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)

The article discusses Friedman's classic claim that economics can be based on irrealistic assumptions. It exploits Samuelson's distinction between two "F-twists" (that is, "it is an advantage for an economic theory to use irrealistic assumptions" vs "the more irrealistic the assumptions, the better the economic theory"), as well as Nagel's distinction between three philosophy-of-science construals of the basic claim. On examination, only one of Nagel's construals seems promising enough. It involves the neo-positivistic distinction between theoretical and non-theoretical ("observable") terms; so (...) Friedman would in some sense argue for the major role of theoretical terms in economics. The paper uses a model-theoretic apparatus to refine the selected construal and check whether it can be made to support the claim. This inquiry leads to essentially negative results for both F-twists, and the final conclusion is that they are left unsupported. (shrink)

This paper considers the prospects for objectivity in reasoning strategies in response to empirical studies that apparently show systematic culture‐based differences in patterns of reasoning. I argue that there is at least one modest class of exceptions to the claim that there are alternative, equally warranted standards of good reasoning: the class that entails the solution of certain well‐structured problems which, suitably chosen, are common, or touchstone, to the sorts of culturally different viewpoints discussed. There is evidence that some cognitive (...) tasks are seen in much the same way across cultures, not least by virtue of the common run of experiences with the world of material objects in early childhood by creatures with similar cognitive endowments. These tasks thus present as similarly structured sets of claims that have similar priority: what is framed, and what is bracketed, or held constant in the background, is shown to be naturally common across cultures. As a consequence, a normative view of reasoning and, by implication, critical thinking can be defended. While this might be a modest sense of objectivity, the high level of intercultural articulation that is able to occur among people of different backgrounds suggests that it provides cognitive scaffolding for a lot of other reasoning tasks as well. (shrink)

Some scholars suspect that thought experiments have something in common with jokes. Moreover, Thomas Kuhn has suggested that what happens to someone who thinks through a thought experiment “is very similar to what happens to a man, like Lavoisier, who must assimilate the result of a new unexpected experimental discovery” (1964: 321). In this paper, I pinpoint the presumed commonalities. I identify, more specifically, what cognitive linguists call “incongruity-resolution” as the problem-solving process not only involved in humor comprehension, but in (...) scientific discovery and thought experimentation as well. (shrink)

Can computer systems ever be considered moral agents? This paper considers two factors that are explored in the recent philosophical literature. First, there are the important domains in which computers are allowed to act, made possible by their greater functional capacities. Second, there is the claim that these functional capacities appear to embody relevant human abilities, such as autonomy and responsibility. I argue that neither the first (Domain-Function) factor nor the second (Simulacrum) factor gets at the central issue in the (...) case for computer moral agency: whether they can have the kinds of intentional states that cause their decisions and actions. I give an account that builds on traditional action theory and allows us to conceive of computers as genuine moral agents in virtue of their own causally efficacious intentional states. These states can cause harm or benefit to moral patients, but do not depend on computer consciousness or intelligence. (shrink)

The Greeks had a ready answer for what happens when the mind suddenly finds the answer to a question for which it had been searching: insight was regarded as a gift of the Muses, its origins were divine. It served to highlight the Greeks'' belief that there are some things which are not meant to be scientifically explained. The essence of insight is that it comes from some supernatural source: unpredicted and unfettered. In other words, the origins of insight are (...) unconscious, and hence, unexplainable. Wittgenstein felt that, as long as there continues to be a noun expression like to have a moment of insight which functions in the same way as the expression to have a hunger pang, thereby inducing us to treat moment of insight as the name of an experience, then people will keep stumbling over the same puzzling difficulties and find themselves staring at something which no explanation seems capable of clearing up. To the founders of AI, this argument reeked of obscurantism. The moment of insight, they felt, is indeed a mystery, but it is one that begs to be explained in causal terms. Indeed, the problem of insight served as one of the leading problems in the evolution of AI. Hence anyone interested in the foundations of AI is compelled to examine the manner in which the early pioneers of the field sought to explain the eureka experience. In this paper I will look at some of the key conceptual developments which paved the way for Newell and Simon''s theory of GPS: the fundamental changes in the notion of the unconscious — the emergence of the cognitive unconscious — which took place in the nineteenth- and early twentieth-century. In so doing, I hope to clarify what Wittgenstein may have had in mind in his strictures against mechanist attempts to analyse the nature of insight. (shrink)

