Runshan Fu, Yan Huang, Nitin Mehta, Param Vir Singh, and Kannan Srinivasan. Unequal Impact of Zestimate on the Housing Market. Accepted at Marketing Science.
We study the impact of Zillow’s Zestimate on housing market outcomes and how the impact differs across socioeconomic segments. Zestimate is produced by a machine learning algorithm using large amounts of data and aims to predict a home’s market value at any time. Zestimate can potentially help market participants in the housing market as identifying the value of a home is a nontrivial task. However, inaccurate Zestimate could also lead to incorrect beliefs about property values and therefore, suboptimal decisions, which would hinder the selling process. Meanwhile, Zestimate tends to be systematically more accurate for rich neighborhoods than poor neighborhoods, raising concerns that the benefits of Zestimate may accrue largely to the rich, which could widen socioeconomic inequality. Using data on Zestimate and housing sales in the United States, we show that Zestimate overall benefits the housing market as on average, it increases both buyer surplus and seller profit. This is primarily because its uncertainty reduction effect allows sellers to be more patient and set higher reservation prices to wait for buyers who truly value the properties, which improves seller-buyer match quality. Moreover, Zestimate actually reduces socioeconomic inequality as our results reveal that both rich and poor neighborhoods benefit from Zestimate but that the poor neighborhoods benefit more. This is because poor neighborhoods face greater prior uncertainty and therefore, would benefit more from new signals.
Qiaochu Wang, Yan Huang, and Param Vir Singh. Algorithmic Lending, Competition, and Strategic Provision of Pre-approval Tools. Accepted at Marketing Science.
We theoretically examine how lenders (banks) decide whether to share the predictions of their credit risk assessment algorithms with potential borrowers using “pre-approval tools” in a competitive environment. Using a multi-stage game theory model, we analyze the strategic decisions of duopoly lenders in offering pre-approval tools for unsecured financial products. Our findings suggest that high algorithm accuracy can sustain an asymmetric revelation equilibrium, with one lender revealing pre-approval outcomes through pre-approval tools while the other does not, even when there is no explicit cost of providing such pre-approval tools. Conversely, low algorithm accuracy prompts both lenders to reveal pre-approval outcomes. These findings diverge from traditional literature, which typically associates asymmetric revelation with differentiated products or revealing cost. Additionally, our results show that mandatory revelation policies could reduce lenders’ incentives to improve algorithmic accuracy, potentially harming social welfare. These insights inform managerial strategies on the use of algorithmic transparency in lending and underscore the need for careful consideration of regulatory policies to balance market efficiency and consumer protection.
Xiyang Hu, Yan Huang, Beibei Li, and Tian Lu. Human-Algorithmic Bias: Source, Evolution, and Impact. Accepted at Management Science. [Finalist, Best Student Paper Award, CIST 2021]
In this study, leveraging a unique repeat decision-making setting in a high-stakes micro-lending context, we aim to uncover the underlying source, evolution dynamics, and associated impacts of bias. We first develop and estimate a structural econometric model of the decision dynamics to understand the source and evolution of potential bias in human evaluators in microloan granting. We find that both preference-based bias and belief-based bias are present in human evaluators' decisions and are in favor of female applicants. Through counterfactual simulations, we quantify the effects of the two types of bias on both fairness and profits. The results show that the elimination of either of the two biases improves the fairness in financial resource allocation, as well as the platform profits. The profit improvement mainly stems from the increase in the approval probability for male borrowers, especially those who would eventually pay back loans. Furthermore, to examine how human biases evolve when being inherited by machine learning (ML) algorithms, we then train a set of state-of-the-art ML algorithms for default risk prediction on both real-world datasets with human biases encoded within and counterfactual datasets with human biases partially or fully removed. By comparing the decision outcomes in different counterfactual settings, we find that even fairness-unaware ML algorithms can reduce bias present in human loan-granting decisions. Interestingly, while removing both types of human biases from the training data can further improve ML fairness, the fairness-enhancing effects vary significantly between new and repeat applicants. Based on our findings, we discuss how to reduce decision bias most effectively in a human-machine learning pipeline.
Liying Qiu, Yan Huang, Param Vir Singh, and Kannan Srinivasan. Personalization, Consumer Search and Algorithmic Pricing. Accepted at Marketing Science.
This study investigates the impact of product ranking systems on artificial intelligence (AI)-powered pricing algorithms. Specifically, we examine the effects of “personalized” and “unpersonalized” ranking systems on algorithmic pricing outcomes and consumer welfare. Our analysis reveals that personalized ranking systems, which rank products in decreasing order of consumer’s utilities, may encourage higher prices charged by pricing algorithms, especially when consumers search for products sequentially on a third-party platform. This is because personalized ranking significantly reduces the ranking-mediated price elasticity of demand and thus incentives to lower prices. Conversely, unpersonalized ranking systems lead to significantly lower prices and greater consumer welfare. These findings suggest that even in the absence of price discrimination, personalization may not necessarily benefit consumers because pricing algorithms can undermine consumer welfare through higher prices. Thus, our study highlights the crucial role of ranking systems in shaping algorithmic pricing behaviors and consumer welfare.
