Billy Wheeler

Chapter 3 investigates the potential for a theory of the metaphysics of ideal laws based around non-governing conceptions of lawhood. Three existing approaches are discussed: Lewis’ best system account and Schrenk’s so-called ‘better best system account’; Unterhuber’s theory of laws as generics, and the inference-ticket view, advocated by some logical empiricists such as Schlick and Toulmin. It is argued that a combination of the best system account and the inference-ticket view offers the best hope for a theory of ideal laws.

Chapter 4 brings together the insights of the first three chapters, and argues that the best way to understand ideal laws is to think of them as rules or algorithms for compressing empirical data. Idealization is explained as a form of lossy compression. Lossy compression is tolerated in scientific theories because of predictive redundancy in our theories. Idealizations in scientific theories and their application are accounted for as compression artefacts left over from the lossy compression. A number of possible objections to this explanation are considered and responses given.

It has been argued that the fundamental laws of physics do not face a ‘problem of provisos’ equivalent to that found in other scientific disciplines (Earman, Roberts and Smith 2002) and there is only the appearance of exceptions to physical laws if they are confused with differential equations of evolution type (Smith 2002). In this paper I argue that even if this is true, fundamental laws in physics still pose a major challenge to standard Humean approaches to lawhood, as they are not in any obvious sense about regularities in behaviour. A Humean approach to physical laws with exceptions is possible, however, if we adopt a view of laws that takes them to be the algorithms in the algorithmic compressions of empirical data. When this is supplemented with a distinction between lossy and lossless compression, we can explain exceptions in terms of compression artefacts present in the application of the lossy laws.

Informational structural realism (ISR) offers a new way to understand the nature of the “structure” that structural realists claim our best scientific theories get right about the world. According to Luciano Floridi, who has given the most detailed formulation of ISR so far, this structure is composed of information representing binary differences. In this paper I assess whether ISR offers a good way to resolve the tension between the no miracle argument (often taken to support scientific realism) and the pessimistic meta-induction (often taken to support antirealism). With regards to this important motivation for structural realism, I shall argue that ISR faces insurmountable difficulties. However, I agree that interpreting “structure” in terms of information can be profitable for the realist. Instead, I offer a new version of ISR that borrows from algorithmic information theory. As a result, a more realist version of ISR is provided.

Objects appear to causally interact with one another in virtual worlds, such as video games, virtual reality, and training simulations. Is this causation real or is it illusory? In this paper I argue that virtual causation is as real as physical causation. I achieve this in two steps: firstly, I show how virtual causation has all the important hallmarks of relations that are causal, as opposed to merely accidental, and secondly, I show how virtual causation is genuine according to one influential metaphysical theory of causation: the mechanistic approach.

That many of our most successful scientific theories involve one or more idealizations poses a challenge to traditional models of theory confirmation. One popular response amongst scientific realists is the “improvement model of confirmation”: if tightening up one or more of the idealizations leads to greater predictive accuracy, then this supports the belief that the theory’s inaccuracy is a result of its idealizations and not because it is wrong. In this article I argue that the improvement model is deeply flawed and that therefore idealizations continue to undermine “success-to-truth” arguments for scientific realism.

Is it possible to gain knowledge about the real world based solely on experiences in virtual reality? According to one influential theory of knowledge, you cannot. Robert Nozick's truth-tracking theory requires that, in addition to a belief being true, it must also be sensitive to the truth. Yet beliefs formed in virtual reality are not sensitive: in the nearest possible world where P is false, you would have continued to believe that P. This is problematic because there is increasing awareness from philosophers and technologists that virtual reality is an important way in which we can arrive at beliefs and knowledge about the world. Here I argue that a suitably modified version of Nozick's sensitivity condition is able to account for knowledge from virtual reality.

We are becoming increasingly dependent on robots and other forms of artificial intelligence for our beliefs. But how should the knowledge gained from the “say-so” of a robot be classified? Should it be understood as testimonial knowledge, similar to knowledge gained in conversation with another person? Or should it be understood as a form of instrument-based knowledge, such as that gained from a calculator or a sundial? There is more at stake here than terminology, for how we treat objects as sources of knowledge often has important social and legal consequences. In this paper, I argue that at least some robots are capable of testimony. I make my argument by exploring the differences between instruments and testifiers on a well-known account of knowledge: reliabilism. On this approach, I claim that the difference between instruments and testifiers as sources of knowledge is that only the latter are capable of deception. As some robots can be designed to deceive, so they too should be recognized as testimonial sources of knowledge.

