Joint Optimization Decision of Online Retailers’ Pricing and Live-Streaming Effort in the Postepidemic Era

The isolation requirements of the coronavirus epidemic and the intuitive display advantages of live-streaming have led to an increasing number of retailers shifting to social live-streaming platforms and e-commerce live-streaming platforms to promote and sell their products in real time. However, the provision of live-streaming services will also incur high live-streaming effort costs. In this paper, we develop two decision models for retailers to sell goods through a single online shop and both online shop and live-streaming room; we also present the optimal decisions of pricing and live-streaming efforts. Furthermore, we identify the profitability conditions for retailers to determine when to provide live-streaming services. In addition, we examine the impact of the provision of live-streaming services on the optimal price and live-streaming effort. We obtain three findings. First, there is a unique optimal decision on the price and live-streaming effort under certain conditions. Second, when the effect coefficient of the live-streaming room reaches a certain threshold, there are enough customers who enter the live-streaming room to watch and buy and it is profitable for retailers to provide live-streaming service. Finally, the optimal price and live-streaming effort increase with the increase in average return loss, the effect coefficient of live-streaming effort, and the extra return rate and decrease with the increase in the proportion of customers who choose to buy in the online shop and the price discount coefficient in the live-streaming room.

Specificity of the work of physicians in inpatient healthcare facilities of the Gomel region in the situation of the COVID-19 pandemic

Objective. To study the specificity of the functioning of inpatient healthcare facilities in the Gomel region in the spread of the coronavirus disease COVID-19.Materials and methods. A survey among 78 physicians of the inpatient healthcare facilities was conducted, which provided an opportunity to analyze the specificity of the work in the spread of the coronavirus disease COVID-19 and related difficulties.Results. According to the respondents, in the situation of the COVID-19 pandemic, there has been a considerable growth in working hours and intensity of the work of the physicians in the inpatient healthcare facilities. In the vast majority of the cases (74.7 %), the physicians in the inpatient healthcare facilities underwent training on prevention, diagnostics, adoption of anti-epidemic measures in the detection and treatment of COVID-19 patients. Nevertheless, only 25.6 % of the respondents considered their professional competence level with regard to their work with COVID-19 patients to be high. Moreover, the physicians` satisfaction rate for the existing strategies for rendering medical care to COVID-19 patientswas 6.87 ± 2.06 outof 10.Conclusion. To a large extent, the physicians of inpatient healthcare facilities of the Gomel region during their work with patients are concerned about such circumstances as longer working hours and increased working intensity, complexity of making balanced and optimal decisions regarding the organization of the work of inpatient healthcare facilities during the pandemic, as well as issues related to contact with patients (worrying about getting infected and infecting family, organization of childcare due to the closure of schools and preschool institutions during quarantine and others).

Mathematically optimal decisions in forensic age assessment

Forensic age estimation generally involves considerable amounts of uncertainty. Forensic age indicators such as teeth or skeleton images predict age only approximately, and this is likely to remain true even for future forensic age indicators. Thus, forensic age assessment should aim to make the best possible decisions under uncertainty. In this paper, we apply mathematical theory to make statistically optimal decisions to age assessment. Such an application is fairly straightforward assuming there is a standardized procedure for obtaining age indicator information from individuals, assuming we have data from the application of this procedure to a group of persons with known ages, and assuming the starting point for each individual is a probability distribution describing prior knowledge about the persons age. The main problem is then to obtain such a prior. Our analysis indicates that individual priors rather than a common prior for all persons may be necessary. We suggest that caseworkers, based on individual case information, may select a prior from a menu of priors. We show how information may then be collected over time to gradually increase the robustness of the decision procedure. We also show how replacing individual prior distributions for age with individual prior odds for being above an age limit cannot be recommended as a general method. Our theoretical framework is applied to data where the maturity of the distal femur and the third molar is observed using MRI. As part of this analysis we observe a weak positive conditional correlation between maturity of the two body parts.

