Groundwater compatibility with formation water and pay zone rocks in Pervomaysk oil-gas-condensate field to maintain formation pressure

The paper describes the research results in determining the compatibility of groundwater from Aptain-Albian-Cenomanian aquifer with formation water and pay zone rocks in U1 layer sediments, Pervomaysk oil field

Using Response Times to Measure Ability on a Cognitive Task

I show how using response times as a proxy for effort coupled with an explicit process-based model can address a long-standing issue of how to separate the effect of cognitive ability on performance from the effect of motivation. My method is based on a dynamic stochastic model of optimal effort choice in which ability and motivation are the structural parameters. I show how to estimate these parameters from the data on outcomes and response times in a cognitive task. In a laboratory experiment, I find that performance on a Digit-Symbol test is a noisy and biased measure of cognitive ability. Ranking subjects by their performance leads to an incorrect ranking by their ability in a substantial number of cases. These results suggest that interpreting performance on a cognitive task as ability may be misleading

Essays in Behavioral Labor and Welfare Economics

In my dissertation, I study three questions in behavioral labor and welfare economics using experimental methods: what is the role of task difficulty on effort, how to measure ability and motivation in a more rigorous way, and what are the economic consequences of stochastic choice in a risk setting. In the first chapter, I study the effect of task difficulty on workers’ effort and compare it to the effect of monetary rewards in a tightly controlled laboratory experiment. I find that task difficulty has an inverse-U effect on effort, and that this effect is quantitatively large when compared to the effect of conditional rewards. Difficulty acts as an important mediator of monetary rewards: they are most effective at the medium level of difficulty. I show that the inverse-U pattern of effort response to difficulty is not consistent with the Expected Utility model but is consistent with the Rank-Dependent Utility (RDU) model that allows for probability weighting. I structurally estimate the RDU model and find that it fits the data well. These findings suggests that 1) task difficulty is a useful and costless tool to stimulate effort, 2) to elicit the maximum amount of effort, the task has to be reasonably challenging, and 3) the design of optimal incentive schemes for workers should take into account task difficulty. In the second chapter, I develop a novel method for estimating ability and motivation from the outcomes and response time on a cognitive test. The proposed method is based on a dynamic stochastic model of optimal effort choice that features a psychologically plausible mechanism of decision-making. In a laboratory experiment, I find substantial heterogeneity among subjects in terms of their estimated ability and motivation that is partially attributed to their demographic characteristics and preferences. Test scores turns out to be a very imprecise measure of true ability. The observed variation in test scores is mostly due to variation in motivation rather than ability. I find no association between estimated measures of ability and motivation and their self-reported counterparts. Looking at the relative importance of ability versus motivation on the success on a cognitive task, I find that motivation plays a slightly bigger role than ability. In the third chapter, which is a joint work with Dr. Glenn W. Harrison, Dr. Morten Lau and Dr. Don Ross, we study the welfare costs of stochastic choice. Theoretical work on stochastic choice mainly focuses on the sources of choice randomness, and less on its economic conse- quences. We attempt to close this gap by developing a method of extracting information about the monetary costs of noise from structural estimates of preferences and choice randomness. Our method is based on allowing a degree of noise in choices in order to rationalize them by a given structural model. To illustrate the approach, we consider risky binary choices made by a sample of the general Danish population in an artefactual field experiment. The estimated welfare costs are small in terms of everyday economic activity, but they are considerable in terms of the actual stakes of the choice environment. Higher welfare costs are associated with higher age, lower education, and lower income

The Economics of Babysitting a Robot

I study the welfare effect of automation on workers in a setting where technology is complementary but imperfect. Using a modified task-based framework, I argue that imperfect complementary automation can impose non-pecuniary costs on workers via a behavioral channel. The theoretical model suggests that a critical factor determining the welfare effect of imperfect complementary automation is the automatability of the production process. I confirm the model\u27s predictions in an experiment that elicits subjects\u27 revealed preference for automation. Increasing automatability leads to a significant increase in the demand for automation. I explore additional drivers of the demand for automation using machine learning analysis and textual analysis of choice reasons. The analysis reveals that task enjoyment, performance, and cognitive flexibility are the most important predictors of subjects\u27 choices. There is significant heterogeneity in how subjects evaluate imperfect complementary automation. I discuss the implications of my results for workers\u27 welfare, technology adoption, and inequality

