GENERAL
| SCHOOL | School of Economics and Political Science |
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| DEPARTMENT | Sociology |
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| Level of Study | Undergraduate |
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| COURSE CODE | 431113 |
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| COURSE TITLE | Stochastic Models for the Social Sciences (PS) |
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| Independent Teaching Activities |
Weekly Teaching Hours |
CREDIT UNITS |
| — |
| Course Type | Special Background |
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| Prerequisite Courses | No |
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| Teaching and Examination Language | Greek |
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| THE COURSE IS OFFERED TO ERASMUS STUDENTS | No |
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| Course Webpage (URL) | |
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(2) LEARNING OUTCOMES
Upon completion of the course, students are expected to be able to:
- understand the basic principles of stochastic models and probabilistic processes
- understand and apply stochastic models to the study of social behaviors, population changes, inequalities, information diffusion, crime, unemployment, and political behavior.
- have theoretical training but to understand the practical applications of the theories to real social data.
General Capabilities
- Cultivation of reflective thought and critical thinking,
- Search, analysis and synthesis of data and information, using the necessary tools: literature search, project execution: individual or team work
- Working in an interdisciplinary environment
- Generation of new research ideas
- Respect for diversity and multiculturalism, demonstration of social, professional, and ethical responsibility and gender sensitivity
(3) COURSE CONTENT
Stochastic Models for the Social Sciences
The course introduces the basic principles of stochastic models and probabilistic processes, with applications in the analysis of social phenomena and data. It focuses on the modeling of uncertainty, randomness, and the dynamics of social systems, utilizing tools from probability theory, stochastic processes, statistics, and econometrics.
The goal of the course is the understanding and application stochastic models for the study of social behaviors, population changes, inequalities, information diffusion, criminality, unemployment, and political behavior.
The course combines theoretical foundation with practical applications on real social data.
Thematic Units
- Introduction to stochastic models and the social sciences
- Probability theory and random variables
- Probability distributions and applications to social data
- Stochastic processes and dynamic social phenomena
- Markov chains and social mobility
- Multiplicative and population stochastic processes
- Diffusion models and social networks
- Choice and decision models under uncertainty
- Event history analysis
- Stochastic unemployment and labor market models
- Models of inequality, income, and social stratification
- Monte Carlo simulations and computational social science
- Agents and agent-based models of social behavior
- Applications of stochastic models in public policy and forecasting social trends
(4) TEACHING & LEARNING METHODS – EVALUATION
| DELIVERY METHOD | Face to face |
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| Information and Communication Technologies |
- PowerPoint presentation,
- Display of supporting audiovisual material
- Support of the learning process through the e-class electronic platform
|
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| Activity |
Semester Workload |
| Lectures |
39 |
| Work |
39 |
| Standalone Study |
69 |
| Participation in exams |
3 |
| Course Summary |
150 |
Student Evaluation
| Process Description Evaluation |
- Attendance and Participation (10%)
- Preparation and Presentation of a Project (40%)
- Final Exam (50%)
Includes:
Comparative evaluation of theories & critical evaluation of a specific case
They are listed and available on e-class
|
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(5) RECOMMENDED BIBLIOGRAPHY
Suggested Bibliography
- Papadimitriou, K., Probability and Statistics for Social Sciences, Critique.
- Michaelides, N., Statistical Analysis of Social Data, Gutenberg.
- Kalogirou, S., Quantitative Methods and Modeling, Papazissis.
- Hatzigiannis, D., Stochastic Processes – Theory and Applications, Tziolas.
Related Scientific Journals
- Journal of Mathematical Sociology (Taylor & Francis)
- Sociological Methods & Research (SAGE)
