• Title/Summary/Keyword: Pricing Agent

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Decision Rules of Intelligent Agents for Purchase Pricing Decision (거래가격 결정을 위한 에이전트의 의사결정규칙에 대한 연구)

  • Chu Seok-Chin
    • The Journal of Information Systems
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    • v.14 no.2
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    • pp.55-74
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    • 2005
  • In order to purchase a product cheaper, a lot of customers have been trying to search one or more marketplaces. Ever since the commercial use of the Internet, several types of marketplaces have been operating successfully on the Internet. Some of them are online shopping malls, auction markets, and group-buying markets. They have the price settlement mechanisms of their own. Online shopping malls where many stores are located support a customer to purchase the product that matches his/her requests such as price, function, design, and so forth. In online auction market, a customer can buy the product by making bids sequentially and competitively until a final price is reached. In online group-buying market, a customer can purchase the product by aggregating the orders from several buyers so that cheaper prices can be negotiated. The cheaper customers could purchase the same product item, the more satisfied they would be. However, it is very difficult for the customer to determine the marketplace to purchase, considering different kinds of marketplaces at the same time. Even though the purchasing price is cheapest in one marketplace, it is very difficult for customers to convince it the cheapest for all marketplaces. Therefore, rules and methods have been developed for purchase decision making in multiple marketplaces to reach the optimal purchase decision as a whole. They can maximize customer's utility and resolve the conflicts with other marketplaces through multi-agent negotiation.

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Auction Prices Generation Agent Using Case-base Reasoning (사례 기반 추론에 의한 경매 가격 생성 에이전트)

  • Ko, Min-Jung;Lee, Yong-Kyu
    • The Journal of Society for e-Business Studies
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    • v.11 no.2
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    • pp.31-48
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    • 2006
  • Commercial internet auction systems have been successfully used recently. In those systems, because auction prices of auction items are given by sellers only, the success bid rate can be decreased due to the large difference between the reserve price and the normal price. In this paper, we propose an agent that generates auction prices to sellers based on past auction data and item prices gathered from the web Through performance experiments, we show that the successful bid rate increases by preventing sellers from making unreasonable reserve prices. Using the pricing agent, we design and implement an XML-based auction system on the web.

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Dynamic Limit and Predatory Pricing Under Uncertainty (불확실성하(不確實性下)의 동태적(動態的) 진입제한(進入制限) 및 약탈가격(掠奪價格) 책정(策定))

  • Yoo, Yoon-ha
    • KDI Journal of Economic Policy
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    • v.13 no.1
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    • pp.151-166
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    • 1991
  • In this paper, a simple game-theoretic entry deterrence model is developed that integrates both limit pricing and predatory pricing. While there have been extensive studies which have dealt with predation and limit pricing separately, no study so far has analyzed these closely related practices in a unified framework. Treating each practice as if it were an independent phenomenon is, of course, an analytical necessity to abstract from complex realities. However, welfare analysis based on such a model may give misleading policy implications. By analyzing limit and predatory pricing within a single framework, this paper attempts to shed some light on the effects of interactions between these two frequently cited tactics of entry deterrence. Another distinctive feature of the paper is that limit and predatory pricing emerge, in equilibrium, as rational, profit maximizing strategies in the model. Until recently, the only conclusion from formal analyses of predatory pricing was that predation is unlikely to take place if every economic agent is assumed to be rational. This conclusion rests upon the argument that predation is costly; that is, it inflicts more losses upon the predator than upon the rival producer, and, therefore, is unlikely to succeed in driving out the rival, who understands that the price cutting, if it ever takes place, must be temporary. Recently several attempts have been made to overcome this modelling difficulty by Kreps and Wilson, Milgram and Roberts, Benoit, Fudenberg and Tirole, and Roberts. With the exception of Roberts, however, these studies, though successful in preserving the rationality of players, still share one serious weakness in that they resort to ad hoc, external constraints in order to generate profit maximizing predation. The present paper uses a highly stylized model of Cournot duopoly and derives the equilibrium predatory strategy without invoking external constraints except the assumption of asymmetrically distributed information. The underlying intuition behind the model can be summarized as follows. Imagine a firm that is considering entry into a monopolist's market but is uncertain about the incumbent firm's cost structure. If the monopolist has low cost, the rival would rather not enter because it would be difficult to compete with an efficient, low-cost firm. If the monopolist has high costs, however, the rival will definitely enter the market because it can make positive profits. In this situation, if the incumbent firm unwittingly produces its monopoly output, the entrant can infer the nature of the monopolist's cost by observing the monopolist's price. Knowing this, the high cost monopolist increases its output level up to what would have been produced by a low cost firm in an effort to conceal its cost condition. This constitutes limit pricing. The same logic applies when there is a rival competitor in the market. Producing a high cost duopoly output is self-revealing and thus to be avoided. Therefore, the firm chooses to produce the low cost duopoly output, consequently inflicting losses to the entrant or rival producer, thus acting in a predatory manner. The policy implications of the analysis are rather mixed. Contrary to the widely accepted hypothesis that predation is, at best, a negative sum game, and thus, a strategy that is unlikely to be played from the outset, this paper concludes that predation can be real occurence by showing that it can arise as an effective profit maximizing strategy. This conclusion alone may imply that the government can play a role in increasing the consumer welfare, say, by banning predation or limit pricing. However, the problem is that it is rather difficult to ascribe any welfare losses to these kinds of entry deterring practices. This difficulty arises from the fact that if the same practices have been adopted by a low cost firm, they could not be called entry-deterring. Moreover, the high cost incumbent in the model is doing exactly what the low cost firm would have done to keep the market to itself. All in all, this paper suggests that a government injunction of limit and predatory pricing should be applied with great care, evaluating each case on its own basis. Hasty generalization may work to the detriment, rather than the enhancement of consumer welfare.

