On the semantic characteristics of information

Shreider, Yu. A.

doi:10.1016/s0020-0271(65)80003-1

Cited by 14 publications

(12 citation statements)

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“…The basic idea of the opinion revision rule is that no opinion change is expected if the persuasion is either too far or too close to the already existing opinion [15,36,60]. The opinion revision rule is not Bayesian, because the standard Bayesian approach does not apply to processes of persuasion and advising; see the second section for more details.…”

Section: Discussionmentioning

confidence: 99%

Opinion Dynamics with Confirmation Bias

Allahverdyan

Galstyan

2014

PLoS ONE

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BackgroundConfirmation bias is the tendency to acquire or evaluate new information in a way that is consistent with one's preexisting beliefs. It is omnipresent in psychology, economics, and even scientific practices. Prior theoretical research of this phenomenon has mainly focused on its economic implications possibly missing its potential connections with broader notions of cognitive science.Methodology/Principal FindingsWe formulate a (non-Bayesian) model for revising subjective probabilistic opinion of a confirmationally-biased agent in the light of a persuasive opinion. The revision rule ensures that the agent does not react to persuasion that is either far from his current opinion or coincides with it. We demonstrate that the model accounts for the basic phenomenology of the social judgment theory, and allows to study various phenomena such as cognitive dissonance and boomerang effect. The model also displays the order of presentation effect–when consecutively exposed to two opinions, the preference is given to the last opinion (recency) or the first opinion (primacy) –and relates recency to confirmation bias. Finally, we study the model in the case of repeated persuasion and analyze its convergence properties.ConclusionsThe standard Bayesian approach to probabilistic opinion revision is inadequate for describing the observed phenomenology of persuasion process. The simple non-Bayesian model proposed here does agree with this phenomenology and is capable of reproducing a spectrum of effects observed in psychology: primacy-recency phenomenon, boomerang effect and cognitive dissonance. We point out several limitations of the model that should motivate its future development.

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Section: Discussionmentioning

confidence: 99%

Opinion Dynamics with Confirmation Bias

Allahverdyan

Galstyan

2014

PLoS ONE

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“…Propositions and predicates are symbolic knowledge units in the logical approach to information theory developed in works of Bar-Hillel and Carnap [12], Hintikka [13,14] and some other authors. Shreider [15] interpreted symbolic knowledge units as texts in a thesaurus. Many researchers employ mental schemas as cognitive/symbolic knowledge units in the brain (cf., for example, [16][17][18]) One more possibility for W ses is the set, or more exactly, a multiset, W Sit of logical representations of situations possible in a world U.…”

Section: Epistemic Spacesmentioning

confidence: 99%

Weighted E-Spaces and Epistemic Information Operators

Burgin

2014

Information

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Information is usually related to knowledge. Here, we present a broader picture in which information is associated with epistemic structures, which form cognitive infological systems as basic recipients and creators of cognitive information. Infological systems are modeled by epistemic spaces, while operators in these spaces are mathematical models of information. Information that acts on epistemic structures is called cognitive information, while information that acts on knowledge structures is called epistemic information. The latter brings new and updates existing knowledge, being of primary importance to people. In this paper, both types of information are studied as operators in epistemic spaces based on the general theory of information. As a synthetic approach, which reveals the essence of information, organizing and encompassing all main directions in information theory, the general theory of information provides efficient means for such a study. Different types of information dynamics representation use tools from various mathematical disciplines, such as the theory of categories, functional analysis, mathematical logic and algebra. In this paper, we base our exploration of information and knowledge dynamics on functional analysis further developing the mathematical stratum of the general theory of information.Keywords: information; knowledge; epistemic structure; epistemic space; weighted epistemic space; epistemic information operator; vector bundle; continuity; boundedness definition of the rectangular closure R(X), there are points x and z from X such x = (a, w) and z = (b, y) with a, b ∈ X e. By the properties of metric,By initial conditions, d(x, z) < k. At the same time, by the definition of the metric d and Corollary 2, we have: d((c, w), (a, w)Proposition is proved because p and q are arbitrary points from R(X). □Reducing the problem of boundedness to rectangular sets, now we find conditions of boundedness for rectangular sets.Proposition 3. A rectangular subset X of the space E of the vector bundle E = (E, p E , E e ) is bounded if and only if the projection X e = p E (X) of X and the fiber projection σ(X) of X are uniformly bounded. Proof. Necessity. Let us assume that the projection X e = p E (X) of X is unbounded. It means that for any positive number k, there are two points a and b in X e such that d(a, b) > k. As X e is the projection of X, there are two points x and z in X such that a = p E (x) and b = p E (z). By the definition of the metric in the space E, d(x, z) ≥ d(a, b) > k. Consequently, X is also unbounded. Now, let us suppose that the fiber projection σ(X) of X is not uniformly bounded. It means that for any positive number k, there are two points u and v in σ(X) such that d v (u, v) > k. As σ(X) is a projection of X, there are points x = (a, u) and z = (b, v) from the space X. By Corollary 2, d(x, z) ≥ k as by choice of the points u and v, d v (u, v) > k. Thus, the space X is not bounded.Then by the Law of Contraposition, if the space X is bounded, then the projection X e = p E ...

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“…For those who prefer to have an exact definition contrary to a broader perspective, it is possible to define a cognitive infological system as the system of knowledge. This approach was used by [31,32].…”

Section: Ontological Principle O2a (The Special Transformation Princimentioning

confidence: 99%

Section: Example 44mentioning

confidence: 99%

“…Thesaurus is a natural infological system [7,32]. An efficient representation of a thesaurus is a set of words, or more generally, of texts in some languages, which are information representations, as well as information carriers when these texts are in a material form, e.g., strings of symbols on paper or states of a computer memory [32]. Thus, it is natural to take sets of words (or texts) as objects of the category C. Then morphisms of this category are multiple computations ( [37]) performed by systems/automata from some class, e.g., Turing machines, inductive Turing machines or finite automata.…”

Section: Example 44mentioning

confidence: 99%

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Information Operators in Categorical Information Spaces

Burgin

2010

Information

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The general theory of information (GTI) is a synthetic approach, which reveals the essence of information, organizing and encompassing all main directions in information theory. On the methodological level, it is formulated as system of principles explaining what information is and how to measure information. The goal of this paper is the further development of a mathematical stratum of the general theory of information based on category theory. Abstract categories allow us to construct flexible models for information and its flow. Now category theory is also used as unifying framework for physics, biology, topology, and logic, as well as for the whole mathematics, providing a base for analyzing physical and information systems and processes by means of categorical structures and methods. There are two types of representation of information dynamics, i.e., regularities of information processes, in categories: the categorical representation and functorial representation. Here we study the categorical representations of information dynamics, which preserve internal structures of information spaces associated with infological systems as their state/phase spaces. Various relations between information operators are introduced and studied in this paper. These relations describe intrinsic features of information, such as decomposition and complementarity of information, reflecting regularities of information processes

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On the semantic characteristics of information

Cited by 14 publications

References 3 publications

Opinion Dynamics with Confirmation Bias

Opinion Dynamics with Confirmation Bias

Weighted E-Spaces and Epistemic Information Operators

Information Operators in Categorical Information Spaces

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