Multi-Agent simulation is primary technology in AI. MASim methodologies/technologies are still not sufficiently mature though, its scientific significance is getting quite high to understand and analyze complex mega-scale systems, such as human societies. Data mining is another primary AI technology to retrieve hidden information or knowledge from big data. However, real data for the mining does not always include essential elements of a target complex system. Thus, a simulation is promising way to generate meaningful data which is hard to obtain in the real world. In order to understand diverse mega-scale complex systems such as a human brain, social systems, Internet, and WWW, it is not enough to simply dig out knowledges from the vein of data. It is required to establish new "constructive data mining process" consisting of iterative processes of the generation of data veins and exploration of new knowledge from them. Therefore, we try to harness multi-agent simulation and data mining technologies and find the best mix of MASim and DM technologies. To understand mega-scale complex phenomena, technologies/methodologies for simulation, knowledge discovery, and computational modeling are required. Although MASim researchers are good at working on the implementation of tools for multi-agent simulations and the design of computational model, they are not necessarily experts of knowledge discovery who can extract essentials of complex systems. On the other hand, DM researchers are technicians for knowledge discovery though, it is usually hard for them to actively analyze obtained knowledge through simulations. Thus, the workshop should be of interest to researchers addressing complex systems from different viewpoints, such as modeling, implementation, and data analysis.