Q&A: Phase 24.3 — PerspectiveTaker

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Q&A: Phase 24.3 — PerspectiveTaker 🔄

Q1: What is the practical maximum recursion depth for Level-k reasoning?

A: In behavioral game theory experiments, humans rarely exceed Level-2 reasoning (Camerer et al. 2004 found mean τ ≈ 1.5 in most settings). Our default max_recursion_depth is 3 (Level-3 / "Deep"), which covers >95% of the Poisson mass. Level-4+ adds negligible strategic value but exponential cost. The configurable timeout_ms (500ms default) serves as a hard cap—if Level-3 exceeds the budget, we return the Level-2 result.

Q2: How does the LRU cache interact with belief updates?

A: The cache key is (agent_id, context_hash, level). When BeliefTracker updates an agent's belief model, we register an invalidation hook that evicts all cache entries for that agent. This ensures perspective simulations always reflect the latest beliefs. The cache primarily helps when:

• The same agent is queried multiple times in one social cognition cycle
• Context hasn't changed between queries
• Multiple components (SocialPredictor, SocialOrchestrator) query the same perspective

Q3: How does bounded rationality affect simulation quality?

A: The bounded_rationality parameter (0.0–1.0) controls a softmax temperature over action values:

P(action_i) = exp(V_i / T) / Σ exp(V_j / T)
where T = 1 / bounded_rationality

• bounded_rationality = 1.0 → T = 1.0 → moderate randomness (perfectly rational)
• bounded_rationality = 0.5 → T = 2.0 → high randomness (bounded rational)
• bounded_rationality = 0.1 → T = 10.0 → nearly random (very bounded)

This models the empirical finding that real agents don't perfectly optimize—they make mistakes, satisfice, and use heuristics.

Q4: How is CommonGround computed between multiple agents?

A: CommonGround computation:

1. Take perspective of each agent in the group
2. Find intersection of believed_state across all perspectives
3. Identify shared goals (goals present in all intention models)
4. Identify disagreements (propositions where agents' beliefs diverge beyond threshold)
5. alignment_score = |shared_beliefs| / |union_of_all_beliefs|

This is a computational approximation of Stalnaker's common ground concept from pragmatics.

Q5: Can PerspectiveTaker detect when an agent is lying?

A: Indirectly. If we know the ground truth (from WorldModel 13.1) and an agent communicates something inconsistent with it, BeliefTracker marks a divergence. PerspectiveTaker can then check: does the agent believe the false statement (honest mistake) or do they believe the truth but communicated otherwise (deception)? This requires comparing the agent's believed_state with their communicated claims. The check_false_belief method handles the belief-side; deception detection additionally requires SocialOrchestrator analysis.

Q6: How does emotional state from EmpathyEngine (21.3) modulate perspective simulation?

A: Emotional state affects the reasoning_model parameter in the constructed Perspective. For example:

• High anxiety → more conservative action selection (lower bounded_rationality)
• High anger → more aggressive/competitive goal weighting
• High trust → cooperative strategy bias

This is implemented as an emotional adjustment to the softmax temperature and goal prior weights, based on appraisal theory (Scherer 2001).

Q7: What is the fallback when PerspectiveTaker has no belief model for an agent?

A: If BeliefTracker has no model for agent X (never observed), PerspectiveTaker falls back to a default perspective:

• believed_state = ground truth (assume agent knows what we know)
• goals = empty (no inferred intentions)
• reasoning_level = NAIVE (Level-0)
• confidence = 0.1 (very low)

This graceful degradation ensures the system never crashes on unknown agents, but the low confidence signals to downstream components that this perspective is speculative.