• Journal of Korean Institute of Industrial Engineers
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• v.32 no.3
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• pp.172-179
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• 2006

Over the past few years, a considerable number of studies have been conducted on modeling the bargaining game using artificial agents on within-model interaction. However, very few attempts have been made at study on between-model interaction. This paper investigates the interaction and co-evolutionary process among heterogeneous artificial agents in the bargaining game. We present two kinds of the artificial agents participating in the bargaining game. They play some bargaining games with their strategies based on genetic algorithm (GA) and reinforcement learning (RL). We compare agents' performance between two agents under various conditions which are the changes of the parameters of artificial agents and the maximal number of round in the bargaining game. Finally, we discuss which agents show better performance and why the results are produced.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.4
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• pp.1683-1689
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• 2013

Applying Markov Stochastic Process theory, this paper attempts to suggest a tentative model explaining how private information may cause bargaining delay. It is shown that the bargaining delay is critically dependent on the specification of information. It turns out that the delay tends to be longer in bargaining where information is imperfect. This means that bargaining models frequently can have an infinite delay under imperfect information while they have finite delay of bargaining before reaching the agreements if information is perfect. Other interesting result is that bargaining delay may depend on who makes the offer first. And it is also shown that bargaining tends to end earlier if both players (seller and buyer) can make offers in turn than the case where only one side make a offer.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.292-300
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• 2020

According to the development of artificial intelligence technology, attempts have been made to interpret phenomena in various fields of the real world such as economic, social, and scientific fields through computer simulations using virtual artificial agents. In the existing artificial agent-based bargaining game analysis, there was a problem that did not reflect the cost incurred when the stage progresses in the real-world bargaining game and the depreciation of the bargaining target over time. This study intends to observe the effect on the bargaining game by adding the cost incurred in the bargaining stage and depreciation of the bargaining target over time (bargaining cost) to the previous artificial agent-based bargaining game model. As a result of the experiment, it was observed that as the cost incurred in the bargaining stage increased, the two artificial agents participating in the game had a share close to half the ratio and tried to conclude the negotiation in the early stage.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.3
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• pp.209-222
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• 1997

In this paper, we study a bargaining order problem where one buyer sequentially bargains with two sellers whose reservation prices are unknown to the buyer but correlated. Our main question is who the buyer should bargain first with to maximize his expected payoff. This type of problem is widely applicable to business and political situations where one party negotiates with multiple parties sequentially. One of the most important element in a sequential bargaining is "linkage effect" which exists when the aggreement of the previous bargaining affects the outcome of the following bargaining. To examine "linkage effect", we assume that the sellers'objects are similar so that the sellers' reservation prices are correlated. In addition, to consider incomplete information aspect regarding reservation prices, it is assumed that the sellers' reservation prices are unknown to the buyer. That is, we deal with one sided incomplete information case. In our model, there are two stages in each of which the buyer meets one seller. Since we are concerned with the bargaining order, we consider two different bargaining orders. Using game theory, we find a perfect Bayesian equilibrium and compute the buyer's expected payoff for each bargaining order. Finally we identify the advantageous bargaining order for the buyer by comparing the expected payoffs obtained under two different bargaining orders. Our results are as follows: the advantageous bargaining order depends on the prior probability of the seller type. However, in general, the buyer should bargain first with the seller whose object is less valuable to the buyer. The basic reason for our result is that the buyer wants to experiment in the first stage to find out the sellers' reservation prices and in doing so, to minimize the experimental cost and maximize potential gain in case of negotiation failure in the first stage. in the first stage.

• Journal of Korean Society of Transportation
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• v.23 no.4 s.82
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• pp.37-45
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• 2005

This paper presents a relationship between Nash bargaining game and Wardrop user equilibrium, which has been widely used in transportation modeling for route choice problem. Wardrop user equilibrium assumes that drivers in road network have perfect information on the traffic conditions and they choose their optimal paths without cooperation each other. In this regards, if the bargaining game process is introduced in route choice modeling, we may avoid the strong assumptions to some extent. For such purpose, this paper derives a theorem that Nash bargaining solution is equivalent to Wardrop user equilibrium as the barging process continues and prove it with some numerical examples. The model is formulated based on two-person bargaining game. and n-person game is remained for next work.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.549-557
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• 2014

Recently, analysis of bargaining game using evolutionary computation is essential issues in field of game theory. In this paper, we observe a bargaining game using co-evolution between two heterogenous artificial agents. In oder to model two artificial agents, we use a particle swarm optimization and a differential evolution. We investigate algorithm parameters for the best performance and observe that which strategy is better in the bargaining game under the co-evolution between two heterogenous artificial agents. Experimental simulation results show that particle swarm optimization outperforms differential evolution in the bargaining game.