Qiaochu Wang, Yan Huang, Param Vir Singh, and Kannan Srinivasan. Algorithms, Artificial Intelligence, and Simple Rule-Based Pricing. Working paper.
Automated pricing strategies in e-commerce can be broadly categorized into two forms -- simple rule-based such as undercutting the lowest price, and more sophisticated artificial intelligence (AI) powered algorithms, such as reinforcement learning (RL) algorithms. Although simple rule-based pricing remains the most widely used strategy, a few retailers have started adopting pricing algorithms powered by AI. RL algorithms are particularly appealing for pricing due to their abilities to autonomously learn an optimal policy and adapt to changes in competitors’ pricing strategies and market environment. Despite the common belief that RL algorithms hold a significant advantage over rule-based strategies, our extensive pricing experiments demonstrate that when competing against RL pricing algorithms, simple rule-based algorithms may result in higher prices and benefit all sellers, compared to scenarios where multiple RL algorithms compete against each other. To further validate our findings, we estimate a non-sequential search structural demand model using individual-level data from a large e-commerce platform and conduct counterfactual simulations. The results show that in a real-world demand environment, simple rule-based algorithms outperform RL algorithms when facing other RL competitors. Our research sheds new light on the effectiveness of automated pricing algorithms and their interactions in competitive markets, and provides practical insights for retailers in selecting the appropriate pricing strategies.
Qiaochu Wang, Yan Huang, Stefanus Jasin, and Param Vir Singh. 2023. Algorithmic Transparency with Strategic Users. Management Science, (69:4): 2297-2317. [AIS Senior Scholar Best Publication of 2023 Award; Best Student Paper Award, CIST 2019]
Recently, there has been an increased demand for algorithmic transparency to combat algorithmic bias and discrimination, and improve accountability and trust in algorithms. However, firms have been reluctant to make their algorithms transparent, arguing that transparency opens the door to user gaming. In this paper, we theoretically study the impact of algorithmic transparency in a hiring context where a firm is using an algorithm to distinguish between more desirable (high-talent) and less desirable (low-talent) job applicants. Most arguments concluding that gaming by strategic users will negatively affect the prediction accuracy of transparent algorithms are based on the assumption that only the less desirable individuals will game. We show that under certain conditions, algorithmic transparency can increase the incentives for the more desirable individuals to improve on the features that are costly to mimic to further differentiate themselves from the less desirable individuals, leading to a higher prediction accuracy.
Runshan Fu, Yan Huang, and Param Vir Singh. 2021. Crowds, Lending, Machine, and Bias. Information Systems Research, (32:1): 72-92. [Runner Up, Best Paper Published in Information Systems Research, 2021]
The first question people may have when deciding whether to use machine learning algorithms for decision-making is whether machines can improve upon humans’ decisions. In this paper, we answer the question in the context of peer-to-peer lending, where investment decisions are traditionally made by a crowd of investors. We carefully design a contraction procedure to perform a fair comparison between the decisions made by the crowd and by the machine in terms of borrower and investor welfare. We show that the machine learning algorithm can simultaneously improve investors’ payoff irrespective of their risk preferences, and provide more lending opportunities to borrowers with fewer alternative funding options. We also find that the machine is biased in gender and race. We propose a general debias method and show that the debiased algorithm can still increase investor and borrower welfare while minimizing the bias.
Yi Gao, Zhe Wang, and Yan Huang. Generative AI, Creativity Competition, and Platform Regulation: An Economic Analysis. Submitted to Management Science.
We employ a game-theoretic approach to analyze the impact of generative AI tools on various market outcomes, including creators' creativity levels, pricing strategies, and consumer welfare, and to explore the platform's optimal regulatory strength for these AI tools. Our results show that while generative AI tools can facilitate content creation by reducing creative costs and enhancing content quality, they may lead to a decline in content creativity compared to scenarios without such tools, due to creators’ reliance on AI. Through analyzing creators’ adoption decisions of generative AI, interestingly, we find that asymmetric adoption can be sustained in equilibrium among symmetric creators since such asymmetric adoption decisions alleviate creativity competition, ultimately benefiting both adopting and non-adopting creators. Additionally, our findings suggest that higher AI intelligence does not always incentivize creators to adopt generative AI tools, and with higher AI intelligence, the platform may not always benefit from encouraging creators to adopt AI. Furthermore, although generative AI has the potential to improve content quality, this does not necessarily translate into higher consumer welfare. Lastly, we highlight the divergence between the regulatory objectives of platforms and policymakers, offering insights for developing effective policies to improve market outcomes.
