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Record W2031787181 · doi:10.1109/wcnc.2012.6214194

Duopoly price competition of WLAN service providers in presence of heterogeneous user demand

2012· article· en· W2031787181 on OpenAlexaff
Abhinav Kumar, Ranjan K. Mallik, Robert Schober

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDuopolyNash equilibriumMonopolyService providerCompetition (biology)MicroeconomicsBusinessService (business)Social WelfareCournot competitionGame theoryComputer scienceIndustrial organizationEconomicsMarketing

Abstract

fetched live from OpenAlex

In the presence of several wireless local area network (WLAN) service providers, the users have to make a choice. The price charged and the congestion experienced by the users play an important role in making this choice. In this paper, we analyze the duopoly price competition between two WLAN service providers in the presence of four types of users. We prove that the distribution of heterogeneous user demand is governed by the Wardrop equilibrium. We also show the existence of the Nash equilibrium between competing WLAN service providers. It is further shown through analysis that the social welfare in Nash equilibrium is close to its maximal value. We find that compared to a strictly regulated monopoly, an unregulated WLAN duopoly market results in significant transfer of the surplus from service providers to users with negligible losses in efficiency.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations5
Published2012
Admission routes1
Has abstractyes

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