The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given the focus on the (...) relationship between a single model and its supposed target system, and the neglect of the actual representational means with which scientists construct models. I therefore suggest an alternative account of models as epistemic tools. This amounts to regarding them as concrete artefacts that are built by specific representational means and are constrained by their design in such a way that they facilitate the study of certain scientific questions, and learning from them by means of construction and manipulation. (shrink)

Mechanistic philosophy of science views a large part of scientific activity as engaged in modelling mechanisms. While science textbooks tend to offer qualitative models of mechanisms, there is increasing demand for models from which one can draw quantitative predictions and explanations. Casini et al. (Theoria 26(1):5–33, 2011) put forward the Recursive Bayesian Networks (RBN) formalism as well suited to this end. The RBN formalism is an extension of the standard Bayesian net formalism, an extension that allows for modelling the hierarchical (...) nature of mechanisms. Like the standard Bayesian net formalism, it models causal relationships using directed acyclic graphs. Given this appeal to acyclicity, causal cycles pose a prima facie problem for the RBN approach. This paper argues that the problem is a significant one given the ubiquity of causal cycles in mechanisms, but that the problem can be solved by combining two sorts of solution strategy in a judicious way. (shrink)

This is a paper about the methodology of normative ethics. I claim that much work in normative ethics can be interpreted as modelling, the form of inquiry familiar from science, involving idealised representations. I begin with the anti-theory debate in ethics, and note that the debate utilises the vocabulary of scientific theories without recognising the role models play in science. I characterise modelling, and show that work with these characteristics is common in ethics. This establishes the plausibility of my interpretation. (...) Taking methodological inspiration from modelling in science gives us new tools for managing idealisations, and a new perspective on pluralism. I demonstrate why this interpretation is a fruitful way of interpreting ethics, by looking at three case studies. First, I return to the anti-theory debate and argue that modelling opens up a new middle ground. Second, I argue that a modelling lens offers a new way of understanding impossibility theorems in population ethics, and their bearing on ethics as a whole. Finally, I show how viewing our work as modelling can be deployed in debates within ethics, using the debate over prioritarianism as an example. I close with further methodological suggestions for those who choose to see themselves as modellers. I discuss the role of counterexamples, our responses to moral disagreement, and the training of new ethicists. (shrink)

Over the last decade, fully distributed models have become dominant in connectionist psychological modelling, whereas the virtues of localist models have been underestimated. This target article illustrates some of the benefits of localist modelling. Localist models are characterized by the presence of localist representations rather than the absence of distributed representations. A generalized localist model is proposed that exhibits many of the properties of fully distributed models. It can be applied to a number of problems that are difficult for fully (...) distributed models, and its applicability can be extended through comparisons with a number of classic mathematical models of behaviour. There are reasons why localist models have been underused, though these often misconstrue the localist position. In particular, many conclusions about connectionist representation, based on neuroscientific observation, can be called into question. There are still some problems inherent in the application of fully distributed systems and some inadequacies in proposed solutions to these problems. In the domain of psychological modelling, localist modelling is to be preferred. Key Words: choice; competition; connectionist modelling; consolidation; distributed; localist; neural networks; reaction-time. (shrink)

This monograph offers a critical introduction to current theories of how scientific models represent their target systems. Representation is important because it allows scientists to study a model to discover features of reality. The authors provide a map of the conceptual landscape surrounding the issue of scientific representation, arguing that it consists of multiple intertwined problems. They provide an encyclopaedic overview of existing attempts to answer these questions, and they assess their strengths and weaknesses. The book also presents a comprehensive (...) statement of their alternative proposal, the DEKI account of representation, which they have developed over the last few years. They show how the account works in the case of material as well as non-material models; how it accommodates the use of mathematics in scientific modelling; and how it sheds light on the relation between representation in science and art. The issue of representation has generated a sizeable literature, which has been growing fast in particular over the last decade. This makes it hard for novices to get a handle on the topic because so far there is no book-length introduction that would guide them through the discussion. Likewise, researchers may require a comprehensive review that they can refer to for critical evaluations. This book meets the needs of both groups. (shrink)

This article discusses work on implementing emotional and cultural models into synthetic graphical characters. An architecture, FAtiMA, implemented first in the antibullying application FearNot! and then extended as FAtiMA-PSI in the cultural-sensitivity application ORIENT, is discussed. We discuss the modelling relationships between culture, social interaction, and cognitive appraisal. Integrating a lower level homeostatically based model is also considered as a means of handling some of the limitations of a purely symbolic approach. Evaluation to date is summarised and future directions discussed.

