems, the systems O, P, Z, and QC. These systems differ in the number of inferences they licence (O ⊂ P ⊂ Z ⊂ QC). LP systems that license more inferences enjoy the possible reward of deriving more true and informative conclusions, but with this possible reward comes the risk of drawing more false or uninformative conclusions. In (...) the first part of the paper, we present the four systems and extend each of them by theorems that allow one to compute almost-tight lower-probability-bounds for the conclusion of an inference, given lower-probability-bounds for its premises. In the second part of the paper, we investigate by means of computer simulations which of the four systems provides the best balance of reward versus risk. Our results suggest that system Z offers the best balance. (shrink)
It's uncontroversial that notions of idealization and approximation are central to understanding computer simulations and their rationale. What's not so clear is what exactly these notions come to. Two distinct forms of approximation will be distinguished and their features contrasted with those of idealizations. These distinctions will be refined and closely tied to computer simulations by means of Scott-Strachey denotational programming semantics. The use of this sort of semantics also provides a convenient format for argumentation in favor of (...) several theses I shall propose concerning the role computer implemented approximations and idealizations play in fixing what the acceptance of an underlying scientific theory is or should be. (shrink)
Thomas & Karmiloff-Smith (T&K-S) correctly identify Residual Normality (RN) as a critical assumption of some theorising about mental structure within developmental psychology. However, their simulations provide only weak support for the conditions under which RN may occur because they explore closely related architectures that share a learning algorithm. It is suggested that more work is required to establish the limits of RN.
Most previous works on responsible conduct of research have focused on good practices in laboratory experiments. Because computation now rivals experimentation as a mode of scientific research, we sought to identify the responsibilities of researchers who develop or use computational modeling and simulation. We interviewed nineteen experts to collect examples of ethical issues from their experiences in conducting research with computational models. We gathered their recommendations for guidelines for computational research. Informed by these interviews, we describe the respective professional responsibilities (...) of developers and users of computational models in research. In particular, we examine whether developers should disclose the full computational codes, and we explain how developers and users should minimize harms from improper uses of models. (shrink)
Recent innovations in computer software development have produced a new breed of pet, AIBO 2, a robotic pet that simulates the behavior of real pets. This paper argues that software developers who create such simulations have ethical responsibilities to product users and to society. The paper concludes with some general ethical guidelines for software developers to follow when engaged in projects involving real-world simulations.
We introduce new proof systems for propositional logic, simple deduction Frege systems, general deduction Frege systems, and nested deduction Frege systems, which augment Frege systems with variants of the deduction rule. We give upper bounds on the lengths of proofs in Frege proof systems compared to lengths in these new systems. As applications we give near-linear simulations of the propositional Gentzen sequent calculus and the natural deduction calculus by Frege proofs. The length of a proof is the number of (...) lines (or formulas) in the proof. A general deduction Frege proof system provides at most quadratic speedup over Frege proof systems. A nested deduction Frege proof system provides at most a nearly linear speedup over Frege system where by "nearly linear" is meant the ratio of proof lengths is O(α(n)) where α is the inverse Ackermann function. A nested deduction Frege system can linearly simulate the propositional sequent calculus, the tree-like general deduction Frege calculus, and the natural deduction calculus. Hence a Frege proof system can simulate all those proof systems with proof lengths bounded by O(n · α(n)). Also we show that a Frege proof of n lines can be transformed into a tree-like Frege proof of O(n log n) lines and of height O(log n). As a corollary of this fact we can prove that natural deduction and sequent calculus tree-like systems simulate Frege systems with proof lengths bounded by O(n log n). (shrink)
Robots, as well as software agents, can be of use in biology as implementations of a theory rather than as simulations of specific real world target systems. Such implementations generate hypotheses rather than representing them. Their behavior is not predicted, but rather observed, and is not expected to duplicate that of a target system. Scientific knowledge is gained through the testing of generated hypotheses.
