MATHEMATICS FOR MODELLING IN MANAGEMENT
- Academic year
- 2022/2023 Syllabus of previous years
- Official course title
- MATHEMATICS FOR MODELLING IN MANAGEMENT
- Course code
- PHD168 (AF:402250 AR:218814)
- Modality
- On campus classes
- ECTS credits
- 6
- Degree level
- Corso di Dottorato (D.M.45)
- Educational sector code
- SECS-S/06
- Period
- 1st Term
- Course year
- 1
- Where
- VENEZIA
- Moodle
- Go to Moodle page
Contribution of the course to the overall degree programme goals
This course addresses some classical and more recent advances in the context of network theory. We will analyze the main social network structures, their properties and the basic tools of mathematics of networks. Finally, we will study how information, innovation and opinions spread through networks due to social interactions.
Expected learning outcomes
Students will be able to critically read, analyze, present and discuss academic papers related to the applications of network theory in the field of management.
Pre-requirements
Mathematics
• Number sets - Powers and their properties - Logarithms and their properties - Equations – Inequalities
• The notion of real function - Graphs of functions – Linear and quadratic functions – Logarithmic and exponential functions
• Derivatives - Rates of change - Increasing/decreasing functions – Convexity and concavity
• Rules for differentiation - Maxima/Minima
• Indefinite integrals - Definite integrals - Improper integrals
• Basics of matrix algebra
Suggested reference:
K. Sydsaeter, P. Hammond and A. Strom (2016). Essential Mathematics for Economic Analysis (V edition), Pearson. Chapters 1-9.
Statistics
• Basic notions of probability theory
• Mean, median, variance , standard deviation
• Hypothesis testing
• Correlation (e.g., how to interpret a correlation coefficient)
• Linear Regression (e.g., how to interpret a regression coefficient)
• Types of variables (e.g., continuous, ordinal, categorical, dummy)
• Basic familiarity with computers and productivity software, like excel
Suggested reference:
OpenStax (2013). Introductory Statistics. Rice University. Free download of the pdf at: https://d3bxy9euw4e147.cloudfront.net/oscms-prodcms/media/documents/IntroductoryStatistics-OP_LXn0jei.pdf
Contents
1. Networks and social networks. Examples and applications
2. The mathematics of networks 1 (adjacency matrices, degree, connectivity)
3. The mathematics of networks 2 (components, paths and degree distribution)
4. Metrics and measures (centrality, similarity): hubs and influencers
5. The mean field approximation (from the Bass ’69 model to the new media)
6. Diffusions on networks and social interactions – SIR and SIS models
7. Random walks on graphs. The De Groot model for consensus
8. Opinion leaders and social influence: an application to advice networks
Referral texts
Newman, M. Networks: an introduction. Oxford University Press, Second Edition, 2018. [Ch. 1-3, 6-8, 17]
Supplementary material and discussion papers will be provided by the instructor.
Assessment methods
The written exam consists in a set of problems (exercises) related to the program seen in class and in some theoretical questions. Mock-ups will be provided to students during the course. The structure of the ongoing activities will be discussed at the beginning of the course.
Teaching methods
Further information
Accommodation and support services for students with disabilities and students with specific learning impairments 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.