Peter Lipton

Bio: Peter Lipton is an academic researcher from University of Cambridge. The author has contributed to research in topics: Scientific theory & Philosophy of science. The author has an hindex of 15, co-authored 28 publications receiving 2318 citations.

Inference to the best explanation

Abstract: How do we go about weighing evidence, testing hypotheses, and making inferences? According to this work, we infer the hypothesis that would, if correct, provide the best explanation of the available evidence. Articulating the model of "Inference to the Best Explanation" requires an account of what makes one explanation better than another. Lipton distinguishes between the explanation best supported by the evidence - the likeliest explanation - and the explanation that would, if true, provide the most understanding - the loveliest explanation. He argues that an illuminating version of "Inference to the Best Explanation" must rely on the latter notion, and provides a new account of what makes one explanation lovelier than another. He does this by analyzing the structure of contrastive explanations, explanations that answer the form "why P rather than Q?". The analysis of contrastive explanation is then shown to support a strong version of "Inference to the Best Explanation" that reveals how explanatory considerations can be a guide to inference.

Testing hypotheses: prediction and prejudice.

Abstract: Observations that fit a hypothesis may be made before or after the hypothesis is formulated. Can that difference be relevant to the amount of support that the observations provide for the hypothesis? Philosophers of science and statisticians are both divided on this question, but there is an argument that predictions ought to count more than accommodations, because of the risk of “fudging” that accommodations run and predictions avoid.

Is the best good enough

Abstract: L'A. examine des formes intermediaires de scepticisme avec l'intention de demontrer que meme si les scientifiques possedent des pouvoirs considerables d'induction, la croyance rationnelle en la theorie reste impossible. Il affirme que certaines de ces formes intermediaires de scepticisme sont instables et aboutissent soit au doute radical humenien, soit au realisme modere. Il s'interesse specialement a l'argument sceptique issu de la sous-consideration

What Good is an Explanation

Abstract: We are addicted to explanation, constantly asking and answering why-questions. But what does an explanation give us? I will consider some of the possible goods, intrinsic and instrumental, that explanations provide.

Social Theory of International Politics

Abstract: Drawing upon philosophy and social theory, Social Theory of International Politics develops a theory of the international system as a social construction. Alexander Wendt clarifies the central claims of the constructivist approach, presenting a structural and idealist worldview which contrasts with the individualism and materialism which underpins much mainstream international relations theory. He builds a cultural theory of international politics, which takes whether states view each other as enemies, rivals or friends as a fundamental determinant. Wendt characterises these roles as 'cultures of anarchy', described as Hobbesian, Lockean and Kantian respectively. These cultures are shared ideas which help shape state interests and capabilities, and generate tendencies in the international system. The book describes four factors which can drive structural change from one culture to another - interdependence, common fate, homogenization, and self-restraint - and examines the effects of capitalism and democracy in the emergence of a Kantian culture in the West.

Mendelian randomization: genetic anchors for causal inference in epidemiological studies

Abstract: Observational epidemiological studies are prone to confounding, reverse causation and various biases and have generated findings that have proved to be unreliable indicators of the causal effects of modifiable exposures on disease outcomes. Mendelian randomization (MR) is a method that utilizes genetic variants that are robustly associated with such modifiable exposures to generate more reliable evidence regarding which interventions should produce health benefits. The approach is being widely applied, and various ways to strengthen inference given the known potential limitations of MR are now available. Developments of MR, including two-sample MR, bidirectional MR, network MR, two-step MR, factorial MR and multiphenotype MR, are outlined in this review. The integration of genetic information into population-based epidemiological studies presents translational opportunities, which capitalize on the investment in genomic discovery research.

Mendelianrandomization:geneticanchorsforcausal inference in epidemiological studies

Abstract: Observational epidemiological studies are prone to confounding, reverse causation and various biases and have generated findings that have proved to be unreliable indicators of the causal effects of modifiable exposures on disease outcomes. Mendelian randomization (MR) is a method that utilizes genetic variants that are robustly associated with such modifiable exposures to generate more reliable evidence regarding which interventions should produce health benefits. The approach is being widely applied, and various ways to strengthen inference given the known potential limitations of MR are now available. Developments of MR, including two-sample MR, bidirectional MR, network MR, two-step MR, factorial MR and multiphenotype MR, are outlined in this review. The integration of genetic information into population-based epidemiological studies presents translational opportunities, which capitalize on the investment in genomic discovery research.

The Cambridge handbook of thinking and reasoning

Abstract: The Cambridge Handbook of Thinking and Reasoning is the first comprehensive and authoritative handbook covering all the core topics of the field of thinking and reasoning Written by the foremost experts from cognitive psychology, cognitive science, and cognitive neuroscience, individual chapters summarize basic concepts and findings for a major topic, sketch its history, and give a sense of the directions in which research is currently heading The volume also includeswork related to developmental, social and clinical psychology, philosophy, economics, artificial intelligence, linguistics, education, law, and medicine Scholars and students in all these fields and others will find this to be a valuable collection

Verification and Validation in Scientific Computing

Abstract: Advances in scientific computing have made modelling and simulation an important part of the decision-making process in engineering, science, and public policy. This book provides a comprehensive and systematic development of the basic concepts, principles, and procedures for verification and validation of models and simulations. The emphasis is placed on models that are described by partial differential and integral equations and the simulations that result from their numerical solution. The methods described can be applied to a wide range of technical fields, from the physical sciences, engineering and technology and industry, through to environmental regulations and safety, product and plant safety, financial investing, and governmental regulations. This book will be genuinely welcomed by researchers, practitioners, and decision makers in a broad range of fields, who seek to improve the credibility and reliability of simulation results. It will also be appropriate either for university courses or for independent study.