Adaptive Task Allocation in Human-Machine Teams with Trust and Workload Cognitive Models
Bibliographic record
Abstract
In mixed-initiative systems where teams of humans and automated agents collaborate to perform decision-making tasks, determining factors of joint performance include human cognitive workload and the level of trust placed by the operators in the automation. Both workload and trust are dynamic variables that change over time based on current task allocation and on the result of past interactions. In this paper, we propose a methodology leveraging quantitative models of trust and workload to automatically and dynamically suggest efficient task allocations in mixed human-machine systems. Our approach is based on a Markov decision process framework and is presented for concreteness in the context of a human-machine team performing repeated binary decision-making tasks. Simulation results show the emergence of interesting automation behaviors such as seeking trust, attempting to repair trust after an error and adjusting human workload for optimal performance. Overall, the human-aware dynamic task allocation strategy shows the potential of significant team performance improvement compared to a static task distribution, even in the presence of significant errors in the trust and workload models used.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".