The present paper analyses the topic of scientific discovery and the problem of the existence of a logical framework involved in such endeavour. We inquire how several non-monotonic logic frameworks and other formalisms can account for such a task. In the same vein, we analyse some key aspects of the historical and theoretical debate surrounding scientific discovery, in particular, the context of discovery and context of justification context distinction. We present an argument concerning the weakening of the discovery/justification context dichotomy (...) based on the descriptive accent contained in the non-monotonic logic perspective together with its epistemological concerns. (shrink)

Problem solving has recently become a central topic both in the philosophy of science and in cognitive science. This paper integrates approaches to problem solving from these two disciplines and discusses the epistemological consequences of such an integration. The paper first analyzes problem solving as getting a true answer to a question. It then explores some stages of cognitive activity relevant to question answering that have been delineated by historians and philosophers of science and by cognitive psychologists and artificial intelligencers. (...) The traditional opposition between discovery and justification is challenged. It is suggested that epistemology may be conceptualized, in part, as the critical assessment of problem-solving strategies. (shrink)

How do vague notions about how one might understand certain physical phenomena get transformed into scientific concepts such as “field”, “quark”, and “gene”? Philosophers of as disparate views as Reichenbach and Feyerabend have held that the process through which scientific concepts emerge is not a reasoned process. In a manner completely mysterious and unanalyzable, scientific concepts emerge fully grown, like Athena from the head of Zeus. However, when one examines actual cases of concept formation in science, a different picture can (...) be painted: Scientific concepts do not pop out of heads, but are constructed in response to specific problems by utilizing methods appropriate to the solution of the problem. Thus, as with other aspects of the scientific enterprise, concept formation is a problem-solving process that involves the application of systematic procedures in the attempt to solve specific problems. It is a reasoned process. (shrink)

Both “optimists” and “sceptics” in regard to extraterrestrial intelligence tend to hold the view that we are entitled to an epistemically clear position: either there will be a signal, in the sufficiently general sense, proving the existence of extraterrestrial intelligence, or no such signal is forthcoming. The distinction, I wish to argue here, is not at all so clear-cut. On the contrary, there are arguments, intrinsic to the subject matter, to the effect that the detection of ETI will be a (...) protracted affair characterized by uncertainty at every step. Such view of SETI discovery mandates different policies from those conventionally discussed in the literature. We should not gear our expectations and publicly promote the view that the Contact will be a clear-cut, Archimedean “Eureka!”-style discovery. In contrast, the tempo and mode of the process of discovery might significantly influence societal and political reactions to the discovery. We should be prepared for such a protracted unfolding of events. (shrink)

Se explora la naturaleza de los problemas lacunae a partir del análisis de los conceptos elaborados, respectivamente, por Larry Laudan, Theo Kuipers y Atocha Aliseda. Sugeriré que los problemas lacunae pueden surgir del debilitamiento de la noción de ‘falsación empírica’ y repercutir en la noción de ‘implicación lógica’.

Intelligence can be understood as a form of rationality, in the sense that an intelligent system does its best when its knowledge and resources are insufficient with respect to the problems to be solved. The traditional models of rationality typically assume some form of sufficiency of knowledge and resources, so cannot solve many theoretical and practical problems in Artificial Intelligence (AI). New models based on the Assumption of Insufficient Knowledge and Resources (AIKR) cannot be obtained by minor revisions or extensions (...) of the traditional models, and have to be established fully according to the restrictions and freedoms provided by AIKR. The practice of NARS, an AI project, shows that such new models are feasible and promising in providing a new theoretical foundation for the study of rationality, intelligence, consciousness, and mind. (shrink)

Scientists routinely solve the problem of supplementing one’s store of variables with new theoretical posits that can explain the previously inexplicable. The banality of success at this task obscures a remarkable fact. Generating hypotheses that contain novel variables and accurately project over a limited amount of additional data is so difficult—the space of possibilities so vast—that succeeding through guesswork is overwhelmingly unlikely despite a very large number of attempts. And yet scientists do generate hypotheses of this sort in very few (...) tries. I argue that this poses a dilemma: either the long history of scientific success is a miracle, or there exists at least one method or algorithm for generating novel hypotheses with at least limited projectibility on the basis of what’s available to the scientist at a time, namely a set of observations, the history of past conjectures, and some prior theoretical commitments. In other words, either ordinary scientific success is miraculous or there exists a logic of discovery at the heart of actual scientific method. (shrink)

