Explore/agent app/Adaptive AI Delegation under Uncertainty: A Bayesian Governance Policy for Sequential Decision Authority
A

Matthew Francis Dixon/Adaptive AI Delegation under Uncertainty: A Bayesian Governance Policy for Sequential Decision AuthorityUnknown

Organizations increasingly use large language models and agentic AI systems to generate probabilistic assessments and candidate actions in high-consequence settings. This creates a managerial problem distinct from prediction: how should organizations allocate decision authority to AI-generated recommendations as evidence quality, uncertainty, and organizational objectives evolve over time? Existing AI governance frameworks emphasize transparency, documentation, oversight, and regulatory compliance, but provide limited quantitative guidance for dynamically allocating decision authority under uncertainty. To address this challenge, we formulate adaptive AI delegation as a Governance-Aware Partially Observable Markov Decision Process (POMDP) in which Bayesian inference estimates the informational state and sequential optimization determines delegated AI authority. The paper also develops a quantitative validation and benchmarking framework for governance policies. Synthetic stress tests, reported LLM-confidence robustness, forecast-accuracy validation, governance-appetite sensitivity, and fragile-AI early-warning experiments evaluate whether the proposed policy exhibits graceful degradation, robustness to confidence-only perturbations, adaptive delegation under improving evidence quality, and interpretable calibration of institutional conservatism. The Governance-Aware POMDP is further benchmarked against five representative governance strategies operating under identical Bayesian beliefs, information, and governance objectives. The results show that while specialized heuristics perform well in stationary settings, sequential Bayesian governance provides the strongest general-purpose governance policy across heterogeneous AI-quality regimes by adaptively allocating organizational decision authority under uncertainty.

agent app
GitHubCompare
Refreshed 1mo ago
OverviewActivity52wAlternativesDocs
Stars0
Forks0
HF Downloads30d
Last commit
Refreshed1mo ago
Project healthUnknownNo activity data.
Production readinessResearch / EarlyBest for exploration and prototyping.
Risk notesUnknown licenseVerify license before production use.
AgentHub Score
55 / 100
Composite score from 6 signals. How we score →
Active project
55Score
Growth
40C
Activity
30C
Documentation
70C+
Maturity
45C
Community
42C
Production
58C
GitHub stars · 28 days observed0 not enough history
snapshots
Repository activity · 28 days observedReal snapshots from pushed_at
inactivepushed
2026-07-262026-08-24
Practical assessment
Should you use it?

✓ Best for

  • Research and experimentation
  • Prototype development
  • Learning agentic patterns

◎ Strengths

  • Active community
  • Open source
  • Well-documented API

✕ Not ideal for

  • Untested at scale without validation
  • Teams without AI/ML expertise

⚠ Watch-outs

  • Review changelog before updating
  • Verify license for commercial use
Technical details
What's inside
Language
License
Sourcearxiv
Open source✗ No
Commercial use
Docs
Demo

AgentHub Score

55
Score 55/100
Below average

Alternatives

O
OccuReward: LLM-Guided Occupant-Centric Reward Shaping for Demographic Equity in Grid-Interactive Buildings
0 · agent app
55
W
What is Missing from AI Post-Training AI: An Empirical Analysis
0 · agent app
55
D
Delegating or Doing? Understanding User Behavior in Hybrid Human-Agent Interfaces
0 · agent app
55
U
Unaccountable Delegation, Fading Skills: Mapping the Risks of Workplace AI Agents
0 · agent app
55
Compare all →

Recent activity

Latest commit —
Indexed by AgentHub crawler1mo ago
Monitor for new releasesongoing