Although positive incentives for cooperators and/or negative incentives for free-riders in social dilemmas play an important role in maintaining cooperation, there is still the outstanding issue of who should pay the cost of incentives. The second-order free-rider problem, in which players who do not provide the incentives dominate in a game, is a well-known academic challenge. In order to meet this challenge, we devise and analyze a meta-incentive game that integrates positive incentives (rewards) and negative incentives (punishments) with second-order incentives, which are incentives for other players’ incentives. The critical assumption of our model is that players who tend to provide incentives to other players for their cooperative or non-cooperative behavior also tend to provide incentives to their incentive behaviors. In this paper, we solve the replicator dynamics for a simple version of the game and analytically categorize the game types into four groups. We find that the second-order free-rider problem is completely resolved without any third-order or higher (meta) incentive under the assumption. To do so, a second-order costly incentive, which is given individually (peer-to-peer) after playing donation games, is needed. The paper concludes that (1) second-order incentives for first-order reward are necessary for cooperative regimes, (2) a system without first-order rewards cannot maintain a cooperative regime, (3) a system with first-order rewards and no incentives for rewards is the worst because it never reaches cooperation, and (4) a system with rewards for incentives is more likely to be a cooperative regime than a system with punishments for incentives when the cost-effect ratio of incentives is sufficiently large. This solution is general and strong in the sense that the game does not need any centralized institution or proactive system for incentives. Although social dilemmas can be resolved if punishing non-cooperators or rewarding cooperators works, such rewards and punishments, i.e., external incentives, entail certain expenses. As a result, a cooperative player who shirks his or her duty to provide an incentive to other players will emerge, and he or she will be more advantageous than an incentive-provider. In fact, the problem of excluding such cooperative incentive-non-providers, or second-order free-riders, is a well-known academic challenge. In order to meet this challenge, we devise and analyze a meta-incentive game that integrates positive incentives (rewards) and negative incentives (punishments) with second-order incentives, which are incentives for other players’ incentives. In this paper, we solve the replicator dynamics for a simple version of the game and analytically categorize the game types into four groups. We show that second-order incentives for first-order reward are necessary for cooperative regimes. This solution is general and strong in the sense that the game does not need any centralized institution or proactive system for incentives.

Inbound tourism plays an important role in local economies. To stimulate local economies, it is necessary to attract foreign tourists to various areas of a country. This research aims to develop a method of extracting the locations of tourist destinations in a country and to understand what characteristics foreign tourists expect of areas near tourist attractions compared with what domestic tourists expect. In this paper, a tourist destination is defined as a small area that has places of interests for tourists such as historic sites, theme parks, hotels, and restaurants. The methods proposed in this paper are applied to data acquired from Twitter and Foursquare in Japan. The proposed method successfully extracts the locations of tourist destinations and characterizes those locations based on the points of interest in the neighborhood. The results indicate that foreign tourists who come to Japan expect nightlife spots (bars, nightclubs, etc.) to be located in the neighborhood of tourist destinations, in contrast to the expectations of domestic tourists. The proposed methods are applicable to not only Japan, but to any country.

Why do people use social media, even though they must pay certain costs for generating content? The purpose of this study is to answer a more pointed question: What type of mechanism can encourage the continuous participation of users in social media? To address this question, we clarify various conditions that must be met to motivate users to voluntarily provide information in social media. In this work, we use the framework of ``the public goods game'' to describe user behaviors such as posting articles and comments. We first describe the communication carried out in specific social media by extending the metanorms game, which issues both rewards for cooperation and punishments for non-cooperation. We further develop a simulation model to clarify the conditions needed to create a cooperation-dominant environment, which we define as a world where nearly everyone cooperates in the sense used in game theory. From the results of our agent-based simulation, it is clear that the rewards for cooperation and the rewards for rewards given by others can work to encourage cooperation. Furthermore, we analyze the relationships between the costs and benefits of cooperation by using dynamics analysis. Consequently, we clarify that cooperative behaviors are dominant when the benefits of cooperation are larger than the costs of rewarding other users.

In this paper, we focus on the Werewolf Game. The Werewolf Game is an advanced communication-game in which winning or losing is directly linked to one's success or failure in communication. Therefore, we expect exponential developments in artificial intelligence by studying the Werewolf Game. In this current study, we propose a psychological model that considers multiple perspectives to model the play of a human such as inferring the intention of the other side. As one of the psychological models, we constructed a “one's self model” that models the role of others as viewed from their own viewpoint. In addition, to determine whether one's opinion is reliable after inferring other's intentions, we also constructed an “others model” that models the role of others as viewed from their viewpoints. Combining these models, we showed through experimentation that a combined approach achieved better results, i.e., higher win percentages.