Herding towards pygmalion: Examining the cultural dimension of market and bank based systems.

The study aims to not only detect the presence of herd behavior in the countries studied, but also to examine the effect of cultural dimensions and market/bank-based systems on the herding behavior of financial market investors. The study employs the Cross-Sectional Standard Deviation and Cross-Sectional Absolute Deviation methods to analyze daily data from public companies traded in the capital markets in Emerging Seven and Group of Seven economies. The results suggest that being a member of E7-G7, a Future Oriented (FO), and a Performance Oriented (PO) cultures are the most important factors in explaining herd behavior. Additionally, the study found that the Ridge Classifier and CatBoost Classifier algorithms arethe most superior model for estimating herd behavior periods determined by the CSSD and CASD models, respectively. The feature selection results show that the Assertiveness (A) in-group collectivism (GC) are the three most important explanatory factors of the herd behavior.

The influence of emotional intelligence and behavioural biases on mutual fund churning frequency: Evidence from India.

Behavioural finance invalidates the rationalistic assumptions of the efficient market hypothesis by proposing a realistic explanation for overreaction and underreaction. These phenomena are caused by investors making financial decisions based on their emotions without realizing them. This study attempts to establish the effect of investors' emotional intelligence on behavioural biases, namely, herding, overconfidence bias, and disposition effects, and its consequences for the churning frequency of mutual fund portfolios. This quantitative cross-sectional study was undertaken to collect data from 499 mutual fund investors using a self-administered questionnaire. We found that the disposition effect has a negative impact and that overconfidence bias positively affects the churning frequency of mutual fund portfolios. Furthermore, emotional intelligence, particularly its subconstructs, affects herding bias and overconfidence bias, ultimately impacting the churning frequency of investors. An investor with a higher level of self-motivation is likely to have a disposition effect and herding bias. Investors with disposition impact may be encouraged to reorganize their portfolio if there are any schemes that have been providing negative returns for more than two years or that are not projected to offer significant returns in the future. By understanding how they stack up on each emotional intelligence metric, investors and financial advisors may focus on avoiding the biases that could jeopardize long-term portfolio returns.

Testing and characterization of herding dogs' behaviors.

Breeding for phenotype in herding dogs (HDs) mainly relies on their performance in national field trial competitions, which has been shown to be inadequate for identifying HDs suited for real livestock farming conditions. In this study, a different field trial with a new scoring system consisting of 28 items to consider was designed to assess young HDs, the results of which culminated in a statement of adequate phenotype (AP) or non-adequate phenotype (NAP). An AP HD was defined as being able: to control the direction of a flock, to keep it grouped close to a handler when needed, to confront animals it is dealing with in a respectful manner, and able to create movement of the flock without excessive disturbance, threatening or attacking it through chasing, or uncontrolled biting. This innovative trial is composed of a pre-test (PT) and a test (T) phase. To evaluate its efficiency in detecting AP/NAP, 460 French Border Collies aged between 8 and 24 mo, underwent the trial. Its average duration (PT + T) was 3 min and 16 s (SD = 26 s). According to experts' assessments (Gold Standard), 16.5% of tested HD reached an AP score, and the Idele scoring system correctly identified 93.3% of them (sensitivity). Specificity and accuracy values were of 96.1% and 95.7%, respectively (P value < 0.0004). Recursive feature elimination identified 25 of the 118 features (categories of items) from the scoring system as significant predictors of AP/NAP. An AP HD was statistically defined as a dog who completed the PT and T phases, showed keenness, correct position in relation to the handler, and absence of prey drive. Four environmental effects significantly influenced AP/NAP: the field trial session, the owner's experience with HDs, the conditions of the HD's first contact with livestock, and the type of livestock with which the HD is accustomed to working (P-values <0.0005, <0.05, <0.05, and <0.007, respectively). Inter-evaluator agreement was substantial (0.70). The field trial proved to be a short, easily implemented, standardized, reproducible method for detecting AP/NAP. Hence, the field trial and its scoring system could provide a basis for a breeding program based on phenotype pending additional testing of HDs and genetic analyses.

