Bibliographic record
Abstract
This paper examines a number of theoretical and practical issues concerning the use of decision-theoretic planning to implement agents in market-based systems. The markets considered here result from the decomposition of complex, intra-organizationai resource allocation problems such as manufacturing scheduling. Although these problems can be formulated as monolithic optimization problems, they tend to be much too large to solve in practice. Markets provide a means of decomposing large resource allocation problems and distributing the computation of a solution over many processors. An important precondition of efficient markets is agent-level rationality. Decision theoretic planning can be used to implement economic rationality and thus decision theoretic planning agents fit well into market-based approaches. The primary challenge in building decision theoretic planning agents is the size of the agent’s state space. Although marketbased decomposition results in agent-level problems that are much smaller than the original resource allocation problem, the price mechanism used to achieve independence of the agent-level problems requires that the agents plan over a large number of different resource contingencies. This requirement exacerbates the state space explosion that characterizes decision-theoretic planning. The application of state space reduction techniques such as structured dynamic programming and reachability analysis are shown to yield significant reductions in the effective size of the agent-level problems and thereby increase the applicability of decision theoretic planning techniques in market-based systems. 1
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".