This article addresses the physical chemical processes underlying biological self-organisation by which a homogenous solution of reacting chemicals spontaneously self-organises. Theoreticians have predicted that self-organisation can arise from a coupling of reactive processes with molecular diffusion. In addition, the presence of an external field, such as gravity, at a critical moment early in the process may determine the morphology that subsequently develops. The formation, in-vitro, of microtubules, a constituent of the cellular skeleton, shows this type of behaviour. The preparations spontaneously (...) self-organise by reaction-diffusion and the morphology that develops depends upon the presence of gravity at a critical bifurcation time early in the process. Here, we present numerical simulations of a population of microtubules that reproduce this behaviour. Microtubules can grow from one end whilst shrinking from the other. The shrinking end leaves behind a chemical trail of high tubulin concentration. Neighbouring microtubules preferentially grow into these regions, whilst avoiding regions of low tubulin concentration. The chemical trails produced by individual microtubules thus activate and inhibit the formation of neighbouring microtubules and this progressively leads to self-organisation. Gravity acts by way of its directional interaction with the macroscopic density fluctuations present in the solution arising from microtubule disassembly. (shrink)
The personal computer has become the primary research tool in many scientific and engineering disciplines. The role of the computer has been extended to be an experimental and modelling tool both for convenience and sometimes necessity. In this paper some of the relationships between real models and virtual models, i.e. models that exist only as programs and data structures, areexplored. It is argued that the shift from experimenting with real objects to experimentation with computer models and simulations may also (...) require a new approach for evaluating scientific theories derived from these models. Accepting the additional sets of assumptions that are associated with computer models and simulations requires ‘leaps of faith’, which we may not want to make in order to preserve scientific rigor. There are problems in providing acceptable arguments and explanations as to why a particular computer model or simulation should be judged scientifically sound, plausible, or even probable. These problems not only emerge from models that are particularly complex, but also in models that suffer from being too simplistic. (shrink)
Barsalou's interesting model might benefit from defining simulation and clarifying the implications of prior critiques for simulations (and not just for perceptual symbols). Contrary to claims, simulators (or frames) appear, in the limit, to be amodal. In addition, the account of abstract terms seems extremely limited.
The aim of this work was to compare experimental investigations on effects of lidocaine, calcium and, BRL 34915 on reentries to simulated data obtained by use of a model of propagation based on the Huygens' constriction method already described in previous works. Calcium and lidocaine effects are investigated on anisotropic conduction conditions. In both cases, reduction in conduction velocities are observed. In lidocaine case, a refractory area is located along the longitudinal axis. In agreement with experimental electrical mapping, the (...) class='Hi'>simulations show that the stabilization of reentrant excitation is mainly due to the existence of this refractory area around which the reentrant circuit can develop. The experimental study shows that BRL 34915 has both arrhythmogenic and antiarrhythmic effects. A detailed electrophysiological analysis has shown that drug infusion act on normal cardiac cells by decreasing the relative and absolute refractory period. BRL 34915 action is simulated by a decrease in the refractory period showing that the time frequency of the reentrant activity is increased and that the spatial size where the reentry is developing is becoming smaller. These two effects are arrhythmogenic, the simulated data being so in good agreement with the experimental ones. (shrink)
A slight modification of Webb's diagrammatic representation of the dimensions for describing models is proposed which extends it to cover a range of theoretical models as well as material models. It is also argued that beyond a certain level robotic simulations could offer a number of real advantages over computer simulations of organisms interacting with their environment.
According to the "experimenter's regress", disputes about the validity of experimental results cannot be closed by objective facts because no conclusive criteria other than the outcome of the experiment itself exist for deciding whether the experimental apparatus was functioning properly or not. Given the frequent characterization of simulations as "computer experiments", one might worry that an analogous regress arises for computer simulations. The present paper analyzes the most likely scenarios where one might expect such a "simulationist's regress" to (...) surface, and, in doing so, discusses analogies and disanalogies between simulation and experimentation. I conclude that, on a properly broadened understanding of robustness, the practice of simulating mathematical models can be seen to have sufficient internal structure to avoid any special susceptibility to regress-like situations. (shrink)
The three volumes that make up Noise in Nonlinear Dynamical Systems comprise a collection of specially written authoritative reviews on all aspects of the subject, representative of all the major practitioners in the field.
