BACHELOR S DEGREE IN BUSINESS ADMINISTRATION

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1 BACHELOR S DEGREE IN BUSINESS ADMINISTRATION Course Decision Analysis Code Module Character Credits 6 Cross sectional formation Compulsory Area Attendance 2.7 Non Attendance Year 3 Semester 5 Quantitative Methods 3.3 Department Professor Cristina del Campo Campos STATISTICS AND OPERATIONAL RESEARCH II Coordinator: Jose María Maroto campocc@ccee.ucm.es SYNOPSIS BRIEF DESCRIPTION Decision Analysis (DA) is the discipline comprising the philosophy, theory, methodology, and professional practice necessary to address important decisions in a formal manner. DA includes many procedures, methods, and tools for identifying, representing, and formally assessing important aspects of a decision, as well as translating the formal representation of a decision and its corresponding recommendation into insight for the decision maker and other stakeholders. PRE-REQUISITES Descriptive statistics, Probability Theory and Probability distribution models. AIMS & OBJECTIVES To know the rational fundamentals and the methods to aid the decision-maker, enabling the student to learn about decision making under certainty, uncertainty and under risk, both from a multivariate and univariate perspective. COMPETENCES General: CG1, CG2, CG3

2 Cross-sectional: CT1, CT2, CT4, CT5 Specific: CE3, CE4, CE5, CE6 LEARNING METHODOLOGY A mixed methodology of teaching and learning will be used in all educational activities with the aim of encouraging students to develop a collaborative and cooperative attitude in the pursuit of knowledge. TEACHING ACTIVITIES % OF TOTAL CREDITS ATTENDANCE Lectures 20% 100% Classes 20% 75% Tutorials 6% 100% Assessment activities 4% 100% Homeworks and class assignments 20% 0% Time to study 30% 0% ASSESSMENT Examination % Share of Final Grade 60% Final Exam Other Activities % Share of Final Grade 10% Active participation in the classroom and in the seminars Other activities % Share of Final Grade 30% Cases and exercises solution ASSESSMENT CRITERIA In the ordinary call (February) the student must decide (in the first and a half month) whether to follow the continuous assessment or not. If the student decides not to follow the continuous assessment and does not sit for the final exam (February), the rating will be NP (not evaluated). If the student follows the continuous assessment, the final grade will be obtained applying the criteria set in the course tab, even if he/she does not sit for the final exam. In the extraordinary call (June), if the student does not sit for the exam, the rating will be NP (not evaluated), regardless whether he/she followed the continuous assessment. In the case the student sits for the exam, the grade will be the result of applying the evaluation criteria set in the course tab. Continuous assessment in the extraordinary examination: in case one student has failed the ordinary examination, having attended the final exam and participated in the continuous assessment, the mark to be considered as continuous assessment for that extraordinary examination will be the final mark obtained in the ordinary examination.

3 IMPORTANT NOTICE: Students are not allowed to use any programmable calculator in the final examination. Only non-programmable scientific calculators will be allowed. Similarly, no electronic translator is allowed in the final examination.

4 Tentative Schedule Week TOPIC In the classroom Outside the classroom 1 Introduction to course. Chapter 1: Introduction Read chapter 1 materials. Read chapter 2 materials. 2 Chapter 2: Decisions under Risk. Axiomatic approach Exercises chapter 2 Study chapter 2 and solve Read chapter 3 3 Chapter 3: Historical antecedents. Decision problem Modelling. Decision criteria 4 Chapter 3 (Cont.): Sequential decision processes. Bayesian decision analysis 5 Chapter 3 (Cont.): Information efficiency Chapter 4: Decisions under Strict Uncertainty 6 Chapter 5: Multi-criteria Decision Making. Introduction 7 Chapter 6: Historical antecedents. Continuous Multicriteria Decision Methods. Efficient solutions. Exercises chapter 2 Exercises chapter 3 Practical case 1 Exercises chapter 3 and 4 Exercises chapter 5 Practical case 2 Seminar 1: Linear Programming and Excel Exercises chapter 6 materials. Study chapter 3 and solve Study chapter 3 and solve Study chapter 3 and 4 and solve Read chapter 5 materials. Study chapter 5 and solve Read chapter 6 materials. 8 Chapter 6: Compromise Programming. Exercises chapter 6 9 Chapter 6: Compromise Programming (Cont.). Goal Programming 10 Chapter 6 (Cont): Compromise programming 11 Chapter 6 (Cont.): Goal programming. Chapter 7. Discrete Multicriteria Decision Problems. Exercises chapter 6 Practical case 3 Seminar 2: Compromise Programming Practical case 4 Exercises chapter 6 12 Chapter 6: Goal programming (Cont.). Seminar 4: Goal programming 13 Chapter 7. Discrete Multicriteria Decision Problems. Exercises chapter 7 Practical case 5 Read chapter 7 materials. Study chapter 7 and solve 14 Chapter 7: Discrete MCDM problems (Cont.). Seminar 4: AHP Study chapter 7 and solve Read chapter 8 materials. 15 Chapter 8: Two Player Games Historical Exercises chapter 8 Study chapter 8 and solve

5 antecedents. Non constant-sum games. Nash equilibrium.

6 RESOURCES GENERAL BIBLIOGRAPHY - Balakrishnan, N., Render, B. and Stair, R.M. (2007): Managerial Decision Modeling with Spreadsheets and Student CD Package, International Edition, 2/E, Pearson Higher Education. - French, S., Maule, J. and Papamichail, N. (2009): Decision Behaviour, Analysis and Support, Cambridge University Press. - Forman, E.; Selly, M.A. Decision by Objectives (how to convince others that you are right). ( - Gibbons, R. (1992): A Primer in Game Theory, Pearson Academic. - Hillier, F.S. and Liberman, G.J. (2010): Introduction to Operations Research, Mc.Graw-Hill. - Roy, B. (1996): Multicriteria Methodology for Decison Aiding, Kluwer Academic Publishers. - Vincke, P. (1988): L Aide Multicritère à la Décision, Éditions de L Université de Bruxelles. FURTHER READING - Barba -Romero, S. and Pomerol, S.J. (1997): Decisiones Multicriterio. Servicio de Publicaciones de la Universidad de Alcalá de Henares. - Barbolla, R., Cerdá, E. and Sanz, P. (2001): Optimización, cuestiones, ejercicios y aplicaciones a la economía. Colección Prentice Práctica, Prentice Hall, Madrid. - García Aguado, J., Martínez Rodríguez, E., Redondo Palomo, R. and del Campo Campos, C. (2013): Métodos de Decisión Unicriterio. Ejercicios resueltos, Prentice Hall, Madrid. - García Aguado, A., Martínez Rodríguez, E., del Campo Campos, C. and Lopez Zafra, J.M. (2013): Técnicas multicriterio de ayuda a la decisión, Prentice Hall, Madrid. - Guerrero, F. (1994): Curso de Optimización. Programación Matemática. Ed. Ariel Económica. OTHER RESOURCES - E-learning platform: Campus Virtual - Software: Microsoft - Excel, Expert Choice