Course
ARTIFICIAL ECONOMICS: SIMULATION AND COMPUTATIONAL METHODS
Teacher
ROSELLA CASTELLANO
Knowledge and understanding: to carry out calculations with matrices and vectors; to plot basic mathematical functions, to know how to look for the zeroes of a function; to know how to draw an histogram and to implement Monte Carlo simulations; to represent a network through a matrix, to compute the paths on the network; to compute the most common centrality measures; to create random networks; to identify clusters.
Applying knowledge and understanding: to apply random network to economics and social complex problems.
Making judgements: to have a comprehensive and critical view of real world networks.
Communication skills: to know how to give a proper interpretation of the results and to communicate and represent them to a target audience.
Learning skills: the student is supposed to have passed the basic university exam of calculus to participate in a profitable way to the course; at the end of the course the student is supposed to have acquired and to be able to manage the main tools and issues treated in the course.
Applying knowledge and understanding: to apply random network to economics and social complex problems.
Making judgements: to have a comprehensive and critical view of real world networks.
Communication skills: to know how to give a proper interpretation of the results and to communicate and represent them to a target audience.
Learning skills: the student is supposed to have passed the basic university exam of calculus to participate in a profitable way to the course; at the end of the course the student is supposed to have acquired and to be able to manage the main tools and issues treated in the course.
The goal of this course is to provide the students with a base set of methodological tools useful to face the study of social networks. In this respect, we will enters in the details of the complex network analysis, starting from the very beginning and going on step by step toward the most used methods characterizing complex network analysis.
In the following, a more detailed list of the topics is provided:
1) Introduction to complex networks: an introduction; historical traits and useful softwares; representation of network and clustering coefficients; the Erdös Bacon number, visit of a network; small world and connected components; minimum spanning tree and some centrality measures; centrality measures and structure of networks (2 CFU)
2) Networks with specific topologies: network topology; Erdös-Rényi and Wattts-Strogatz networks; replicating properties of real world networks, the Barabasi and Albert model; assortativity and beyond; diffusion of networks (2 CFU).
3) Communities and advanced topics: epidemic spreading, immunization and forecast; K-core and k-shells, community detections, advanced topics, case studies (2 CFU);
In the following, a more detailed list of the topics is provided:
1) Introduction to complex networks: an introduction; historical traits and useful softwares; representation of network and clustering coefficients; the Erdös Bacon number, visit of a network; small world and connected components; minimum spanning tree and some centrality measures; centrality measures and structure of networks (2 CFU)
2) Networks with specific topologies: network topology; Erdös-Rényi and Wattts-Strogatz networks; replicating properties of real world networks, the Barabasi and Albert model; assortativity and beyond; diffusion of networks (2 CFU).
3) Communities and advanced topics: epidemic spreading, immunization and forecast; K-core and k-shells, community detections, advanced topics, case studies (2 CFU);
- Teaching slides available on the moodle page
- Video lessons;
- Reading materials;
- Summary questions and Auto evaluation tests.
- Project Work agreed and discussed with the teacher
Summary questions and Self Evaluation tests. Development and analysis of a case study.
Oral discussion of a case study whose topic must be agreed with the teacher. The case study must be presented in the form of a short dissertation which must be sent to the teacher at least fifteen days before the exam.
The final evaluation will result from the grade of the case study, possibly added to the points obtained through the oral presentation
Social networks, innovation network, economics, finance and business.