• Korean Management Science Review
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• v.17 no.3
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• pp.49-60
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• 2000

The proliferation of electronic commerce(EC) has led manufactureres to consider Internet based marketing as a salient candidate for strategic diversification of marketing channel. In this case, each manufacturer can build its own Web store or rent an existing special EC store. Such decision making of ‘build’ or ‘rent’ can be analyzed by a game model which derives the Nash solutions for price and profit considering degree of competition with other competitive manufacturers. In this paper, to overcome the drawbacks of the traditional linear demand function, we first propose a new linear demand function which incorporates not only the price difference between competitive products but the awareness of Web stores perceived by the consumers, then design the game models to analyze the characteristics of three typical types of marketing channel in duopoly market. Based on the Nash solutions of the game models, we analyzed the effect of the degree of competition and Web awareness in selecting the optimal marketing channel.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.4
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• pp.71-80
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• 1997

In this paper, we consider with a market pioneering game among symmetric firms in highly competitive situation. To describe the puzzling situation of timing competition, we construct a dynamic game model and explore the equilibrium solution. As a result, we find a subgame perfect mixed strategy Nash equilibrium conceptually defined by 't$_{0}$ + .elsilon. equilibrium'. Our major finding s include : i) market entry will be occurred in sequential manner even though the condition of each firm is symmetric ii) the optimal timing of market pioneering will be advanced until almost all of the monopolist's profit is dissipated, iii) as the market position of the pioneer is stronger, the timings of the pioneer and the follower are separated, iv) and as the slope of the profit flow is steeper, the entry timing of the two players will be pooled together.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.231-238
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• 2003

This paper presents a virtual grouping method used in multi-user network games, which reduces a response time and losses of response data. The proposed method divides each group into virtual groups and transmits data in them after dividing an overall map on a game into several fixed regions and grouping them. And this paper derives the optimal number of groups minimizing a given cost function. The proposed method if shown to be useful by comparing with a general grouping method.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.403-405
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• 2000

This paper presents a N-player game theory application for analyzing power transactions in a deregulated energy marketplace such as PoolCo, where, participants, especially, generating entities, maximize their net profits through optimal bidding strategies (i.e., bidding prices and bidding generations). In this paper, the electricity market for power transactions is modeled as a non-cooperative. N-player game with complete information, where the solution is determined in a continuous strategy domain having recourse to the Nash equilibrium idea.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.5
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• pp.161-168
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• 2020

Predicting outcome of the sports enables teams to establish their strategy by analyzing variables that affect overall game flow and wins and losses. Many studies have been conducted on the prediction of the outcome of sports events through statistical techniques and machine learning techniques. Predictive performance is the most important in a game prediction model. However, statistical and machine learning models show different optimal performance depending on the characteristics of the data used for learning. In this paper, we propose a new ensemble model to predict English Premier League soccer games using statistical models and the machine learning models which showed good performance in predicting the results of the soccer games and this model is possible to select a model that performs best when predicting the data even if the data are different. The proposed ensemble model predicts game results by learning the final prediction model with the game prediction results of each single model and the actual game results. Experimental results for the proposed model show higher performance than the single models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4748-4765
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• 2016

We consider a virtualized cognitive radio (CR) network, where multiple virtual network operators (VNOs) who own different virtual cognitive base stations (VCBSs) share the same physical CBS (PCBS) which is owned by an infrastructure provider (InP), sharing the spectrum with the primary user (PU). The uplink scenario is considered where the secondary users (SUs) transmit to the VCBSs. The PU is protected by constraining the interference power from the SUs. Such constraint is applied by the InP through pricing the interference. A Stackelberg game is formulated to jointly maximize the revenue of the InP and the individual utilities of the VNOs, and then the Stackelberg equilibrium is investigated. Specifically, the optimal interference price and channel allocation for the VNOs to maximize the revenue of the InP and the optimal power allocation for the SUs to maximize the individual utilities of the VNOs are derived. In addition, a low‐complexity ±‐optimal solution is also proposed for obtaining the interference price and channel allocation for the VNOs. Simulations are provided to verify the proposed strategies. It is shown that the proposed strategies are effective in resource allocation and the ±‐optimal strategy achieves practically the same performance as the optimal strategy can achieve. It is also shown that the InP will not benefit from a large interference power limit, and selecting VNOs with higher unit rate utility gain to share the resources of the InP is beneficial to both the InP and the VNOs.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.1
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• pp.1-9
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• 1988

The purpose of this paper is study the sensitivity analysis of matrix game. Teh sensitivity analysis of matrix is classified into two types. Type one is to find the characteristic region of an element of the pay off matrix in which the value of the current optimal strategy remains as an optimum. Type two is to find that in which the basis of the current optimal strategy does not change. This paper shows the characteristic regions of basic and nonbasic strategies. Further it is found that the characteristic regions of type one and two are same in the case that the element is that of at least one player's nonbasic strategy.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.468-471
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• 2003

The A＊ algorithm is usually used in game programming, mainly because it is fast in finding a optimal path to goal. In this paper. This algorithm was utilized for path finding, HIMM(Histogramic In-Motion Mapping) method is used in map-building. Map is updated continuously with range data sampled by PSD sensors From the map, A＊ algorithm finds a optimal path and sends subsequently the most suitable point to the Eyebot. A＊ algorithm has been tested on the Eyebot in various unknown maps of unknown and proved to work well. It could escape the local minimum, also.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.60-62
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• 2001

This paper shows the optimal bidding strategy determination method using Nash equilibrium concept by defining bidding strategy vector. This vector is 2-dimension vector whose components are generation amount and generation cost. Thereby we are able to make all possible strategies and their's payoff table. And then we erase dominated strategies one by one so that we obtain Nash equilibrium, the optimal bidding strategy of generation bidding game.

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.1
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• pp.1-11
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• 2011

This article examines the impact of electronic commerce on environmental welfare. In particular, we analyze a game model of price competition between offline and online firms when consumption taxes are imposed on both offline and online transactions that produce environmental pollution. We investigate the properties of optimal taxation between offline and online markets and demonstrate that there is an optimal difference between the two taxes, depending upon not only the transaction cost between offline and online consumption, but also the environmental damage cost. We also investigate the effect of tax-free online transactions on tax revenues, and the financial feasibility of the optimal taxation.