Economics is a culturally and politically powerful and contested discipline, and it has been that way as long as it has existed. For some commentators, economics is the "queen of the social sciences", while others view it as a "dismal science" (and both of these epithets allow for diverse interpretations; see Mäki 2002). Economics is also a discipline that deals with a dynamically complex subject matter and has a tradition of reducing this complexity by using systematic procedures of simplification. Nowadays, (...) these procedures involve for the most part building and using mathematical models (for an overview of the philosophical issues, see Morgan and Knuuttila 2011). In the dominant circles of the discipline, one is not regarded as a serious economist having a professional expert view on any given economic or social issue without having a model about it. Much of the power of the discipline and its characteristic contestations therefore involve models and modelling: the successes and failures of the dismal queen are those of modeling. The issues involved in conomic modeling have been made particularly acute once again by the financial crisis of 2008-2009 and its aftermath: the discipline of economics is among the candidates for the major blame for failure. I will first outline some thoughts about the characteristics disciplinary conventions that guide and constrain modeling in economics. I will then summarize my account of the very ideas of models and modeling. Finally, within the framework of that account, I will highlight some major issues of contestation and sketch the respective notions of potential success and failure in economic modeling with illustrations. These notions are motivated by my subscription to a (flexible and discipline-sensitive) realist philosophy of science (e.g. Mäki 2005). (shrink)

We used agent-based modelling to highlight the advantages and disadvantages of several management styles in biology, ranging from centralized to egalitarian ones. In egalitarian groups, all team members are connected with each other, while in centralized ones, they are only connected with the principal investigator. Our model incorporated time constraints, which negatively influenced weakly connected groups such as centralized ones. Moreover, our results show that egalitarian groups outperform others if the questions addressed are relatively simple or when the communication among (...) agents is limited. Complex epistemic spaces are explored best by centralized groups. They outperform other team structures because the individual members can develop their own ideas with less interference of the opinions of others. The optimal ratio between time spent on experimentation and dissemination varies between different organizational structures. Furthermore, if the evidence is shared only after a relevant degree of certainty is reached, all investigated groups epistemically profit. We discovered that the introduction of seminars to the model changes the epistemic performance in favour of weakly connected teams. Finally, the abilities of the principal investigator do not seem to outperform cognitive diversity, as group performances were not strongly influenced by the increase of her abilities. (shrink)

We claim that the process of theoretical model refinement in economics is best characterised as robustness analysis: the systematic examination of the robustness of modelling results with respect to particular modelling assumptions. We argue that this practise has epistemic value by extending William Wimsatt's account of robustness analysis as triangulation via independent means of determination. For economists robustness analysis is a crucial methodological strategy because their models are often based on idealisations and abstractions, and it is usually difficult to tell (...) which idealisations are truly harmful. (shrink)

We present here a new method for bioethics: systemic modelling. In this method, the complex phenomenon being studied (e.g. personalized medicine, genetic testing, gene therapy, genetically modified organisms) is modelled as a whole, to shed light on its organization and functioning, and major (bio)ethical issues and solutions for their resolution are then identified. This systemic modelling method is ideal for use in the identification of solutions, rather than their validation, with other methods then used to test the solutions found. We (...) provide a description and reproducible instructions for the application of systemic modelling in bioethics, together with a brief example of the application of this method to the study of the impact of personalized medicine on French society. (shrink)

ABSTRACT Is there something specific about modelling that distinguishes it from many other theoretical endeavours? We consider Michael Weisberg’s thesis that modelling is a form of indirect representation through a close examination of the historical roots of the Lotka–Volterra model. While Weisberg discusses only Volterra’s work, we also study Lotka’s very different design of the Lotka–Volterra model. We will argue that while there are elements of indirect representation in both Volterra’s and Lotka’s modelling approaches, they are largely due to two (...) other features of contemporary model construction processes that Weisberg does not explicitly consider: the methods-drivenness and outcome-orientedness of modelling. 1Introduction 2Modelling as Indirect Representation 3The Design of the Lotka–Volterra Model by Volterra 3.1Volterra’s method of hypothesis 3.2The construction of the Lotka–Volterra model by Volterra 4The Design of the Lotka–Volterra Model by Lotka 4.1Physical biology according to Lotka 4.2Lotka’s systems approach and the Lotka–Volterra model 5Philosophical Discussion: Strategies and Tools of Modelling 5.1Volterra’s path from the method of isolation to the method of hypothesis 5.2The template-based approach of Lotka 5.3Modelling: methods-driven and outcome-oriented 6Conclusion. (shrink)

