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1. Bilateral Trade with Correlated Values

   Dobzinski, Shahar; Shaulker, Ariel (June 2024, ACM)

   Mohar, Bojan; Shinkar, Igor; O'Donnell, Ryan (Ed.)

   We study the bilateral trade problem where a seller owns a single indivisible item, and a potential buyer seeks to purchase it. Previous mechanisms for this problem only considered the case where the values of the buyer and the seller are drawn from independent distributions. In contrast, this paper studies bilateral trade mechanisms when the values are drawn from a joint distribution. We prove that the buyer-offering mechanism guarantees an approximation ratio of e/e−1 ≈ 1.582 to the social welfare even if the values are drawn from a joint distribution. The buyer-offering mechanism is Bayesian incentive compatible, but the seller has a dominant strategy. We prove the buyer-offering mechanism is optimal in the sense that no Bayesian mechanism where one of the players has a dominant strategy can obtain an approximation ratio better than e/e−1. We also show that no mechanism in which both sides have a dominant strategy can provide any constant approximation to the social welfare when the values are drawn from a joint distribution. Finally, we prove some impossibility results on the power of general Bayesian incentive compatible mechanisms. In particular, we show that no deterministic Bayesian incentive-compatible mechanism can provide an approximation ratio better than 1+ln2/2≈ 1.346. 

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2. Bargaining and Exclusion With Multiple Buyers

   https://doi.org/10.3982/ECTA19675  

   Abreu, Dilip; Manea, Mihai (March 2024, Econometrica)

   A seller trades withqout ofnbuyers who have valuationsa₁ ≥ a₂ ≥ ⋯ ≥ a_n > 0 via sequential bilateral bargaining. Whenq < n, buyer payoffs vary across equilibria in the patient limit, but seller payoffs do not, and converge to max_l≤q+1[(a₁+a₂+⋯+a_l−1)/2+a_l+1+⋯+a_q+1]. Ifl^*is the (generically unique) maximizer of this optimization problem, then each buyeri < l^*trades with probability 1 at the fair pricea_i/2, while buyersi ≥ l^*are excluded from trade with positive probability. Bargaining with buyers who face the threat of exclusion is driven by asequential outside option principle: the seller can sequentially exercise the outside option of trading with the extra marginal buyerq + 1, then with the new extra marginal buyerq, and so on, extracting full surplus from each buyer in this sequence and enhancing the outside option at every stage. A seller who can serve all buyers (q = n) may benefit from creating scarcity by committing to exclude some remaining buyers as negotiations proceed. Anoptimal exclusion commitment, within a general class, excludes a single buyer but maintains flexibility about which buyer is excluded. Results apply symmetrically to a buyer bargaining with multiple sellers. 

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3. Selling to a No-Regret Buyer

   Braverman, Mark; Mao, Jieming; Schneider, Jon; Weinberg, S. Matthew (January 2018, Economics and Computation)

   We consider the problem of a single seller repeatedly selling a single item to a single buyer (specifically, the buyer has a value drawn fresh from known distribution $$D$$ in every round). Prior work assumes that the buyer is fully rational and will perfectly reason about how their bids today affect the seller's decisions tomorrow. In this work we initiate a different direction: the buyer simply runs a no-regret learning algorithm over possible bids. We provide a fairly complete characterization of optimal auctions for the seller in this domain. Specifically: 1) If the buyer bids according to EXP3 (or any ``mean-based'' learning algorithm), then the seller can extract expected revenue arbitrarily close to the expected welfare. This auction is independent of the buyer's valuation $$D$$, but somewhat unnatural as it is sometimes in the buyer's interest to overbid. 2) There exists a learning algorithm $$\mathcal{A}$$ such that if the buyer bids according to $$\mathcal{A}$$ then the optimal strategy for the seller is simply to post the Myerson reserve for $$D$$ every round. 3) If the buyer bids according to EXP3 (or any ``mean-based'' learning algorithm), but the seller is restricted to ``natural'' auction formats where overbidding is dominated (e.g. Generalized First-Price or Generalized Second-Price), then the optimal strategy for the seller is a pay-your-bid format with decreasing reserves over time. Moreover, the seller's optimal achievable revenue is characterized by a linear program, and can be unboundedly better than the best truthful auction yet simultaneously unboundedly worse than the expected welfare. 

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4. Sentiment Recognition for Short Annotated GIFs Using Visual-Textual Fusion

   https://doi.org/10.1109/TMM.2019.2936805  

   Liu, T; You, Q; Wang, Y; Dong, X; Dai, X; Luo, J. (January 2020, IEEE transactions on multimedia)

   With the rapid development of social media, visual sentiment analysis from image or video has become a hot spot in visual understanding researches. In this work, we propose an effective approach using visual and textual fusion for sentiment analysis of short GIF videos with textual descriptions. We extract both sequence-level and frame-level visual features for each given GIF video. Next, we build a visual sentiment classifier by using the extracted features. We also define a mapping function, which converts the sentiment probability from the classifier to a sentiment score used in our fusion function. At the same time, for the accompanying textual annotations, we employ the Synset forest to extract the sets of the meaningful sentiment words and utilize the SentiWordNet3.0 model to obtain the textual sentiment score. Then, we design a joint visual-textual sentiment score function weighted with visual sentiment component and textual sentiment one. To make the function more robust, we introduce a noticeable difference threshold to further process the fused sentiment score. Finally, we adopt a grid search technique to obtain relevant model hyper-parameters by optimizing a sentiment aware score function. Experimental results and analysis extensively demonstrate the effectiveness of the proposed sentiment recognition scheme on three benchmark datasets including the TGIF dataset, GSO-2016 dataset, and Adjusted-GIFGIF dataset. 

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5. Social motivation predicts gaze following between 6 and 14 months

   https://doi.org/10.1111/infa.12544  

   Zeng, Guangyu; Leung, Tiffany S.; Maylott, Sarah E.; Saunders, Thea A.; Messinger, Daniel S.; Llabre, Maria M.; Simpson, Elizabeth A. (May 2023, Infancy)

   Infants vary in their ability to follow others’ gazes, but it is unclear how these individual differences emerge. We tested whether social motivation levels in early infancy predict later gaze following skills. We longitudinally tracked infants’ (N = 82) gazes and pupil dilation while they observed videos of a woman looking into the camera simulating eye contact (i.e., mutual gaze) and then gazing toward one of two objects, at 2, 4, 6, 8, and 14 months of age. To improve measurement validity, we used confirmatory factor analysis to combine multiple observed measures to index the underlying constructs of social motivation and gaze following. Infants’ social motivation—indexed by their speed of social orienting, duration of mutual gaze, and degree of pupil dilation during mutual gaze—was developmentally stable and positively predicted the development of gaze following—indexed by their proportion of time looking to the target object, first object look difference scores, and first face‐to‐object saccade difference scores—from 6 to 14 months of age. These findings suggest that infants’ social motivation likely plays a role in the development of gaze following and highlight the use of a multi‐measure approach to improve measurement sensitivity and validity in infancy research. 

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