feat: decision rule prompts, theta_trust enforcement, spatial messaging, and scenario history filtering#45
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Changes to be committed: modified: agentevac/simulation/main.py modified: agentevac/simulation/spawn_events.py modified: agentevac/utils/replay.py modified: sumo/Repaired.netecfg modified: sumo/Repaired.sumocfg
Module updated: agentevac/utils/replay.py
- Fixed RouteReplay._load_schedule(...) so it only reads step and veh_id for replayable events:
- route_change
- departure_release
- Non-replayable events like agent_cognition and metrics_snapshot are now ignored without touching veh_id.
Cause
- The loader was accessing rec["veh_id"] before checking the event type.
- metrics_snapshot records do not have veh_id, so replay loading crashed with KeyError.
Verification
1. python3 -m py_compile agentevac/utils/replay.py passed.
2. Reproduced the failing case with a small local script:
- one route_change
- one agent_cognition
- one metrics_snapshot
- replay load now succeeds and only indexes the route-change step.
…s and agent communication
…t_round_timeline.py
…gs for documentation
…g, and per-agent heterogeneity - Add compute_signal_conflict() using Jensen-Shannon divergence in belief_model.py - Restructure all three LLM prompts (pre-departure, destination, route) to expose raw env vs. social disagreement via your_observation/neighbor_assessment/ information_conflict/combined_belief fields - Add conflict_assessment field to all Pydantic response models - Add conflict recording to metrics (record_conflict_sample, compute_average_signal_conflict) - Implement distance-based noise scaling (proposal Eq. 1): effective sigma scales with fire margin / reference distance via DIST_REF_M config - Add per-agent parameter heterogeneity via sample_profile_params() with truncated normal distributions; configurable via *_SPREAD env vars (default 0 = legacy) - Fix stale subjective_information reference in scenarios.py - Add experiment stage scripts (stages 0-5) for RQ1/RQ2/RQ3 sweeps - Add comprehensive tests for all new features (291 tests passing) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
… recording - Add observation-based exposure function for no_notice scenario that uses agent belief state and route length instead of route-specific fire data - Enable expected_utility in all three scenarios (no_notice, alert_guided, advice_guided) with scenario-aware LLM policy text - Update menu filtering to retain travel time and utility for no_notice agents - Fix NET_FILE default from .rou.xml (route file) to .net.xml (network file), which caused EDGE_SHAPE to be empty and all exposure scores to be zero - Fix exposure recording to fire only on decision rounds instead of every simulation step, preventing dilution of the exposure average - Add Repaired.net.xml to repo; update SUMO configs to use local net file Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…e parameters Use SIM_END_TIME_S (default 1200s) to control simulation duration instead of relying on getMinExpectedNumber(), which terminated early when no agents had departed yet. Remove dummy t_0 vehicle from route file. Add --sim-end-time CLI flag and SIM_END_TIME_S env var. Update fire source growth rates and timing for more aggressive spread scenarios. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add input-hash caching (Plan C) across all 3 LLM call sites to skip redundant API calls when agent inputs haven't changed between rounds - Add parallel LLM dispatch (Plan A) for process_pending_departures using ThreadPoolExecutor — two-phase collect-then-process pattern fires all non-cached predeparture LLM calls concurrently (up to MAX_CONCURRENT_LLM) - Add 4 new fields to AgentRuntimeState for cache state tracking - Add RQ1–RQ4 experiment runner scripts for automated parameter sweeps Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Stop the simulation loop as soon as every spawned vehicle has departed and arrived at its destination, instead of running until SIM_END_TIME_S. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Restructure LLM decision prompts with explicit priority levels (safety > official guidance > risk assessment), add EOC guidance_source to operator briefings, and fix early termination to check actual arrivals via metrics.arrived_count() instead of active vehicle count. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…dule-plots # Conflicts: # agentevac/agents/agent_state.py # agentevac/analysis/metrics.py # agentevac/simulation/main.py # sumo/Repaired.netecfg # sumo/Repaired.sumocfg
…etwork updates - Record per-step edge traces via getRoadID() for faithful replay of actual routes taken (not just planned routes) - Add departure destination choice: agents pick a destination via LLM before spawning, so vehicles head the right direction from step zero (parallel LLM dispatch via ThreadPoolExecutor) - Fix early termination to check arrived_count instead of empty vehicle list (prevents premature exit when SUMO defers vehicle insertion) - Replay mode reads to_edge from departure records for correct initial routing - Update SUMO network with new shelter edges (E#S0, E#S1, E#S2) and refreshed spawn_events - Refine scenario prompt suffixes and DecisionModel schema (situation_summary field, expanded reason descriptions) - Update forecast_layer and corresponding tests Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…dule-plots # Conflicts: # agentevac/simulation/main.py # sumo/Repaired.net.xml # sumo/Repaired.netecfg # sumo/Repaired.sumocfg
…selection - Record departure destination choice in metrics via record_decision_snapshot so destination_choice_share counts all agents (not just those processed by process_vehicles) - Replace fixed-offset vehicle selection with round-robin so all agents get mid-route LLM re-evaluation over successive decision ticks Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The experiment runner lacked a --sumo-seed argument, so RQ scripts passed the seed via env var prefix which broke under dash (/bin/sh). Add --sumo-seed to experiments.py and rewrite all four RQ scripts to use POSIX-compatible for-loops with the new flag. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Record each agent's sampled psychological parameters (theta_trust, theta_r, theta_u, gamma, lambda_e, lambda_t) and write them to an agent_profiles JSON file alongside the metrics file. Enables post-hoc verification of population heterogeneity distributions in RQ4 runs. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…stances The old thresholds (danger ≤100m, risky ≤300m, buffered ≤700m) were calibrated for a city-block mental model. In the actual simulation, the minimum observed margin is 619m, so every agent always classified the fire as "safe" and never perceived risk. Scale thresholds to danger ≤1200m, risky ≤2500m, buffered ≤5000m so agents meaningfully perceive fire hazard. Also scale RISK_DECAY_M from 80 to 960 to keep the exponential risk curve proportional. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
