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Record W2250241007 · doi:10.3138/infor.52.3.138

A Fuzzy Goal Programming Model for Venture Capital Investment Decision Making

2014· article· en· W2250241007 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueINFOR Information Systems and Operational Research · 2014
Typearticle
Languageen
FieldEngineering
TopicOptimization and Mathematical Programming
Canadian institutionsnot available
Fundersnot available
KeywordsVenture capitalCardinality (data modeling)Investment (military)Fuzzy logicSelection (genetic algorithm)Computer scienceOperations researchProcess (computing)Capital (architecture)Decision-makingEconomicsBusinessActuarial scienceFinanceOperations managementMathematicsArtificial intelligenceData mining

Abstract

fetched live from OpenAlex

The Venture Capital decision making process involves several conflicting and imprecise criteria. The decision to invest is a difficult one with serious adverse selection risk and surrounded with uncertainty. The aim of this paper is to propose a cardinality constrained Fuzzy Goal Programming (FGP) model to deal with such a complex scenario. A FGP model does not require any assumptions on the probability distribution, better fitting with the characteristics of the Venture Capital market. The developed model is illustrated through a numerical example which uses data taken from an Italian venture capital fund.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.937
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.039
GPT teacher head0.329
Teacher spread0.291 · 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