Online research in philosophy

The neuropsychopharmacological methods and theories used to investigate the nature of depression have been viewed as suspect for a variety of philosophical and scientific reasons. Much of this criticism aims to demonstrate that biochemical- and neurological-based theories of this mental illness are defective, due in part because the methods used in their service are consistently invalidated, failing to induce depression in pre-clinical animal models. Neuropsychopharmacologists have been able to stave off such criticism by showing that their methods are context (...) and domain-sensitive, and that the worth of an animal model is relative to its purpose – thereby creating logical space for the question of whether there could ever be a “good” animal model of depression. I contend that this sort of response implicitly leans on Feyerabendian principles in the philosophy of science, and exemplify this connection using a standard taxonomy of behavioral models of depression. I then take one central Feyerabendian principle – methodological and theoretical pluralism – and show how it maps onto the neuropsychopharmacological research tradition as it is currently practiced. (shrink)

The seminal 1993 article by LaFollette and Shanks “Animal Models in Biomedical Research: Some Epistemological Worries” introduced an influential taxonomy into the debate about the value of animal experimentation. The distinction they made between hypothetical and causal analog models served to highlight a concern regarding extrapolating results obtained in animal models to human subjects, which endures today. Although their taxonomy has made a significant contribution to the field, we maintain that it is flawed, and instead, we offer (...) a new practice-oriented taxonomy of animal models as a means to allow philosophers, modelers, and other interested parties to discuss the epistemic merits and shortcomings, purpose, and predictive capacities of specific modeling practices. (shrink)

Any account of extrapolation from animal models to humans must confront two basic challenges: explain how extrapolation can be justified even when there are causally relevant differences between model and target, and explain how the suitability of a model can be established given only limited information about the target. We argue that existing approaches to extrapolation—either in terms of capacities or mechanisms—do not adequately address these challenges. However, we propose a further elaboration of the mechanisms approach that provides a (...) better treatment of this issue. The central concept in our proposal is what we term comparative process tracing. (shrink)

In-vivo phenotyping of genetically engineered mouse models for amyotrophic lateral sclerosis is established by combining BT-MRI and CASL G. Vanhoutte1, E. Storkebaum2, P. Carmeliet2, A. Van der Linden1.

In general, we endorse Aggleton & Brown's thesis that the neuroanatomy of amnesia comprises two functionally distinct systems, but we are disappointed in the lack of detail regarding the critical functional contribution of the hippocampus. We also take issue with the characterization of the cortical areas surrounding the hippocampus, particularly the decreased emphasis on the cortical input to the hippocampus.

This book concludes by considering whether growing counter calls to reduce our consumption of meat/dairy products in the face of climate change threats are in ...

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)

This paper focuses on abduction as explicit or readily formulatable inference to possible explanatory hypotheses--as contrasted with inference to conceptual innovations or abductive logic as a cycle of hypotheses, deduction of consequences and inductive testing. Inference to an explanation is often a matter of projection or extrapolation of elements of accepted theory for the solution of outstanding problems in particular domains of inquiry. I say "projections or extrapolation" of accepted theory, but I mean to point to something broader and suggest (...) how elements of accepted theory constrain emergent models and plausible inferences to explanations--in a quasi-rationalist fashion. I draw on illustrations from quantum gravity below just because there is so little direct evidence available in the field. It is in such cases that Peirce's discussions of abductive inference provide the most plausible support for the idea of a logic of abduction--as inference to readily formulatable explanatory hypotheses. The possible need for conceptual innovation points to the limits on the possibility of a logic of abduction of a more rationalistic character--selecting uniquely superior explanations. Abduction conceived as a repeated cycle of inquiry also points to limits on our expectations for an abductive logic. My chief point is that the character of inference to an explanation, viewed below as embedded within arguments from analogy, is so little compelling, as a matter of logical form alone, that there will always be a pluralism of plausible alternatives among untested hypotheses and inferences to them--calling for some comparative evaluation. This point leads on to some consideration of the virtues of hypotheses--as a description of the range of this pluralism. (shrink)

This volume provides a general overview of the basic ethical and philosophical issues of animal rights. It asks questions such as: Do animals have moral rights? If so, what does this mean? What sorts of mental lives do animals have, and how should we understand welfare? By presenting models for understanding animals' moral status and rights, and examining their mental lives and welfare, David DeGrazia explores the implications for how we should treat animals in connection with our diet, zoos, (...) and research. Animal Rights distinguishes itself by combining intellectual rigor with accessibility, offering a distinct moral voice with a non-polemical tone. (shrink)

Mechanistic explanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of (...) techniques for abstracting the functional properties of the system, which may not coincide with its mechanistic organization. I describe these techniques and show that despite being non-mechanistic, these cognitive models can satisfy the normative constraints on good explanations. (shrink)

The development of causal modelling since the 1950s has been accompanied by a number of controversies, the most striking of which concerns the Markov condition. Reichenbach's conjunctive forks did satisfy the Markov condition, while Salmon's interactive forks did not. Subsequently some experts in the field have argued that adequate causal models should always satisfy the Markov condition, while others have claimed that non-Markovian causal models are needed in some cases. This paper argues for the second position by considering (...) the multi-causal forks, which are widespread in contemporary medicine. (shrink)

The paper examines philosophical issues that arise in contexts where one has many different models for treating the same system. I show why in some cases this appears relatively unproblematic (models of turbulence) while others represent genuine difficulties when attempting to interpret the information that models provide (nuclear models). What the examples show is that while complementary models needn’t be a hindrance to knowledge acquisition, the kind of inconsistency present in nuclear cases is, since it (...) is indicative of a lack of genuine theoretical understanding. It is important to note that the differences in modeling do not result directly from the status of our knowledge of turbulent flows as opposed to nuclear dynamics—both face fundamental theoretical problems in the construction and application of models. However, as we shall, the ‘problem context(s)’ in which the modeling takes plays a decisive role in evaluating the epistemic merit of the models themselves. Moreover, the theoretical difficulties that give rise to inconsistent as opposed to complementary models (in the cases I discuss) impose epistemic and methodological burdens that cannot be overcome by invoking philosophical strategies like perspectivism, paraconsistency or partial structures. (shrink)

