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)

Forthcoming, Studies in the History and Philosophy of Science Abstract: The epistemic problem of assessing the support that some evidence confers on a hypothesis is considered using an extended example from the history of meteorology. In this case, and presumably in others, the problem is to develop techniques of data analysis that will link the sort of evidence that can be collected to hypotheses of interest. This problem is solved by applying mathematical tools to structure the data and connect it (...) to the competing hypotheses. I conclude that mathematical innovations provide crucial epistemic links between evidence and theories precisely because the evidence and theories are mathematically described. (shrink)

During the last two decades the role of quantitative genetics in evolutionary theory has expanded considerably. Quantitative genetic-based models addressing long term phenotypic evolution, evolution in multiple environments (phenotypic plasticity) and evolution of ontogenies (developmental trajectories) have been proposed. Yet, the mathematical foundations of quantitative genetics were laid with a very different set of problems in mind (mostly the prediction of short term responses to artificial selection), and at a time in which any details of the genetic machinery were virtually (...) unknown. In this paper we discuss what a model is in population biology, and what kind of model we need in order to address the complexities of phenotypic evolution. We review the assumptions of quantitative genetics and its most recent accomplishments, together with the limitations that such assumptions impose on the modelling of some aspects of phenotypic evolution. We also discuss three alternative appr oaches to the theoretical description of evolutionary trajectories (nonlinear dynamics, complexity theory and optimization theory), and their respective advantages and limitations. We conclude by calling for a new theoretical synthesis, including quantitative genetics and not necessarily limited to the other approaches here discussed. (shrink)

The present paper presents a philosophical analysis of earth science, a discipline that has received relatively little attention from philosophers of science. We focus on the question of whether earth science can be reduced to allegedly more fundamental sciences, such as chemistry or physics. In order to answer this question, we investigate the aims and methods of earth science, the laws and theories used by earth scientists, and the nature of earth-scientific explanation. Our analysis leads to the tentative conclusion that (...) there are emergent phenomena in earth science but that these may be reducible to physics. However, earth science does not have irreducible laws, and the theories of earth science are typically hypotheses about unobservable (past) events or generalised - but not universally valid - descriptions of contingent processes. Unlike more fundamental sciences, earth science is characterised by explanatory pluralism: earth scientists employ various forms of narrative explanations in combination with causal explanations. The main reason is that earth-scientific explanations are typically hampered by local underdetermination by the data to such an extent that complete causal explanations are impossible in practice, if not in principle. (shrink)

Feminist philosophers have discussed the prospects for assessing values empirically, particularly given the ongoing threat of sexism and other oppressive values influencing science and society. Some advocates of such tests now champion a “values as evidence” approach, and they criticize Helen Longino’s contextual empiricism for not holding values to the same level of empirical scrutiny as other claims. In this paper, we defend contextual empiricism by arguing that many of these criticisms are based on mischaracterizations of Longino’s position, overstatements of (...) certain claims, and false dichotomies. Her contextual empiricism not only allows for the empirical support and disconfirmation of values, but Longino explicitly discusses when values can be empirically adjudicated and emphasizes the crucial role of the community for standards of evidence. We support contextual empiricism and elaborate a less direct account of “values as heuristics” by reviewing Longino’s theory of evidence and then using a case study from Elisabeth Lloyd on the biology of female orgasm, demonstrating the disconfirmation of androcentric values in evolutionary science. Within Longino’s and Lloyd’s contextual empiricism, values do not get treated as empirical evidence to be directly assessed by individuals, but rather values are heuristic tools to build models whose use can be validated or invalidated by communities based on their empirical fruitfulness in the logic and pragmatics of research questions in specific historical and cultural contexts. (shrink)

