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Record W3002919848 · doi:10.1080/0952813x.2020.1716857

Communicative bottlenecks lead to maximal information transfer

2020· article· en· W3002919848 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Experimental & Theoretical Artificial Intelligence · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicGame Theory and Applications
Canadian institutionsMila - Quebec Artificial Intelligence Institute
Fundersnot available
KeywordsComputer sciencePartition (number theory)Information transferSignallingTheoretical computer scienceSimple (philosophy)Stability (learning theory)Property (philosophy)Empirical evidenceMathematical economicsMachine learningMathematicsTelecommunications

Abstract

fetched live from OpenAlex

This paper presents new analytic and numerical analysis of signalling games that give rise to informational bottlenecks – that is to say, signalling games with more state/act pairs than available signals to communicate information about the world. I show via simulation that agents learning to coordinate tend to favour partitions of nature which provide maximal information transfer. This is true despite the fact that nothing from an initial analysis of the stability properties of the underlying signalling game suggests that this should be the case. As a first pass to explain this, I note that the underlying structure of our model favours maximal information transfer in regard to the simple combinatorial properties of how the agents might partition nature into kinds. However, I suggest that this does not perfectly capture the empirical results; thus, several open questions remain.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.611
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.167
GPT teacher head0.418
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it