A Cased-Based Reasoning Decision Support System/SYSTEME DU SUPPORT DECISION CBR DANS L'ACQUISITION GOVERNMENTALE
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.
Bibliographic record
Abstract
Abstract: Each bidding contractors estimates his likely costs of carrying out the work detailed in the project schedules and adds a percentage markup to form the bid value. The value of the markup crucially influences the chances of a bidder winning the contract. Clearly, a low markup value should increases the chance of winning but decrease the profit, whilst a high markup should increase the profit but decrease the chance of winning the contract. It is very difficult for contractors to decide a proper markup, which happens to produce a satisfactory balance between the probability of winning the contract and the profit generated as a result of winning the contract. This paper presents a case-based reasoning decision support system (CBR-DSS) that assists contractors in solving markup estimation problem. The CRR-DSS uses successful cases of previous completed projects to derive solution to new project markup estimation problem. The principle of the CBR-DSS is to analogy new project with previous projects. Key Words: Case-Based Reasoning, DSS, Bidding, Markup Resume: Chaque contracteur demande estime son cout d'application d'un travail detaille dans les horaires et ajoute un percentage de maquillage pour avoir l'offre qui influence crucialement une eventuelle reussite d'un contrat. Evidemment une petite valeur de maquillage doit augmenter les chances de gagner mais reduire le profit tandis que un grand maquillage doit augmenter le profit mais reduire les chances d'arriver a un contract. Il est tres difficile pour les contracteurs de decider une offre convenable, qui eventuellement produit une balance de satisfaction entre la probabilite d'achever le contrat et le profit considere comme une reussite d'un contrat. Ce document presente un systeme du support decision rationnel base sur les cas (CBR-DSS) qui permet aux contracteurs de s'engager dans la solution des problemes estimes et demandes. Le CRR-DSS utilise des reussites de programmes pre-acheves qui servent a resoudre les problemes d'estimation dans un nouveau programme. Le principe de CBR-DSS est trouver les solutions pour de nouveaux programmes par analogie ceux pre-acheves. Mots cles: Raisonnement base sur les cas, DSS; offre, maquillage, acquisition Governmentale 1. INTRODUCTION The bidding decision is a complex decision-making process that is affected by a lot of factors, especially for markup decision-making process. In fact, the markup, M, which is the price quoted minus the cost, is usually taken as the key decision variable and the total expected profit is then the product of the estimated cost, the markup chosen and the probability, P (m), of winning the contract with a markup M. Each bidding contractor estimates his likely costs of carrying out the work detailed in the project schedules and adds a percentage markup to form the bid value. The value of the markup crucially influences the chances of a bidder winning the contract. Clearly, a low markup value should increases the chance of winning but decrease the profit, whilst a high markup should increase the profit but decrease the chance of winning the contract. Strategic markup bidding assumes that the bidder applies a markup that happens to produce a balance between the probability of winning the contract and the profit generated as a result of winning the contract. A special case of strategic markup bidding id optimal bidding, defined as applying a markup that happens to maximize expected profit, i.e. the product of the probability of winning the contract and the profit generated as a result of winning the contract. The literature on strategic markup bidding is quite extensive and several reviews have been published. All the work to date has been based on two bivariate models. The Friedman model compares the strategic bidder with the lowest bidders. However, the Friedman model has been frequently criticized as demanding unrealistic amounts of data to estimate the model parameters, especially for construction contract auction. …
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it