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Although it is helpful to appreciate the general nature of explanations, we might reasonably want more than this. As this book is part of the Understanding Life series, we may expect to delve into details about kinds of explanations that are specific to the life sciences.
It is widely held that science is a (if not the) primary source of our knowledge of the world around us. Further, most accept that scientific knowledge is the best confirmed and well-supported kind of knowledge that we have of the world. But, how do scientific explanations lead to scientific knowledge? The short answer is that they do so via a method known as “inference to the best explanation” (IBE), sometimes called “abduction.” Before we get into the details of IBE, let’s take a quick look at an obvious way that scientific explanations give us scientific knowledge.
A general way of appreciating some of the main ideas of the previous chapter is to recognize that explanations aim at providing understanding. Scientists and philosophers agree that understanding is a (if not the) primary epistemic goal of scientific inquiry. Both explanation and prediction tend to be closely related to understanding. We want explanations in science because we want to understand why the world is as it is and how things happen. And, once we understand various phenomena, we can make accurate predictions about them. One simple, and widely accepted, way of assessing the quality of a given explanation is to look at the understanding it provides. Roughly, the better an explanation, the more understanding that explanation (if true) would provide. As philosopher Peter Lipton explained, the explanation that is the best is simply the explanation that, if true, would provide the deepest understanding of the phenomena being explained. That being said, some worry because it seems that we might misjudge how well we understand something.
In this chapter we’re looking at the relation between scientific explanations and predictions. It is tempting to think that the only difference between explanations and predictions is that one looks back and tells us how or why things happened as they did, and the other looks forward and tells us how or why certain things will (or are likely to) happen. This thought can seem particularly plausible when we consider that in many cases a good scientific hypothesis will both explain phenomena and allow us to make accurate predictions. Despite its initial plausibility, the idea that explanation and prediction are symmetrical is mistaken. The way to see this is to take a look at a particular theory of scientific explanation that entails this relationship between explanation and prediction. The particular theory of scientific explanation in question, the covering law model, which we discussed in Chapter 2, is false. One of the reasons that this theory of explanation fails helps illustrate the fact that explanation and prediction are not symmetrical.
Explanation is central to our lives, in general. We seem to have an innate (or nearly so) drive to explain and seek explanations. When our favorite app is not working, we want to know why, and we want to know how to fix it. When trying to understand why people engage in an odd behavior – refusing to wear masks during the COVID-19 pandemic, say – we want an explanation. What reasons do they have for doing something that seems so clearly misguided? Why are they resistant to expert advice on the issue? Ultimately, we seek explanations to help us understand and navigate the world around us.
While it isn’t necessary to do so, it’s often good to start a book by saying something that is clearly true. So, let’s do that. Science has had (and continues to have) a significant impact upon our lives. This fact is undeniable. Science has revealed to us how different species arise, the causes of our world’s changing climate, many of the microphysical particles that constitute all matter, among many other things. Science has made possible technology that has put computing power that was almost unimaginable a few decades ago literally in the palms of our hands. A common smartphone today has more computing power than the computers that NASA used to put astronauts on the Moon in 1969! There are, of course, many additional ways in which science has solved various problems and penetrated previously mysterious phenomena. A natural question to ask at this point is: why discuss this? While we all (or at least the vast majority of us!) appreciate science and what it has accomplished for modern society, there remain – especially among portions of the general public – confusions about science, how it works and what it aims to achieve. The primary goal of this book is to help address some specific confusions about one key aspect of science: how it explains the world.
In the previous chapter we discussed the importance of accurate explanations. Without explanations that are in fact accurate we cannot have genuine understanding. In this chapter we will explore whether false scientific theories can be used to generate accurate scientific explanations. Before jumping into this, let’s first briefly recall the relationship between scientific theories and scientific explanations. Scientific theories consist of laws, models, and principles. Together these components of scientific theories offer broad generalizations about the nature of the world.
Even though explanation plays a central role in science, it is not enough to simply come up with explanations. Scientists (and everyone else) must also evaluate explanations. After all, it’s clear that not every explanation is a good one, as well as that some explanations are better than others. For example, evolutionary theory provides a much better explanation of the diversity of life than, say, the hypothesis that all organisms appeared at the same time in their present form. But what makes one explanation better than another? Relatedly, how can we tell which of a set of competing hypotheses provides the best explanation?
All people desire to know. We want to not only know what has happened, but also why it happened, how it happened, whether it will happen again, whether it can be made to happen or not happen, and so on. In short, what we want are explanations. Asking and answering explanatory questions lies at the very heart of scientific practice. The primary aim of this book is to help readers understand how science explains the world. This book explores the nature and contours of scientific explanation, how such explanations are evaluated, as well as how they lead to knowledge and understanding. As well as providing an introduction to scientific explanation, it also tackles misconceptions and misunderstandings, while remaining accessible to a general audience with little or no prior philosophical training.
