scholarly journals Analogical Reasoning as an Inference Scheme

Dialogue ◽  
2021 ◽  
pp. 1-21
Author(s):  
Bernard Walliser ◽  
Denis Zwirn ◽  
Hervé Zwirn
Keyword(s):  
Analogical Reasoning ◽  
General Scheme ◽  
Formal Representation ◽  
Analogical Inference ◽  
Different Types

Abstract Despite its importance in various fields, analogical reasoning has not yet received a unified formal representation. Our contribution proposes a general scheme of inference that is compatible with different types of logic (deductive, probabilistic, non-monotonic). Firstly, analogical assessment precisely defines the similarity of two objects according to their properties, in a relative rather than absolute way. Secondly, analogical inference transfers a new property from one object to a similar one, thanks to an over-hypothesis linking two sets of properties. The belief strength in the conclusion is then directly related to the belief strength in this meta-hypothesis.

Inferring Causal Relations by Analogy

2017 ◽  
Author(s):  
Keith J. Holyoak ◽  
Hee Seung Lee
Keyword(s):  
Analogical Reasoning ◽  
Causal Model ◽  
Causal Knowledge ◽  
Common Pattern ◽  
Causal Relations ◽  
Analogical Inference ◽  
Psychological Evidence ◽  
Inductive Inferences ◽  
Novel Target ◽  
Constituent Elements

When two situations share a common pattern of relationships among their constituent elements, people often draw an analogy between a familiar source analog and a novel target analog. This chapter reviews major subprocesses of analogical reasoning and discusses how analogical inference is guided by causal relations. Psychological evidence suggests that analogical inference often involves constructing and then running a causal model. It also provides some examples of analogies and models that have been used as tools in science education to foster understanding of critical causal relations. A Bayesian theory of causal inference by analogy illuminates how causal knowledge, represented as causal models, can be integrated with analogical reasoning to yield inductive inferences.

scholarly journals Confirmation Based on Analogical Inference: Bayes Meets Jeffrey

2019 ◽  
Vol 50 (2) ◽  
pp. 174-194
Author(s):  
Christian J. Feldbacher-Escamilla ◽  
Alexander Gebharter
Keyword(s):  
Analogical Reasoning ◽  
Moral Reasons ◽  
Analogical Inference ◽  
Jeffrey Conditionalization ◽  
Bayesian Update

AbstractCertain hypotheses cannot be directly confirmed for theoretical, practical, or moral reasons. For some of these hypotheses, however, there might be a workaround: confirmation based on analogical reasoning. In this paper we take up Dardashti, Hartmann, Thébault, and Winsberg’s (2019) idea of analyzing confirmation based on analogical inference Bayesian style. We identify three types of confirmation by analogy and show that Dardashti et al.’s approach can cover two of them. We then highlight possible problems with their model as a general approach to analogical inference and argue that these problems can be avoided by supplementing Bayesian update with Jeffrey conditionalization.

Relational Reasoning in a Neurally Plausible Cognitive Architecture

2005 ◽  
Vol 14 (3) ◽  
pp. 153-157 ◽  
Author(s):  
John E. Hummel ◽  
Keith J. Holyoak
Keyword(s):  
Analogical Reasoning ◽  
A Priori ◽  
Cognitive Architecture ◽  
Relational Reasoning ◽  
Natural Basis ◽  
Analogical Inference ◽  
Analogical Mapping ◽  
Design Requirements ◽  
Human Thinking ◽  
Propositional Structures

Human mental representations are both flexible and structured—properties that, together, present challenging design requirements for a model of human thinking. The Learning and Inference with Schemas and Analogies (LISA) model of analogical reasoning aims to achieve these properties within a neural network. The model represents both relations and objects as patterns of activation distributed over semantic units, integrating these representations into propositional structures using synchrony of firing. The resulting propositional structures serve as a natural basis for memory retrieval, analogical mapping, analogical inference, and schema induction. The model also provides an a priori account of the limitations of human working memory and can simulate the effects of various kinds of brain damage on thinking.