Computer models are frequently used tools in hydrological research. Many decisions related to the model set-up and configuration have to be made before a model can be run, influencing the model results. This study is an empirical investigation of the motivations for certain modeling decisions. Fourteen modelers from three different institutes were interviewed about their modeling decisions. In total, 83 different motivations were identified. Most motivations were related to the team of the modeler and the modelers themselves, “Experience from colleagues” (...) was the most frequently mentioned motivation. Both institutionalization and internalization were observed: a modeler can introduce a concept that subsequently becomes the teams' standard, or a modeler can internalize the default team approach. These processes depend on the experience of the modeler. For model selection, two types of motivations were identified: experience (from colleagues or the modelers themselves), and model vision (the model has assets that align with the modeling vision). Model studies are mainly driven by context, such as time constraints, colleagues, and facilities at the institute, rather than epistemic (such as aligning with the modeling vision). The role of local context in the construction of and the value assigned to models shows that models are social constructs, making model results time, and place dependent. To account for this context in the estimation of the robustness of model results, we need a diversity of opinions, perspectives, and approaches. This requires transparent modeling procedures and an explicit modeling vision for each model study. (shrink)

This book addresses key conceptual issues relating to the modern scientific and engineering use of computer simulations. It analyses a broad set of questions, from the nature of computer simulations to their epistemological power, including the many scientific, social and ethics implications of using computer simulations. The book is written in an easily accessible narrative, one that weaves together philosophical questions and scientific technicalities. It will thus appeal equally to all academic scientists, engineers, and researchers in industry interested in questions (...) related to the general practice of computer simulations. (shrink)

The geosciences include a wide spectrum of disciplines ranging from paleontology to climate science, and involve studies of a vast range of spatial and temporal scales, from the deep-time history of microbial life to the future of a system no less immense and complex than the entire Earth. Modeling is thus a central and indispensable tool across the geosciences. Here, we review both the history and current state of model-based inquiry in the geosciences. Research in these fields makes use of (...) a wide variety of models, such as conceptual, physical, and numerical models, and more specifically cellular automata, artificial neural networks, agent-based models, coupled models, and hierarchical models. We note the increasing demands to incorporate biological and social systems into geoscience modeling, challenging the traditional boundaries of these fields. Understanding and articulating the many different sources of scientific uncertainty – and finding tools and methods to address them – has been at the forefront of most research in geoscience modeling. We discuss not only structuralmodel uncertainties, parameter uncertainties, and solution uncertainties, but also the diverse sources of uncertainty arising from the complex nature of geoscience systems themselves. Without an examination of the geosciences, our philosophies of science and our understanding of the nature of model-based science are incomplete. (shrink)

This article argues that common intuitions regarding (a) the specialness of ‘use-novel’ data for confirmation and (b) that this specialness implies the ‘no-double-counting rule’, which says that data used in ‘constructing’ (calibrating) a model cannot also play a role in confirming the model’s predictions, are too crude. The intuitions in question are pertinent in all the sciences, but we appeal to a climate science case study to illustrate what is at stake. Our strategy is to analyse the intuitive claims in (...) light of prominent accounts of confirmation of model predictions. We show that on the Bayesian account of confirmation, and also on the standard classical hypothesis-testing account, claims (a) and (b) are not generally true; but for some select cases, it is possible to distinguish data used for calibration from use-novel data, where only the latter confirm. The more specialized classical model-selection methods, on the other hand, uphold a nuanced version of claim (a), but this comes apart from (b), which must be rejected in favour of a more refined account of the relationship between calibration and confirmation. Thus, depending on the framework of confirmation, either the scope or the simplicity of the intuitive position must be revised. (shrink)

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

In engineering, as in other scientific fields, researchers seek to confirm their models with real-world data. It is common practice to assess models in terms of the distance between the model outputs and the corresponding experimental observations. An important question that arises is whether the model should then be ‘tuned’, in the sense of estimating the values of free parameters to get a better fit with the data, and furthermore whether the tuned model can be confirmed with the same data (...) used to tune it. This dual use of data is often disparagingly referred to as ‘double-counting’. Here, we analyse these issues, with reference to selected research articles in engineering. Our example studies illustrate more and less controversial practices of model tuning and double-counting, both of which, we argue, can be shown to be legitimate within a Bayesian framework. The question nonetheless remains as to whether the implied scientific assumptions in each case are apt from the engineering point of view. (shrink)

