In recent years, there have been some attempts to defend the legitimacy of a non-inductive generative logic of discovery whose strategy is to analyze a variety of constraints on the actual generation of explanatory hypotheses. These proposed new theories, however, are only weakly generative (relying on sophisticated processes of elimination) rather than strongly generative (embodying processes of correction).This paper develops a strongly generative theory which holds that we can come to know something new only as a variant of what we (...) already know — and that the novelty of this variant is not thereby eliminated nor beyond our powers of characterization, a double requirement that is vital for resolving the Meno paradox. In this light, the discovery of a new hypothesis is taken as the correction of an antecedent hypothesis in response to the discrepancies between the predictions generated by that antecedent hypothesis and the desired result (e.g. the actual data to be explained). This process comprises two parallel operations: the first, which demonstrates the positive role of the facts in generating new explanations, involves a mapping between multiple hypotheses and sets of predictions generated from those hypotheses, for the purpose of taking the actual data as a determinable variant of neighboring sets of predictions. This mapping permits the facts to indicate how corrective adjustments in the working hypothesis should be made; the second operation, which demonstrates the positive role of explanations in generating new facts, involves a mapping between differently construed versions of the actual data and the conceptualizations derived from those perceptual versions, for the purpose of taking the working hypothesis as a determinable variant of these neighboring conceptualizations. This mapping permits a given hypothesis to generate predictions increasingly closer to the actual facts. (shrink)

Abductive Reasoning: Logical Investigations into Discovery and Explanation is a much awaited original contribution to the study of abductive reasoning, providing logical foundations and a rich sample of pertinent applications. Divided into three parts on the conceptual framework, the logical foundations, and the applications, this monograph takes the reader for a comprehensive and erudite tour through the taxonomy of abductive reasoning, via the logical workings of abductive inference ending with applications pertinent to scientific explanation, empirical progress, pragmatism and belief revision.

New computer systems of discovery create a research program for logic and philosophy of science. These systems consist of inference rules and control knowledge that guide the discovery process. Their paths of discovery are influenced by the available data and the discovery steps coincide with the justification of results. The discovery process can be described in terms of fundamental concepts of artificial intelligence such as heuristic search, and can also be interpreted in terms of logic. The traditional distinction that places (...) studies of scientific discovery outside the philosophy of science, in psychology, sociology, or history, is no longer valid in view of the existence of computer systems of discovery. It becomes both reasonable and attractive to study the schemes of discovery in the same way as the criteria of justification were studied: empirically as facts, and logically as norms. (shrink)

In recent years there has been a great deal of discussion about the prospects of developing a “naturalized epistemology,” though different authors tend to interpret this label in quite different ways.1 One goal of this paper is to sketch three projects that might lay claim to the “naturalized epistemology” label, and to argue that they are not all equally attractive. Indeed, I’ll maintain that the first of the three – the one I’ll attribute to Quine – is simply incoherent. There (...) is no way we could get what we want from an epistemological theory by pursuing the project Quine proposes. The second project on my list is a naturalized version of reliabilism. This project is not fatally flawed in the way that Quine’s is. However, it’s my contention that the sort of theory this project would yield is much less interesting than might at first be thought. (shrink)

In recent years there has been a great deal of discussion about the prospects of developing a ‘naturalized epistemology’, though different authors tend to interpret this label in quite different ways. One goal of this paper is to sketch three projects that might lay claim to the ‘naturalized epistemology’ label, and to argue that they are not all equally attractive. Indeed, I'll maintain that the first of the three—the one I'll attribute to Quine—is simply incoherent. There is no way we (...) could get what we want from an epistemological theory by pursuing the project Quine proposes. The second project on my list is a naturalized version of reliabilism. This project is not fatally flawed in the way that Quine's is. However, it's my contention that the sort of theory this project would yield is much less interesting than might at first be thought. (shrink)