I argue that verbal models should be included in a philosophical account of the scientific practice of modelling. Weisberg (2013) has directly opposed this thesis on the grounds that verbal structures, if they are used in science, only merely describe models. I look at examples from Darwin's On the Origin of Species (1859) of verbally constructed narratives that I claim model the general phenomenon of evolution by natural selection. In each of the cases I look at, a particular scenario is (...) described that involves at least some fictitious elements but represents the salient causal components of natural selection. I pronounce the importance of prioritising observation of scientific practice for the philosophy of modelling and I suggest that there are other likely model types that are excluded from philosophical accounts. (shrink)

Scientists confronted with multiple explanatory hypotheses as a result of their abductive inferences, generally want to reason further on the different hypotheses one by one. This paper presents a modal adaptive logic MLA s that enables us to model abduction in such a way that the different explanatory hypotheses can be derived individually. This modelling is illustrated with a case study on the different hypotheses on the origin of the Moon.

Despite efforts from regulatory agencies (e.g. NIH, FDA), recent systematic reviews of randomised controlled trials (RCTs) show that top medical journals continue to publish trials without requiring authors to report details for readers to evaluate early stopping decisions carefully. This article presents a systematic way of modelling and simulating interim monitoring decisions of RCTs. By taking an approach that is both general and rigorous, the proposed framework models and evaluates early stopping decisions of RCTs based on a clear and consistent (...) set of criteria. The framework allows decision analysts to generate and quickly answer ‘what-if’ questions by simulating alternate trial scenarios. I illustrate the framework with a case study of an RCT that was stopped early due to harm. This was a trial of vitamin A supplement in relation to HIV transmission from mother-to-child through breastfeeding. (shrink)

In the last decade, many arguments have emerged for encouraging public participation in environmental policy making and management While some have argued that, in democratic societies, people simply have a right to a participatory role, others base arguments for public participation on the idea that lay people may have access to knowledge which is unknown to officially sanctioned experts. Local people may count as experts about aspects of their neighbourhood or they may have insights into the behaviour of plant operators (...) that is thought to give rise to pollution. This paper reports on a novel empirical approach to analysing and capturing such 'lay' understandings. This technique, developed in ESRC-funded work in the UK, uses community mapping exercises in urban centres to produce spatial representations of local knowledges about air pollution and related problems of noise and odour. In the paper the technique is outlined, presenting data from the three-city case study. The paper concludes by assessing the ways in which participatory modelling can contribute to the local governance of air quality. (shrink)

Many causal systems studied by sciences such as biology, pharmacology, and economics feature causal cycles. Most accounts of causal modelling currently on the market are, however, explicitly designed to study acyclic structures. This chapter focuses on causal cycles and the challenges such cycles pose for causal modelling. First, we distinguish between different types of causal cycles. Then we introduce causal models and discuss a selection of general challenges for cyclic models when it comes to representation, prediction, and causal discovery. Finally, (...) we zoom in on a concrete case from biochemistry: the PI3K/mTOR signalling network that plays a crucial role in regulating the cell cycle. Using this case we discuss some possible pitfalls for the application of causal modelling tools to complex biological cases. (shrink)

A theory of vagueness gives a model of vague language and of reasoning within the language. Among the models that have been offered are Degree Theorists’ numerical models that assign values between 0 and 1 to sentences, rather than simply modelling sentences as true or false. In this paper, I ask whether we can benefit from employing a rich, well-understood numerical framework, while ignoring those aspects of it that impute a level of mathematical precision that is not present in the (...) modelled phenomenon of vagueness. Can we ignore apparent implications for the phenomena by pointing out that it is just a model and that the unwanted features are mere artefacts? I explore the distinction between representors and artefacts and criticise the strategy of appealing to features as mere artefacts in defence of a theory. I focus largely on theories using numerical resources, but also consider other, related theories and strategies, including theories appealing to non-linear structures. (shrink)

The strategies of action employed by a human subject in order to perceive simple 2-D forms on the basis of tactile sensory feedback have been modelled by an explicit computer algorithm. The modelling process has been constrained and informed by the capacity of human subjects both to consciously describe their own strategies, and to apply explicit strategies; thus, the strategies effectively employed by the human subject have been influenced by the modelling process itself. On this basis, good qualitative and semi-quantitative (...) agreement has been achieved between the trajectories produced by a human subject, and the traces produced by a computer algorithm. The advantage of this reciprocal modelling option, besides facilitating agreement between the algorithm and the empirically observed trajectories, is that the theoretical model provides an explanation, and not just a description, of the active perception of the human subject. (shrink)