This paper argues that the difference between contemporary software intensive scientific practice and more traditional non-software intensive varieties results from the characteristically high conditionality of software. We explain why the path complexity of programs with high conditionality imposes limits on standard error correction techniques and why this matters. While it is possible, in general, to characterize the error distribution in inquiry that does not involve high conditionality, we cannot characterize the error distribution in inquiry that depends on software. Software intensive (...) science presents distinctive error and uncertainty modalities that pose new challenges for the epistemology of science. (shrink)

What is it about simulation models that has led some practitioners to treat them as potential sources of empirical data on the real-world systems being simulated; that is, to treat simulations as ‘artificial worlds’within which to perform computational ‘experiments’? Here we use the work of Richard Levins as a starting point in identifying the appeal of this model building strategy, and proceed to account for why this appeal is strongest for computational modellers. This analysis suggests a perspective on simulation modelling (...) that makes room for ‘artificial worlds’ as legitimate science without having to accept that they should be treated as sources of empirical data. (shrink)

This paper explicates the philosophical and epistemological background of the MIRRORS project, which is the starting point of the various contributions in this issue. Developments in the philosophy of science will be discussed, especially the watershed work of Kuhn, in order to analyze further developments in the sociology of science, particularly starting from the Strong Programme. Finally, it will be shown how a multidisciplinary approach in Science & Technology (S&T) studies, as opposed to an interdisciplinary one, is to be preferred. (...) Specifically, the connection between this approach and the modelling and idealizational approaches to science is stressed and defended as the best available approach for the formation of a conscientious democratic knowledge-based society. (shrink)

Global environmental science, in its current configuration as predominantly interdisciplinary earth systems analysis, owes its existence to technological development in three respects. (1) Environmental impacts of globalization of corporate and military industrial development linked to widespread use of new technologies prompted investigation of ways to understand and anticipate the global nature of such impacts. (2) Extension of the reach of technology itself demands extension of attempts to anticipate and control the environment in which the technology is to function. Thus as (...) the reach becomes global, the environment in question is also global. (3) Such global studies cannot get far without the development of command, control and information technologies (computers, satellites, automated remote sensing devices) which are crucial for data gathering, storage, and analysis and for the simulation modeling, crucial to theory testing and prediction. This network of dependence on technological development gives the global environmental sciences a rather distinctive epistemological profile, one in which some distinctions that we had thought were clear, on the basis of models of classic laboratory sciences (such as those between experiment and deduction or representation and instrument), turn out to be far from clear. In consequence there needs to be a careful evaluation of the extent to which, or the ways in which, these sciences can provide bases for policy decisions. (shrink)

This article argues that common intuitions regarding (a) the specialness of ‘use-novel’ data for confirmation and (b) that this specialness implies the ‘no-double-counting rule’, which says that data used in ‘constructing’ (calibrating) a model cannot also play a role in confirming the model’s predictions, are too crude. The intuitions in question are pertinent in all the sciences, but we appeal to a climate science case study to illustrate what is at stake. Our strategy is to analyse the intuitive claims in (...) light of prominent accounts of confirmation of model predictions. We show that on the Bayesian account of confirmation, and also on the standard classical hypothesis-testing account, claims (a) and (b) are not generally true; but for some select cases, it is possible to distinguish data used for calibration from use-novel data, where only the latter confirm. The more specialized classical model-selection methods, on the other hand, uphold a nuanced version of claim (a), but this comes apart from (b), which must be rejected in favour of a more refined account of the relationship between calibration and confirmation. Thus, depending on the framework of confirmation, either the scope or the simplicity of the intuitive position must be revised. 1 Introduction2 A Climate Case Study3 The Bayesian Method vis-à-vis Intuitions4 Classical Tests vis-à-vis Intuitions5 Classical Model-Selection Methods vis-à-vis Intuitions 5.1 Introducing classical model-selection methods 5.2 Two cases6 Re-examining Our Case Study7 Conclusion. (shrink)

