In this paper, we simulate virtual societies under several communication conditions between agents using multi-agent systems whose agents are based on cognitive economic efficiency, to get the clue to understand the modern society which consists of complex human relationships. The research about social structures and a conceptualization of group problem solving have a long and rich history. Heider, a founder of the theory of perceptual balance, defined the relationship between three notions (P, O, X) as “positive (+1) ,” “negative (-1)” and judged the “balance” or “imbalance” of the group by using the product of these three signs in this cycle. Cartwright and Harary expanded Heider's balance theory (structural balance) to the groups consist of more than three actors in order to explain more realistic social problems. We use these theories to social simulations. Furthermore, we adopt the idea of cognitive economic efficiency to the agents. The model simulates social actors making positive and negative selection of each other in efforts to reach balanced cognitive states. We observe the influence on dissimilarity or the formation of groups on our social simulations as a result of several communication networks and the ways of choosing communication partners, especially for sharing common perceptions, which influences their society.

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Because of the recession of recent years, as an urgent step, firms adopt many strategies to cut costs. Therefore, introducing “work sharing” is suggested to cut costs. To propose “work sharing” which aims to keep temporary employees, it is important to consider the inherent problem that they leave the organization within a short period of time. In this study, we focus on the change of human relationships by temporary employees and information sharing on those relationships. We propose the effective “work sharing” method considering temporary employees. To evaluate the method, we also propose the dynamic network model which represents structural change of the network through information sharing. In our model, nodes and links correspond to employees and human relationships, respectively. We evaluate the effect of cost cutting methods “mass firing of temporary employees” and “work sharing”. The results show that introducing “work sharing” in ascending order of closeness centrality can cut costs with the highest working efficiency.

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In a market with network externalities, consumers have an incentive to purchase products/services from a firm with a large customer share, because they can greatly benefit by using the products/services that have already been used by many other customers. Thus, when a firm obtains more customers than any other firm at the initial stage, it tends to acquire an overwhelming number of customers at the final stage of product/service diffusion. In this paper, by using the artificial market simulation method, we study the present strategies of a firm with a smaller customer share where it offers its products/services to some consumers without any initial cost. We examine the effectiveness of three types of such strategies to acquire a larger customer share as compared to other firms. We find that the final number of customers that the firm obtains by using these strategies depends on not only the types and number of consumers that receive the products/services free of cost but also the timing when the firm implements the strategies. In particular, we find that the firm's present strategy of offering its products/services to new customers whose friends have not had any purchase experience with the firm in its earlier stage can dramatically increase the possibility of the firm acquiring a larger customer share in the market.

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Relationship Marketing (RM) has been one of the main topics in marketing. However, sales persons tend to chase short-term orientation sales strategy in their activities. Additionally although “Trust” is recognized as the key component of RM, some empirical analysis indicates “Trust” doesn't have positive effects on sales performance such as revenue. Objective of this paper is to get knowledge of limitation of “Trust” at seller-buyer relationship. Multi Agent-based Simulation model (MAS) is adopted as the method for the purpose of analyzing limitation of RM. Watkins and Hill use MAS to examine how RM effects on sellers' sales performances. They showed “Trust” sales style has better sales performances than Non-Trust type sales style in the long-run term. However, they don't assume sellers can change their sales strategies in the short term. Therefore, it is hard for their model to apply to real business situation. This paper expands Hill and Watkins' MAS model. Additional condition is that sellers can change their sales strategies in 1 simulation. By this extension, it is shown that Trust's positive effects turn unclear. The important suggestion for traditional RM theory is the advantage of “Trust” sales is dismissed under the short term orientation strategy. Implications for sales department is that sales manager should monitor market all more deeply and widely for the purpose of achieving and enjoying long-term RM's advantage.