The debate between the Mendelians and the (largely Darwinian) biometricians has been referred to by R. A. Fisher as ‘one of the most needless controversies in the history of science’ and by David Hull as ‘an explicable embarrassment’. The literature on this topic consists mainly of explaining why the controversy occurred and what factors prevented it from being resolved. Regrettably, little or no mention is made of the issues that figured in its resolution. This paper deals with the latter topic (...) and in doing so reorients the focus of the debate as one between Karl Pearson and R. A. Fisher rather than between the biometricians and the Mendelians. One reason for this reorientation is that Pearson's own work in 1904 and 1909 suggested that Mendelism and biometry could, to some extent, be made compatible, yet he remained steadfast in his rejection of Mendelism. The interesting question then is why Fisher, who was also a proponent of biometric methods, was able to synthesise the two traditions in a way that Pearson either could not or would not. My answer to this question involves an analysis of the ways in which different kinds of assumptions were used in modelling Mendelian populations. I argue that it is these assumptions, which lay behind the statistical techniques of Pearson and Fisher, that can be isolated as the source of Pearson's rejection of Mendelism and Fisher's success in the synthesis. (shrink)

The origins of human social cooperation confound simple evolutionary explanation. But from Darwin and Durkheim onward, theorists (anthropologists and sociologists especially) have posited a potential link with another curious and distinctively human social trait that cries out for explanation: religion. This dissertation explores one contemporary theory of the co-evolution of religion and human social cooperation: the signalling theory of religion, or religious signalling theory (RST). According to the signalling theory, participation in social religion (and its associated rituals and sanctions) acts (...) as an honest signal of one’s commitment to a religiously demarcated community and its way of doing things. This signal would allow prosocial individuals to positively assort with one another for mutual advantage, to the exclusion of more exploitative individuals. In effect, the theory offers a way that religion and cooperation might explain one another, but which that stays within an individualist adaptive paradigm. My approach is not to assess the empirical adequacy of the religious signalling explanation or contrast it with other explanations, but rather to deal with the theory in its own terms – isolating and fleshing out its core commitments, explanatory potential, and limitations. The key to this is acknowledging the internal complexities of signalling theory, with respect to the available models of honest signalling and the extent of their fit (or otherwise) with religion as a target system. The method is to take seriously the findings of formal modelling in animal signalling and other disciplines, and to apply these (and methods from the philosophy of biology more generally) to progressively build up a comprehensive picture of the theory, its inherent strengths and weaknesses. The first two chapters outline the dual explanatory problems that cooperation and religion present for evolutionary human science, and surveys contemporary approaches toward explaining them. Chapter three articulates an evolutionary conception of the signalling theory, and chapters four to six make the case for a series of requirements, limitations, and principles of application. Chapters seven and eight argue for the value of formal modelling to further flesh out the theory’s commitments and potential and describe some simple simulation results which make progress in this regard. Though the inquiry often problematizes the signalling theory, it also shows that it should not be dismissed outright, and that it makes predictions which are apt for empirical testing. (shrink)

Economic models describe individuals in terms of underlying characteristics, such as taste for some good, sympathy level for another player, time discount rate, risk attitude, and so on. In real life, such characteristics change through experiences: taste for Mozart changes through listening to it, sympathy for another player through observing his moves, and so on. Models typically ignore change, not just for simplicity but also because it is unclear how to incorporate change. I introduce a general axiomatic framework for defining, (...) analysing and comparing rival models of change. I show that seemingly basic postulates on modelling change together have strong implications, like irrelevance of the order in which someone has his experiences and ‘linearity’ of change. This is a step towards placing the modelling of change on solid axiomatic grounds and enabling non-arbitrary incorporation of change into economic models. (shrink)

Econophysics is a new and exciting cross-disciplinary research field that applies models and modelling techniques from statistical physics to economic systems. It is not, however, without its critics: prominent figures in more mainstream economic theory have criticized some elements of the methodology of econophysics. One of the main lines of criticism concerns the nature of the modelling assumptions and idealizations involved, and a particular target are ‘kinetic exchange’ approaches used to model the emergence of inequality within the distribution of individual (...) monetary income. This article will consider such models in detail, and assess the warrant of the criticisms drawing upon the philosophical literature on modelling and idealization. Our aim is to provide the first steps towards informed mediation of this important and interesting interdisciplinary debate, and our hope is to offer guidance with regard to both the practice of modelling inequality, and the inequality of modelling practice. _1_ Introduction _1.1_ Econophysics and its discontents _1.2_ Against burglar economics _2_ Modelling Inequality _2.1_ Mainstream economic models for income distribution _2.2_ Econophysics models for income distribution _3_ Idealizations in Kinetic Exchange Models _3.1_ Binary interactions _3.2_ Conservation principles _3.3_ Exchange dynamics _ 4 _ Fat Tails and Savings _ 5 _ Evaluation. (shrink)

