Fuzzy Logic and Corporate Governance Theories

[Excerpt] “Fuzzy logic is a theory that categorizes concepts or things belonging to more than one group. A methodology that explains how things function in multiple groups (not fully in one group or another) offers advantages when no one definition or membership in a group accounts for belonging to multiple groups. The principal/agent model of corporate governance has some characteristics of fuzzy logic theory. Under traditional agency theory of corporate governance, shareholders, directors, and senior corporate officers each belong to groups having multiple attributes. In the principal/agent model of corporate governance, shareholders are owners or principals; directors are shareholders and agents of the corporation; and senior corporate officers are directors’ agents, shareholders’ agents, and agents of the corporation. Each one functions within multiple groups serving multiple agency roles, and each owes fiduciary duties that vary depending on whose agent they are functioning as. Such a multi-dimensional role for corporate actors is a consequence of multi-definitional corporate purpose within agency theory of governance. This multi-dimensional group membership is not easily reconciled within agency theory and is therefore not always explained. However, traditional corporate governance theory can borrow another basic tenet of fuzzy logic theory. Fuzzy theory not only accounts for membership in multiple groups, but also explains how things work because they are multidimensional or ambiguous. This article seeks to explain the ambiguities of corporate governance theory and suggests a framework that accounts for the multi-agent role of senior corporate officers of public companies. It offers a kind of fuzzy logic theory for understanding the fiduciary duties of senior officers. The purpose of this article is to evaluate other models of corporate governance that account for the multi-agent role of senior officers of public companies and assess the ability of various models to hold senior officers accountable to the corporation.

Learning from AI : new trends in database technology

Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database

Rejecting Mathematical Realism while Accepting Interactive Realism

Indispensablists contend that accepting scientific realism while rejecting mathematical realism involves a double standard. I refute this contention by developing an enhanced version of scientific realism, which I call interactive realism. It holds that interactively successful theories are typically approximately true, and that the interactive unobservable entities posited by them are likely to exist. It is immune to the pessimistic induction while mathematical realism is susceptible to it

Modeling of Traceability Information System for Material Flow Control Data.

This paper focuses on data modeling for traceability of material/work flow in information layer of manufacturing control system. The model is able to trace all associated data throughout the product manufacturing from order to final product. Dynamic data processing of Quality and Purchase activities are considered in data modeling as well as Order and Operation base on lots particulars. The modeling consisted of four steps and integrated as one final model. Entity-Relationships Modeling as data modeling methodology is proposed. The model is reengineered with Toad Data Modeler software in physical modeling step. The developed model promises to handle fundamental issues of a traceability system effectively. It supports for customization and real-time control of material in flow in all levels of manufacturing processes. Through enhanced visibility and dynamic store/retrieval of data, all traceability usages and applications is responded. Designed solution is initially applicable as reference data model in identical lot-base traceability system

LCM and MCM: specification of a control system using dynamic logic and process algebra

LCM 3.0 is a specification language based on dynamic logic and process algebra, and can be used to specify systems of dynamic objects that communicate synchronously. LCM 3.0 was developed for the specification of object-oriented information systems, but contains sufficient facilities for the specification of control to apply it to the specification of control-intensive systems as well. In this paper, the results of such an application are reported. The paper concludes with a discussion of the need for theorem-proving support and of the extensions that would be needed to be able to specify real-time properties

A multi-INT semantic reasoning framework for intelligence analysis support

Lockheed Martin Corp. has funded research to generate a framework and methodology for developing semantic reasoning applications to support the discipline oflntelligence Analysis. This chapter outlines that framework, discusses how it may be used to advance the information sharing and integrated analytic needs of the Intelligence Community, and suggests a system I software architecture for such applications

Making AI Meaningful Again

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy