Exploring the Effects of Swarm Degradations on Trustworthiness Perceptions, Reliance Intentions, and Reliance Behaviors
Bibliographic record
Abstract
Swarms comprise robotic assets operating autonomously through local control laws. Research on human-swarm interaction (HSwI) investigates how human operators collaborate with swarms to accomplish shared goals. Researchers have begun to investigate the role of trust in HSwI, specifically which aspects of robotic swarms affect human trust. Through a human factors lens, the present research builds on earlier HSwI work and investigates the effect of swarm asset degradations on trustworthiness perceptions, reliance intentions, and reliance behaviors. Results showed that trustworthiness perceptions of and intentions to rely on swarms (but not reliance behaviors) were correlated, demonstrating the relation between theoretically relevant antecedents to trust in HSwI contexts. Contrary to past work, the results showed no statistical evidence that asset degradations differentially affect trustworthiness perceptions, reliance intentions, or reliance behaviors. Limitations of the current work (e.g., heterogeneity of post-intervention foraging behavior, sample size) are discussed and followed with future research suggestions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".