Evidence map›Paper›PMID 37271173›Full record

ReviewPhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2023

How could we make a social robot? A virtual bargaining approach.

Nick Chater

Abstract readReview
In one paragraph

Review in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Introduction to 'Cognitive artificial intelligence'.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023
    Article
  2. Symbols and grounding in large language models.Philosophical transactions. Series A, Mathematical, physical, and engineering 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

1 author.

Nick ChaterBehavioural Science Group, Warwick Business School, University of Warwick, Scarman Road, Coventry CV4 7AL, UK.ORCID 0000-0002-9745-0686

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

What is required to allow an artificial agent to engage in rich, human-like interactions with people? I argue that this will require capturing the process by which humans continually create and renegotiate 'bargains' with each other. These hidden negotiations will concern topics including who should do what in a particular interaction, which actions are allowed and which are forbidden, and the momentary conventions governing communication, including language. Such bargains are far too numerous, and social interactions too rapid, for negotiation to be conducted explicitly. Moreover, the very process of communication presupposes innumerable momentary agreements concerning the meaning of communicative signals, thus raising the threat of circularity. Thus, the improvised 'social contracts' that govern our interactions must be implicit. I draw on the recent theory of virtual bargaining, according to which social partners mentally simulate a process of negotiation, to outline how these implicit agreements can be made, and note that this viewpoint raises substantial theoretical and computational challenges. Nonetheless, I suggest that these challenges must be met if we are ever to create AI systems that can work collaboratively alongside people, rather than serving primarily as valuable special-purpose computational tools. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.

Indexed as

NegotiatingRoboticsArtificial IntelligenceHumansSocial Interactionartificial intelligenceconventionsnormssocial interactionvirtual bargaining

Identifiers

PMID37271173
PMCPMC10239680

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.