OVERVIEW

The course introduces students to optimization models and decision support methods used for the analysis and resolution of decision problems arising in economics and social sciences. Some of the problems have been already studied from a teorethical view point in some of the 1° and 2° year courses of the bachelor in Economics, thus the attention will be focused on how to obtain a solution for the different problems adressed using specific software.

AIMS AND CONTENT

LEARNING OUTCOMES

The course aims to provide students with a basic knowledge about the main quantitative methods to support  decision-making processes in  economics, both in certain and uncertain environments. The course also aims to let the students be able to use the reference software environments for solving the different problems faced. From a methodological point of view will be introduced single decision maker optmization problems, in particular using convex programming models to optimize different objective functions (such as profit and utility maximization and cost minimization), decision models under uncertainty and risk and game theory decision methods to deal with situations of strategic interaction. 

AIMS AND LEARNING OUTCOMES

The course aims to present the subject in its theoretical, methodological and applicative features in order to provide students with knowledge of the applicable models and methodologies. The practical laboratories aim to provide the student with the knowledge and skills to use specific software in order 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 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.
• Use the 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.
• Ability to communicate effectively in written and oral form, adaptation of the communication to the context, use of various sources and aids, critical thinking, ability to use, process and evaluate information, argumentative skills.
• Ability to identify own abilities, focus, handle complexity, think critically, make decisions, work independently, ask for support, handle stress.
• Ability to manage social interactions, cooperative attitude, constructive communication in different environments, ability to respect others and their needs, willingness to overcome prejudices, express and understand different points of view, conflict management, ability to build trust, empathy.
• Awareness with respect to own learning strategies, organization and evaluation of personal learning according to what is understood and learned, understanding of own needs and ways of developing skills, ability to identify and pursue learning goals.
• L’insegnamento contribuisce al potenziamento delle soft skills, in particolare la competenza alfabetico-funzionale (livello avanzato), la competenza personale e quella sociale (livello avanzato) e la capacità di imparare ad imparare (livello avanzato) e concorre all'ottenimento dei rispettivi Open Badge.

PREREQUISITES

Suggested: Mathematics and Statistics

TEACHING METHODS

Lectures, analysis of case studies, exercises and labs using software (Excel and optimization software). Activities with active, interactive and constructive teaching techniques, such as Flipped Classroom, Team Based Learning and Problem based learning, will be proposed. The teaching mode of the calendar classes will be communicated on the Aulaweb page of the teaching (registration required and recommended).

Working students and students with certified SLD (Specific Learning Disorders), disability or other special educational needs are advised to contact the teacher at the beginning of the course to agree on teaching and examination arrangements so to take into account individual learning patterns, while respecting the teaching objectives.

SYLLABUS/CONTENT

For each part of the course, the theoretical discussion intended to provide basic theory content, will be complemented by the practical/laboratory part in the computer lab using specific software tools (Excel solver and optimization software).

Part I:

• Introduction to decision-making and problem solving: from real problems to mathematical models.
• Decision models classification.
• Introduction to non-linear, convex, linear and integer mathematical programming.
• Resource allocation problems arising in the production of goods and services
• Supply and demand matching problems
• Linear programming, duality, economic interpretation and sensitivity
• Introducing non-linear hypotheses on prices and costs

Part II:

• Study of functions in several variables: unconstrained maxima and minima.
• Constrained optimization, Lagrangian functions and economic interpretation of Lagrange multipliers.
• Consumer choice models and utility maximization.
• Company choice models: cost minimization and profit maximization.
• Models for selecting investments and portfolio oprimization.

Part III: 

• Decision trees and decision theory considering uncertainty and risk.
• Optimization of the expected economic value of decisions.
• Utility Theory.
• Introduction to Game Theory.
• Study and modeling situations of strategic interactions (Pure and mixed strategies). Game theory and optimization.

RECOMMENDED READING/BIBLIOGRAPHY

The slides used by the teacher, text books and other additional handouts for foreign students will be communicated at the beginning of the course and published on the course Aulaweb page

TEACHERS AND EXAM BOARD

Exam Board

ELENA TANFANI (President)

ANNA FRANCA SCIOMACHEN

DANIELA AMBROSINO (President Substitute)

LESSONS

LESSONS START

Sem: I

27 Sept 2023

Class schedule

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 (alone or in groups) or a Lab test. The sincronous and asynconous activities proposed will be also evaluated. 

ASSESSMENT METHODS

The written test is aimed at assessing the degree of knowledge and learning of the theoretical topics discussed during the lectures. While the critical evaluation and reasoning and the ability to apply the acquired knowledge are evaluated through the project work or the lab test. The written and project work/lab test are worth 90% of the grade; participation in the proposed activities and team work contributes up to 10% to the final grade.

Exam schedule

FURTHER INFORMATION

Not compulsory.

The course is available 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 labs and the use of software to solve case studies, team based learning and problem based learning activities.