• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.915-930
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• 2011

There are a variety of methods to model game results and many methods exist for the case of paired comparison data. Among them, the Bradley-Terry model is the most widely used to derive a latent preference scale from paired comparison data. It has been applied in a variety of fields in psychology and related disciplines. We applied this model to the data of Korean Baseball League. It shows that the loglinear Bradley-Terry model of defensive rate and save is optimal in terms of AIC. Also some categorical characteristics, such as east team and west team, existence of golden glove winning players, team(s) with seasonal pitching leader, and team(s) with home advantage, influenced the game result significantly. As a result, the suggested models can be further utilized to predict future game results.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.3-8
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• 2013

In this paper, we present an energy-efficient base station operation with base station sharing in wireless cellular networks. Firstly, cost functions are modeled related with the amount of energy usage and traffic load. We use game theory and formulate an energy bill game, where the players are the service operators which are operating base stations and their strategies are the bill of their base station energy consumption and hand-over traffic load to the others service operators. We show that the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy bill game. Simulation results confirm that the proposed approach can reduce the energy bill of the service operator, and show the relationship between the energy cost saving and various parameters.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.375-381
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• 2005

Usually genetic algorithms are used to design optimal system. However the performance of the algorithm is determined by the fitness function and the system environment. It is expected that a co-evolutionary algorithm, two populations are constantly interact and co-evolve, is one of the solution to overcome these problems. In this paper we propose three types of co-evolutionary algorithm to solve GA-Hard problem. The first model is a competitive co-evolutionary algorithm that solution and environment are competitively co-evolve. This model can prevent the solution from falling in local optima because the environment are also evolve according to the evolution of the solution. The second algorithm is schema co-evolutionary algorithm that has host population and parasite (schema) population. Schema population supply good schema to host population in this algorithm. The third is game model-based co-evolutionary algorithm that two populations are co-evolve through game. Each algorithm is applied to visual servoing, robot navigation, and multi-objective optimization problem to verify the effectiveness of the proposed algorithms.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.99-106
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• 2004

This paper presents some comparisons between Cournot-Nash and Stackelberg game in bi-level program, composed of both upper level program and lower level one. The upper level can be formulated to optimize a specific objective function, while the lower formulated to express travelers' behavior patterns corresponding to the design parameter of upper level problem. This kind of hi-level program is to determine a design parameter, which leads the road network to an optimal state. Bi-level program includes traffic signal control, traffic information provision, congestion charge and new transportation mode introduction as well as road expansion. From the view point of game theory, many existing algorithms for bi-level program such as IOA (Iterative Optimization Assignment) or IEA (Iterative Estimation Assignment) belong to Cournot-Nash game. But sensitivity-based algorithms belongs to Stackelberg one because they consider the reaction of the lower level program. These two game models would be compared by using an example network and show some results that there is no superiority between the models in deterministic case, but in stochastic case Stackelberg approach is better than that of Cournot-Nash one as we expect.

• Asia Marketing Journal
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• v.16 no.1
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• pp.35-69
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• 2014

The impact of competition on pricing has been studied in the context of counterfactual merger analyses where expected optimal prices in a hypothetical monopoly are compared with observed prices in an oligopolistic market. Such analyses would typically assume static decision making by consumers and firms and thus have been applied mostly to data obtained from consumer packed goods such as cereal and soft drinks. However such static modeling approach is not suitable when decision makers are forward looking. When it comes to the markets for durable products with indirect network effects, consumer purchase decisions and firm pricing decisions are inherently dynamic as they take into account future states when making purchase and pricing decisions. Researchers need to take into account the dynamic aspects of decision making both in the consumer side and in the supplier side for such markets. Firms in a two-sided market typically subsidize one side of the market to exploit the indirect network effect. Such pricing behaviors would be more prevalent in competitive markets where firms would try to win over the battle for standard. While such qualitative expectation on the relationship between pricing behaviors and competitive structures could be easily formed, little empirical studies have measured the extent to which the distinct pricing structure in two-sided markets depends on the competitive structure of the market. This paper develops an empirical model to measure the impact of competition on optimal pricing of durable products under indirect network effects. In order to measure the impact of exogenously determined competition among firms on pricing, we compare the equilibrium prices in the observed oligopoly market to those in a hypothetical monopoly market. In computing the equilibrium prices, we account for the forward looking behaviors of consumers and supplier. We first estimate a demand function that accounts for consumers' forward-looking behaviors and indirect network effects. And then, for the supply side, the pricing equation is obtained as an outcome of the Markov Perfect Nash Equilibrium in pricing. In doing so, we utilize numerical dynamic programming techniques. We apply our model to a data set obtained from the U.S. video game console market. The video game console market is considered a prototypical case of two-sided markets in which the platform typically subsidizes one side of market to expand the installed base anticipating larger revenues in the other side of market resulting from the expanded installed base. The data consist of monthly observations of price, hardware unit sales and the number of compatible software titles for Sony PlayStation and Nintendo 64 from September 1996 to August 2002. Sony PlayStation was released to the market a year before Nintendo 64 was launched. We compute the expected equilibrium price path for Nintendo 64 and Playstation for both oligopoly and for monopoly. Our analysis reveals that the price level differs significantly between two competition structures. The merged monopoly is expected to set prices higher by 14.8% for Sony PlayStation and 21.8% for Nintendo 64 on average than the independent firms in an oligopoly would do. And such removal of competition would result in a reduction in consumer value by 43.1%. Higher prices are expected for the hypothetical monopoly because the merged firm does not need to engage in the battle for industry standard. This result is attributed to the distinct property of a two-sided market that competing firms tend to set low prices particularly at the initial period to attract consumers at the introductory stage and to reinforce their own networks and eventually finally to dominate the market.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.2
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• pp.1-13
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• 2009

