Monday
July 27

08:30 - 09:00

Opening and welcome (30 minutes)

• Introduction to lecturers

• Structure of the week

• Introduction to collaborative work and expectations of the students

• Welcome from the lecturers and the student representative

• Group photo

Hang Xiong

Dean of HZAU/NU

09:00 - 12:00

Lecture 1: Social complexity

• Complex (Adaptive) Systems

• Social complexity

Wander Jager

Lecture 2: Modelling and simulation in social sciences

• Systems theory

• Model types (Markov, System Dynamics, ABM)

• Matching a modeling approach to the study objective

12:00 - 14:30

Lunch   Break

14:30-17:30

Lecture 3: Introduction to NetLogo and syntax

• Basic programming and getting started with NetLogo

• Interacting with agents via the command center

• Code tab, buttons, sliders and procedures

• Monitor and plot

• Programming best-practices

• Building your first NetLogo model

Team Project

• Description of learning tasks and collaborative work for the summer school

• Modeling task to be performed in groups

• Suggestions for model design and prototype development

  in NetLogo

Koen van Dam

17:00-17:30 Behavioral game

1. Heroes + Cowards game

2. Paperclip game

• Play the game in a suitable location (maybe with a smaller group rather than all)

• Discussion and showing the NetLogo model on opinion

  dynamics

Zhanli (Jerry) Sun

Koen van Dam

Wander Jager

19:00-21:00

Self-study

Tuesday
July 28

08:30-11:30

Lecture 4: ABM modeling process & NetLogo skills

•  Further introduction to NetLogo syntax

•  Debugging NetLogo programmes

•  Further materials for NetLogo programming

• Agent-based Modeling procedure

Zhanli (Jerry) Sun,   Koen van Dam

Lecture 5: Agent decision making

• Agent behavioral rules: from theory to formalisation

• From a simple consumer decision model (Netlogo) towards Consumat and HUMAT

11:30-14:30

Lunch   Break

14:30-17:30

Lecture 6: Simulation experiment with NetLogo

• Behavioural space and sensitivity analysis

• Verification, Validation, and Calibration of Simulation Models

Georgiy Bobashev

 Hang Xiong

Lecture 7: ABM documentation (ODD)

• What is ODD?

• Why ODD?

• How to use ODD?

• Examples

                                                                   Self-study

Wednesday

July 29

08:30-11:30

Lecture 8: Spatial Agent-Based Model (ABM+GIS)

• Introduction to GIS

• Using GIS in NetLogo

• Examples of spatially explicit models

Koen van Dam

Zhanli (Jerry) Sun

Lecture 9: (Spatial) synthetic population

• Theory and case studies

11:30-14:30

Lunch   Break

14:30-17:30

Lecture 10: Participatory ABM

• Introduction to participatory ABM + modelling games (Jerry)

• Participatory modelling practice with stakeholders (Teng)

Zhanli (Jerry) Sun

Teng Li

15:30-17:30 Exercise: Team project

• Support for groups from lecturers and Q&A

                                                                 Self-study

Thursday

Lecture 11: ABM in policy decision making

• ABM as a policy-decision-making tool & examples  

• ABM as a dialogue tool in communicating with stakeholders (general public)

Hang Xiong

11:30-14:30

Lunch   Break

Lecture 12: Machine Learning for Social Science

• Introduction to Reinforcement learning and decision

  making

• Q-learning

• Multi-armed Bandit Learning

                                                               Self-study

Friday

Lecture 13: Emerging models in the social model 

(1 hour)

Xiaohua Yu

Lecture 14: CGE & CGE+AI

• Introduction to CGE

• AI-assisted invocation, explanation, and modification of CGE models.

11:30-14:30

Lunch   Break