Zhaohui (Zoey) Jiang, Yan Huang, and Damian R. Beil. 2022. The Role of Feedback in Dynamic Crowdsourcing Contests: A Structural Empirical Analysis. Management Science (68:7), 4858-4877. [Finalist, IBM Best Student Paper Award for INFORMS Service Science, 2019]
This paper empirically investigates the impact of performance feedback (e.g., star-ratings) on the outcome of crowdsourcing contests, and finds that, contrary to the common belief that frequent and timely performance feedback is helpful in improving the contest outcome, delaying feedback until the later stage of the contest is optimal. We develop a dynamic structural model to capture the economic processes that drive contest participants’ behavior, and estimate the model using a data set collected from a major online crowdsourcing design platform. The model captures key features of crowdsourcing contests, including a large participant pool, entries by new participants throughout the contest, exploitation and exploration behaviors (quantified with a computer vision algorithm) by contest incumbents, and participants’ strategic choice among these entry, exploration, and exploitation decisions in a dynamic game. Using counter-factual simulations, we show that the late feedback policy leads to a better overall contest outcome. This is because performance feedback helps guide creators’ exploration and exploitation decisions, but can have a discouraging effect on entries and incumbents’ follow-up actions. The late feedback policy attains the former benefit while mitigating the latter problem, by only giving feedback after many creators have had a chance to enter.
He Huang, Yan Huang, Zhijun Yan, and Han Zhang. 2022. Social Influence, Competition, and Free Riding: Examining Seller Interactions Within an Online Social Network. Management Information Systems Quarterly (46:3): 1817-1832.
In this paper, we empirically study how a focal seller’s effort and sales performance are affected by the effort and sales performance of other sellers she is connected to (i.e., inviter and invitees) and the commissions she has received. We find evidence for social influence, competition, and free riding among connected sellers: Inviter effort has a positive impact on the focal seller’s effort, indicating social influence; inviter performance has a negative impact on the focal seller’s effort and performance, indicating competition. Interestingly, invitee effort/performance has no significant effect on the focal seller. Therefore, social influence and competition among connected sellers are one-directional and happen in the “top-down” manner. We also find that higher commissions from invitees’ sales performance reduce the focal seller’s effort, indicating free riding.
Zhaohui (Zoey) Jiang, Yan Huang, and Damian R. Beil. 2021. The Role of Problem Specification in Crowdsourcing Contest for Design Problems: A Theoretical and Empirical Analysis. Manufacturing & Service Operations Management (23:3): 637-656.
This paper studies the impact of problem specification in crowdsourcing creative contests. At first sight, one may think seekers should specify their problems in the most thorough manner possible. However, we show that an overly specified problem may backfire, especially when the seeker is not careful about the types of information he/she provides. We develop a game-theoretic model featuring different types of information (categorized as “conceptual objectives” or “execution guidelines”) conveyed in problem specifications. We show theoretically and verify empirically that, with more conceptual objectives disclosed in the problem specification, the number of participants in a contest eventually decreases; with more execution guidelines in the problem specification, the trial effort provision by each participant increases; and the best solution quality always increases with more execution guidelines, but eventually decreases with more conceptual objectives. Therefore, to maximize the best solution quality in crowdsourced design problems, seekers should always provide more execution guidelines, but only a moderate number of conceptual objectives.
Manqi (Maggie) Li, Yan Huang, and Amitabh Sinha. 2020. Data-Driven Promotion Planning for Paid Mobile Applications. Information Systems Research (31:3): 1007-1029.
In this paper, we empirically estimate a demand model for paid mobile apps and quantify the effect of price promotions on download volume. The estimation results reveal two interesting characteristics of the demand of paid apps: (1) the magnitude of the direct promotion effect changes with time within a multi-day promotion; and (2) due to the visibility effect (i.e., apps ranked high on the download chart are more visible to consumers), a price promotion also has an indirect effect on download volume through affecting app rank, and this effect can persist after the promotion ends. Based on the empirically estimated demand model, we propose a promotion planning heuristic and show that the proposed policy can increase the app lifetime revenue by around 10%.
