A critical realist approach to agent-based modeling: Unlocking prediction in non-positivist paradigms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Information systems (IS) scholarship and practice aim to predict phenomena and outcomes of IS use. These phenomena of IS use are typically set in multi-leveled, dynamic, and complex contexts that lend explanation to the non-positivist tradition in IS research. However, limited methodological options exist to make predictions. In this research, we propose stratified agent-based modeling, a step-by-step approach that enables prediction in non-positivist paradigms. Drawing upon the critical realist philosophy of science, which suggests ontological stratification and assumes open systems, we adopt a retroduction-based explanation formation and agent-based modeling to simulate different potential states of a complex system. The critical step in combining critical realism with agent-based modeling involves identifying and codifying the underlying generative mechanisms (i.e., causal powers) into various components of the agent-based model. We propose four steps toward prediction under the critical realist paradigm: (1) capturing the phenomenon, (2) identifying the generative mechanism, (3) building the agent-based model, and (4) simulating states of the system. We present an exemplar of our proposed approach that investigates the effectiveness of strategies to combat malicious content propagation in social networks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it