Lec 1 computational modeling - Introduction.pdf
- 1. COMPUTATIONAL MODELLING FOR THE SOCIAL SCIENCES • Introduction And Course Overview Faculty of Economics and Political Science The Department of Socio-Computing
- 2. • Discussion - Background (previous courses) - Expectations
- 3. • A methodology that allows the study of various phenomena, • By building models to solve large and complex problems. • Model is a representation of a system using general rules and concepts Computational
- 4. •Heat from the Sun causes water on Earth (in oceans, lakes etc) to evaporate (turn from liquid into gas) and rise into the sky. This water vapor collects in the sky in the form of clouds. • Water falls from the sky in the form of rain, snow etc, this process is called precipitation. Types of models: Conceptual model 4
- 5. Mathematical model It is a description of a system using mathematical concepts and language. Types of Models: 5
- 6. Physical model It is a smaller or larger physical copy of an object. You can touch and see, the object may be small (an atom) or large (Solar System). Types of models: 6
- 7. Computational Model • Is the application of computers to advance science, largely the modeling and simulating of the real world
- 8. •allow to: ✓ Better understand ✓ Make predictions of the future of the phenomena ✓ Be ready for any unexpected changes that may occur. Computational Models
- 9. ✓Individual behaviours ✓Interactions ✓Relations ✓Conflicts ✓Groups ✓Societies • The Social sciences are academic disciplines concerned with studying societies, individual behaviour, and the interactions between the individuals in these societies. Social sciences
- 11. • Scientists believed that the study of individual decisions and behaviours can not fully explain the social phenomena that emerge when people interact in organizations, institutions, and societies. • Social phenomena are complex ones. They are difficult to predict and analyze using traditional methods of analysis. Complexity of Social Science Phenomena
- 12. •Experiments on Revolutions Complexity of Social Science Phenomena
- 14. Social Simulation Agent Based Models Social Networks Analysis (SNA) Management Information (MIS) Social network analysis (SNA) is the mapping and measuring of relationships and flows between people, groups, computers, and other connected information/knowledge entities. In SNA, the nodes in the network are the people and groups while the links show relationships or flows between the nodes. 15 Computational models
- 15. Social Simulation Agent Based Models Social Networks Analysis (SNA) Management Information (MIS) Management Information System (MIS): is a computer-based system that provides managers with the tools to organize, evaluate and efficiently manage departments within an organization. 16 Computational Models
- 16. Social Simulation Agent Based Models Social Networks Analysis (SNA) Management Information System ( MIS) Social simulation: Is the application of computer-based methods and technologies to replicate social behavior in various environments and scenarios. Researchers design the program to model a social situation and then observe the behavior of individuals when the program runs. 17 Computational Models
- 17. Social Simulation Agent Based Models Social Networks Analysis (SNA) Management Information System (MIS) Agent-Based Model is a class of computational models for simulating agents' actions and interactions to assess their effects on the system as a whole. Agent-Based Model (ABM) consists of modelling different societies as artificial agents and placing them in a computer-simulated society to observe the behaviours of the agents. 18 Computational Models
- 18. To program ABM the researcher needs to program: Agents, An environment, Rules. When running an ABM the outcome is unpredictable, i.e., we do not know what the aggregated product of these interactions will be. 19 Social Simulation Agent Based Models Social Networks Analysis (SNA) Management Information System (MIS) Computational Models
- 19. ABM Applications
- 20. Course Structure 1. What is Agent-Based Modeling and Why Should You Use It? 2. Beginning with Simple Models 3. Extending Models 4. A Full Model 5. The Architecture of an Agent-Based Model 6. Analyzing Agent-Based Models 7. Verification, Validation, and Replication 8. Application and History of ABM 9. Advanced ABM
- 21. Contacting Us • Email: [email protected] • Room 55, third floor, old Building. • Office Hours: Sunday 12:30-1:30 Tuesday: 12:30-1:30 • Check Thinqi (https://thinqi.cu.edu.eg/) for power point presentations
- 22. Course information Course material: Lectures notes, and Lab notes. Grading: • Final term exam: 50 marks. • Midterm : 20 marks. • Lectures’ quizzes and assignments : 15 marks. • Lab: 15 marks 23
- 23. Software • NetLogo • http://ccl.northwestern.edu/netlogo • Go through the tutorial
- 24. Recommended Book • An Introduction to Agent-Based Modeling • Uri Wilensky and William Rand • Available at MIT Press and Amazon https://mitpress.mit.edu/books/introduction-agent-based-modeling http://www.intro-to-abm.com/
- 25. 31 Thanks