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/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))