PremC1F/emotion-engine

Emotion Engine Dataset

204,520 agent-step records from a five-condition controlled experiment on emergent emotional appraisal in generative agents.

Paper: Emergent Emotional Appraisal in Generative Agents via Predictive World Modeling Author: Prem Babu Kanaparthi Code: github.com/premxai/emotion_engine

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What This Dataset Is

Five emotion-engine architectures were compared in Ghost Town — a 12-agent survival simulation across 6 scenario variants and 8 random seeds.

The central finding: Condition D (trained only to predict future world events) independently rediscovers OCC cognitive appraisal signatures (fear, grief, suspicion) with zero emotion rules, labels, or reward shaping.

Each record is one agent at one timestep: the full observation vector, emotion state, latent representation, action taken, 12 future-event prediction probabilities, and outcome metadata.

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Dataset Structure

Splits

Split	Condition	Scenario	Seeds	Records
baseline	Baseline (no emotion)	all 6	0–7	40,907
condition_a	Hand-coded OCC rules	all 6	0–7	40,921
condition_b	Behavioral cloning	all 6	0–7	40,838
condition_c	Emotion dynamics model	all 6	0–7	40,916
condition_d	Predictive world modeling	all 6	0–7	40,938

Total: 204,520 records

Scenarios in Every Split

Scenario	Primary OCC Signature	Description
standard_night	Fear	1 ghost per night, standard threat
high_ghost_pressure	Fear (extreme)	3 simultaneous ghosts per night
storm_scarcity	Stress + Grief	Food supply reduced 70% by storm
ally_death	Grief	June Carter dies at step 1 (scripted)
betrayal_refusal	Suspicion	Rival pairs initialized with negative ties
crowded_shelter	Fear + Suspicion	Shelter capacity halved (6 slots / 12 agents)

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Fields

Each record contains the following fields:

Identity

Field	Type	Description
run_id	string	Unique run identifier, e.g. condition_d_standard_night_seed0_12-agent
step	int	Timestep within run (0–71, 3 simulated days × 24 steps/day)
agent_id	string	Agent name (one of 12 fixed agents)
condition	string	baseline_0, condition_a, condition_b, condition_c, condition_d
seed	int	Random seed (0–7)
scenario	string	One of the 6 scenario variants

Observation (14 dimensions)

Field	Type	Description
obs_visible_ghosts	int	Number of ghosts within sensor range
obs_visible_deaths	int	Number of ally deaths witnessed this step
obs_nearby_allies	int	Allies within 5 tiles
obs_trusted_allies	int	Allies with positive social tie nearby
obs_supplies_seen	int	Supply units visible
obs_in_shelter	bool	Agent is inside a safe building
obs_nearest_refuge_distance	float	Tiles to closest safe building
obs_steps_since_ghost_seen	int	Steps since last ghost sighting (capped at 20)
obs_steps_since_ally_died	int	Steps since last witnessed death (capped at 20)
obs_steps_since_betrayal	int	Steps since last betrayal received (capped at 20)
obs_ally_deaths_witnessed	int	Cumulative ally deaths witnessed
obs_betrayals_received	int	Cumulative betrayals received
obs_average_trust	float	Mean social tie value across all agents [−1, 1]
obs_graph_tension	float	Mean negative tie magnitude

Emotion / Affect (Condition D only — zeros for others)

Field	Type	Description
affect_fear	float	Fear activation [0, 1]
affect_grief	float	Grief activation [0, 1]
affect_trust	float	Trust activation [0, 1]
affect_stress	float	Stress activation [0, 1]
affect_relief	float	Relief activation [0, 1]
affect_suspicion	float	Suspicion activation [0, 1]

Latent Representation (8 dimensions)

Field	Type	Description
latent_0 … latent_7	float	Internal learned representation (8-dim)

Future-Event Predictions (Condition D only — zeros for others)

