Julian Reiss

Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science , but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology and semantics of models and theories, and the relation between experimentation and theorising, and submit that simulations demand a fundamentally new philosophy of science in many respects. The aim of this paper is to critically evaluate these claims. Our conclusion will be sober. We argue that these claims are overblown and that simulations, far from demanding a new metaphysics, epistemology, semantics and methodology, raise few if any new philosophical problems. The philosophical problems that do come up in connection with simulations are not specific to simulations and most of them are variants of problems that have been discussed in other contexts before.

This paper reviews all major theories of evidence such as the Bayesian theory, hypothetico-deductivism, satisfaction theories, error-statistics, Achinstein's explanationist theory and Cartwright's argument theory. All these theories fail to take adequate account of the context in which a hypothesis is established and used. It is argued that the context of an inquiry determines important facts about what evidence is, and how much and what kind has to be collected to establish a hypothesis for a given purpose.

Two approaches to evidential reasoning compete in the biomedical and social sciences: the experimental and the pragmatist. Whereas experimentalism has received considerable philosophical analysis and support since the times of Bacon and Mill, pragmatism about evidence has been neither articulated nor defended. The overall aim is to fill this gap and develop a theory that articulates the latter. The main ideas of the theory will be illustrated and supported by a case study on the smoking/lung cancer controversy in the 1950s

This paper aims (a) to provide characterizations of realism and instrumentalism that are philosophically interesting and applicable to economics; and (b) to defend instrumentalism against realism as a methodological stance in economics. Starting point is the observation that ‘all models are false’, which, or so I argue, is difficult to square with the realist's aim of truth, even if the latter is understood as ‘partial’ or ‘approximate’. The three cheers in favour of instrumentalism are: (1) Once we have usefulness, truth is redundant. (2) There is something disturbing about causal structure. (3) It's better to do what one can than to chase rainbows.

Thought experiments are ubiquitous in science and especially prominent in domains in which experimental and observational evidence is scarce. One such domain is the causal analysis of singular events in history. A long‐standing tradition that goes back to Max Weber addresses the issue by means of ‘what‐if’ counterfactuals. In this paper I give a descriptive account of this widely used method and argue that historians following it examine difference makers rather than causes in the philosopher’s sense. While difference making is neither necessary nor sufficient for causation, to establish difference makers is more consistent with the historians’ more ultimate purposes. †To contact the author, please write to: Department of Philosophy, Erasmus University, 3000 DR Rotterdam, The Netherlands; e‐mail: [email protected].

In recent years, a number of philosophers have attempted to fix paradoxes of the counterfactual account of causation by making causation contrastive. In this framework, causation is understood to be not a two-place relationship between a cause and an effect but a three or four-place relationship between a cause, an effect and a contrast on the side of the cause, the effect or both. I argue that contrasting helps resolving certain paradoxes only if an account of admissibility of the chosen set of contrasts is given. I show by means of numerous examples that it is contextual features that determine admissibility. This way, context becomes part of the semantics of causation. I finally argue that once contextualised, explicit contrasting is redundant: causation is therefore a three-place relationship between a cause, an effect and a context

Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science, but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology and semantics of models and theories, and the relation between experimentation and theorising, and submit that simulations demand a fundamentally new philosophy of science in many respects. The aim of this paper is to critically evaluate these claims. Our conclusion will be sober. We argue that these claims are overblown and that simulations, far from demanding a new metaphysics, epistemology, semantics and methodology, raise few if any new philosophical problems. The philosophical problems that do come up in connection with simulations are not specific to simulations and most of them are variants of problems that have been discussed in other contexts before.

This paper examines Patrick Suppes’ probabilistic theory of causality understood as a theory of causal inference, and draws some lessons for empirical economics and contemporary debates in the foundations of econometrics. It argues that a standard method of empirical economics, multiple regression, is inadequate for most but the simplest applications, that the Bayes’ nets approach, which can be understood as a generalisation of Suppes’ theory, constitutes a considerable improvement but is still subject to important limitations, and that the currently fashionable ‘design-based approach’ suffers from the same flaws Suppes anticipated a long time ago. It then sketches an alternative in response, one that differs drastically from the formalisms Suppes endorsed but is consistent with his pragmatic general take on science.

This paper presents an outline of a methodology of ‘evidence-based economics’. The question whether an economic statement is evidence-based must be answered on three different levels. The first level concerns measurement: it asks whether claims made about economic quantities such as inflation, unemployment, growth or poverty are justified by the data and measurement procedures. The second level concerns induction: it asks whether claims made about the relations between economic quantities (such as ‘number of babies born predicts growth’, ‘change in money causes change in monetary income’, ‘non-borrowed reserves can be used to control the interest rate’), are justified by the inference procedures. The third level concerns idealisation: it asks whether the quantities and relations selected are justified by the stated aim of the inquiry. The paper provides a discussion of these three types of investigation and of some solutions that have been offered.