Detectives and scientists are in the business of reasoning from observations to explanations. This they often do by raising cunning questionsduring their inquiries. But to substantiate this claim we need to know how questions arise and how they are nurtured into more specific hypotheses. I shall discuss what the problem is, and then introduce the so-called interrogative model of inquiry which makes use of an explicit logic of questions. On this view, a discovery processes can be represented as a model-based (...) game in which an inquirer subjects a source of information to a series of strategically organized questions. Strategic principles and why-questions are especially important in heuristical reasoning. Why-questions have their own peculiar nature among questions. They indicate that the inquirer's expectations are somehow disappointed, and that is cognitively challenging. In a finished argument why-questions can be omitted, but in the search for more specific questions they are highly important. As a detetective example I shall analyze Sherlock Holmes reasoning in Silver Blace, the scientific one is A.R. Wallace's discovery of the principle of natural selection. In both of these examples the meaning of questions, especially of well-chosen why-questions, of strategic principles, and of highly structured background knowledge come to the fore. Good questions frequent those who have orderly expectations, based on experience and expertise (detectives!) or highly structured background theories (scientists!). (shrink)

Popper is well known for rejecting a logic of discovery, but he is only justified in rejecting the same type of logic of discovery that is denied by consequentialism. His own account of hypothesis generation, based on a natural selection analogy, involves an error-eliminative logic of discovery and the differences he admits between biological and conceptual evolution suggest an error-corrective logic of discovery. These types of logics of discovery are based on principles of plausibility that are used in the generation (...) as opposed to the preliminary evaluation of hypotheses. The normative relevance of these principles is grounded in the distinction between strategic and definitory rules. (shrink)

Popper is well known for rejecting a logic of discovery, but he is only justified in rejecting the same type of logic of discovery that is denied by consequentialism. His own account of hypothesis generation, based on a natural selection analogy, involves an error-eliminative logic of discovery and the differences he admits between biological and conceptual evolution suggest an error-corrective logic of discovery. These types of logics of discovery are based on principles of plausibility that are used in the generation (...) as opposed to the preliminary evaluation of hypotheses. The normative relevance of these principles is grounded in the distinction between strategic and definitory rules. (shrink)

The paper consists of a reflexive exercise in which Herbert Simon's views concerning science are applied to his own research. It argues that what connected his ventures into so many different disciplinary domains was a search for complex, hierarchical systems. In the process, the paper establishes a close connection between Simon's insights and his focus on simulation. Instead of simulating Simon on a computer, though, it simulates Simon on paper. This exercise is then contrasted with Simon's own attempts to simulate (...) science. This comparison leads to a reflexive evaluation of Simon's simulations. (shrink)

It is undeniable that much scientific work is model-based. Despite this, the justification for this reliance on models is still controversial. A particular difficulty here is the fact that many scientific models are based on assumptions that do not describe the exact details of many or even any empirical situations very well. This raises the question of why it is that, despite their frequent lack of descriptive accuracy, employing models is scientifically useful.One..

We investigate the context of discovery of two significant achievements of twentieth century biochemistry: the chemiosmotic mechanism of oxidative phosphorylation and the dark reaction of photosynthesis. The pursuit of these problems involved discovery strategies such as the transfer, recombination and reversal of previous causal and mechanistic knowledge in biochemistry. We study the operation and scope of these strategies by careful historical analysis, reaching a number of systematic conclusions: even basic strategies can illuminate “hard cases” of scientific discovery that go far (...) beyond simple extrapolation or analogy; the causal–mechanistic approach to discovery permits a middle course between the extremes of a completely substrate-neutral and a completely domain-specific view of scientific discovery; the existing literature on mechanism discovery underemphasizes the role of combinatorial approaches in defining and exploring search spaces of possible problem solutions; there is a subtle interplay between a fine-grained mechanistic and a more coarse-grained causal level of analysis, and both are needed to make discovery processes intelligible. (shrink)

We investigate the context of discovery of two significant achievements of twentieth century biochemistry: the chemiosmotic mechanism of oxidative phosphorylation and the dark reaction of photosynthesis. The pursuit of these problems involved discovery strategies such as the transfer, recombination and reversal of previous causal and mechanistic knowledge in biochemistry. We study the operation and scope of these strategies by careful historical analysis, reaching a number of systematic conclusions: even basic strategies can illuminate “hard cases” of scientific discovery that go far (...) beyond simple extrapolation or analogy; the causal–mechanistic approach to discovery permits a middle course between the extremes of a completely substrate-neutral and a completely domain-specific view of scientific discovery; the existing literature on mechanism discovery underemphasizes the role of combinatorial approaches in defining and exploring search spaces of possible problem solutions; there is a subtle interplay between a fine-grained mechanistic and a more coarse-grained causal level of analysis, and both are needed to make discovery processes intelligible. (shrink)

