Imprecise Data Sets as a Source of Ambiguity: A Model and Experimental Evidence

In this study, an innovative approach that combines least square support vector regression (LSSVR) with uncertainty theory to enhance its performance in dealing with low-quality or imprecise data from real-world be proposed. The resulting model, called uncertain least square support vector regression (ULSSVR), incorporates chance constraints and simplified parameter selection, which are critical to handle imprecise observations. A numerical algorithm called the conjugate residual method (CR) is introduced to reduce the computational complexity of the model solution. The experimental results using both small and medium-sized datasets demonstrate the superior performance of ULSSVR in terms of prediction accuracy and generalization ability compared to other models such as uncertain support vector regression (USVR), uncertain linear lodel, uncertain polynomial model, and uncertain growth models. ULSSVR not only improves prediction accuracy by at least 28.49% but also demonstrates faster computational speed. Overall, ULSSVR presents a promising solution for data science and internet applications where dealing with imprecise and low-quality data is a common challenge.

The literature discusses risk aversion as one of the behavioral determinants of technology adoption. However, little attention has been paid to measuring ambiguity aversion of poor people in developing countries or in finding the role of ambiguity aversion in technology adoption. Risk experiments in the previous studies have been designed in such a way that individuals face the risky and/or ambiguous situations alone. Individuals in the real world, especially farmers in developing countries, are likely to get information from peers before making any decision regarding a new innovation that has an ambiguous nature. This paper addresses two broad issues. The first issue is to measure the risk and ambiguity preferences of Bangladeshi rural farmers. The paper investigates whether the attitudes toward uncertainty (risk and ambiguity) differ when farmers face the uncertainty alone versus when they are allowed to communicate with peer groups of 3 or 6. It also investigates whether farmers’ demographic characteristics affect their attitudes toward uncertainty or not. A second issue is to find whether measures of attitudes toward uncertainty is same across different groups of subjects using experimental lotteries. To do so, this paper replicates the same experiments with groups of students in two universities in Bangladesh. Finally, the paper also investigates whether demographic variables affect the attitudes toward risk and ambiguity aversion or not. It finds that risk attitudes of farmers and students are same when deciding alone. However, farmers tend to show higher variation in risk aversion than students sample when deciding in a group of 3. In the latter case, farmers tend to show less risk aversion than students. While disaggregating the measured risk attitudes across gender, female students tend to show more risk aversion as well as higher variation in risk aversion than male students in the sample. The study also finds that The study also finds that students’ and farmers’ demographic characteristics affect both risk and ambiguity aversion.

The first paper of this dissertation in Chapter II, “Insurance for Technology Adoption: An Experimental Evaluation of Schemes and Subsidies with Maize Farmers in Mexico”, analyzes experimentally how bundling the purchase of a risky technology, namely a higher yielding maize seed variety, with different insurance schemes, affects the total take-up of that variety. In this regard, the paper looks at the effects of (1) partial insurance versus full insurance, (2) geographical versus local basis risk, and (3) fair versus below-fair premium. This is the first paper to evaluate insurance schemes with different levels of risk reduction, basis risk and premium subsidies regarding their effect on technology adoption. The results add to the debate on insurance serving as a potential tool for incentivizing agricultural producers to adopt more productive, but more risky technologies, and thereby enabling them to escape poverty (Carter et al. 2016; Fan et al. 2013; Lybbert and Carter 2014; Nicola 2015; World Bank 2013). The second paper in Chapter III, “The Relationship between Farmers’ Shock Experiences and their Uncertainty Preferences - Experimental Evidence from Mexico” addresses the relationship between farmers’ uncertainty preferences, sociodemographic characteristics and their experience of adverse harvest shocks. Uncertainty preferences refer to a range of preference parameters as derived from Cumulative Prospect Theory (Kahneman and Tversky 1979; Tversky and Kahneman 1992), namely risk aversion, loss aversion and probability weighting, as well as ambiguity aversion (Ellsberg 1961). A series of incentivized lottery games are used to estimate these parameters with the sample of Mexican maize farmers, controlling for (1) sociodemographic characteristics and (2) the severity of experienced maize harvest losses. While there are several field studies examining the effect of shocks on risk preferences with subjects from developing countries, only few look at preferences beyond Expected Utility Theory and take into account Cumulative Prospect Theory, and none has looked at ambiguity aversion. Therefore, this paper sheds light on the role that the experience of adverse random shocks, as well as a range of sociodemographic variables, have in explaining one’s uncertainty preferences. The third paper in Chapter IV, “Formal Insurance, Risk Sharing, and the Dynamics of Other-Regarding Preferences”, analyzes how selectively providing formal insurance to members of a risk sharing network affects informal transfers and, subsequently, the dynamics of other-regarding preferences within that network. Many poor households in developing countries are excluded from formal financial markets and therefore rely on the mutual exchange within informal risk sharing networks to protect themselves against adverse income shocks. Social interactions in the aftermath of such shocks have been found to strengthen the social ties among members of these networks, while formal insurance has been found to crowd-out these transfers. Similarly, this third paper finds that when some members of risk sharing networks become formally insured, it affects the informal exchange of transfers among members, as well as their other-regarding preferences. This is the first study to explore the effect of insurance on other-regarding preferences in that context. In order to do so, an incentivized, three-stage experimental design with a baseline and an ex-post measurement of altruism, trust and trustworthiness through dictator and trust games is implemented with random and anonymous groups of three. Between the baseline and the ex-post measurement, a solidarity game is played with the same anonymous groups as in the ex-post measurement of other-regarding preferences, during which the shock structure and the availability of formal insurance are varied exogenously. The findings suggest that the effect of insurance depends on (1) the covariance structure of shocks and (2) is different for the insured and non-insured members within a network. Insurance either decreases trust levels of the uninsured or increases trust levels of the insured subjects towards the other network members, depending on whether the shocks affects one or more than one network member at a time. Trustworthiness and altruism remain unaffected by insurance. Furthermore, the analysis indicates that the results are driven by a change in the dynamics of the transfer behavior within the network induced by formal insurance. Specifically, there is evidence that subjects increase trust levels towards their network members after receiving higher transfers relative to the maximum possible value from them, but not after receiving higher transfers in absolute terms.