Field	Type	Description
pred_ghost_nearby_t3	float	P(ghost visible in 3 steps)
pred_my_death_t5	float	P(this agent dies in 5 steps)
pred_health_drop_t3	float	P(health decreases in 3 steps)
pred_nearby_death_t5	float	P(an ally dies in 5 steps)
pred_help_success_t5	float	P(help action succeeds in 5 steps)
pred_refusal_received_t5	float	P(refusal received in 5 steps)
pred_tie_increase_t5	float	P(social tie increases in 5 steps)
pred_shelter_achieved_t2	float	P(agent in shelter in 2 steps)
pred_storm_onset_t3	float	P(storm starts in 3 steps)
pred_scarcity_t5	float	P(supply scarcity in 5 steps)
pred_graph_tension_t3	float	P(social tension increases in 3 steps)
pred_valence_t5	float	P(positive valence in 5 steps)

Action & Outcome

Field	Type	Description
action	string	Action taken: hide, gather_supplies, seek_safe_house, seek_hospital, refuse_help, warn, patrol
goal	string	Agent goal state at this step
reward_survival	float	Survival reward component
reward_shelter	float	Shelter reward component
reward_health	float	Health reward component
reward_social	float	Social reward component
total_reward	float	Sum of reward components
alive	bool	Agent survived this step
health	float	Health percentage (0–100)
sheltered	bool	Agent is in a safe building
time_of_day	string	day, dusk, night, dawn

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Loading the Dataset

With the datasets library (recommended)

from datasets import load_dataset

# Load a specific condition
ds = load_dataset("PremC1F/emotion-engine", "condition_d")

# Load all conditions
ds = load_dataset("PremC1F/emotion-engine", "all")

# Filter to a specific scenario
night_only = ds["train"].filter(lambda x: x["scenario"] == "standard_night")

# Filter to ghost-present steps
threat_steps = ds["train"].filter(lambda x: x["obs_visible_ghosts"] > 0)

Reproduce the Fear→Shelter result

from datasets import load_dataset
import numpy as np
from scipy.stats import fisher_exact

ds = load_dataset("PremC1F/emotion-engine", "condition_d", split="train")
df = ds.to_pandas()

ghost_present = df[df["obs_visible_ghosts"] > 0]
ghost_absent  = df[df["obs_visible_ghosts"] == 0]

shelter_given_ghost    = (ghost_present["action"] == "seek_safe_house").mean()
shelter_given_no_ghost = (ghost_absent["action"]  == "seek_safe_house").mean()

# Contingency table
a = (ghost_present["action"] == "seek_safe_house").sum()
b = (ghost_present["action"] != "seek_safe_house").sum()
c = (ghost_absent["action"]  == "seek_safe_house").sum()
d = (ghost_absent["action"]  != "seek_safe_house").sum()

_, p = fisher_exact([[a, b], [c, d]])
print(f"Ghost present  → shelter: {shelter_given_ghost:.1%}")
print(f"Ghost absent   → shelter: {shelter_given_no_ghost:.1%}")
print(f"Fisher p = {p:.2e}")
# Expected: Ghost present → shelter: 99.0%, p = 3.3e-113

Reproduce the Suspicion Decay result

from datasets import load_dataset

ds = load_dataset("PremC1F/emotion-engine", "condition_d", split="train")
df = ds.to_pandas()

refusals = df[df["action"] == "refuse_help"].copy()

def bucket(steps):
    if steps <= 5:  return "recent"
    if steps <= 10: return "fading"
    return "forgotten"

refusals["bucket"] = refusals["obs_steps_since_betrayal"].apply(bucket)
all_social = df.copy()
all_social["bucket"] = all_social["obs_steps_since_betrayal"].apply(bucket)

for b in ["recent", "fading", "forgotten"]:
    denom = (all_social["bucket"] == b).sum()
    numer = ((refusals["bucket"] == b)).sum()
    print(f"{b}: {numer/denom:.1%} refusal rate")
# Expected: recent 64.6%, fading 4.5%, forgotten 35.2%

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Citation

@article{kanaparthi2026emotion,
  author  = {Kanaparthi, Prem Babu},
  title   = {Emergent Emotional Appraisal in Generative Agents
             via Predictive World Modeling},
  year    = {2026},
}

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License

MIT License.