Complexity sciences have become famous worldwide thanks to several popular books that served as echo chambers of their promises. These consisted in departing from “classical science” defined as deterministic, reductionist, analytic and mono-disciplinary. Their founders and supporters declared that complexity sciences were going to give rise (or that they have given rise) to a post-Laplacian, antireductionist, holistic and interdisciplinary approach. By taking a closer look at their content and practices, I argue in this article that, because of their physics-oriented, computationalist, (...) and mathematical assumptions, complexity sciences have paradoxically produced knowledge at odds with these four tenets. (shrink)

Deep learning (DL) has become increasingly central to science, primarily due to its capacity to quickly, efficiently, and accurately predict and classify phenomena of scientific interest. This paper seeks to understand the principles that underwrite scientists’ epistemic entitlement to rely on DL in the first place and argues that these principles are philosophically novel. The question of this paper is not whether scientists can be justified in trusting in the reliability of DL. While today’s artificial intelligence exhibits characteristics common to (...) both scientific instruments and scientific experts, this paper argues that the familiar epistemic categories that justify belief in the reliability of instruments and experts are distinct, and that belief in the reliability of DL cannot be reduced to either. Understanding what can justify belief in AI reliability represents an occasion and opportunity for exciting, new philosophy of science. (shrink)

Computer models and simulations have become, since the 1960s, an essential instrument for scientific inquiry and political decision making in several fields, from climate to life and social sciences. Philosophical reflection has mainly focused on the ontological status of the computational modeling, on its epistemological validity and on the research practices it entails. But in computational sciences, the work on models and simulations are only two steps of a longer and richer process where operations on data are as important as, (...) and even more time and energy-consuming than modeling itself. Drawing on two study cases—computational embryology and computational epidemiology—this article contributes to filling the gap by focusing on the operations of producing and re-using data in computational sciences. The different phases of the scientific and artisanal work of modelers include data collection, aggregation, homogenization, assemblage, analysis and visualization. The article deconstructs the ideas that data are self-evident informational aggregates and that data-driven approaches are exempted from theoretical work. More importantly, the paper stresses the fact that data are constructed and theory laden not only in their fabrication, but also in their reusing. (shrink)

Computer simulations are often claimed to be opaque and thus to lack transparency. But what exactly is the opacity of simulations? This paper aims to answer that question by proposing an explication of opacity. Such an explication is needed, I argue, because the pioneering definition of opacity by P. Humphreys and a recent elaboration by Durán and Formanek are too narrow. While it is true that simulations are opaque in that they include too many computations and thus cannot be checked (...) by hand, this doesn’t exhaust what we might want to call the opacity of simulations. I thus make a fresh start with the natural idea that the opacity of a method is its disposition to resist knowledge and understanding. I draw on recent work on understanding and elaborate the idea by a systematic investigation into what type of knowledge and what type of understanding are required if opacity is to be avoided and why the required sort of understanding, in particular, is difficult to achieve. My proposal is that a method is opaque to the degree that it’s difficult for humans to know and to understand why its outcomes arise. This proposal allows for a comparison between different methods regarding opacity. It further refers to a kind of epistemic access that is important in scientific work with simulations. (shrink)

Even though the philosophy of simulation is intended as a comprehensive reflection about the practice of computer simulation in contemporary science, its output has been disproportionately shaped by research on equation-based simulation in the physical and climate sciences. Hence, the particularities of alternative practices of computer simulation in other scientific domains are not sufficiently accounted for in the current philosophy of simulation literature. This article centres on agent-based social simulation, a relatively established type of simulation in the social sciences, to (...) exemplify this claim. The analysis advanced has a twofold goal. First, it shows that the philosophy of agent-based social simulation, mostly developed by practitioners themselves, is, on one hand, heavily influenced by the methodological features of agent-based modelling and by the loose and fragmented character of social theory and, on the other hand, distinctively shaped by contrasting views of what it implies to do social research with virtual or artificial societies. Second, it suggests ways in which cross-fertilisation could enrich the philosophical understanding of computer simulation both in agent-based social simulation and in the philosophy of simulation. (shrink)