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DSS Architectures to Support Data Mining Activities for Supply Chain Management (데이터 마이닝을 활용한 공급사슬관리 의사결정지원시스템의 구조에 관한 연구)

  • Jhee, Won-Chul;Suh, Min-Soo
    • Asia pacific journal of information systems
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    • v.8 no.3
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    • pp.51-73
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    • 1998
  • This paper is to evaluate the application potentials of data mining in the areas of Supply Chain Management (SCM) and to suggest the architectures of Decision Support Systems (DSS) that support data mining activities. We first briefly introduce data mining and review the recent literatures on SCM and then evaluate data mining applications to SCM in three aspects: marketing, operations management and information systems. By analyzing the cases about pricing models in distribution channels, demand forecasting and quality control, it is shown that artificial intelligence techniques such as artificial neural networks, case-based reasoning and expert systems, combined with traditional analysis models, effectively mine the useful knowledge from the large volume of SCM data. Agent-based information system is addressed as an important architecture that enables the pursuit of global optimization of SCM through communication and information sharing among supply chain constituents without loss of their characteristics and independence. We expect that the suggested architectures of intelligent DSS provide the basis in developing information systems for SCM to improve the quality of organizational decisions.

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Implementation of the Agent using Universal On-line Q-learning by Balancing Exploration and Exploitation in Reinforcement Learning (강화 학습에서의 탐색과 이용의 균형을 통한 범용적 온라인 Q-학습이 적용된 에이전트의 구현)

  • 박찬건;양성봉
    • Journal of KIISE:Software and Applications
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    • v.30 no.7_8
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    • pp.672-680
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    • 2003
  • A shopbot is a software agent whose goal is to maximize buyer´s satisfaction through automatically gathering the price and quality information of goods as well as the services from on-line sellers. In the response to shopbots´ activities, sellers on the Internet need the agents called pricebots that can help them maximize their own profits. In this paper we adopts Q-learning, one of the model-free reinforcement learning methods as a price-setting algorithm of pricebots. A Q-learned agent increases profitability and eliminates the cyclic price wars when compared with the agents using the myoptimal (myopically optimal) pricing strategy Q-teaming needs to select a sequence of state-action fairs for the convergence of Q-teaming. When the uniform random method in selecting state-action pairs is used, the number of accesses to the Q-tables to obtain the optimal Q-values is quite large. Therefore, it is not appropriate for universal on-line learning in a real world environment. This phenomenon occurs because the uniform random selection reflects the uncertainty of exploitation for the optimal policy. In this paper, we propose a Mixed Nonstationary Policy (MNP), which consists of both the auxiliary Markov process and the original Markov process. MNP tries to keep balance of exploration and exploitation in reinforcement learning. Our experiment results show that the Q-learning agent using MNP converges to the optimal Q-values about 2.6 time faster than the uniform random selection on the average.

Exploring the influence of commuter's variable departure time in autonomous driving car operation (자율주행차 운영 환경하에서 통근자 출발시간 선택의 영향에 관한 연구)

  • Kim, Chansung;Jin, Young-Goun;Park, Jiyoung
    • Journal of the Korea Convergence Society
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    • v.9 no.5
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    • pp.7-14
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    • 2018
  • The purpose of this study is to analyze the effect of commuter's departure time on transportation system in future traffic system operated autonomous vehicle using agent based model. Various scenarios have been set up, such as when all passenger choose a similar departure time, or if the passenger chooses a different departure time. Also, this study tried to analyze the effect of road capacity. It was found that although many of the scenarios had been completed in a stable manner, many commuters were significantly coordinated at the desired departure time. In particular, in the case of a reduction in road capacity or in certain scenarios, it has been shown that, despite excessive schedule adjustments, many passengers are unable to commute before 9 o'clock. As a result, it is suggested that traffic management and pricing policies are different from current ones in the era of autonomous car operation.

