The Effect of the Network Structure Differences on the Diffusion of Items

This paper presents a multiagent-based simulation approach to the effect of network structure difference on the diffusion of items. Recently, the rapid spread of information and communication technology induces multiplexing of our communication space. As a result, diffusion of products in the market has been changed because the communication affects our behaviors and state of own mind. Network externality is an effect that value of a product depends on the market penetration. It has been known that markets of products having network externality are greatly influenced by interpersonal communication. In this paper, we constructed two network, “offline network” and “online network” that refer to networks before and after the development of information and communication technologies, focusing on such changes and analyzed the differences between network structures. We verified the effect that difference of network structures affects the diffusion of items in network effect markets. We discussed the property of diffusion process in each network and how easily monopolistic diffusion can occur depending on the network structures. Introduction Recently, a socializing method and other parties of association have been changed because of the development of information and communication technologies such as the Internet and mobile phones. For example, mobile phones make it possible to communicate with anyone, anytime, and the Internet and Social Network Service (SNS) make it easy for people who have something in common to communicate through online communities. It is thought that communication and exchanging information with others, and their behavior, affects our own actions and psychology. The network effect (also called network externality or demand-side economies of scale) in markets is an example of interaction with others producing a major effect on individuals (Katz and Shapiro, 1985). Briefly, the network effect means that as the number of users an item has increases, that item becomes more attractive to others. In this situation, it is thought that the diffusion rate of items within one group of friends affects the decision about whether or not to buy something. When two similar items are in a race to be the most popular, it is common for only one item to be shared exclusively because of positive feedback and items selling faster if they are already doing well. This phenomenon is called “winnertakes-all” by Arthur (1996). There has been much previous research about the network effect (e.g., Ozawa and Nakayama (2010); Weitzel et al. (2003)). Kaneko et al. (2006, 2005) constructed a model that considered the asymmetricity of information and analyzed customers’ purchase decision-making processes. In the model each consumer had different information with respect to others’ purchase behavior, and they reported that the market becomes inefficient if customers are unaware of each other’s behavior. Kawamura and Ohuchi (2005) analyzed the effectiveness of the present strategy, in which businesses provide their services without charge. They examined the effectiveness of two present strategies: a simple present strategy and friend present strategy, and discussed the effectiveness of present strategies in different network structures. Iba et al. (2001) analyzed the format competition of video cassette recorders, that is to say a typical standard race interaction between customers, by the artificial market model with multiagent approach. The simulation observed the emergence of locality, which is caused by the local influence, and the results showed that the local clusters provide the brakes on the winner-take-all phenomenon. Mizutani (2002) proposed a simulation model with evincive individual relationships. He used network structures that describe the relationship in urban and provincial areas, and showed that differences to these structures can affect the diffusion race. In a network effect market, the situation at the early stage is significant in terms of the final diffusion result (Liebowits and Margolis, 1994). Therefore, we need to examine the early stage and come up with a definition for the customer group that decides to buy an item at the early stage and how the diffusion process proceeds from that point. Additionally, it seems to be important to study acquaintance network structures that show the relationships between customers. We propose a model that draws on Mizutani (2002)’s model that focuses on the relationship between network structure differences and the diffusion state. First, we deALIFE 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems