50,224 research outputs found

    Styles of Scientific Reasoning: A Cultural Rationale for Science Education?

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    In this paper, we contend that what to teach about scientific reasoning has been bedeviled by a lack of clarity about the construct. Drawing on the insights emerging from a cognitive history of science, we argue for a conception of scientific reasoning based on six “styles of scientific reasoning.” Each “style” requires its own specific ontological and procedural entities, and invokes its own epistemic values and constructs. Consequently, learning science requires the development of not just content knowledge but, in addition, procedural knowledge, and epistemic knowledge. Previous attempts to develop a coherent account of scientific reasoning have neglected the significance of either procedural knowledge, epistemic knowledge, or both. In contrast, “styles of reasoning” do recognize the need for all three elements of domain-specific knowledge, and the complexity and situated nature of scientific practice. Most importantly, “styles of reasoning” offer science education a means of valorizing the intellectual and cultural contribution that the sciences have made to contemporary thought, an argument that is sorely missing from common rationales for science education. Second, the construct of “styles of reasoning” offers a more coherent conceptual schema for the construct of scientific reasoning—one of the major goals of any education in the sciences

    Queries, rules and definitions as epistemic statements in concept languages

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    Concept languages have been studied in order to give a formal account of the basic features of frame-based languages. The focus of research in concept languages was initially on the semantical reconstruction of frame-based systems and the computational complexity of reasoning. More recently, attention has been paid to the formalization of other aspects of frame-based languages, such as non-monotonic reasoning and procedural rules, which are necessary in order to bring concept languages closer to implemented systems. In this paper we discuss the above issues in the framework of concept languages enriched with an epistemic operator. In particular, we show that the epistemic operator both introduces novel features in the language, such as sophisticated query formulation and closed world reasoning, and makes it possible to provide a formal account for some aspects of the existing systems, such as rules and definitions, that cannot be characterized in a standard first-order framework

    Learnability with PAC Semantics for Multi-agent Beliefs

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    The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence. In an influential paper, Valiant recognised that the challenge of learning should be integrated with deduction. In particular, he proposed a semantics to capture the quality possessed by the output of Probably Approximately Correct (PAC) learning algorithms when formulated in a logic. Although weaker than classical entailment, it allows for a powerful model-theoretic framework for answering queries. In this paper, we provide a new technical foundation to demonstrate PAC learning with multi-agent epistemic logics. To circumvent the negative results in the literature on the difficulty of robust learning with the PAC semantics, we consider so-called implicit learning where we are able to incorporate observations to the background theory in service of deciding the entailment of an epistemic query. We prove correctness of the learning procedure and discuss results on the sample complexity, that is how many observations we will need to provably assert that the query is entailed given a user-specified error bound. Finally, we investigate under what circumstances this algorithm can be made efficient. On the last point, given that reasoning in epistemic logics especially in multi-agent epistemic logics is PSPACE-complete, it might seem like there is no hope for this problem. We leverage some recent results on the so-called Representation Theorem explored for single-agent and multi-agent epistemic logics with the only knowing operator to reduce modal reasoning to propositional reasoning

    Coping with uncertainty in public health: the use of heuristics

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    The observation that experts and lay people use cognitive shortcuts or heuristics to arrive at judgements about complex problems is certainly not new. But what is new is the finding that a group of reasoning strategies, which have been maligned by philosophers and logicians alike, have demonstrable value in helping members of the public come to a judgement about public health problems. These problems, which span food safety crises, immunization scares and risks associated with exposure to environmental toxins, presuppose knowledge and expertise which falls outside of the epistemic and technical competence of most members of the public. Notwithstanding the complexity of these problems, they are not perceived by lay people to be wholly unintelligible or incomprehensible. This short communication reports on the findings of a questionnaire-based investigation into the use of these reasoning strategies by 879 members of the public. The results reveal a rational competence on the part of lay people which has been hitherto unexamined, and which may be usefully exploited in all aspects of public health work

    Epistemic virtues, metavirtues, and computational complexity

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    I argue that considerations about computational complexity show that all finite agents need characteristics like those that have been called epistemic virtues. The necessity of these virtues follows in part from the nonexistence of shortcuts, or efficient ways of finding shortcuts, to cognitively expensive routines. It follows that agents must possess the capacities – metavirtues –of developing in advance the cognitive virtues they will need when time and memory are at a premium

    Reasoning about Knowledge and Strategies: Epistemic Strategy Logic

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    In this paper we introduce Epistemic Strategy Logic (ESL), an extension of Strategy Logic with modal operators for individual knowledge. This enhanced framework allows us to represent explicitly and to reason about the knowledge agents have of their own and other agents' strategies. We provide a semantics to ESL in terms of epistemic concurrent game models, and consider the corresponding model checking problem. We show that the complexity of model checking ESL is not worse than (non-epistemic) Strategy LogicComment: In Proceedings SR 2014, arXiv:1404.041
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