Catálogo de publicaciones - libros

The aim of this paper is to discuss the possibility of understanding human social interaction in web communities by analogy with a disease propagation model from epidemiology. When an article is submitted by an individual to a social web service, it is potentially influenced by other participants. The submission sometimes starts a long and argumentative chain of articles, but often does not. This complex behavior makes management of server resources difficult and a more theoretical methodology is required. This paper tries to express these complex human dynamics by analogy with infection by a virus. In this first report, by fitting an epidemiological model to Bulletin Board System (BBS) logs in terms of a numerical triple, we show that the analogy is reasonable and beneficial because the analogy can estimate the community size despite the submitter’s information alone being observable.

This paper illustrates how to compare different agent-based models and how to compare an agent-based model with real data. As examples we investigate ARFIMA models, the probability density function, and the spectral density function. We illustrate the methodology in an analysis of the agent-based model developed by Levy, Levy, Solomon (2000), and confront it with the S&P 500 for a comparison with real life data.

This paper presents the computational results obtained in the strategy experiments in an artificial futures market with human subjects. Participants submit their own strategy files and they receive the performances of all the market participants in order to improve for the next round. After two-round experiments, simulations with only machine agents are run. We find that the time series data support so-called stylized facts in some regards and that experiments of human subjects seem to make the prices be closer to a theoretical value.

Conjoint analysis is a statistical technique to reveal customers’ invisible preference using series of questions regarding tradeoffs in products. In this paper, we propose a new variant of this technique that uses products layout and customers’ actions in a store instead of conjoint cards and answers. We demonstrate the effectiveness of this method by making agent-based in-store simulator that can reproduce the congestion in a store. The parameters of the agents in the simulator were determined by our technique – video-based conjoint analysis.

Recommender System provides certain products adapted to a target user, from a large number of products. One of the most successful recommendation algorithms is Collaborative Filtering, and it is used in many websites. However, the recommendation result is influenced by community characteristics such as the number of users and bias of users’ preference, because the system uses ratings of products by the users at the recommendation.

In this paper, we evaluate an effect of community characteristics on recommender system, using multi-agent based simulation. The results show that a certain number of ratings are necessary to effective recommendation based on collaborative filtering. Moreover, the results also indicate that the number of necessary ratings for recommendation depends on the number of users and bias of the users’ preference.

This paper explores for service and of new service from the transportation viewpoint. For this purpose, we analyze transport networks of railway, subway, and waterbus, and have revealed the following implications: (1)   criterion proposed by Latora [7,8] and criterion in the complex network literature can be applied as quantitative evaluation criteria for service in a transportation domain; and (2) new services are highly embedded among networks, , the analyses of the combined networks have the great potential for finding new services that cannot be found by analyzing a single network.

This paper proposes a diversity generating mechanism for an evolutionary algorithm that determines the basic structure of Multilayer Perceptron (MLP) classifiers and simultaneously estimates the coefficients of the models. We apply a modified version of a recently proposed diversity enhancement mechanism [1], that uses a variable population size and periodic partial reinitializations of the population in the form of a saw-tooth function. Our improvement on this standard scheme consists of guiding saw-tooth reinitializations by considering the variance of the best individuals in the population, performing the population restart when the difference of variance between two consecutive generations is lower than a percentage of the previous variance. The empirical results over six benchmark datasets show that the proposed mechanism outperforms the standard saw-tooth algorithm. Moreover, results are very promising in terms of classification accuracy, yielding a state-of-the-art performance.

The edited -nearest neighbor consists of the application of the -nearest neighbor classifier with an edited training set, in order to reduce the classification error rate. This edited training set is a subset of the complete training set in which some of the training patterns are excluded. In recent works, genetic algorithms have been successfully applied to generate edited sets. In this paper we propose three improvements of the edited -nearest neighbor design using genetic algorithms: the use of a mean square error based objective function, the implementation of a clustered crossover, and a fast smart mutation scheme. Results achieved using the breast cancer database and the diabetes database from the UCI machine learning benchmark repository demonstrate the improvement achieved by the joint use of these three proposals.

This paper presents a novel approach to solve multiclass classification problems using pure evolutionary techniques. The proposed approach is called Evolution of Geometric Structures algorithm, and consists in the evolution of several geometric structures such as hypercubes, hyperspheres, hyperoctahedrons, etc. to obtain a first division of the samples space, which will be re-evolved in a second step in order to solve samples belonging to two or more structures. We have applied the EGS algorithm to a well known multiclass classification problem, where our approach will be compared with several existing classification algorithms.

This paper presents a novel hybrid algorithm for combinatorial optimization problems based on mixing the cross-entropy (CE) method and a Hopfield neural network. The algorithm uses the CE method as a global search procedure, whereas the Hopfield network is used to solve the constraints associated to the problems. We have shown the validity of our approach in several instance of the generalized frequency assignment problem.