In this paper I propose an account of representation for scientific models based on Kendall Walton’s ‘make-believe’ theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, (...) I demonstrate an important advantage my account has over other theories of scientific representation. All existing theories analyse scientific representation in terms of relations, such as similarity or denotation. By contrast, my account does not take representation in modelling to be essentially relational. For this reason, it can accommodate a group of models often ignored in discussions of scientific representation, namely models which are representational but which represent no actual object. (shrink)

The word “model” is highly ambiguous, and there is no uniform terminology used by either scientists or philosophers. Here, a model is considered to be a representation of some object, behavior, or system that one wants to understand. This article presents the most common type of models found in science as well as the different relations—traditionally called “analogies”—between models and between a given model and its subject. Although once considered merely heuristic devices, they are now seen as indispensable (...) to modern science. There are many different types of models used across the scientific disciplines, although there is no uniform terminology to classify them. The most familiar are physical models such as scale replicas of bridges or airplanes. These, like all models, are used because of their “analogies” to the subjects of the models. A scale model airplane has a structural similarity or “material analogy” to the full scale version. This correspondence allows engineers to infer dynamic properties of the airplane based on wind tunnel experiments on the replica. Physical models also include abstract representations which often include idealizations such as frictionless planes and point masses. Another, but completely different type of model, is constituted by sets of equations. These mathematical models were not always deemed legitimate models by philosophers. Model-to-subject and model-to-model relations are described using several different types of analogies: positive, negative, neutral, material, and formal. (shrink)

A distinction is made between theory-driven and phenomenological models. It is argued that phenomenological models are significant means by which theory is applied to phenomena. They act both as sources of knowledge of their target systems and are explanatory of the behaviors of the latter. A version of the shell-model of nuclear structure is analyzed and it is explained why such a model cannot be understood as being subsumed under the theory structure of Quantum Mechanics. Thus its representational (...) capacity does not stem from its close link to theory. It is shown that the shell model yields knowledge about the target and is explanatory of certain behaviors of nuclei. Aspects of the process by which the shell model acquires its representational capacity are analyzed. It is argued that these point to the conclusion that the representational status of the model is a function of its capacity to function as a source of knowledge and its capacity to postulate and explain underlying mechanisms that give rise to the observed behavior of its target. (shrink)

The aim of this paper is to show that every topological space gives rise to a wealth of topological models of the modal logic S4.1. The construction of these models is based on the fact that every space defines a Boolean closure algebra (to be called a McKinsey algebra) that neatly reflects the structure of the modal system S4.1. It is shown that the class of topological models based on McKinsey algebras contains a canonical model that can (...) be used to prove a completeness theorem for S4.1. Further, it is shown that the McKinsey algebra MKX of a space X endoewed with an alpha-topologiy satisfies Esakia's GRZ axiom. (shrink)

In this paper we argue that structural explanations are an effective way of explaining well known relativistic phenomena like length contraction and time dilation, and then try to understand how this can be possible by looking at the literature on scientific models. In particular, we ask whether and how a model like that provided by Minkowski spacetime can be said to represent the physical world, in such a way that it can successfully explain physical phenomena structurally. We conclude by (...) claiming that a partial isomorphic approach to scientific representation can supply an answer only if supplemented by a robust injection of pragmatic factors. (shrink)

This paper looks closely at previously enunciated axioms that specifically include phenomenology as the sense of a self in a perceptual world. This, we suggest, is an appropriate way of doing science on a first-person phenomenon. The axioms break consciousness down into five key components: presence, imagination, attention, volition and emotions. The paper examines anew the mechanism of each and how they interact to give a single sensation. An abstract architecture, the Kernel Architecture, is introduced as a starting point for (...) building computational models. The thrust of the paper is to relate the axioms to the kernel architecture and indicate that this opens a way of discussing some first-person issues: tests for consciousness, animal consciousness and Higher Order Thought. (shrink)

This paper aims 1) to introduce the notion of theoretical story as a resource and source of constraint for the construction and assessment of models of phenomena; 2) to show the relevance of this notion for a better understanding of the role and nature of values in scientific activity. The reflection on the role of values and value judgments in scientific activity should be attentive, I will argue, to the distinction between models and the theoretical story that guides (...) and constrains their construction. The aim of scientific activity is to develop understanding of phenomena, and something that serves this aim and contributes to the development of understanding has a cognitive value. Cognitive values are the features that something that plays a role in scientific activity should have so that it can serve its aim. I will focus my attention on the features of the theoretical story and of the models. (shrink)

I argue that, contrary to common opinion, (i) unintended models do not pose a significant problem for syntactic approaches to scientific theories, (ii) in syntactic approaches, scientific theories can be as well connected to the world as in semantic ones, and (iii) some syntactic approaches are at least as language independent as semantic ones. Based on these results, I argue that syntactic and semantic approaches fare equally well when it comes to (iv) capturing the theory-observation relation, (v) ease of (...) application, and (vi) accommodating the use of models in the sciences. (shrink)

Against the tradition, which has considered measurement able to produce pure data on physical systems, the unavoidable role played by the modeling activity in measurement is increasingly acknowledged, particularly with respect to the evaluation of measurement uncertainty. This paper characterizes measurement as a knowledge-based process and proposes a framework to understand the function of models in measurement and to systematically analyze their influence in the production of measurement results and their interpretation. To this aim, a general model of measurement (...) is sketched, which gives the context to highlight the unavoidable, although sometimes implicit, presence of models in measurement and, finally, to propose some remarks on the relations between models and measurement uncertainty, complementarily classified as due to the idealization implied in the models and their realization in the experimental setup. (shrink)

Models such as the simple pendulum, isolated populations, and perfectly rational agents, play a central role in theorising. It is now widely acknowledged that a study of scientific representation should focus on the role of such imaginary entities in scientists’ reasoning. However, the question is most of the time cast as follows: How can fictional or abstract entities represent the phenomena? In this paper, I show that this question is not well posed. First, I clarify the notion of representation, (...) and I emphasise the importance of what I call the “format” of a representation for the inferences agents can draw from it. Then, I show that the very same model can be presented under different formats, which do not enable scientists to perform the same inferences. Assuming that the main function of a representation is to allow one to draw predictions and explanations of the phenomena by reasoning with it, I conclude that imaginary models in abstracto are not used as representations: scientists always reason with formatted representations. Therefore, the problem of scientific representation does not lie in the relationship of imaginary entities with real systems. One should rather focus on the variety of the formats that are used in scientific practice. (shrink)

