When Does Information Sharing Improve Decentralized Discovery? Aggregation, Independent Rescue, and Equilibrium Selection
Information sharing can simultaneously improve pooled estimates and eliminate independent rescue actions, and this paper formally separates these two effects in exact finite discovery models Under a registered incremental-sharing protocol, a sharing step improves discovery if and only if pooled residual error contracts faster than an independent rescue attempt In a two-agent Bayesian game with hidden common and independent signal sources, the selected equilibrium yields a strict positive sharing
Analysis
TL;DR
- Information sharing can simultaneously improve pooled estimates and eliminate independent rescue actions, and this paper formally separates these two effects in exact finite discovery models
- Under a registered incremental-sharing protocol, a sharing step improves discovery if and only if pooled residual error contracts faster than an independent rescue attempt
- In a two-agent Bayesian game with hidden common and independent signal sources, the selected equilibrium yields a strict positive sharing interval at signal accuracy 3/5, but results are equilibrium-selection-dependent rather than universal
- Exact bounded registries exhibit four structural phenomena: compression, aggregation, neutral curves, and a bounded zero mixed class
- The models are purely synthetic and finite, with no empirical human or organizational data used
Why It Matters
This work provides a rigorous theoretical foundation for understanding when and why information sharing succeeds or fails in decentralized multi-agent systems, which is directly relevant to distributed AI, federated learning, and multi-agent coordination. The equilibrium-selection dependence highlights a critical design consideration: sharing protocols cannot be evaluated in isolation from the strategic environment in which agents operate.
Technical Details
- The paper introduces a registered incremental-sharing protocol within exact finite discovery models, deriving a precise threshold condition: sharing improves discovery exactly when pooled residual error contracts faster than an independent rescue attempt
- A centralized action-budget profile demonstrates that equal per-agent accuracy can coexist with different portfolio values, revealing a decoupling between individual competence and collective outcome
- The two-agent Bayesian game features a hidden mixture of common and independent signal sources, with equilibrium analysis showing that the registered selected equilibrium produces a strict positive sharing interval at signal accuracy of 3/5
- Four structural properties of exact bounded registries are identified: compression (information density increases), aggregation (signals combine non-trivially), neutral curves (boundaries where sharing is indifferent), and a bounded zero mixed class (regions where no mixed strategy equilibrium exists)
- The analysis is entirely synthetic and finite; no real-world datasets or organizational data are employed
Industry Insight
- Multi-agent AI systems should explicitly model equilibrium selection when designing information-sharing protocols, as the same protocol can yield divergent outcomes under different strategic assumptions
- The 3/5 accuracy threshold provides a concrete benchmark for practitioners evaluating whether decentralized agents are sufficiently competent to benefit from sharing versus operating independently
- The decoupling of individual accuracy from portfolio value suggests that team composition strategies should optimize for complementary signal structures rather than merely maximizing individual agent performance
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