Course information | Decision Making

Course Syllabus

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Modalità di verifica dell'apprendimento

Sustainable Development Goals

SALUTE E BENESSERE | ENERGIA PULITA E ACCESSIBILE | CONSUMO E PRODUZIONE RESPONSABILI

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Learning area

APPLIED EXPERIMENTAL PSYCHOLOGICAL SCIENCES

Learning objectives

Knowledge and understanding

Understand the ideal standards of decision-making both in individual and interactive context
Understand why people fail to cope with ideal standards
Heuristics in decision-making and associated biases
Prospect theory and associated formal modeling of decision making
Understand how indirect suggestions can influence decisions (nudging)

Applying knowledge and understanding

Determination of the optimal course of action in different contexts, with examples from clinical decision making and economic decisions
Analysis of the typical decision course of individuals, with critical analysis of their limits
Use of professional software for building and visualizing decision trees

The course will explore and discuss the main theories, recent experimental evidence, and applications on human decision making. Students will also learn basic use of a software for building and visualizing decision trees.

Detailed program

Choice under certainty
Judgment under risk and uncertainty
Choice under risk and uncertainty
Intertemporal choice
Prospect theory and Nudging
Decision Trees with sensitivity analysis
Cost-Effectiveness Analysis
Advice taking
Human - AI collaborative decision making

Prerequisites

Teaching methods

The course will be held in presence. Teaching methods include lectures, short films, classroom discussions, group work, and exercises. Smartphone apps that allow students to respond to open or closed questions in real-time will be utilized. Once a week, lectures will be held in a computer lab to (i) work on short presentations discussing a common topic chosen by the teacher, and (ii) learn and practice using software for building decision trees. All course materials will be available on the course's e-learning website. Additionally, a group chat will enable interaction with both fellow students and the teacher. The teaching approach will be one-third interactive and two-thirds lecture-based.

Assessment methods

The exam includes a written test to be performed in a computer lab. The test involves three parts: a multiple response test, open questions and an exercise with a decision tree software. The exam aims at ascertaining the effective acquisition of both theoretical knowledge and the ability to connect and apply the different topics of the course. The answers to each question will be evaluated for correctness, argumentative capacity, synthesis, ability to form links among the different areas, and the ability to present the phenomena critically. The activities performed during the course will be part of the overall evaluation.