Leibniz frequently argued that reasons are to be weighed against each other as in a pair of scales, as Professor Marcelo Dascal has shown in his article "The Balance of Reason." In this kind of weighing it is not necessary to reach demonstrative certainty – one need only judge whether the reasons weigh more on behalf of one or the other option However, a different kind of account about rational decision-making can be found in some of Leibniz's writings. In his (...) article "Was Leibniz's Deity an Akrates?" Professor Jaakko Hintikka has argued that Leibniz developed a new vectorial model for rational decisions which is better suited to complicated decisions, where values are complementary to each other. This model, related closely to his work in metaphysics and the philosophy of mind, is a heuristic device which helps in finding rational combinations - and in an ideal case an optimum - between plural inclinations to the good. I shall argue that Leibniz applies more or less implicitly both of these models in his practical rationality. In simple situations he applied the pair of scales model and in more complicated situations he applied the vectorial model. (shrink)

Mechanistic models in molecular systems biology are generally mathematical models of the action of networks of biochemical reactions, involving metabolism, signal transduction, and/or gene expression. They can be either simulated numerically or analyzed analytically. Systems biology integrates quantitative molecular data acquisition with mathematical models to design new experiments, discriminate between alternative mechanisms and explain the molecular basis of cellular properties. At the heart of this approach are mechanistic models of molecular networks. We focus on the articulation (...) and development of mechanistic models, identifying five constraints which guide the articulation of models in molecular systems biology. These constraints are not independent of one another, with the result that modeling becomes an iterative process. We illustrate the use of these constraints in the modeling of the mechanism for bistability in the lac operon. (shrink)

We argue that non-epistemic values, including moral ones, play an important role in the construction and choice of models in science and engineering. Our main claim is that non-epistemic values are not only “secondary values” that become important just in case epistemic values leave some issues open. Our point is, on the contrary, that non-epistemic values are as important as epistemic ones when engineers seek to develop the best model of a process or problem. The upshot is that (...) class='Hi'>models are neither value-free, nor depend exclusively on epistemic values or use non-epistemic values as tie-breakers. (shrink)

Given a classical theory T, a Kripke model K for the language L of T is called T-normal or locally PA just in case the classical L-structure attached to each node of K is a classical model of T. Van Dalen, Mulder, Krabbe, and Visser showed that Kripke models of Heyting Arithmetic (HA) over finite frames are locally PA, and that Kripke models of HA over frames ordered like the natural numbers contain infinitely many PA-nodes. We show that (...) Kripke models of the latter sort are in fact PA-normal. This result is extended to a somewhat larger class of frames. (shrink)

We observe that the classification problem for countable models of arithmetic is Borel complete. On the other hand, the classification problems for finitely generated models of arithmetic and for recursively saturated models of arithmetic are Borel; we investigate the precise complexity of each of these. Finally, we show that the classification problem for pairs of recursively saturated models and for automorphisms of a fixed recursively saturated model are Borel complete.

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)

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)

Collapse models predict the spontaneous collapse of the wave function, in order to avoid the emergence of macroscopic superpositions. In their mass-dependent formulation, they claim that the collapse of any system’s wave function depends on its mass. Neutral K, D, B mesons are oscillating systems that are given by Nature as superposition of two distinct mass eigenstates. Thus they are unique laboratory for testing collapse models that are sensitive to the mass. In this paper we derive—for the single (...) mesons and bipartite entangled mesons—the effect of the mass-proportional CSL (Continuous Spontaneous Localization) collapse model on the dynamics on neutral mesons. We compare the theoretical prediction with experimental data from different accelerator facilities. (shrink)

Models as Make-Believe offers a new approach to scientific modelling by looking to an unlikely source of inspiration: the dolls and toy trucks of children's games of make-believe.

The idea that biorobots can be used as a testbed for the evaluation of hypotheses about how an animal functions is supported. Generation of realistic feedback is a major advantage of biorobotic models. Nevertheless, skeptics can only be convinced that this approach is valid if significant biological insights are generated from its application.

Climate change presents us with a problem of intergenerational justice. While any costs associated with climate change mitigation measures will have to be borne by the world’s present generation, the main beneficiaries of mitigation measures will be future generations. This raises the question to what extent present generations have a responsibility to shoulder these costs. One influential approach for addressing this question is to appeal to neo-classical economic cost–benefit analyses and so-called economy-climate “integrated assessment models” to determine what course (...) of action a principle of intergenerational welfare maximization would require of us. I critically examine a range of problems for this approach. First, integrated assessment models face a problem of underdetermination and induction: They are very sensitive to a number of highly conjectural assumptions about economic responses to a temperature and climate regime, for which we have no empirical evidence. Second, they involve several simplifying assumptions which cannot be justified empirically. And third, some of the assumptions underlying the construction of economic models are intrinsically normative assumptions that reflect value judgments of the modeler. I conclude that, while integrated assessment models may play a useful role as “toy models,” their use as tools for policy optimization is highly problematic. (shrink)

The paper presents four open problems concerning recursively saturated models of Peano Arithmetic. One problems concerns a possible converse to Tarski's undefinability of truth theorem. The other concern elementary cuts in countable recursively saturated models, extending automorphisms of countable recursively saturated models, and Jonsson models of PA. Some partial answers are given.

http://dx.doi.org/10.5007/1808-1711.2008v12n1p73 This paper aims at discussing from the point of view of a pragmatic stance the concept of model as an abstract replica. According to this view, scientific models are abstract structures different from set-theoretic models. The view of models argued for here stems from the conceptions of some important philosophers of science who elaborated on the notion of model, such as Suppe, Cartwright, Hempel, and Nagel. Differently from all those authors, however, the conception of model argued (...) for here is typically pragmatic, not semantic, i.e. it has not to do with the interpretation of scientific theories, but with the explanation and construction of given circumstances (both abstract and concrete), from the point of view of the theory. (shrink)

We show that there are continuum many different non-Fregean sentential logics that have adequate models. The proof is based on the construction of a special class of models of the power of the continuum.

A model M of PA has the omega-property if it has a subset of order type omega that is coded in an elementary end extension of M. All countable recursively saturated models have the omega-property, but there are also models with the omega-property that are not recursively saturated. The papers is devoted to the study of structural properties of such models.

