This paper deals with two types of two-person non-zero-sum games in extensive form. We formulate quadratic programming problems whose decision variables correspond to behavioral strategies of players and show that optimal solutions to the formulated problems are equilibrium solutions of the games. We give two examples and demonstrate how to obtain equilibrium solutions by using the relation between the equilibrium solutions and the quadratic programming problems.
In everyday life it is a common experience that when carrying out mental activities such as memory or arithmetic tasks, there is a greater psychological impression of annoyance caused by noise; with performance worsening because of this noise. This tendency is more apparent in the case of meaningful noise, such as music or conversation, than it is with meaningless noise, such as that from road traffic. This study considered whether masking meaningful noise with meaningless steady noise can reduce the psychological impression of annoyance from noise experienced during completion of a mental task. Specifically, the authors first discussed how the psychological impression of annoyance from noise and performance correlated to an index such as the percentage of correct answers or reaction time would change under the influence of meaningless noise or an interested/uninterested meaningful noise. Then, how the above items change when meaningful noise is masked with meaningless noise was investigated.
One advantage of evolutionary multiobjective optimization (EMO) algorithms over classical approaches is that many non-dominated solutions can be simultaneously obtained by their single run. This paper shows how this advantage can be utilized in genetic rule selection for the design of fuzzy rule-based classification systems. Our genetic rule selection is a two-stage approach. In the first stage, a pre-specified number of candidate rules are extracted from numerical data using a data mining technique. In the second stage, an EMO algorithm is used for finding non-dominated rule sets with respect to three objectives. Since one of the three objectives is to maximize a classification rate on training patterns, the evolution of rule sets tends to overfit to training patterns. The question is whether the other two objectives with respect to complexity work as a safeguard against the over-fitting. In this paper, we examine the effect of the three-objective formulation on the generalization ability of obtained rule sets through computational experiments where many non-dominated rule sets are generated using an EMO algorithm for a number of high-dimensional pattern classification problems. Finally, we demonstrate that an ensemble of generated fuzzy rule-based systems leads to high generalization ability.
In this paper, we deal with lotteries for financing for preservation of the global commons and carry out simulations on an agent-based artificial society model. Especially, focusing on the preservation of the global commons as the atmospheric quality, when it does not become excessively worse, we show the effectiveness of lotteries with selective incentives, compared to voluntary contributions without those incentives. We also provide sensitivity analysis for our model.
In general it is impossible to know the learning effect of students before teaching them. Therefore, teachers have to predict it in order to perform actual teaching effectively and efficiently. In this paper, we propose a method to predict the error rates of the students for a learning problem and analyze how to teach students effectively through the prediction results. For this purpose, a multi-layer neural network (MNN) model is used. In this model, the input variables are five aptitude abilities of a student and the output variables are three error rates. It is confirmed that the prediction values obtained by using this MNN are reasonable as compared with the experimental results. Moreover, from the sensitivity analysis, the aptitude abilities to reduce the error rates are identified. This result makes it possible to predict the error rates of a learning problem in advance. By using these results, teachers can instruct students more effectively and efficiently.