Algorithmic Prediction of Human Strategic Behaviour

参与者战略性推理的设置在人工智能中很普遍;它们包括任何与人类交互的系统。这些系统包括大量参与者占数十亿美元交易的系统，如广告拍卖和无线电频谱分配，以及仅与少数参与者进行交互的系统，如计算机游戏或智能用户界面。无论这种与人类互动的系统的规模如何，替代设计都必须通过尝试每一个来评估-通常是一个非常昂贵和耗时的提议-或者通过基于参与者将如何反应的模型的预测性能来评估。因此，准确的行为模型是改善代理设计和系统本身设计的宝贵工具。这种模型的一个可能的候选者是博弈论，这是理解战略互动的标准数学框架。然而，实验数据表明，标准的博弈论模型对人类行为的描述很差。这种不足源于他们的假设，即参与者是完全理性的代理人。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。拟议的研究将开发更强大的模型，考虑到现实的特定领域的问题结构。要考虑的现实主义将包括：参与者与大型行动集;推理其他参与者的大型集合;接收和学习反馈，因为他们与系统交互;推理其他参与者的学习。我将在我以前的工作的基础上构建模型，预测在这些更丰富的环境中的行为。我将重点讨论两种战略互动。第一个是在相对固定的环境中与人类的互动。具体的领域将是分配安全资源用于恶意软件和欺诈检测，更真实的游戏以及最佳的流量建议。另一种互动是为协调多人行为而设计政策和激励措施，重点是设计不能“玩游戏”的同行分级激励措施。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。有一个很大的机会，行为知情的机制设计，因为现有的工作仍然是非常依赖于完整的理性模型。

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.