Effects of communication, group selection, and social learning on risk and ambiguity attitudes: Experimental evidence from Bangladesh

We study information aggregation in a dynamic trading model. We show theoretically that separable securities, introduced by Ostrovsky in the context of Expected Utility, no longer aggregate information if some traders have imprecise beliefs and are ambiguity averse. Moreover, these securities are prone to manipulation as the degree of information aggregation can be influenced by the initial price set by the uninformed market maker. These observations are also confirmed in our laboratory experiment using prediction markets. We define a new class of strongly separable securities, which are robust to the above considerations and show that they characterize information aggregation in both strategic and non-strategic environments. We derive several testable predictions, which we are able to confirm in the laboratory. Finally, we show theoretically that strongly separable securities are both sufficient and necessary for information aggregation but, strikingly, there does not exist a security that is strongly separable for all information structures.

Despite holding valuable information, unstructured data pose challenges for efficient recognition due to the difficulties in feature extraction using traditional Von-Neumann architecture systems, which are limited by power and time bottlenecks. Although biological neural signals offer crucial insights, they require more effective recognition solutions due to inherent noise and the vast volumes of data. Inspired by the human brain, neuromorphic systems have emerged as promising alternatives because of their parallelism, low power consumption, and error tolerance. By leveraging deep neural networks (DNNs), these systems can recognize imprecise data through two key processes: learning (feature extraction) and testing (feature matching and recognition). During the learning phase, DNNs extract and store unique features such as weight changes in synapse units. In the testing phase, new data are compared with the stored features for recognition. The parallelization of the neuromorphic system enables the efficient processing of large, imprecise datasets with minimal energy consumption. Nevertheless, the hardware implementation is essential for determining the full potential of DNNs. This paper focuses on synapse devices, which are the core units for hardware DNN implementations, and presents a biomedical application example: a rat neural signal recognition system implemented using a synapse device-based neuromorphic system.

The human aerodigestive tract (ADT) is the crossroads of breathing, swallowing, and vocalizing, yet precise data on when anatomic and physiologic changes occur is poorly understood. A central determiner of ADT function is laryngeal position, which until the second year is intranarial, allowing near simultaneous breathing and swallowing. While infants have been called “obligate” nose breathers, oral tidal respiration is evidenced by transient larynx/nasopharynx unlocking by the third month and even earlier. ADT development is hypothesized to be associated with clinicopathologies reflecting developmental errors. Tracking these changes may determine if a circumscribed time period in infancy exists during which structural changes in ADT development can be found consistently to occur. This project reviewed the literature of three documented infant ADT related clinicopathologies (sleep apneas, SIDS, excessive crying) to see if any nexus in occurrence frequency could be identified. In large scale trials, these three disorders were found to show a peak incidence between 2–4 months and dramatically decreased by 6 months. These data indicate that the critical period of infant ADT development may lie between 2–4 months. Whether these changes are functional, anatomical, or both remain unclear.