Using an example of a computer simulation of the convective structure of a red giant star, this paper argues that simulation is a rich inferential process, and not simply a "number crunching" technique. The scientific practice of simulation, moreover, poses some interesting and challenging epistemological and methodological issues for the philosophy of science. I will also argue that these challenges would be best addressed by a philosophy of science that places less emphasis on the representational capacity of theories (and ascribes (...) that capacity instead to models) and more emphasis on the role of theory in guiding (rather than determining) the construction of models. (shrink)
A number of recent discussions comparing computer simulation and traditional experimentation have focused on the significance of “materiality.” I challenge several claims emerging from this work and suggest that computer simulation studies are material experiments in a straightforward sense. After discussing some of the implications of this material status for the epistemology of computer simulation, I consider the extent to which materiality (in a particular sense) is important when it comes to making justified inferences about target systems on the basis (...) of experimental results. (shrink)
Journal of Experimental and Theoretical Artificial Intelligence (Vol. 19, No. 1, 2007) What is the difference between a simulation of X and simply another instance of X? Is there a point at which the ‘‘virtual reality’’ of a model becomes the real thing? This paper examines these questions using cases taken from recent developments in evolutionary engineering and artificial life research. By implementing the Darwinian mechanism and setting it to work on a design problem, scientists and engineers find that evolution (...) not only can improve prior designs, but also produce novel technological solutions. Artificial life systems Tierra and Avida which operate at a higher level of abstraction than evolutionary engineering applications. I analyze simulation as a rational concept ‘‘S simulates R’’ and argue that it always includes some relevant property P, of R, that is captured but that there is always also some other that it omits, and that pragmatic factors fix what counts as relevant. The border between a simulation and an instance can change depending upon the context. I show that in one sense, evo-technology and artificial life simulate organic evolution, but in another relevant sense they are instances of evolution itself. Biologists can use such systems to experimentally test evolutionary hypotheses such as those involving the evolution of complex features and altruism. This analysis suggests lines for future research on broader questions about models classification and confirmation. (shrink)
Whether simulation models provide the right kind of understanding comparable to that of analytic models has been and remains a contentious issue. The assessment of understanding provided by simulations is often hampered by a conflation between the sense of understanding and understanding proper. This paper presents a deflationist conception of understanding and argues for the need to replace appeals to the sense of understanding with explicit criteria of explanatory relevance and for rethinking the proper way of conceptualizing the role (...) of a single human mind in the collective scientific understanding. (shrink)
This paper, which is based on recent empirical research at the University of Leeds, the University of Edinburgh, and the University of Bristol, presents two difficulties which arise when condensed matter physicists interact with molecular biologists: (1) the former use models which appear to be too coarse-grained, approximate and/or idealized to serve a useful scientific purpose to the latter; and (2) the latter have a rather narrower view of what counts as an experiment, particularly when it comes to computer (...) class='Hi'>simulations, than the former. It argues that these findings are related; that computer simulations are considered to be undeserving of experimental status, by molecular biologists, precisely because of the idealizations and approximations that they involve. The complexity of biological systems is a key factor. The paper concludes by critically examining whether the new research programme of ‘systems biology’ offers a genuine alternative to the modelling strategies used by physicists. It argues that it does not. (shrink)
Cognitively-oriented theories have dominated the recent history of the study of emotion. However, critics of this perspective suggest the role of the body in the experience of emotion is largely ignored by cognitive theorists. As an alternative to the cognitive perspective, critics are increasingly pointing to William James’ theory, which emphasized somatic aspects of emotions. This emerging emphasis on the embodiment of emotions is shared by those in the field of AI attempting to model human emotions. Behavior-based agents in AI (...) are attempts to model the role the body might play in the experiencing of emotions. Progress in creating such behavior-based models that function in their environments has been slow, suggesting some potential problems with Jamesian alternatives to cognitive perspectives of emotions. Heidegger’s and Merleau-Ponty’s conceptions of embodiment are suggested as alternatives to James’ and as means for addressing the shortcomings of the cognitive perspective. (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)
We discuss two research projects in material science in which the results cannot be stated with an estimation of the error: a spectroscopic ellipsometry study aimed at determining the orientation of DNA molecules on diamond and a scanning tunneling microscopy study of platinum-induced nanowires on germanium. To investigate the reliability of the results, we apply ideas from the philosophy of models in science. Even if the studies had reported an error value, the trustworthiness of the result would not depend on (...) that value alone. (shrink)