This article compares the features of a logic of discovery for the "friends of discovery" and for Karl Popper. It argues that the account given by Popper is the same as that of the "friends of discovery." The comparison will unsystematically exhibit that Popper proposes such a logic and will submit that the epistemological significance of a logic of discovery is to be sought in a configuration of ideas and transactions deemed regulated by or mirroring rationality rather than in creative (...) processes as such. (shrink)

The paper provides a neurophilosophical assessment of a controversy between two neuroeconomic models that compete to identify the putative object of neural utility: goods or actions. We raise two objections to the common view that sees the ‘good-based’ model prevailing over the ‘action-based’ model. First, we suggest extending neuroeconomic model discrimination to all of the models’ neurophilosophical assumptions, showing that action-based assumptions are necessary to explain real-world value-based decisions. Second, we show that the good-based model’s presumption of introducing a normative (...) neural definition of economic choice would arbitrarily restrict the domain of economic choice and consequently of economics. (shrink)

The extent of harm and suffering caused by the coronavirus pandemic has prompted a debate about whether the epidemic could have been contained, had the gravity of the crisis been predicted earlier. In this paper, the philosophical debate on predictive reasoning is framed by Hume’s problem of induction. Hume argued that it is rationally unjustified to move from the finite observations of past incidences to the predictions of future events. Philosophy has offered two major responses to the problem of induction: (...) the pragmatic induction of Peirce and the critical rationalism of Popper. It is argued that of these two, Popper’s critical rationalism provides a more potent tool for preparing for unanticipated events such as the Covid-19 pandemic. Popper’s notion of risky predictions equips strategic foresight with clear hypotheticals regarding potential crisis scenarios. Peirce’s pragmatic induction, instead, leans on probabilities that are slower to be amended as unexpected events start unfolding. The difference between the two approaches is demonstrated through a case study of the patterns of reasoning within the World Health Organization in the early stages of the coronavirus pandemic. (shrink)

Does the viability of the discovery program depend on showing either (1) that methods of generating new problem solutions, per se, have special probative weight (the per se thesis); or, (2) that the original conception of an idea is logically continuous with its justification (anti-divorce thesis)? Many writers have identified these as the key issues of the discovery debate. McLaughlin, Pera, and others recently have defended the discovery program by attacking the divorce thesis, while Laudan has attacked the discovery program (...) by rejecting the per se thesis. This disagreement over the central issue has led to communication breakdown. I contend that both friends and foes of discovery mistake the central issues. Recognizing a form of divorce helps rather than hurts the discovery program. However, the per se thesis is not essential to the program (nor is the related debate over novel prediction); hence, the status of the per se thesis is a side issue. With these clarifications in hand, we can proceed to the next stage of the discovery debate--the development (or revival) of a generative conception of justification which goes beyond consequentialism to forge a strong linkage of generation (or rather, generatability) with justification. (shrink)

According to a traditional view, scientific laws and theories constitute algorithmic compressions of empirical data sets collected from observations and measurements. This article defends the thesis that, to the contrary, empirical data sets are algorithmically incompressible. The reason is that individual data points are determined partly by perturbations, or causal factors that cannot be reduced to any pattern. If empirical data sets are incompressible, then they exhibit maximal algorithmic complexity, maximal entropy and zero redundancy. They are therefore maximally efficient carriers (...) of information about the world. Since, on algorithmic information theory, a string is algorithmically random just if it is incompressible, the thesis entails that empirical data sets consist of algorithmically random strings of digits. Rather than constituting compressions of empirical data, scientific laws and theories pick out patterns that data sets exhibit with a certain noise.Author Keywords: Algorithmic randomness; Compression; Empirical data; Information; Law; Pattern. (shrink)

Essential to Peirce's distinction among three kinds of reasoning, deduction, induction and abduction, is the claim that each is correlated to a unique species of validity irreducible to that of the others. In particular, abductive validity cannot be analyzed in either deductive or inductive terms, a consequence of considerable importance for the logical and epistemological scrutiny of scientific methods. But when the full structure of abductive argumentation — as viewed by the mature Peirce — is clarified, every inferential step in (...) the process can be seen to dissolve into familiar forms of deductive and inductive reasoning. Specifically, the final stage is a special type of practical inference which, if correct, is deductively valid, while the creative phase, surprisingly, is not inferential at all. In neither is abduction a type of inference to the best explanation. The result is a major reassessment of the relevance of Peirce's views to contemporary methodological studies. (shrink)

