Predictive models of human behavior in strategic settings

拟议的项目将开发通用模型，准确预测真实的人在战略环境中的行为。这些模型很重要，因为这些设置的设计-例如，谷歌建立的规则是在搜索关键词的基础上进行广告位拍卖，这在很大程度上取决于参与者的行为模式。具体地，设计者选择产生最佳预测行为的规则，例如，优化设计者的收入或所有参与者的福利。目前，参与者通常被建模为“经济理性”的行为。当所有参与者都是大公司时，这种假设可以说是合理的。然而，最近大量的实验证据表明，它往往不能适用于人类个体。因此，被认为是最优的设计在实践中可能表现得不太理想。如果人们只是表现出非确定性，这可能是故事的结局。然而，人们不仅仅是非理性的--他们是可以预见的非理性。在过去十年左右的时间里，行为博弈理论家已经将人们偏离理性的各种系统方式进行了分类。然而，这些文献并没有告诉我们如何模拟真实的人在任意策略设置中的行为。我的长期研究目标是建立人类行为的完整预测模型，并使用机器学习的最先进方法验证这些模型。本提案中描述的工作将遵循四个相互关联的工作线程：（1）对人类战略游戏中的显着性效应进行建模;（2）利用深度学习的最新想法来自动化特征发现;（3）开发预测一个人的战略推理深度的模型;（4）开发一个人的战略推理深度模型。以及（4）研究如何优化Google的广告拍卖设计，以便在我们的模型预测的行为下获得更好的性能。总的来说，更准确的投标人行为预测模型对改善谷歌关键字拍卖的运营有直接的影响-这是加拿大小企业的关键收入驱动力-实际上是对各种行业市场的优化。

The proposed project will develop general models that accurately predict how real people behave in strategic settings. Such models are important because the design of such settings--e.g., the rules that Google establishes to run its auctions for advertising slots alongside search keywords--depends crucially on models of participant behavior. Specifically, a designer chooses rules that yield the best predicted behavior, e.g., optimizing the designer's revenue or the welfare of all participants. Currently, participants are usually modeled as behaving "economically rationally". This assumption is arguably reasonable when all participants are large corporations. However, a wealth of recent experimental evidence shows that it very often fails to hold for individual human beings. Thus, designs said to be optimal are likely to perform suboptimally in practice. If people simply behaved nondeterministically, this might be the end of the story. However, people are not just irrational--they are predictably irrational. Over the last decade or so, behavioral game theorists have catalogued a wide range of systematic ways in which people deviate from rationality. However, this literature doesn't quite tell us how to model how real people behave in arbitrary strategic settings. My long-term research goal is to produce full predictive models of human behavior, and to validate these models using state-of-the-art methods from machine learning. The work described in this proposal will pursue four interrelated threads of work: (1) modeling the effect of salience in human strategic play; (2) leveraging the recent idea of deep learning to automate feature discovery; (3) developing models that predict a person's depth of strategic reasoning; and (4) investigating ways that Google's ad auction design could be optimized for better performance under the sort of behavior predicted by our models. Overall, more accurate predictive models of bidder behavior have immediate implications for improving the operation of Google's keyword auctions--a key revenue driver for small businesses across Canada--and indeed for the optimization of markets across a wide variety of industries.