David Lewis (1969) introduced sender-receiver games as a way of investigating how meaningful language might evolve from initially random signals. In this report I investigate the conditions under which Lewis signaling games evolve to perfect signaling systems under various learning dynamics. While the 2-state/2- term Lewis signaling game with basic urn learning always approaches a signaling system, I will show that with more than two states suboptimal pooling equilibria can evolve. Inhomogeneous state distributions increase the likelihood of pooling equilibria, but (...) learning strategies with negative reinforcement or certain sorts of mutation can decrease the likelihood of, and even eliminate, pooling equilibria. Both Moran and APR learning strategies (Bereby-Meyer and Erev 1998) are shown to promote successful convergence to signaling systems. A model is presented that illustrates how a language that codes state-act pairs in an order-based grammar might evolve in the context of a Lewis signaling game. The terms, grammar, and the corresponding partitions of the state space co-evolve to generate a language whose structure appears to reflect canonical natural kinds. The evolution of these apparent natural kinds, however, is entirely in service of the rewards that accompany successful distinctions between the sender and receiver. Any metaphysics grounded on the structure of a natural language that evolved in this way would track arbitrary, but pragmatically useful distinctions. (shrink)
Using connectionist modelling, Thomas & Karmiloff-Smith (T&K-S) claim that developmental disorders in children are characterised by atypical trajectories and an ultimate functional architecture that is fundamentally different from normal. We argue that there is no empirical evidence for these claims in any developmental disorder and that the available evidence provides support for Residual Normality in both developmental and acquired disorders. We also refute the claim that modular accounts cannot encompass developmental trajectories in children with developmental disorders.
This essay proposes an alternative way of studying video games: as thought experiments akin to the narrative thought experiments that are frequently used in philosophy. This perspective incorporates insights from the narratological and ludological perspectives in game studies and highlights the philosophical significance of games. Video game thought experiments are similar to narrative thought experiments in many respects and can perform the same functions. They also have distinctive advantages over narrative thought experiments, as they situate counterfactuals in more complex, developed (...) contexts and present them to players who are participants in game worlds, rather than simply observers. (shrink)
Experimental activity is traditionally identified with testing the empirical implications or numerical simulations of models against data. In critical reaction to the ‘tribunal view’ on experiments, this essay will show the constructive contribution of experimental activity to the processes of modeling and simulating. Based on the analysis of a case in fluid mechanics, it will focus specifically on two aspects. The first is the controversial specification of the conditions in which the data are to be obtained. The second is (...) conceptual clarification, with a redefinition of concepts central to the understanding of the phenomenon and the conditions of its occurrence. (shrink)
This paper draws attention to an increasingly common method of using computer simulations to establish evidential standards in physics. By simulating an actual detection procedure on a computer, physicists produce patterns of data (‘signatures’) that are expected to be observed if a sought-after phenomenon is present. Claims to detect the phenomenon are evaluated by comparing such simulated signatures with actual data. Here I provide a justification for this practice by showing how computer simulations establish the reliability of detection (...) procedures. I argue that this use of computer simulation undermines two fundamental tenets of the Bogen–Woodward account of evidential reasoning. Contrary to Bogen and Woodward’s view, computer-simulated signatures rely on ‘downward’ inferences from phenomena to data. Furthermore, these simulations establish the reliability of experimental setups without physically interacting with the apparatus. I illustrate my claims with a study of the recent detection of the superfluid-to-Mott-insulator phase transition in ultracold atomic gases. (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)
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)
The essays in this volume make it abundantly clear that there is no shortage of disagreement about the plausibility of the simulation theory. As we see it, there are at least three factors contributing to this disagreement. In some instances the issues in dispute are broadly empirical. Different people have different views on which theory is favored by experiments reported in the literature, and different hunches about how future experiments are likely to turn out. In 3.1 and 3.3 we will (...) consider two cases that fall under this heading. With a bit of luck these disputes will be resolved as more experiments are done and more data become available. Faulty arguments are a second source of disagreement. In 3.2 and 3.4 we will set out two dubious arguments advanced by our critics and try to explain exactly why we think they are mistaken. The third source of disagreement is terminological. Terms like "theory-theory," "simulation theory" and a number of others are often not clearly defined, and they are used in different ways by different authors. (Worse yet, we suspect they are sometimes used in different ways by a single author on different occasions). Thus it is sometimes the case that what appears to be a substantive disagreement turns out to be simply a verbal dispute. Moreover, since the labels "theory-theory" and "simulation theory" are each used to characterize a broad range of theories, it may well turn out that some of the theories falling under both headings are correct. In Sections 1 and 2, we will set out a variety of different views for which the labels "theory-theory" and "simulation theory" might be used. As we proceed we'll point out a number of places where disagreements diminish when distinctions among different versions of the theory-theory and the simulation theory are kept clearly in mind. (shrink)
Par un procédé d'objections/réponses, nous passons d'abord en revue certains des arguments en faveur ou en défaveur du caractère empirique de la simulation informatique. A l'issue de ce chemin clarificateur, nous proposons des arguments en faveur du caractère concret des objets simulés en science, ce qui légitime le fait que l'on parle à leur sujet d'une expérience, plus spécifiquement d'une expérience concrète du second genre.
