Implementation Issues of Hydrological Expert Systems - A Civil Engineering Case Study

Bidding Decision Model Based on Prospect Theory and Game Theory

“…In order to assign an implement probability for each bidding strategy to all participants, this study adopted FPR to estimate the relative importance of the bidding strategies to each participant. This study adopt linguistic terms are { AH (Absolutely highly important), VH (Very highly important), SH (Strongly highly important), WH (Weakly highly important), EQ (Equally important), WL (Weakly lowly important), SL (Strongly lowly important), VL (Very strongly lowly important), AL (Absolutely lowly important)} and the intensity of linguistic terms are {AH (9), VH (7), SH (5), WH (3), EQ (1), WL (1/3), SL (1/5), VL (1/7), AL (1/9)}. For example, the assessment by the experts of A-company are {VL, EQ, WH, SH}, {AL, WL, EQ, AH}, {VL, WL, WH, VH}, {AL, EQ, EQ, AH} and {SL, EQ, WH, AH}, via the computational process [14], relative weights can be calculated as {0.1086, 0.2644, 0.2964, 0.2491, 0.0815}.…”

“…Early mark-up scale estimation models employed probability theory to predict the probability of winning a particular contract [8]. Recently, expert system [9], case-based reasoning [10], neural network [11], analytical hierarchy process [12], and fuzzy set theory [13] are adopted in bidding decision making. A BD-MCPM model was proposed [14], which combine FPR with CPT to help primary decision maker (PDM) determine which projects should be bid and the optimal mark-up size.…”