880021 :Social Intelligence (CSAI/HAIT/DJ/BDM)

General info

Instruction language English
Type of Instruction 14 x 2 hours lectures plus online contact hours (Lecture schedule)
Type of exams written exam, papers, presentation (Examination schedule)
Course load:6 ECTS credits
Registration:Enrollment via Blackboard before start of lectures
Blackboard InfoLink to Blackboard (When you see 'Guest are not allowed in this course', please login at Blackboard itself)


dr. M.M. van Zaanen (coordinator)

dr. E.E. van der Vaart


At the end of the unit, students will be able to
  • describe the idea of wisdom of the crowd as used in collective intelligence systems;
  • apply crowdsourcing collection systems to particular problems;
  • list a range of collective intelligence applications;
  • describe the internals of components that are used in collective intelligence applications;
  • analyze, break down and design complex collective intelligence end-user systems from simple components.

The following academic skills will be applied in this course:
  • formulate a research question and collect and interpret data in order to answer the research question;
  • give a clear and academically adequate presentation for a specific target audience;
  • communicate and cooperate in an (international and/or interdisciplinary) team.


The Internet allows for information to be shared all over the world and anyone with an Internet connection can access this information whenever and whereever they want.  Nowadays people not only consume information, they also collectively produce and share information. For example, every day, millions of people write tweets about things that interest them, rate songs they listen to, post videos on YouTube and add information to the world wide web in a myriad of other ways.

This collectively produced data, often called Web 3.0, can be the source of information useful for companies and researchers alike.  In this course we will examine the idea of social intelligence and wisdom of the crowd.  Additionally, we can simulate the behavior underlying collective intelligence, including alternative forms of collective behavior, such as ant colony optimization. Some of the topics we will discuss are recommender systems and crowdsourcing.

Questions that arise from this are, for instance: How do we recommend a song to a Spotify user or a book to an Amazon client?  How do we extract relevant information? Can we effectively use collective behavior to identify (near) optimal solutions to find the best path in a complex environment? How do we evaluate these methods?



The final grade of the course consists of the rounded weighted average of the written exam (70%), a group assignment (10%) and two individual assignments (10% each). All of the assignments (and written exam) are compulsory to pass the course. It is not required for all assignments and written exam to be passes, only the final grade (rounded, weighted average) should be a pass in order to pass the course. For the written exam a resit is possible. For the assignments revisions (resits) are not allowed. Additional (non-graded) compulsory assignments do require a pass in order to pass for the course (pass/fail). Assignments have non-negotiable deadlines. Assignments handed in after the deadline will not be accepted and will lead to a fail for the course.

Labor Market

During this course, you will use a crowdsourcing system that is used to collect data from the crowd.  This system is not only used for academic purposes, but is actively used by companies. Additionally, several systems that are used by successful internet companies, such as Amazon and Google, are discussed.

Obligatory attendence

For some lectures attendence is obligatory. Information will be made available on Blackboard at the start of the course.

Compulsory Reading

  1. Online articles and slides.

Required Prerequisites


Recommended option for

  • Master Business Communication and Digital Media ( 2015, 2016, 2017 )
  • Master Communication Design ( 2015, 2016, 2017 )
  • Master Human Aspects of Information Technology ( 2015, 2016 )
  • Master Data Journalism ( 2015, 2016 )
  • Master Communication and Information Sciences ( 2015, 2016, 2017 )
  • Cognitive Science and Artificial Intelligence ( 2017 )