This paper argues that at least one of the following propositions is true: (1) the human species is very likely to go extinct before reaching a “posthuman” stage; (2) any posthuman civilization is extremely unlikely to run a significant number of simulations of their evolutionary history (or variations thereof); (3) we are almost certainly living in a computer simulation. It follows that the belief that there is a significant chance that we will one day become posthumans who run ancestor- (...) class='Hi'>simulations is false, unless we are currently living in a simulation. A number of other consequences of this result are also discussed. (shrink)
There are a variety of topics in the philosophy of science that need to be rethought, in varying degrees, after one pays careful attention to the ways in which computer simulations are used in the sciences. There are a number of conceptual issues internal to the practice of computer simulation that can benefit from the attention of philosophers. This essay surveys some of the recent literature on simulation from the perspective of the philosophy of science and argues that philosophers (...) have a lot to learn by paying closer attention to the practice of simulation. (shrink)
The SIMS model claims that it is by means of an embodied simulation that we determine the meaning of an observed smile. This suggests that crucial interpretative work is done in the mapping that takes us from a perceived smile to the activation of one's own facial musculature. How is this mapping achieved? Might it depend upon a prior interpretation arrived at on the basis of perceptual and contextual information?
This paper examines the causal basis of our ability to attribute emotions to music, developing and synthesizing the existing arousal, resemblance and persona theories of musical expressivity to do so. The principal claim is that music hijacks the simulation mechanism of the brain, a mechanism which has evolved to detect one's own and other people's emotions.
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)
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.
People are minded creatures; we have thoughts, feelings and emotions. More intriguingly, we grasp our own mental states, and conduct the business of ascribing them to ourselves and others without instruction in formal psychology. How do we do this? And what are the dimensions of our grasp of the mental realm? In this book, Alvin I. Goldman explores these questions with the tools of philosophy, developmental psychology, social psychology and cognitive neuroscience. He refines an approach called simulation theory, which starts (...) from the familiar idea that we understand others by putting ourselves in their mental shoes. Can this intuitive idea be rendered precise in a philosophically respectable manner, without allowing simulation to collapse into theorizing? Given a suitable definition, do empirical results support the notion that minds literally create (or attempt to create) surrogates of other peoples mental states in the process of mindreading? Goldman amasses a surprising array of evidence from psychology and neuroscience that supports this hypothesis. (shrink)
This paper is concerned with the problem of selfidentification in the domain of action. We claim that this problem can arise not just for the self as object, but also for the self as subject in the ascription of agency. We discuss and evaluate some proposals concerning the mechanisms involved in selfidentification and in agencyascription, and their possible impairments in pathological cases. We argue in favor of a simulation hypothesis that claims that actions, whether overt or covert, are centrally simulated (...) by the neural network, and that this simulation provides the basis for action recognition and attribution. (shrink)
The goal of the present article is to contribute to the epistemology and methodology of computer simulations. The central thesis is that the process of simulation modeling takes the form of an explorative cooperation between experimenting and modeling. This characteristic mode of modeling turns simulations into autonomous mediators in a specific way; namely, it makes it possible for the phenomena and the data to exert a direct influence on the model. The argumentation will be illustrated by a case (...) study of the general circulation models of meteorology, the major simulation models in climate research. (shrink)
This paper contributes to an ongoing debate regarding the cognitive processes involved when one person predicts a target person's behavior and/or attributes a mental state to that target person. According to simulation theory, a person typically performs these tasks by employing some part of her brain as a simulation of what is going on in a corresponding part of the brain of the target person. I propose a general intuitive analysis of what 'simulation' means. Simulation is a particular way of (...) using one process to acquire knowledge about another process. What distinguishes simulation from other ways of acquiring knowledge is that simulation requires, for its non-accidental success, that the simulating process reflect significant aspects of the simulated process. This conceptual work is of independent philosophical interest, but it also enables me to argue for two conclusions that are of great significance to the debate about mental simulation theory. First, I argue that, in order to stake a non-trivial claim, simulation theory must hold that mental simulation involves what I call concretely similar processes. Second, I argue for the surprising conclusion that a significant class of cases that simulation theorists have claimed as intuitive cases of simulation do not actually involve simulation, after all. I close by sketching an alternative account that might handle these problematic cases. (shrink)