An inverse scattering theory and two inversion algorithms for the reconstruction of the dielectric profile of an inaccessible, layered, and dispersionless half-space are presented. The source and receiver are located at a distance above the inaccessible region. The source is an artificial electromagnetic source which is assumed to be capable of radiating plane waves at normal incidence for all the positive frequencies. In this case the receiver measures the reflection coefficient phase and amplitude for all the positive frequencies. Alternatively, the source is radiating an impulsive plane wave and the receiver measures the impulse response of the inaccessible region. From either of these data, the dielectric profile of the inaccessible region is constructed uniquely and exactly. Two numerical algorithms are developed and their performance is examined for the cases of precise and imprecise data. It is also shown that if the inaccessible half-space is made of an inhomogeneous layer of known thickness on top of a uniform half-space of known dielectric constant, then the average value of the refractive index profile of the inhomogeneous layer and the refractive index at the edges of the layer can be estimated from the minimum and maximum values of the reflection coefficient amplitude. Results are given for these estimates for five different inhomogeneous layers.

This paper develops a BCC input relaxation model for identifying inputcongestion as a severe form of inefficiency of decision-making units in fuzzy data envelopment analysis. The possibility approach is presented to obtain the models equivalent to fuzzy models. We use a one-model approach to determine input congestion based on the BCC input relaxation model. A numerical example is given to illustrate the proposed model and identify the congestion with precise and imprecise data. The proposed model is also used to determine the congestion in 16 hospitals using four fuzzy inputs and two fuzzy outputs with a symmetrical triangular membership function.

PurposeThe purpose of the research is to determine the perspective directions of state regulation of competition between human and artificial intellectual capital in Industry 4.0 and to develop scientific and methodological recommendations for their implementation. For this, the directions of state regulation of competition between human and artificial intellectual capital are described, monitoring of competition between human and artificial intellectual capital by the example of modern Russia (2019) is performed and scientific and practical recommendations for state regulation of competition between human and artificial intellectual capital are developed, with their approbation by the example of modern Russia (2019).Design/methodology/approachA method of expert evaluation is used for collection of the information and empirical data. The method of comparative analysis is used for comparing the successfulness of implementing the distinguished directions of state regulation of competition between human and artificial intellectual capital (4.1) according to the official statistics to their current evaluation according to the interested parties. Also, future evaluation (forecasts) according to the interested parties (until 2045) is determined.FindingsIt is substantiated that during evaluation of state regulation of competition between human and artificial intellectual capital in Industry 4.0, one cannot use only the official statistics, as these data are fragmentary and indirect. Fuller and more precise data are provided by assessment according to the interested parties. They allow determining the current and the future state of affairs and, based on it, compiling a forecast and developing a long-term strategy of state regulation of competition between human and artificial intellectual capital in Industry 4.0.Originality/valueThe perspective directions of state regulation of competition between human and artificial intellectual capital in Industry 4.0 are as follows: stimulation of competition in the market of intellectual capital, social risk management of the market of intellectual capital, managing international competition in the market of intellectual capital and ecological risk management of the intellectual capital market. As the experience of modern Russia shows, even at the initial stage of transition to Industry 4.0, the measures of state regulation of competition between human and artificial intellectual capital are not enough, but their deficit is moderate. In the course of development of Industry 4.0, the necessity for the measures of regulation will grow, and their deficit will increase. That's why there's a need for strategic approach to their implementation, which envisages their systemic reconsideration and supplementing. An author's algorithm is offered for this.

Home-Field Advantage or a Matter of Ambiguity Aversion? Local Bias Among German Individual Investors

