Agent-based model (ABM) development needs information on system components and interactions. Qualitative narratives contain contextually rich system information beneficial for ABM conceptualization. Traditional qualitative data extraction is manual, complex, and time- and resource-consuming. Moreover, manual data extraction is often biased and may produce questionable and unreliable models. A possible alternative is to employ automated approaches borrowed from Artificial Intelligence. This study presents a largely unsupervised qualitative data extraction framework for ABM development. Using semantic and syntactic Natural Language Processing tools, our methodology extracts information on system agents, their attributes, and actions and interactions. In addition to expediting information extraction for ABM, the largely unsupervised approach also minimizes biases arising from modelers’ preconceptions about target systems. We also introduce automatic and manual noise-reduction stages to make the framework usable on large semi-structured datasets. We demonstrate the approach by developing a conceptual ABM of household food security in rural Mali. The data for the model contain a large set of semi-structured qualitative field interviews. The data extraction is swift, predominantly automatic, and devoid of human manipulation. We contextualize the model manually using the extracted information. We also put the conceptual model to stakeholder evaluation for added credibility and validity.

More from our Archive

• DOI: 10.1093/bioinformatics/btad424 2023

  Evaluation of AlphaFold-Multimer prediction on multi-chain protein complexes

  Wensi Zhu, Aditi Shenoy, Petras Kundrotas, Arne Elofsson

• DOI: 10.3390/a16070337 2023

  A Hybrid Model for Analysis of Laser Beam Distortions Using Monte Carlo and Shack–Hartmann Techniques: Numerical Study and Experimental Results

  Ilya Galaktionov, Julia Sheldakova, Alexander Nikitin, Vladimir Toporovsky, Alexis Kudryashov

• DOI: 10.1002/jcc.27187 2023

  Accurate thermochemistry at affordable cost by means of an improved version of the JunChS‐F12 model chemistry

  S. Di Grande, M. Kállay, V. Barone

• DOI: 10.1093/bioinformatics/btad437 2023

  SBOannotator: a Python tool for the automated assignment of systems biology ontology terms

  Nantia Leonidou, Elisabeth Fritze, Alina Renz, Andreas Dräger

• DOI: 10.3390/a16070324 2023

  A Fast Surrogate Model-Based Algorithm Using Multilayer Perceptron Neural Networks for Microwave Circuit Design

  Mohammad (Behdad) Jamshidi, Salah I. Yahya, Saeed Roshani, Muhammad Akmal Chaudhary, Yazeed Yasin Ghadi, Sobhan Roshani

• DOI: 10.1093/bioinformatics/btad418 2023

  KG-Hub—building and exchanging biological knowledge graphs

  J Harry Caufield, Tim Putman, Kevin Schaper, Deepak R Unni, Harshad Hegde, Tiffany J Callahan, Luca Cappelletti, Sierra A T Moxon, Vida Ravanmehr, Seth Carbon, Lauren E Chan, Katherina Cortes, Kent A Shefchek, Glass Elsarboukh, Jim Balhoff, Tommaso Fontana, Nicolas Matentzoglu, Richard M Bruskiewich, Anne E Thessen, Nomi L Harris, Monica C Munoz-Torres, Melissa A Haendel, Peter N Robinson, Marcin P Joachimiak, Christopher J Mungall, Justin T Reese

• DOI: 10.1093/bioinformatics/btad203 2023

  A gated graph transformer for protein complex structure quality assessment and its performance in CASP15

  Xiao Chen, Alex Morehead, Jian Liu, Jianlin Cheng

• DOI: 10.1093/bioinformatics/btad228 2023

  TSignal: a transformer model for signal peptide prediction

  Alexandru Dumitrescu, Emmi Jokinen, Anja Paatero, Juho Kellosalo, Ville O Paavilainen, Harri Lähdesmäki

• DOI: 10.1093/bioinformatics/btad411 2023

  MDTips: a multimodal-data-based drug–target interaction prediction system fusing knowledge, gene expression profile, and structural data

  Xiaoqiong Xia, Chaoyu Zhu, Fan Zhong, Lei Liu

• DOI: 10.3390/a16070343 2023

  Deep Learning for Detecting Verticillium Fungus in Olive Trees: Using YOLO in UAV Imagery

  Marios Mamalis, Evangelos Kalampokis, Ilias Kalfas, Konstantinos Tarabanis