Ichiro Fukuda

Reciprocal Altruism-based Cooperation in a Social Network Game

Cooperative behaviors are common in humans and are fundamental to our society. Theoretical and experimental studies have modeled environments in which the behaviors of humans, or agents, have been restricted to analyze their social behavior. However, it is important that such studies are generalized to less restrictive environments to understand human society. Social network games (SNGs) provide a particularly powerful tool for the quantitative study of human behavior. In SNGs, numerous players can behave more freely than in the environments used in previous studies; moreover, their relationships include apparent conflicts of interest and every action can be recorded. We focused on reciprocal altruism, one of the mechanisms that generate cooperative behavior. This study aims to investigate cooperative behavior based on reciprocal altruism in a less restrictive environment. For this purpose, we analyzed the social behavior underlying such cooperative behavior in an SNG. We focused on a game scenario in which the relationship between the players was similar to that in the Leader game. We defined cooperative behaviors by constructing a payoff matrix in the scenario. The results showed that players maintained cooperative behavior based on reciprocal altruism, and cooperators received more advantages than noncooperators. We found that players constructed reciprocal relationships based on two types of interactions, cooperative behavior and unproductive communication.

Lightweight Interactions for Reciprocal Cooperation in a Social Network Game

The construction of reciprocal relationships requires cooperative interactions during the initial meetings. However, cooperative behavior with strangers is risky because the strangers may be exploiters. In this study, we show that people increase the likelihood of cooperativeness of strangers by using lightweight non-risky interactions in risky situations based on the analysis of a social network game (SNG). They can construct reciprocal relationships in this manner. The interactions involve low-cost signaling because they are not generated at any cost to the senders and recipients. Theoretical studies show that low-cost signals are not guaranteed to be reliable because the low-cost signals from senders can lie at any time. However, people used low-cost signals to construct reciprocal relationships in an SNG, which suggests the existence of mechanisms for generating reliable, low-cost signals in human evolution.

Environmentally Driven Migration in a Social Network Game

Cooperative behaviors are common in humans, and they are the fundamental basis of our society. Theoretical and experimental studies have modeled environments where the behaviors of humans, or agents, have been restricted to analyze their social behavior. However, it is important that such studies can be generalized to a less restrictive environment in order to understand human society. Social network games (SNGs) provide a powerful tool for the quantitative study of human behavior using a less restrictive environment than in previous studies. We focused on multilevel selection, one of the theoretical frameworks used to study the evolution of cooperation. The evolution of cooperation by multilevel selection requires that the continual assortment between cooperators and noncooperators is generated; thus, humans may have acquired mechanisms that ensure assortment (e.g., migration between groups). This study aims to investigate this mechanism in a less restrictive environment. For this purpose, we researched the effect of migration based on data analysis in an SNG where the players could behave more freely than was possible in the environments used in the previous studies. We showed that players maintained assortment between cooperators and defectors in this SNG, where it appears that environmentally driven migration generated the assortment.

Limitations of Time Resources in Human Relationships Determine Social Structures

The number of possible social relationships a single human being can be involved in is limited, and the distribution of strengths of such relationships show significant skew. This skewness suggests that costs and benefits of the social interactions required to bond with others (social grooming) depend on the strength of the social relationships: if it involved uniform costs and benefits, the distribution would not be skew. In this paper, we show that the cost of social grooming increases with the strength of social relationships, and its gradient increases the width and shallowness of these relationships as evident from an analysis of data from six communication systems. We show that narrow and deep social relationships require higher costs per relationship than do wide and shallow ones, using a comparison with a null model where social grooming costs were assumed to be independent of the strength of social relationships. This may be due to increase in communication volumes, such as number of characters and duration of calls, along with an increase in the strength of social relationships. We test this hypothesis using an individual-based simulation where social grooming costs are assumed to increase linearly with the strength of social relationships; this is the simplest assumption. The results of this simulation suggest that the gradient of social grooming costs increases the width and shallowness of social relationships.

How Do Newcomers Blend into a Group?: Study on a Social Network Game

Cooperative behaviors are common in humans, and they are the fundamental basis of our society. We focused on multilevel selection, one of the theoretical frameworks used to study the evolution of cooperation. The evolution of cooperation by multilevel selection requires that the continual assortment between cooperators and noncooperators is generated, thus, humans may have acquired mechanisms that ensure assortment, e.g., migration between groups. However, human migration involves taking risks, because migrators cannot know whether they should join new groups. In a similar way, it is also too risky to interact with newcomers for existing members. This study aims to investigate this migration behavior that may generate the assortment between cooperators and noncooperators in a less restrictive environment. Especially, we explore how humans resolve the migration risks. Therefore, we research social interaction between newcomers (migrators) and existing group members in a social network game (SNG), wherein players can behave more freely compared to the environments used in previous studies. Our result suggests that migration which generated assortment between cooperators and noncooperators may have not been probably risky in human society. Moreover, we found interesting differences between newcomers and existing group members in social interaction.

Analytical method of web user behavior using Hidden Markov Model

We propose a new analytical method to classify web user behavior based on such latent states of users as intention, interest, or motivation. First, we put the clickstream data of many users into a Hidden Markov Model in which the number of hidden states is large enough to build a state transition network. Since the variable hidden states represent different latent states of users, the movement on the state transition network can represent user behavior. Second, we divide each piece of clickstream data into sessions, which we classify using network movement as feature values. These cluster labels represent the latent states of users during their stay in the web service. In this paper, we applied our method to the data of a social network game named Girl Friend BETA, which is an online game that is mainly provided on social networking services. We observed the following hidden states that represent the variable latent states of users, such as enthusiasm for the main contents of the service, playing basic content, and daily routines that are well observed by visiting the service: e.g. receiving login bonuses. Also, we classified the sessions by the latent states of users, such as light user sessions, low motivation sessions, and sessions in which users seem addicted to the main contents.

Managing a complicated workflow based on dataflow-based workflow scheduler

Diversifying requirements forces a data analytics platform to deal with complicated workflows including various combinations of data integration. We have developed a new workflow scheduler for our Hadoop-based data analytics platform to cope with the issue. The new scheduler enables fine-grained dependency management efficiently on a huge monolithic workflow and can improve the utilization efficiency of computational resources while keeping the maintainability of the workflow. This paper describes the design of the new workflow scheduler and introduces how we have been operating the data analytics platform based on the scheduler.