Multiple analogical proportions

2021 ◽  
pp. 1-18
Author(s):  
Henri Prade ◽  
Gilles Richard
Keyword(s):  
Artificial Intelligence ◽  
Linear Algebra ◽  
Building Block ◽  
Analogical Reasoning ◽  
Weak Form ◽  
Boolean Logic ◽  
Real Numbers ◽  
Analogical Inference ◽  
Logical Modeling

Analogical proportions are statements of the form “a is to b as c is to d”, denoted a : b : : c : d, that may apply to any type of items a, b, c, d. Analogical proportions, as a building block for analogical reasoning, is then a tool of interest in artificial intelligence. Viewed as a relation between pairs ( a , b ) and ( c , d ), these proportions are supposed to obey three postulates: reflexivity, symmetry, and central permutation (i.e., b and c can be exchanged). The logical modeling of analogical proportions expresses that a and b differ in the same way as c and d, when the four items are represented by vectors encoding Boolean properties. When items are real numbers, numerical proportions – arithmetic and geometric proportions – can be considered as prototypical examples of analogical proportions. Taking inspiration of an old practice where numerical proportions were handled in a vectorial way and where sequences of numerical proportions of the form x 1 : x 2 : ⋯ : x n : : y 1 : y 2 : ⋯ : y n were in use, we emphasize a vectorial treatment of Boolean analogical proportions and we propose a Boolean logic counterpart to such sequences. This provides a linear algebra calculus of analogical inference and acknowledges the fact that analogical proportions should not be considered in isolation. Moreover, this also leads us to reconsider the postulates underlying analogical proportions (since central permutation makes no sense when n ⩾ 3) and then to formalize a weak form of analogical proportion which no longer obeys the central permutation postulate inherited from numerical proportions. But these weak proportions may still be combined in multiple weak analogical proportions.

Signs and terminology: Science caught between language and perception

Bionomina ◽  
2011 ◽  
Vol 4 (1) ◽  
pp. 1-41 ◽  
Author(s):  
Lars M. VOGT
Keyword(s):  
Natural Kinds ◽  
Scientific Progress ◽  
Semantic Transparency ◽  
Formal Representation ◽  
Negative Effects ◽  
Conceptual Content ◽  
Scientific Terminology ◽  
New Methods ◽  
Different Types ◽  
Scientific Terms

After briefly discussing various problems that can result from linguistic ambiguities, I attempt to provide an introduction and overview of various aspects and theories that are relevant to scientific terminology. These include a general semiotics (i.e., theory of signs), the distinction of semantic conceptual content and aesthetic nonconceptual content and their relationship to each other, a discussion of different types of scientific concepts (e.g., essentialistic and cluster classes, natural kinds, type approach), and the importance of specifying epistemological recognition criteria for empirical concepts in addition to their ontological theoretical definitions and the specification of contexts in which the concepts are used and on which theories they depend upon. I then provide a distinction of raw data, data, metadata, information and knowledge, and discuss the relation between images and data and how efforts to standardize data and metadata can affect scientific terminology. I briefly introduce new methods and techniques for increasing semantic transparency and communicability in science, which include the organization and the management of scientific terms within taxonomies, and their formal representation in ontologies. The usefulness of terminological standardization and its possible negative effects on scientific progress is then discussed, and finally the question is addressed of whether one can distinguish types of terminologies that benefit from standardization from those that could suffer from it.

On the Load Balancing of Business Intelligence Reporting Systems

2010 ◽  
pp. 42-57 ◽  
Author(s):  
Leszek Kotulski ◽  
Dariusz Dymek
Keyword(s):  
Load Balancing ◽  
Data Warehouse ◽  
Distributed System ◽  
Auxiliary Information ◽  
Formal Representation ◽  
Timing Diagrams ◽  
General Rules ◽  
Formal Tool ◽  
Reporting Systems ◽  
Different Types

The UML model consists of several types of diagrams representing different aspects of the modeled system. To assure the universality and flexibility, the UML involves only a few general rules about dependence among different types of diagrams. In consequence people can have the different methodologies based on the UML, but in the same time we haven’t the formal tool for assure the vertical cohesion of created model. To test and reach the vertical cohesion of the model some auxiliary information about the relations among the elements belonging to different types of diagrams should be remembered. In this chapter the authors present the method of formal representation of such information in a form of the relation, called Accomplish Relation. This method is based only on the UML properties and is independent from any methodology. Additionally, they show how to use the UML timing diagrams for representing the users’ requirements in association with use cases. To illustrate the usefulness of this approach we present how it can be used for load balancing of distributed system in case of a Reporting Systems based on Data Warehouse concept.

