Claus Beisbart

We consider a decision board with representatives who vote on proposals on behalf of their constituencies. We look for decision rules that realize utilitarian and (welfarist) egalitarian ideals. We set up a simple model and obtain roughly the following results. If the interests of people from the same constituency are uncorrelated, then a weighted rule with square root weights does best in terms of both ideals. If there are perfect correlations, then the utilitarian ideal requires proportional weights, whereas the egalitarian ideal requires equal weights. We investigate correlations that are in between these extremes and provide analytic arguments to connect our results to Barberà and Jackson (J Polit Econ 114(2):317–339, 2006) and to Banzhaf voting power.

We develop a utilitarian framework to assess different decision rules for the European Council of Ministers. The proposals to be decided on are conceptualized as utility vectors and a probability distribution is assumed over the utilities. We first show what decision rules yield the highest expected utilities for different means of the probability distri- bution. For proposals with high mean utility, simple bench- mark rules (such as majority voting with proportional weights) tend to outperform rules that have been proposed in the political arena. For proposals with low mean utility, it is the other way round. We then compare the expected utilities for smaller and larger countries and look for Pareto- dominance relations. Finally, we provide an extension of the model, discuss its restrictions, and compare our approach with assessments of decision rules that are based on the Penrose measure of voting power.

The mean majority deficit in a two-tier voting system is a function of the partition of the population. We derive a new square-root rule: For odd-numbered population sizes and equipopulous units the mean majority deficit is maximal when the member size of the units in the partition is close to the square root of the population size. Furthermore, within the partitions into roughly equipopulous units, partitions with small even numbers of units or small even-sized units yield high mean majority deficits. We discuss the implications for the winner-takes-all system in the US Electoral College.

To provide an introduction to this book, we explain the motivation to publish this volume, state its main goal, characterize its intended readership, and give an overview of its content. To this purpose, we briefly summarize each chapter and put it in the context of the whole volume. We also take the opportunity to stress connections between the chapters. We conclude with a brief outlook.The main motivation to publish this volume was the diagnosis that the validation of computer simulation needs more attention in practice and in theory. The aim of this volume is to improve our understanding of validation. To this purpose, computer scientists, mathematicians, working scientists from various fields, as well as philosophers of science join efforts. They explain basic notions and principles of validation, embed validation in philosophical frameworks such as Bayesian epistemology, detail the steps needed during validation, provide best practice examples, reflect upon challenges to validation, and put validation in a broader perspective. As we suggest in our outlook, the validation of computer simulations will remain an important research topic that needs cross- and interdisciplinary efforts. A key issue is whether, and if so, how very rigorous approaches to validation that have proven useful in, e.g., engineering can be extended to other disciplines.

This chapter clarifies the concept of validation of computer simulations by comparing various definitions that have been proposed for the notion. While the definitions agree in taking validation to be an evaluationEvaluation, they differ on the following questions: What exactly is evaluated—results from a computer simulation, a model, a computer codeCode? What are the standardsStandard of evaluationEvaluation––truthTruth, accuracyAccuracy, and credibilityCredibility or also something else? What type of verdict does validation lead to––that the simulation is such and such good, or that it passes a testTest defined by a certain threshold? How strong needs the case to be for the verdict? Does validation necessarily proceed by comparing simulation outputsOutput with measured dataData? Along with these questions, the chapter explains notions that figure prominently in them, e.g., the concepts of accuracy and credibility. It further discusses natural answers to the questions as well as arguments that speak in favor and against these answers. The aim is to obtain a better understandingUnderstanding of the options we have for defining validation and how they are related to each other.

Bayesian epistemologyEpistemology offers a powerful framework for characterizing scientific inference. Its basic idea is that rational belief comes in degrees that can be measured in terms of probabilities. The axioms of the probability calculus and a rule for updatingUpdating emerge as constraints on the formation of rational belief. Bayesian epistemologyEpistemology has led to useful explications of notions such asConfirmation confirmation. It thus is natural to ask whether Bayesian epistemologyEpistemology offers a useful framework for thinking about the inferences implicit in the validation of computer simulations. The aim of this chapter is to answer this question. Bayesian epistemologyEpistemology is briefly summarized and then applied to validation. UpdatingUpdating is shown to form a viable method for data-driven validation. Bayesians can also express how a simulation obtains prior credibilityCredibility because the underlying conceptual modelConceptual model is credible. But the impact of this prior credibilityCredibility is indirect since simulations at best provide partial and approximate solutions to theConceptual model conceptual model. Fortunately, this gap between the simulations and the conceptual model can be addressed using what we call Bayesian verification. The final part of the chapter systematically evaluates the use of Bayesian epistemologyEpistemology in validation, e.g., by comparing it to a falsificationist approach. It is argued that Bayesian epistemologyEpistemology goes beyond mere calibrationCalibration and that it can provide the foundations for a sound evaluationEvaluation of computer simulations.

