Integrating individual, organizational and market level reasoning for agent coordination
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
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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