There are three major shopping festivals in China: double eleven (Nov. 11), double twelve (Dec. 12) and 6.18. It’s somewhat akin to Black Friday yet still slightly different. All these three festivals emerged in the past decade, and are promoted mainly by E-commerce platforms. They are basically artificial festivals tailored for consumerism. Naturally (though still interesting and enjoyable to observe and experience), when online platforms started to offer huge discounts, local retail stores simultaneously begin to offer discounts. And these discounts sometimes don’t match. ...

The song casts a magical spell. The first line of the chorus, with the lyric ’the boy is mine’, masterfully escalates the tension built up from the pre-chorus––however though, by disrupting the tension’s anticipating flow, with a striking and beautiful fermata on the tempo. This innovative technique, rarely seen in recent pop productions, perfectly ignites the song’s whimsical and provocative allure. the music video match the audacious and dramatic flair of the song. Start from here to listen to the editing of the music video that almost completes this avant-garde, iconic production. ...

Ariana Grande’s albums invariably feature at least one so-called ‘bad-girl anthem’. Take her debut album ‘Yours Truly’, arguably the most innocent of her career, which includes the sultry track The Way. This song is a collaboration with her then-boyfriend Mac Miller, and it famously includes the line, “I got a bad boy, you must admit it.” Her subsequent albums—My Everything and Dangerous Woman—further shaped her image as a love-craving icon. Even Sweetener offers R.E.M., which lyrically flirts with: ...

Consider the following RMAB problem instance. (For a review of what is an RMAB, check out here). A platform coordinates a total of $N$ volunteers for various tasks. At each time point, a task $k$ out of $K$ possible tasks arrives randomly with probability $f_k$, and the platform must decide whom to notify (activate) based on the likelihood of acceptance $p_{k, i}, i\in [N]$ and the potential reward for each volunteer incentivized, exogeneously given as $w_k$. ...

Consider an RMAB instance with $N$ arms, where each arm $i \in [N]$ has a finite state space $\mathbb S_i$ and can receive an action $y_i^t \in \{0, 1\}$ (representing not pulling or pulling the arm, respectively) at each time step $t$. The state of arm $i$ at time $t$ is denoted by $s_i^t$. Depending on the action taken, a reward $r_i(s_i^t, y_i^t)$ is accrued. As a decision maker, our objective is to maximize the averaged total reward over an infinite time horizon, under a constraint that only $B$ arms can be pulled at any time step. This setup can be formalized as follows: ...

Decision-making in dynamic environments over time presents unique challenges, particularly when the conditions influencing decisions are constantly changing. In such scenarios, traditional decision-making models often fall short. This is where Restless Multi-Armed Bandits (RMAB) come into play. The method provides a robust framework for modeling and optimizing decisions over time. Here’s a brief introduction of the concept of RMAB and the Whittle Index Policy. understanding Restless Multi-Armed Bandits what are RMAB? The multi-armed bandit problem is a classic version of the problem of optimal allocation of activity under certainty: simply saying, one has $n$ projects, the state of project $i\in [n]$ being denoted by $x_i(t)$​. ...

A memoir of Market Mechanism Design course’s final presentation report, based on: Algorithmic Classification and Strategic Effort Jon Kleinberg and Manish Raghavan | ACM SIGecom Exchanges, Vol. 18, No. 2, November 2020, Pages 53–60 motivation: difference in modelling strategic behavior and objectives–between econ/CS perspectives The principal-agent and strategic machine learning literatures appear to share a common goal: how should one structure a decision-making rule to account for the strategic actions of decision subjects? ...

speaker: Zizhuo Wang | Prof., The Chinese University of Hong Kong-Shenzhen. Takeaway: point of view of bridging practice and research. For industry solutions: Need to consider a lot of details Need to be fast and intepretation, therefore requires simple solutions Don’t care about theory. For academic works: Need abstract to focus Graced with time Expect some generality, therefore theory. TALK ABSTRACT: In the past years, there have been growing number of companies in China that adopt OR methods in their operations. There are also several interesting research questions that arise in those application problems. In this talk, we present some recent projects and some of the research questions that come from those scenarios. ...

speaker: Ming Hu | Prof., University of Toronto Keys: random product offering/demand allocation/pricing algorithm. TALK ABSTRACT: First, we study blind boxes as a novel selling mechanism in which buyers purchase sealed packages containing unknown items, with the chance of uncovering rare or special items. We show how such product randomization introduced by the blind box can improve the seller’s profitability over traditional separate selling. Second, we study how an e-commerce platform should assign sequentially arriving customers to sellers who compete to sell identical products on the platform. The allocation rule may be random and dependent on the sellers’ inventory levels. We show how such demand fulfillment randomization can incentivize sellers to hold more inventory and improve the platform’s profitability and customer welfare. Third, we study randomized promotions in which the firm randomly offers discounts over time to sequentially arriving customers with heterogeneous valuations and patience levels. We show how price randomization can improve the firm’s profitability beyond deterministic pricing policies. ...

speaker: Peng Sun | Prof., Duke University TALK ABSTRACT: New prescription drugs require regulatory approval before drug makers can sell them. In some countries, regulators may conditionally approve a drug, which allows sales to begin before the developer has proven the drug’s efficacy. After further testing, the regulator may either grant final approval or reject the drug. We show that conditional approval not only speeds access to drugs but also encourages the development of drugs that would not have been pursued otherwise. Using mechanism design principles, we show that regulators should conditionally approve a drug even if it is ex-ante less likely to prove efficacious, under certain conditions. The regulator should approve the drug to encourage investment, especially when only the firm knows the testing cost. However, drugs that are less likely to prove efficacious should only be conditionally approved for a portion of the patient population if that is enough to motivate testing. Additionally, the impact of conditional approval is greater when the firm’s revenue increases over time. Finally, a regulator should sometimes commit that in the future it will grant final approval for a drug that narrowly misses the efficacy threshold in return for the firm testing an otherwise unprofitable drug.