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Ambiguity is ubiquitous in language; lexical ambiguity refers to instances where a single word has multiple meanings. The current investigation examined homonyms, words that have the same orthography and pronunciation in English but multiple meanings (e.g., BANK, meaning "financial institution" or "river's edge"). The processing of homonyms requires the engagement of executive control processes, for example, to select the appropriate meaning of the homonym while reducing interference from other meanings. Executive function processes are known to change over the lifespan and may be impacted by experiential factors such as bilingualism. The present study uses event-related brain potentials as an index of lexical access to examine whether bilingualism influences homonym processing in older adults. The results indicate that patterns of lexical access differ as a function of bilingual status in older adults and compared to young adults, suggesting that language experience may moderate language processing in both young and older adults, at least in situations where language processing is demanding on executive function, such as in the processing of homonyms. Importantly, we show that older bilinguals show a somewhat similar pattern of ambiguity processing as their younger counterparts, while age differences were observed in monolinguals when comparing across studies. This suggests that bilingual language experience may have a mitigating impact on age-related changes in ambiguity processing. [ABSTRACT FROM AUTHOR]

Introduction: Using the conservation of resources (COR) theory, our study explores the interaction between role ambiguity and leader-member exchange (LMX) quality on burnout using work addiction as a mediator among Canadian first-level healthcare managers. Methods: Cross-sectional data was collected among 165 first-level managers working in healthcare with the support of interprofessional associations in Canada. Linear regression was used to test the presented hypotheses. Results: Work addiction fully mediated the positive relationship between role ambiguity and burnout among first-level managers. In addition, high LMX exacerbated both the direct and indirect effects of role ambiguity. Conclusion: Our study contributes by identifying role ambiguity as a context under which LMX can have adverse effects for first-level managers in healthcare. Moreover, work addiction acted as a mediator, theorized as a risky resource investment which depletes managers' resources. Having a good relationship with their team further entices managers to develop a pathological relationship with their work to protect its members, which in turn is related to higher levels of burnout. [ABSTRACT FROM AUTHOR]

This paper presents the first comprehensive study of a data-driven formulation of the distributionally robust second order stochastic dominance constrained problem (DRSSDCP) that hinges on using a type-1 Wasserstein ambiguity set. It is, furthermore, for the first time shown to be axiomatically motivated in an environment with distribution ambiguity. We formulate the DRSSDCP as a multistage robust optimization problem and further propose a tractable conservative approximation that exploits finite adaptability and a scenario-based lower bounding problem. We then propose the first exact optimization algorithm for this DRSSDCP. We illustrate how the data-driven DRSSDCP can be applied in practice on resource-allocation problems with both synthetic and real data. Our empirical results show that, with a proper adjustment of the size of the Wasserstein ball, DRSSDCP can reach acceptable out-of-sample feasibility yet still generating strictly better performance than what is achieved by the reference strategy. Optimization with stochastic dominance constraints has recently received an increasing amount of attention in the quantitative risk management literature. Instead of requiring that the probabilistic description of the uncertain parameters be exactly known, this paper presents a comprehensive study of a data-driven formulation of the distributionally robust second order stochastic dominance constrained problem (DRSSDCP) that hinges on using a type-1 Wasserstein ambiguity set. This formulation allows us to identify solutions with finite sample guarantees and solutions that are asymptotically consistent when observations are independent and identically distributed. It is, furthermore, shown to be axiomatically motivated in an environment with distribution ambiguity. Leveraging recent results in the field of robust optimization, we further formulate the DRSSDCP as a multistage robust optimization problem and further propose a tractable conservative approximation that exploits finite adaptability and a scenario-based lower bounding problem, both of which can reduce to linear programs under mild conditions. We then propose, to the best of our knowledge, the first exact optimization algorithm for this DRSSDCP, the efficiency of which is confirmed by our numerical results. Finally, we illustrate how the data-driven DRSSDCP can be applied in practice on resource-allocation problems with both synthetic and real data. Our empirical results show that, with a proper adjustment of the size of the Wasserstein ball, DRSSDCP can reach "acceptable" out-of-sample feasibility yet still generating strictly better performance than what is achieved by the reference strategy. Funding: This research was partially supported by the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2016-05208], the Canada Research Chair program [Grant 950-230057], and Groupe d'études et de recherche en analyse des décisions, and it was enabled in part by support provided by Calcul Quebec (https://www.calculquebec.ca/en/) and the Digital Research Alliance of Canada (https://www.alliancecan.ca/). Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2387. [ABSTRACT FROM AUTHOR]