Oberflächen : drei Projektvorstellungen der Werkstatt für Visuelle Anthropologie ; (mit einem Kommentar von Valerij Podoroga)

Oberfläche. Projektvorstellung: AES [Lev Evzoviè, Evgenij Svjackij] – Körperoberfläche; Dimitrij Gutov – Tabula Rasa; Die Textur der Hautoberfläche – Der Abstand zum Sichtbaren; Die Oberfläche als weiße Leinwand. – Die Lüge des Künstlers; Valerij Podoroga: Kommentar ( Januar–Februar 1996); Oberfläche. Abschließende Diskussion – Vorbei reden

The Power of Incentives

I study an optimal design of monetary incentives in experiments where incentives are a treatment variable. I introduce the Budget Minimization problem in which a researcher chooses the level of incentives that allows her to detect a predicted treatment effect while minimizing her expected budget. The Budget Minimization problem builds upon the power analysis and structural modeling. It extends the standard optimal design approach by explicitly making the budget a part of the objective function. I show theoretically that the problem has an interior solution under fairly mild conditions. I illustrate the applications of the Budget Minimization problem using existing experiments and offer a practical guide for implementing it. My approach adds to the experimental economists' toolkit for an optimal design, however, it also challenges some conventional design recommendations

Give me a challenge or give me a raise

I study the effect of task difficulty on workers’ effort. I find that task difficulty has an inverse-U effect on effort and that this effect is quantitatively large, especially when compared to the effect of conditional monetary rewards. Difficulty acts as a mediator of monetary rewards: conditional rewards are most effective at the intermediate or high levels of difficulty. The inverse-U pattern of effort response to difficulty is inconsistent with many popular models in the literature, including the Expected Utility models with the additively separable cost of effort. I propose an alternative mechanism for the observed behavior based on non-linear probability weighting. I structurally estimate the proposed model and find that it successfully captures the behavioral patterns observed in the data. I discuss the implications of my findings for the design of optimal incentive schemes for workers and for the models of effort provision

On (σ,τ)(\sigma,\tau)-derivations of group algebra as category characters

For the space of $(\sigma,\tau)$-derivations of the group algebra $\mathbb{C} [G]$ of discrete countable group $G$, the decomposition theorem for the space of $(\sigma,\tau)$-derivations, generalising the corresponding theorem on ordinary derivations on group algebras, is established in an algebraic context using groupoids and characters. Several corollaries and examples describing when all $(\sigma,\tau)$-derivations are inner are obtained. Considered in details cases on $(\sigma,\tau)-$nilpotent groups and $(\sigma,\tau)$-$FC$ groups

Accounting and monitoring tools enhancement for Run 3 in the ATLAS distributed computing

The ATLAS experiment at the LHC utilizes complex multicomponent distributed systems for processing (PanDA WMS) and managing (Rucio) data. The complexity of the relationships between components, the amount of data being processed and the continuous development of new functionalities of the critical systems are the main challenges to consider when creating monitoring and accounting tools able to adapt to this dynamic environment in a short time. To overcome these challenges, ATLAS uses the unified monitoring infrastructure (UMA) provided by CERN-IT since 2018, which accumulates information from distributed data sources and then makes it available for different ATLAS distributed computing user groups. The information is displayed using Grafana dashboards. Based on the information provided, they can be grouped as “data transfers”, “site accounting”, “jobs accounting” and so on. These monitoring tools are used daily by ATLAS members to spot and fix issues. In addition, LHC Run 3 required the implementation of significant changes in the monitoring and accounting infrastructure to collect and process data collected by ATLAS during the LHC run. This paper describes the recent enhancements to the UMA-based monitoring and accounting dashboards