Purpose‐Based Expert Finding in a Portfolio Management System
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
Most of the research in the area of expert finding focuses on creating and maintaining centralized directories of experts' profiles, which users can search on demand. However, in a distributed multiagent‐based software environment, the autonomous agents are free to develop expert models or model fragments for their own purposes and from their viewpoints. Therefore, the focus of expert finding is shifting from the collection at one place as much data about a expert as possible to accessing on demand from various agents whatever user information is available at the moment and interpreting it for a particular purpose. This paper outlines purpose‐based expert modeling as an approach for finding an expert in a multiagent portfolio management system in which autonomous agents develop expert agent models independently and do not adhere to a common representation scheme. This approach aims to develop taxonomy of purposes that define a variety of context‐dependent user modeling processes, which are used by the users' personal agents to find appropriate expert agents to advise users on investing strategies.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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