Since their inception in the 1980s, complexity sciences have been described as a revolutionary new domain of research. By describing some of the practices and assumptions of its representatives, the present article shows that this field is an association of subdisciplines laying on existing disciplinary footholds. The general question guiding us here is: On what basis do complexity scientists consider their inquiry methods and results as valuable? To answer it, I describe five “epistemic argumentative regimes,” namely the ways in which (...) complexity scientists argue the credibility of their research, and five “ontological views,” that is the ways in which they interpret the material and formal causes of their study objects and models. Finally, the article proposes the term of “regime of evidence” to designate the specific combination of one ontological view with one or more epistemic argumentative regimes. (shrink)

We argue that for scientists and science communicators to build usable knowledge for various publics, they require social and political capital, skills in boundary work, and ethical acuity. Drawing on the context of communicating seasonal climate predictions to farmers in Australia, we detail four key issues that scientists and science communicators would do well to reflect upon in order to become effective and ethical intermediaries. These issues relate to the boundary work used to link science and values and thereby construct (...) public identities, emplacement, that is, the importance of situating knowledge in relation to the places with which people identify, personal and organizational processes of reflexivity, and the challenges of developing and maintaining the social and political capital necessary to simultaneously represent people’s identities and lifeworlds and the climate systems that affect them. Through a discourse analysis of in-depth interviews with Australian agro-climatologists, we suggest that three distinct “modes of extension” are apparent, namely, discursive, conceptual, and contextual. Our participants used these three modes interdependently to create knowledge that has salience, credibility, and legitimacy. They thereby generated new narratives of place, practice, and identity for Australian agriculture. (shrink)

A longstanding philosophical premise perceives simplicity as a desirable attribute of scientific theories. One of several raised justifications for this notion is that simple theories are more likely to indicate the true makeup of natural systems. Qualitatively parsimonious hypotheses and theories keep to a minimum the number of different postulated entities within a system. Formulation of such ontologically simple working hypotheses proved to be useful in the experimental probing of narrowly defined bio systems. It is less certain, however, whether qualitatively (...) parsimonious theories are effective indicators of the true nature of complex biological systems. This paper assesses the success of ontologically simple theories in envisaging the makeup of three complex systems in bacteriology, immunology, and molecular biology. Evidence shows that parsimonious theories completely misconstrued the actual ontologically complex constitutions of the three examined systems. Since evolution and selective pressures typically produce ontologically intricate rather than simple bio systems, qualitatively parsimonious theories are mostly inapt indicators of the true nature of complex biological systems. (shrink)

What does it mean to trust the results of a computer simulation? This paper argues that trust in simulations should be grounded in empirical evidence, good engineering practice, and established theoretical principles. Without these constraints, computer simulation risks becoming little more than speculation. We argue against two prominent positions in the epistemology of computer simulation and defend a conservative view that emphasizes the difference between the norms governing scientific investigation and those governing ordinary epistemic practices.

The Fukushima nuclear accident from 2011 provided an occasion for the public display of radiation maps generated using decision-support systems for nuclear emergency management. Such systems rely on computer models for simulating the atmospheric dispersion of radioactive materials and estimating potential doses in the event of a radioactive release from a nuclear reactor. In Germany, as in Japan, such systems are part of the national emergency response apparatus and, in case of accidents, they can be used by emergency task forces (...) for planning radioprotection countermeasures. In this context, the paper addresses the epistemology of dose projections by critically analyzing some of the sources of epistemic opacity and non-knowledge affecting them, and the different methods and practices used by German radioprotection experts to improve their trustworthiness and reliability. It will be argued that dose projections are part of an entire radioprotection regime or assemblage built around the belief that the effects of nuclear accidents can be effectively mitigated thanks to the simulation technologies underlying different protocols and practices of nuclear preparedness. And, as the Fukushima experience showed, some of these expectations will not be met in real emergencies due to the inherent uncertainties entailed by the use of dose projections when planning protective countermeasures. (shrink)

