Evidence map›Paper›PMID 35589759›Full record

ArticleScientific reports2022

Delegation to artificial agents fosters prosocial behaviors in the collective risk dilemma.

Elias Fernández Domingos, Inês Terrucha, Rémi Suchon, Jelena Grujić, Juan C Burguillo, Francisco C Santos, Tom Lenaerts

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
6.1field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 28 citations in OpenAlex.

  1. Article
  2. Humans program artificial delegates to accurately solve collective-risk dilemmas but lack precision.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  3. Article
  4. Article
  5. Article
  6. A new sociology of humans and machines.Nature human behaviour · 2024
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. The future of theoretical evolutionary game theory.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 6 institutions in 5 countries.

Elias Fernández DomingosMachine Learning Group, Computer Science Department, Université Libre de Bruxelles, 1050, Brussels, Belgium. elias.fernandez.domingos@ulb.be.
Inês TerruchaArtificial Intelligence Lab, Computer Science Department, Vrije Universiteit Brussel, 1050, Brussels, Belgium.
Rémi SuchonMachine Learning Group, Computer Science Department, Université Libre de Bruxelles, 1050, Brussels, Belgium.
Jelena GrujićMachine Learning Group, Computer Science Department, Université Libre de Bruxelles, 1050, Brussels, Belgium.
Juan C BurguilloatlanTTic Research Center, E.E. Telecom., Universidade de Vigo, 36310, Vigo, Spain.
Francisco C SantosINESC-ID and Instituto Superior Técnico, Universidade de Lisboa, IST-Taguspark, 2744-016, Porto Salvo, Portugal. franciscocsantos@tecnico.ulisboa.pt.
Tom LenaertsMachine Learning Group, Computer Science Department, Université Libre de Bruxelles, 1050, Brussels, Belgium. Tom.Lenaerts@ulb.be.
Vrije Universiteit Brussel · BEiMinds · BEInstituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento · PTUniversidade de Vigo · ESUniversité Catholique de Lille · FRUniversity of California, Berkeley · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Home assistant chat-bots, self-driving cars, drones or automated negotiation systems are some of the several examples of autonomous (artificial) agents that have pervaded our society. These agents enable the automation of multiple tasks, saving time and (human) effort. However, their presence in social settings raises the need for a better understanding of their effect on social interactions and how they may be used to enhance cooperation towards the public good, instead of hindering it. To this end, we present an experimental study of human delegation to autonomous agents and hybrid human-agent interactions centered on a non-linear public goods dilemma with uncertain returns in which participants face a collective risk. Our aim is to understand experimentally whether the presence of autonomous agents has a positive or negative impact on social behaviour, equality and cooperation in such a dilemma. Our results show that cooperation and group success increases when participants delegate their actions to an artificial agent that plays on their behalf. Yet, this positive effect is less pronounced when humans interact in hybrid human-agent groups, where we mostly observe that humans in successful hybrid groups make higher contributions earlier in the game. Also, we show that participants wrongly believe that artificial agents will contribute less to the collective effort. In general, our results suggest that delegation to autonomous agents has the potential to work as commitment devices, which prevent both the temptation to deviate to an alternate (less collectively good) course of action, as well as limiting responses based on betrayal aversion.

Indexed as

AltruismCooperative BehaviorAutomationGame TheoryHumansMotivationSocial Behavior

Identifiers

PMID35589759
PMCPMC9119388
OpenAlexW4280586858

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.