feat: 添加观点传播系统

- 新增 Stance 数据结构 (optimism/fear)
- 情绪影响 stance (happy→乐观, anxious→恐惧)
- 实现 apply_social_influence 社交影响函数
- 确定性随机选择接触对象
- 单次变化限制 ±0.1

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

engine-python/app/engine.py

@@ -6,6 +6,7 @@ from .models import (
 )
 from .global_events import GLOBAL_EVENT_POOL, GlobalEvent
 from .opinions import generate_opinions
+from .social_influence import apply_social_influence
 
 MAX_EVENTS = 20
 MAX_MEMORY_PER_AGENT = 3
@@ -86,6 +87,9 @@ def process_events(state: WorldState, events: List[Event]) -> WorldState:
     # 生成角色对事件的观点
     generate_opinions(state)
 
+    # 应用社交影响
+    apply_social_influence(state)
+
     return state
 
 
@@ -119,6 +123,9 @@ def check_and_trigger_global_event(
         # 生成角色对新事件的观点
         generate_opinions(state)
 
+        # 应用社交影响
+        apply_social_influence(state)
+
         result = GlobalEventResult(
             triggered=True,
             event=global_event.to_info()

engine-python/app/models.py

@@ -38,6 +38,12 @@ class Opinion(BaseModel):
     tick: int   # 生成时的 tick
 
 
+class Stance(BaseModel):
+    """角色立场"""
+    optimism: float = Field(default=0.5, ge=0.0, le=1.0)
+    fear: float = Field(default=0.5, ge=0.0, le=1.0)
+
+
 class AgentState(BaseModel):
     emotion: Emotion = Emotion.CALM
     goal: str = ""
@@ -45,6 +51,8 @@ class AgentState(BaseModel):
     opinion: Optional[Opinion] = None
     # 记录已评论过的事件，防止重复生成
     commented_effects: List[str] = Field(default_factory=list)
+    # 角色立场
+    stance: Stance = Field(default_factory=Stance)
 
 
 class WorldState(BaseModel):

engine-python/app/opinions.py

@@ -1,6 +1,13 @@
 """角色事件评论系统 - 基于规则生成观点"""
 from typing import Dict, List, Optional
-from .models import WorldState, AgentState, Opinion, Emotion, WorldEffect
+from .models import WorldState, AgentState, Opinion, Emotion, WorldEffect, Stance
+
+# 情绪对 stance 的影响
+EMOTION_STANCE_EFFECTS: Dict[str, Dict[str, float]] = {
+    "happy": {"optimism": 0.1, "fear": -0.05},
+    "calm": {"optimism": 0.0, "fear": 0.0},
+    "anxious": {"optimism": -0.05, "fear": 0.1},
+}
 
 
 # 观点模板：effect_type -> emotion -> 观点列表
@@ -123,6 +130,20 @@ def get_opinion_text(
     return templates[index]
 
 
+def update_stance_from_emotion(agent: AgentState) -> None:
+    """根据情绪更新 stance（单次变化不超过 ±0.1）"""
+    emotion_key = agent.emotion.value
+    effects = EMOTION_STANCE_EFFECTS.get(emotion_key, {})
+
+    # 更新 optimism
+    new_optimism = agent.stance.optimism + effects.get("optimism", 0.0)
+    agent.stance.optimism = max(0.0, min(1.0, new_optimism))
+
+    # 更新 fear
+    new_fear = agent.stance.fear + effects.get("fear", 0.0)
+    agent.stance.fear = max(0.0, min(1.0, new_fear))
+
+
 def generate_opinions(state: WorldState) -> None:
     """为所有 agent 生成对当前活跃事件的观点"""
     # 无活跃效果时，清空 opinion
@@ -155,6 +176,9 @@ def generate_opinions(state: WorldState) -> None:
             tick=state.tick
         )
 
+        # 根据情绪更新 stance
+        update_stance_from_emotion(agent)
+
         # 记录到 memory
         memory_entry = f"[{agent_id}对{active_effect.name}的看法] {text}"
         agent.memory.append(memory_entry)

