Decision Support System Technology in Economics

Entry requirements: Basic understanding of economics terminology

Credits: 4

Course: Core

Language of the course: Russian

Lecturer

Sergey Ivanov

Objectives

Students will learn:

  • To understand key principles of decision making in economics
  • To employ various decision support technologies
  • To solve economical problems in various settings and to find solutions
  • To use decision making methods under certainty, uncertainty and risk
  • To use modelling methods for decision making in economics

Contents

  • Key concepts and definitions of decision making theory: Definition of decision making theory. Tasks of decision making theory. Elements of decision making process and classification of tasks. Classification of models and decision making methods.
  • Multi-objective optimization: Foundations of multi-objective optimization tasks. Mathematical model of design project. Internal, output and external parameters of design project. Restrictions. Operativity domain. Local (partial) criteria. Local estimates. Criteria space. Problem definition of multi-objective optimization. Problems of solving multi-objective optimization tasks. Noncomparability of decisions. Criteria normialization. Selecting optimality principle. Registration of criteria priority. Computing the optimum of vector optimization task. Main areas of solution methods in vector optimization.
  • Pareto efficiency: Pareto efficiency. Pareto dominance relation. Pareto optimality. Analytical methods for building Pareto set. Trade-off curve (Pareto front). Computation of trade-off curves. Narrowing method for Pareto optimal solutions.
  • Solution methods for vector optimization tasks: Methods of substituting vector criterion with nonvector criterion. Additive criterion of optimality. Multiplicative optimality criterion. Ideal point method. Problems of building generalized criterion for vector optimization tasks. Difficulties with building generalized criterion. Formal definition of generalized criterion. Ranging partial criteria. Methods of deining weighting coefficient.
  • Sequential optimization methods: Global criterion method. Sequential concession method. Lexicographic criterion. Partial criteria equality method.
  • Decision making under uncertainty: Decision making under uncertainty. Laplace criterion, Savage criterion, Hurwitz criterion, minimax criterion.
  • Decision making under risk: Decision making under risk. Expected value criterion (profit and cost); combination of expected value and dispersion, ultimate level criterion; most likely outcome criterion. Experimental data in decision making under risk. Decision trees.
  • Game theory: Key concepts and definitions. Antagonistic games. Payoff matrix. Game value. Saddle point. Mixed strategies. Reducing matrix game to a linear programming task.

Format

Lectures, practical sessions and labs

Assessment

Attendance is mandatory. Students cannot miss more than one class. Students should complete all the assignments. The final grade is based on the student performance throughout the course: 40% individual work; 60% final exam.