Chapter 3 addresses open online knowledge sharing. Open sharing is becoming more important in all major sectors in society, including science, politics, education and innovation, knowledge products (videos, textbooks and databases). This sharing includes both the domain of expert-produced scientific knowledge and massive amounts of citizen-produced practical knowledge. Because of lower publishing costs, Open Access has become the new dominant trend that makes research accessible to everyone. Increased production of open textbooks gives a more readable access to scientific knowledge and reaches a much wider audience. In addition, scientific knowledge construction processes are becoming transparent. This includes the establishment of many more open digital databases that allow anyone both to make their own contributions and get free access to all the data (e.g. citizen science project like eBird). There is also experimentation with making knowledge construction processes more open, both within scientific discourse (e.g. Polymath project) and the development of encyclopedic knowledge (e.g. Wikipedia). In addition, the recent decade has seen an enormous increase in amateur-produced practical knowledge, not only texts, but an abundance of images and videos. Enthusiasts share their skills and passions concerning any activity that might be of interest to other like-minded persons. It also includes the sharing of political opinions, for example with new digital technologies like argument mapping. Even some companies in the business sector have begun sharing more of its corporate knowledge.
This paper identifies the potential benefits of data sharing and open science, supported by artificial intelligence tools and services, and dives into the challenges to make data open and findable, accessible, interoperable, and reusable (FAIR).
This chapter introduces the idea of scientific cultures as complex, multifaceted sets of norms and values, shared within a given scientific community, about the appropriate social practice of scientific teaching and research. It identifies seven dimensions of scientific cultures:
1. The attitude towards existing scientific knowledge
2. The approach to problem-solving
3. The scope of research ambitions
4. The degree of autonomy given to individual scientists within a research team
5. The importance given to rank and seniority
6. The attitudes towards difference within the lab or research organization, and finally
7. The approach to inward- and outward-facing communication
This chapter details each of these dimensions using Western-trained Asian scientists’ comparative accounts of their early training in Asia and their subsequent training in the West. This chapter also documents the significant variation in each of these dimensions not only between Asia and the West, but also within each of these world regions at the level of countries, universities, and also individual labs. This helps debunk the idea of a single Asian or Western scientific culture.
The aim of this chapter is to provide a thorough account of the fact that Ernst Mach and Friedrich Nietzsche are often associated with each other in the specialised literature on the history of philosophy and the philosophy of science. I argue that the consistency which can be discovered between them is much more substantial than one may imagine. On the basis of their conception of knowledge and truth, it is possible to outline a complete parallelism between their approach to the issue concerning our intellectual relationship with the external world. In fact, Mach and Nietzsche dealt with the very same questions and indeed pursued a common general aim, namely the elimination of worn-out conceptions from the world of modern culture. Furthermore, I will maintain that Mach’s and Nietzsche’s research interests converge on the classic problem of realism versus anti-realism, and that it is in the light of this particular issue that their own views can be compared.
This chapter introduces the main concepts, context, focus, and framework of the book. The centre of analysis concerns the scientific engagement techniques of judges, which refers to a host of practices with which international courts and tribunals assess and interact with the scientific dimensions of environmental disputes. Judicial engagement with science will be evaluated with respect to four distinctive stages of the adjudicatory process, notably, the framing of disputes, the process of scientific fact-finding, causal inquiry, and the standard of review. The scope of this study extends to environmental disputes, broadly understood, which appear in the case law of the International Court of Justice, international arbitral tribunals, regional human rights courts, investment arbitral tribunals, the World Trade Organization dispute settlement bodies, and the International Tribunal for the Law of the Sea. The focus of this book is on the reasoning techniques with which international judges can legitimately justify their choices regarding competing science-based claims.
This chapter summarizes the main findings of the book and shows that the judicial function bestows a truly interdisciplinary task on judges, which confers epistemic responsibilities on legal adjudicators. It argues that judges deciding international environmental disputes should be more active and thorough in terms of reflecting on the scientific profile of disputes both in the judicial assessment and in their reasoning.
Chapter 8 considers the widespread epistemic dependence that characterizes “big science,” and uses the information economy framework to dispel the worry that such dependence is inconsistent with the standards for scientific knowledge. This leads to a new argument against reductionism in the epistemology of testimony. First, reductionism is shown to be untenable for scientific knowledge. Second, if reductionism must be rejected for scientific knowledge, then it should be rejected more generally. This second idea can be vindicated in two ways. First, anti-reductionism about scientific knowledge entails anti-reductionism about knowledge in general, since anti-reductionism is best understood as the thesis that some transmitted knowledge cannot be reduced to generated knowledge. Second, if anti-reductionism is required for scientific knowledge, then reductionism for non-scientific knowledge is unmotivated. The most elegant position is anti-reductionism about knowledge transmission in general.
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