Anchors on prices of consumer goods only hold when decisions are hypothetical

We elicit willingness to pay for different types of consumption goods, systematically manipulating irrelevant anchors (high vs. low) and incentives to provide true valuations (hypothetical questions vs. Becker-DeGroot-Marschak mechanism). On top of a strong hypothetical bias, we find that anchors only make a substantial, significant difference in the case of hypothetical data, the first experiments to directly document such an interaction. This finding suggests that hypothetical market research methods may deliver lower quality data. Moreover, it contributes to the discussion examining the mechanism underlying the anchoring effect, suggesting it could partly be caused by insufficient conscious effort to drift away from the anchor.

Fear effect in discrete prey-predator model incorporating square root functional response

In this work, an interaction between prey and its predator involving the effect of fear in presence of the predator and the square root functional response is investigated. Fixed points and their stability condition are calculated. The conditions for the occurrence of some phenomena namely Neimark-Sacker, Flip, and Fold bifurcations are given. Base on some hypothetical data, the numerical simulations consist of phase portraits and bifurcation diagrams are demonstrated to picturise the dynamical behavior. It is also shown numerically that rich dynamics are obtained by the discrete model as the effect of fear.

Numerical investigation of turbocharger turbine temperature field based on conjugate heat transfer

Aiming at the issues of low accuracy and poor feasibility of the analytical results of the turbocharger turbine temperature field under operating conditions, a full-domain conjugate heat transfer numerical model was established by the conjugate heat transfer and finite volume method. The temperature field characteristics of each component of the turbocharger turbine were analyzed. The numerical and experimental test results were compared and analyzed. The global conjugate heat transfer model avoids the input of a large number of hypothetical data on the interface between fluid and solid in the traditional model, and makes the calculation process closer to the actual situation. Through the comparison with the experimental results, the accuracy of the turbine temperature field obtained by the global conjugate heat transfer model is more reasonable and more accurate than that of the traditional model, which verifies the reliability and accuracy of the global conjugate heat transfer model.

Different kinds of generalized rough sets based on neighborhoods with a medical application

Approximation space can be said to play a critical role in the accuracy of the set’s approximations. The idea of “approximation space” was introduced by Pawlak in 1982 as a core to describe information or knowledge induced from the relationships between objects of the universe. The main objective of this paper is to create new types of rough set models through the use of different neighborhoods generated by a binary relation. New approximations are proposed representing an extension of Pawlak’s rough sets and some of their generalizations, where the precision of these approximations is substantially improved. To elucidate the effectiveness of our approaches, we provide some comparisons between the proposed methods and the previous ones. Finally, we give a medical application of lung cancer disease as well as provide an algorithm which is tested on the basis of hypothetical data in order to compare it with current methods.

A topological reduction for predicting of a lung cancer disease based on generalized rough sets

The present work proposes new styles of rough sets by using different neighborhoods which are made from a general binary relation. The proposed approximations represent a generalization to Pawlak’s rough sets and some of its generalizations, where the accuracy of these approximations is enhanced significantly. Comparisons are obtained between the methods proposed and the previous ones. Moreover, we extend the notion of “nano-topology”, which have introduced by Thivagar and Richard [49], to any binary relation. Besides, to demonstrate the importance of the suggested approaches for deciding on an effective tool for diagnosing lung cancer diseases, we include a medical application of lung cancer disease to identify the most risk factors for this disease and help the doctor in decision-making. Finally, two algorithms are given for decision-making problems. These algorithms are tested on hypothetical data for comparison with already existing methods.

Isplativost rada u Hrvatskoj i Sloveniji: analiza pomoću mikrosimulacijskog modela

WORK INCENTIVES IN CROATIA AND SLOVENIA: ANALYSIS USING MICROSIMULATION MODELS The paper analyzes the impact of the tax-benefit system on work incentives in Croatia and Slovenia. Unemployed and inactive persons and their hypothetical transitions to employment are considered. As the main indicator of work incentives, the participation tax rate (PTR) is estimated, as it represents a portion of additional income that is lost because taxes increase and benefits decrease in transition of a person from non-employment into employment. Unlike previous research, which was made for both countries on hypothetical data, in this paper for the first time the calculations and analysis of PTR are based on survey data, which for both countries gives a realistic picture of the situation in the field of work incentives. The analysis is carried out on data and the tax-benefit system for 2017, and the main tool used is EUROMOD, a tax-benefit microsimulation model for the EU countries. The results show that the average PTR in Croatia is of a moderate size of 31.3%, while in Slovenia it is 11.3 percentage points higher. People with higher number of dependent children and those with lower level of market income obtained by other household members are more likely to have a high PTR in both countries, and in Croatia people with only primary education will also have it. Key words: participation tax rate, work incentives, EUROMOD, Croatia, Slovenia

