金融从业者广泛借助博弈与谜题来处理不确定性与复杂性，形成跨领域的共享逻辑结构。核心方法包括源自赌博与资产配置的 Kelly 准则，以及 Keynes 将市场比作“美人竞赛”的二阶预期框架。职业扑克、填字、狼人杀与其他策略游戏提供风险管理、模式识别与心理稳态训练。统计上，游戏提供可重复的高-维度决策环境，使投资者在信息不完整、对手行为不可观测的条件下练习最优行动。与此同时，金融并非游戏：错误会影响养老金、客户储蓄及实际交易关系。
职业扑克选手 Vanessa Selbst 在十余年、数百万手牌的训练中形成基于概率阈值的下注策略：当获胜概率超过 50% 时即应下注并按优势比例调整规模，而非等待确定性。她赢得 3 条世界扑克大赛手链与约 1200 万美元奖金，并在 2014 年登顶 Global Poker Index。进入 Jane Street 交易后，她将同样的统计直觉用于期权定价，例如评估 0.50 美元溢价的合约在极端条件下升至 10 美元的概率。她强调回顾获胜与失败的策略同等重要，以避免幸存者偏差。
游戏在华尔街制度化应用的规模不断扩大。Jane Street 营收超 70 亿美元并开发交易模拟游戏 Figgie，使用 40 张特制卡牌、350 美元起始筹码、4 分钟快速交换、基于目标花色的 10 美元计价体系，训练快速决策与价格发现。该游戏被纳入实习与新员工课程；学术界亦发表策略论文。疫情以来狼人杀等不完全信息游戏在高管与交易员间流行，用以训练逻辑推断与角色识别。跨活动的统计共性在于：高变异度环境、信息不对称、博弈式最优回应，以及将人类行为建模为随机变量。
Finance professionals use games and puzzles to navigate uncertainty, relying on shared quantitative frameworks such as the Kelly criterion for bet sizing and Keynes’ beauty-contest model of second-order expectations. Poker, crosswords, and deduction games train risk calibration, pattern recognition, and psychological stability by providing repeated high-dimensional decision environments with hidden information. Yet finance is not a game: misjudgments affect pensions, client savings, and counterparties, underscoring limits to pure gamesmanship.
Professional poker player Vanessa Selbst developed probability-threshold decision rules through millions of hands, arguing that bets should be made whenever win odds exceed 50% and scaled to expected value rather than certainty. Her record includes three World Series of Poker bracelets and roughly $12 million in winnings; she was the top Global Poker Index player in 2014. At Jane Street she applies similar probabilistic reasoning to options, evaluating scenarios such as a $0.50-over-fair-value contract jumping to $10. She stresses analyzing winning and losing decisions alike to avoid survivorship bias.
Games are increasingly institutionalized on Wall Street. Jane Street, with revenue above $7 billion, created the trading simulation Figgie, built on 40 specialized cards, $350 starting chips, four-minute rapid exchanges, and $10 goal-suit scoring to train fast judgment and price discovery. The game is used in internships and new-hire training and has spawned academic strategy papers. Werewolf and similar incomplete-information games have spread among executives and traders, reinforcing skills in deduction and role inference. Across activities, the statistical commonalities include high-variance environments, asymmetric information, strategic best responses, and modeling human behavior as stochastic input.