Fixing Sample Biases in Experimental Data Using Agent-Based Modelling
(2020) 14th Social Simulation Conference, 2018 In Springer Proceedings in Complexity p.155-159- Abstract
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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/215622e4-e9a7-4434-981a-89ab01aff344
- author
- Farjam, Mike LU and Bravo, Giangiacomo
- publishing date
- 2020
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Agent-based modelling, Bias, Experiment, Methodology
- host publication
- Advances in Social Simulation - Looking in the Mirror, 2018
- series title
- Springer Proceedings in Complexity
- editor
- Wijermans, Nanda ; Bravo, Giangiacomo ; Borit, Melania and Verhagen, Harko
- pages
- 5 pages
- publisher
- Springer Nature
- conference name
- 14th Social Simulation Conference, 2018
- conference location
- Stockholm, Sweden
- conference dates
- 2018-08-20 - 2018-08-24
- external identifiers
-
- scopus:85087890066
- ISSN
- 2213-8692
- 2213-8684
- ISBN
- 978-3-030-34127-5
- 9783030341268
- DOI
- 10.1007/978-3-030-34127-5_14
- language
- English
- LU publication?
- no
- id
- 215622e4-e9a7-4434-981a-89ab01aff344
- date added to LUP
- 2021-02-19 10:31:33
- date last changed
- 2024-10-03 21:05:00
@inproceedings{215622e4-e9a7-4434-981a-89ab01aff344, abstract = {{<p>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.</p>}}, author = {{Farjam, Mike and Bravo, Giangiacomo}}, booktitle = {{Advances in Social Simulation - Looking in the Mirror, 2018}}, editor = {{Wijermans, Nanda and Bravo, Giangiacomo and Borit, Melania and Verhagen, Harko}}, isbn = {{978-3-030-34127-5}}, issn = {{2213-8692}}, keywords = {{Agent-based modelling; Bias; Experiment; Methodology}}, language = {{eng}}, pages = {{155--159}}, publisher = {{Springer Nature}}, series = {{Springer Proceedings in Complexity}}, title = {{Fixing Sample Biases in Experimental Data Using Agent-Based Modelling}}, url = {{http://dx.doi.org/10.1007/978-3-030-34127-5_14}}, doi = {{10.1007/978-3-030-34127-5_14}}, year = {{2020}}, }