Fixing Sample Biases in Experimental Data Using Agent-Based Modelling

Mike Farjam, Giangiacomo Bravo

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review


We present how agent-based models can be used to correct for biases in a sample. The approach is generally useful for behavioural experiments where participants interact over time. The model we developed copied mechanics of a behavioural experiment conducted earlier, and agents in the model faced the same strategic choices as human participants did. We used the data from the experiment to calibrate agent behaviour such that agents reproduced patterns observed in the experiment. After this learning phase, we resampled agents such that their characteristics (political orientation) were similar to those found in the real world. We found that after the correction for the bias, agents produced patterns closer to those commonly found.

Original languageEnglish
Title of host publicationAdvances in Social Simulation - Looking in the Mirror, 2018
EditorsNanda Wijermans, Giangiacomo Bravo, Melania Borit, Harko Verhagen
Place of PublicationCham
PublisherSpringer Nature
Number of pages5
ISBN (Electronic)978-3-030-34127-5
ISBN (Print)9783030341268
Publication statusPublished - 2020
Externally publishedYes
Event14th Social Simulation Conference, 2018 - Stockholm, Sweden
Duration: 2018 Aug 202018 Aug 24

Publication series

NameSpringer Proceedings in Complexity
ISSN (Print)2213-8684
ISSN (Electronic)2213-8692


Conference14th Social Simulation Conference, 2018

Subject classification (UKÄ)

  • Social Sciences Interdisciplinary

Free keywords

  • Agent-based modelling
  • Bias
  • Experiment
  • Methodology


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