Herding dogs are extremely useful in handling all types of livestock. They are selectively bred for success in herding dog competitions, but the abilities which that to success in these are not always the same as those needed on a farm. To produce efficient herding dogs, other abilities therefore needed to be tested, via new trials. So, a new field trial, with a specific scoring system, was set up and tried out using 460 young French Border Collies, with the aim of correctly assessing herding dogs that would be fully adapted to French farmers' needs. This corresponded to an "Adequate Phenotype". A herding dog with an adequate phenotype was defined as being able: to control the direction of a flock, to keep it grouped close to the handler when needed, to confront the animals it is dealing with in a respectful manner, and able to create movement of the flock without excessive disturbance, threatening or attacking through chasing or uncontrolled biting. The new protocol was found efficient in detecting such herding dogs. Moreover, it was easy to set up, short, standardized, and reproducible.

Determinants of users' unverified information sharing on social media platforms: A herding behavior perspective.

The proliferation of unverified or false information by irresponsible users can significantly amplify the spread of misinformation or fake news. Despite growing research on unverified information sharing, a comprehensive understanding of the varying influences of different factors and strategies to mitigate this issue remains under investigation. To address this research gap, this study, rooted in the theory of herd behavior, develops, and tests a model theorizing the reasons behind social media users' unverified information sharing. Data was collected from 510 respondents across six regions of China using a convenience sampling method. The collected data was analyzed using Mplus. The results from this study indicated that perceived severity, state uncertainty, and herding have a significant positive influence on unverified information sharing. These results enrich the understanding of unverified information-sharing behavior among Chinese social media users. Drawing from these results, we suggest platform administrators and policymakers mitigate herd behavior tendencies and stem the spread of misinformation by disseminating timely, accurate, and authoritative information. Since this action will reduce users' perceptions of severity and uncertainty. Social media users are also advised to stay vigilant over the implications of herd behavior and foster a more critical attitude towards information sharing.

Herding during different types of crises: The COVID-19 health crisis and Russia-Ukraine political crisis.

We analysed herding behaviour in the recent pandemic and conflict. We employed the cross-sectional dispersion of daily stock returns to estimate herding's intensity in the Saudi stock market. We conducted all analyses for the entire sample and four sub-samples. Additionally, we investigate the existence of the asymmetry in the investors' responses; whether there are differences between up and down markets and between high-volatility and low-volatility days. We found that herding did not occur in the pre-COVID-19, occurred in the during-COVID-19, disappeared in the post-COVID-19 and did not occur during the Russia-Ukraine conflict. Robustness checks confirm our finding that herding manifested in the during-COVID-19 period. Additionally, no difference exists between bearish and bullish or high-and low-volatility days, pushing aside the asymmetry in the investors' responses. This study may raise investors' awareness of their cognitive bias's influence on their decisions, improving market efficiency by increasing the rationality of investors' decisions.

A framework for performance enhancement of classifiers in detection of prostate cancer from microarray gene.

Prostate cancer is a major world health problem for men. This shows how important early detection and accurate diagnosis are for better treatment and patient outcomes. This study compares different ways to find Prostate Cancer (PCa) and label tumors as normal or abnormal, with the goal of speeding up current work in microarray gene data analysis. The study looks at how well several feature extraction methods work with three feature selection strategies: Harmonic Search (HS), Firefly Algorithm (FA), and Elephant Herding Optimization (EHO). The techniques tested are Expectation Maximization (EM), Nonlinear Regression (NLR), K-means, Principal Component Analysis (PCA), and Discrete Cosine Transform (DCT). Eight classifiers are used for the task of classification. These are Random Forest, Decision Tree, Adaboost, XGBoost, and Support Vector Machine (SVM) with linear, polynomial, and radial basis function kernels. This study looks at how well these classifiers work with and without feature selection methods. It finds that the SVM with radial basis function kernel, using DCT for feature extraction and EHO for feature selection, does the best of all of them, with an accuracy of 94.8 % and an error rate of 5.15 %.

The study on mechanical model considering optimal self-adaption in the bottleneck area.