• ETRI Journal
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• v.37 no.1
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• pp.197-202
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• 2015

A smart grid is a modernized electrical grid that uses information about the behaviors of suppliers and consumers in an automated fashion to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity. In the operation of a smart grid, demand side management (DSM) plays an important role in allowing customers to make informed decisions regarding their energy consumption. In addition, it helps energy providers reduce peak load demand and reshapes the load profile. In this paper, we propose a new DSM scheduling scheme that makes use of the day-ahead pricing strategy. Based on the Rubinstein-Stahl bargaining model, our pricing strategy allows consumers to make informed decisions regarding their power consumption, while reducing the peak-to-average ratio. With a simulation study, it is demonstrated that the proposed scheme can increase the sustainability of a smart grid and reduce overall operational costs.

• KDI Journal of Economic Policy
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• v.35 no.1
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• pp.137-146
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• 2013

This paper outlines the findings of a model of plea bargaining with multiple defendants, in which a prosecutor makes plea offer sequentially. It is shown that plea discount can be minimized with sequential offers and that not all of defendants shall be induced to plead guilty. By allowing sequential offer, a prosecutor has more power in the plea bargaining, which may increase social welfare by giving appropriate level of punishment to the guilty.

• Membrane and Water Treatment
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• v.14 no.2
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• pp.65-76
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• 2023

Given the limited water resources and the presence of multiple decision makers with different and usually conflicting objectives in the exploitation of water resources systems, especially dam's reservoirs; therefore, the decision to determine the optimal allocation of reservoir water among decision-makers and stakeholders is a difficult task. In this study, by combining a fuzzy VIKOR technique or fuzzy multi-criteria decision making (FMCDM) and the Young's bilateral bargaining model, a new method was developed to determine the optimal quantitative and qualitative water allocation of dam's reservoir water with the aim of increasing the utility of decision makers and stakeholders and reducing the conflicts among them. In this study, by identifying the stakeholders involved in the exploitation of the dam reservoir and determining their utility, the optimal points on trade-off curve with quantitative and qualitative objectives presented by Mojarabi et al. (2019) were ranked based on the quantitative and qualitative criteria, and economic, social and environmental factors using the fuzzy VIKOR technique. In the proposed method, the weights of the criteria were determined by each decision maker using the entropy method. The results of a fuzzy decision-making method demonstrated that the Young's bilateral bargaining model was developed to determine the point agreed between the decisions makers on the trade-off curve. In the proposed method, (a) the opinions of decision makers and stakeholders were considered according to different criteria in the exploitation of the dam reservoir, (b) because the decision makers considered the different factors in addition to quantitative and qualitative criteria, they were willing to participate in bargaining and reconsider their ideals, (c) due to the use of a fuzzy-logic based decision-making approach and considering different criteria, the utility of all decision makers was close to each other and the scope of bargaining became smaller, leading to an increase in the possibility of reaching an agreement in a shorter time period using game theory and (d) all qualitative judgments without considering explicitness of the decision makers were applied to the model using the fuzzy logic. The results of using the proposed method for the optimal exploitation of Iran's 15-Khordad dam reservoir over a 30-year period (1968-1997) showed the possibility of the agreement on the water allocation of the monthly total dissolved solids (TDS)=1,490 mg/L considering the different factors based on the opinions of decision makers and reducing conflicts among them.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.208-209
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• 2016

This study explores how bargaining power and partnership effect on the SCM main results namely flexibility, agility, and control of uncertainty. To test the suggested model, this study used a data set generated from online and offline survey. The 140 sets of data collected, which companies use SCM, were tested against the model using SPSS 23 and smartPLS 3.2.3. The results of this study are as follows. First, Bargaining power had an effect on partnership. Second, partnership had an effect on the SCM agility, flexibility, control of uncertainty.