Table of Contents

  • A Bibliometric Study of Agent-Based Modeling Literature on the SSCI Database
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    The purpose of this study is to investigate the characteristics of the international literature using Agent-Based Modeling (ABM) in SSCI during 1997–2009. The results of this study reveal the fact that the growth of international literature using ABM is still well perceived. Most of the literature is from various institutions in the USA. According to Bradford’s Law, eight core journals in ABM are identified and analyzed. Moreover, the frequency distributions of the author productivity match the generalized Lotka’s Law. Applications of ABM are mainly found in the fields of social science/interdisciplinary studies, economics, and environmental studies.
  • Examining the Effects of Traders’ Overconfidence on Market Behavior
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    Much attention has been paid in the past decade to how traders’ psychological factors affect market properties. Overconfidence is one of most important characteristics of traders. Under an agent-based modeling framework, this paper examines how traders’ overconfidence affects market properties. The preliminary results have shown that overconfidence increases market volatility, price distortion, and trading volume. Some stylized facts such as the fat-tail of the return distribution and volatility clustering would be more evident.
  • Communities, Anti-Communities, Pan-Community as Social Order
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    A society of agents who can freely attack others or not inevitably evolves into a battling society (a “war of all against all”). We investigated whether the Friend Selection Strategies based on Attribute and Reputation in Group (FSS-ARG) lead to the emergence of social order. FSS-ARG require an agent to evaluate whether others are his “friends” or “enemies”, based on whether others were peaceful or hostile to his group and whether others have the same attribute as his group or not. We carried out evolutionary simulations with FSS-ARG. As a result, we found that four types of social states, what we call “battling society”, “communities”, “anti-communities” and “pan-community”, have emerged. For example, the “communities” consist of two mutually hostile communities, in each of which all members have the sane attribute and are friendly to all other members. So, the “communities” can remind us of Max Weber’s “in-group/out-group morality”.
  • Bayesian Analysis Method of Time Series Data in Greenhouse Gas Emissions Trading Market
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    This paper proposes the Bayesian analysis method (BAM) to classify the time series data which derives the complicated phenomena in the international greenhouse gas emissions trading. Our investigation compared the results using the method of Discrete Fourier transform (DFT) and BAM. Such comparisons have revealed the following implications: (1) BAM is superior to DFT in terms of classifying time series data by the different distances; and (2) the different distances in BAM show the importance of 1% influence of emission reduction targets.
  • Learning Backward Induction: A Neural Network Agent Approach
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    This paper addresses the question of whether neural networks (NNs), a realistic cognitive model of human information processing, can learn to backward induce in a two-stage game with a unique subgame-perfect Nash equilibrium. The NNs were found to predict the Nash equilibrium approximately 70% of the time in new games. Similarly to humans, the neural network agents are also found to suffer from subgame and truncation inconsistency, supporting the contention that they are appropriate models of general learning in humans. The agents were found to behave in a bounded rational manner as a result of the endogenous emergence of decision heuristics.
  • Large Scale Crowd Simulation of Terminal Station Area When Tokai Earthquake Advisory Information Is Announced Officially
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    On the assumption of advisory information concerning an imminent Tokai earthquake being officially announced, as a case example we developed a LSCS (Large Scale Crowd Simulator) for the Nagoya Station area, where several terminal stations are concentrated; in the model, agents played people on their way home, and such factors as the routes selected by agents and the spatial restrictions, e.g. passages, were taken into consideration. Basic on SOARS (Spot Oriented Agent Role Simulator) platform, we conducted a large-scale crowd simulation with 160,000 agents and analysis the change of space density in 1 h to compare to the estimates given by Nagoya City, we analysis the result and also refer to LSCSver1.0 for implementing much higher functions.
  • The Flow of Information Through People’s Network and Its Effect on Japanese Public Pension System
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    In this article, we would like to verify a positive or negative policy impact that comes from decreasing or increasing people’s distrust in Japanese public pension system. For the sake of tackling these issues, firstly, we pick up some network models that fit well in real people’s network. Secondly, we put the information, which is concerning about Japanese public pension system, on agent-to-agent network. Finally, we ascertain the effect of releasing the information and its expansion on Japanese public pension system. Consequently, it is revealed that releasing information over again have a profound effect on reducing distrust in public pension system and on pension premium fund. With releasing information to a limited extent and to a limited number of agents, there is limited effect on reducing agent’s distrust.
  • Comprehensive Analysis of Information Transmission Among Agents: Similarity and Heterogeneity of Collective Behavior
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    Recent development of Information and Communication Technology enables us to collect and store data on human activities both circumstantially and comprehensively. In such circumstances it is necessary to consider trade-off between personal privacy and public utility. In the present article I discuss methods to quantify comprehensive states of human activities without private information and propose a measure to characterize global states of societies from a holistic point of view based on an information-theoretic methodology. By means of the proposed method I investigate participants’ states of the foreign exchange market during the period of the recent financial crisis which started around the middle of 2008. The results show that drastic changes of market states frequently occurred at the foreign exchange market during the period of global financial crisis starting from 2008.
