Criticism is the habitus of the contemplative intellect, whereby we try to recognize with probability the genuine quality of a l- erary work by using appropriate aids and rules. In so doing, c- tain general and particular points must be considered. The art of interpretation or hermeneutics is the habitus of the contemplative intellect of probing into the sense of somewhat special text by using logical rules and suitable means. Note : Hermeneutics differs from criticism as the part does from the whole. Antonius Gvilielmus Amo Afer (1727) There is no such thing as absolute truth. At best it is a subj- tive criterion, but one based upon valuation. Unfortunately, too many people place their fate in the hands of subjective without properly evaluating it. Arnold A. Kaufmann and Madan M. Gupta The development of cost benefit analysis and the theory of fuzzy decision was divided into two inter-dependent structures of identification and measurement theory on one hand and fuzzy value theory one the other. Each of them has sub-theories that constitute a complete logical system.

The genus of definitions for the theoretical sciences is (the province of) the habitus of the intellective intention, for the practical sciences, however, that of the effective intention; the objects and ends constitute the specific differ ence There is nothing in the intellect that has not already been in the senses, that is, in the sensory organs, that has not already been in sensible things from which are distinguished things not perceptible to the senses. Nothing can be of the mind, sensation and the thing inferred therefrom except the operation itself. Real learning is cognition of things in themselves. It thus has the basis of its certainty in the known thing. This is established in two ways: by demon stration in the case of contemplative things, and by induction in the case of things perceptible to the senses. In contrast with real learning there is pos sible, probable and fictive learning. Antonius Gvilielmus Amo Afer (1827) This research has been long in the making. Its conception began in my last years in the doctoral program at Temple University, Philadelphia, Pa. It was simultaneously conceived with my two books on the Neo Keynesian Theory of Optimal aggregate investment and output dynamics [201] [202] as well as reflections on the methodology of decision-choice rationality and development economics [440] [441]. Economic theories and social policies were viewed to have, among other things, one impor tant thing in common in that they relate to decision making under different.

This monograph is devoted to the identification and measurement theory of costs and benefits in a fuzzy information environment. The process of cost-benefit analysis is presented, requiring the development of real cost-benefit databases and the construction of cost-benefit criterion. These steps are accomplished with various theoretical constructs that provide sets of self-contained algorithms for application. This book integrates cost-benefit analysis, theory of fuzzy decisions and social decisions into unified decision algorithms accessible to practitioners, researchers, and graduate students. It features the essentials of fuzzy mathematics and algorithms in a comprehensive way, exposing a multi-disciplinary approach for the development of cost-benefit decision making in the framework of fuzziness and soft computing.

This volume presents an analysis of the problems and solutions of the market mockery of the democratic collective decision-choice system with imperfect information structure composed of defective and deceptive structures using methods of fuzzy rationality. The book is devoted to the political economy of rent-seeking, rent-protection and rent-harvesting to enhance profits under democratic collective decision-choice systems. The toolbox used in the monograph consists of methods of fuzzy decision, approximate reasoning, negotiation games and fuzzy mathematics. The monograph further discusses the rent-seeking phenomenon in the Schumpeterian and Marxian political economies where the rent-seeking activities transform the qualitative character of the general capitalism into oligarchic socialism and making the democratic collective decision-choice system as an ideology rather than social calculus for resolving conflicts in preferences in the collective decision-choice space without violence.

This 41st volume covers Application of Bayesan Belief Networks to Highway Construction to Virtual Reality Software and Technology.

We do not perceive the present as it is and in totality, nor do we infer the future from the present with any high degree of dependability, nor yet do we accurately know the consequences of our own actions. In addition, there is a fourth source of error to be taken into account, for we do not execute actions in the precise form in which they are imaged and willed. Frank H. Knight [R4.34, p. 202] The “degree” of certainty of confidence felt in the conclusion after it is reached cannot be ignored, for it is of the greatest practical signi- cance. The action which follows upon an opinion depends as much upon the amount of confidence in that opinion as it does upon fav- ableness of the opinion itself. The ultimate logic, or psychology, of these deliberations is obscure, a part of the scientifically unfathomable mystery of life and mind. Frank H. Knight [R4.34, p. 226-227] With some inaccuracy, description of uncertain consequences can be classified into two categories, those which use exclusively the language of probability distributions and those which call for some other principle, either to replace or supplement.

