# DECISION MAKING METHODS FOR ECONOMICS

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iten
Code
80675
2019/2020
CREDITS
6 credits during the 3nd year of 8699 Economics (L-33) GENOVA

6 credits during the 3nd year of 8766 Mathematical Statistics and Data Management (L-35) GENOVA

SCIENTIFIC DISCIPLINARY SECTOR
MAT/09
LANGUAGE
Italian
TEACHING LOCATION
GENOVA (Economics)
semester
1° Semester
Prerequisites
Teaching materials

OVERVIEW

The course introduces students to optimization models and decision support methods used for the solution of decision problems arising in economics. Some of the problems have been already studied from a teorethical view point in the 1° and 2° year thus the attention will be focused on the solutions search.

## AIMS AND CONTENT

LEARNING OUTCOMES

The course aims to provide students with adequate knowledge of the main quantitative methods of support to decision-making in the field of economics and to be able to use, with some mastery, the reference software environments. From a methodological point of view, specific situations of competitive, co-operative, gaming-related interaction situations, and convex programming algorithms will be illustrated to optimize certain objective functions, such as profit maximization and utility and minimization of the Costs.

AIMS AND LEARNING OUTCOMES

The aim of the course is to introduce students to models and methods of optimization, decision theory and game theory that can be used to solve decision problems in the economic and social field.

The course aims to present the subject in its theoretical, methodological and applicative aspects in order to provide students with knowledge of the applicable models and methodologies. The theoretical and practical laboratory aims to provide the student with the knowledge and skills to use specific software to solve the practical problems faced.

At the end of the course the students should have acquired skills that allow them to understand, describe and solve different types of real problems, developing models and solution methods and using, with a certain mastery, the reference software environments.
In particular, students at the end of the course will:

• Know and understand the main tools and methods of decision analysis that allow them to formalize individual decisions in the economic and social application field.
• Know the main elements of a problem of choice in terms of decision makers involved, type of data available, variables, constraints and objectives.
• Apply the acquired knowledge to describe, formalize and solve the problems and the related single-decider optimization models as well as those with social and economic interaction components in situations of applicative interest.
• Use both the conceptual and the operational level of the knowledge acquired with independent evaluation and critical reasoning skills, developing original models applicable in different application contexts.
• Have acquired a correct terminology and technical language to clearly communicate the main elements of the decision problem under consideration.
• Have acquired useful skills in the field of applied mathematics to access the most quantitative master's degree classes in the economic and quantitative area.
• Have developed learning skills that will allow them to deepen and apply independently the main topics of the discipline in the working contexts in which they will operate.

PREREQUISITES

Mathematics

Teaching methods

Lectures, analysis of case studies, computer lab exercises and lessons using ad hoc software tools.

SYLLABUS/CONTENT

For each part of the course, the theoretical discussion, intended to provide basic content, will be complemented by the practical/laboratory part in the computer classroom through the use of appropriate software tools.

Part I:

- Introduction to decision-making and problem solving: from real problems to mathematical models. - Classification of decision models. - Functional study in multiple variables: maximum and minimum unbounded. - Constrained optimization, Lagrangian functions, and economical interpretation of Lagrange multipliers.

Part II: - Introduction to non-linear, convex, linear, and mathematical mathematical programming models. - Consumer choice models and maximization of utility. - Business choice models: minimizing costs and maximizing profit. - Demand and Supply Models. - Models for selecting investment under risk.

Part III: - Decision trees and decisions theory in uncertainty and risk. - Optimizing the expected economic value of the decisions. - Utility theory. - Introduction to Game Theory. - Studying and modeling situations of interaction between subjects, competitive and cooperative, pure and mixed strategies. Game theory and optimization.

The text books and other additional handouts for foreign students will be communicated at the beginning of the course and published on Aulaweb

## TEACHERS AND EXAM BOARD

Ricevimento: Wednesday h.13.30-14.30 - Department of Economics - I floor

Exam Board

ELENA TANFANI (President)

ANNA FRANCA SCIOMACHEN

DANIELA AMBROSINO

## LESSONS

Teaching methods

Lectures, analysis of case studies, computer lab exercises and lessons using ad hoc software tools.

LESSONS START

Sem: I

16 Sept 2019 - 13 Dec 2019

ORARI

L'orario di tutti gli insegnamenti è consultabile su EasyAcademy.

Vedi anche:

DECISION MAKING METHODS FOR ECONOMICS

## EXAMS

Exam description

The verification of the achievement of the expected learning outcomes is evaluated with a written test and with a project work (in groups) and / or a practical test in the computer lab.

Assessment methods

The written test is aimed at assessing the degree of knowledge and learning of the theoretical topics discussed during the lessons. While the capacity for critical evaluation and reasoning and the ability to apply the acquired knowledge are evaluated through the project work and/or practical exam.

## FURTHER INFORMATION

Not compulsory.

The teaching is present on aulaweb. All students are invited to periodically consult the page of this course on the AulaWeb portal (http://www.aulaweb.unige.it/), where they will find further information and updates.

It should be noted that to take into account the findings of last year's teaching assessment questionnaires, more time will be devoted to the laboratory part and case study analysis.