Events & Seminars

We study a novel strategy deployed by user-generated content (UGC) platforms to address underprovision. This strategy involves the use of an additional low-barrier content curation tool that allows users to post low-effort content distinct from the high-effort content that is standard on the platforms. It remains theoretically ambiguous whether the introduction of this low-barrier content tool complements or cannibalizes the contribution of high-effort content. By leveraging a quasi-experiment on a large Chinese online question-and- answer (Q&A) platform, we identify the causal effects of the low-barrier content tool on incentives for users to contribute high-effort content (i.e., answers to others' questions) in a difference-in-differences framework. We find that the use of this low-barrier tool complements the contributed answers; adopters of the newly introduced low-barrier content tool increase their volume of contributed answers without compromising the

The Locker Alliance Network (LAN) is a recent smart nation initiative introduced in Singapore for parcel pickup by customers, to improve the efficiency of the last mile operation. This government facility is open to all logistic service providers (LSPs) operating in the country. With more parcels being shifted to locker stations, the number of visits to home locations could be drastically reduced, and the length of the delivery trips to homes will decrease. However, in the case of LAN, the carriers have to substitute these home deliveries with visits to the locker stations, on a separate delivery trip. The challenge is to determine the appropriate size of the LAN (number and location of locker stations), since having too many or too few of these stations may increase the total length of delivery trips instead. Furthermore, given the interoperable nature of the system, how should the government design the network of locker stations to serve all LSPs operating in the city?
We develop a network design model to address these questions. For a given delivery profile, say from an LSP, we first develop a model to jointly minimize the length of the two delivery trips (to home locations and to locker stations). We show that this can be formulated as a Prize-Collecting TSP problem, and reformulated as a second-order cone problem (SOCP) under the logit choice model. We develop a heuristic policy with provable approximation guarantee based on its LP relaxation, for this class of network design problem. Our analysis shows also that there is an optimal number of locker stations needed for efficient operations, beyond which the efficiency of the last mile operations will deteriorate. More importantly, we can use the model to design the interoperable network for multiple LSPs, with possibly different delivery volumes, as long as they have similar footprints. We show that the network expands (almost) in a nested fashion in this case, i.e., the optimal networks for LSPs with smaller scale are (almost) contained in the optimal network for the larger LSPs. Therefore, the optimal interoperable network is very close to the optimal network for the largest LSP, and the optimal density of the locker network is dictated by the optimal density of the largest LSP operating in the country. Participation of the largest LSP is therefore crucial in any government-run interoperable system to increase the efficiency of last mile delivery operations.

Recent social justice movements have rekindled interests in supporting minority-owned businesses, and in response, popular online business-review platforms launched features to mark minority-owned businesses. We ask: Do such markers increase the willingness to visit and support the businesses? In a politically-divided environment, the markers may have heterogeneous effects across those who support the cause of helping minority-owned businesses and those who do not. Moreover, the markers may also act as a reminder of biases and prompt consumers to expect different levels of quality purely based on the ethnicity of the business owner. We conducted two online experiments—using restaurant business as the context—and found that, in aggregate, the markers seemed to have the intended effect (i.e., a positive effect on users’ willingness to visit and support the corresponding restaurants). However, the markers had such an effect only on users who supported the cause of helping minority-owned businesses. For those who did not, the markers had little effect for restaurants that matched positive biases based on the ethnicity of ownership, and worse, the markers actually backfired when the restaurants did not match the biases. We discuss the research and practical implications of our findings.

Although conventional wisdom suggests that product recommendations should benefit consumers, there is a lack of evidence on how they help consumers, specifically, whether product recommendations can algorithmically identify products aligned to consumers’ preferences and thus help them find higher-value products. We estimate the benefit of the collaborative filtering (CF) recommendation system by conducting a randomized field experiment on a US apparel retailer’s website. We collect unique data on the affinity scores computed by a CF algorithm to estimate how product recommendations help consumers search for higher-value products that are lower-priced, fit their tastes better, or both. We show that the discovery of lower-priced and better-fit products are the underlying reasons for higher purchase probability (lower likelihood of failed search efforts) of consumers under recommendations. We further find a higher benefit of recommendations in product categories with higher price dispersion and heterogeneity in consumers’ tastes, which provide additional evidence for these underlying reasons. Finally, we find that consumers substitute other search tools on the website with product recommendations when available. Our findings have implications for online retailers, policymakers, regulators, and the design of recommendation systems.

In this talk, we consider infinite horizon Markov decision processes (MDPs) with "fast-slow" structure, meaning that certain parts of the state space move "fast" (and are more influential) while other parts of the state space transition more slowly (and are less influential). Examples of this type of structure arise in a number of practical applications: multi-product inventory control and pricing, machine maintenance, multi-class queueing, and energy demand response. We propose an approximate value iteration algorithm based on the idea of periodically "freezing" the slow states, solving a set of simpler finite-horizon MDPs, and applying value iteration to an auxiliary MDP that transitions on a slower timescale (and smaller discount factor). We present analyses of the regret of policies generated by our approach, along with empirical results that demonstrate its computational benefits.

