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- Source: inbox/archive/2020-12-00-da-costa-active-inference-discrete-state-spaces.md - Domain: ai-alignment - Extracted by: headless extraction cron (worker 3) Pentagon-Agent: Theseus <HEADLESS>
53 lines
4.2 KiB
Markdown
53 lines
4.2 KiB
Markdown
---
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type: claim
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domain: ai-alignment
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secondary_domains: [critical-systems, collective-intelligence]
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description: "Knowledge bases as discrete state-spaces enable EFE-based policy selection where research directions are policies minimizing expected free energy"
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confidence: experimental
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source: "Da Costa et al. (2020), Active Inference on Discrete State-Spaces: A Synthesis (applied to knowledge base architecture)"
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url: "https://www.sciencedirect.com/science/article/pii/S0022249620300857"
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created: 2026-03-11
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depends_on: ["active-inference-unifies-perception-action-planning-learning-under-free-energy-minimization.md"]
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challenged_by: []
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---
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# Discrete state-space active inference provides formal foundation for research direction selection by modeling knowledge bases as state-spaces where policies minimize expected free energy
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The discrete-state formulation of active inference (Da Costa et al., 2020) provides a mathematical foundation for implementing research direction selection in knowledge base systems. The key insight is that structured knowledge bases can be modeled as discrete state-spaces where:
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1. **States** are configurations of claims, confidence levels, and relationships
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2. **Actions** are research activities (reading sources, extracting claims, enriching existing claims)
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3. **Policies** are possible research directions (which domain to explore, which source to read next)
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4. **Observations** are the evidence and claims extracted from sources
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In this formulation, the optimal research direction is the policy that minimizes expected free energy—balancing:
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- **Epistemic value** (information gain): How much does this research direction reduce uncertainty about important claims?
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- **Pragmatic value** (preference alignment): How well does this direction advance stated research goals?
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This framework formalizes what the Residue prompt does informally: structured exploration that balances learning (epistemic) with goal-directed progress (pragmatic). The 6x improvement from structured exploration protocols can be understood as better approximation of EFE minimization compared to unstructured human coaching.
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The discrete-state formulation is particularly well-suited to knowledge bases because:
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- Claims are discrete (they exist or don't)
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- Confidence levels are discrete (proven/likely/experimental/speculative)
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- Research directions are discrete choices (finite set of sources, domains, enrichment targets)
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- State transitions are deterministic (reading a source either yields claims or doesn't)
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## Evidence
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- Da Costa et al. (2020) provides complete mathematical framework for active inference on discrete state-spaces, explicitly covering planning and decision-making as policy selection minimizing EFE
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- The framework is mathematically proven to be optimal for intelligent behavior in discrete domains
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- [[structured exploration protocols reduce human intervention by 6x because the Residue prompt enabled 5 unguided AI explorations to solve what required 31 human-coached explorations]] demonstrates empirical performance consistent with EFE-minimizing behavior
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## Limitations
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This is an architectural proposal grounded in formal theory but not yet implemented in the knowledge base system. The claim is that the mathematical framework *provides foundation for* implementation, not that it has been validated in practice. Confidence is experimental because the application to knowledge base architecture is novel, though the underlying active inference theory is well-established.
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---
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Relevant Notes:
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- [[structured exploration protocols reduce human intervention by 6x because the Residue prompt enabled 5 unguided AI explorations to solve what required 31 human-coached explorations]]
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- [[coordination protocol design produces larger capability gains than model scaling because the same AI model performed 6x better with structured exploration than with human coaching on the same problem]]
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- [[as AI-automated software development becomes certain the bottleneck shifts from building capacity to knowing what to build making structured knowledge graphs the critical input to autonomous systems]]
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Topics:
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- [[domains/ai-alignment/_map]]
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- [[foundations/collective-intelligence/_map]]
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- [[foundations/critical-systems/_map]]
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