  • Exchange Rate Forecasting with Hybrid Genetic Algorithms
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    In recent years, Artificial Intelligence (AI) methods have proven to be successful tools for forecasting in the sectors of business, finance, medical science and engineering. In this study, we employ a Genetic Algorithm (GA) to select the optimal variable weights in order to predict exchange rates; subsequently, Genetic Algorithms, Particle Swam Optimization (PSO) and Back Propagation Network (BPN) are utilized to construct three models: GA_​_GA, GA_​_PSO, GA_​_BPN to compare results with a traditional regression model. Fundamentally, we expect enhanced variable selection to provide improved forecasting performance. The results of our experiments indicate that the GA_​_GA model achieves the best forecasting performance and is highly consistent with the actual data.
  • Landscape Analysis of Possible Outcomes
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    The behavior of a complex social system is unpredictable because both the uncertainties and the complex interactions in the system affect its future behavior. Existing scenario analysis methods focus on the effects of complex interactions of the system upon the system’s behavior, rather than the uncertainties in the system. The purpose of this paper is to develop a novel scenario analysis method that mainly focuses on evaluating a range of possible outcomes in a system based on selected uncertainties. We validate this method by applying it to a case example in which the configuration of an evaluation system for a sales division is examined.
  • Short Time Correction to Mean Variance Analysis in an Optimized Two-Stock Portfolio
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    The effect of short time correlation in stock prices on a two-stock portfolio under the framework of Mean Variance Analysis is investigated. The theory of Markowitz on portfolio management, based on a long time scale analysis of return and variance, is first optimized over a selection of pair of stocks from the Hang Seng Index and then corrected by the return of short time scales of the stocks. Several choices of short time returns, from 1 to 5 days in the past, are studied. The cumulative return is highest when the returns of the two-stock portfolio in the past 2 days are included in the correction to the “modified Sharpe ratio”. The testing data cover the period between Jan 10, 2007 and July 21, 2009 for 24 blue chip stocks from the Hang Seng Index. The strategy is compared to the average return of these 24 stocks as well as to the Hang Seng Index in the same period. We conclude that our strategy has a positive return over most of the days of the testing period, including a very stable positive performance in the period of market crash. The variation of the cumulative return of our strategy is less than both the average returns of the chosen stocks or the Hang Seng Index, thereby providing a portfolio with a smaller risk but still attractive return. This strategy of time dependent mean variance analysis to include both the long and short time scale data appears to be a good investment scheme for conservative investors who prefer stable return even during market downturn.
  • Cognitive-Costed Agent Model of the Microblogging Network
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    Microblogging is a new paradigm spreading in social web services that provides us a light-weight, speedy way of communication. In the microblogging system, users post short messages just as quickly as chatting. Users can easily communicate with each other. However, the system brings users huge cognitive costs since they always need to follow up their friends’ posts every second. Can such cognitive costs affect the structure of the microblogging network? Here we extracted data from the most major microblogging service: Twitter. We find that the network structure in Twitterhas some features: power law decay in the small degree range, link reciprocity and asymmetry between distributions of the in-degree and out-degree. To explain such characteristics, we introduce a simple stochastic agent model based on the Barabási–Albert model. With the mathematical analysis and computational experiments, we confirm that even such a simple model explains well the behavior of the observed data.
  • Identification of Voting with Individual’s Feet Through Agent-Based Modeling
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    This paper describes an agent-based simulation model to analyze migration behaviors of individual in several political regions. The model was originally discussed by Tiebout in 1956 as “Voting with Feet,” however, the validity of the model has not been examined very carefully. In the proposed agent simulation model, plural political decisions in each region are made by the corresponding regional government, and the inhabitants will vote the decisions based on their preferences. Both governments and individuals are modeled as decision making agents. The intensive simulation studies have revealed the emergence of decision groups and how the decisions have been made.
  • Boundary Organizations: An Evaluation of Their Impact Through a Multi-Agent System
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    Modern environmental issues imply that decision-makers consider simultaneously various dimensions, such as science and economics. To take into account opinions from experts of different fields, they can rely on boundary organizations, institutions able to cross the gap between different domains and act beyond the boundaries. By encouraging a flow of useful information, they provide a better understanding of a situation characterized by uncertainty, increasing the efficiency of the decision-making process. Though never formally proved, this hypothesis is widely accepted based on the observation of existing institutions. In this paper, we observe the impact of boundary organizations through an agent-based model of continuous opinion dynamics over two dimensions where heterogeneous experts distinguished by credibility and uncertainty interact. We conclude that boundary organizations significantly reduce the diversity of opinions expressed and increase the number of experts agreeing to emerging positions, which confirms their positive impact on the efficiency of decision-making.