Course Category：Professional Basic Course
Prerequisites：Advanced Mathematics, Linear Algebra, Probability, Linear Programming and Nonlinear Programming.
Aims & Requirements：
This is a basic course for graduate students in Operations Research & Control Theory and Management Sciences and Engineering, but it can also be an elective for students of majors like Applied Mathematics. Through the study of this course, students are supposed to master the main concepts, basic theories and methods, acquaint with the trends of development of Decision Analysis, so as to lay foundation for further study and research.
Chapter 1 Introduction
Chapter 2 Bayes Decision Making Theory
Bayes theorem; sufficient statistic; lose function; Bayes risk; Bayes Analysis; sensitivity of Bayes regulations.
Chapter 3 Utility Theory
Construction of utility function; relation between risk and utility; application of utility function.
Chapter 4 Multi-Attribute and Multi-objective Decision Making
Priority order; Multi-Attribute value function; AHP; fuzzy synthetic methods; weighted sum method; Electre method; objective program method; method of idle points movement; sequential decision.
Chapter 5 Group Decision Making
Social choice function; social welfare function; group utility function; applications of group decision making.
Chapter 6 Multi-Period Decision Making and Dynamic Programming
 G. Gregory, Decision Analysis, Pitman Publishing Co., London, 1988.
 M. Chapman, Decision Analysis, Majesty, London, 1980.
Author：Shouyang Wang (Academy of Mathematics and Systems Science)