• The Korean Journal of Financial Management
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• v.2 no.1
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• pp.55-75
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• 1986

This article is to analyse the economic efficiency of capital market, which plays a role of resource allocation in terms of financial claims such as stock and bond. It provides various contributions to the welfare theoretical aspects of modern capital market theory. The key feature that distinguishes the theory described here from traditional welfare theory is the presence of uncertainty. Securities has time dimensions and the state and outcome of the future are really uncertain. This problem resulting from this uncertainty can be solved by complete market, but it has a weak power to explain real stock market. Capital Market is faced with the uncertainity because it is a kind of incomplete market. Individuals and firms in capital market made their consumption-investment decision by their own criteria, i. e. the maximization of expected utility form intertemporal consumption and the maximization of the market value of firm. We noted that allocative decisions that had to be made in the economy could be naturally subdivided into two groups. One set of decisions concerned the allocation of first-period resources among consumption $C_i$, investment in risky firms $I_j$, and riskless investment M. The other decisions concern the distribution among individuals of income available in the second period $Y_i(\theta)$. Corresponing to this grouping, the theoretical analysis of efficiency has also been dichotomized. The optimality of the distribution of output in the second period is distributive efficiency" and the optimality of the allocation of first-period resources is 'the efficiency of investment'. We have found in the distributive efficiency that the conditions for attainability is the same as the conditions for market optimality. The necessary and sufficient conditions for attainability or market optimality is that (1) all utility functions are such that -$\frac{{U_i}^'(Y_i)}{{U_i}^"(Y_i)}={\mu}_i+{\lambda}Y_i$-linear risk tolerance function where the coefficients ${\mu}_i$ and $\lambda$ are independent of $Y_i$, and (2) there are homogeneous expectations, i. e. ${\Large f}_i(\theta)={\Large f}(\theta)$ for every i. On the other hand, the efficiency of investment has disagreement about optimal investment level. The investment level for market rule will not generally lead to Pareto-optimal allocation of investment. This suboptimality is caused by (1)the difference of Diamond's decomposable production function and mean-variance valuation model and (2) the selection of exelusive investment or competitive investment. In conclusion, this article has made an analysis of conditions and processes of Pareto-optimal allocation of resources in capital marker and tried to connect with significant issues in modern finance.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.393-405
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• 2000

Box and Draper( 19(5) listed some properties of a design that should be considered in design selection. But it is impossible that one design criterion from optimal experimental design theory reflects many potential objectives of an experiment, because the theory was originally based on the underlying model and its strict assumption about the error structure. Therefore, when it is neces::;ary to implement multi-objective experimental design. it is common practice to balance out the several optimal design criteria so that each design criterion involved benefits in terms of its relative "high" efficiency. In this study, we proposed several composite design criteria taking the case of heteroscedastic model. WVhen the heteroscedasticity is present in the model. the well known equivalence theorem between 1)- and C-optimality no longer exists and furthermore their design characteristics are sometimes drastically different. We introduced three different design criteria for this purpose: constrained design, combined design, and minimax design criteria. While the first two methods do reflect the prior belief of experimenter, the last one does not take it into account. which is sometimes desirable. Also we extended this method to the case when there are uncertainties concerning the error structure in the model. A simple algorithm and concluslOn follow.On follow.

• Hwankyungkyoyuk
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• v.13 no.2
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• pp.38-50
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• 2000

We are evidently Home Economicus, egoistic rational utility maximiger, and all the capitalism economic situation make us adapt to such life, and recognize that it is rational to act like that. This can be demonstrated in Prisoner′s Dilemma(PD) which always select the non-cooperative choice for free rider in rational selection process of public goods. This paper notice the "what is problem\ulcorner"The problem is not in free rider itself but in free rider egoism. The practical behavior of free rider egoism can be explained by way of Prisoner′s Dilemma. In PD situation, the prisoner makes a rational choice, non-cooperative alternative, but he doesn′arrive at preto-optimality. It is dilemma. Why can′t he arrive \ulcorner Because he is isolated from other prisoner. So we call it prisoner′s dilemma. The PD situation can be compared with our real economic life, which, we think, have kept by rational choice of the public goods. We actually have made our life as an individual one although we organized communities of capitalism. Of course, we know each others as members of same society, but each individual being can′t secure the belief, which has composed basis of community. So, it is very similar and common between PD situation and our real economic life in the production of public goods. We conclude that this non-cooperative process of PD situation can be utilized as instrument of EE. So this non-cooperative process can show us the effectiveness of EE as follows. \circled1 Game situation life PD can be used as good instrument for explaining the rational selection dilemma(error) to Homo-Economicus, the rational agent, with the optimal and rational language. \circled2 We can show that the selection result is dilemma, not arrive pareto - optimality. \circled3 The dilemma can be resolved with accomplishing the good communal life based on the belief, not on the isolation.