This monograph is a treatment on optimal fuzzy rationality as an enveloping of decision-choice rationalities where limited information, vagueness, ambiguities and inexactness are essential characteristics of our knowledge structure and reasoning processes. The volume is devoted to a unified system of epistemic models and theories of decision-choice behavior under total uncertainties composed of fuzzy and stochastic types. The unified epistemic analysis of decision-choice models and theories begins with the question of how best to integrate vagueness, ambiguities, limited information, subjectivity and approximation into the decision-choice process. The answer to the question leads to the shifting of the classical paradigm of reasoning to fuzzy paradigm. This is followed by discussions and establishment of the epistemic foundations of fuzzy mathematics where the nature and role of information and knowledge are explicated and represented. The epistemic foundation allows total uncertainties that constrain decision-choice activities, knowledge enterprise, logic and mathematical structures as our cognitive instruments to be discussed in reference to the phenomena of fuzzification, defuzzification and fuzzy logic. The discussions on these phenomena lead us to analyze and present models and theories on decision-choice rationality and the needed mathematics for problem formulation, reasoning and computations. The epistemic structures of two number systems made up of classical numbers and fuzzy numbers are discussed in relation to their differences, similarities and relative relevance to decision-choice rationality. The properties of the two number systems lead to the epistemic analysis of two mathematical systems that allow the partition of the mathematical space in support of decision-choice space of knowledge and non-knowledge production into four cognitively separate but interdependent cohorts whose properties are analyzed by the methods and techniques of category theory. The four cohorts are identified as non-fuzzy and non-stochastic, non-fuzzy and stochastic both of which belong to the classical paradigm and classical mathematical space; and fuzzy and non-stochastic, and fuzzy and stochastic cohorts both of which belong to the fuzzy paradigm and fuzzy mathematical space. The differences in the epistemic foundations of the two mathematical systems are discussed. The discussion leads to the establishment of the need for fuzzy mathematics and computing as a new system of reasoning in both exact and inexact sciences. The mathematical structures of the cohorts are imposed on the decision-choice process to allow a grouping of decision-choice models and theories. The corresponding classes of decision-choice theories have the same characteristics as the logico-mathematical cohorts relative to the assumed information-knowledge structures. The four groupings of models and theories on decision-choice activities are then classified as: 1) non-fuzzy and non-stochastic class with exact and full information-knowledge structure (no uncertainty), 2) non-fuzzy and stochastic class with exact and limited information-knowledge structure (stochastic uncertainty), 3) fuzzy and non-stochastic class with full and fuzzy information-knowledge structure (fuzzy uncertainty) and 4) Fuzzy and stochastic class with fuzzy and limited information-knowledge structure (fuzzy and stochastic uncertainties). All these different classes of decision choice problems have their corresponding rationalities which are fully discussed to present a unified logical system of theories on decision-choice process. The volume is concluded with epistemic discussions on the nature of contradictions and paradoxes viewed as logical decision-choice problems in the classical paradigm, and how these contradictions and paradoxes may be resolved through fuzzy paradigm and the methods and techniques of optimal fuzzy decision-choice rationality. The logical problem of sorites paradox with its resolution is given as an example. Interested audience includes those working in the areas of economies, decision-choice theories, philosophy of sciences, epistemology, mathematics, computer science, engineering, cognitive psychology, fuzzy mathematics and mathematics of fuzzy-stochastic processes.

The genus of definitions for the theoretical sciences is (the province of) the habitus of the intellective intention, for the practical sciences, however, that of the effective intention; the objects and ends constitute the specific differ ence There is nothing in the intellect that has not already been in the senses, that is, in the sensory organs, that has not already been in sensible things from which are distinguished things not perceptible to the senses. Nothing can be of the mind, sensation and the thing inferred therefrom except the operation itself. Real learning is cognition of things in themselves. It thus has the basis of its certainty in the known thing. This is established in two ways: by demon stration in the case of contemplative things, and by induction in the case of things perceptible to the senses. In contrast with real learning there is pos sible, probable and fictive learning. Antonius Gvilielmus Amo Afer (1827) This research has been long in the making. Its conception began in my last years in the doctoral program at Temple University, Philadelphia, Pa. It was simultaneously conceived with my two books on the Neo Keynesian Theory of Optimal aggregate investment and output dynamics [201] [202] as well as reflections on the methodology of decision-choice rationality and development economics [440] [441]. Economic theories and social policies were viewed to have, among other things, one impor tant thing in common in that they relate to decision making under different.