Course 2025-2026 a.y.

21050 - ALGORITHMIC DECISION-MAKING

Department of Economics

Course taught in English
31
ACME (6 credits - I sem. - OP  |  SECS-P/01) - AFC (6 credits - I sem. - OP  |  SECS-P/01) - AI (6 credits - I sem. - OP  |  SECS-P/01) - CLELI (6 credits - I sem. - OP  |  SECS-P/01) - CLMG (6 credits - I sem. - OP  |  SECS-P/01) - DES-ESS (6 credits - I sem. - OP  |  SECS-P/01) - DSBA (6 credits - I sem. - OP  |  SECS-P/01) - EMIT (6 credits - I sem. - OP  |  SECS-P/01) - ESS (6 credits - I sem. - OP  |  SECS-P/01) - FIN (6 credits - I sem. - OP  |  SECS-P/01) - GIO (6 credits - I sem. - OP  |  SECS-P/01) - IM (6 credits - I sem. - OP  |  SECS-P/01) - MM (6 credits - I sem. - OP  |  SECS-P/01) - PPA (6 credits - I sem. - OP  |  SECS-P/01)
Course Director:
MARTINO BANCHIO

Classes: 31 (I sem.)
Instructors:
Class 31: MARTINO BANCHIO


Suggested background knowledge

Graduate-level mathematical maturity is recommended.

Mission & Content Summary

MISSION

This course explores the theoretical foundations and practical applications of algorithmic decision-making, bridging concepts from economics, computer science, and social sciences. As algorithms increasingly influence critical decisions in areas such as finance, healthcare, law, and public policy, understanding their design, impact, and ethical implications becomes paramount.

CONTENT SUMMARY

Algorithmic Decision Making under Ambiguity:

- Adversarial Bandits

 

Algorithmic Game Theory:
- Learning in Games

- Mechanism Design

- Algorithmic Mechanism Design

- Offline and Online Matching


Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...

  1. Understand Core Concepts: Explain fundamental concepts of algorithmic decision-making, including various types of algorithms (e.g., supervised learning, reinforcement learning, matching algorithms) and decision-theoretic frameworks.

  2. Model Decision Problems: Recognize decision-making problems and distinguish appropriate algorithmic and economic models to represent them.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  1. Analyze Algorithmic Impact: Critically analyze the incentive properties of algorithmic systems, their decision-making performance and their economic applications.

  2. Implement & Experiment: Gain practical experience in implementing simple algorithmic decision systems and conducting empirical analysis of their performance and properties.


Teaching methods

  • Lectures
  • Guest speaker's talks (in class or in distance)

DETAILS

Depending on availability, we will have one or more practitioners as guest speakers, telling us about how the concepts we study get applied in online markets.


Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
   x x
  • Collaborative Works / Assignment (report, exercise, presentation, project work etc.)
 x    
  • Active class participation (virtual, attendance)
 x    

ATTENDING AND NOT ATTENDING STUDENTS

  • Midterm Exam: 40%

  • Final Exam/Project: 50% 

  • Class Participation: 10% 

 

Midterm and Final exams will comprise exercises and projects that test the student's ability to translate abstract concepts in applied contexts. Applications will require an understanding of the theoretical concepts as well as the ability to translate practical considerations in model constraints, as predicated in the classroom.


Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

  • Bandit Algorithms: Slivkins, Aleksandrs. "Introduction to Multi-Armed Bandits." arXiv preprint arXiv:1904.07272 (2019). (Available online)

  • Algorithmic Game Theory: Roughgarden, Tim. "Twenty Lectures on Algorithmic Game Theory." Cambridge University Press, 2016. (Selected chapters/notes will be assigned, purchase not necessary)
Last change 03/06/2025 12:11