The leading Intelligent Design theorist William Dembski (Rowman & Littlefield, Lanham MD, 2002) argued that the first No Free Lunch theorem, first formulated by Wolpert and Macready (IEEE Trans Evol Comput 1: 67–82, 1997), renders Darwinian evolution impossible. In response, Dembski’s critics pointed out that the theorem is irrelevant to biological evolution. Meester (Biol Phil 24: 461–472, 2009) agrees with this conclusion, but still thinks that the theorem does apply to simulations of evolutionary processes. According to Meester, the theorem (...) shows that simulations of Darwinian evolution, as these are typically set in advance by the programmer, are teleological and therefore non-Darwinian. Therefore, Meester argues, they are useless in showing how complex adaptations arise in the universe. Meester uses the term teleological inconsistently, however, and we argue that, no matter how we interpret the term, a Darwinian algorithm does not become non-Darwinian by simulation. We show that the NFL theorem is entirely irrelevant to this argument, and conclude that it does not pose a threat to the relevance of simulations of biological evolution. (shrink)
People love to pretend, and to watch others pretending. From story-telling to plays to movies to virtual reality, we keep getting better at making people feel like they are watching imagined places and events. We also keep getting better at role-playing, i.e., creating enviroments where several people can see what happens when they all pretend they are different people in another time and place. Eventually such role-playing simulations may get so good that people will often forget that it is (...) just a simulation. (shrink)
In a series of recent papers, Jane Heal (1994, 1995a, 1995b, 1996a, 1996b) has developed her own quite distinctive version of simulation theory and offered a detailed critique of the arguments against simulation theory that we and our collaborators presented in earlier papers. Heal's theory is clearly set out and carefully defended, and her critique of our arguments is constructive and well informed. Unlike a fair amount of what has been written in this area in recent years, her work is (...) refreshingly free of obscurity; it generates more light than heat. While we have many disagreements with Heal, we also find much that we can agree with and learn from. In this paper we hope to advance the discussion by saying where we agree and how we think we can build on that agreement. We'll also explain where we disagree and why. (shrink)
This paper examines the relationship between simulation and experiment. Many discussions of simulation, and indeed the term "numerical experiments," invoke a strong metaphor of experimentation. On the other hand, many simulations begin as attempts to apply scientific theories. This has lead many to characterize simulation as lying between theory and experiment. The aim of the paper is to try to reconcile these two points of viewto understand what methodological and epistemological features simulation has in common with experimentation, while at (...) the same time keeping a keen eye on simulation's ancestry as a form of scientific theorizing. In so doing, it seeks to apply some of the insights of recent work on the philosophy of experiment to an aspect of theorizing that is of growing philosophical interest: the construction of local models. (shrink)
It is sometimes said that simulation can serve as epistemic substitute for experimentation. Such a claim might be suggested by the fast-spreading use of computer simulation to investigate phenomena not accessible to experimentation (in astrophysics, ecology, economics, climatology, etc.). But what does that mean? The paper starts with a clarification of the terms of the issue and then focuses on two powerful arguments for the view that simulation and experimentation are ‘epistemically on a par’. One is based on the claim (...) that, in experimentation, no less than in simulation, it is not the system under study that is manipulated but a system that ‘stands-in’ for it. The other one highlights the pervasive use of models in experimentation. It will be argued that these arguments, as compelling as they might seem, are each based on a mistaken interpretation of experimentation and that, far from simulation and experimentation being epistemically on a par, they do not have the same epistemic function, do not produce the same kind of epistemic results. (shrink)
For philosophers, the current phase of the debate with which this volume is concerned can be taken to have begun in 1986, when Jane Heal and Robert Gordon published their seminal papers (Heal, 1986; Gordon, 1986; though see also, for example, Stich, 1981; Dennett, 1981). They raised a dissenting voice against what was becoming a philosophical orthodoxy: that our everyday, or folk, understanding of the mind should be thought of as theoretical. In opposition to this picture, Gordon and Heal argued (...) that we are not theorists but simulators. For psychologists, the debate had begun somewhat earlier when Heider (1958) produced his work on lay psychology; and in more recent times the psychological debate had continued in developmental psychology and in work on animal cognition. (shrink)
