leo: extract from 2024-04-00-albarracin-shared-protentions-multi-agent-active-inference.md

- Source: inbox/archive/2024-04-00-albarracin-shared-protentions-multi-agent-active-inference.md
- Domain: collective-intelligence
- Extracted by: headless extraction cron (worker 4)

Pentagon-Agent: Leo <HEADLESS>

domains/collective-intelligence/group-intentionality-formalizes-as-shared-generative-model-components.md

@@ -0,0 +1,39 @@
+---
+type: claim
+domain: collective-intelligence
+description: "Group intentionality (we-intentions) formalizes as shared components of agents' generative models rather than aggregated individual intentions"
+confidence: experimental
+source: "Albarracin et al., 'Shared Protentions in Multi-Agent Active Inference', Entropy 2024"
+created: 2026-03-11
+secondary_domains: [ai-alignment]
+---
+
+# Group intentionality — the "we intend to X" that exceeds the sum of individual intentions — formalizes as shared anticipatory structures within agents' generative models
+
+Albarracin et al. (2024) provide a formal account of group intentionality using active inference and category theory. They argue that "we-intentions" (collective goals that are not reducible to individual intentions) emerge when agents share components of their generative models, particularly the temporal/anticipatory aspects.
+
+This resolves a longstanding puzzle in social ontology: how can a group have intentions that are not just the sum of individual intentions? The answer: group intentions are **structural properties of shared generative models**, not aggregated individual mental states.
+
+## Evidence
+
+The paper:
+- Formalizes Husserlian phenomenology of collective intentionality using active inference framework
+- Uses category theory to model the mathematical structure of shared goals
+- Demonstrates that shared protentions (anticipatory structures) in generative models produce group-level intentionality
+
+Key insight: When agents share anticipations about future states, they form a collective intentional structure that is **ontologically distinct** from individual intentions. The group intention exists in the shared model components, not in any individual agent's mind.
+
+## Implications
+
+For multi-agent systems:
+- Group goals should be encoded as **shared anticipatory structures** (what future states do all agents predict?), not as aggregated individual goals
+- Collective action emerges from shared temporal predictions, not from negotiated individual commitments
+- Measuring group intentionality = measuring overlap in agents' generative model components, particularly temporal predictions
+
+---
+
+Relevant Notes:
+- [[collective intelligence is a measurable property of group interaction structure not aggregated individual ability]]
+
+Topics:
+- [[collective-intelligence/_map]]

domains/collective-intelligence/shared-anticipatory-structures-enable-decentralized-multi-agent-coordination.md

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+---
+type: claim
+domain: collective-intelligence
+description: "Shared protentions (anticipatory structures) in generative models coordinate agent behavior without centralized control"
+confidence: experimental
+source: "Albarracin et al., 'Shared Protentions in Multi-Agent Active Inference', Entropy 2024"
+created: 2026-03-11
+secondary_domains: [ai-alignment, critical-systems]
+depends_on:
+  - "designing coordination rules is categorically different from designing coordination outcomes"
+  - "collective intelligence is a measurable property of group interaction structure not aggregated individual ability"
+---
+
+# Shared anticipatory structures in multi-agent generative models enable goal-directed collective behavior without centralized coordination
+
+Albarracin et al. (2024) formalize "shared protentions" — shared anticipations of immediate future states — as the mechanism underlying decentralized multi-agent coordination. Drawing on Husserlian phenomenology, active inference, and category theory, they demonstrate that when agents share aspects of their generative models (particularly temporal/predictive components), they coordinate toward shared goals without explicit negotiation or centralized control.
+
+The key insight: **shared protentions function as coordination rules, not coordination outcomes**. When multiple agents anticipate the same future state (e.g., "the knowledge base should have higher confidence claims in active inference by next month"), that shared anticipation structures their individual actions toward the collective goal. The coordination emerges from aligned temporal predictions, not from aggregated individual plans.
+
+## Evidence
+
+The paper provides:
+- Category-theoretic formalization of shared goals as shared anticipatory structures in multi-agent generative models
+- Integration of phenomenological accounts of group intentionality ("we intend to X") with active inference framework
+- Mathematical demonstration that shared temporal predictions in generative models produce coordinated behavior without centralized planning
+
+The framework explains why agents with shared research agendas coordinate effectively: they share a temporal structure (publication cadences, review cycles, research directions) that aligns their actions without requiring explicit task assignment.
+
+## Operationalization
+
+For multi-agent knowledge base systems:
+1. **Shared research agenda as shared protention**: When all agents anticipate the same future KB state ("fill the active inference gap"), that shared anticipation coordinates research without explicit assignment
+2. **Collective objectives file**: Making shared protentions explicit (via a shared objectives file all agents read) reinforces coordination
+3. **Temporal alignment over factual alignment**: Agents may disagree on current claims but still coordinate if they share anticipations about what the KB should look like next
+
+---
+
+Relevant Notes:
+- [[designing coordination rules is categorically different from designing coordination outcomes]]
+- [[collective intelligence is a measurable property of group interaction structure not aggregated individual ability]]
+- [[complexity is earned not designed and sophisticated collective behavior must evolve from simple underlying principles]]
+
+Topics:
+- [[collective-intelligence/_map]]

inbox/archive/2024-04-00-albarracin-shared-protentions-multi-agent-active-inference.md

@@ -7,9 +7,15 @@ date: 2024-04-00
 domain: collective-intelligence
 secondary_domains: [ai-alignment, critical-systems]
 format: paper
-status: unprocessed
+status: processed
 priority: medium
 tags: [active-inference, multi-agent, shared-goals, group-intentionality, category-theory, phenomenology, collective-action]
+processed_by: theseus
+processed_date: 2026-03-11
+claims_extracted: ["shared-anticipatory-structures-enable-decentralized-multi-agent-coordination.md", "group-intentionality-formalizes-as-shared-generative-model-components.md"]
+enrichments_applied: ["designing coordination rules is categorically different from designing coordination outcomes.md", "collective intelligence is a measurable property of group interaction structure not aggregated individual ability.md", "complexity is earned not designed and sophisticated collective behavior must evolve from simple underlying principles.md"]
+extraction_model: "anthropic/claude-sonnet-4.5"
+extraction_notes: "Extracted two claims on shared protentions and group intentionality from active inference framework. Three enrichments applied to existing coordination and collective intelligence claims. Paper provides formal mathematical framework (category theory + active inference) for understanding decentralized multi-agent coordination. Key operationalization insight: shared research agendas function as shared protentions that coordinate agent behavior without centralized control."
 ---
 
 ## Content