Bayesian reverse-engineering is a research strategy for developing three-level explanations of behavior and cognition. Starting from a computational-level analysis of behavior and cognition as optimal probabilistic inference, Bayesian reverse-engineers apply numerous tweaks and heuristics to formulate testable hypotheses at the algorithmic and implementational levels. In so doing, they exploit recent technological advances in Bayesian artificial intelligence, machine learning, and statistics, but also consider established principles from cognitive psychology and neuroscience. Although these tweaks and heuristics are highly pragmatic in character and (...) are often deployed unsystematically, Bayesian reverse-engineering avoids several important worries that have been raised about the explanatory credentials of Bayesian cognitive science: the worry that the lower levels of analysis are being ignored altogether; the challenge that the mathematical models being developed are unfalsifiable; and the charge that the terms ‘optimal’ and ‘rational’ have lost their customary normative force. But while Bayesian reverse-engineering is therefore a viable and productive research strategy, it is also no fool-proof recipe for explanatory success. (shrink)

Future professionals should be prepared for scientific reasoning, i.e., to construct and apply scientific knowledge, in order to analyze and solve problems in their professional practice. Yet, future practitioners’ scientific reasoning skills often seem to be deficient when solving practical problems. This dissertation explores to what extent collaboration may foster the engagement of future practitioners in scientific reasoning: i.e., in epistemic processes and in referring to scientific content knowledge. Therefore, two studies were conducted to compare collaborative and individual problem solving (...) of pre-service teachers regarding their scientific reasoning. Study 1 investigates the effect of group heterogeneity with respect to problem solving scripts on scientific reasoning. Study 2 explores to what extent Epistemic Network Analysis can serve as a methodological approach for measuring scientific reasoning. As part of Study 1, pre-service teachers solved an educational problem either as individuals or as pairs. Collaboration showed a mixed effect on scientific reasoning processes: pairs engaged more in explaining and reasoning about the problem and drew more conclusions, while individuals engaged more in generating solutions. Additional analyses showed that the more heterogeneous pairs were regarding their members’ problem solving scripts the more they engaged in hypothesizing and evaluating evidence and the less they engaged in generating solutions. Finally, pairs less often referred to scientific content than individuals did during problem solving. Study 2 further analyzed the data by applying Epistemic Network Analysis. This method has the advantage of analyzing patterns of connections between epistemic processes, i.e., epistemic networks of scientific reasoning. The central epistemic process for pairs was evidence evaluation, which they frequently used in combination with hypothesizing and communicating and scrutinizing. On the other hand, the most characteristic process in individuals’ scientific reasoning was solution generation, which very often co-occurred with hypothesizing and evidence evaluation. The overall results indicate that if the aim is to develop a more reflective understanding of the problem, future practitioners should collaborate with each other, especially in heterogeneous settings. However, they should be supported to share knowledge regarding scientific theories and evidence as well as to reach a mutual understanding on the problem after a certain time so as to be able to have the capacity of generating solutions. Moreover, the different effects of collaboration on the process and content aspects of scientific reasoning imply that scientific reasoning might not be a unidimensional construct, and its process and content levels should be differentiated in future research. A further important methodological implication is that the process aspect of scientific reasoning can be analyzed as a network of interconnected skills and such analysis might bring more explanatory value than the mere reliance on frequencies of occurrences of isolated activities. (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)

Global Cumulative Treatment Analysis is a novel clinical research model combining expert knowledge, and treatment coordination based upon global information-gain, to treat every patient optimally while efficiently searching the vast space that is the realm of cancer research.

In Éléments de l'art de la teinture, Claude-Louis Berthollet organized and described knowledge of a chemical craft in terms of contemporary chemical science. The resulting intellectual structure of his treatise established a programme and method for the subsequent improvement of dyeing. Berthollet's descriptive and hierarchical systematization of knowledge rendered problems intelligible and isolated them so that they could be attacked and solved by methodical experimentation. This double-edged processes of solving practical problems, first cognitively and then experimentally, provides a key to (...) understanding the way science has contributed to rapid and systematic technological change. (shrink)