In the research field of Human-agent interaction, the uncanny valley is the crucial issue to realize co-existence of human and artificial agents. It is referred to as the phenomenon that human can feel repulsive against the agents whose appearance is considerably humanlike. There has been just theoretical based but not verifiable model providing an explanation for how it occurs. We hypothesized that when human observes that humanlike agent, s/he perceives it as both human and non-human, and the contradiction between the perceptions causes him or her to elicit negative response against it. We conducted the experiment where the participants were asked to judge whether face of agents or a person was depicted as that of a real person, with their eye tracked and their gaze direction estimated. The results indicated that observers had two-step information processing to the agent. Above all, we proposed a model generating the human negative response against humanlike agents, taking into consideration of the function of the brain regions such as amygdala, prefrontal cortex, hippocampus, and striatum. To verify the model, the transition of the emotional value (namely, positive or negative) was simulated on the basis of a qualitative description for the model. It is suggested that the model be proposed which is verifiable with many findings in the field of neuroscience.

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Gaming is widely spreading in education. In gaming, learners make decisions iteratively in a simulation environment that replicates a real-world problem, called a game, and study what the proper decisions are in the game before making decisions in the problem. In the game, we need an expert agent that always makes proper decisions and from which the learner can learn such decisions by watching its behavior and/or by investigating it. However, it is difficult to develop such an expert agent manually because it is a very heavy task to implement knowledge of human experts on the problem into the agent, and sometimes it is also hard to extract such knowledge from the experts. Instead, we propose to automatically develop an agent that acts rightly as human experts with evolutionary computation. In particular, in this paper, we evolve such an agent for a research career design game where the player first determines his/her goal and has to decide his/her choices many times to achieve the goal afterward, based on a huge number of different situations. The experimental result shows that the evolved agent is better than or, at least, similar to agents created by a human expert.

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Recently, recommender systems have attracted attention as systems that collect the enormous amount of information on the Web and suggests information to users. Recommender systems help users find the products that they want. There is a close relationship between a recommender system and the long tail because the performance of them is evaluated by not only accuracy metrics but also long tail metrics. Collaborative filtering (CF) is a typical recommender system. It is described as technology used to support the long tail. However, CF is prone to be biased towards recommending hit products. In this paper, we propose a system that recommends niche products if an item is similar to the user's preference. We will reduce the bias in top-N recommendation by using the interest in a keyword. The interest is computed from information gain, which is used to choose attributes in decision tree learning and to select features in machine learning. The results from the experiments show that the proposed system outperformed item-based CF in recommending niche products. In most existing studies focused on the long tail, niche products are recommended at the cost of accuracy. However, in our study, not only are niche products recommended but accuracy is also improved.

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It is often the case that a single person belongs to multiple communities and expresses different opinions by community. However, the lack of consistency of his opinions is subject to criticism when they are uncovered. Recently, opinion formation mainly occurs in online communities instead of real communities and this affects the opinion formation environment as follows: 1) the inconsistency of opinions is easily disclosed because many online communities are not closed, and 2) there are some people too sensitive to such inconsistency. These become social pressures making people rather silent in communities and cause many phenomena including silent majority/vocal minority and opinion polarization. It is important to clarify how these changes affect opinion formation processes. Our goal is to reveal the dynamics of opinion formation in multiple communities, considering the dilemma of people between the adaptation to each of communities and the lack of consistency. We make an agent based model based on the Bounded Confidence Model on multiplex network structures. The model is novel in considering conflict between agent's different opinions across multiple communities. As a result of simulation, it is found that probability of disclosure of the lack of consistency could cause opinion formation processes differently by how agents are sensitive to the lack of consistency. Only when agents do not overreact to the lack of consistency, the larger the probability of disclosure is, the smaller the variation of opinions within communities becomes. On the other hand, when agents overreact to the lack of consistency, the mechanism of opinion formation is different. Furthermore, we reveal the mechanism of the influence of social pressure to the opinion formation processes. These results can explain real world phenomena and offer suggestions for the facilitation of consensus formation in the age of Internet.

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Social Tagging System (STS) which is one of the content management techniques is widely adopted in the online content sharing service. Using STS, users can give any strings (tags) to contents as annotations. It is important to know the usage of tag statistics for accomplishing an effective database design and the information navigation. The frequency of tag usage as well as their dynamics are similar to the ones found in the natural language. It is possible to reproduce the branching process of the tag dynamics using a classical model called Yule-Simon process. Another characteristic aspect of tags is the tag co-occurrence generated from the simultaneous use of tags. Using the tag co-occurrence, STS is able to reconstitute the hierarchy of tags, and recommend the tag which is probably used next. However, Yule-Simon process does not consider the tag co-occurrence and thus how the tag co-occurrence is generated from the model like Yule-Simon has not been addressed yet. In this paper, we propose to expand the Yule-Simon process to model the tag co-occurrence. From the point of view of network hierarchy, we confirm the similarity in the structure of the tag co-occurrence with the empirical data obtained from a social network service called ‘RoomClip’. The present result suggested that this simple model like extended Yule-Simon process generates the tag co-occurrence feature.

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We analyzed the impact of position-based market maker, which tries to maintain its neutral position, to the competition among stock exchanges by an artificial market simulation approach. In the previous study, we built an artificial market model and investigated for the impact of non-position-based market maker's spread to the markets' shares of trading volumes. However it had the serious problem that the non-position-based market maker is too simple to manage its own position properly and so we could not judge weather the result of previous study is correct or not. Thus in this study, we made a position-based market maker and explored the competition, in terms of taking markets' shares of trading volumes, between two artificial financial markets that have exactly the same specifications except existing a market maker, the non-position-based market maker or the position-based market maker. As a result, we found that the position-based market maker can acquire the share of trading volumes from the competitor even though its spread is bigger than bid-offer-spread of the competitor. Moreover, we revealed that position-based market maker can get a profit even in the situation that its spread or tick sizes of the stock exchanges are small. In addition to that, position-based market maker made a profit in almost all experiments which we conducted in this research by changing its spread and tick sizes of markets. At last, we confirmed that position-based market maker can manage its position properly compared to non-position-based market maker. In conclusion, the position-based market maker can not only supply liquidity to stock exchanges and contribute to acquire the share from the competitor as well as the non-position-based market maker does, but also manage its own position properly and make a profit.

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Efficient market hypothesis is widely accepted in financial market studies and entails the unpredictability of future stock prices. In this study, we show that a simple analysis can classify short-term stock price changes with an 82.9% accuracy. Our analysis uses the order book information of high-frequency trading. The volume of high-frequency trading, which is responsible for short-term stock price changes, is increasing dramatically; therefore, our study suggests the importance of analyzing short-term market fluctuations, an aspect that is not well studied in conventional market theories. The experimental results also suggest the importance of the new data representation and analysis methods we propose, neither of which have been thoroughly investigated in conventional financial studies.

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