Enhancing Online User-Facing Systems through Dynamic Experimentation

由于各种各样的原因，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状况。即使设计师在如何创建这样的软件上花了很多心思，它也经常不能满足用户的需求——任何网站或应用程序的用户都可以证明，软件不能帮助他们实现目标，这让他们感到沮丧。人机交互的一个持续的挑战是，很难知道一项技术的有效性，直到它被部署到真正的用户身上，并收集数据。我的目标是理解：我们如何构建计算系统，使它们能够不断地改变和增强，甚至在部署给用户之后？例如，如果一个非营利组织创建了一个介绍其工作和邀请捐款的网站，它如何将网站部署到公众面前，然后不断增强对用户的信息和呼吁？******我的研究计划将探索以可扩展和有效的方式更好地使用随机A/B实验来改进面向用户的软件设计的方法。我将探索如何利用用户和设计师的人类智慧来生成可以通过实验评估的新版本的技术组件（例如，通过不同的信息让用户向慈善机构捐款）。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，并衡量它们的相对功效。我还将研究如何汇集人类智慧，鼓励设计师之间、设计师和用户之间的实验协同设计，以及用户的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，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（例如多武装强盗和其他算法，用于权衡强化学习的探索和利用）。这项研究可以探索如何使用实验将人类计算和强化学习的工作结合起来，确定计算原理，以不断改进帮助人类的现实世界系统

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.**