ECONOMICS

In this paper we consider the problem of the firm's optimal plans for factor usage under conditions of profit maximization. Two factors of capital and labor are considered in a dynamic choice system where labor is endowed with a capacity to learn on the job but capital is not.

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All the questions raised in the previous chapters fall under either the set of questions of ontological type or the set of questions of epistemological type. Most of the questions are of epistemological type. The theory of the knowledge square is a metatheory on epistemology and knowledge production. The point of entry of the metatheory is that the information input for knowledge production is defective due to fuzziness and incompleteness that create qualitative and quantitative inexactness for all sciences. Fuzziness is associated with qualitative disposition of information while incompleteness is associated with quantitative disposition of information about epistemic objects with neutrality of time. The point of departure is a search for an appropriate symbolism that will incorporate fuzziness and incompleteness of information with a further search for appropriate logic of operations that will allow exact equivalences to be abstracted from inexactness. All the debates in the last analysis are about what we claim to know about what there is in the ontological space and the process of knowing. In the epistemic process of knowing, the solution to the problem of exactness forces us to choose between two principles that must be taken as intuitive assumptions. They are the ontological principle of exactness and inexactness, on one hand, and the epistemological principle of exactness and inexactness on the other. The inter-relational structures between ontology and epistemology regarding exactness and inexactness are displayed in .0.

We have discussed the potential and possibility spaces and their relationships to the knowledge-production process as viewed from the theory of the knowledge square. These conceptual relationships were connected to the possible-world space, the explanatory science, prescriptive science and the defective knowledge structure. On the basis of the defective knowledge structure, the roles of explanatory theory and the corresponding explanatory rationality were projected. Similarly, the prescriptive theory and the corresponding prescriptive rationality were discussed .We shall now turn our attention to the probability space and show how it is connected to the possibility space and then to the universal object set in a backward regressive process of the knowledge-production process. The possibility space and its construct on the basis of the fuzzy paradigm to deal with the problem of possibilistic uncertainty provide us with the analytical structure of possibilistic reasoning under conditions of defective information structure that is constrained by quality and quantity deficiencies about epistemic elements with neutrality of time.

This essay is devoted to epistemics of Savage axioms and Ellsberg's paradox in decision-choice actions in relation to fuzzy optimal decision-choice rationality in the space of uncertainties. The uncertainty space is partitioned into non-fuzzy stochastic sub-space and fuzzy-stochastic sub-space. The Savage axioms are argued to emerge from the former which is vagueness-free while Ellsberg thought experiment leading to his paradox takes place in the latter that contains vagueness. The explanation of the rise of the paradox is shown to be the result of non-comparable decision-choice subspaces in which both of them work. The topologies of the two sub-spaces and the required mathematics and logic are considered in their epistemic forms. Criticisms are offered on some attempts to explain and resolve the paradox. The paradox, it is argued, cannot be logically resolved in the space in which the Savage axioms are created. It can not either be resolved in the fuzzy-stochastic space with classical paradigm under Aristotelian logic where all propositions are either true or false, it is argued. A resolution of the paradox as it is related to probability estimates is offered through fuzzy logic, mathematics and computational methods of optimal fuzzy decision-choice rationality. The essay is concluded with an epistemic reflection. .

In this essay, we present a logical framework for developing a comprehensive benefit information set for cost-benefit analysis where subjectivity and approximations are some important characteristics of the decision space. The concept of benefit, its components and their relationships to decision and choice are discussed. A benefit space equipped with a measure is presented. This is linked with a computational process through a technique of benefit identification matrices where the benefit space and the society are partitioned into real benefit characteristics and social classes respectively. From the elements in the benefit identification matrix, distributional weights of benefit intensities are computed by the logic of fuzzy decision and mathematical programming leading to a formula for computing total benefit. The method is argued to be compatible with direct assessment (contingent valuation method) and indirect assessment (market revealed approach).