ObjectivesTo assess whether associations between positive or negative weight commentary and body-related emotions, internalized weight bias, and weight worry differ by weight status among young adult males and females.MethodsParticipants were from the Nicotine Dependence in Teens study, initiated in 1999–2000. For this cross-sectional analysis, self-report data collected online in 2023 were available for 687 young adults (57% female; Mean age = 35.3 years). Sex-stratified analyses compared mean scores for eight body image-related indicators by frequent positive or negative weight commentary (yes/no) and weight status (lower weight vs. higher weight). Moderation was tested using product terms in multivariable linear regression.ResultsAmong females, 44% reported frequent positive commentary (47% lower weight; 41% higher weight) and 13% reported frequent negative commentary (10% lower weight; 16% higher weight). Positive commentary was associated with lower shame, guilt, embarrassment, and internalized weight bias, with stronger protective effects among females with higher weight. Negative commentary was associated with greater body-related distress and weight-related worry, also with stronger effects among females with higher weight. Among males, positive and negative commentary showed modest associations with body image–related indicators, and there was little evidence that weight status modified these associations.ConclusionsAssociations between weight commentary and body image-related indicators were moderated by weight status in females but not in males. For women with higher weight, positive remarks were somewhat protective, while negative remarks appeared to have disproportionately adverse effects. Findings suggest the need for weight-neutral, sensitive approaches to weight discussions in clinical and social settings.

Interactions between humans and physical features of the escape area can considerably impede collective movement of panicked crowds. The turning angle is one of the physical features that must be designed carefully as angled or circuitous egress routes such as corridors are unavoidable features of mass gathering places. Previous studies on crowd disasters have highlighted the importance of considering turning movements, particularly under panic situations. However, few qualitative and quantitative studies have addressed this phenomenon. One reason for the limited study might be the lack of empirical data to validate the predictions from mathematical models. In this work, empirical data collected from panicking ants and data from a crowd dynamic simulation model are used to describe how right-angled egress paths work ineffectively compared with straight egress paths during the collective panic egress. Empirical data with panicking ants and simulation results show that right-angled egress paths are more than 20% ineffective compared with straight paths of the same dimensions. That is, right-angled egress paths decrease the flow rate and increase the escape times significantly compared with those of straight egress paths. Results suggest that it is possible to study evacuation strategies and design solutions that can prevent crowd disasters by using empirical data collected from biological entities.

In vitro primary satellite cell growth and differentiation within litters of pigs

BackgroundTranscatheter aortic valve implantation (TAVI) for symptomatic aortic stenosis is considered a minimally invasive procedure. Body mass index (BMI) has been rarely evaluated for pulmonary complications after TAVI. This study aimed to assess the influence of BMI on pulmonary complications and other related outcomes after TAVI.MethodsThe clinical data of 109 patients who underwent TAVI in our hospital from May 2018 to April 2021 were retrospectively analyzed. Patients were divided into three groups according to BMI: low weight (BMI < 21.9 kg/m2, n = 27), middle weight (BMI 21.9–27.0 kg/m2, n = 55), and high weight (BMI > 27.0 kg/m2, n = 27); and two groups according to vascular access: through the femoral artery (TF-TAVI, n = 94) and through the transapical route (TA-TAVI, n = 15). Procedure endpoints, procedure success, and adverse outcomes were evaluated according to the Valve Academic Research Consortium (VARC)-2 definitions.ResultsHigh-weight patients had a higher proportion of older (p < 0.001) and previous percutaneous coronary interventions (p = 0.026), a higher percentage of diabetes mellitus (p = 0.026) and frailty (p = 0.032), and lower glomerular filtration rate (p = 0.024). Procedure success was similar among the three groups. The 30-day all-cause mortality of patients with low-, middle-, and high weights was 3.7% (1/27), 5.5% (3/55), and 3.7% (1/27), respectively. In the multivariable analysis, middle- and high-weight patients exhibited similar overall mortality (middle weight vs. low weight, p = 0.500; high weight vs. low weight, p = 0.738) and similar intubation time compared with low-weight patients (9.1 ± 7.3 h vs. 8.9 ± 6.0 h vs. 8.7 ± 4.2 h in high-, middle-, and low-weight patients, respectively, p = 0.872). Although high-weight patients had a lower PaO2/FiO2 ratio than low-weight patients at baseline, transitional extubation, and post extubation 12th hour (p = 0.038, 0.030, 0.043, respectively), there were no differences for post extubation 24th hour, post extubation 48th hour, and post extubation 72nd hour (p = 0.856, 0.896, 0.873, respectively). Chronic lung disease [odds ratio (OR) 8.038, p = 0.001] rather than high weight (OR 2.768, p = 0.235) or middle weight (OR 2.226, p = 0.157) affected postoperative PaO2/FiO2 after TAVI.ConclusionsWe did not find the existence of an obesity paradox after TAVI. BMI had no effect on postoperative intubation time. Patients with a higher BMI should be treated similarly without the need to deliberately extend the intubation time for TAVI.