engine-python/app/social_influence.py

@@ -0,0 +1,126 @@
+"""社交影响系统 - 角色间观点传播"""
+from typing import Dict, List, Tuple
+from .models import WorldState, AgentState, Stance
+
+# 单次 stance 变化上限
+MAX_STANCE_CHANGE = 0.1
+
+# 影响强度系数
+INFLUENCE_STRENGTH = 0.05
+
+
+def get_contact_targets(
+    agent_id: str,
+    all_agent_ids: List[str],
+    tick: int
+) -> List[str]:
+    """
+    确定性地选择 1-2 个接触对象
+    基于 agent_id 和 tick 确保可复现
+    """
+    # 排除自己
+    others = [aid for aid in all_agent_ids if aid != agent_id]
+    if not others:
+        return []
+
+    # 基于 hash 确定性选择
+    seed = hash(agent_id) + tick
+
+    # 选择 1 或 2 个目标
+    num_targets = 1 + (seed % 2)
+    num_targets = min(num_targets, len(others))
+
+    targets = []
+    for i in range(num_targets):
+        idx = (seed + i * 7) % len(others)
+        target = others[idx]
+        if target not in targets:
+            targets.append(target)
+
+    return targets
+
+
+def calculate_influence(
+    agent_stance: Stance,
+    target_stance: Stance
+) -> Tuple[float, float]:
+    """
+    计算社交影响产生的 stance 变化
+
+    逻辑：
+    - 相近立场 → 强化原有立场
+    - 相反立场 → 产生反向影响
+
+    返回: (optimism_delta, fear_delta)
+    """
+    # 计算差异
+    opt_diff = target_stance.optimism - agent_stance.optimism
+    fear_diff = target_stance.fear - agent_stance.fear
+
+    # 相近度判断（差异小于 0.3 视为相近）
+    opt_similar = abs(opt_diff) < 0.3
+    fear_similar = abs(fear_diff) < 0.3
+
+    opt_delta = 0.0
+    fear_delta = 0.0
+
+    if opt_similar:
+        # 相近 → 强化原有立场（向中间靠拢后再强化）
+        opt_delta = opt_diff * INFLUENCE_STRENGTH
+    else:
+        # 相反 → 反向影响（抵抗）
+        opt_delta = -opt_diff * INFLUENCE_STRENGTH * 0.5
+
+    if fear_similar:
+        fear_delta = fear_diff * INFLUENCE_STRENGTH
+    else:
+        fear_delta = -fear_diff * INFLUENCE_STRENGTH * 0.5
+
+    # 限制单次变化幅度
+    opt_delta = max(-MAX_STANCE_CHANGE, min(MAX_STANCE_CHANGE, opt_delta))
+    fear_delta = max(-MAX_STANCE_CHANGE, min(MAX_STANCE_CHANGE, fear_delta))
+
+    return opt_delta, fear_delta
+
+
+def apply_social_influence(state: WorldState) -> None:
+    """
+    应用社交影响：每个 agent 与 1-2 个其他 agent 接触
+    所有变化写入 world_state
+    """
+    agent_ids = list(state.agents.keys())
+    if len(agent_ids) < 2:
+        return
+
+    # 收集所有变化（先计算，后应用，避免顺序影响）
+    stance_changes: Dict[str, Tuple[float, float]] = {}
+
+    for agent_id in agent_ids:
+        agent = state.agents[agent_id]
+        targets = get_contact_targets(agent_id, agent_ids, state.tick)
+
+        total_opt_delta = 0.0
+        total_fear_delta = 0.0
+
+        for target_id in targets:
+            target = state.agents[target_id]
+            opt_delta, fear_delta = calculate_influence(
+                agent.stance, target.stance
+            )
+            total_opt_delta += opt_delta
+            total_fear_delta += fear_delta
+
+        # 限制总变化
+        total_opt_delta = max(-MAX_STANCE_CHANGE, min(MAX_STANCE_CHANGE, total_opt_delta))
+        total_fear_delta = max(-MAX_STANCE_CHANGE, min(MAX_STANCE_CHANGE, total_fear_delta))
+
+        stance_changes[agent_id] = (total_opt_delta, total_fear_delta)
+
+    # 应用所有变化
+    for agent_id, (opt_delta, fear_delta) in stance_changes.items():
+        agent = state.agents[agent_id]
+        new_opt = agent.stance.optimism + opt_delta
+        new_fear = agent.stance.fear + fear_delta
+
+        agent.stance.optimism = max(0.0, min(1.0, new_opt))
+        agent.stance.fear = max(0.0, min(1.0, new_fear))

engine-python/app/state.py

@@ -1,6 +1,6 @@
 import json
 from pathlib import Path
-from .models import WorldState, AgentState, GlobalMeter
+from .models import WorldState, AgentState, GlobalMeter, Stance
 
 STATE_FILE = Path(__file__).parent.parent / "state.json"
 
@@ -12,8 +12,18 @@ def get_default_state() -> WorldState:
         weather="sunny",
         town_mood=0,
         agents={
-            "alice": AgentState(emotion="calm", goal="探索小镇", memory=[]),
-            "bob": AgentState(emotion="calm", goal="与人交流", memory=[]),
+            "alice": AgentState(
+                emotion="calm",
+                goal="探索小镇",
+                memory=[],
+                stance=Stance(optimism=0.6, fear=0.4),
+            ),
+            "bob": AgentState(
+                emotion="calm",
+                goal="与人交流",
+                memory=[],
+                stance=Stance(optimism=0.4, fear=0.6),
+            ),
         },
         events=[],
         global_meter=GlobalMeter(value=0, threshold=100, cooldown=0),