Many Ways to Be Lonely: Fine-Grained Characterization of Loneliness and Its Potential Changes in COVID-19

Loneliness has been associated with negative outcomes for physical and mental health. Understanding how people express and cope with various forms of loneliness is critical for early screening and targeted interventions to reduce loneliness, particularly among vulnerable groups such as young adults. To examine how different forms of loneliness and coping strategies manifest in loneliness self-disclosure, we built a dataset, FIG-Loneliness (FIne-Grained Loneliness) by using Reddit posts in two young adult-focused forums and two loneliness related forums consisting of a diverse age group. We provided annotations by trained human annotators for binary and fine-grained loneliness classifications of the posts. Trained on FIG-Loneliness, two BERT-based models were used to understand loneliness forms and authors' coping strategies in these forums. Our binary loneliness classification achieved an accuracy above 97%, and fine-grained loneliness category classification reached an average accuracy of 77% across all labeled categories. With FIG-Loneliness and model predictions, we found that loneliness expressions in the young adults related forums were distinct from other forums. Those in young adult-focused forums were more likely to express concerns pertaining to peer relationship, and were potentially more sensitive to geographical isolation impacted by the COVID-19 pandemic lockdown. Also, we showed that different forms of loneliness have differential use in coping strategies

A Unifying Framework for Combining Complementary Strengths of Humans and ML toward Better Predictive Decision-Making

Hybrid human-ML systems are increasingly in charge of consequential decisions in a wide range of domains. A growing body of empirical and theoretical work has advanced our understanding of these systems. However, existing empirical results are mixed, and theoretical proposals are often mutually incompatible. In this work, we propose a unifying framework for understanding conditions under which combining the complementary strengths of humans and ML leads to higher quality decisions than those produced by each of them individually -- a state which we refer to as human-ML complementarity. We focus specifically on the context of human-ML predictive decision-making and investigate optimal ways of combining human and ML predictive decisions, accounting for the underlying sources of variation in their judgments. Within this scope, we present two crucial contributions. First, taking a computational perspective of decision-making and drawing upon prior literature in psychology, machine learning, and human-computer interaction, we introduce a taxonomy characterizing a wide range of criteria across which human and machine decision-making differ. Second, formalizing our taxonomy allows us to study how human and ML predictive decisions should be aggregated optimally. We show that our proposed framework encompasses several existing models of human-ML complementarity as special cases. Last but not least, an initial exploratory analysis of our framework presents a critical insight for future work in human-ML complementarity: the mechanism by which we combine human and ML judgments should be informed by the underlying causes of divergence in their decisions.Comment: 21 pages, 1 figur

Modeling Attrition in Recommender Systems with Departing Bandits

Traditionally, when recommender systems are formalized as multi-armed bandits, the policy of the recommender system influences the rewards accrued, but not the length of interaction. However, in real-world systems, dissatisfied users may depart (and never come back). In this work, we propose a novel multi-armed bandit setup that captures such policy-dependent horizons. Our setup consists of a finite set of user types, and multiple arms with Bernoulli payoffs. Each (user type, arm) tuple corresponds to an (unknown) reward probability. Each user's type is initially unknown and can only be inferred through their response to recommendations. Moreover, if a user is dissatisfied with their recommendation, they might depart the system. We first address the case where all users share the same type, demonstrating that a recent UCB-based algorithm is optimal. We then move forward to the more challenging case, where users are divided among two types. While naive approaches cannot handle this setting, we provide an efficient learning algorithm that achieves $\tilde{O}(\sqrt{T})$ regret, where $T$ is the number of users.Comment: Accepted at AAAI 202

A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed Bandits

Personalized recommender systems suffuse modern life, shaping what media we read and what products we consume. Algorithms powering such systems tend to consist of supervised learning-based heuristics, such as latent factor models with a variety of heuristically chosen prediction targets. Meanwhile, theoretical treatments of recommendation frequently address the decision-theoretic nature of the problem, including the need to balance exploration and exploitation, via the multi-armed bandits (MABs) framework. However, MAB-based approaches rely heavily on assumptions about human preferences. These preference assumptions are seldom tested using human subject studies, partly due to the lack of publicly available toolkits to conduct such studies. In this work, we conduct a study with crowdworkers in a comics recommendation MABs setting. Each arm represents a comic category, and users provide feedback after each recommendation. We check the validity of core MABs assumptions-that human preferences (reward distributions) are fixed over time-and find that they do not hold. This finding suggests that any MAB algorithm used for recommender systems should account for human preference dynamics. While answering these questions, we provide a flexible experimental framework for understanding human preference dynamics and testing MABs algorithms with human users. The code for our experimental framework and the collected data can be found at https://github.com/HumainLab/human-bandit-evaluation.Comment: Accepted to CHI. 16 pages, 6 figure

A novel flow-guide device for uniform exhaust in a central air exhaust ventilation system

Exhaust ventilation system with one central fan and multiple terminals has been widely used for the heat and contaminant removal in building environment. Conventional design without pressure balancing leads to uneven distribution of exhaust airflow rate among the multiple outlets. Existed balancing methods usually uses dampers (constant-air-volume valve or regulating valve), tapered duct, or varied inlet area. However, these methods result in higher fan energy consumption, or complicated construction and on-site commissioning. In this paper, a flow-guide device was developed for adjusting the pressure distribution of duct branches. This new device is integrated with the interflow Tee-junction and does not need any commissioning or regulating. The resistance performance of the device responding to the structural parameter was derived using the CFD simulation and experiment. The negative direct resistance featured by the device was found to effectively benefit exhaust at the outlets farther away from the central fan. The ductwork hydraulic model based on the Bernoulli's law of airflow and the fitted resistance correlations were further proposed to fulfill the parametric design. Finally, full-scale test was carried out for a central exhaust system installed with the flow-guide devices referring to a factory workshop with heat and contaminant sources. Compared to the system without the devices, the total rate of the system increased by 25%. Discrepancy of exhaust rate decreased by 78% and uneven degree decreased by 82%, which well meets the engineering balancing requirement. Meanwhile, total resistance of the system reduced 23.8% owing to the negative loss the devices bring