The rise of the internet has precipitated the birth and downfall of numerous industries, but perhaps no industry has been transformed to the same degree as news production. Journalism has seen the rise of content aggregation, the proliferation of fake news through social media, and the decimation of local reporting capacity, all of which have served to hollow the newspaper industry. In this work, we examine the downstream effects of this decline industry on an outcome of significant theoretical and practical significance: political corruption. As newspapers are an important investigative arm of local communities, it is possible that the closure of community media will embolden corrupt actors who believe they are less likely to be detected following the closure of a local newspaper. To estimate any effect, we employ a difference in difference approach, exploiting the phased closure of major daily newspapers across the country. Results indicate a significant increase in federal corruption charges in federal districts following closure. Further, we observe no evidence that the rise in online newsvendors and the democratization of the press ameliorates this effect. This highlights the important role of the “fourth estate” in inhibiting corruption in governance and the need to conceptualize the punitive societal effects of the internet more expansively.

Emission from diesel trucks such as Nitrogen Oxides causes severe air pollution. However, direct regulation on trucking companies for their use of diesel trucks typically falls out of the jurisdiction of local governments. A legislative alternative is to regulate other sectors that prompt diesel truck usage in the local region, called indirect emission sources. The first of such regulations is Southern California's Rule 2305, the Warehouse Indirect Source Rule (ISR). Passed in May 2021, the ISR holds local warehouses responsible for the diesel truck trips to their facilities through a mitigation fee. The goal of the ISR is to incentivize warehouses to hire electric semi-trucks to improve air quality and thus public health. Motivated by this new policy, we explore the environmental impact of the ISR and the industry burden that it introduces, compared to a hypothetical direct source rule (DSR) that regulates trucking companies. We find that ISR can indeed lead to higher adoption of electric semi-trucks than DSR, especially when the mitigation fees for diesel truck trips are small. However, using the mitigation fee collected from warehouses to subsidize trucking companies’ electric semi-truck investments, a current practice of the ISR, can backfire and reduce industry adoption of electric semi-trucks. Interestingly, depending on the distribution of truck trips' distances, a higher mitigation fee for using diesel trucks can also lead to lower adoption of electric semi-trucks. Finally, we explore the practical implications of the ISR using real warehouse data from Southern California.

The undesirable but inevitable consequence of running promotions is that consumers can be trained to time their purchases strategically. In this paper, we study randomized promotions, where the firm randomly offers discounts over time, as an alternative strategy of intertemporal price discrimination. Specifically, we consider a base model where a monopolist sells a single product to a market with a constant stream of two market segments. The segments are heterogeneous in both their product valuations and patience levels. The firm pre-commits to a price distribution, and in each period, a price is randomly drawn from the chosen distribution. We characterize the optimal price distribution as a randomized promotion policy and show that it serves as an intertemporal price discrimination mechanism such that high-valuation customers would purchase immediately at a regular price upon arrival and low-valuation customers would wait for a random promotion. Compared against the optimal cyclic pricing policy, which is optimal within the strategy space of all deterministic pricing policies, the optimal randomized pricing policy beats the optimal cyclic pricing policy if low-valuation customers are sufficiently patient and the absolute discrepancy between high and low customer valuations is large enough. We extend the model in two directions. We first consider Markovian pricing policies where prices are allowed to be intertemporally correlated in a Markovian fashion. This additional maneuver allows the firm to reap an even higher profit when low-valuation customers are sufficiently patient, by avoiding consecutive promotions but on average running the promotion more frequently with a smaller discount size. We then consider a model with multiple customer segments, and show that a two-point price distribution remains optimal and our conclusion from the two-segment base model still holds under certain conditions that are adopted in the literature. Our results imply that the firm may want to deliberately randomize promotions in the presence of forward-looking customers.

Causal decision making (CDM) at scale has become a routine part of business, and increasingly, CDM is based on machine learning. Businesses algorithmically target offers, incentives, and recommendations to affect consumer behavior. Recently, we have seen an acceleration of research related to CDM and causal effect estimation (CEE) using machine-learned models. This article highlights an important perspective: CDM is not the same as CEE, and counterintuitively, accurate CEE is not necessary for accurate CDM. Technically, the estimand of interest is different, and this has important implications both for modeling and for the use of statistical models for CDM. In this talk, I will highlight three implications. (1) We should carefully consider the objective function of the causal machine learning, and if possible, optimize for accurate “treatment assignment” rather than for accurate effect-size estimation. (2) Confounding affects CDM and CEE differently. The upshot here is that for supporting CDM it may be just as good or even better to learn with confounded data as with unconfounded data. (3) Causal statistical modeling may not be necessary at all to support CDM because a proxy target for statistical modeling might do as well or better. This third observation helps to explain at least one broad common CDM practice that seems “wrong” at first blush—the widespread use of noncausal models for targeting interventions. The last two implications are particularly important in practice, as acquiring (unconfounded) data on both “sides” of the counterfactual for modeling can be quite costly and often impracticable. These observations also open substantial research ground that I will discuss at the end of the talk.

We study the problem of community detection in multi-layer networks, where pairs of nodes can be related in multiple modalities. We introduce a general framework, i.e., mixture multi-layer stochastic block model (MMSBM), which includes many earlier models as special cases. We propose a tensor-based algorithm (TWIST) to reveal both global/local memberships of nodes, and memberships of layers. We show that the TWIST procedure can accurately detect the communities with small misclassification error as the number of nodes and/or the number of layers increases. Numerical studies confirm our theoretical findings. To our best knowledge, this is the first systematic study on the mixture multi-layer networks using tensor decomposition. The method is applied to two real datasets: worldwide trading networks and malaria parasite genes networks, yielding new and interesting findings.

Based on joint work with Bing-Yi Jing, Ting Li and Zhongyuan Ly.