Theory and Approaches of Unascertained Group Decision-Making

The question of how to aggregate structural uncertainties of multiple variables to determine the group preference, on the basis of  the complexity of group decision-making, is a problem that urgently waits for a resolution. This book expresses the complexity existing in each group decision-making process by employing complex networks and studying various methods of aggregating preference information from various unascertained, time-series based decision-making groups’ structures. It covers the decision-making mechanism of unascertained preference information, aggregation of unascertained preference information, and various methods of aggregating preference information in dynamic environments.

Tackling the question of how to effectively aggregate uncertain preference information in multiple structures given by decision-making groups, Theory and Approaches of Unascertained Group Decision-Making focuses on group aggregation methods based on uncertainty preference information. It expresses the complexity existing in each group decision-making process by employing complex networks and studying various methods of aggregating preference information from various unascertained, time-series based decision-making groups'' structures.

Written by Jianjun Zhu, director of the Chinese Society for Optimization, Overall Planning, and Economic Mathematics, the book starts by examining case studies of group decision making. It discusses consistency measuring and ranking methods of interval number reciprocal judgment matrix and interval number complementary judgment matrix. Next, it presents an unascertained number preference and a three-point interval number preference.

Investigating linguistic preferences, the text provides two consistencies definitions. It considers the aggregating methods of several uncertain preferences as well as the multistage aggregating model of uncertain preference. The final chapter proposes an aggregating model of multistage linguistic information based on TOPSIS.

The research in this book is supported by a number of scientific foundations from the People’s Republic of China, including the National Natural Science Foundation of China; the Social Science Foundation of China, Jiangsu Province; and the Soft Science Foundation of China.