The Paternalistic Turn in Behavioral Law and Economics: A Critique

The application of behavioral economics to law and economics has taken a paternalistic turn. Behavioralists believe that the fundamental assumptions regarding individual behavior in standard theory do not reflect reality. If individuals are not “rational” in the standard economic sense, then there will be decisionmaking failures: people cannot be relied upon to make individually optimal decisions and thus to maximize welfare as they see it. This Article is organized as follows. Part One is a prelude and gives context. Part Two discusses the fundamental normative standard in behavioral public policy: true preferences. I then proceed to outline the causes of the divergence between true preferences and actual observed preferences. Part Three analyzes some of the knowledge problems is ascertaining the presence of cognitive and behavioral biases. Part Four presents a case study of the difficulties of behavioral policy analysis in the area of consumer credit. Part Five concludes.

Learning Optimal Decision Sets and Lists with SAT

Decision sets and decision lists are two of the most easily explainable machine learning models. Given the renewed emphasis on explainable machine learning decisions, both of these machine learning models are becoming increasingly attractive, as they combine small size and clear explainability. In this paper, we define size as the total number of literals in the SAT encoding of these rule-based models as opposed to earlier work that concentrates on the number of rules. In this paper, we develop approaches to computing minimum-size “perfect” decision sets and decision lists, which are perfectly accurate on the training data, and minimal in size, making use of modern SAT solving technology. We also provide a new method for determining optimal sparse alternatives, which trade off size and accuracy. The experiments in this paper demonstrate that the optimal decision sets computed by the SAT-based approach are comparable with the best heuristic methods, but much more succinct, and thus, more explainable. We contrast the size and test accuracy of optimal decisions lists versus optimal decision sets, as well as other state-of-the-art methods for determining optimal decision lists. Finally, we examine the size of average explanations generated by decision sets and decision lists.

Regional Collision Risk Prediction System at a Collision Area Considering Spatial Pattern

To reduce the risk of collision in territorial sea areas, including trade ports and entry waterways, and to enhance the safety and efficiency of ship passage, the International Maritime Organization requires the governing body of every country to establish and operate a vessel traffic service (VTS). However, previous studies on risk prediction models did not consider the locations of near collisions and actual collisions and only employed a combined collision risk index in surveillance sea areas. In this study, we propose a regional collision risk prediction system for a collision area considering spatial patterns using a density-based spatial clustering of applications with noise (DBSCAN). Furthermore, a fuzzy inference system based on a near collision (FIS-NC) and long short-term memory (LSTM) is adopted to help a vessel traffic service operator (VTSO) make timely optimal decisions. In the local spatial pattern stage, the ship trajectory was determined by identifying the actual-collision and near-collision locations simultaneously. Finally, the system was developed by learning a sequence dataset from the extracted trajectory of the ship when a collision occurred. The proposed system can recommend an action faster than the fuzzy inference system based on the near-collision location. Therefore, using the developed system, a VTSO can quickly predict ship collision risk situations and make timely optimal decisions at dangerous surveillance sea areas.

Hidden temporal structure of the ongoing task impacts detection strategy and is reflected in pupillary dynamics

Estimating temporal regularities in incoming sensory inputs supports optimal decisions in noisy environments. In particular, inferred temporal structure can ease the detection of likely target events. Here we postulated that timely urgency signals can adapt subjects' decision-making to the ongoing task temporal structure, possibly through neuromodulatory tone. To test this hypothesis, we used an auditory change detection task in which targets followed a block-based temporal contingency, unbeknownst to participants. False alarm occurrences were driven by the distribution of target timings, indicating that participants adapted their behavior to the ongoing temporal structure. Task-evoked pupillary responses were larger for blocks with earliest target timings, and correlated with individual subjects' behavioral adaptation. Individual pupil responses matched an urgency signal extracted from a decision model fitted to behavior. This work demonstrates that internal temporal expectation can be tracked through pupillary dynamics, suggesting a role of neuromodulatory systems in context-dependent modulation of decision variable dynamics.

Optimal Decisions and Risk Assessment in Sustainable Supply Chains

Due to environmental preoccupations and potential economic benefits [...]