Yan Huang, Stefanus Jasin, and Puneet Manchanda. 2019. “Level Up”: Leveraging Skill and Engagement to Maximize Player Retention in Online Video Games. Information Systems Research (30:3): 927-947. [INFORMS eBusiness Best Paper Award, 2017]
In this paper, we investigate the evolution of online video game players’ (gamers’) latent engagement state and its implications for gamer matching strategies. We first build a Hidden Markov Model (HMM) to capture the evolution of gamers’ latent engagement as a function of their game-play outcome, and estimate the HMM using a longitudinal data set obtained from a major international video gaming company, which contains detailed game-play history of a large random sample of gamers. The estimation results show that high-, medium- and low-engagement-state gamers respond differently to motivational factors such as achievement and challenge. We use the estimated HMM to develop a matching algorithm that learns each gamer’s current engagement state and exploits that learning to match each gamer to a game round that maximizes his/her game-play volume. We show that our matching algorithm increases game-play, leading to economically significant revenue gains for the company.
Yan Huang, Param Vir Singh, and Anindya Ghose. 2015. A Structural Model of Employee Behavioral Dynamics in Enterprise Social Media. Management Science (61:12): 2825-2844. [Finalist, Best Paper Published in the IS Department of Management Science in 2014-2016]
This paper empirically examines employee behavioral dynamics in enterprise blogs, which have been widely adopted by firms as new platforms for employees to freely share knowledge and expertise. Firms that adopt enterprise blogs often find excessive leisure-related posts on their blogging platform, and are concerned that such posts will undermine the very goal of enterprise blogging. We analyze employees’ social media content creation and consumption behavior on the enterprise blog site of a large IT service firm, and find a significant positive readership spillover from leisure-related posts to work-related posts. Therefore, a policy that prohibits leisure-related activities will have a detrimental effect on employees’ work-related content creation and consumption.
Yan Huang, Param Vir Singh, and Kannan Srinivasan. 2014. Crowdsourcing New Product Ideas under Consumer Learning. Management Science (60:9): 2138-2159. [Finalist, 2019 INFORMS TIMES Best Paper Award]
In this paper, we empirically study idea contributors’ behavior in crowdsourced ideation initiatives, where individuals post ideas for the firm to implement and vote on other’s ideas, and the firm then decides which ideas to implement. Although popular, crowdsourced ideation faces increasing criticism as the number of ideas generated often declines over time. We show that the downward trend in the number of ideas contributed is in fact a sign of market efficiency rather than failure. We develop and estimate a dynamic structural model that captures the economic mechanisms shaping individuals’ idea contribution behavior. We find that in the initial stages of the crowdsourcing process, individuals tend to underestimate the cost the firm incurs to implement their ideas, but overestimate the potential of their ideas. Hence, the idea market is initially overcrowded with low-potential ideas. However, individuals learn about their abilities to come up with high-potential ideas from peer voting, and learn about the firm’s cost structure from its responses to all consumer-generated ideas. Over time, contributors of low-potential ideas eventually drop out, while contributors of high-potential ideas remain active, and the crowdsourcing market becomes more efficient.
Brett Danaher, Yan Huang, Michael D. Smith, and Rahul Telang. 2014. An Empirical Analysis of Digital Music Bundling Strategies. Management Science (60:6): 1413-1433.
This paper examines the pricing and bundling strategies in digital music markets where both single songs and albums are available to consumers. We collaborate with a major music label to conduct a pricing experiment. We use data from this pricing experiment to empirically estimate a structural model of consumer choices, and then evaluate welfare under various policy-relevant counterfactual scenarios. Our results show that tiered pricing (setting different prices for songs in different popularity tiers) coupled with reduced album pricing increases revenue while also increasing consumer surplus.
Helen (Shuxuan) Zeng, Yan Huang, Gordon Burtch, and Michael Smith. Operational Decision-making Around Movie Piracy and Theatrical Release. Minor Revision at Manufacturing & Service Operations Management.
We examine moviegoers' choices between consuming content through legal theatrical channels and illegal piracy channels, focusing on two key factors: the picture quality of pirated sources (influenced by studio security investments) and the costs associated with legal channels, including ticket prices and consumer transportation costs (driven by screening volume). Using a structural model and counterfactual simulations, we find that both factors significantly affect consumer behavior. The availability of high-quality pirated content since the first week of a movie's release reduces theatrical revenue by 7.9% over the first eight weeks, compared to a scenario with only low-quality piracy. This impact is especially pronounced for smaller movies relative to blockbusters. While reducing the cost of legal consumption (e.g., via lower ticket prices or increased screen volume) has limited effectiveness, moderately enhancing the value or quality of the theatrical experience, such as through investment in better technology, can effectively offset the impact of high-quality pirated content.
ARTICLES IN EDITED BOOKS/VOLUMES:
Runshan Fu, Yan Huang, and Param Vir Singh. 2020 Artificial Intelligence and Algorithmic Bias: Source, Detection, Mitigation and Implications. INFORMS Tutorials in Operations Research, INFORMS.
Yan Huang, Stephanie Lee, and Yong Tan. 2019. Structural Econometric Models. INFORMS Tutorials in Operations Research, INFORMS.