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Record W2002503368 · doi:10.1049/iet-com.2013.0296

Wireless local area network service providers’ price competition in presence of heterogeneous user demand

2013· article· en· W2002503368 on OpenAlexaff
Abhinav Kumar, Ranjan K. Mallik, Robert Schober

Bibliographic record

VenueIET Communications · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOligopolyRevenueMonopolyDuopolyEconomic surplusCompetition (biology)Price discriminationService providerMicroeconomicsRevenue modelNash equilibriumBusinessService (business)Computer scienceIndustrial organizationEconomicsCournot competitionMarketingFinance

Abstract

fetched live from OpenAlex

Consider wireless local area network (WLAN) service providers (SPs) operating in an overlapping service area. The SPs compete with each other to attract users. The price charged is utilised by the SPs as a tool to maximise revenue, resulting in a price competition between the WLAN SPs. The users are assumed to be selfish, trying to maximise their individual utility. They have varied sensitivity towards quality of service experienced and the price charged. In such a scenario, the user demand distribution is the one that achieves Wardrop equilibrium. Approximate analytical expressions are obtained for the best response of SPs to each other's price. Existence of a Nash equilibrium (NE) between the competing SPs is proved and the price vector at which the NE occurs is obtained. It is found that, while in one extreme monopoly leads to very high revenue for WLAN SPs with minimal consumer surplus, in the other extreme unregulated duopoly/oligopoly leads to high consumer surplus at the cost of minimal revenue generation for the competing SPs. Thus, price regulation is proposed in the WLAN market for equitable distribution of the surplus among the SPs and the users.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.220
Teacher spread0.207 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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