Data visualization in the time of coronavirus

Currently, we observe a proliferation of data visualizations about Covid-19 in the media, which makes it a convenient time to study the topic from the perspective of different disciplines, including information design and mathematics. If, on the one hand, the abundance of such pandemic representations would already be a legitimate reason to address the issue, on the other hand, it is not the central motivation of the present discussion. The uniqueness of the epidemiological phenomenon that we are experiencing highlights new aspects regarding the production and use of data visualizations, one of which is its diversification beyond counting and visual representation of events related to the virus spread. In this sense, the article discusses, through the analysis of examples, three different approaches for this type of schematic representation, namely: visualization of hypothetical data, visualizations based on secondary data, and visualization for social criticism and self-reflection. Ultimately, we can argue that design contributes to the production of data visualizations that can help people to understand the causes and implications involved in the new coronavirus and encourage civic responsibility through self-care and the practice of social distancing.

A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results

Inference is the process of using facts we know to learn about facts we do not know. A theory of inference gives assumptions necessary to get from the former to the latter, along with a definition for and summary of the resulting uncertainty. Any one theory of inference is neither right nor wrong but merely an axiom that may or may not be useful. Each of the many diverse theories of inference can be valuable for certain applications. However, no existing theory of inference addresses the tendency to choose, from the range of plausible data analysis specifications consistent with prior evidence, those that inadvertently favor one’s own hypotheses. Because the biases from these choices are a growing concern across scientific fields, and in a sense the reason the scientific community was invented in the first place, we introduce a new theory of inference designed to address this critical problem. We introduce hacking intervals, which are the range of a summary statistic one may obtain given a class of possible endogenous manipulations of the data. Hacking intervals require no appeal to hypothetical data sets drawn from imaginary superpopulations. A scientific result with a small hacking interval is more robust to researcher manipulation than one with a larger interval and is often easier to interpret than a classical confidence interval. Some versions of hacking intervals turn out to be equivalent to classical confidence intervals, which means they may also provide a more intuitive and potentially more useful interpretation of classical confidence intervals. This paper was accepted by J. George Shanthikumar, big data analytics.

Student Perceptions of Bullying

Students have faced or done unusual things one of which is the act of bullying. As individuals who are in the early adult group, students should already have a correct and precise understanding of bullying. However, there are still many bullying incidents that occur among students. This research is quantitative research with descriptive approach. The respondents were new students in the academic year 2020/2021 at one of the universities in Jakarta which has 513 students. The collection method uses Likert scale model which is distributed through google form. Data analysis was carried out using percentiles to describe the categories of perceptions that new students have of bullying. Based on hypothetical data (89,3%) and empirical (50,49%) indicates that the majority of new students have a neutral perceptions of bullying. That means the majority of new students give inconsistent assessments of aggressive behaviors repeated by individuals or groups against weak individuals. The next conclusion based on hypothetical data (0%) and empirical (28,65%) shows that there are still new students who have a bad perception of bullying or it can be interpreted that there are still students who are wrong in making judgment against bullying.

Detecting Instruction Effects—Deciding Between Covariance Analytical and Change-Score Approach

The article focuses on estimating effects in nonrandomized studies with two outcome measurement occasions and one predictor variable. Given such a design, the analysis approach can be to include the measurement at the previous time point as a predictor in the regression model (ANCOVA), or to predict the change-score of the outcome variable (CHANGE). Researchers demonstrated that both approaches can result in different conclusions regarding the reported effect. Current recommendations on when to apply which approach are, in part, contradictory. In addition, they lack direct reference to the educational and instructional research contexts, since they do not consider latent variable models in which variables are measured without measurement error. This contribution assists researchers in making decisions regarding their analysis model. Using an underlying hypothetical data-generating model, we identify for which kind of data-generating scenario (i.e., under which assumptions) the defined true effect equals the estimated regression coefficients of the ANCOVA and the CHANGE approach. We give empirical examples from instructional research and discuss which approach is more appropriate, respectively.