W4315786278 Peer-reviewed research AI × management and organizations

Artificial Intelligence: Can Seemingly Collusive Outcomes Be Avoided?

Ibrahim Abada · Xavier Lambin

Also recorded as: doi:10.1287/mnsc.2022.4623

Management Science

Recorded claims
1
coded from the inspected source
Evidence classes
1
crossref-api-metadata
Recorded limitations
3
stated, never hidden

From the hash-bound evidence releases

Recorded claims

Each claim states what the inspected source says, at the recorded location—bounded by its scope and graded by its confidence. Nothing here is a synthesis across works.

A market model in which independent machine-learning algorithms buy and sell a storable good reports seemingly collusive behavior arising from imperfect exploration and identifies decentralized learning or intervention during learning as possible regulatory responses.

confidence: high crossref-api-metadata apw-c1-obot-journals

Scope: publisher-deposited abstract metadata; algorithmic market conduct, firm profit objectives, and regulatory intervention

Structured relationships

In-corpus citations

Only source-supplied cites relationships whose two endpoints are admitted are shown. Invocation evidence remains a separate relationship.

Boundaries

Limitations & independence

Recorded at coding time, carried with the work forever. A claim without its limits is not evidence.

Recorded limitations

  • Abstract-level Crossref metadata only; the blocked publisher surface was not retried.
  • Crossref supplies only a year and month for publication, so published_date is null rather than an invented day.
  • Full text was not inspected.

Source independence

publisher-only