ADVANCED MICROECONOMICS
- Anno accademico
- 2026/2027 Programmi anni precedenti
- Titolo corso in inglese
- ADVANCED MICROECONOMICS
- Codice insegnamento
- PHD107 (AF:753804 AR:450096)
- Lingua di insegnamento
- Inglese
- Modalità
- In presenza
- Crediti formativi universitari
- 6
- Livello laurea
- Corso di Dottorato (D.M.226/2021)
- Settore scientifico disciplinare
- STAT-04/A
- Periodo
- 2° Periodo
- Anno corso
- 1
- Sede
- VENEZIA
Inquadramento dell'insegnamento nel percorso del corso di studio
Risultati di apprendimento attesi
1. Knowledge and understanding:
1.1. Knowing models and algorithms;
1.2. Knowing how to present and interpret the results produced by these models and algorithms to answer research questions (widely interpreted);
1.3. Knowing the hypotheses at the basis of the models and algorithms studied during the course and their logical consequences on the output.
2. Applying knowledge and understanding:
2.1. Applying the models and algorithms studied in the course to answer a research question in social choice;
2.2. Comparing the results produced by alternative models or algorithms based on different set of hypotheses and their limitations concerning the research questions at hand;
2.3. Applying economic theory to assess the plausibility of alternative set of assumptions fed into the models or algorithms.
3. Evaluation and project skills:
3.1. Designing novel models or procedures (or tweaking existing one) to address an economic research question of interest;
3.2. Evaluating the contribution provided by models and algorithms available in the economic literature to address a research question of interest;
3.3. Thinking about the design of models and algorithms aimed at improving the existing knowledge base or address specific issues.
4. Lifelong learning skills
4.1 Master complex modeling and algorithmic reasoning;
4.2 Master theoretical methodologies to guide applications;
4.3 Ability to exploit new tools and adapt them to different contexts.
Prerequisiti
Contenuti
1) AI Demand.
2) AI Supply.
3) AI Pricing.
4) AI Policy.
Testi di riferimento
J. Gans (2025), The Microeconomics of Artificial Intelligence .
R. Spiegler (2024), The Curious Culture of Economic Theory.
Teaching notes.
Modalità di verifica dell'apprendimento
Problems sets combine formative and summative evaluation.
Modalità di esame
Il/la docente ha il dovere di vigilare affinché siano rispettate le regole di autenticità e originalità delle prove d'esame. Di conseguenza, nei casi in cui vi sia il sospetto di un comportamento irregolare, l'esame può prevedere un ulteriore approfondimento, contestuale alla prova d'esame, che potrà essere realizzato anche in modalità differente rispetto alle modalità sopra riportate.
Graduazione dei voti
B+ 3,25 B 3,00 B- 2,75 B/C 2,5
C+ 2,25 C 2,00 C- 1,75 C/D 1,5
Metodi didattici
Interactive learning is strongly encouraged, but it is expected to be carried out mainly away from class, because the standard UniVE format is too compact --- leaving no sufficient time for students to handle all the material covered.
Altre informazioni
Ca’ Foscari abides by Italian Law (Law 17/1999; Law 170/2010) regarding support services and accommodation available to students with disabilities. This includes students with mobility, visual, hearing and other disabilities (Law 17/1999), and specific learning impairments (Law 170/2010). If you have a disability or impairment that requires accommodations (i.e., alternate testing, readers, note takers or interpreters) please contact the Disability and Accessibility Offices in Student Services: disabilita@unive.it.