Verification and validation are methods with which computer simulations are tested. While many practitioners draw a clear line between verification and validation and demand that the former precedes the latter, some philosophers have suggested that the distinction has been over-exaggerated. This chapter clarifies the relationship between verification and validation. Regarding the latter, validation of the conceptual and of the computational modelComputational model are distinguished. I argue that, as a method, verification is clearly different from validation of either of the models. However, the methods are related to each other as follows: If we allow that the validation of the computational model need not include the comparisonComparison between simulation output and measured data, then the computational model may be validated by validating the conceptual model independently and by verifying the simulation with respect to it. This is often not realistic, however, because, in most cases, the conceptual modelConceptual model cannot be validated independently from the simulation. In such cases, the computational modelComputational model is verified with the aim to use it as an appropriate substitute for the conceptual model. Then simulation output is compared to measured data to validate both the computational and the conceptual model. I analyze the underlying inferences and argue that they require some prior confidence in the conceptual model and in verification. This suggests that verification precede validation that proceeds via a comparisonComparison between simulation outputOutput and measured data. Recent arguments to the effect that the distinction between verification and validation is not clear-cut do not refute these results, or so I argue against philosopher E. Winsberg.

Many questions about the fundamentals of some area take the form “What is …?” It does not come as a surprise then that, at the dawn of Western philosophy, Socrates asked the questions of what piety, courage, and justice are. Nor is it a wonder that the philosophical preoccupation with computer simulations centered, among other things, about the question of what computer simulations are. Very often, this question has been answered by stating that computer simulation is a species of a well-known method, e.g., experimentation. Other answers claim at least a close relationship between computer simulation and another method. In any case, correct answers to the question of what a computer simulation is should help us to better understand what validation of simulations is. The aim of this chapter is to discuss the most important proposals to understand computer simulation in terms of another method and to trace consequences for validation. Although it has sometimes been claimed that computer simulations are experiments, there are strong reasons to reject this view. A more appropriate proposal is to say that computer simulations often model experiments. This implies that the simulation scientists should to some extent imitate the validation of an experiment. But the validation of computer simulations turns out to be more comprehensive. Computer simulations have also been conceptualized as thought experimentsThought experiment or close cousins of the latter. This seems true, but not very telling since thought experiments are not a standardStandard method and since it is controversial how they contribute to our acquisition of knowledge. I thus consider a specific view on thought experiments to make some progress on understanding simulations and their validation. There is finally a close connection between computer simulation and modeling, and it can be shown that the validation of a computer simulation is the validation of a specific model, which may either be thought to be mathematical or fictional.

The paper provides a systematic overview of recent debates in epistemology and philosophy of science on the nature of understanding. We explain why philosophers have turned their attention to understanding and discuss conditions for “explanatory” understanding of why something is the case and for “objectual” understanding of a whole subject matter. The most debated conditions for these types of understanding roughly resemble the three traditional conditions for knowledge: truth, justification and belief. We discuss prominent views about how to construe these conditions for understanding, whether understanding indeed requires conditions of all three types and whether additional conditions are needed.

What is the status of a cat in a virtual reality environment? Is it a real object? Or part of a fiction? Virtual realism, as defended by D. J. Chalmers, takes it to be a virtual object that really exists, that has properties and is involved in real events. His preferred specification of virtual realism identifies the cat with a digital object. The project of this paper is to use a comparison between virtual reality environments and scientific computer simulations to critically engage with Chalmers’s position. I first argue that, if it is sound, his virtual realism should also be applied to objects that figure in scientific computer simulations, e.g. to simulated galaxies. This leads to a slippery slope because it implies an unreasonable proliferation of digital objects. A philosophical analysis of scientific computer simulations suggests an alternative picture: The cat and the galaxies are parts of fictional models for which the computer provides model descriptions. This result motivates a deeper analysis of the way in which Chalmers builds up his realism. I argue that he buys realism too cheap. For instance, he does not really specify what virtual objects are supposed to be. As a result, rhetoric aside, his virtual realism isn’t far from a sort of fictionalism.

This unique volume introduces and discusses the methods of validating computer simulations in scientific research. The core concepts, strategies, and techniques of validation are explained by an international team of pre-eminent authorities, drawing on expertise from various fields ranging from engineering and the physical sciences to the social sciences and history. The work also offers new and original philosophical perspectives on the validation of simulations. Topics and features: introduces the fundamental concepts and principles related to the validation of computer simulations, and examines philosophical frameworks for thinking about validation; provides an overview of the various strategies and techniques available for validating simulations, as well as the preparatory steps that have to be taken prior to validation; describes commonly used reference points and mathematical frameworks applicable to simulation validation; reviews the legal prescriptions, and the administrative and procedural activities related to simulation validation; presents examples of best practice that demonstrate how methods of validation are applied in various disciplines and with different types of simulation models; covers important practical challenges faced by simulation scientists when applying validation methods and techniques; offers a selection of general philosophical reflections that explore the significance of validation from a broader perspective. This truly interdisciplinary handbook will appeal to a broad audience, from professional scientists spanning all natural and social sciences, to young scholars new to research with computer simulations. Philosophers of science, and methodologists seeking to increase their understanding of simulation validation, will also find much to benefit from in the text.

