Algorithmic Prediction of Human Strategic Behaviour

参与者策略性推理的设置在人工智能中很普遍，包括任何与人类互动的系统。这些系统的范围从拥有大量参与者的系统(如广告拍卖和无线电频谱分配)到仅与少数参与者的互动(如计算机游戏或智能用户界面)，这些系统的交易金额达到数十亿美元。不管这样一个与人类互动的系统的规模有多大，替代设计必须要么通过尝试每一种设计--通常是一个非常昂贵和耗时的命题--进行评估，要么通过基于参与者对它们的反应模型预测的性能来进行评估。因此，准确的行为模型对于改进代理的设计和系统本身的设计都是宝贵的工具。这种模型的一个可能候选者是博弈论，它是理解战略互动的标准数学框架。然而，实验数据已经证明，标准博弈论模型对人类行为的描述很差。这种不充分源于他们认为参与者是完全理性的代理人的假设。然而，由于算法的进步以及计算能力和数据可用性的提高，我们现在有能力超越经典的经济理性假设，准确预测实际的人类行为。拟议的研究将通过考虑现实的特定领域的问题结构来开发更强大的模型。要考虑的现实主义将包括：具有大动作集的参与者；关于其他参与者的大集合的推理；在他们与系统交互时从反馈中接收和学习；关于其他参与者的学习的推理。我将在我以前工作的基础上，构建预测这些更丰富环境中行为的模型。我将重点介绍两种战略互动。首先是在相对固定的环境中与人类的互动。具体的领域将是为恶意软件和欺诈检测分配安全资源，在游戏中更现实地发挥作用，以及最佳流量建议。另一种互动是为协调多个人的行为而设计的政策和激励措施，重点是设计不能“玩弄”的同行评级激励措施。设计战略环境本身的第二个问题被称为机制设计，它是算法博弈论中一个日益重要的领域。行为知情的机制设计有很大的机会，因为现有的工作仍然非常依赖完全理性模式。

Settings in which participants reason strategically are widespread in artificial intelligence; they include any system that interacts with a human. These range from systems with large numbers of participants accounting for billions of dollars in transactions, such as ad auctions and radio spectrum allocation, to interactions with just a few participants, such as computer games or intelligent user interfaces. Regardless of the scale of such a system for interacting with humans, alternative designs must be evaluated either by trying each one out--frequently a very expensive and time-consuming proposition--or via their predicted performance based on a model of how participants will react to them. Hence, accurate behavioural models are valuable tools for improving both the design of agents and the design of the systems themselves. One possible candidate for such a model is game theory, the standard mathematical framework for understanding strategic interactions. However, it has been demonstrated from experimental data that standard game theoretic models describe human behaviour poorly. This inadequacy stems from their assumption that participants are perfectly rational agents. However, thanks to algorithmic advances and increased computing power and data availability, we now have the ability to go beyond the classical economic rationality assumptions to accurately predict actual human behaviour. The proposed research will develop substantially stronger models by taking realistic domain-specific problem structure into account. The realism to be considered will include: participants with large action sets; reasoning about large sets of other participants; receiving and learning from feedback as they interact with a system; reasoning about the learning of other participants. I will build on my previous work to construct models that predict behaviour in these richer environments. I will focus on two kinds of strategic interactions. The first is interactions with humans in relatively fixed environments. The specific domains will be allocation of security resources for malware and fraud detection, more realistic play in games, and optimal traffic advice. The other kind of interaction is the design of policies and incentives for coordinating the behaviour of multiple people, with a focus on the design of peer grading incentives that cannot be "gamed". The second problem of designing the strategic environment itself is referred to as mechanism design, and it is an increasingly important area of algorithmic game theory. There is a great opportunity for behaviourally informed mechanism design, as existing work is still very tied to the full rationality model.