Appreciate I have an opportunity to talk “New concepts and trends of hybrid MCDM model for tomorrow” including my two new books and a series of recent published SSCI/SCI journal papers for sharing with our Colleagues of National Taipei University for solving actual/real world problems in business and economics by Academic Speaker in this talk. In recent I develop and propose several important new concepts and trends in the MCDM field (including data mining of cause-effects in Rough Set Theory for MCDM) for solving actual/real world problems. First, the traditional model assumes the criteria are independently and hierarchically structured (Statistics and Economics are unrealistic in the real world); however, in reality, problems are often characterized by interdependent criteria/dimensions and may even exhibit feedback-like cause-effects. Second, relatively good solutions from the existing alternatives are replaced by aspiration levels to fit today’s competitive markets (avoid “Choose the best among inferior choices/options/alternatives”, i.e., avoid “Pick the best apple among a barrel of rotten apples”). Third, the emphasis in the field has shifted from ranking and selection when determining the most preferable approaches to performance improvement of existing methods based on influential network relation map (INRM) combining rough set theory (we need a systematic approach to problem-solving; instead of addressing the systems of the problem, we need to identify the sources of the problem; i.e., avoid “stop-gap piecemeal (腳痛醫腳頭痛醫頭)). Fourth, information fusion techniques, one plus one ( ) is larger than two, including the fuzzy integral method, have been developed to aggregate the performances for suitable the real world problems. Finally, the original fixed resources in multi-objective programming are divided such that both decision and objective spaces are changeable (called “Changeable Spaces Programming”). In these two books and a series of our recently published papers, we add several new concepts and trends for tomorrow's world that could be thought of as an attempt to complete the original MCDM methods in solving the actual/real world problems.