The current e-marketplace provides many tools and benefits that bring sellers and buyers together, and promote trading within cyberspace. And due to certain unique features of e-commerce, the competition also takes on characteristics different from those found in traditional commerce. This paper analyses both the competition between sellers, and the stable state in e-marketplace through a proposed model that applies evolutionary game theory. The purpose is to better understand these relations and the current state within emarketplace, as well as provide a tool for sellers to increase their profits. Here, the sellers are divided into four categories based on their scale (Large, Small) and sales strategy (Aggressive, Conservative). By developing Asymmetrical Competition Game Model in E-Marketplace (ACGME) in Nash Equilibrium, we analyze the composition of different sellers and how this proportion is affected by asymmetry among sellers. Finally, we conduct a simulation experiment to verify the effectiveness of our proposed model. Table 1: Four competition models in the e-marketplace. Large vs. Large Large vs. Small Small vs. Large Small vs. SmallThe rest of this paper is structured as follows. The next section references and discusses literature 650

Decision-making of market entry and incumbents defense is one of the most important issues in business activities as well as the research hotspot in academic community. This paper adopts game theory to study market-entry decisions of e-retailers in e-marketplace environment. All e-retailers are classified into two categories: one belongs to "giants" who have a great quantity of investments and aim to earn large profits. Another belongs to "dwarfs" who invest not so much and are less eager to make profit. To study the different characters between them and make entry decision in virtual business world, an e-commerce market entry game model is proposed. Using this model, a simulation of market entry decision is formed and a game tree is drawn to analyze the equilibrium. In addition to pure strategy equilibriums, the mixed strategy equilibriums are also analyzed to explore the probability of entrance into the market. Finally, through quantitative analyses, the proposed model demonstrates effective application for e-commerce context and meaningful advice for entrants to make entry or exit decision.

One of the biggest challenges in e-commerce is to utilize data mining methods for the improvement of profitability for both platform hosts and e-commerce vendors. Taking Alibaba as an example, the more efficient method of operation is to collect hosting service fees from the vendors that use the platform. The platform defines a service fee value and the vendors can decide whether to accept or not. In this sense, it is necessary to create an analytical tool to improve and maximize the profitability of this partnership. This work proposes a dynamic in-cooperative E-Commerce Game Model (E-CGM). In E-CGM, the platform hosting company and the e-commerce vendors have their payoff functions calculated using backwards induction and their activities are simulated in a game where the goal is to achieve the biggest payoff. Taking into consideration various market conditions, E-CGM obtains the Nash equilibrium and calculates the value for which the service fee would yield the most profitable result. By comparing the data mining results obtained from a set of real data provided by Alibaba, E-CGM simulated the expected transaction volume based on a selected service fee. The results demonstrate that the proposed model using game theory is suitable for e-commerce studies and can help improve profitability for the partners of an online business model.