This volume offers an integrated understanding of how the theory of general relativity gained momentum after Einstein had formulated it in 1915. Chapters focus on the early reception of the theory in physics and philosophy and on the systematic questions that emerged shortly after Einstein's momentous discovery. They are written by physicists, historians of science, and philosophers, and were originally presented at the conference titled Thinking About Space and Time: 100 Years of Applying and Interpreting General Relativity, held at the University of Bern from September 12-14, 2017. By establishing the historical context first, and then moving into more philosophical chapters, this volume will provide readers with a more complete understanding of early applications of general relativity and of related philosophical issues. Because the chapters are often cross-disciplinary, they cover a wide variety of topics related to the general theory of relativity. These include: Heuristics used in the discovery of general relativity Mach's Principle The structure of Einstein's theory Cosmology and the Einstein world Stability of cosmological models The metaphysical nature of spacetime The relationship between spacetime and dynamics The Geodesic Principle Symmetries Thinking About Space and Time will be a valuable resource for historians of science and philosophers who seek a deeper knowledge of the uses of general relativity, as well as for physicists and mathematicians interested in exploring the wider historical and philosophical context of Einstein's theory.

Computer simulations and experiments share many important features. One way of explaining the similarities is to say that computer simulations just are experiments. This claim is quite popular in the literature. The aim of this paper is to argue against the claim and to develop an alternative explanation of why computer simulations resemble experiments. To this purpose, experiment is characterized in terms of an intervention on a system and of the observation of the reaction. Thus, if computer simulations are experiments, either the computer hardware or the target system must be intervened on and observed. I argue against the first option using the non-observation argument, among others. The second option is excluded by e.g. the over-control argument, which stresses epistemological differences between experiments and simulations. To account for the similarities between experiments and computer simulations, I propose to say that computer simulations can model possible experiments and do in fact often do so.

If we are to constrain our place in the world, two principles are often appealed to in science. According to the Copernican Principle, we do not occupy a privileged position within the Universe. The Cosmological Principle, on the other hand, says that our observations would roughly be the same, if we were located at any other place in the Universe. In our paper we analyze these principles from a logical and philosophical point of view. We show how they are related, how they can be supported and what use is made of them. Our main results are: 1. There is a logical gap between both principles insofar as the Cosmological Principle is significantly stronger than the Copernican Principle. 2. A step that is often taken for establishing the Cosmological Principle on the base of the Copernican Principle and observations is not incontestable as it stands, but can be supplemented with a different argument. 3. The Cosmological Principle might be crucial for cosmology to the extent it is not supported by empirical evidence.

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

Kant famously thought that mathematics contains synthetic a priori truths. In his book, Wille defends a version of the Kantian thesis on not-so-Kantian grounds. Wille calls his account neo-Kantian, because it makes sense of Kantian tenets by using a methodology that takes the linguistic and pragmatic turns seriously.Wille's work forms part of a larger project in which the statuses of mathematics and proof theory are investigated. The official purpose of the present book is to answer the question: what is mathematics. Wille sets himself the task of finding a definition that enables him to distinguish between mathematics and proof theory. His solution reads roughly as follows. Mathematics is about how to generate synthetic a priori knowledge by acting within some calculus. This definition does not seem to be a promising starting point, because in this way a very controversial claim becomes true by definition. However, Wille's strategy is to make sense of his definition during the course of his argument and to show that the definition is appropriate regarding research that is commonly taken to be mathematics.The second part of the book explains what ‘acting within some calculus’ amounts to. In Wille's view, mathematics should be characterized in …

With this Editorial, we’d like to inform our readers about some news concerning the editorship of the Journal for General Philosophy of Science:Since its foundation by Alwin Diemer, Lutz Geldsetzer and Gert König in 1970, JGPS was edited by the three founders until 2004. In 2005 and 2008, respectively, Helmut Pulte and Gregor Schiemann joined JGPS as new editors, while Lutz Geldsetzer and Gert König retired by the end of 2008. In 2013, Ulrich Krohs joined the editorial team and thereby restored the ‘trinity’ of the starting years of JGPS.Gregor Schiemann has decided to resign from his editorship with the end of 2014 and has assigned Claus Beisbart as his successor in consultation with the other editors and the publisher. Since 2012, Prof. Dr. Dr. Beisbart is working as a philosopher of science at the University of Bern. His areas of expertise include philosophy of physics, particularly of cosmology, epistemology of model building and simulation..