Causal modelling provides a powerful set of tools for identifying causal structure from observed correlations. It is well known that such techniques fail for quantum systems, unless one introduces 'spooky' hidden mechanisms. Whether one can produce a genuinely quantum framework in order to discover causal structure remains an open question. Here we introduce a new framework for quantum causal modelling that allows for the discovery of causal structure. We define quantum analogues for core features of classical causal modelling techniques, including (...) the causal Markov condition and faithfulness. Based on the process matrix formalism, this framework naturally extends to generalised structures with indefinite causal order. (shrink)

1. Introduction; Elisabeth A. Lloyd and Eric Winsberg.- Section 1: Confirmation and Evidence.- 2. The Scientific Consensus on Climate Change: How Do We Know We’re Not Wrong?; Naomi Oreskes.- 3. Satellite Data and Climate Models Redux.- 3a. Introduction to Chapter 3: Satellite Data and Climate Models; Elisabeth A. Lloyd.- Ch. 3b Fact Sheet to "Consistency of Modelled and Observed Temperature Trends in the Tropical Troposphere"; Benjamin D. Santer et al..- Ch. 3c Reprint of "Consistency of Modelled and Observed Temperature Trends (...) in the Tropical Troposphere"; Benjamin D. Santer et al..- 4. The Role of ’Complex’ Empiricism in the Debates about Satellite Data and Climate Models; Elisabeth A. Lloyd.- 5. Reconciling Climate Model/Data Discrepancies: The Case of the Trees That Didn’t Bark; Michael Mann.- 6. Downscaling of Climate Information; Linda O. Mearns et al..- Section 2: Uncertainties and Robustness.- 7. The Significance of Robust Model Projections; Wendy S. Parker.- 8. Building Trust, Removing Doubt? Robustness Analysis and Climate Modeling; Jay Odenbaugh.- Section 3: Climate Models as Guides to Policy.- 9. Climate Model Confirmation: From Philosophy to Predicting Climate in the Real World; Reto Knutti.- 10. Uncertainty in Climate Science and Climate Policy; Jonathan Rougier and Michel Crucifix.- 11. Communicating Uncertainty to Policy Makers: The Ineliminable Role of Values; Eric Winsberg.- 12. Modeling Climate Policies: A Critical Look at Integrated Assessment Models; Mathias Frisch.- 13. Modelling Mitigation and Adaptation Policies to Predict their Effectiveness: The Limits of Randomized Controlled Trials; Alexandre Marcellesi and Nancy D. Cartwright. (shrink)

The ability to convert human somatic cells into induced pluripotent stem cells (iPSCs) is allowing the production of custom‐tailored cells for drug discovery and for the study of disease phenotypes at the cellular and molecular level. IPSCs have been derived from patients suffering from a large variety of disorders with different severities. In many cases, disease related phenotypes have been observed in iPSCs or their lineage‐specific progeny. Several proof of concept studies have demonstrated that these phenotypes can be reversed in (...) vitro using approved drugs. However, several challenges must be overcome to take full advantage of this technology. Here, we highlight recent advances in the field and discuss the main challenges associated with this technology as it applies to disease modelling. (shrink)

Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and ‘average’ Bayesian models of legal arguments that have been built independently and with no attempt to make them consistent (...) in terms of variables, causal assumptions or parameterization. The approach involves assessing whether competing models of legal arguments are explained or predict facts uncovered before or during the trial process. Those models that are more heavily disconfirmed by the facts are given lower weight, as model plausibility measures, in the Bayesian model comparison and averaging framework adopted. In this way a plurality of arguments is allowed yet a single judgement based on all arguments is possible and rational. (shrink)