In this paper, we develop theoretical game models to determine the level of government subsidies for banks to provide policy loans to Innovative SMEs(small and medium sized enterprises) through banks, which otherwise would not finance them for the sake of their own profitability. For this, we compare net cash flows of each bank using different strategies against high risk innovative SMEs. A bank can decide whether to provide them loans or not In each period. Following Kim(2003)'s Infinite horizon model on the soft budget constraint, we introduce a situation in which banks compete against each other for higher net long-term payoffs from their loans to innovative SMEs and non-innovative SMEs. From the models, we show that competition among banks in general leads to a tighter decision against innovative SMEs, as a Nash equilibrium. It is not because the government bank is simply loose in providing loans, but because competition among commercial banks for fewer riskier borrowers results in tighter loan decisions against innovative SMEs. Thus, the competitive market for policy loans to innovative SMEs fails to reach the socially optimal level of loans for innovative SMMs. Commercial banks in the competitive market may require additional supports from the government to make up for the differences in their payoffs to support innovative SMEs, possibly much riskier due to moral hazards and poor discounted cash flows. The monopolistic government bank might also request such supports from the government to fund otherwise unqualified SMEs. We calculate an optimal level of governmental support for banks to guarantee funding such high-risk innovative SMEs over periods without deviating from their optimal Nash equilibrium policies.

• Journal of Advanced Navigation Technology
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• v.11 no.4
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• pp.437-446
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• 2007

In this paper, we numerically induced the optimal values of optical phase conjugator (OPC) position and dispersion coefficients of fiber sections, which can improve the bit error rate (BER) and design the adaptive WDM transmission system, as a function of the extinction ratio (ER) of 10 dB and 20 dB in $16{\times}40$ Gb/s WDM transmission system. It is confirmed that these optimal parameter values for effectively compensating overall WDM channels are dependence on the extinction ratio of signals as well as modulation format, transmitted channel numbers, which were investigated in previous researches. It is also confirmed that ER of 20 dB has the advantage of designing flexible WDM systems using optimal parameters than ER of 10 dB.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1171-1180
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• 2019

Emotion is a psycho-physiological process that plays an important role in human interactions. Affective computing is centered on the development of human-aware artificial intelligence that can understand and regulate emotions. This field of study is also critical as mental diseases such as depression, autism, attention deficit hyperactivity disorder, and game addiction are associated with emotion. Despite the efforts in emotions recognition and emotion detection from nonstationary, detecting emotions from abnormal EEG signals requires sophisticated learning algorithms because they require a high level of abstraction. In this paper, we investigated LSTM hyperparameters for an optimal emotion EEG classification. Results of several experiments are hereby presented. From the results, optimal LSTM hyperparameter configuration was achieved.

• Journal of Game and Entertainment
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• v.2 no.1
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• pp.35-44
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• 2006

Objective of this study is to develop a 3D car navigation system that shows the driving direction to a destination through real-time 3-D panoramic views of the route. For the purpose, a new searching process was established to find the optimal driving direction based on the driver's current location and the real-time traffic situation and the TIP (tour into the picture) method was extended to implement a wide virtual environment. A virtual environment was built up by applying the extended TIP method to the panoramic images taken at a constant distance from a real road, and then, displayed 3-D navigation as clear as the real images. The car navigation system developed in this study provides the optimal driving direction and real-time traffic situation using 2-D navigation module and 3D navigation module.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.3
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• pp.463-468
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• 2016

The overall performance of multiuser systems significantly depends on how effectively and fairly manage resources shared by them. The efficient resource management strategies are even more important for multimedia users since multimedia data is delay-sensitive and massive. In this paper, we focus on resource allocation based on a game-theoretic approach, referred to as Nash bargaining solution (NBS), to provide a quality of service (QoS) guarantee for each user. While the NBS has been known as a fair and optimal resource management strategy, it is challenging to find the NBS efficiently due to the computationally-intensive task. In order to reduce the computation requirements for NBS, we propose an approach that requires significantly low complexity even when networks consist of a large number of users and a large amount of resources. The proposed approach linearizes utility functions of each user and formulates the problem of finding NBS as a convex optimization, leading to nearly-optimal solution with significantly reduced computation complexity. Simulation results confirm the effectiveness of the proposed approach.