Mirror neurons and systems are often appealed to as mechanisms enabling mindreading, i.e., understanding other people’s mental states. Such neural mirroring processes are often treated as instances of mental simulation rather than folk psychological theorizing. I will call into question this assumed connection between mirroring and simulation, arguing that mirroring does not necessarily constitute mental simulation as specified by the simulation theory of mindreading. I begin by more precisely characterizing “mirroring” (Sect. 2) and “simulation” (Sect. 3). Mirroring results in a (...) neural process in an observer that resembles a neural process of the same type in the observed agent. Although simulation is often characterized in terms of resemblance (Goldman, Simulating minds: The philosophy, psychology, and neuroscience of mindreading, 2006), I argue that simulation requires more than mere interpersonal mental resemblance: A simulation must have the purpose or function of resembling its target (Sect. 3.1). Given that mirroring processes are generated automatically, I focus on what is required for a simulation to possess the function of resembling its target. In Sect. 3.2 I argue that this resemblance function, at least in the case of simulation-based mindreading, requires that a simulation serve as a representation or stand-in of what it resembles. With this revised account of simulation in hand, in Sect. 4 I show that the mirroring processes do not necessarily possess the representational function required of simulation. To do so I describe an account of goal attribution involving a motor mirroring process that should not be characterized as interpersonal mental simulation. I end in Sect. 5 by defending the conceptual distinction between mirroring and simulation, and discussing the implications of this argument for the kind of neuroscientific evidence required by simulation theory. (shrink)
The distinction between analog and digital representation is reexamined; it emerges that a more fundamental distinction is that between symbolic and analog simulation. Analog simulation is analyzed in terms of a (near) isomorphism of causal structures between a simulating and a simulated process. It is then argued that a core concept, naturalistic analog simulation, may play a role in a bottom-up theory of adaptive behavior which provides an alternative to representational analyses. The appendix discusses some formal conditions for naturalistic analog (...) simulation. (shrink)
The theory-theory claims that the explanation and prediction of behavior works via the application of a theory, while the simulation theory claims that explanation works by putting ourselves in others' places and noting what we would do. On either account, in order to develop a prediction or explanation of another person's behavior, one first needs to have a characterization of that person's current or recent actions. Simulation requires that I have some grasp of the other person's behavior to project myself (...) upon; whereas theorizing requires a subject matter to theorize about. The frame problem shows that multiple, true characterizations are possible for any behavior or situation. However, only one or a few of these characterizations are relevant to explaining or predicting behavior. Since different characterizations of a behavior lead to different predictions or explanations, much of the work of interpersonal interpretation is done in the process of finding this characterization - that is, prior to either theorizing or simulating. Moreover, finding this characterization involves extensive knowledge of the physical, cultural, and social worlds of the persons involved. (shrink)
The theory of mind debate has reached a “hybrid consensus” concerning the status of theory-theory and simulation-theory. Extant hybrid models either specify co-dependency and implementation relations, or distribute mentalizing tasks according to folk-psychological categories. By relying on a non-developmental framework these models fail to capture the central connection between simulation and theory. I propose a “dynamic” hybrid that is informed by recent work on the nature of similarity cognition. I claim that Gentner’s model of structure-mapping allows us to understand simulation (...) as a process in which psychological representations are aligned, causing the spontaneous abstraction of theoretical generalizations about the psychological domain. (shrink)
Motor imagery typically involves an experience as of moving a body part. Recent studies reveal close parallels between the constraints on motor imagery and those on actual motor performance. How are these parallels to be explained? We advance a simulative theory of motor imagery, modeled on the idea that we predict and explain the decisions of others by simulating their decision-making processes. By proposing that motor imagery is essentially off-line motor action, we explain the tendency of motor imagery to mimic (...) motor performance. We close by arguing that a simulative theory of motor imagery gives (modest) support to and illumination of the simulative theory of decision-prediction. (shrink)