The story of the fall and rise of Zahavi’s handicap principle is one of a battle between models. Early attempts at formal modeling produced negative results and, unsurprisingly, scepticism about the principle. A major change came in 1990 with Grafen’s production of coherent models of a handicap mechanism of honest signalling. This paper’s first claim is that acceptance of the principle, and its dissemination into other disciplines, has been driven principally by that, and subsequent modeling, rather than by (...) empirical results. Secondly, there is a vast literature on biological signalling but few studies that make all of the observations necessary to diagnose the handicap mechanism. My final claim is that many of the applications of costly signalling theory in other disciplines are conceptually confused. Misinterpretations of what is meant by costly signalling are common. Demonstrating that a signal is costly is insufficient and is not always necessary in order to prove that, and explain why, a signal is honest. In addition to the biological modelling of signals, there is an economic literature on the same subject. The two run in parallel in the sense that they have had little mutual interaction. Additionally, it is the biological modelling that has been picked up, and often misapplied, by other disciplines. (shrink)

Hidden Markov models (HMMs) have been successful for modelling the dynamics of carefully dictated speech, but their performance degrades severely when used to model conversational speech. Since speech is produced by a system of loosely coupled articulators, stochastic models explicitly representing this parallelism may have advantages for automatic speech recognition (ASR), particularly when trying to model the phonological effects inherent in casual spontaneous speech. This paper presents a preliminary feasibility study of one such model class: loosely coupled HMMs. Exact model (...) estimation and decoding is potentially expensive, so possible approximate algorithms are also discussed. Comparison of one particular loosely coupled model on an isolated word task suggests loosely coupled HMMs merit further investigation. An approximate algorithm giving performance which is almost always statistically indistinguishable from the exact algorithm is also identified, making more extensive research computationally feasible. (shrink)

Modelling contagious diseases needs to include a mechanistic knowledge about contacts between hosts and pathogens as specific as possible, e.g., by incorporating in the model information about social networks through which the disease spreads. The unknown part concerning the contact mechanism can be modelled using a stochastic approach. For that purpose, we revisit SIR models by introducing first a microscopic stochastic version of the contacts between individuals of different populations (namely Susceptible, Infective and Recovering), then by adding a random perturbation (...) in the vicinity of the endemic fixed point of the SIR model and eventually by introducing the definition of various types of random social networks. We propose as example of application to contagious diseases the HIV, and we show that a micro-simulation of individual based modelling (IBM) type can reproduce the current stable incidence of the HIV epidemic in a population of HIV-positive men having sex with men (MSM). (shrink)

Modelling with Words is an emerging modelling methodology closely related to the paradigm of Computing with Words introduced by Lotfi Zadeh. This book is an authoritative collection of key contributions to the new concept of Modelling with Words. A wide range of issues in systems modelling and analysis is presented, extending from conceptual graphs and fuzzy quantifiers to humanist computing and self-organizing maps. Among the core issues investigated are - balancing predictive accuracy and high level transparency in learning - scaling (...) linguistic algorithms to high-dimensional data problems - integrating linguistic expert knowledge with knowledge derived from data - identifying sound and useful inference rules - integrating fuzzy and probabilistic uncertainty in data modelling. (shrink)

This paper examines the process of modelling a complex empirical phenomenon in modern astrophysics: extended extragalactic radio sources. I show that modelling is done piecemeal, addressing selected striking or puzzling features of that phenomenon separately and individually. The result is various independent and separate sub-models concerned only with limited aspects of the same phenomenon. Because the sub-models represent features of the same physical phenomenon, they need to be reasonably consistent with each other - a criterion not always fully adhered to (...) - and there needs to be a way to conceptually `re-unite' the sub-models to form an overall-model. Visualisation, that is, supplying a concrete interpretation of abstract, theoretical sub-models, aids this modelling process. My case study further endorses the view that modelling is `work in progress', i.e. a form of developing knowledge whereby models represent, not replicate, a phenomenon. (shrink)

What is it for a group to believe something? A summative account assumes that for a group to believe that p most members of the group must believe that p. Accounts of this type are commonly proposed in interpretation of everyday ascriptions of beliefs to groups. I argue that a nonsummative account corresponds better to our unexamined understanding of such ascriptions. In particular I propose what I refer to as the joint acceptance model of group belief. I argue that group (...) beliefs according to the joint acceptance model are important phenomena whose aetiology and development require investigation. There is an analogous phenomenon of social or group preference, which social choice theory tends to ignore. (shrink)

The following considerations concern modelling Belief Dynamics (BD) not just in the sense of a formalization, but rather in the sense of building a computational model and implementing the corresponding data structures and algorithms of recomputing beliefs. The purpose of such a project is to illustrate some ideas about belief changes in a Web of Beliefs (WoB) to explore and deepen one's understanding of belief changes by trying to implement or improve corresponding algorithms.

