Sequential Cursed Equilibrium

Cursed equilibrium posits that players in a Bayesian game neglect the relationship between their opponent's actions and their opponent's type (Eyster and Rabin, 2005). Sequential cursed equilibrium generalizes this idea to extensive games, including those with endogenous private information. It predicts that players neglect the information content of hypothetical events, but make correct inferences from observed events -- as is consistent with data from experiments on hypothetical thinking

Dyadic Reinforcement Learning

Mobile health aims to enhance health outcomes by delivering interventions to individuals as they go about their daily life. The involvement of care partners and social support networks often proves crucial in helping individuals managing burdensome medical conditions. This presents opportunities in mobile health to design interventions that target the dyadic relationship -- the relationship between a target person and their care partner -- with the aim of enhancing social support. In this paper, we develop dyadic RL, an online reinforcement learning algorithm designed to personalize intervention delivery based on contextual factors and past responses of a target person and their care partner. Here, multiple sets of interventions impact the dyad across multiple time intervals. The developed dyadic RL is Bayesian and hierarchical. We formally introduce the problem setup, develop dyadic RL and establish a regret bound. We demonstrate dyadic RL's empirical performance through simulation studies on both toy scenarios and on a realistic test bed constructed from data collected in a mobile health study

Systematic elucidation of the traditional Chinese medicine prescription Danxiong particles via network pharmacology and molecular docking

Purpose: To investigate the pharmacological effect of the traditional Chinese medicine (TCM) prescription Danxiong particles (TDX105) and its mechanism of action.Methods: The active compound and targets of TDX105 were investigated via network pharmacology. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were enriched, and protein-protein interaction network (PPI) was constructed. A network of ‘components-targets-pathways’ was developed with Cytoscape 3.8.0 software, while the formation of molecular docking analysis was conducted using Autodock vina software.Results: There were 304 compounds and 482 targets identified in total. Genes with degree ≥ mean node values were selected as the crucial targets, and string database was to be combined to 64 targets identified with cytoscape so as to draw a protein interaction map. A total of 137 pathways were enriched from 64 targets involving mainly 10 pathways, for example, PI3K-Akt signaling pathway, pathways in cancer, human cytomegalovirus infection and focal adhesion. Then, compound-target and compoundtarget- pathways were constructed using cytoscape (3.8.0). Finally, the five most active compounds, viz, quercetin, myricetin, luteolin, ellagic acid and kaempferol, and the top ten targets AKT1, GAPDH, TP53, ALB, EGFR, MAPK3, JUN, MAPK1, SRC and ESR1 were selected for molecular docking. These targets and compounds had strong interactions through a combination of hydrogen bonds and hydrophobic forces.Conclusion: The mechanism of action of TDX105 has been successfully explained using the combination of network pharmacology and molecular docking. This may offer a solid foundation to the clinical use of TDX105, and further strengthen the prospects of its development for clinical use

Neural Attentive Session-based Recommendation

Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. Previous work only considers the user's sequential behavior in the current session, whereas the user's main purpose in the current session is not emphasized. In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem. Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later. We then compute the recommendation scores for each candidate item with a bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session representations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental results show that NARM outperforms state-of-the-art baselines on both datasets. Furthermore, we also find that NARM achieves a significant improvement on long sessions, which demonstrates its advantages in modeling the user's sequential behavior and main purpose simultaneously.Comment: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. arXiv admin note: text overlap with arXiv:1511.06939, arXiv:1606.08117 by other author

The mediation effect of political interest on the connection between social trust and wellbeing among older adults

Previous research has established significant positive associations between social trust and wellbeing among older adults. This study aimed to obtain a deeper understanding of the relationship between different sources of social trust and wellbeing by examining the mediational role of political interest. A sample of 4,406 Italian residents aged 65 years and over was extracted from a national cross-sectional survey during 2013 in Italy, representative of the non-institutionalised population. Measures included trust in people, trust in institutions, political interest, life satisfaction and self-perceived health. Mediation path analysis and structural equation modelling were used to test the mediation effects of political interest on the relationship between trust in people and trust in institutions with life satisfaction and self-perceived health. Associations between trust in people, life satisfaction and self-perceived health, and between trust in institutions and life satisfaction were partially mediated by political interest, while the association between trust in institutions and self-perceived health was fully mediated by political interest. Having high levels of political interest may thus enhance the relationship between social trust and wellbeing among older adults. These results suggest that interventions to enhance wellbeing in older adults may benefit from examining individuals’ levels of political interest

Dark energy from conformal symmetry breaking

The breakdown of conformal symmetry in a conformally invariant scalar-tensor gravitational model is revisited in the cosmological context. Although the old scenario of conformal symmetry breaking in cosmology containing scalar field has already been used in many earlier works, it seems that no special attention has been paid for the investigation on the possible connection between the breakdown of conformal symmetry and the existence of dark energy. In this paper, it is shown that the old scenario of conformal symmetry breaking in cosmology, if properly interpreted, not only has a potential ability to describe the origin of dark energy as a symmetry breaking effect, but also may resolve the coincidence problem.Comment: 11 pages, minor revision, published online in EPJ

Testing an integrated destination image model across residents and tourists

Tourism research has yet to confirm whether an integrated destination image model is applicable in predicting the overall destination image and behavioral intentions of local residents. This study examines whether the cognitive, affective and overall image - hypothesized to be predictors of behavioral intentions - are applicable to residents and tourists in the resort city of Eilat. The proposed model allowed for the distinct effect of each image component on overall image and behavior to be closely examined. The findings support the applicability of the model to local residents and also showed that among tourists, the affective component exerted a greater influence than the cognitive on overall destination image and future behavior. These findings have theoretical and practical implications for research on destination image

Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data

Advances in digital technology have greatly increased the ease of collecting intensive longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of behavior changes. Such data are typically multilevel (e.g., with repeated measures nested within individuals), and are inevitably characterized by some degrees of missingness. Previous studies have validated the utility of multiple imputation as a way to handle missing observations in ILD when the imputation model is properly specified to reflect time dependencies. In this study, we illustrate the importance of proper accommodation of multilevel ILD structures in performing multiple imputations, and compare the performance of a multilevel multiple imputation (multilevel MI) approach relative to other approaches that do not account for such structures in a Monte Carlo simulation study. Empirical EMA data from a tobacco cessation study are used to demonstrate the utility of the multilevel MI approach, and the implications of separating participant- and study-initiated EMAs in evaluating individuals’ affective dynamics and urge