This book examines the philosophical conception of abductive reasoning as developed by Charles S. Peirce, the founder of American pragmatism. It explores the historical and systematic connections of Peirce's original ideas and debates about their interpretations. Abduction is understood in a broad sense which covers the discovery and pursuit of hypotheses and inference to the best explanation. The analysis presents fresh insights into this notion of reasoning, which derives from effects to causes or from surprising observations to explanatory theories. The (...) author outlines some logical and AI approaches to abduction as well as studies various kinds of inverse problems in astronomy, physics, medicine, biology, and human sciences to provide examples of retroductions and abductions. The discussion covers also everyday examples with the implication of this notion in detective stories, one of Peirce’s own favorite themes. The author uses Bayesian probabilities to argue that explanatory abduction is a method of confirmation. He uses his own account of truth approximation to reformulate abduction as inference which leads to the truthlikeness of its conclusion. This allows a powerful abductive defense of scientific realism. This up-to-date survey and defense of the Peircean view of abduction may very well help researchers, students, and philosophers better understand the logic of truth-seeking. (shrink)

Biochemists often adopt what may be called the “Strategy of Decomposition” for the causal discovery of biochemical pathway dynamic behaviours. This involves decomposing a pathway into a set of isolated parts, which are then analysed separately. It is assumed that knowledge gained of the isolated parts can then be used to explain the dynamic behaviours of the whole pathway. My thesis addresses the extent to which use of the Strategy of Decomposition is warranted. I evaluate two challenges contained in Bechtel (...) and Richardson’s Discovering Complexity. The first challenge is that pathways lack the ‘modular’ structure assumed in the Strategy of Decomposition. Bechtel and Richardson take biochemists to use a concept of modularity called ‘near decomposability’. The second challenge is that pathways have ‘Pathway Emergent’ behaviours. I reject both challenges. I show that near decomposability is the wrong type of modularity to apply to pathways, and that the occurrence of Pathway Emergence has not been established. I argue that an underlying problem with Bechtel and Richardson’s analyses is that they overstate the consequences of feedback and nonlinearity for the Strategy of Decomposition. Instead, the analysis of pathway modularity and emergence needs to be centered on the context-sensitivity of pathways’ ‘local causal laws’. I identify that the type of modularity assumed in the Strategy of Decomposition is ‘causal law modularity’, which requires the invariance of local causal laws. I also identify a necessary condition for Pathway Emergence: a pathway must manifest at least one local causal law that is not manifested by its isolated parts. I argue that the use of the Strategy of Decomposition may often be unwarranted. This is because the local causal laws of pathways are highly context-sensitive, and pathways might often not be causal law modular. This context-sensitivity also leaves open the possibility of Pathway Emergence. (shrink)

Organisms change their shape and behavior during ontogenesis in response to incentives—what biologists call “phenotypic plasticity” or what is called here more specifically “behavioral plasticity.” Such plasticity is usually in the direction of enhancing welfare or fitness. In light of basic concepts in economics, such behavioral plasticity is nothing but rationality. Such rationality is not limited to organisms with neural systems. It also characterizes brainless organisms such as plants, fungi, and unicellular organisms. The gist of the article is the distinction (...) between rationality and intelligence. Whereas rationality is ubiquitous in all organisms, intelligence varies in degrees depending on division of labor that may involve the evolution of a neural system. This article aims to defend the universality of rationality—the Organismus oeconomicus hypothesis. It argues that neither the notion of bounded rationality of behavioral economics nor Herbert Simon’s “procedural rationality” can ultimately undermine the Organismus oeconomicus hypothesis. (shrink)

How do scientific models represent in a way that enables us to discover new truths about reality and draw inferences about it? Contemporary accounts of scientific discovery answer this question by focusing on the cognitive mechanisms involved in the generation of new ideas and concepts in terms of a special sort of reasoning—or model-based reasoning—involving imagery. Alternatively, I argue that answering this question requires that we recognise the crucial role of the propositional imagination in the construction and development of models (...) for the purpose of generating hypotheses that are plausible candidates for truth. I propose simple fictionalism as a new account of models as Waltonian games of make-believe and suggest that models can lead to genuine scientific discovery when they are used as representations that denote real world phenomena and generate two main kinds of theoretical hypotheses, model-world comparisons and direct attributions. (shrink)

