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Should We Believe That Our Current Best Scientific Theories Are Approximately True, or Will Future Theories Look Substantially Different?

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Scientific and technological advances have had profound effects on human life. In the 19th century, most families could expect to lose one or more children to disease. Today, in the United States and other developed countries, the death of a child from disease is uncommon. Every day we rely on technologies made possible through the application of scientific knowledge and processes. The computers and cell phones which we use, the cars and airplanes in which we travel, the medicines that we take, and many of the foods that we eat were developed in part through insights obtained from scientific research

Science has boosted living standards, has enabled humans to travel into Earth’s orbit and to the moon, and has given us new ways of thinking about ourselves and the universe.

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Up to this point in history, however, most biomedical interventions, whether successful or not, have attempted to restore something perceived to be deficient, such as vision, hearing or mobility. Even when these interventions have tried to improve on nature – say with anabolic steroids to stimulate muscle growth or drugs such as Ritalin to sharpen focus ­– the results have tended to be relatively modest and incremental. But thanks to recent scientific developments in areas such as biotechnology, information technology and nanotechnology, humanity may be on the cusp of an enhancement revolution. In the next two or three decades, people may have the option to change themselves and their children in ways that, up to now, have existed largely in the minds of science fiction writers and creators of comic book superheroes. Both advocates for and opponents of human enhancement spin a number of possible scenarios. Some talk about what might be called “humanity plus” – people who are still recognizably human, but much smarter, stronger and healthier. Others speak of “post-humanity,” and predict that dramatic advances in genetic engineering and machine technology may ultimately allow people to become conscious machines – not recognizably human, at least on the outside. This enhancement revolution, if and when it comes, may well be prompted by ongoing efforts to aid people with disabilities and heal the sick. Indeed, science is already making rapid progress in new restorative and therapeutic technologies that could, in theory, have implications for human enhancement. It seems that each week or so, the headlines herald a new medical or scientific breakthrough. In the last few years, for instance, researchers have implanted artificial retinas to give blind patients partial sight. Other scientists successfully linked a paralyzed man’s brain to a computer chip, which helped restore partial movement of previously non-responsive limbs. Still others have created synthetic blood substitutes, which could soon be used in human patients. One of the most important developments in recent years involves a new gene-splicing technique called “clustered regularly interspaced short palindromic repeats.” Known by its acronym, CRISPR, this new method greatly improves scientists’ ability to accurately and efficiently “edit” the human genome, in both embryos and adults.

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The view that simplicity is a virtue in scientific theories and that, other things being equal, simpler theories should be preferred to more complex ones has been widely advocated in the history of science and philosophy, and it remains widely held by modern scientists and philosophers of science. It often goes by the name of “Ockham’s Razor.” The claim is that simplicity ought to be one of the key criteria for evaluating and choosing between rival theories, alongside criteria such as consistency with the data and coherence with accepted background theories (Fitzpatrick, S. 2009). Simplicity, in this sense, is often understood ontologically, in terms of how simple a theory represents nature as being—for example, a theory might be said to be simpler than another if it posits the existence of fewer entities, causes, or processes in nature in order to account for the empirical data. However, simplicity can also been understood in terms of various features of how theories go about explaining nature—for example, a theory might be said to be simpler than another if it contains fewer adjustable parameters, if it invokes fewer extraneous assumptions, or if it provides a more unified explanation of the data. Preferences for simpler theories are widely thought to have played a central role in many important episodes in the history of science. Simplicity considerations are also regarded as integral to many of the standard methods that scientists use for inferring hypotheses from empirical data, the most of common illustration of this being the practice of curve-fitting. Indeed, some philosophers have argued that a systematic bias towards simpler theories and hypotheses is a fundamental component of inductive reasoning quite generally. However, though the legitimacy of choosing between rival scientific theories on grounds of simplicity is frequently taken for granted, or viewed as self-evident, this practice raises a number of very difficult philosophical problems

A common concern is that notions of simplicity appear vague, and judgments about the relative simplicity of particular theories appear irredeemably subjective. Thus, one problem is to explain more precisely what it is for theories to be simpler than others and how, if at all, the relative simplicity of theories can be objectively measured. In addition, even if we can get clearer about what simplicity is and how it is to be measured, there remains the problem of explaining what justification, if any, can be provided for choosing between rival scientific theories on grounds of simplicity (Crick, F. 1988). For instance, do we have any reason for thinking that simpler theories are more likely to be true?

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Thus, similar reasoning would undergird all of our beliefs about the future and about the unobserved. Are such beliefs justified? Again, Hume thinks not, since the above argument, and all arguments like it, contain an unsupported premise, namely the second premise, which might be called the Principle of the Uniformity of Nature (PUN)

Why should we believe this principle to be true? Hume insists that we provide some reason in support of this belief. Because the above argument is an inductive rather than a deductive argument, the problem of showing that it is a good argument is typically referred to as the “problem of induction.” We might think that there is a simple and straightforward solution to the problem of induction, and that we can indeed provide support for our belief that PUN is true.

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Crick, F. 1988. What Mad Pursuit: a Personal View of Scientific Discovery. New York: Basic Books.

Dowe, D, Gardner, S., and Oppy, G. 2007. Bayes not bust! Why simplicity is no problem for Bayesians. British Journal for the Philosophy of Science, 58, 709-754.

Contra Forster and Sober (1994), argues that Bayesians can make sense of the role of simplicity in curve-fitting.

Duhem, P. 1954. The Aim and Structure of Physical Theory. Princeton: Princeton University Press.

Fitzpatrick, S. 2009. The primate mindreading controversy: a case study in simplicity and methodology in animal psychology. In R. Lurz (ed.), The Philosophy of Animal Minds. New York: Cambridge University Press.

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