Teaching Methodologies
Lectures will be held, combining exposition and interaction, integrated with real case studies. Simulation activities are also planned. Finally, Guest Lectures and Discussion Panels led by experts are scheduled to take place.
Learning Results
1. Understand the fundamental principles of microeconomics.
Competencies to acquire: Understanding of individual and market behaviors.
2. Explore the basic concepts and techniques of AI, including its economic applications.
Competencies to acquire: Knowledge of AI principles and identification of its economic applications.
3. Critically analyze the intersection between microeconomics and AI.
Competencies to acquire: Ability to critically analyze, identify opportunities, and challenges.
4. Develop practical skills in applying economic models and identifying AI algorithms.
Competencies to acquire: Practical skills in applying economic models and identifying AI algorithms to solve complex problems.
5. Recognize and evaluate the ethical and social implications of using AI in economic decision-making.
Competencies to acquire: Critical evaluation of ethical and social implications.
Program
Introduction to Microeconomics and AI
- • Microeconomic principles
- • Introduction to artificial intelligence and its applications
- • Historical development and intersections of economic theory and AI
Microeconomic Theory
- • Consumer theory: Preferences, utility, and demand
- • Producer theory: Costs, production, and supply
- • Market equilibrium and efficiency
AI in Market Dynamics
- • Price discrimination and personalized pricing
- • Dynamic pricing strategies
- • Auction mechanisms and negotiation
AI and Firm Behavior
- • Strategic interactions and game theory
- • Competitive strategy and market entry
- • Product differentiation and innovation
AI and Consumer Behavior
- • Recommendation systems and personalized advertising
- • Behavioral economics
- • Information asymmetry and adverse selection
Welfare Implications and Policy Considerations
- • Trade-off between efficiency and equity
- • Market regulation
- • Ethical considerations in AI-driven markets
Internship(s)
NAO
Bibliography
McConnell, C. R., Brue, S. L., & Flynn, S. M. (2022). Microeconomics. 22nd Edition. McGrawHill. ISBN 9781265271442.
Norvig, P., & Russell, S. (2021). Artificial Intelligence: A Modern Approach (Global Edition). 4th Edition. Pearson Education Limited. Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press.