• Genomics & Informatics
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• v.16 no.4
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• pp.28.1-28.6
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• 2018

Due to the increasing interest in synonymous codons, several codon bias-related terms were introduced. As one measure of them, the tRNA adaptation index (tAI) was invented about a decade ago. The tAI is a measure of translational efficiency for a gene and is calculated based on the abundance of intracellular tRNA and the binding strength between a codon and a tRNA. The index has been widely used in various fields of molecular evolution, genetics, and pharmacology. Afterwards, an improved version of the index, named specific tRNA adaptation index (stAI), was developed by adapting tRNA copy numbers in species. Although a subsequently developed webserver (stAIcalc) provided tools that calculated stAI values, it was not available to access pre-calculated values. In addition to about 100 species in stAIcalc, we calculated stAI values for whole coding sequences in 148 species. To enable easy access to this index, we constructed a novel web database, named STADIUM (Species-specific tRNA adaptive index compendium). STADIUM provides not only the stAI value of each gene but also statistics based on pathway-based classification. The database is expected to help researchers who have interests in codon optimality and the role of synonymous codons. STADIUM is freely available at http://stadium.pmrc.re.kr.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.467-472
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• 1989

In this study, a design method to obtain a robust optimal regulator for linear multivariable system is presented. When assigning eigenvalues of linear multivatiable system , the feedback gain is not unique. So we can assign robustness index to optimality so that we can fully use the remained degree of freedom.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.4 no.2
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• pp.59-66
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• 2000

We consider a multiobjective fractional variational programming problem (P) involving vector valued functions. By using the concept of proper efficiency, a relationship between the primal problem and parametric multiobjective variational problem is indicated.

• Structural Engineering and Mechanics
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• v.83 no.4
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• pp.537-549
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• 2022

In this paper, a novel multi-feature model predictive control (MPC) framework with real-time and adaptive performances is proposed for intelligent structural control in which some drawbacks of the algorithm including, complex control rule and non-optimality, are alleviated. Hence, Linear Programming (LP) is utilized to simplify the resulted control rule. Afterward, the Whale Optimization Algorithm (WOA) is applied to the optimal and adaptive tuning of the LP weights independently at each time step. The stochastic control rule is also achieved using Kalman Filter (KF) to handle noisy measurements. The Extreme Learning Machine (ELM) is then adopted to develop a data-driven and real-time control algorithm. The efficiency of the developed algorithm is then demonstrated by numerical simulation of a twenty-story high-rise benchmark building subjected to earthquake excitations. The competency of the proposed method is proven from the aspects of optimality, stochasticity, and adaptivity compared to the KF-based MPC (KMPC) and constrained MPC (CMPC) algorithms in vibration suppression of building structures. The average value for performance indices in the near-field and far-field (El earthquakes demonstrates a reduction up to 38.3% and 32.5% compared with KMPC and CMPC, respectively.

• Earthquakes and Structures
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• v.13 no.3
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• pp.221-230
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• 2017

One of the important phases of probabilistic performance-based methodology is establishing appropriate probabilistic seismic demand models (PSDMs). These demand models relate ground motion intensity measures (IMs) to demand measures (DMs). The objective of this paper is selection of the optimal IMs in probabilistic seismic demand analysis (PSDA) of the RC high-rise buildings. In selection process features such as: efficiency, practically, proficiency and sufficiency are considered. RC high-rise buildings with core wall structural system are selected as a case study building class with the three characteristic heights: 20-storey, 30-storey and 40-storey. In order to determine the most optimal IMs, 720 nonlinear time-history analyses are conducted for 60 ground motion records with a wide range of magnitudes and distances to source, and for various soil types, thus taking into account uncertainties during ground motion selection. The non-linear 3D models of the case study buildings are constructed. A detailed regression analysis and statistical processing of results are performed and appropriate PSDMs for the RC high-rise building are derived. Analyzing a large number of results it are adopted conclusions on the optimality of individual ground motion IMs for the RC high-rise building.

• Journal of Communications and Networks
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• v.17 no.4
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• pp.440-448
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• 2015

In this paper, we study the problem of selfish behavior of secondary users (SUs) based on cognitive radio (CR) with the presence of primary users (PUs). SUs are assumed to contend on a channel using the carrier sense multiple access with collision avoidance (CSMA/CA) and PUs do not consider transmission of SUs, where CSMA/CA protocols rely on the random deference of packets. SUs are vulnerable to selfish attacks by which selfish users could pick short random deference to obtain a larger share of the available bandwidth at the expense of other SUs. In this paper, game theory is used to study the systematic cheating of SUs in the presence of PUs in multichannel CR networks. We study two cases: A single cheater and multiple cheaters acting without any restraint. We identify the Pareto-optimal point of operation of a network with multiple cheaters and also derive the Nash equilibrium of the network. We use cooperative game theory to drive the Pareto optimality of selfish SUs without interfering with the activity of PUs. We show the influence of the activity of PUs in the equilibrium of the whole network.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.4
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• pp.399-406
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• 2020

In this paper, we propose a time-varying proportional navigation guidance law that determines the proportional navigation gain in real-time according to the operating situation. When intercepting a target, an unidentified evasion strategy causes a loss of optimality. To compensate for this problem, proper proportional navigation gain is derived at every time step by solving an optimal control problem with the inferred evader's strategy. Recently, deep reinforcement learning algorithms are introduced to deal with complex optimal control problem efficiently. We adapt the actor-critic method to build a proportional navigation gain network and the network is trained by the Proximal Policy Optimization(PPO) algorithm to learn an evasion strategy of the target. Numerical experiments show the effectiveness and optimality of the proposed method.