Climate modelling plays a crucial role for understanding and addressing the climate challenge, in terms of both mitigation and adaptation. It is therefore of central importance to understand to what extent climate models are adequate for relevant purposes, such as providing certain kinds of climate change projections in view of decision-making. In this perspective, the issue of the stability of climate models under small relevant perturbations in their structure seems particularly important. Within this framework, a debate has emerged in the (...) philosophy of science literature about the relevance for climate modelling of the mathematical notion of structural stability. This paper adresses several important foundational and epistemological questions that arise in this context, in particular about the the role of abstract mathematical considerations of a qualitative nature for concrete modelling projects with mainly quantitative purposes. (shrink)

In this paper, I address the issue of scientific modelling in contemporary linguistics, focusing on the generative tradition. In so doing, I identify two common varieties of linguistic idealisation, which I call determination and isolation respectively. I argue that these distinct types of idealisation can both be described within the remit of Weisberg’s :639–659, 2007) minimalist idealisation strategy in the sciences. Following a line set by Blutner :27–35, 2011), I propose this minimalist idealisation analysis for a broad construal of the (...) generative linguistic programme and thus cite examples from a wide range of linguistic frameworks including early generative syntax, Minimalism, the parallel architecture and optimality theory. Lastly, I claim that from a modelling perspective, the dynamic turn in syntax can be explained as a continuation, as opposed to a marked shift, of the generative modelling paradigm. Seen in this light, my proposal is an even broader construal of the generative tradition, along scientific modelling lines. Thus, I offer a lens through which to appreciate the scientific contribution of generative grammar, amid an increased resistance to some of its core theoretical posits, in terms of a brand of structural realism in the philosophy of science and specifically scientific modelling. (shrink)

Modelling human reasoning characterizes the fundamental human cognitive capacity to describe our past experience and use it to form expectations as well as plan and direct our future actions. Natural language semantics analyzes dynamic forms of reasoning in which the real-time order determines the temporal relations between the described events, when reported with telic simple past-tense clauses. It provides models of human reasoning that could supplement ACT-R models.

Webb's scheme for classifying behavioral models is applicable to a wide range of theories and simulations, nonrobotic as well as robotic. It is suggested that a meta-analysis of existing models, characterized according to the proposed scheme, could identify regions of the seven-dimensional modelling space that are particularly likely to lead to new insights in understanding behavior.

This paper deals with causal analysis in the social sciences. We first present a conceptual framework according to which causal analysis is based on a rationale of variation and invariance, and not only on regularity. We then develop a formal framework for causal analysis by means of structural modelling. Within this framework we approach causality in terms of exogeneity in a structural conditional model based which is based on (i) congruence with background knowledge, (ii) invariance under a large variety of (...) environmental changes, and (iii) model fit. We also tackle the issue of confounding and show how latent confounders can play havoc with exogeneity. This framework avoids making untestable metaphysical claims about causal relations and yet remains useful for cognitive and action-oriented goals. (shrink)

What is the connection between modelling thought and modelling the brain? In a model (as understood here), we strip away from the modelled system some non-essential features and retain some essential ones. What are the essential features of thought that are to be re- tained in the model, and conversely, what are its inessential features, that may be stripped away in the model? According to a prevalent view in contemporary science and philoso- phy, thought is a computation, and therefore its (...) essential features are its computational features. A necessary part of the computational view of thought is the idea that the same computation can be realised by, or implemented in, physically heterogeneous systems, an idea known as “Multiple Realizability” of the computational features or properties by the physical ones. I will describe why the very idea of Multiple Realizability, especially in the case of mental computation, entails mind-body dualism, and explore some implications of this conclusion concerning the question of which are the essential features of thought to be retained in modeling it. (shrink)

In the last decade, the UK Government has attempted to implement improved processes and procedures in modelling and analysis in response to the Laidlaw report of 2012 and the Macpherson review of 2013. The Laidlaw report was commissioned after failings during the Intercity West Coast Rail (ICWC) Franchise procurement exercise by the Department for Transport (DfT) that led to a legal challenge of the analytical models used within the exercise. The Macpherson review looked into the quality assurance of Government analytical (...) models in the context of the experience with the Intercity West Coast franchise competition. This paper examines what progress has been made in the 8 years since the Laidlaw report in model building and best practise in government and proposes several recommendations for ways forward. This paper also discusses the Lords Science and Technology Committees of June 2020 that analysed the failings in the modelling of COVID. Despite going on to influence policy, many of the same issues raised within the Laidlaw and Macpherson Reports were also present in the Lords Science and Technology Committee enquiry. We examine the technical and organisational challenges to progress in this area and make recommendations for a way forward. (shrink)

