An argument that the development of scientific practice and growth of scientific knowledge are governed by Darwin’s evolutionary model of descent with modification. Although scientific investigation is influenced by our cognitive and moral failings as well as all of the factors impinging on human life, the historical development of scientific knowledge has trended toward an increasingly accurate picture of an increasing number of phenomena. Taking a fresh look at Thomas Kuhn’s 1962 work, The Structure of Scientific Revolutions, in How Knowledge Grows Chris Haufe uses evolutionary theory to explain both why scientific practice develops the way it does and how scientific knowledge expands. This evolutionary model, claims Haufe, helps to explain what is epistemically special about scientific knowledge: its tendency to grow in both depth and breadth. Kuhn showed how intellectual communities achieve consensus in part by discriminating against ideas that differ from their own and isolating themselves intellectually from other fields of inquiry and broader social concerns. These same characteristics, says Haufe, determine a biological population’s degree of susceptibility to modification by natural selection. He argues that scientific knowledge grows, even across generations of variable groups of scientists, precisely because its development is governed by Darwinian evolution. Indeed, he supports the claim that this susceptibility to modification through natural selection helps to explain the epistemic power of certain branches of modern science. In updating and expanding the evolutionary approach to scientific knowledge, Haufe provides a model for thinking about science that acknowledges the historical contingency of scientific thought while showing why we nevertheless should trust the results of scientific research when it is the product of certain kinds of scientific communities.

An argument that the development of scientific practice and growth of scientific knowledge are governed by Darwin’s evolutionary model of descent with modification. Although scientific investigation is influenced by our cognitive and moral failings as well as all of the factors impinging on human life, the historical development of scientific knowledge has trended toward an increasingly accurate picture of an increasing number of phenomena. Taking a fresh look at Thomas Kuhn’s 1962 work, The Structure of Scientific Revolutions, in How Knowledge Grows Chris Haufe uses evolutionary theory to explain both why scientific practice develops the way it does and how scientific knowledge expands. This evolutionary model, claims Haufe, helps to explain what is epistemically special about scientific knowledge: its tendency to grow in both depth and breadth. Kuhn showed how intellectual communities achieve consensus in part by discriminating against ideas that differ from their own and isolating themselves intellectually from other fields of inquiry and broader social concerns. These same characteristics, says Haufe, determine a biological population’s degree of susceptibility to modification by natural selection. He argues that scientific knowledge grows, even across generations of variable groups of scientists, precisely because its development is governed by Darwinian evolution. Indeed, he supports the claim that this susceptibility to modification through natural selection helps to explain the epistemic power of certain branches of modern science. In updating and expanding the evolutionary approach to scientific knowledge, Haufe provides a model for thinking about science that acknowledges the historical contingency of scientific thought while showing why we nevertheless should trust the results of scientific research when it is the product of certain kinds of scientific communities.

‘This important new book provides a penetrating, novel analysis of the key role played by knowledge when viewed through the lens of Schumpeterian economics. It is loaded with important insights that highlight the primacy of knowledge and innovation to unleash economic growth.’ —David B. Audretsch, Indiana University Bloomington, USA This book combines the tools elaborated by the economics of knowledge and the legacy of Joseph Schumpeter to explore the emergence of the new knowledge economy and the shift away from the manufacturing industries. Antonelli analyzes the characteristics of the innovation process as a creative response based upon the accumulation, generation and exploitation of knowledge. He highlights the new structure of advanced economies, where knowledge is at the same time the prime input and output. With special attention to the limits of the new knowledge growth regime, raised by the role of finance, income distribution and intellectual property rights, this Palgrave Pivot recommends appropriate economic policies based upon an Open Technology approach.

New insights from the science of science Facts change all the time. Smoking has gone from doctor recommended to deadly. We used to think the Earth was the center of the universe and that the brontosaurus was a real dinosaur. In short, what we know about the world is constantly changing. Samuel Arbesman shows us how knowledge in most fields evolves systematically and predictably, and how this evolution unfolds in a fascinating way that can have a powerful impact on our lives. He takes us through a wide variety of fields, including those that change quickly, over the course of a few years, or over the span of centuries.

Peter Munz, a former student of both Popper and Wittgenstein, begins his comparison of the two great twentieth-century philosophers, by explaining that since the demise of positivism there have emerged, broadly speaking, two philosophical options: Wittgenstein, with the absolute relativism of his theory that meaning is a function of language games and that social configurations are determinants of knowledge; and Popper’s evolutionary epistemology – conscious knowledge is a special case of the relationship which exists between all living beings and their environments. Professor Munz examines and rejects the Wittgensteinian position. Instead, Our Knowledge of the Growth of Knowledge, first published in 1985, elaborates the potentially fruitful link between Popper’s critical rationalism and Neo-Darwinism. Read in the light of the latter, Popper’s philosophy leads to the transformation of Kant’s Transcendental Idealism into ‘Hypothetical Realism’, whilst the emphasis on the biological orientation of Popper’s thought helps to illumine some difficulties in Popper’s ‘falsificationism’.

One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science.

Preface1: Learning from Error 2: Ducks, Rabbits, and Normal Science: Recasting the Kuhn's-Eye View of Popper 3: The New Experimentalism and the Bayesian Way 4: Duhem, Kuhn, and Bayes 5: Models of Experimental Inquiry 6: Severe Tests and Methodological Underdetermination7: The Experimental Basis from Which to Test Hypotheses: Brownian Motion8: Severe Tests and Novel Evidence 9: Hunting and Snooping: Understanding the Neyman-Pearson Predesignationist Stance10: Why You Cannot Be Just a Little Bit Bayesian 11: Why Pearson Rejected the Neyman-Pearson (Behavioristic) Philosophy and a Note on Objectivity in Statistics12: Error Statistics and Peircean Error Correction 13: Toward an Error-Statistical Philosophy of Science ReferencesIndex Copyright © Libri GmbH. All rights reserved.