Enhancing Online User-Facing Systems through Dynamic Experimentation

出于各种各样的原因，人们不断地与在线软件系统交互。例如，人们浏览网站是为了获取信息或做出决定，浏览应用程序是为了交流或改善健康。即使设计师仔细考虑如何开发这样的软件，它也往往无法满足用户的需求--网站或应用程序的任何用户都可以证明，软件无法帮助他们实现目标。人机交互中的一个持续挑战是，在一项技术部署到真正的用户并收集数据之前，很难知道它的效率有多高。我的目标是理解：我们如何构建计算系统，以便即使在部署到用户之后，它们也可以永久地更改和增强？例如，如果一个非营利组织创建了一个描述其工作并邀请捐款的网站，它如何向公众部署该网站，然后不断增强其向用户提供的信息和吸引力？ 我的研究项目将探索更好地利用随机A/B实验来改进面向用户的软件设计的方法，以可扩展和高效的方式。我将探索如何利用用户和设计师的人类智慧来生成可以进行实验评估的新版本的技术组件(例如，让用户向慈善机构捐赠的不同消息)。我将研究如何帮助人们进行随机的A/B实验，以创建产品/界面/体验的替代版本，并衡量它们的相对有效性。我还将研究如何汇集人类的智慧，鼓励设计师之间、设计师和用户之间的实验协作设计，以及从用户那里众包想法和实验条件。为了使这样的实验成为持续改进的引擎，我将探索如何最好地应用算法来自动发现哪些条件是有效的，并动态地为未来的用户提供更好的版本(例如多臂强盗和其他用于权衡探索和利用强化学习的算法)。这项研究可以探索如何利用实验将人类计算和强化学习的工作结合在一起，确定帮助人们的现实世界系统不断改进的计算原则。

People are continually interacting with online software systems, for a wide range of reasons. For example, people browse websites to gain information or make decisions, and apps to communicate or improve their health. Even when designers put careful thought into how to create such software, it often fails to meet user needs - any user of a website or app can attest to frustrations in software not helping them achieve their goals. A constant challenge in human-computer interaction is that it is difficult to know how effective a piece of technology is until it is deployed with real users, and data is being collected. I aim to understand: How can we build computational systems so that they can be perpetually changed and enhanced, even after deployment to users? For example, if a non-profit organization created a website describing its work and inviting donations, how could it deploy the website to the public, and then keep enhancing the information and appeals it makes to users? My research program will explore methods for better using randomized A/B experiments in improving the design of user-facing software, in scalable and efficient ways. I will explore how to draw on the human intelligence of users and designers to generate new versions of technology components that can be experimentally evaluated (e.g. different messages to get users to donate to a charity). I will investigate how to help people conduct randomized A/B experiments to create alternative versions of a product/interface/experience, and measure their relative efficacy. I will also investigate how to pool human intelligence in encouraging collaborative design of experiments between designers, between designers and users, and for crowdsourcing ideas and experimental conditions from users. To make such experimentation an engine for continual improvement, I will explore how best to apply algorithms that automatically discover which conditions are effective and dynamically provide better versions to future users (such as multi-armed bandits and other algorithms for trading off exploration and exploitation from reinforcement learning). This research can explore how to use experimentation to bring together work on human computation and reinforcement learning, identifying computational principles for perpetually improving real-world systems that help people.