En-route agents in no_notice mode can now see fire on the first few edges ahead of their current position (VISUAL_LOOKAHEAD_EDGES, default 3). This adds a penalty to the current destination's exposure score, making agents more likely to switch shelters when fire blocks their route. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…ling _observation_based_exposure() now prefers travel_time_s_fastest_path (minutes * 0.3) over len_edges (count * 0.15) to better reflect actual exposure duration. Falls back to edge count when travel time is unavailable. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…y prompts Adds an explicit instruction to all three LLM policy locations (pre-departure, en-route destination, en-route route) requiring agents to only reference information explicitly present in the prompt data. Prevents GPT-4o-mini from fabricating neighbor behaviors, evacuation patterns, or shelter choices that cascade through the messaging system. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Agents in no_notice mode now only perceive fires within FIRE_PERCEPTION_RANGE_M (default 1200m) of their position: - Perception horizon: if no fire is in range, env_signal margins are None → observed_state="unknown" (genuine uncertainty instead of false "safe"). When fire is in range, margins are computed from visible fires only. - Route-level fire data: all reachable menu items gain proximity_blocked_edges and proximity_min_margin_m from visible fires, enabling the utility function to differentiate destinations by hazard. - Exposure scoring: _observation_based_exposure() adds a proximity penalty (blocked * 8.0 + margin_penalty) matching _expected_exposure weights, so routes through visible fire are strongly deprioritised. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…nd compact JSON encoder Functional updates: - Extract AgentMessagingBus and OutboxMessage from main.py into agentevac/agents/messaging.py with spatial filtering support (comm_radius_m parameter for distance-based broadcast delivery) - Add filter_history_for_scenario() in scenarios.py to sanitize agent self-history records before embedding in LLM prompts (strips forecast, advisory, fire metrics per information regime) - Add _CompactLeafEncoder in run_parameters.py for readable parameter log output (leaf dicts rendered on single lines) - Add agentevac/analysis/analyze_run.py run analysis utility - Add tests for messaging spatial filtering (test_messaging.py) and history filtering (test_scenarios.py) - Add Research_Proposal_0317.pptx to .gitignore Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…rust enforcement, and spatial messaging Functional updates in agentevac/simulation/main.py: DEPARTURE PROMPT: - Replace BINDING CONSTRAINT with DECISION RULE prompt — 4 ordered rules (p_danger > theta_r, heuristic_departure_signal, official order, else wait) with stop-at-first-match semantics - Add heuristic_departure_signal field to predeparture prompt, exposing the pre-computed departure model output (covers theta_r, urgency decay via theta_u/gamma, low-confidence precaution, and neighbor departure pressure) THETA_TRUST LLM ENFORCEMENT (predeparture + routing prompts): - When theta_trust=0.0: strip inbox entirely, add BINDING CONSTRAINT instructing LLM to IGNORE all neighbor/social data - When theta_trust>0.0: inject calibrated percentage guidance (e.g., "rely 70% on own observation, 30% on neighbor messages") - Apply _consider_pol, _belief_weigh_pol, trust_policy to all 3 routing prompt locations (destination, route, and predeparture) SCENARIO-AWARE HISTORY: - Filter agent_self_history through filter_history_for_scenario() before embedding in routing prompts (prevents data leakage from historical records) SPATIAL MESSAGING: - Add COMM_RADIUS_M env var for distance-based broadcast filtering - Precompute SPAWN_EDGE_MIDPOINT for pre-departure agent positions - Pass agent positions to messaging.begin_round() for spatial delivery - Include comm_radius_m in messaging config sections of all prompts CONFIGURATION: - Remove inline AgentMessagingBus/OutboxMessage (now imported) - Widen lambda_e bounds to (0.0, 100.0) and lambda_t to (0.0, 100.0) - Add fire source F0_8 at (16348, 6801) with r0=400 - Export fire_sources and fire_events to run parameter log Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Functional updates: - Disable veh3 and veh4 spawn groups (commented out) to reduce toy-model agent count for focused testing - Comment out large-scale spawn groups (veh9–veh41) — reserved for future 100+ agent experiments - Update SUMO network (Repaired.net.xml): add junction J20 and associated internal edges for improved routing near south zone - Update Repaired.netecfg and Repaired.sumocfg timestamps Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
COMM_RADIUS_Menv var enables distance-based broadcast filtering so only nearby agents receive messagesAgentMessagingBusandOutboxMessagemoved from main.py toagentevac/agents/messaging.pyCommits
dcf53ce— refactor: Extract messaging module, addfilter_history_for_scenario(), compact JSON encoder, new tests (54 pass)ea5e61c— feat: Overhaul departure/routing prompts with decision rules, theta_trust enforcement, spatial messagingf17ecac— chore: Update spawn configuration and SUMO network geometryTest plan
test_scenarios.py,test_messaging.py)no_noticewith messaging off — verify agents depart with DECISION RULEadvice_guidedwith messaging off — verify advisory labels update over timeno_noticewith messaging on — verify no degenerate herd behavior🤖 Generated with Claude Code