Using Rules in the Narrative Knowledge Representation Language (NKRL) Environment

2010 ◽  
pp. 50-75 ◽  
Author(s):  
Gian Piero Zarri
Keyword(s):  
Knowledge Representation ◽  
Analogical Reasoning ◽  
Point Of View ◽  
Inference Rules ◽  
Economic Interest ◽  
Formal Representation ◽  
Representation Point ◽  
Knowledge Representation Language ◽  
Representation Language ◽  
High Level

NKRL is a semantic language expressly designed to deal with all sort of ‘narratives’, in particular with those (‘non-fictional narratives’) of an economic interest. From a knowledge representation point of view, its main characteristics consists in the use of two different sorts of ontologies, a standard, binary ontology of concepts, and an ontology of n-ary templates, where each template corresponds to the formal representation of a class of elementary events. Rules in NKRL correspond to high-level reasoning paradigms like the search for causal relationships or the use of analogical reasoning. Given i) the conceptual complexity of these paradigms, and ii) the sophistication of the underlying representation language, rules in NKRL cannot be implemented in a (weak) ‘inference by inheritance’ style but must follow a powerful ‘inference by resolution’ approach. After a short reminder about these two inference styles, and a quick introduction of the NKRL language, the chapter describes in some depth the main characteristics of the NKRL inference rules.

The strange case of urban theory

2020 ◽  
Vol 13 (3) ◽  
pp. 443-459 ◽  
Author(s):  
Clive Barnett
Keyword(s):  
Urban Studies ◽  
Analogical Reasoning ◽  
Wicked Problems ◽  
Explanatory Theory ◽  
Urban Theory ◽  
Different Types ◽  
Styles Of Reasoning

Abstract Recent debates in urban theory have centred on the problem of whether universal concepts can have applications to particular places. These debates could benefit from more serious attention to how urban thought involves styles of analogical reasoning closer in spirit to casuistry than to explanatory theory. The difficult status of ‘the case’ in urban studies is explored through a consideration of different types of universality in this field, leading to a re-consideration of ideas of experimentalism and wicked problems. Further attention should be given to the multiple styles of reasoning through which urban knowledge is produced and circulated.

scholarly journals Analogical Reasoning as an Inference Scheme

2021 ◽  
Author(s):  
Bernard Walliser ◽  
Denis ZWIRN ◽  
Hervé ZWIRN
Keyword(s):  
Analogical Reasoning ◽  
Single Case ◽  
Point Of View ◽  
Analogical Inference ◽  
The Difference ◽  
Definition Of

Abstract Analogy plays an important role in science as well as in non-scientific domains such as taxonomy or learning. We make explicit the difference and complementarity between the concept of analogical statement, which merely states that two objects have a relevant similarity, and the concept of analogical inference, which relies on the former in order to draw a conclusion from some premises. For the first, we show that it is not possible to give an absolute definition of what it means for two objects to be analogous; a relative definition of analogy is introduced, only relevant from some point of view. For the second, we argue that it is necessary to introduce a background over-hypothesis relating two sets of properties; the belief strength of the conclusion is then directly related to the belief strength of the over-hypothesis. Moreover, we assert the syntactical identity between analogical inference and single case induction despite important pragmatic differences.

scholarly journals In Defense of Analogical Reasoning

2008 ◽  
Vol 28 (3) ◽  
pp. 229 ◽  
Author(s):  
Steven Gamboa
Keyword(s):  
Analogical Reasoning ◽  
Scientific Models ◽  
Existence Proof ◽  
Analogical Inference

I offer a defense of ana-logical accounts of scientific models by meeting certain logical objections to the legitimacy of analogical reasoning. I examine an argument by Joseph Agassi that purports to show that all putative cases of analogical inference succumb to the following dilemma: either (1) the reasoning remains hopelessly vague and thus establishes no conclusion, or (2) can be analyzed into a logically preferable non-analogical form. In rebuttal, I offer a class of scientific models for which (a) there is no satisfactory non-analogical analysis, and (b) we can gain sufficient clarity for the legitimacy of the inference to be assessed. This result constitutes an existence proof for a class of analogical models that escape Agassi’s dilemma.

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