scholarly journals A Framework for Measuring Information Asymmetry

2020 ◽  
Vol 34 (03) ◽  
pp. 2983-2990
Author(s):  
Yakoub Salhi
Keyword(s):  
Information Asymmetry ◽  
Knowledge Base ◽  
General Logic ◽  
Rationality Postulates ◽  
Asymmetry Measure

Information asymmetry occurs when an imbalance of knowledge exists between two parties, such as a buyer and a seller, a regulator and an operator, and an employer and an employee. It is a key concept in several domains, in particular, in economics. We propose in this work a general logic-based framework for measuring the information asymmetry between two parties. A situation of information asymmetry is represented by a knowledge base and a set of questions. We define the notion of information asymmetry measure through rationality postulates. We further introduce a syntactic concept, called minimal question subset (MQS), to take into consideration the fact that answering some questions allows avoiding others. This concept is used for defining rationality postulates and measures. Finally, we propose a method for computing the MQSes of a given situation of information asymmetry.

scholarly journals Consolidation via Tacit Culpability Measures: Between Explicit and Implicit Degrees of Culpability

2021 ◽  
Author(s):  
Jandson S. Ribeiro ◽  
Matthias Thimm
Keyword(s):  
Fixed Point ◽  
Knowledge Base ◽  
Special Class ◽  
Belief Change ◽  
Minimal Change ◽  
The Other ◽  
Preference Relations ◽  
Other Hand ◽  
The One ◽  
Rationality Postulates

Restoring consistency of a knowledge base, known as consolidation, should preserve as much information as possible of the original knowledge base. On the one hand, the field of belief change captures this principle of minimal change via rationality postulates. On the other hand, within the field of inconsistency measurement, culpability measures have been developed to assess how much a formula participates in making a knowledge base inconsistent. We look at culpability measures as a tool to disclose epistemic preference relations and build rational consolidation functions. We introduce tacit culpability measures that consider semantic counterparts between conflicting formulae, and we define a special class of these culpability measures based on a fixed-point characterisation: the stable tacit culpability measures. We show that the stable tacit culpability measures yield rational consolidation functions and that these are also the only culpability measures that yield rational consolidation functions.

scholarly journals General information spaces: measuring inconsistency, rationality postulates, and complexity

2021 ◽  
Author(s):  
John Grant ◽  
Francesco Parisi
Keyword(s):  
Knowledge Base ◽  
Knowledge Bases ◽  
General Information ◽  
Propositional Knowledge ◽  
Information Space ◽  
Temporal Database ◽  
Information Spaces ◽  
Spatio Temporal ◽  
World Information ◽  
Rationality Postulates

AbstractAI systems often need to deal with inconsistent information. For this reason, since the early 2000s, some AI researchers have developed ways to measure the amount of inconsistency in a knowledge base. By now there is a substantial amount of research about various aspects of inconsistency measuring. The problem is that most of this work applies only to knowledge bases formulated as sets of formulas in propositional logic. Hence this work is not really applicable to the way that information is actually stored. The purpose of this paper is to extend inconsistency measuring to real world information. We first define the concept ofgeneral information spacewhich encompasses various types of databases and scenarios in AI systems. Then, we show how to transform any general information space to aninconsistency equivalentpropositional knowledge base, and finally apply propositional inconsistency measures to find the inconsistency of the general information space. Our method allows for the direct comparison of the inconsistency of different information spaces, even though the data is presented in different ways. We demonstrate the transformation on four general information spaces: a relational database, a graph database, a spatio-temporal database, and a Blocks world scenario, where we apply several inconsistency measures after performing the transformation. Then we review so-called rationality postulates that have been developed for propositional knowledge bases as a way to judge the intuitive properties of these measures. We show that although general information spaces may be nonmonotonic, there is a way to transform the postulates so they can be applied to general information spaces and we show which of the measures satisfy which of the postulates. Finally, we discuss the complexity of inconsistency measures for general information spaces.

IPO of SMEs and Information Asymmetry

2020 ◽  
Vol 11 (2) ◽  
pp. 173-188
Author(s):  
Joo-Hwan Kim ◽  
Jin-Woo Park
Keyword(s):  
Information Asymmetry

Blockchain in Supply Chain: Case Study of Lottemart Ciputat

2019 ◽  
Vol 5 (1) ◽  
pp. 38-49 ◽  
Author(s):  
B. K. Handoyo ◽  
M. R. Mashudi ◽  
H. P. Ipung
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Information Asymmetry ◽  
New Technology ◽  
Business Case ◽  
Analysis Method ◽  
Chain Management ◽  
Production Processes

Current supply chain methods are having difficulties in resolving problems arising from the lack of trust in supply chains. The root reason lies in two challenges brought to the traditional mechanism: self-interests of supply chain members and information asymmetry in production processes. Blockchain is a promising technology to address these problems. The key objective of this paper is to present qualitative analysis for blockchain in supply chain as the decision-making framework to implement this new technology. The analysis method used Val IT business case framework, validated by the expert judgements. The further study needs to be elaborated by either the existing organization that use blockchain or assessment by the organization that will use blockchain to improve their supply chain management.

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