{
  "centralThesis": "围绕“德州扑克策略的现实启示：决策心理学、风险管理与人类认知：关于德州扑克策略与技巧的研究结论，对于理解人类决策、风险管理和认知偏差有哪些启示？这些研究如何在现实场景中应用或改变我们的思维？”，应先区分当前证据直接支持的结论与仍待验证的推断。",
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      "readerTakeaway": "先明确问题范围和阅读入口。",
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      "readerTakeaway": "读者能够复述当前证据支持的核心认识。",
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      "requirements": [
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        "结论与证据强度相匹配"
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      "title": "目前可以带走的核心结论",
      "transitionFromPrevious": "在问题定向后直接回答研究问题。"
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      "kind": "research_landscape",
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      "readerQuestion": "现有研究主要从哪些问题入口展开？",
      "readerTakeaway": "读者能够理解不同工作围绕哪些问题形成分支。",
      "renderMode": "map",
      "requirements": [
        "按问题而不是论文顺序组织",
        "说明各分支之间的关系"
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      "title": "当前研究版图",
      "transitionFromPrevious": "核心结论之后解释这些认识在研究版图中的关系。"
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      "readerTakeaway": "读者能够区分已获支持的判断与仍待验证的推断。",
      "renderMode": "prose",
      "requirements": [
        "区分缺失信息和反对证据",
        "明确仍需全文或新研究验证的部分"
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      "title": "这些结论能相信到什么程度",
      "transitionFromPrevious": "在全文结尾校准前述判断的适用范围。"
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  ],
  "narrativeArc": [
    "先明确问题范围和阅读入口。",
    "读者能够复述当前证据支持的核心认识。",
    "读者能够理解不同工作围绕哪些问题形成分支。",
    "读者能够区分已获支持的判断与仍待验证的推断。"
  ],
  "omittedModules": [
    {
      "kind": "method_evolution",
      "reason": "未在规划阶段确认足够清晰的问题—方法—代价演进链。"
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  "readerTakeaways": [
    "在德州扑克等不完美信息博弈中，结合博弈论最优（GTO）策略的防御性基础与实时识别、利用对手次优行为的剥削性策略，可以超越仅能避免损失的纯GTO策略，实现长期利润最大化。",
    "面对经济损失或收益等触发因素，人工智能倾向于变得更风险厌恶和理性，而人类则表现出更风险寻求和非理性的倾向，这种差异可作为区分算法与人类决策的“行为特征”。",
    "将人类专家设计的规则技能库与大语言模型结合，可在不需昂贵求解器或大量训练的情况下，使大语言模型在不完美信息博弈中达到接近专家级的表现并显著减少损失。",
    "在高风险博弈中，将基于规则的技能限制与大语言模型结合，能使其在不依赖博弈论求解器的情况下，使大语言模型在不完美信息博弈中接近专家级水平并显著降低损失。",
    "将损失厌恶机制纳入学习模型后，理性扑克策略会自发涌现为主导策略；而仅考虑获胜及获胜幅度时，理性策略无法自发主导。",
    "精英职业扑克玩家之所以能在长期中持续获胜，核心在于他们基于期望值而非已实现结果来评估决策质量，且只在自身能力圈内承担风险。",
    "大型语言模型在扑克中会展现出与其基础模型相关的特定行为风格，且这些风格虽相对高级但并非博弈论最优。",
    "职业扑克玩家的认知架构被概念化为一种需系统训练的决策武器系统，其通过双系统认知协调与对概率扭曲的抵抗来实现极端对抗压力下的心理韧性。"
  ],
  "schemaVersion": 2
}