Integrating individual, organizational and market level reasoning for agent coordination
Bibliographic record
Abstract
In this paper we articulate a multi-level view of agent coordination and provide solutions for an integrated agent architecture that addresses all the levels. At the individual agent level, we model the decision making problem faced by individual agents that need to discover their highest utility goals and the plans to achieve them. Individual level plans normally contain goals that lie outside the agent's control. To achieve them, the agent needs to team up with other agents in the organization. At the organizational level, we show how organizational structures can be used to form the minimum cost teams needed to achieve such goals. Individual and organizational reasoning rely on knowing the utilities of the options available to agents. Often, these utilities are not given in advance, they must be discovered dynamically by market driven interaction. At the market level, we give a constraint optimization formulation to Multi Attribute Utility Theory and introduce interaction processes that allow agents to discover how to cooperate to optimize their objectives. All levels translate their specific models into a common reasoning infrastructure integrating randomized and systematic search.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".