There is a demand to nurture scientific creativity in science education. This paper proposes that the relevant conceptual infrastructure with which to teach scientific creativity is often already included in philosophy of science courses, even those that do not cover scientific creativity explicitly. More precisely, it is shown how paradigm theory can serve as a framework with which to introduce the differences between combinational, exploratory, and transformational creativity in science. Moreover, the types of components given in Kuhn’s disciplinary matrix are (...) argued to indicate a further subdivision within transformational creativity that makes explicit that this most radical type of creativity that aims to go beyond and thus to transform the current paradigm can take many different directions. More generally, it is argued that there are several synergies between the topic of scientific creativity and paradigm theory that can be utilized in most philosophy of science courses at relative ease. Doing so should promote the understanding of scientific creativity among students, provide another way to signify the relevance of paradigm theory, and more strategically be a way of reinforcing the place of philosophy of science in science education. (shrink)

In a discussion of Sherlock Holmes’ “science of deduction” and the related “method of exclusion,” I show that Holmes’ claim that his inferences are deductive makes sense, if we consider his theoretical presuppositions. So, it is more accurate to say that he tries to reduce abduction to deduction than that he confuses them. His theoretical framework, albeit inadequate as a theory of empirical reasoning, can be seen as a basic model of classical (symbolic) AI. The main problems of this approach (...) are surveyed, and abduction is brought into play as both a better characterization of Holmes’ inferences and a better guide for building AI systems. This is a good background for raising the question of creativity because, according to Peirce, it relates to abduction in a substantial way. Is Sherlock Holmes creative? Can machines be creative? An affirmative answer to the second question might be given by a “logic of discovery” but the problem remains that inventing such a logic already presupposes creativity. (shrink)

The ArgumentScientific tools—measurement and calculation instruments, techniques of inference—straddle the line between the context of discovery and the context of justification. In discovery, new scientific tools suggest new theoretical metaphors and concepts; and in justification, these tool-derived theoretical metaphors and concepts are morelikely to be accepted by the scientific community if the tools are already entrenched in scientific practice.Techniques of statistical inference and hypothesis testing entered American psychology first as tools in the 1940s and 1950s and then as cognitive theories (...) in the 1960s and 1970s. Not only did psychologists resists statistical metaphors of mind prior to the institutionalization of inference techniques in their own practice; the cognitive theories they ultimately developed about “the mind as intuitive statistician” still bear the telltale marks of the practical laboratory context in which the tool was used. (shrink)

This paper examines whether Sherlock Holmes’ “Science of Deduction and Analysis,” as reconstructed by Hintikka and Hintikka The sign of three: Peirce, Dupin, Holmes, Indiana University Press, Bloomington, 1983), exemplifies a logic of discovery. While the Hintikkas claimed it does, their approach remained largely programmatic, and ultimately unsuccessful. Their reconstruction must thus be expanded, in particular to account for the role of memory in inquiry. Pending this expansion, the Hintikkas’ claim is vindicated. However, a tension between the naturalistic aspirations of (...) their model and the formal apparatus they built it on is identified. The paper concludes on suggestions for easing this tension without losing the normative component of the Hintikkas’ epistemological model. (shrink)

M. B. Hintikka and J. Hintikka claimed that their reconstruction of the ‘Sherlock Holmes sense of deduction’ can “serve as an explication for the link between intelligence and memory”. The claim is vindicated, first for the single-agent case, where the reconstruction captures strategies for accessing the content of a distributed and associative memory; then, for the multi-agent case, where the reconstruction captures strategies for accessing knowledge distributed in a community. Moreover, the reconstruction of the ‘Sherlock Holmes sense of deduction’ allows (...) to conceptualize those strategies as belonging to a continuum of behavioral strategies. (shrink)

This paper examines critically the reconstruction of the ‘Sherlock Holmes sense of deduction’ proposed jointly by M.B. Hintikka and J. Hintikka in the 1980s, and its successor, the interrogative model of inquiry developed by J. Hintikka and his collaborators in the 1990s. The Hintikkas’ model explicitly used game theory in order to formalize a naturalistic approach to inquiry, but the imi abandoned both the game-theoretic formalism, and the naturalistic approach. It is argued that the latter better supports the claim that (...) the imi provides a ‘logic of discovery’, and safeguards its empirical adequacy. Technical changes necessary to this interpretation are presented, and examples are discussed, both formal and informal, that are better analyzed when these changes are in place. The informal examples are borrowed from Conan Doyle’s The Case of Silver Blaze, a favorite of M.B. and J. Hintikka. (shrink)

