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Record W2487158812 · doi:10.1177/1043463114561754

Norms and trades: An experimental investigation

2015· article· en· W2487158812 on OpenAlexaff
Giuseppe Danese, Luigi Mittone

Bibliographic record

VenueRationality and Society · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAllocative efficiencyRedistribution (election)MicroeconomicsNorm of reciprocityNorm (philosophy)Reciprocity (cultural anthropology)EconomicsPositive economicsSocial psychologyMathematical economicsPsychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

In this paper we study how norms of symmetry and centricity affect the functioning of two ways to allocate resources described in the economic anthropology literature, namely reciprocity and redistribution. The baseline reciprocity study, with no explicit priming of the norm of symmetry, features near-zero levels of allocative efficiency. Consistent with the anthropological framework we use throughout, we find that priming the norm of symmetry among the players through pre-play communication dramatically increases efficiency. Next we study a game of redistribution and find that in the final stages of the game allocative efficiency levels consistently approach 100%, regardless of how the chief comes to acquire centricity in the group. We conclude that reciprocity and redistribution can seldom allocate resources efficiently in the absence of norms of symmetry and centricity in the institutional design. By way of comparison, we confirm a robust finding in the experimental economics literature that a simple market exchange game achieves high efficiency, even when the traders can formulate expectations about each other’s compliance with norms.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

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

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 head0.138
GPT teacher head0.375
Teacher spread0.237 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2015
Admission routes1
Has abstractyes

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