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Record W2255209176 · doi:10.1287/serv.2015.0114

Software Evolution in Web-Service Ecosystems: A Game-Theoretic Model

2016· article· en· W2255209176 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueService Science · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDigital Platforms and Economics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceSoftwareGame theoryWeb serviceService (business)World Wide WebBusinessSoftware engineeringMarketingMicroeconomicsOperating systemEconomics

Abstract

fetched live from OpenAlex

Service orientation is the prevalent paradigm for modular distributed systems, giving rise to service ecosystems defined by software dependencies, which, at the same time, carry business and economic implications. And as software evolves, so do the business relationships among the ecosystem participants, with corresponding economic impact. Therefore, a more comprehensive model of software evolution is necessary in this context, to support the decision-making processes of the ecosystem participants. In this work, we view the ecosystem as a market environment, with providers offering competing services and developing these services to attract more clients by better satisfying their requirements. Based on an economic model for calculating the costs and values associated with service evolution, we develop a game-theoretic model to capture the interactions between providers and clients and support the providers’ decision-making process. We demonstrate the use of our model with a realistic example of a cloud-services ecosystem.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.011
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.013
GPT teacher head0.197
Teacher spread0.184 · 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