The descriptions and theoretical laws scientists write down when they model a system are often false of any real system. And yet we commonly talk as if there were objects that satisfy the scientists’ assumptions and as if we may learn about their properties. Many attempt to make sense of this by taking the scientists’ descriptions and theoretical laws to define abstract or fictional entities. In this paper, I propose an alternative account of theoretical modelling that draws upon Kendall Walton’s (...) ‘make-believe’ theory of representation in art. I argue that this account allows us to understand theoretical modelling without positing any object of which scientists’ modelling assumptions are true. (shrink)

Historically embryogenesis has been among the most philosophically intriguing phenomena. In this paper I focus on one aspect of biological development that was particularly perplexing to the ancients: self-organisation. For many ancients, the fact that an organism determines the important features of its own development required a special model for understanding how this was possible. This was especially true for Aristotle, Alexander, and Simplicius who all looked to contemporary technology to supply that model. However, they did not all agree on (...) what kind of device should be used. In this paper I explore the way these ancients made use of technology as a model for the developing embryo. However, my purpose here is more than just the historical interest of knowing which devices were used by whom and how each of them worked; I shall largely ignore the details of how the various devices actually worked. Instead I shall look at the use of technology from a philosophical perspective. As we shall see, the different choices of device reveal fundamental differences in the way each thinker understood the nature of biological development itself. Thus, the central aim of this paper is to examine, not who used what devices and how they worked, but why they used those particular devices and what they thought their functioning could tell us about the nature of embryological phenomena. (shrink)

Frey sets the challenge for the other authors: to explain why, morally, no humans can be subject to the kinds of experiments that animals are subject to and to explain how researchers can reliablyuse animal models to understand and cure human disease. He thinks that the first challenge has not been met; the second challenge is, unfortunately, not directly addressed in this book. Adrian Morrison states that he “abhors” positions like Frey’s, Peter Singer’s and Tom Regan’s. He asserts that (...) all “human beings stand apart in a moral sense from all other species” (51) and that all are worthy of “special consideration” (50). Regrettably he fails to defend his view by identifying the morally-relevant characteristics that all humans (even those with less intelligence, sentience and autonomy than animals) possess and all animals lack that might make his claim true. That omission prevents him from rationally criticizing opposing views. (shrink)

Background: The requirement that animals be used in research and testing in order to protect humans was formalized in the Nuremberg Code and subsequent national and international laws, codes, and declarations.DiscussionWe review the history of these requirements and contrast what was known via science about animal models then with what is known now. We further analyze the predictive value of animal models when used as test subjects for human response to drugs and disease. We explore the use of (...) animals for models in toxicity testing as an example of the problem with using animal models.SummaryWe conclude that the requirements for animal testing found in the Nuremberg Code were based on scientifically outdated principles, compromised by people with a vested interest in animal experimentation, serve no useful function, increase the cost of drug development, and prevent otherwise safe and efficacious drugs and therapies from being implemented. (shrink)

Theoretical models are an important tool for many aspects of scientific activity. They are used, i.a., to structure data, to apply theories or even to construct new theories. But what exactly is a model? It turns out that there is no proper definition of the term "model" that covers all these aspects. Thus, I restrict myself here to evaluate the function of models in the research process while using "model" in the loose way physicists do. To this end, (...) I distinguish four kinds of models. These are (1) models as special theories, (2) models as a substitute for a theory, (3) toy models and (4) developmental models. I argue that models of the types (3) and (4) are considerably useful in the process of theory construction. This will be demonstrated in an extended case-study from High-Energy Physics. (shrink)

Fundamental theories are hard to come by. But even if we had them, they would be too complicated to apply. Quantum chromodynamics (QCD) is a case in point. This theory is supposed to govern all strong interactions, but it is extremely hard to apply and test at energies where protons, neutrons and ions are the effective degrees of freedom. Instead, scientists typically use highly idealized models such as the MIT Bag Model or the Nambu Jona-Lasinio Model to account for (...) phenomena in this domain, to explain them and to gain nderstanding. Based on these models, which typically isolate a single feature of QCD (confinement and chiral symmetry breaking respectively) and disregard many others, scientists attempt to get a better understanding of the physics of strong interactions. But does this practice make sense? Is it justified to use these models for the purposes at hand? Interestingly, these models do not even provide an accurate description of the mass spectrum of protons, neutrons and pions and their lowest lying excitations well - despite several adjustable parameters. And yet, the models are heavily used. I'll argue that a qualitative story, which establishes an explanatory link between the fundamental theory and a model, plays an important role in model acceptance in these cases. (shrink)

This paper erects a framework for analyzing some idealized models as (what I call) inferentially veridical representations. It adopts a version of the semantic view of theories that focuses on properties, and mobilizes conceptual resources associated with properties and the way that properties are related in various ways. The outcome is an elaboration of some aspects of the analysis of Jones (2005).

Financial theory is in trouble. Market crashes and high volatility are only too familiar to everyone, although the standard theories predict that they hardly ever occur. According to the well-known and (partly due to its simplicity) still widely used random-walk model, the probabilities for price changes of, say, stocks should result in a Gaussian distribution. However, experience tells us that large changes occur far more often than ‘allowed’ by a Gaussian distribution. New models are needed which lead to realistic (...) probability distributions. ‘Econophysicists’ are particularly active in this field by constructing microscopic models of financial markets on the basis of various ideas and tools from physics. But in which sense do these models contribute scientific explanations? In this paper I will investigate what and how one exemplary econophysics model explains. (shrink)

European society, with its steadily increasing welfare levels, is not only concerned with food (safety, prices), but also with other aspects such as biodiversity loss, landscape degradation, and pollution of water, soil, and atmosphere. To a great extent these concerns can be translated into a larger concept named sustainable development, which can be defined as a normative concept by). Sustainability in the food chain means creating a new sustainable agro-food system while taking the institutional element into account. While different concepts (...) of sustainability abound, in recent years, spontaneous groups of consumers called solidarity purchase groups (SPG) have been developing. In short, they are characterized by an economy that is not necessarily local, but ethical and equitable, where social and economic territorial relations tend to develop districts and networks. One of the main characteristics of a SPG is the direct relationships between small farms and their customers; a relationship that is characterized by consumer participation and farmer specialization. This study aims to address issues related to organizational frameworks, at farm and chain level, and to assess those elements that lead to consumer choice and satisfaction. (shrink)

