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Social Simulation and Complex Analysis Systems

Code: PRODEI041     Acronym: SSASC

Classification Keyword
OFICIAL Intelligent Systems

Instance: 2018/2019 - 2S Ícone do Moodle

Active? Yes
Responsible unit: Department of Informatics Engineering
Course/CS Responsible: Doctoral Program in Informatics Engineering

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
PRODEI 2 Syllabus 1 - 6 28 162

Teaching - Hours

Recitations: 2,00
Type Teacher Classes Hour
Recitations Totals 1 2,00
Rosaldo José Fernandes Rossetti 2,00

Teaching language



This course is intended to present and study Complex Systems from a behavioural perspective, where macro-level consequences result from micro-level interactions of entities networking in social phenomena such as co-operation, collaboration, competition, diffusion, foraging and complex societies. The proposed programme aims at presenting all concepts and tools for the practical implementation of social simulations with a diverse range of applications in mind. More specifically, the goals are:

  • To introduce and discuss on concepts and basic characteristics of social systems as a metaphor to analyse complex domains;
  • To present and practice with modelling techniques and simulation tools to analyse complex social systems;
  • To present and define all steps in the life-cycle of simulation project applied to the analysis of complex social systems;
  • To present techniques to test, verify and validate social simulation models;
  • To present appropriate tools to simulate social systems;
  • To carry out a complete social simulation project. 

Learning outcomes and competences

After successfully completing the Social Simulation course unit, a student should be able to:

  • Demonstrate acquaintance with basic concepts, terminologies, methods and techniques in the field of Social Simulation as an approach to analyse complex systems;
  • Select appropriate modelling metaphors to devise social models of diverse complex systems;
  • Devise and carry out a complete social simulation project;
  • Acquire data to and analyse results from social simulation projects;
  • Report results and use social simulation as a decision support tool in a diverse range of applications.

Working method


Pre-requirements (prior knowledge) and co-requirements (common knowledge)

There is no mandatory prerequisite. Knowledge of (object-oriented) programming and statistics are desirable.


The topics to be covered during this Course Unit will include:

  • Review and presentation of basic concepts of modelling and simulation methodologies applied to social sciences;
  • Modelling and Simulation paradigms applied to Social Systems analysis;
  • Models of complex adaptive social dynamics;
  • Agent-based modelling and simulation of artificial societies;
  • Anthropological, Psychological, and Sociological aspects of social behaviour and interactions;
  • Social Games as a tool for behavioural elicitation and assimilation;
  • Network Science and Diffusion processes;
  • Complex Systems analysis from a Social network perspective;
  • Geographical and temporal dimensions of social interactions;
  • Visualisation techniques of social phenomena;
  • Data acquisition, preparation, mining, and analysis of social data sets;
  • Activity-based analysis in social systems;
  • Verification, test, and validation of social simulation models;
  • Social Simulation as a strategic and decision-support tool;
  • Applications of Social Simulation;
  • Tools and Simulation Environments.

Mandatory literature

John H. Miller and Scott E. Page; Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity) , 2007. ISBN: 0691127026
Joshua M. Epstein; Generative Social Science: Studies in Agent-Based Computational Modeling., 2007. ISBN: 0691125473

Teaching methods and learning activities

Passive learning is limited to the initial introduction of concepts. Students will be encouraged to deepen their knowledge by contextualising the state of the art and identifying recent trends. This component will comprise presentations of recent and relevant case studies of the different areas of study taught in this course unit. 

However, the principal teaching method is based on an active learning, being directed to research and project. It aims to integrate students in practical activities, such as the use of simulation tools, implementation of ad-hoc simulators, reviews and readings of scientific papers, as well as structuring students’ knowledge by attending seminars and writing articles. 


Social sciences > Sociology > Societal behaviour
Technological sciences > Engineering > Simulation engineering

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation Weight (%)
Trabalho escrito 60,00
Trabalho laboratorial 40,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (hours)
Elaboração de projeto 33,60
Estudo autónomo 42,00
Frequência das aulas 36,00
Trabalho de investigação 50,40
Total: 162,00

Eligibility for exams

Students are expected to reach a grade >=8 in all components of the continuous assessment.

Calculation formula of final grade

Continuous Assessment (CA) comprises:

  • Preparation/presentation of seminars (S): 20%
  • Preparation/presentation of tutorial (S): 20%
  • Development of a simulation project (P): 40%
  • Writing up a paper on the developed project (A): 20%

Therefore, Final Mark (FM) will be:

  • FM= 0,2*S + 0,2*T + 0,4*P + 0,2*A

The student cannot get a mark below 8 in each of the components, in which case he/she will fail the course.

Examinations or Special Assignments

  • Writing up an article and its presentation;

  • Preparation and presentation of seminars;

  • Medium-size simulation project;

  • Mid-term test.

Internship work/project


Special assessment (TE, DA, ...)

The assessment components are compulsory to all students, even to the ones who do not need to attend classes under special status, in which cases the assessment activities will take place according to students’ availability and schedule.

Classification improvement

Students may improve their grades by improving one or more of the following assessment components:

  • Writing up an article and its presentation;
  • Preparation and presentation of seminars;
  • Medium-size simulation project;
  • Mid-term test.



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