Ever since 1956 when details of the Logic Theorist were published by Newell and Simon, a large literature has accumulated on computational models and theories of the creative process, especially in science, invention and design. But what exactly do these computational models/theories tell us about the way that humans have actually conducted acts of creation in the past? What light has computation shed on our understanding of the creative process? Addressing these questions, we put forth three propositions: (I) Computational models (...) of the creative process are fundamentally flawed as theories of human creativity. Rather, the universal power of computational models lies elsewhere: (II) Computational models of particular acts of creation can serve as effective experiments to test universal hypotheses about creative processes and mechanisms; and (III) Computation-based architectures of the creative mind provide metaphorical frameworks that, like all good metaphors, can serve as rich sources of insight into aspects of the creative process. In this paper, we provide evidence for these three propositions by drawing upon some particular episodes in the cognitive history of science, technology, and art. (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)

The way scientific discovery has been conceptualized has changed drastically in the last few decades: its relation to logic, inference, methods, and evolution has been deeply reloaded. The ‘philosophical matrix’ moulded by logical empiricism and analytical tradition has been challenged by the ‘friends of discovery’, who opened up the way to a rational investigation of discovery. This has produced not only new theories of discovery, but also new ways of practicing it in a rational and more systematic way. Ampliative rules, (...) methods, heuristic procedures and even a logic of discovery have been investigated, extracted, reconstructed and refined. The outcome is a ‘scientific discovery revolution’: not only a new way of looking at discovery, but also a construction of tools that can guide us to discover something new. This is a very important contribution of philosophy of science to science, as it puts the former in a position not only to interpret what scientists do, but also to provide and improve tools that they can employ in their activity. (shrink)

The distinction between the context ofdiscovery and the context of justificationrestricts philosophy of science to the rationalreconstruction of theories, and characterizesscientific discovery as rare, theoreticalupheavals that defy rational reconstruction. Kuhnian challenges to the two contextsdistinction show that non-rational elementspersist in the justification of theories, butgo no further to provide a positive account ofdiscovery. A gradualist theory of discoverydeveloped in this paper shows, with supportfrom ecological cases, that discoveries areroutinely made in ecology by extending modelsto new domains, or by making additions (...) toearlier models. The logic of discovery isphilosophically accessible once it isappreciated that model truth is presumed, evenif counterfactually, in ecologists' applicationof models. A gradualist view shows thatmodels' heuristic power routinely leads todiscoveries. (shrink)

This paper analyzes the rapid and unexpected rise of deep learning within Artificial Intelligence and its applications. It tackles the possible reasons for this remarkable success, providing candidate paths towards a satisfactory explanation of why it works so well, at least in some domains. A historical account is given for the ups and downs, which have characterized neural networks research and its evolution from “shallow” to “deep” learning architectures. A precise account of “success” is given, in order to sieve out (...) aspects pertaining to marketing or sociology of research, and the remaining aspects seem to certify a genuine value of deep learning, calling for explanation. The alleged two main propelling factors for deep learning, namely computing hardware performance and neuroscience findings, are scrutinized, and evaluated as relevant but insufficient for a comprehensive explanation. We review various attempts that have been made to provide mathematical foundations able to justify the efficiency of deep learning, and we deem this is the most promising road to follow, even if the current achievements are too scattered and relevant for very limited classes of deep neural models. The authors’ take is that most of what can explain the very nature of why deep learning works at all and even very well across so many domains of application is still to be understood and further research, which addresses the theoretical foundation of artificial learning, is still very much needed. (shrink)

Case studies in the psychophysics, modeling and localization of human vision are presented as an example of “hands-on” philosophy of the cognitive sciences. These studies also yield important results for familiar problems in philosophy of mind: the explanatory gap surrounding phenomenological feels is not closed by the kinds of investigations surveyed. However, the science is able to explain some sorts of phenomenological facts, such as why the human color space takes the form of the Munsell color solid, or why there (...) is a phenomenologically-pure yellow but not a phenomenologically-pure orange. (shrink)

Recently developed theory using directed graphs permits simple and precise statements about the validity of causal inferences in most cases. Applying this while reading econometric papers can make it easy to understand assumptions that are vague in prose, and to isolate those assumptions that are crucial to support the main causal claims. The method is illustrated here alongside a close reading of the paper that introduced the use of settler mortality to instrument the impact of institutions on economic development. Two (...) causal pathways that invalidate the instrument are found not to be blocked by satisfactory strategies. The estimates in the original paper, and in many that have used the instrument since, should be considered highly suspect. (shrink)