It is a curious fact about mainstream discussions of animal rights that they are dominated by consequentialist defenses thereof, when consequentialism in general has been on the wane in other areas of moral philosophy. In this paper, I describe an alternative, non‐consequentialist ethical framework (combining Kantian and virtue‐ethical elements) and argue that it grants (conscious) animals more expansive rights than consequentialist proponents of animal rights typically grant. The cornerstone of this non‐consequentialist framework is the thought that the virtuous agent is (...) s/he who has the stable and dominating disposition to treat all conscious animals, including non‐human conscious animals, as ends and not mere means. (shrink)

: Animal ethics has presented convincing arguments for the individual value of animals. Animals are not only valuable instrumentally or indirectly, but in themselves. Less has been written about interest conflicts between humans and other animals, and the use of animals in practice. The motive of this paper is to analyze different approaches to interest conflicts. It concentrates on six models, which are the rights model, the interest model, the mental complexity model, the special relations model, the multi-criteria model, (...) and the contextual model. Of these, the contextual model is the strongest, and carries clear consequences for the practical use of animals. (shrink)

A classification of models of reduction into three categories — theory reductionism, explanatory reductionism, and constitutive reductionism — is presented. It is shown that this classification helps clarify the relations between various explications of reduction that have been offered in the past, especially if a distinction is maintained between the various epistemological and ontological issues that arise. A relatively new model of explanatory reduction, one that emphasizes that reduction is the explanation of a whole in terms of its parts (...) is also presented in detail. Finally, the classification is used to clarify the debate over reductionism in molecular biology. It is argued there that while no model from the category of theory reduction might be applicable in that case, models of explanatory reduction might yet capture the structure of the relevant explanations. (shrink)

Scientific research is almost always conducted by communities of scientists of varying size and complexity. Such communities are effective, in part, because they divide their cognitive labor: not every scientist works on the same project. Philip Kitcher and Michael Strevens have pioneered efforts to understand this division of cognitive labor by proposing models of how scientists make decisions about which project to work on. For such models to be useful, they must be simple enough for us to understand (...) their dynamics, but faithful enough to reality that we can use them to analyze real scientific communities. To satisfy the first requirement, we must employ idealizations to simplify the model. The second requirement demands that these idealizations not be so extreme that we lose the ability to describe real-world phenomena. This paper investigates the status of the assumptions that Kitcher and Strevens make in their models, by first inquiring whether they are reasonable representations of reality, and then by checking the models’ robustness against weakenings of these assumptions. To do this, we first argue against the reality of the assumptions, and then develop a series of agent-based simulations to systematically test their effects on model outcomes. We find that the models are not robust against weakenings of these idealizations. In fact we find that under certain conditions, this can lead to the model predicting outcomes that are qualitatively opposite of the original model outcomes. (shrink)

Van Gelder (1995) has recently spearheaded a movement to challenge the dominance of connectionist and classicist models in cognitive science. The dynamical conception of cognition is van Gelder's replacement for the computation bound paradigms provided by connectionism and classicism. He relies on the Watt governor to fulfill the role of a dynamicist Turing machine and claims that the Motivational Oscillatory Theory (MOT) provides a sound empirical basis for dynamicism. In other words, the Watt governor is to be the theoretical (...) exemplar of the class of systems necessary for cognition and MOT is an empirical instantiation of that class. However, I shall argue that neither the Watt governor nor MOT successfully fulfill these prescribed roles. This failure, along with van Gelder's peculiar use of the concept of computation and his struggle with representationalism, prevent him from providing a convincing alternative to current cognitive theories. (shrink)

Much of the philosophical interest of cognitive science stems from its potential relevance to the mind/body problem. The mind/body problem concerns whether both mental and physical phenomena exist, and if so, whether they are distinct. In this chapter I want to portray the classical and connectionist frameworks in cognitive science as potential sources of evidence for or against a particular strategy for solving the mind/body problem. It is not my aim to offer a full assessment of these two frameworks in (...) this capacity. Instead, in this thesis I will deal with three philosophical issues which are (at best) preliminaries to such an assessment: issues about the syntax, the semantics, and the processing of the mental representations countenanced by classical and connectionist models. I will characterize these three issues in more detail at the end of the chapter. (shrink)

A considerable body of recent work in developmental psychology and animal behavior has addressed the cognitive processes required to recognize oneself in a mirror. Most models of such "mirrored self-recognition" (MSR) treat it as the result of inferential processes drawing on the subject’s possession of some sort of mature "self-awareness". The present chapter argues that such an approach to MSR is not obligatory, and suggests some empirical grounds for rejecting it. We also sketch the outlines of an alternative, "embodied" (...) theory of MSR, and propose a way to evaluate it using the tools of adaptive robotics. (shrink)

Few areas of scientific investigation have spawned more alternative approaches than animal behavior: comparative psychology, ethology, behavioral ecology, sociobiology, behavioral endocrinology, behavioral neuroscience, neuroethology, behavioral genetics, cognitive ethology, developmental psychobiology—the list goes on. Add in the behavioral sciences focused on the human animal, and you can continue the list with ethnography, biological anthropology, political science, sociology, psychology (cognitive, social, developmental, evolutionary, etc.), and even that dismal science, economics. Clearly, no reasonable-length chapter can do justice to such a varied collection. We (...) have opted therefore to focus on three of these subdisciplines and to provide a somewhat historical tour of them, mentioning along the way the philosophical points that are of particular interest to us, but allowing the development of these points to be limited only by the imaginations of our readers. For readers seeking a more-traditional historical survey, see Dewsbury (1984a, b) and Burghardt (1985a). Our chosen brief is to write about comparative psychology, ethology, and cognitive ethology, although other approaches, especially neuroscience, will be mentioned where appropriate. These sciences are philosophically significant because they are enmeshed in ancient philosophical questions about the nature of mind and purposeful action and about the differences between humans and other animals. These sciences are also clustered because of their attention to mechanistic explanations of individual animal behavior as opposed to attempting to capture regularities at a population level, such as the game-theoretic strategic models popular among behavioral ecologists. (shrink)

Theories concerning the structure, or format, of mental representation should (1) be formulated in mechanistic, rather than metaphorical terms; (2) do justice to several philosophical intuitions about mental representation; and (3) explain the human capacity to predict the consequences of worldly alterations (i.e., to think before we act). The hypothesis that thinking involves the application of syntax-sensitive inference rules to syntactically structured mental representations has been said to satisfy all three conditions. An alternative hypothesis is that thinking requires the construction (...) and manipulation of the cognitive equivalent of scale models. A reading of this hypothesis is provided that satisfies condition (1) and which, even though it may not fully satisfy condition (2), turns out (in light of the frame problem) to be the only known way to satisfy condition (3). (shrink)

