Catálogo de publicaciones - libros

Multi-agent simulations (MAS) have been used to study the dynamics of social systems. Disaster-related simulation is one of application fields. The simulation is applied to scenarios that are difficult to perform drills in the real world. The results provide useful data such as the amount of time people take to evacuate buildings and how smoothly rescue responders arrive at target points in the buildings. Making use of the simulation results to plan disaster-prevention measure, we need to verify that the simulation results that are reasonable at scenarios that are not confirmed from real data and observations. In this paper, we discuss the standardization process of MAS-based evacuation simulations by examining qualitative differences perceived in our evacuation simulations.

In order to understand pedestrian dynamics, we construct a model of self-propelled disk particles interacting repulsively with no fixed destination. From molecular dynamics simulations, we find that the model exhibits collective motion and transition from a disordered to a polar-ordered, heterogenous state. Binary scattering study suggests that ordering originates from parallel alignment of particles’ velocity after collision. The dependency of alignment tendency on the model parameter agrees well with the behavior of multiparticle systems. We verify that the model reproduces the actual pedestrian phenomena in a straight pathway. Although there is still a gap with empirical findings, especially in high densities, the result implies that pedestrian crowds can spontaneously build up a collective motion even in the situation where they have lost their destinations.

A traffic simulation of Kobe-city was carried out. In order to simulate an actual traffic flow, a road network was constructed utilizing a high-quality digital map data, and an origin-destination information of vehicles was estimated by a geographical population distribution data. The result obtained in this way was incompatible with the traffic census data due to the differences between the simulation and actual traffic, such as routing, OD information and so on. In order to improve the reproducibility of the traffic flow, the parameter search whose adjustable parameter was the speed limit of the road was conducted. This adjustment showed that reproducibility improves. Further improvement of the reproducibility needs to reconsideration of the routing algorithm.

In this paper, we present MOSAIIC, an agent-based model to simulate the road traffic of a city in the context of a catastrophic event. Whether natural (cyclone, earthquake, flood) or human (industrial accident) in origin, catastrophic situations modify both infrastructures (buildings, road networks) and human behaviors, which can have a huge impact on human safety. Because the heterogeneities of human behaviors, of land-uses and of network topology have a great impact on the traffic flows, the agent-based modeling is particularly adapted to this subject. In this paper, we focus on the new traffic model itself: the way geographical data is used to build a network, the various behaviors of our agents, from the individual to the collective level.

In this paper, we discuss how to build a model by SOARS VisualShell intuitively and explain its architecture. SOARS (Agent based simulation modeling language) SOARS Project (), Tanuma et al. (Post-proceedings of AESCS04. Springer, Japan, pp 49–56, 2004) and Tanuma and Deguchi (Inst Electron Inf Commun Eng D J90-D(9):2415–2422, 2007) is a programming language to model social phenomena by agent-based simulation. We aim to make SOARS a simulation description language by which a domain expert can simulate social interactions occurred in the real world by ones conceptual model intuitively. Therefore, a support tool for realizing and achieving specialized concepts is necessary for a domain expert to build and run a simulation model based on his/her only domain knowledge without possessing complex programming skill, and SOARS VisualShell is an application to support such intuitive modeling by SOARS.

We quantified the emotional changes observed in social media after major disasters, focusing especially on the Japanese blog space after the Great East Japan Earthquake in 2011. We checked the appearances of Japanese adjectives and found that special emotion adjectives, such as ‘impatient’, and ‘frustrating’ which involve the want to help others but the person has no means to and feels frustrated, occur with considerably increasing frequency. To visualize social mood, we drew a co-occurrence network of adjectives showing a major topological change at the site of the quake. Measuring emotional changes after an emergency has been difficult, but, our research has the potential to achieve it.

“The pen is mightier than the sword” said in previous times, the role of dispatching information was given to the people with the special trainee, the people called “Mass-Communication”. However, it has become possible for everyone to dispatch the information on social society with the advent of the Web. Accordingly, Enjyo is often observed on Social Networking Services. Enjyo is a phenomenon that leads the tragedy to individual/company who sends the promotion information via a process of consensus formation as the result of many SNS users. In this research, we analyze the value of reputation on social media in some cases with the purpose of modeling Enjyo. In this study, we tried to numerically analyze and model some cases of Enjyo as well as to classify them by using the data on SNS. For achieving our purpose, we proposed a method of measuring a state of Enjyo and applied the case study method for analysis. With this method, the process of the analysis is likely to be influenced by one’s subjective interpretation or assessment. Therefore, we also tried to facilitate its efficiency and accuracy with random sampling. As a result, several patterns of Enjyo were identified. Moreover, one of the cases of appropriate Enjyo extinction was observed.

As social network service (SNS) spreads all over the world, we can no longer live without its influence. The study of SNS would reveal how atmosphere of our society changes as we interact through SNS. Here, we focus our attention on emotions expressed in SNS and investigate how our emotions are affected by others in SNS. To reveal how positive/negative emotions are magnified and diffused through SNS, we analyze a network of emotional words in an electronic bulletin board based on the theory of complex networks.

In this paper I propose a novel method to model real online social networks where the growing scale-free networks have tunable clustering coefficient independently of the average degree and the exponent of the degree distribution. Models based on purely preferential attachment are not able to describe high clustering coefficient of social networks. Beside the attractive popularity my model is based on the fact that if a person knows somebody, probably knows several individuals from his/her acquaintanceship as well. The topological properties of these complex systems were studied and it was found that in my networks the cliques are relevant independently of the system size as usual in social systems.

One of the important factors ignored in the literature in e-marketing is “the color” of a product. While one may be able to identify the dominating color of a product based on the overall impression, it is not easy to mechanize the process to determine the dominating color. Accordingly, in many applications, the color of a product is defined subjectively by those who enter the data. Consequently, the color of a product has been a missing link in e-marketing. The purpose of this research is to fill this gap by developing an algorithmic procedure for identifying the dominating color of a product by analyzing a digital image of the product. The algorithmic procedure enables one to reveal color preferences of consumers by analyzing the digital images of the products obtained from the purchasing records. A recommendation engine is also developed based on color class preference vectors of individual consumers.