Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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2° Year activated in the A.Y. 2021/2022
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1 module among the following (1st year: Big Data epistemology and Social research; 2nd year: Cybercrime, Data protection in business organizations, Comparative and Transnational Law & Technology)2 courses among the following (1st year: Business analytics, Digital Marketing and market research; 2nd year: Logistics, Operations & Supply Chain, Digital transformation and IT change, Statistical methods for Business intelligence)2 courses among the following (1st year: Complex systems and social physics, Discrete Optimization and Decision Making, 2nd year: Statistical models for Data Science, Continuous Optimization for Data Science, Network science and econophysics, Marketing research for agrifood and natural resources)2 courses among the following (1st year: Data Visualisation, Data Security & Privacy, Statistical learning, Mining Massive Dataset, 2nd year: Machine Learning for Data Science)Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Complex systems and social physics (2020/2021)
Teaching code
4S009082
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
FIS/02 - THEORETICAL PHYSICS, MATHEMATICAL MODELS AND METHODS
Period
II semestre dal Mar 1, 2021 al Jun 11, 2021.
Learning outcomes
The aim of the course is to provide the student with the skills of theoretical physics and mathematical physics methods for the modeling and characterization of large sets of data, time series, time sequences, and hierarchical structures in aggregation. Students will also be provided with the methods of mathematical physics for the study of correlation, causation, and aggregation relationships in complex social systems.
At the end of the course the student has to show to have acquired the following skills:
● ability to develop formal models for the qualitative and quantitative analysis of databases, time series, and dynamics of complex systems in interaction for the detection of causal relationships, correlation structures, and forecasting schemes.
Program
Review of thermodynamics and statistical mechanics. System theory. Examples and applications to natural and social systems. Dynamical systems. Correlation, causation, stationarity. Transformations: time domain, linear regression, frequency domain, principal components. Linear dynamical systems. Diagonalization of the dynamics, normal modes, fixed points. Coupled systems. Control of dynamical systems. Nonlinear dynamical systems. Bifurcations, hysteresis, catastrophes. Examples and applications. Linearization. Limit cycles. Control and stabilization. Spatial systems. Agent models. Review of probability and statistics. Distributions: uniform, exponential, Gaussian. Measures of non-Gaussianity. Outliers and rare events, heavy-tailed distributions. Power laws. Power laws in social systems. Complex systems. Phase transitions and critical phenomena. Self-organized criticality. Emergence. Observation and inference. Sampling and inverse problems.
Examination Methods
Oral exam, possibly including the discussion of an essay on a topic covered in the course.