This article provides the foundation for a new predictive theory of animal learning that is based upon a simple logical model. The knowledge of experimental subjects at a given time is described using logical equations. These logical equations are then used to predict a subject’s response when presented with a known or a previously unknown situation. This new theory suc- cessfully anticipates phenomena that existing theories predict, as well as phenomena that they cannot. It provides a theoretical account for phenomena (...) that are beyond the domain of existing models, such as extinction and the detection of novelty, from which “external inhibition” can be explained. Examples of the methods applied to make predictions are given using previously published results. The present theory proposes a new way to envision the minimal functions of the nervous system, and provides possible new insights into the way that brains ultimately create and use knowledge about the world. (shrink)

This paper explores John Maynard Smith’s conceptual work on animal signals. Maynard Smith defined animal signals as traits that (1) change another organism’s behaviour while benefiting the sender, that (2) are evolved for this function, and that (3) have their effects through the evolved response of the receiver. Like many ethologists, Maynard Smith assumed that animal signals convey semantic information. Yet his definition of animal signals remains silent on the nature of semantic information and on the conditions determining its content. (...) I therefore compare three ways to specify the semantic content of animal signals. The first suggestion models semantic content on Maynard Smith’s theory of genetic information. On the second proposal, semantic content is equated with a condition identified by conventional content ascriptions. The third suggestion is to explain semantic content in terms of consumer-based teleosemantics. I show how these accounts equate semantic content with distinct kinds of conditions and how they differ with respect to the kinds of traits that qualify as carrying semantic information. (shrink)

There is widespread belief that connectionist networks are dramatically different from classical or symbolic models. However, connectionists rarely test this belief by interpreting the internal structure of their nets. A new approach to interpreting networks was recently introduced by Berkeley et al. (1995). The current paper examines two implications of applying this method: (1) that the internal structure of a connectionist network can have a very classical appearance, and (2) that this interpretation can provide a cognitive theory that cannot (...) be dismissed as a mere implementation. (shrink)

A questionnaire study was performed among Swedish organic livestock farmers to determine their view of animal welfare and other ethical issues in animal production. The questionnaire was sent to 56.5% of the target group and the response rate was 75.6%. A principal components analysis (exploratory factor analysis) was performed to get a more manageable data set. A matrix of intercorrelations between all pairs of factors was computed. The factors were then entered into a series of multiple regression models to (...) explain five dependent variables. Respondents were well educated and had long experience of farming. 81% were full-time farmers. They generally had a very positive attitude towards organic animal husbandry. They considered allowing animals their natural behavior a central aim, which is in accordance with organic philosophy. Farmers tended to be less approving of concepts like animal rights, dignity, and intrinsic value. When analyzing correlations between the factors, two groups of farmers emerged that were only partially correlated, representing different attitudes and behavioral dispositions. These may be interpreted as two subpopulations of organic livestock farmers in Sweden: those who saw organic farming as a lifestyle (``pioneer attitude'''') and entrepreneurs, who considered making money and new challenges more important. Their view of animal welfare differed. While the pioneers considered natural behavior a key issue, this was less important to the entrepreneurs, who also had a more approving attitude towards invasive operations such as castration and were more critical of the organic standards. (shrink)

How should biological behaviour be modelled? A relatively new approach is to investigate problems in neuroethology by building physical robot models of biological sensorimotor systems. The explication and justification of this approach are here placed within a framework for describing and comparing models in the behavioural and biological sciences. First, simulation models – the representation of a hypothesis about a target system – are distinguished from several other relationships also termed “modelling” in discussions of scientific explanation. Seven (...) dimensions on which simulation models can differ are defined and distinctions between them discussed: 1. Relevance: whether the model tests and generates hypotheses applicable to biology. 2. Level: the elemental units of the model in the hierarchy from atoms to societies. 3. Generality: the range of biological systems the model can represent. 4. Abstraction: the complexity, relative to the target, or amount of detail included in the model. 5. Structural accuracy: how well the model represents the actual mechanisms underlying the behaviour. 6. Performance match: to what extent the model behaviour matches the target behaviour. 7. Medium: the physical basis by which the model is implemented. No specific position in the space of models thus defined is the only correct one, but a good modelling methodology should be explicit about its position and the justification for that position. It is argued that in building robot models biological relevance is more effective than loose biological inspiration; multiple levels can be integrated; that generality cannot be assumed but might emerge from studying specific instances; abstraction is better done by simplification than idealisation; accuracy can be approached through iterations of complete systems; that the model should be able to match and predict target behaviour; and that a physical medium can have significant advantages. These arguments reflect the view that biological behaviour needs to be studied and modelled in context, that is, in terms of the real problems faced by real animals in real environments. Key Words: animal behaviour; levels; models; neuroethology; realism; robotics; simulation. (shrink)

Understanding the mechanisms and the time and spatial evolution of penumbra following an ischemic stroke is crucially important for developing therapeutics aimed at preventing this area from evolving towards infarction. To help in integrating the available data, we decided to build a formal model. We first collected and categorised the major available evidence from animal models and human observations and summarized this knowledge in a flow-chart with the potential key components of an evolving stroke. Components were grouped in ten (...) sub-models that could be modelled and tested independently: the sub-models of tissue reactions, ionic movements, oedema development, glutamate excitotoxicity, spreading depression, NO synthesis, inflammation, necrosis, apoptosis, and reperfusion. Then, we figured out markers, identified mediators and chose the level of complexity to model these sub-models. We first applied this integrative approach to build a model based on cytotoxic oedema development following a stroke. Although this model includes only three sub-models and would need to integrate more mechanisms in each of these sub-models, the characteristics and the time and spatial evolution of penumbra obtained by simulation are qualitatively and, to some extent, quantitatively consistent with those observed using medical imaging after a permanent occlusion or after an occlusion followed by a reperfusion. (shrink)

A distinction is made between two definitions of animal cognition: the one most frequently employed in cognitive sciences considers cognition as extracting and processing information; a more phenomenologically inspired model considers it as attributing to a form of the outside world a significance, linked to the state of the animal. The respective fields of validity of these two models are discussed along with the limitations they entail, and the questions they pose to evolutionary biologists are emphasized. This is followed (...) by a presentation of a general overview of what might be the study of the evolution of knowledge in animals. (shrink)

