Project basics

• Participation in group presentation sessions (both as presenter and discussant): 25% of final grade

• Project report and codes: 25% of final grade

• Timeline:

  • 2.06: First project feedback exercise session
  • 9.06: Second project feedback exercise session and general Q&A session
  • 10.06: last chance to get any feedback from lecturers online via discord
  • 11.06: Deadline to register your group in Teach Center.
  • 23.06: First presentations session
  • 30.06: Second presentations session
  • 10.07: Project report deadline

Projects objectives

With the projects, we want to evaluate the following skills and learning:

• Planning a computational project as in the exercises and course examples: model, simulation, analysis, interpretation

• Describing a model in detail, referring to other relevant models (especially if covered by the course)

• Ability to implement and code the model, showing it works as designed with visualizations and other outputs

• Assessing the project question with systematic analysis of simulations

Project routes

A) Reproducing the analysis of a research paper

• Example: Reproducing the results of Axelrod's culture model paper
• Reflect on similarities or differences in your results
• If paper is short, explore a small extra question or analysis

B) Extending a model from the course

• Example: Neighbor overlap in the Barabasi-Albert model
  • Motivation: online we don't only interact with our friends in a direct manner, but also through common friends. Users with larger and more clustered neighborhoods will be less likely to leave
  • Analysis: measuring average neighbor overlap as network grows
  • Question: does overlap change with node age and network size?

Project routes (II)

C) Designing and analyzing a new model

• Example: privacy concerns in social resilience
  • Model probability to leave a social network increasing with too many friends
  • Study % of users leaving with value of parameter of that increase
  • Check previous work! Is this really a new model?
  • Can be the most challenging approach

Data is not always necessary but desirable in some cases

• Data-driven simulations (e.g. using network data)
• Comparing outcomes with empirical data (hard)
• Quantitatively validating outcomes (harder)
• Calibrating dynamics (hardest)

Modeling project pitfalls

• Not understanding your question

  • Bad: I want to model how people play Pokemon Go
  • Good: I want to understand the role of city size in Pokemon Go players using only two teams
  • Risk: not knowing when you are done and if you are doing it right

• Not searching for previous work
  • Bad: Here is the first model ever for fashion
  • Good: My model is similar to (cite,cite) in this and that aspect
  • Risk: specifying the problem wrong or missing interesting parts

Modeling project pitfalls (II)

• Not being systematic
  • Bad: I made some simulations and they look like this
  • Good: Outcome measure X grows with parameter Y as shown in the boxplot
  • Risks: Not having any substantial analysis and just anecdotes. Going back and forth between model and simulations in an endless loop.

• Being too ambitious
  • Bad: I want to explain polarization
  • Good: I want to understand how the strength of social influence affects polarization of opinions in the social network of German politicians on Twitter
  • Risks: Too much work and open paths, large models lead to complicated analyses. Might have too much previous work behind.