The algorithmic theory of laws claims that the laws of nature are the algorithms in the best possible compression of all empirical data. This position assumes that the universe is compressible and that data received from observing it is easily reproducible using a simple set of rules. However, there are three sources of evidence that suggest that the universe as a whole is incompressible. The first comes from the practice of science. The other two come from the nature of the universe itself: the presence of chaotic behavior and the nature of quantum systems also suggests that the universe is incompressible. This paper evaluates these sources and argues that none provides a convincing case to reject the algorithmic theory of laws.

Computer models and simulations have provided enormous benefits to researchers in the natural and social sciences, as well as many areas of philosophy. However, to date, there has been little attempt to use computer models in the development and evaluation of metaphysical theories. This is a shame, as there are good reasons for believing that metaphysics could benefit just as much from this practice as other disciplines. In this paper I assess the possibilities and limitations of using computer models in metaphysics. I outline the way in which different kinds of model could be useful for different areas of metaphysics, and I illustrate in more detail how agent-based models specifically could be used to model two well-known theories of laws: David Lewis’s "Best System Account" and David Armstrong's "Nomic Necessitation" view. Some logically possible processes cannot be simulated on a standard computing device. I finish by assessing how much of a threat this is to the prospect of metaphysical modeling in general.

This new study provides a refreshing look at the issue of exceptions and shows that much of the problem stems from a failure to recognize at least two kinds of exception-ridden law: ceteris paribus laws and ideal laws. Billy Wheeler offers the first book-length discussion of ideal laws. The key difference between these two kinds of laws concerns the nature of the conditions that need to be satisfied and their epistemological role in the law’s formulation and discovery. He presents a Humean-inspired approach that draws heavily on concepts from the information and computing sciences. Specifically, Wheeler argues that laws are best seen as algorithms for compressing empirical data and that ideal laws are needed as 'lossy compressors' for complex data. Major figures in the metaphysics of science receive special attention such as Ronald Giere, Bas van Fraassen, Nancy Cartwright, David Lewis and Marc Lange. This book is essential reading for philosophers of science and will interest metaphysicians, epistemologists and others interested in applying concepts from computing to traditional philosophical problems.

Humans are becoming increasingly dependent on the ‘say-so' of machines, such as computers, smartphones, and robots. In epistemology, knowledge based on what you have been told is called ‘testimony' and being able to give and receive testimony is a prerequisite for engaging in many social roles. Should robots and other autonomous intelligent machines be considered epistemic testifiers akin to those of humans? This chapter attempts to answer this question as well as explore the implications of robot testimony for the criminal justice system. Few are in agreement as to the ‘types' of agents that can provide testimony. The chapter surveys three well-known approaches and shows that on two of these approaches being able to provide testimony is bound up with the possession of intentional mental states. Through a discussion of computational and folk-psychological approaches to intentionality, it is argued that a good case can be made for robots fulfilling all three definitions.

It has been argued that the fundamental laws of physics do not face a ‘problem of provisos’ equivalent to that found in other scientific disciplines (Earman, Roberts and Smith 2002) and there is only the appearance of exceptions to physical laws if they are confused with differential equations of evolution type (Smith 2002). In this paper I argue that even if this is true, fundamental laws in physics still pose a major challenge to standard Humean approaches to lawhood, as they are not in any obvious sense about regularities in behaviour. A Humean approach to physical laws with exceptions is possible, however, if we adopt a view of laws that takes them to be the algorithms in the algorithmic compressions of empirical data. When this is supplemented with a distinction between lossy and lossless compression, we can explain exceptions in terms of compression artefacts present in the application of the lossy laws.

It is often said that the best system account of laws needs supplementing with a theory of perfectly natural properties. The ‘strength’ and ‘simplicity’ of a system is language-relative and without a fixed vocabulary it is impossible to compare rival systems. Recently a number of philosophers have attempted to reformulate the BSA in an effort to avoid commitment to natural properties. I assess these proposals and argue that they are problematic as they stand. Nonetheless, I agree with their aim, and show that if simplicity is interpreted as ‘compression’, algorithmic information theory provides a framework for system comparison without the need for natural properties.