Knowledge of the backgrounds of students of behaviour working in the field of applied animal behavior science may help us to recognize their influence on conclusions reached in a particular study and on more general points of view. This recognition may result in a speed up of the progress in this science, to the benefit of science and animals. Some types are: (1) Eco-ethologists (ethologists of the hunters-type). They like to stalk healthy wild animals in their natural environment. They are (...) less interested in the abnormal behavior of domestic animals under husbandry circumstances. (2) Behaviorists. These are psychologists that still think in a man-animal dichotomy. They are not interested in animals for their own sake but as models for human behavior. (3) Behavior physiologists. These biologists are not primarily interested in behavior. Because of the type of experiments they perform they have an aversity against animal protectionists. (4) Ethologists of the farmers type. These ethologists want to posses animals, collect animal species, take care of them and breed them. They are able to speak on approximately the same wavelength as farmers as well as animal protectionists. (5) Zootechnicians of the farmers type. These scientists want to make a living out of animals and like to take care for them. They are also able to speak at approximately the same wavelength as farmers and animal protectionists. (shrink)

This article starts with an overview of the author’s personal involvement—as an Operations Research consultant—in several engineering case-studies that may raise ethical questions; e.g., case-studies on nuclear waste, water management, sustainable ecology, military tactics, and animal welfare. All these case studies employ computer simulation models. In general, models are meant to solve practical problems, which may have ethical implications for the various stakeholders; namely, the modelers, the clients, and the public at large. The article further presents an overview (...) of codes of ethics in a variety of disciples. It discusses the role of mathematical models, focusing on the validation of these models’ assumptions. Documentation of these model assumptions needs special attention. Some ethical norms and values may be quantified through the model’s multiple performance measures, which might be optimized. The uncertainty about the validity of the model leads to risk or uncertainty analysis and to a search for robust models. Ethical questions may be pressing in military models, including war games. However, computer games and the related experimental economics may also provide a special tool to study ethical issues. Finally, the article briefly discusses whistleblowing. Its many references to publications and websites enable further study of ethical issues in modeling. (shrink)

We emphasize the feature of Webb's presentation that bears most directly on contemporary research with real animals. Many neuroscience modelers erroneously conclude that a model that performs like an animal must have achieved this goal through processes analogous with those used by the animal. A simulation failure justifies rejecting a model, but success does not justify acceptance. However, an important benefit of models, successful or otherwise, is to stimulate new research.

A study of the problem of animal souls as treated by Pierre Bayle in his article on Rorarius in the Dictionnaire. Early modern philosophers, if they rejected dualism, tended—as Bayle shows—to be driven either to materialism or to panpsychism.

Animals detect and acquire resources through a sequence of shape changes. This process is tightly coupled to the sensory and mechanical ecology of the animal. Building physical models allow us to prescind from modeling these aspects of the environment, which may not yet be described or suitably abstracted. The significance of this hybrid of physical modeling and experimentation to the acquisition of scientific knowledge is discussed.

Knowledge of the backgrounds of students of behaviour working in the field of applied animal behavior science may help us to recognize their influence on conclusions reached in a particular study and on more general points of view. This recognition may result in a speed up of the progress in this science, to the benefit of science and animals. Some types are: (1) Eco-ethologists (ethologists of the hunters-type). They like to stalk healthy wild animals in their natural environment. They are (...) less interested in the abnormal behavior of domestic animals under husbandry circumstances. (2) Behaviorists. These are psychologists that still think in a man-animal dichotomy. They are not interested in animals for their own sake but as models for human behavior. (3) Behavior physiologists. These biologists are not primarily interested in behavior. Because of the type of experiments they perform they have an aversity against animal protectionists. (4) Ethologists of the farmers type. These ethologists want to posses animals, collect animal species, take care of them and breed them. They are able to speak on approximately the same wavelength as farmers as well as animal protectionists. (5) Zootechnicians of the farmers type. These scientists want to make a living out of animals and like to take care for them. They are also able to speak at approximately the same wavelength as farmers and animal protectionists. (shrink)

Summary. The viability of the proposal that human cognition involves the utilization of nonsentential models is seriously undercut by the fact that no one has yet given a satisfactory account of how neurophysiological circuitry might realize representations of the right sort. Such an account is offered up here, the general idea behind which is that high-level models can be realized by lower—level computations and, in turn, by neural machinations. It is shown that this account can be usefully applied (...) to deal with problems in fields ranging from artificial intelligence to the philosophy of science. (shrink)

Paper presented at the Heidegger Circle 2011. Although Aristotle’s influence on young Heidegger’s thought has been studied at length, such studies have almost exclusively focused on his interpretation of Aristotle’s ethics, physics and metaphysics. I will rather address Heidegger’s appropriation of Aristotle’s ontology of life. Focusing on recently published or recently translated courses of the mid 20’s (mainly SS 1924, WS 1925-26 and SS 1926), I hope to uncover an important aspect of young Heidegger’s thought left unconsidered: namely, that Dasein’s (...) existential structures – Befindlichkeit, Understanding and being-with-one-another through language – arose from his close reading of Aristotle’s ontology of life, of animal life. (shrink)

According to higher-order thought accounts of phenomenal consciousness it is unlikely that many non-human animals undergo phenomenally conscious experiences. Many people believe that this result would have deep and far-reaching consequences. More specifically, they believe that the absence of phenomenal consciousness from the rest of the animal kingdom must mark a radical and theoretically significant divide between ourselves and other animals, with important implications for comparative psychology. I shall argue that this belief is mistaken. Since phenomenal consciousness might be almost (...) epiphenomenal in its functioning within human cognition, its absence in animals may signify only relatively trivial differences in cognitive architecture. Our temptation to think otherwise arises partly as a side-effect of imaginative identification with animal experiences, and partly from mistaken beliefs concerning the aspects of common-sense psychology that carry the main explanatory burden, whether applied to humans or to non-human animals. (shrink)

