Humans program artificial delegates to accurately solve collective-risk dilemmas but lack precision
Bibliographic record
Abstract
In an era increasingly influenced by autonomous machines, it is only a matter of time before strategic individual decisions that impact collective goods will also be made virtually through the use of artificial delegates. Through a series of behavioral experiments that combine delegation to autonomous agents and different choice architectures, we pinpoint what may get lost in translation when humans delegate to algorithms. We focus on the collective-risk dilemma, a game where participants must decide whether or not to contribute to a public good, where the latter must reach a target in order for them to keep their personal endowments. To test the effect of delegation beyond its functionality as a commitment device, participants are asked to play the game a second time, with the same group, where they are given the chance to reprogram their agents. As our main result we find that, when the action space is constrained, people who delegate contribute more to the public good, even if they have experienced more failure and inequality than people who do not delegate. However, they are not more successful. Failing to reach the target, after getting close to it, can be attributed to precision errors in the agent's algorithm that cannot be corrected amid the game. Thus, with the digitization and subsequent limitation of our interactions, artificial delegates appear to be a solution to help preserving public goods over many iterations of risky situations. But actual success can only be achieved if humans learn to adjust their agents' algorithms.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".