A comparative survey of integrated learning systems

This paper presents the duction framework for unifying the three basic forms of inference - deduction, abduction, and induction - by specifying the possible relationships and influences among them in the context of integrated learning. Special assumptive forms of inference are defined that extend the use of these inference methods, and the properties of these forms are explored. A comparison to a related inference-based learning frame work is made. Finally several existing integrated learning programs are examined in the perspective of the duction framework

Explanatory Value and Probabilistic Reasoning

The question of how judgments of explanatory value (should) inform probabilistic inference is well studied within psychology and philosophy. Less studied are the questions: How does probabilistic information affect judgments of explanatory value? Does probabilistic information take precedence over causal information in determining explanatory judgments? To answer these questions, we conducted two experimental studies. In Study 1, we found that probabilistic information had a negligible impact on explanatory judgments of event-types with a potentially unlimited number of available, alternative explanations; causal credibility was the main determinant of explanatory value. In Study 2, we found that, for event-token explanations with a definite set of candidate alternatives, probabilistic information strongly affected judgments of explanatory value. In the light of these findings, we reassess under which circumstances explanatory inference is probabilistically sound

Unification and confirmation

According to the traditional requirement, formulated by William Whewell in his account of the “consilience of inductions” in 1840, a scientific hypothesis should have unifying power in the sense that it explains and predicts several mutually independent phenomena. Variants of this notion of consilience or unification include deductive, inductive, and approximate systematization. Inference from surprising phenomena to their theoretical explanations was called abduction by Charles Peirce. As a unifying theory is independently testable by new kinds of phenomena, it should also receive confirmation from its empirical success. The study of the prospects of probabilistic Bayesianism to motivate this kind of criterion for abductive confirmation is shown to lead to two quite distinct conceptions of unification.; De acuerdo con un requisito tradicional, formulado por William Whewell en su explicación de la "consiliencia de las inducciones" en 1840, una hipótesis científica debería tener poder unificador, en el sentido de que explique y prediga varios fenómenos mutuamente independientes. Las variantes de esta noción de consiliencia o unificación incluyen la sistematización deductiva, inductiva y aproximada. Charles Peirce llamó abducción a la inferencia que va de fenómenos sorprendentes hasta sus explicaciones teóricas. Puesto que una teoría unificadora puede contrastarse independientemente a partir de nuevas clases de fenómenos, también debería recibir confirmación a partir de su éxito empírico. Se muestra que el estudio de las perspectivas del bayesianismo probabilístico para motivar este tipo de criterio para la confirmación abductiva conduce a dos concepciones distintas de la unificación, vinculación (linking up) y anulación (screening off), y en ambos casos puede observarse que la teoría unificadora recibe apoyo probabilístico a partir de fenómenos empíricos

Explanatory Value and Probabilistic Reasoning

The question of how judgments of explanatory value (should) inform probabilistic inference is well studied within psychology and philosophy. Less studied are the questions: How does probabilistic information affect judgments of explanatory value? Does probabilistic information take precedence over causal information in determining explanatory judgments? To answer these questions, we conducted two experimental studies. In Study 1, we found that probabilistic information had a negligible impact on explanatory judgments of event-types with a potentially unlimited number of available, alternative explanations; causal credibility was the main determinant of explanatory value. In Study 2, we found that, for event-token explanations with a definite set of candidate alternatives, probabilistic information strongly affected judgments of explanatory value. In the light of these findings, we reassess under which circumstances explanatory inference is probabilistically sound

Explanatory Judgment, Probability, and Abductive Inference

Abductive reasoning assigns special status to the explanatory power of a hypothesis. But how do people make explanatory judgments? Our study clarifies this issue by asking: (i) How does the explanatory power of a hypothesis cohere with other cognitive factors? (ii) How does probabilistic information affect explanatory judgments? In order to answer these questions, we conducted an experiment with 671 participants. Their task was to make judgments about a potentially explanatory hypothesis and its cognitive virtues. In the responses, we isolated three constructs: Explanatory Value, Rational Acceptability, and Entailment. Explanatory judgments strongly cohered with judgments of causal relevance and with a sense of understanding. Furthermore, we found that Explanatory Value was sensitive to manipulations of statistical relevance relations between hypothesis and evidence, but not to explicit information about the prior probability of the hypothesis. These results indicate that probabilistic information about statistical relevance is a strong determinant of Explanatory Value. More generally, our study suggests that abductive and probabilistic reasoning are two distinct modes of inference

The role of abduction in production of new ideas in design

The pragmatist philosopher Peirce insisted that besides deduction and induction there is a third main form of inference, abduction, which is the only type of inference capable of producing new ideas. Also he defined abduction as a stage of the methodological process in science, where hypotheses are formed to explain anomalies. Basing on these seminal ideas, scholars have proposed modified, widened or alternative definitions of abduction and devised taxonomies of abductive inferences. Influenced by Peirce’s seminal writings and subsequent treatments on abduction in philosophy of science, design scholars have in the last 40 years endeavoured to shed light on design by means of the concept of abduction. The first treatment was provided by March in 1976. He viewed that abduction, which he called “productive reasoning”, is the key mode of reasoning in design. He also presented a three-step cyclic design process, similar to Peirce’s methodological process in science. Among the many other later treatments of design abduction, Roozenburg’s definition of explanatory and innovative abduction is noteworthy. However, an evaluation of the related literature suggests that research into abduction in design is still in an undeveloped stage. This research shows gaps in coverage, lack of depth and diverging outcomes. By focusing on the differences between science and design as well as on empirical knowledge of different phenomena comprising design, new conceptions of abduction in design are derived. Given the differences of context, abduction in design shows characteristics not yet found or identified in science. For example, abduction can occur in connection to practically all inference types in design; it is a property of an inference besides an inference itself. A number of the most important abductive inference types as they occur in design are identified and discussed in more detail.Peer reviewe