In this paper we1 assess the potential for research on nonhuman animals to address questions about the phenomenology of painful experiences. Nociception, the basic capacity for sensing noxious stimuli, is widespread in the animal kingdom. Even rel- atively primitive animals such as leeches and sea slugs possess nociceptors, neurons that are functionally specialized for sensing noxious stimuli (Walters 1996). Vertebrate spinal cords play a sophisticated role in processing and modulating nociceptive signals, providing direct control of some motor responses to noxious (...) stimuli, and a basic capacity for Pavlovian and instrumental conditioning (Grau et al. 1990; Grau 2002). Higher brain systems provide additional layers of association, top-down control, and cognition. In humans, at least, these higher brain systems also give rise to the conscious experiences that are characteristic of pain. What can be said about the experiences of other animals who possess nervous systems that are similar but not identical to humans? (shrink)

There has recently been an increase in interest in the role of models in science, of which the Pavia workshop on model-based reasoning is a manifestation. One result of this increased attention has been a proliferation of views on what models are and how they are used in science. In this presentation I will develop a unified interpretation of the nature and role of models in science. Central to this interpretation is an understanding of the relationships between (...) models and other elements of an understanding of science, particularly theories, data, and analogy. My conclusion will be that models play a much larger role in science than even the most ardent enthusiasts for models have typically claimed. Modeling, on my view, is not at all ancillary to doing science, but central to constructing scientific accounts of the natural world. (shrink)

Not all models are explanatory. Some models are data summaries. Some models sketch explanations but leave crucial details unspecified or hidden behind filler terms. Some models are used to conjecture a how-possibly explanation without regard to whether it is a how-actually explanation. I use the Hodgkin and Huxley model of the action potential to illustrate these ways that models can be useful without explaining. I then use the subsequent development of the explanation of the action (...) potential to show what is required of an adequate mechanistic model. Mechanistic models are explanatory. (shrink)

Scientific models represent aspects of the empirical world. I explore to what extent this representational relationship, given the specific properties of models, can be analysed in terms of propositions to which truth or falsity can be attributed. For example, models frequently entail false propositions despite the fact that they are intended to say something "truthful" about phenomena. I argue that the representational relationship is constituted by model users "agreeing" on the function of a model, on the fit (...) with data and on the aspects of a phenomenon that are modelled. Model users weigh the propositions entailed by a model and from this decide which of these propositions are crucial to the acceptance and continued use of the model. Thus, models represent phenomena when certain propositions they entail are true, but this alone does not exhaust the representational relationship. Therefore, the constraints that produce the choice of the relevant propositions in a model must also be examined and their analysis contributes to understanding the relationship between models and phenomena. (shrink)

Most recent philosophical thought about the scientific representation of the world has focused on dyadic relationships between language-like entities and the world, particularly the semantic relationships of reference and truth. Drawing inspiration from diverse sources, I argue that we should focus on the pragmatic activity of representing, so that the basic representational relationship has the form: Scientists use models to represent aspects of the world for specific purposes. Leaving aside the terms "law" and "theory," I distinguish principles, specific conditions, (...) models, hypotheses, and generalizations. I argue that scientists use designated similarities between models and aspects of the world to form both hypotheses and generalizations. (shrink)

Do non-human animals have rights? The answer to this question depends on whether animals have morally relevant mental properties. Mindreading is the human activity of ascribing mental states to other organisms. Current knowledge about the evolution and cognitive structure of mindreading indicates that human ascriptions of mental states to non-human animals are very inaccurate. The accuracy of human mindreading can be improved with the help of scientific studies of animal minds. But the scientific studies by themselves do not by themselves (...) solve the problem of how to map psychological similarities (and differences) between humans and animals onto a distinction between morally relevant and morally irrelevant mental properties. The current limitations of human mindreading – whether scientifically aided or not – have practical consequences for the rational justification of claims about which rights (if any) non-human animals should be accorded. (shrink)

I argue for an intentional conception of representation in science that requires bringing scientific agents and their intentions into the picture. So the formula is: Agents (1) intend; (2) to use model, M; (3) to represent a part of the world, W; (4) for some purpose, P. This conception legitimates using similarity as the basic relationship between models and the world. Moreover, since just about anything can be used to represent anything else, there can be no unified ontology of (...) models. This whole approach is further supported by a brief exposition of some recent work in cognitive, or usage-based, linguistics. Finally, with all the above as background, I criticize the recently much discussed idea that claims involving scientific models are really fictions. (shrink)

Contemporary literature in philosophy of science has begun to emphasize the practice of modeling, which differs in important respects from other forms of representation and analysis central to standard philosophical accounts. This literature has stressed the constructed nature of models, their autonomy, and the utility of their high degrees of idealization. What this new literature about modeling lacks, however, is a comprehensive account of the models that figure in to the practice of modeling. This paper offers a new (...) account of both concrete and mathematical models, with special emphasis on the intentions of theorists, which are necessary for evaluating the model-world relationship during the practice of modeling. Although mathematical models form the basis of most of contemporary modeling, my discussion begins with more traditional, concrete models such as the San Francisco Bay model. (shrink)

Models occupy a central role in the scientific endeavour. Among the many purposes they serve, representation is of great importance. Many models are representations of something else; they stand for, depict, or imitate a selected part of the external world (often referred to as target system, parent system, original, or prototype). Well-known examples include the model of the solar system, the billiard ball model of a gas, the Bohr model of the atom, the Gaussian-chain model of a polymer, (...) the MIT bag model of quark confinement, the Lorenz model of the atmosphere, the Lotka-Volterra model of the predator-prey interaction, or the hydraulic model of an economy, to mention just a few. All these models represent their target systems (or selected parts of them) in one way or another. (shrink)

My charge in this chapter is to set out the positive case supporting massively modular models of the human mind.1 Unfortunately, there is no generally accepted understanding of what a massively modular model of the mind is. So at least some of our discussion will have to be terminological. I shall begin by laying out the range of things that can be meant by ‘modularity’. I shall then adopt a pair of strategies. One will be to distinguish some things (...) that ‘modularity’ definitely can’t mean, if the thesis of massive modularity is to be even remotely plausible. The other will be to look at some of the arguments that have been offered in support of massive modularity, discussing what notion of ‘module’ they might warrant. It will turn out that there is, indeed, a strong case in support of massively modular models of the mind on one reasonably natural understanding of ‘module’. But what really matters in the end, of course, is the substantive question of what sorts of structure are adequate to account for the organization and operations of the human mind, not whether or not the components appealed to in that account get described as ‘modules’. So the more interesting question before us is what the arguments that have been offered in support of massive modularity can succeed in showing us about those structures, whatever they get called. (shrink)