In den vergangenen Jahren hat die Europäische Union (EU) wiederholt versucht, ihre Institutionen zu reformieren. Als der Entwurf für eine Europäische Verfassung und später der Vertrag von Lissabon ausgehandelt wurden, betraf einer der meistdiskutiertesten Streitpunkte die Frage, nach welcher Entscheidungsregel der EU-Ministerrat abstimmen sollte. Diese Frage ist eine genuin normative Frage. Deshalb sollten auch politische Philosophen und Ethiker etwas zu dieser Frage beitragen können. Im folgenden wollen wir uns dieser Herausforderung stellen und alternative Entscheidungsregeln für den EU-Ministerrat bewerten. Dabei erweisen sich die Methoden der probablistische Modellierung und der Simulation sozialer Prozesse als unerlässlich.1 Damit wird deutlich, wie Simulationen auch innerhalb der angewandten politischen Philosophie als Methode eingesetzt werden können.

In physics, objects are often assigned vector characteristics such as a specific velocity. How can this be understood from a metaphysical point of view – is assigning an object a vector characteristic to attribute it an intrinsic property? As a short review of Newtonian, special relativistic and general relativistic physics shows, if we wish to assign some object a vector characteristic, we have to relate it to something – call it S. If S is to be different from the original object – and I argue for this assumption – then having the specific vector characteristic is not an intrinsic property under different readings of “intrinsic”. For instance, I argue that lonely objects with different orientations do not constitute metaphysically distinct possibilities. There are, however, difficulties in applying the loneliness test, and they concern the role of the space‐time.

The most promising accounts of ontic probability include the Spielraum conception of probabilities, which can be traced back to J. von Kries and H. Poincaré, and the best system account by D. Lewis. This paper aims at comparing both accounts and at combining them to obtain the best of both worlds. The extensions of both Spielraum and best system probabilities do not coincide because the former only apply to systems with a special dynamics. Conversely, Spielraum probabilities may not be part of the best system, e.g. if a broad class of random devices is not often used. Spielraum probabilities have the potential to provide illuminating explanations of frequencies with which outcomes of trials on gambling devices arise. They ultimately fail to account for such frequencies though because they are compatible with frequencies that grossly differ from the values of the respective probabilities. I thus follow recent proposals by M. Strevens and M. Abrams and strengthen the definition of Spielraum probabilities by adding a further condition that restricts some actual frequencies. The resulting account remains limited in scope, but stands objections raised against it in the recent literature: it is neither circular nor based upon arbitrary choices of a measure or of physical variables. Nor does it lack any explanatory power.

What do we mean to say when we call some person a good business manager? And where do the criteria flow from by which we judge people good business managers? I answer these questions by drawing on von Wright's distinction between several varieties of goodness. We can then discriminate between instrumental, technical and moral senses of the expression “to be a good business manager”. The first two senses presume that business managers have a characteristic task or that they engage in typical activities. It is natural to specify this task and these activities by appealing to goods and to the good of a business. These goods may then be identified as goods for people. Insofar as the welfare of people is of moral value, we obtain a morally loaded account of business management. I argue against this account and plead for a more sober understanding of economic goods and of the good of a business. The latter can be understood in analogy to the good of an individual person, but is largely determined by the goals of the founders and owners. Economic goods are things that are wanted by some people and thus exchanged on markets. If this is so, then there is no direct conceptual path from instrumentally or technically good business managers to morality even though there are certain affinities. To conclude, I trace the consequences for our understanding of business ethics.

The voting power of a voter—the extent to which she can affect the outcome of a collective decision—is often quantified in terms of the probability that she is critical. This measure is extended to a series of power measures of different ranks. The measures quantify the extent to which a voter can be part of a group that can jointly make a difference as to whether a bill passes or not. It is argued that the series of these measures allow for a more appropriate assessment of voting power, particularly of a posteriori voting power in case the votes are stochastically dependent. Also, the new measures discriminate between voting games that cannot be distinguished in terms of the probability of only criticality.

We provide welfarist evaluations of decision rules for federations of states and consider models, under which the interests of people from different states are stochastically dependent. We concentrate on two welfarist standards; they require that the expected utility for the federation be maximized or that the expected utilities for people from different states be equal. We discuss an analytic result that characterizes the decision rule with maximum expected utility, set up a class of models that display interstate dependencies and run simulations for different dependency scenarios in the European Union. We find that, under positive correlations, the welfare distribution tends to be less sensitive to the choice of the decision rule, whereas it can be important under negative correlations. The results that Beisbart and Bovens (SCW 29, p. 581, 2007) have found for two types of models without interstate dependencies are relatively stable. There are exceptions, though, under which the way the welfare distribution is shaped by a decision rule is significantly affected by dependencies.