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In this paper, we propose a voting simulation model for increasing voter turnout and reducing the number of polling places. First we develop utility functions of an elector for voting and not voting. Then we adjust a regional voting paremeter that combines the utility functions according to the voter turnout in each region. Through adjusting process using the actual turnout rate for each region, we could minimize the difference between the estimated voter turnout rate and the actual one in each area. Using the estimated parameters, we employ a two-objective genetic algorithm (NSGA-II) to find a polling place assignment to maximize the voter turnout rate and minimize the number of polling places without being lower than the actual rate by reallocating polling places. The simulation results show that we can increase the voter turnout rate and reduce the number of polling places without being lower than the actual rate.

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This paper describes a method to design a large-scale social simulation with the environmental information on parallel computer. To implement a large-scale Multi-Agent Simulation (MAS), a large number of agents and a large size of environmental information should be implemented in the simulation program. In this paper, we propose a representation technique to implement a simulation program on parallel computer. Our proposed technique is to divide environmental information into some sub-environmental information. We firstly explain the proposed technique and a rule which mediate between agents for parallel MAS. We illustrate how to implement sugarscape-based simulation program with the proposed technique for parallel computer system. Then we evaluate the simulation program on a PC cluster system for the large-size environmental information. The simulation results show that there is no significant gap between the experimental data obtained by the proposed technique and the non-parallel technique. We also show that the computation time of the simulation program with the proposed technique can be improved on a PC cluster system.

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We have proposed the Distributed Cooperative Storage System. Objects are inserted into several peers distributedly according to their hashed value. The storage sytem uses the P2P technology and it enables to construct an overlay network in the application layer. In the general P2P network, objects are replicated over many peers to achieve high availability. In this paper, we propose novel replication model called the "Distributed Interval Tree" that peers manage the "Interval Tree" distributedly.

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Mathematical programming has been applied to various fields. However for many actual problems, the assumption that the parameters involved in the problem are deterministic known data is often unjustified. These data contain uncertainty and are thus represented as random variables, since they represent information about the future. Decision-making under conditions of uncertainty involves potential risk. Stochastic programming deals with optimization under uncertainty. A stochastic programming problem with recourse is referred to as a two-stage stochastic problem. We consider the stochastic programming problem with simple integer recourse in which the value of the recourse variable is restricted to a multiple of a nonnegative integer. The algorithm of a dynamic slope scaling procedure to solve the problem is developed by using the property of the expected recourse function. The numerical experiments show that the proposed algorithm is quite efficient. The stochastic programming model defined in this paper is quite useful for a variety of design and operational problems.

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Recently, a lot of methods of using the neural network for the state space construction of a mobile robot are proposed. When robot is put on a different environment, it is not possible to behave robustly because these methods make a robot adjust to one static environment. On the other hand, Subsumption Architecture (SA) is not suitable for tasks of depending on the structure of an environment though it is expected that it can behave robustly even by the dynamic environment. The robustness of SA is declined when robot adjusts to a specific environment by neural network. In this paper, we propose the hybrid model which is consisted of SA and Dynamics-Based Self-organizing Incremental Neural Network (DBSOINN). DBSOINN is modified The Self-organizing Incremental Neural Network (SOINN) for state space construction of the reinforcement learning. The effectiveness of this proposal was confirmed by the simulation experiment that the mobile agent behaves in the environment which is composed of plural mazes. The proposed model is able to use plural DBSOINN appropriately at the maze which changes dynamically.

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In this paper, a new localization approach for a team of robots which utilizes emergent properties of their formation behavior is proposed. Formation behavior, for example, flocking, rendezvous and so on, regulates the robots to achieve its corresponding task. At times, some of this synchronized behavior generate spin-off effects that include geometric patterns on them. Therefore, it seems to be a reasonable question whether it is possible to utilize the pattern. Firstly, the authors discuss Takayama's control strategy which is proposed for target enclosure formation, which is a typical formation for Robocup. Then they propose a simple and useful expansion of Monte Carlo localization to use the emergent pattern of this formation. The proposed algorithms are confirmed by a series of realistic computer simulations.

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