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本期话题在线教育直播节目动态定价在线广告拍卖对抗点云5月6日-7日晚8:00-9:30AI TIME特别邀请了六位优秀的讲者跟大家共同开启AI+行为经济学专场！哔哩哔哩直播通道扫码关注AITIME哔哩哔哩官方账号观看直播链接：https://live.bilibili.com/21813994★ 邀请嘉宾 ★李涛：清华大学经济管理学院管理科学与工程系博士，导师为徐心教授。
李涛即将加入中国科学技术大学管理学院担任特任副教授。研究兴趣：在线教育，游戏化，社交推荐，信息设计。多篇论文发表在国际信息系统年会（ICIS）, 美国经济学年会(AEA)，实验设计与分析大会（DAE）等国际会议，并曾获国际信息系统年会“在线教育领域“最佳学生论文奖。目前工作论文正在包括Management Science和MIS Quarterly在内的期刊评审中。
报告题目：在线教育中的智能学习策略设计摘要：The rapid development of e-learning draws increasing attention to the issue of how to better structure learners’ learning process using technologies. This study focuses on how to optimize the structuring of learning sessions in e-learning contexts from the perspective of interleaving (i.e., whether to mix different topics in a learning session). From the lens of cognitive load theory, this study theorizes the effect of interleaving on learning performance and how such an effect may vary with learner type (i.e., weak versus strong learners) and the relatedness between interleaved topics. We designed and implemented a personalized learning system with three different session designs, namely, non-interleaving, interleaving, and related-interleaving, by leveraging machine learning technologies. Results from a two-month field experiment at a middle school suggested that interleaved learning did not improve learning performance in general and even had a negative influence on weak learners. We further found that when interleaving was implemented with related topics, it improved strong learners’ performance and worked equally well as non-interleaved learning for weak learners. This study deepens our understanding of the mechanisms underlying the interleaving effect and its boundaries. It also demonstrates how interleaving can be implemented in a personalized e-learning system.论文标题：Should online learning be interleaved? Theory and evidence from a field experiment.
许晟伟 黄致焕 许晟伟：北京大学图灵班四年级本科生，导师为孔雨晴老师，研究兴趣：计算经济学、人机交互、机器学习等。黄致焕：北京大学图灵班四年级本科生，导师为孔雨晴老师，研究兴趣：计算经济学、算法博弈论、理论计算机等；热爱算法竞赛，曾获The 2017 ACM-ICPC Asia Nanning Regional Contest冠军，中国大学生程序设计竞赛（CCPC-2020）长春站亚军等。
报告题目：直播节目信息流对观众感知质量的影响摘要：Information flow measures, over the duration of a game, the audience’s belief of who will win, and thus can reflect the amount of surprise in a game. To quantify the relationship between information flow and audiences' perceived quality, we conduct a case study where subjects watch one of the world’s biggest esports events, LOL S10. In addition to eliciting information flow, we also ask subjects to report their rating for each game. We find that the amount of surprise in the second half of the game plays a dominant role in predicting the rating. This suggests the importance of incorporating when the surprise occurs, in addition to the amount of surprise, in perceived quality models, which is missing in the existing literature. For content providers, it implies that everything else being equal, it is better for twists to be more likely to happen toward the end of a show rather than uniformly throughout.论文标题：SURPRISE! and When to Schedule It.
陈梦静：清华大学交叉信息研究院博士生五年级，导师为唐平中老师，研究方向为机制设计、动态定价及市场设计。相关研究成果发表在AAAI，IJCAI等国际会议上。
报告题目：通过价格调度——乘车共享市场调度建模与动态价格设计摘要：Over the past few years, ride-sharing has emerged as an effective way to relieve traffic congestion. A key problem for the ride-sharing platforms is to come up with a revenue-optimal (or welfare-optimal) pricing scheme and a vehicle dispatching policy that incorporate geographic and temporal information. We aim to tackle this problem by introducing a unified model that takes into account both travel time and driver redirection. We tackle the non-convexity problem using the ``ironing'' technique and formulate the optimization problem as a Markov decision process (MDP), where the states are the driver distributions and the decision variables are the prices. Our main finding is to give an efficient algorithm that computes the exact revenue (or welfare) optimal randomized pricing schemes. We conduct empirical analysis of our solution with real data and show its significant advantages of balancing the demand and supply over fixed pricing schemes as well as those prevalent surge-based pricing schemes论文标题：Dispatching Through Pricing：Modeling Ride-Sharing Markets and Designing Dynamic Prices论文链接：https://www.ijcai.org/Proceedings/2019/0024.pdf
汪勋：清华大学交叉信息研究院博士生四年级，导师为唐平中老师，研究方向为机制设计和在线广告拍卖。相关研究成果发表在AAAI，AAMAS等国际会议上。
报告题目：在线广告拍卖中针对广告主的优惠券设计问题摘要：Online platforms sell advertisements via auctions (e.g., VCG and GSP auction) and revenue maximization is one of the most important tasks for them. Many revenue increment methods are proposed, like reserve pricing, boosting, coupons and so on. The novelty of coupons rests on the fact that coupons are optional for advertisers while the others are compulsory. Recent studies on coupons have limited applications in advertising systems because they only focus on second price auctions and do not consider the combination with other methods. In this work, we study the coupon design problem for revenue maximization in the widely used VCG auction. Firstly, we examine the bidder strategies in the VCG auction with coupons. Secondly, we cast the coupon design problem into a learning framework and propose corresponding algorithms using the properties of VCG auction. Then we further study how to combine coupons with reserve pricing in our framework. Finally, extensive experiments are conducted to demonstrate the effectiveness of our algorithms based on both synthetic data and industrial data.论文标题：Coupon Design in Advertising Systems
袁源：Yuan Yuan is a PhD Candidate in the Institute for Data, Systems, and Society (IDSS) at Massachusetts Institute of Technology. He will be joining the Purdue Krannert School of Management in Fall 2021. He researches social and economic networks by applying cutting-edge computational methods, including machine learning, causal inference, and experimental design, to large-scale network data. He is especially interested in how social ties are formed and stabilized, and how social ties mediate social contagion, social exchange, prosocial behavior, and information diffusion.报告题目：对抗点云的生成和探究摘要：Randomized experiments, or "A/B" tests, remain the gold standard for evaluating the causal effect of a policy intervention or product change. However, experimental settings, such as social networks, where users are interacting and influencing one another, may violate conventional assumptions of no interference for credible causal inference. Existing solutions to the network setting include accounting for the fraction or count of treated neighbors in a user's network, yet most current methods do not account for the local network structure beyond simply counting the number of neighbors. Our study provides an approach that accounts for both the local structure in a user's social network via motifs as well as the treatment assignment conditions of neighbors. We propose a two-part approach. We first introduce and employ "causal network motifs", which are network motifs that characterize the assignment conditions in local ego networks; and then we propose a tree-based algorithm for identifying different network interference conditions and estimating their average potential outcomes. Our approach can account for social network theories, such as structural diversity and echo chambers, and also can help specify network interference conditions that are suitable to each experiment. We test our method on a synthetic network setting and on a real-world experiment on a large-scale network, which highlight how accounting for local structures can better account for different interference patterns in networks.论文标题：Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests论文链接：https://arxiv.org/abs/2010.09911
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