The essay presents a theory of efficient prices for cost-benefit analysis in a fuzzy space. The approach proceeds by taking consumers' income, and producers' outputs and costs as given. The price preferences of consumers and producers are elicited and then ...

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The inventory control system of a firm operating under Marshallian market conditions is developed as part of an optimal production planning problem. The control process is analyzed through the differences between the paths of supply and demand prices that lead to variations in the ...

This chapter is devoted to presenting the development of the theory of fuzzy-stochastic entropy through the construct of hybrid measures of general uncertainty from the possibility and probability spaces. It builds the epistemic frame from the discussions on the differences and similarities of possibility and probability spaces and their relationships to the certainty-uncertainty and necessity-freedom dualities with relational continuum and unity within the quality-quantity duality. The necessity is related to necessary conditions of knowing while the freedom is related to sufficient conditions of knowing. The discussions are extended to the construction of the possibility set and the probability set with analytical tools of indicator functions under a specific paradigm of thought as applicable to information conditions within the exact-inexact duality.

Engineering Costs and Production Economics, 18 (1989) 105115 Elsevier Science Publishers BV, Amsterdam Printed in The Netherlands 105 THE INTERNAL STRUCTURE AND THE FUNCTIONAL REPRESENTATION OF CAPITALADJUSTMENT COSTS Kofi Kissi Dompere Department ...

In this essay, we present a logical framework for developing a comprehensive benefit information set for cost-benefit analysis where subjectivity and approximations are some important characteristics of the decision space. The concept of benefit, its components and their relationships to ...

In this essay, we present a logical framework for developing a comprehensive benefit information set for cost-benefit analysis where subjectivity and approximations are some important characteristics of the decision space. The concept of benefit, its components and their relationships to decision and choice are discussed. A benefit space equipped with a measure is presented. This is linked with a computational process through a technique of benefit identification matrices where the benefit space and the society are partitioned into real benefit characteristics and social classes respectively. From the elements in the benefit identification matrix, distributional weights of benefit intensities are computed by the logic of fuzzy decision and mathematical programming leading to a formula for computing total benefit. The method is argued to be compatible with direct assessment (contingent valuation method) and indirect assessment (market revealed approach).

In this essay, we present a logical framework for developing a comprehensive benefit information set for cost-benefit analysis where subjectivity and approximations are some important characteristics of the decision space. The concept of benefit, its components and their relationships to decision and choice are discussed. A benefit space equipped with a measure is presented. This is linked with a computational process through a technique of benefit identification matrices where the benefit space and the society are partitioned into real benefit characteristics and social classes respectively. From the elements in the benefit identification matrix, distributional weights of benefit intensities are computed by the logic of fuzzy decision and mathematical programming leading to a formula for computing total benefit. The method is argued to be compatible with direct assessment (contingent valuation method) and indirect assessment (market revealed approach).

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This essay presents structural categories of theories of optimization. It begins with the classical system leading to the establishment of an entry point of fuzzy optimization from the logic of the classical optimization. Four categories of optimization problems are identified. They are exact (non-fuzzy) and nonstochastic, exact (non-fuzzy) and stochastic categories that are associated with classical laws of thought and mathematics. The other categories are fuzzy and non-stochastic, and fuzzy-stochastic problems that are associated with fuzzy laws of thought and fuzzy mathematics. The canonical structures of the problems and their solutions are presented. From these structures, similarities and differences in the problem structures and corresponding solutions are abstracted and discussed. The similarities and differences in the problem-solution structures of different categories are attributed to properties of exactness and completeness about information-knowledge structures in which the optimization problems are formulated and solved. The assumed degrees of exactness and completeness establish defective informationknowledge structure that generates uncertainties and produces inter-category differences in the optimization problem. The specific differences of intra-category algorithms are attributed to differences in the assumed functional relationships of the variables that establish the objective and constraint sets. The essay is concluded with taxonomy of solution structures and discussions on future research directions.

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