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Performance evaluation (“rating”) systems not only provide information to users but also motivate the rated worker. This article solves for the optimal (effort-maximizing) rating within the standard career concerns framework. We prove that this rating is a linear function of past observations. The rating, however, is not a Markov process, but rather the sum of two Markov processes. We show how it combines information of different types and vintages. An increase in effort may adversely affect some (but not all) future ratings.

We study trading behavior and the properties of prices in informationally complex markets. Our model is based on the single-period version of the linear-normal framework of Kyle (1985). We allow for essentially arbitrary correlations among the random variables involved in the model: the value of the traded asset, the signals of strategic traders and competitive market makers, and the demand from liquidity traders. We show that there always exists a unique linear equilibrium, characterize it analytically, and illustrate its properties with a number of applications. We then use this characterization to study the informational efficiency of prices as the number of strategic traders becomes large. If liquidity demand is positively correlated (or uncorrelated) with the asset value, then prices in large markets aggregate all available information. If liquidity demand is negatively correlated with the asset value, then prices in large markets aggregate all information except that contained in liquidity demand.

We demonstrate that lottery markets can exhibit the “hot-hand” phenomenon, in which past winning numbers tend to have a greater share of the betting proportion in future draws even though past and future events are independent. This is surprising as previous works have instead documented the presence of an opposite effect, the “gambler’s fallacy” in the U.S. lottery market. The current literature also suggests that the gambler’s fallacy prevails when random numbers are generated by mechanical devices, such as in lottery games. We use two sets of naturally occurring data to show that both the gambler’s fallacy and the hot-hand fallacy can exist in different types of lottery games. We then run online experimental studies that mimic lottery games with one, two, or three winning numbers. Our experimental results show that the number of winning prizes impacts behavior. In particular, whereas a single-prize game leads to a strong presence of the gambler’s fallacy, we observe a significant increase in hot-hand behavior in multiple-prize games with two or three winning numbers. This paper was accepted by David Simchi-Levi, behavioral economics.

We study preferences estimated from finite choice experiments and provide sufficient conditions for convergence to a unique underlying “true” preference. Our conditions are weak and, therefore, valid in a wide range of economic environments. We develop applications to expected utility theory, choice over consumption bundles, and menu choice. Our framework unifies the revealed preference tradition with models that allow for errors.

At an initial time, an individual forms a belief about a future random outcome. As time passes, the individual may obtain, privately or subjectively, further information, until the outcome is eventually revealed. How can a protocol be devised that induces the individual, as a strict best response, to reveal at the outset his prior assessment of both the final outcome and the information flows he anticipates and, subsequently, what information he privately receives? The protocol can provide the individual with payoffs that depend only on the outcome realization and his reports. We develop a framework to design such protocols, and apply it to construct simple elicitation mechanisms for common dynamic environments. The framework is general: we show that strategyproof protocols exist for any number of periods and large outcome sets. For these more general settings, we build a family of strategyproof protocols based on a hierarchy of choice menus, and show that any strategyproof protocol can be approximated by a protocol of this family.

Atezolizumab successfully reinvigorated JC virus immunity in a patient in Belgium with progressive multifocal leukoencephalopathy, as demonstrated by clinical, virologic, and radiologic response to treatment. However, the treatment also resulted in immune reconstitution inflammatory syndrome and life-threatening immune-related adverse events. These conditions were treated with corticosteroids, leading to treatment resistance.

Opportunistic viral infections of the central nervous system represent a significant cause of morbidity and mortality among an increasing number of immunocompromised patients. Since antiviral treatments are usually poorly effective, the prognosis generally relies on the ability to achieve timely immune reconstitution. Hence, strategies aimed at reinvigorating antiviral immune activity have recently emerged. Among these, virus-specific T-cells are increasingly perceived as a principled and valuable tool to treat opportunistic viral infections. Here we briefly discuss how to develop and select virus-specific T-cells, then review their main indications in central nervous system infections, including progressive multifocal leukoencephalopathy, CMV infection, and adenovirus infection. We also discuss their potential interest in the treatment of progressive multiple sclerosis, or EBV-associated central nervous system inflammatory disease. We finish with the key future milestones of this promising treatment strategy.

Two heterogeneous agents contribute over time to a joint project and collectively decide its scale. A larger scale requires greater cumulative effort and delivers higher benefits upon completion. We show that the efficient agent prefers a smaller scale, and preferences are time-inconsistent: as the project progresses, the efficient (inefficient) agent’s preferred scale shrinks (expands). We characterize the equilibrium outcomes under dictatorship and unanimity, with and without commitment. We find that an agent’s degree of efficiency is a key determinant of control over the project scale. From a welfare perspective, it may be desirable to allocate decision rights to the inefficient agent.

The Canadian Earth System Model version 5.0 (CanESM5.0), the most recent major version of the global climate model developed at the Canadian Centre for Climate Modelling and Analysis (CCCma) at Environment and Climate Change Canada (ECCC), has been used extensively in climate research and for providing future climate projections in the context of climate services. Previous studies have shown that CanESM5.0 performs well compared to other models and have revealed several model biases. To address these biases, the CCCma has recently initiated the “Analysis for Development” (A4D) activity, a coordinated analysis activity in support of CanESM development. Here we describe the goals and organization of this effort and introduce two variants (“p1” and “p2”) of a new CanESM version, CanESM5.1, which features important improvements as a result of the A4D activity. These improvements include the elimination of spurious stratospheric temperature spikes and an improved simulation of tropospheric dust. Other climate aspects of the p1 variant of CanESM5.1 are similar to those of CanESM5.0, while the p2 variant of CanESM5.1 features reduced equilibrium climate sensitivity and improved El Niño–Southern Oscillation (ENSO) variability as a result of intentional tuning of the atmospheric component. The A4D activity has also led to the improved understanding of other notable CanESM5.0 and CanESM5.1 biases, including the overestimation of North Atlantic sea ice, a cold bias over sea ice, biases in the stratospheric circulation and a cold bias over the Himalayas. It provides a potential framework for the broader climate community to contribute to CanESM development, which will facilitate further model improvements and ultimately lead to improved climate change information.

The Gulf of St. Lawrence (GSL), along with the St. Lawrence Estuary, is the largest estuarine system in North America. It is a biologically productive sea and an important fishing ground in Canada. The objectives of this study are to determine how climate changes will affect primary production in the GSL and on the eastern Scotian Shelf, and to determine the drivers of the changes. We use a regional ocean model forced with downscaled output of the Max-Planck Institute Earth System Model to study the changes in net primary production under the climate change scenario of Representative Concentration Pathway 8.5 (RCP8.5). Results reveal a projected 13.4% decrease in annual primary production across the GSL system over the next 70 years. This decline primarily stems from reduced nutrient concentrations in the upper layer, despite the increase in nutrients supplied by rivers. Enhanced freshwater influx and ocean surface warming contribute to heightened stratification, that in turn reduce the vertical nutrient fluxes from deeper layers. This mechanism affects the upwelling of nutrient-rich water at the head of the Laurentian Channel in the Lower St. Lawrence Estuary, leading to a 22% reduction in the vertical nitrate flux in the future. Additionally, nutrient concentrations in the water masses entering the GSL at the Strait of Belle Isle and at Cabot Strait are also reduced. Due to declining sea-ice concentration, the phytoplankton bloom is expected to occur between 9 and 23 days earlier under a warmer climate, and last for up to 24 days longer, depending on the GSL subregions, compared to the historical period (2001-2020). Biological productivity at higher trophic levels, and thus fisheries productivity, could be negatively impacted under the RCP8.5 climate change scenario.