In this paper, I critique a recent claim made by Stokhof and van Lambalgen (2011) (hereafter S&vL) that linguistics and science are at odds as to the models and constructions they employ. I argue that their distinction between abstractions and idealisations, the former belonging to the methodology of science and the latter to linguistics, is not a real one. I show that the majority of their arguments are flawed and evidence they cite misleading. Contrary to this distinction, I argue that (...) linguistics, like some variants of the scientific enterprise, uses a minimalist method of idealisation (Weisberg, 2007b), one which includes abstractions (as defined by S&vL) and other idealisations not uncommon to scientific model-building. Finally, I offer an alternative account of the problems cited by S&vL as a direct result of the modelling choices of linguists as opposed to the methods they use to define such models. I do so through the use of the specific example of the treatment of tense and aspect in the mainstream literature. (shrink)

I argue that normative formal epistemology (NFE) is best understood as modelling, in the sense that this is the reconstruction of its methodology on which NFE is doing best. I focus on Bayesianism and show that it has the characteristics of modelling. But modelling is a scientific enterprise, while NFE is normative. I thus develop an account of normative models on which they are idealised representations put to normative purposes. Normative assumptions, such as the transitivity of comparative credence, are characterised (...) as modelling idealisations motivated normatively. I then survey the landscape of methodological options: what might formal epistemologists be up to? I argue the choice is essentially binary: modelling or theorising. If NFE is theorising it is doing very poorly: generating false claims with no clear methodology for separating out what is to be taken seriously. Modelling, by contrast, is a successful methodology precisely suited to the management of useful falsehoods. Regarding NFE as modelling is not costless, however. First, our normative inferences are less direct and are muddied by the presence of descriptive idealisations. Second, our models are purpose-specific and limited in their scope. I close with suggestions for how to adapt our practice. (shrink)

The paper outlines the analysis of certain question types in the Commitment Space framework, as presented in Krifka (2015). The two basic ideas are: Assertions and most questions involve commitments of speaker and addressee to the truth of a proposition, and questions consist in restricting the continuation of the conversation to answers to the question. The main focus is on breadth, not depth and the detailed comparison with alternative approaches, and on semantic modelling, not on the syntactic and prosodic realizations. (...) Topics are polar questions and their bias, alternative questions, constituent questions, high negation in questions, declarative questions, root vs. embedded questions, and deliberative questions. (shrink)

Modelling nursing activities: electronic patient records and their discontents A fully integrated and operating EPR in a clinical setting is hard to find: most applications can be found in outpatient or general practice settings or in isolated hospital wards. In clinical work practice problems with the electronic patient record (EPR) are frequent. These problems are at least partially due to the models of health care work embedded in EPRs. In this paper we will argue that these problems are at least (...) partially due to the models of health care work embedded in current EPRs. We suggest that these models often contain projections of nurses’ and doctors’ work as it should be performed on the ward, rather than depicting how work is actually performed. We draw upon sociological insights to elucidate the fluid and pragmatic nature of healthcare work and give recommendations for the development of an empirically based EPR, which can support the work of nurses and other health care providers. We argue that these issues are of great importance to the nursing profession, since the EPR will help define the worksettings of the future. Since it is a tool that will impact the development of the nursing profession, nurses have and should have a stake in its development. (shrink)

Psychiatric researchers typically assume that the modelling of psychiatric symptoms is not influenced by psychiatric categories; symptoms are modelled and then grouped into a psychiatric category. I highlight this primarily through analysing research domain criteria. RDoC’s importance makes it worth scrutinizing, and this assessment also serves as a case study with relevance for other areas of psychiatry. RDoC takes inadequacies of existing psychiatric categories as holding back causal investigation. Consequently, RDoC aims to circumnavigate existing psychiatric categories by directly investigating the (...) causal basis of symptoms. The unique methodological approach of RDoC exploits the supposed lack of influence of psychiatric categories on symptom modelling, taking psychiatric symptoms as the same regardless of which psychiatric category is employed or if no psychiatric category is employed. But this supposition is not always true. I will show how psychiatric categories can influence symptom modelling, whereby identical behaviours can be considered as different symptoms based on an individual’s psychiatric diagnosis. If the modelling of symptoms is influenced by psychiatric categories, then psychiatric categories will still play a role, a situation which RDoC researchers explicitly aim to avoid. I discuss four ways RDoC could address this issue. This issue also has important implications for factor analysis, cluster analysis, modifying psychiatric categories, and symptom based approaches. (shrink)