Designing Intelligent Agent System for Purchase Decision Making in Retail Electronic Commerce (전자상거래에서의 소비자 구매의사결정을 지원하는 지능형 에이전트 시스템의 설계)

  • Chu Seok Chin;Hong June S.
    • Journal of Intelligence and Information Systems
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    • v.10 no.2
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    • pp.147-163
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    • 2004
  • For the purchase of a cheaper product on the Internet, many customers have been trying to search online shopping mall sites and visit comparison-pricing shops that compare prices and other criteria of the product. Others have been participating into online auction markets or group-buying markets. However, a lot of online shopping malls, auction markets, and group-buying markets provide the same product with different prices. Since these marketplaces have different price settlement mechanism, it is very difficult for the customers to determine marketplace to purchase, considering different kinds of marketplaces at the same time. To overcome such limitations, decision rules and solution procedures for purchase decision making are necessary, which can cover multiple marketplaces simultaneously. For this purpose, purchase decision making in each market must be conducted to maximize customer's utility, and conflicts with other marketplaces must be resolved. Therefore, we have developed the rules and methods that can negotiate cooperatively the purchase decision making in several marketplaces, and designed an architecture of Intelligent Buyer Agent and a message structure to support the idea.

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An Empirical Study on the Effect of Respondent Bias in PSM : Case in Apartment Pricing (PSM 가격평가 주체에 따른 아파트 가격결정 효용성 실증연구)

  • Cho, Han-Jin;Kim, Jong-Lim
    • Land and Housing Review
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    • v.7 no.4
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    • pp.217-223
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    • 2016
  • PSM is widely used pricing tool in field by the reason of data collection convenience and analytical intuitiveness. However, In high involvement environment, strategic respondent bias influence in reducing the price. By using 3 empirical cases of LH apartment for sale, We found that latent consumers' recognition of the range of acceptable and the range of optimal price are lower than real estate agent representative respondents'. This phenomenon is considered loss aversion effect of prospect theory to reduce loss by reducing price, and more influenced in high involvement situation than latent consumer respondents'. Also we found PSM result using real estate representative data is more useful in real market than latent consumers data distorted by loss aversion effects. The meaning of this study is finding some limitation in PSM using consumer data generally used. In further study, development of PSM measurement tool to minimize the effect of strategic bias are need to be studied. Also some new approaches in reinterpretation of the range of acceptable price and the range of optimal price are need to be followed.

Cost-effectiveness Analysis of Pharmacologic Treatment in Hypercholesterolemia (고콜레스테롤혈증 치료 약물들에 대한 비용-효과 분석)

  • 정경래;문옥륜
    • Health Policy and Management
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    • v.9 no.3
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    • pp.70-94
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    • 1999
  • This paper was performed for a cost-effectiveness analysis of pharmacologic treatment of hypercholesterolemia. Agents modeled were cholestyramine, gemfibrozil. bezafibrate, lovastatin, pravastatin, simvastatin. Pharmacologic effectiveness was estimated by regression from reported clinical trials. Pharmacologic effects were expressed as the percent change of blood cholesterol level. Cost estimates included patients' travel expenses and time loss as well as resource consumption in the health care sector. Bezafibrate was the most efficient agent for reducing total cholesterol levels, having an cost over 1 year of ₩31.400 per percent reduction in total cholesterol. Simvastatin (10mg/d) was also efficient(₩33,100 per percent reduction). Chole styramine(8g/d) was least efficient at ₩90,200. For low-density lipoprotein cholesterol. simvastatin(10mg/d) was most efficient, at ₩23,200 per percent reduction, followed by lovastatin(20mg/d) at ₩28,000. Gemfibrozil was least efficient at ₩77,800 per percent reduction. For high-density lipoprotein cholesterol. bezafibrate(400mg/d) was most efficient at ₩39,300 per percent increase of high-density lipoprotein cholesterol. Cholestyramine was least efficient at ₩514,700. Analyses combining low-density lipoprotein cholesterol and high-density cholesterol effects suggest that bezafibrate(600mg/d) and simvastatin (10mg/d) were most efficient for reducing cardiovascular risk. The cost-effectiveness analysis results show that both simvastatin and bezafibrate could be efficient treatment. Simvastatin provide more effective treatment at higher cost, whereas bezafibrate is more cost-effective, as it may be less effective, at lower cost. Therefore, clinicians should choose reasonable treatment according to the patient's needs This pharmacoeconimc analysis will provide a guideline for efficient pharmacologic treatment and also be reference data for pricing new drugs.

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