Scientific models consist of fictitious elements and assumptions. Various attempts have been made to answer the question of how a model, which is sometimes viewed as a fiction, can explain or predict the target phenomenon adequately. I examine two accounts of models-as-fictions which are aiming at disentangling the myth of representing the reality by fictional models. I argue that both views have their own weaknesses in spite of many virtues. I propose to re-evaluate the problems of representation from a novel (...) perspective in which some of the model representations can be regarded as fictional representations. I argue that this type of model representation is credible despite being a fictional representation of the reality. (shrink)

This article on the epistemology of computational models stems from an analysis of the Gaïa hypothesis. It begins with James Kirchner’s criticisms of the central computational model of GH: Daisyworld. Among other things, the model has been criticized for being too abstract, describing fictional entities and trying to answer counterfactual questions. For these reasons the model has been considered not testable and therefore not legitimate in science, and in any case not very interesting since it explores non actual issues. This (...) criticism implicitly assumes that science should only be involved in the making of models that are “actual” and “specific”. I challenge both of these criticisms in this article. First by showing that although the testability—understood as the comparison of model output with empirical data—is an important procedure for explanatory models, there are plenty of models that are not testable. The fact that these are not testable has nothing to do with their being “abstract” or “what-if” but with their being predictive models. Secondly, I argue that “abstract” and “what-if” models aim at epistemic purposes distinct from those pursued by “actual and specific models”. Abstract models are used to propose how-possibly explanation or to pursue theorizing. What-if models are used to attribute causal or explanatory power to a variable of interest. The fact that they aim at different epistemic goals entails that it may not be accurate to consider the choice between different kinds of model as a “strategy“. (shrink)

1. Overview and organizing themes 2. Historical Review: Aristotle to Mill 3. Logic of method and critical responses 3.1 Logical constructionism and Operationalism 3.2. H-D as a logic of confirmation 3.3. Popper and falsificationism 3.4 Meta-methodology and the end of method 4. Statistical methods for hypothesis testing 5. Method in Practice 5.1 Creative and exploratory practices 5.2 Computer methods and the ‘third way’ of doing science 6. Discourse on scientific method 6.1 “The scientific method” in science education and as seen (...) by scientists 6.2 Privileged methods and ‘gold standards’ 6.3 Scientific method in the court room 6.4 Deviating practices 7. Conclusion Bibliography Academic Tools Other Internet Resources Related Entries . (shrink)

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

Many examples of calibration in climate science raise no alarms regarding model reliability. We examine one example and show that, in employing Classical Hypothesis-testing, it involves calibrating a base model against data that is also used to confirm the model. This is counter to the "intuitive position". We argue, however, that aspects of the intuitive position are upheld by some methods, in particular, the general Cross-validation method. How Cross-validation relates to other prominent Classical methods such as the Akaike Information Criterion (...) and Bayesian Information Criterion is also discussed. (shrink)

This article on the epistemology of computational models stems from an analysis of the Gaia hypothesis (GH). It begins with James Kirchner’s criticisms of the central computational model of GH: Daisyworld. Among other things, the model has been criticized for being too abstract, describing fictional entities (fictive daisies on an imaginary planet) and trying to answer counterfactual (what-if) questions (how would a planet look like if life had no influence on it?). For these reasons the model has been considered not (...) testable and therefore not legitimate in science, and in any case not very interesting since it explores non actual issues. This criticism implicitly assumes that science should only be involved in the making of models that are ‘‘actual’’ (by opposition to what-if) and ‘‘specific’’ (by opposition to abstract). I challenge both of these criticisms in this article. First by showing that although the testability—understood as the comparison of model output with empirical data—is an important procedure for explanatory models, there are plenty of models that are not testable. The fact that these are not testable (in this restricted sense) has nothing to do with their being ‘‘abstract’’ or ‘‘what-if’’ but with their being predictive models. Secondly, I argue that ‘‘abstract’’ and ‘‘what-if’’ models aim at (respectable) epistemic purposes distinct from those pursued by ‘‘actual and specific models’’. Abstract models are used to propose how-possibly explanation or to pursue theorizing. What-if models are used to attribute causal or explanatory power to a variable of interest. The fact that they aim at different epistemic goals entails that it may not be accurate to consider the choice between different kinds of model as a ‘‘strategy‘‘. (shrink)

