There Is No Pure Empirical Reasoning

The justificatory force of empirical reasoning always depends upon the existence of some synthetic, a priori justification. The reasoner must begin with justified, substantive constraints on both the prior probability of the conclusion and certain conditional probabilities; otherwise, all possible degrees of belief in the conclusion are left open given the premises. Such constraints cannot in general be empirically justified, on pain of infinite regress. Nor does subjective Bayesianism offer a way out for the empiricist. Despite often-cited convergence theorems, subjective Bayesians cannot hold that any empirical hypothesis is ever objectively justified in the relevant sense. Rationalism is thus the only alternative to an implausible skepticism

Confirmation, Decision, and Evidential Probability

Henry Kyburg’s theory of Evidential Probability offers a neglected tool for approaching problems in confirmation theory and decision theory. I use Evidential Probability to examine some persistent problems within these areas of the philosophy of science. Formal tools in general and probability theory in particular have great promise for conceptual analysis in confirmation theory and decision theory, but they face many challenges. In each chapter, I apply Evidential Probability to a specific issue in confirmation theory or decision theory. In Chapter 1, I challenge the notion that Bayesian probability offers the best basis for a probabilistic theory of evidence. In Chapter 2, I criticise the conventional measures of quantities of evidence that use the degree of imprecision of imprecise probabilities. In Chapter 3, I develop an alternative to orthodox utility-maximizing decision theory using Kyburg’s system. In Chapter 4, I confront the orthodox notion that Nelson Goodman’s New Riddle of Induction makes purely formal theories of induction untenable. Finally, in Chapter 5, I defend probabilistic theories of inductive reasoning against John D. Norton’s recent collection of criticisms. My aim is the development of fresh perspectives on classic problems and contemporary debates. I both defend and exemplify a formal approach to the philosophy of science. I argue that Evidential Probability has great potential for clarifying our concepts of evidence and rationality

Uncovering unknown unknowns: towards a Baconian approach to management decision-making

Bayesian decision theory and inference have left a deep and indelible mark on the literature on management decision-making. There is however an important issue that the machinery of classical Bayesianism is ill equipped to deal with, that of “unknown unknowns” or, in the cases in which they are actualised, what are sometimes called “Black Swans”. This issue is closely related to the problems of constructing an appropriate state space under conditions of deficient foresight about what the future might hold, and our aim is to develop a theory and some of the practicalities of state space elaboration that addresses these problems. Building on ideas originally put forward by Bacon (1620), we show how our approach can be used to build and explore the state space, how it may reduce the extent to which organisations are blindsided by Black Swans, and how it ameliorates various well-known cognitive biases

A Tool-Based View of Theories of Evidence

Philosophical theories of evidence have been on offer, but they are mostly evaluated in terms of all-or-none desiderata — if they fail to meet one of the desiderata, they are not a satisfactory theory. In this thesis, I aim to accomplish three missions. Firstly, I construct a new way of evaluating theories of evidence, which I call a tool-based view. Secondly, I analyse the nature of what I will call the various relevance-mediating vehicles that each theory of evidence employs. Thirdly, I articulate the comparative core of evidential reasoning in the historical sciences, one which is overlooked in major theories of evidence. On the first mission, I endorse a meta-thesis of pluralism on theories of evidence, namely a tool-based view. I regard a theory of evidence as a purpose-specific and setting-sensitive tool which has its own strengths, difficulties and limitations. Among the major theories of evidence I have reviewed, I focus on Achinstein’s explanationist theory, Cartwright’s argument theory and Reiss’s inferentialist account, scrutinising and evaluating them against the purposes they set out and the scope of their applications. On the second mission, I note that there is no such thing as intrinsically ‘being evidence’. Rather, I hold that relevance-mediating vehicles configure data, materials or claims in such ways that some of them are labelled evidence. I identify the relevance-mediating vehicles that the theories of evidence employ. On the final mission, I argue that the likelihoodist account is an appropriate tool for explaining the evidential reasoning in poorly specified settings where likelihoods can be only imprecisely compared. Such settings, I believe, are typical in the historical sciences. Using the reconstruction of proto-sounds in historical linguistics as a case study, I formalise the rationale behind it by means of the law of likelihood

The Material Theory of Induction and the Epistemology of Thought Experiments

John D. Norton is responsible for a number of influential views in contemporary philosophy of science. This paper will discuss two of them. The material theory of induction claims that inductive arguments are ultimately justified by their material features, not their formal features. Thus, while a deductive argument can be valid irrespective of the content of the propositions that make up the argument, an inductive argument about, say, apples, will be justified (or not) depending on facts about apples. The argument view of thought experiments claims that thought experiments are arguments, and that they function epistemically however arguments do. These two views have generated a great deal of discussion, although there hasn’t been much written about their combination. I argue that despite some interesting harmonies, there is a serious tension between them. I consider several options for easing this tension, before suggesting a set of changes to the argument view that I take to be consistent with Norton’s fundamental philosophical commitments, and which retain what seems intuitively correct about the argument view. These changes require that we move away from a unitary epistemology of thought experiments and towards a more pluralist position

Inference to the best explanation in science

This thesis defends inference to the best explanation (IBE) by giving an account of explanatory 'loveliness' in science. I begin by presenting IBE in generic form and showing how it out-performs rival accounts of induction. I then trace a path through the early literature which emphasises the role of background belief in determining loveliness. I then introduce crucial features of Lipton's account of IBE. I argue that Lipton's remarks on loveliness, through minimal, support the background-dependent view and that, appropriately construed, the view does not trivialise IBE.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Evidence-Based Beliefs in Many-Valued Modal Logics

Rational agents, humans or otherwise, build their beliefs from evidence – a process which we call consolidation. But how should this process be carried out? In this thesis, we study a multi-agent logic of evidence and the question how agents should form beliefs in this logic. The main contributions of this thesis are twofold. First, we present and study a many-valued modal logic, and show how it can be suitable for modelling multi-agent scenarios where each agent has access to some evidence, which in turn can be processed into beliefs. This is a technical and practical contribution to many-valued modal logics. Second, we open new paths for research in the field of evidence logics: we show a new approach based on many-valued logics, we highlight the concept of consolidations and the importance of looking at their dynamic nature, and build a methodology based on rationality postulates to evaluate them

(Im)probable stories:combining Bayesian and explanation-based accounts of rational criminal proof

A key question in criminal trials is, ‘may we consider the facts of the case proven?’ Partially in response to miscarriages of justice, philosophers, psychologists and mathematicians have considered how we can answer this question rationally. The two most popular answers are the Bayesian and the explanation-based accounts. Bayesian models cast criminal evidence in terms of probabilities. Explanation-based approaches view the criminal justice process as a comparison between causal explanations of the evidence. Such explanations usually take the form of scenarios – stories about how a crime was committed. The two approaches are often seen as rivals. However, this thesis argues that both perspectives are necessary for a good theory of rational criminal proof. By comparing scenarios, we can, among other things, determine what the key evidence is, how the items of evidence interrelate, and what further evidence to collect. Bayesian probability theory helps us pinpoint when we can and cannot conclude that a scenario is likely to be true. This thesis considers several questions regarding criminal evidence from this combined perspective, such as: can a defendant sometimes be convicted on the basis of an implausible guilt scenario? When can we assume that we are not overlooking scenarios or evidence? Should judges always address implausible innocence scenarios of the accused? When is it necessary to look for new evidence? How do we judge whether an eyewitness is reliable? By combining the two theories, we arrive at new insights on how to rationally reason about these, and other questions surrounding criminal evidence