This paper addresses the question of how scientists determine which type of hypothesis is most suitable for tackling a particular problem by examining the historical case of the anomalous β spectrum in early nuclear physics, a puzzle that occasioned the most diverse hypotheses amongst physicists at the time. It is shown that such determinations are most often implicitly informed by scientists' individual perspectives on the structural relations between the various elements of the theory and the problem at hand. In addition (...) to this main result, it is suggested that Wolfgang Pauli's neutrino idea may well have been an adaptation of Ernst Rutherford's original and older neutron idea, which would provide evidence that the adaptation of older ideas is a more common practice than is often thought. (shrink)

Kelly suggested that it was useful to consider anyone as functioning as a scientist, in the business of applying theories, making hypotheses and predictions and testing them out in the practice of everyday life. One of Charles Peirce’s major contributions was to develop the disciplines of logic and the philosophy of science. We can deepen and enrich our understanding of Kelly’s vision by looking at what Peirce has to say about the process of science. For Peirce, the essence of science (...) was the application of the laws of inference. He developed a much broader concept of logic, elaborating the processes of deduction and induction and adding to these the logic of hypothetical inference, or ‘abduction’, even as Kelly broadened it further in his “departure from classical logic”. Examining the implications of these three forms of inference allows us to elaborate the dynamics involved in the process of construing, ordinacy and the cycles of experience, creativity and decision making. This is the second of a three part series examining the relationship between the work of Peirce and Kelly. The third will include a look at phenomenology, bipolarity, the self, dialogical process and sociological considerations. (shrink)

Heuristics are often invoked in the philosophical, psychological, and cognitive science literatures to describe or explain methodological techniques or "shortcut" mental operations that help in inference, decision-making, and problem-solving. Yet there has been surprisingly little philosophical work done on the nature of heuristics and heuristic reasoning, and a close inspection of the way(s) in which "heuristic" is used throughout the literature reveals a vagueness and uncertainty with respect to what heuristics are and their role in cognition. This dissertation seeks to (...) remedy this situation by motivating philosophical inquiry into heuristics and heuristic reasoning, and then advancing a theory of how heuristics operate in cognition. I develop a positive working characterization of heuristics that is coherent and robust enough to account for a broad range of phenomena in reasoning and inference, and makes sense of empirical data in a systematic way. I then illustrate the work this characterization does by considering the sorts of problems that many philosophers believe heuristics solve, namely those resulting from the so-called frame problem. Considering the frame problem motivates the need to gain a better understanding of how heuristics work and the cognitive structures over which they operate. I develop a general theory of cognition which I argue underwrites the heuristic operations that concern this dissertation. I argue that heuristics operate over highly organized systems of knowledge, and I offer a cognitive architecture to accommodate this view. I then provide an account of the systems of knowledge that heuristics are supposed to operate over, in which I suggest that such systems of knowledge are concepts. The upshot, then, is that heuristics operate over concepts. I argue, however, that heuristics do not operate over conceptual content, but over metainformational relations between activated and primed concepts and their contents. Finally, to show that my thesis is empirically adequate, I consider empirical evidence on heuristic reasoning and argue that my account of heuristics explains the data. (shrink)

One important problem in the philosophy of science is whether there can be a normative theory of discovery, as opposed to a normative theory of justification. Although the possibility of developing a logic of scientific discovery has been often doubted by philosophers, it is particularly interesting to consider how the basic insights of a normative theory of discovery have been turned into an effective research program in computer science, namely the research field of machine learning. In this paper, I introduce (...) some current research on statistical models to a philosophical audience. In particular, I will stress those features of statistical models that make them plausible computational counterparts of scientific theories. After noticing how these models allow for the main kinds of inference that are a trademark of scientific theories, I will focus on the problem of learning statistical models from data. The analysis will show how machine learning is casting new light on traditional problems in the philosophy of science. More precisely, I will explore the implications of some results in statistical learning with respect to the role of simplicity in theory choice and to the role of scalability (as a formal property of induction) in making scientific discovery effective. (shrink)