. Through examining a case study of a major fluids modelling code, this paper charts two key properties of software as a material for building models. Scientific software development is characterized by piecemeal growth, and as a code expands, it begins to manifest frustrating properties that provide an important axis of motivation in the laboratory. The first such feature is a tendency towards brittleness. The second is an accumulation of supporting technologies that sometimes cause scientists to express a frustration with (...) the bureaucracy of highly regulated working practices. Both these features are important conditions for the pursuit of research through simulation. (shrink)

This article has two objectives: (1) to map some of the structural limitations to scientific or rational public planning; and (2) to explore the implications of this for a reconceptualization of the legitimacy of public planning. It is argued that some of the limitations to planning are inherent to the planning process in the sense that they cannot be fully mitigated through the refinement of procedures. They come to represent sources of “basic boundedness” that have to be addressed through a (...) radical reinterpretation of the policy process itself. (shrink)

Much of the scientific process involves “selective ignorance”: we include certain aspects of the systems we are considering and ignore others. This is inherent in the models that we utilize as proxies for biological systems. Our goal usually is to isolate components of these systems and consider them at only certain temporal and spatial scales. The scales and questions induce different metrics for what might be considered a “good” model. The study of mathematical and computational models is replete with differing (...) views of the terms verification, validation, corroboration, and so on. I have often argued that criteria for determination of model utility should be established prior to model construction, but this is rarely done in the application of models in biology. The question I address is whether it is feasible to develop a general approach to model evaluation, that includes all the forms of models typically applied in biology—animal and cell/tissue culture ones as well as mathematical and computational ones. (shrink)

My aim in this paper is to articulate an account of scientific modeling that reconciles pluralism about modeling with a modest form of scientific realism. The central claim of this approach is that the models of a given physical phenomenon can present different aspects of the phenomenon. This allows us, in certain special circumstances, to be confident that we are capturing genuine features of the world, even when our modeling occurs independently of a wholly theoretical motivation. This framework is illustrated (...) using a recent debate from meteorology. (shrink)

Frank Knight (1921) famously distinguished the epistemic modes of certainty, risk, and uncertainty in order to characterize situations where deterministic, probabilistic or possibilistic foreknowledge is available. Because our probabilistic knowledge is limited, i.e. because many systems, e.g. the global climate, cannot be described and predicted probabilistically in a reliable way, Knight's third category, possibilistic foreknowledge, is not simply swept by the probabilistic mode. This raises the question how to justify possibilistic predictionsincluding the identication of the worst case. The development of (...) such a modal methodology is particularly vital with respect to predictions of climate change. I show that a methodological dilemma emerges when possibilistic predictions are framed in traditional terms and argue that a more nuanced conceptual framework, distinguishing dierent types of possibility, should be used in order to convey our uncertain knowledge about the future. The new conceptual scheme, however, questions the applicability of standard rules of rational decision-making, thus generating new challenges. (shrink)

When can macroscopic data about a system be used to set parameters in a microfoundational simulation? We examine the epistemic viability of tweaking parameter values to generate a better fit between the outcome of a simulation and the available observational data. We restrict our focus to microfoundational simulations—those simulations that attempt to replicate the macrobehavior of a target system by modeling interactions between microentities. We argue that tweaking can be effective but that there are two central risks. First, tweaking risks (...) overfitting the simulation to the data and thus compromising predictive accuracy; and second, it risks compromising the microfoundationality of the simulation. We evaluate standard responses to tweaking and propose strategies to guard against these risks. (shrink)

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