英文标题：
《Predicting Human Cooperation》
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作者：
John J. Nay, Yevgeniy Vorobeychik
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最新提交年份：
2016
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英文摘要：
  The Prisoner\'s Dilemma has been a subject of extensive research due to its importance in understanding the ever-present tension between individual self-interest and social benefit. A strictly dominant strategy in a Prisoner\'s Dilemma (defection), when played by both players, is mutually harmful. Repetition of the Prisoner\'s Dilemma can give rise to cooperation as an equilibrium, but defection is as well, and this ambiguity is difficult to resolve. The numerous behavioral experiments investigating the Prisoner\'s Dilemma highlight that players often cooperate, but the level of cooperation varies significantly with the specifics of the experimental predicament. We present the first computational model of human behavior in repeated Prisoner\'s Dilemma games that unifies the diversity of experimental observations in a systematic and quantitatively reliable manner. Our model relies on data we integrated from many experiments, comprising 168,386 individual decisions. The computational model is composed of two pieces: the first predicts the first-period action using solely the structural game parameters, while the second predicts dynamic actions using both game parameters and history of play. Our model is extremely successful not merely at fitting the data, but in predicting behavior at multiple scales in experimental designs not used for calibration, using only information about the game structure. We demonstrate the power of our approach through a simulation analysis revealing how to best promote human cooperation.
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中文摘要：
囚徒困境一直是广泛研究的主题，因为它对于理解个人私利和社会利益之间的紧张关系非常重要。囚徒困境（叛逃）中的严格主导策略，当双方都参与时，是相互有害的。重复囚徒困境可以带来作为平衡的合作，但叛逃也是如此，这种模糊性很难解决。研究囚徒困境的众多行为实验突显出，参与者经常合作，但合作的程度因实验困境的具体情况而显著不同。我们提出了第一个重复囚徒困境博弈中人类行为的计算模型，该模型以系统和定量可靠的方式统一了实验观察的多样性。我们的模型依赖于我们从许多实验中整合的数据，包括168386个个体决策。计算模型由两部分组成：第一部分仅使用结构博弈参数预测第一阶段的动作，第二部分同时使用博弈参数和游戏历史预测动态动作。我们的模型不仅在拟合数据方面非常成功，而且在预测未用于校准的实验设计中的多尺度行为方面也非常成功，只使用了有关游戏结构的信息。我们通过模拟分析展示了我们的方法的力量，揭示了如何最好地促进人类合作。
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Science and Game Theory        计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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