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c1d0c23 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | from typing import Dict, Any, List, Optional
import json
from dataclasses import dataclass, field
import tinytroupe.openai_utils as openai_utils
from tinytroupe.agent.social_types import Content
@dataclass
class TraitProfile:
openness: float = 0.5
conscientiousness: float = 0.5
extraversion: float = 0.5
agreeableness: float = 0.5
controversiality_tolerance: float = 0.5
information_seeking_behavior: float = 0.5
visual_content_preference: float = 0.5
class TraitBasedBehaviorModel:
def __init__(self, model: str = "gpt-4"):
self.model = model
def compute_action_probability(self, persona: Any, action_type: str, content: Optional[Content] = None) -> float:
"""
Compute probability of an action based on persona traits.
"""
traits = persona.behavioral_traits
if not traits:
return 0.5
base_prob = 0.5
if action_type == "engage":
# Example logic
if content and content.format == "video":
base_prob = self.apply_trait_modifiers(base_prob, {"visual_content_preference": traits.get("visual_content_preference", 0.5)})
base_prob = self.apply_trait_modifiers(base_prob, {"openness": traits.get("openness", 0.5)})
return base_prob
def apply_trait_modifiers(self, base_probability: float, traits: Dict[str, float]) -> float:
"""
Apply trait modifiers to a base probability.
"""
prob = base_probability
for trait, value in traits.items():
# Simple linear adjustment for now
# values > 0.5 increase probability, < 0.5 decrease it
modifier = (value - 0.5) * 0.2
prob += modifier
return max(0.0, min(1.0, prob))
def generate_trait_profile_from_description(self, description: str) -> Dict[str, float]:
"""
Use LLM to infer traits from persona descriptions.
"""
prompt = f"""
Analyze the following persona description and infer their behavioral traits on a scale of 0.0 to 1.0.
Description: {description}
Traits to infer:
- openness (Openness to new ideas/novel content)
- conscientiousness (Posting regularity, thoughtfulness)
- extraversion (Sharing frequency, network activity)
- agreeableness (Commenting positivity, conflict avoidance)
- controversiality_tolerance (Engagement with divisive topics)
- information_seeking_behavior (Long-form vs short-form preference)
- visual_content_preference (Image/video vs text preference)
Provide the result in JSON format.
"""
response = openai_utils.client().send_message(
[
{"role": "system", "content": "You are an expert psychologist and persona modeler."},
{"role": "user", "content": prompt}
],
temperature=0.3,
response_format={"type": "json_object"}
)
try:
traits = json.loads(response["content"])
return traits
except Exception:
return TraitProfile().__dict__
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