It aims to solve the problem that the evacuation state of pedestrians depicted by the traditional social force model in a crowded multiexit scenario has a relatively large difference with the actual state, especially the 'optimal path' considered by the self-driving force is the problem of shortest path, and the multiexit evacuation mode depicted by the 'herd behavior' is the local optimum problem. Through in-depth analysis of actual evacuation data of pedestrians and causes of problem, a new crowd evacuation optimization model is established in order to effectively improve the simulation accuracy of crowd evacuation in a multi-exit environment. The model obtains the direction of motion of pedestrians using a field model, fully considers the factors such as exit distance, distribution of pedestrians and regional crowding degree, makes a global optimization for the self-driving force in the social force model using a centralized and distributed network model, and makes a local optimization for it using an elephant herding algorithm, so as to establish a new evacuation optimization method for optimal self-adaption in the bottleneck area. The performance status is compared between the improved social force model and the new model by experiments, and the key factors that affect the new model are analyzed in an in-depth manner. The results show that the new model can optimize the optimal path choice at the early stage of evacuation and improve the evacuation efficiency of pedestrians at the late stage, so as to ensure relatively even distribution of pedestrians at each exit, and also make the simulated evacuation process be more real; and the improvement in overall evacuation efficiency is greater when the number of pedestrians to be evacuated is larger. Therefore, the new model provides a method to solve the phenomenon of disorder in overall pedestrian evacuation due to excessive crowd density during the process of multi-exit evacuation.

Climate change, cultural continuity and ecological grief: Insights from the Sámi Homeland.

Arctic regions are warming significantly faster than other parts of the globe, leading to changes in snow, ice and weather conditions, ecosystems and local cultures. These changes have brought worry and concern and triggered feelings of loss among Arctic Indigenous Peoples and local communities. Recently, research has started to address emotional and social dimensions of climate change, framed through the concept of ecological grief. In this study, we examine sociocultural impacts of climate change and expressions of ecological grief among members of reindeer herding communities in the Sámi Homeland in Finland. Results indicate that ecological grief is felt in connection to major environmental concerns in the area: changes in winter weather and extreme weather events, Atlantic salmon decline and land use changes, which all have cultural and social consequences. Our results indicate that ecological grief is strongly associated with ecological losses, but also with political decisions regarding natural resource governance.

An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study.

Computer-aided diagnosis (CAD) systems play a vital role in modern research by effectively minimizing both time and costs. These systems support healthcare professionals like radiologists in their decision-making process by efficiently detecting abnormalities as well as offering accurate and dependable information. These systems heavily depend on the efficient selection of features to accurately categorize high-dimensional biological data. These features can subsequently assist in the diagnosis of related medical conditions. The task of identifying patterns in biomedical data can be quite challenging due to the presence of numerous irrelevant or redundant features. Therefore, it is crucial to propose and then utilize a feature selection (FS) process in order to eliminate these features. The primary goal of FS approaches is to improve the accuracy of classification by eliminating features that are irrelevant or less informative. The FS phase plays a critical role in attaining optimal results in machine learning (ML)-driven CAD systems. The effectiveness of ML models can be significantly enhanced by incorporating efficient features during the training phase. This empirical study presents a methodology for the classification of biomedical data using the FS technique. The proposed approach incorporates three soft computing-based optimization algorithms, namely Teaching Learning-Based Optimization (TLBO), Elephant Herding Optimization (EHO), and a proposed hybrid algorithm of these two. These algorithms were previously employed; however, their effectiveness in addressing FS issues in predicting human diseases has not been investigated. The following evaluation focuses on the categorization of benign and malignant tumours using the publicly available Wisconsin Diagnostic Breast Cancer (WDBC) benchmark dataset. The five-fold cross-validation technique is employed to mitigate the risk of over-fitting. The evaluation of the proposed approach's proficiency is determined based on several metrics, including sensitivity, specificity, precision, accuracy, area under the receiver-operating characteristic curve (AUC), and F1-score. The best value of accuracy computed through the suggested approach is 97.96%. The proposed clinical decision support system demonstrates a highly favourable classification performance outcome, making it a valuable tool for medical practitioners to utilize as a secondary opinion and reducing the overburden of expert medical practitioners.

Screen interaction behavior in companion dogs: results from a dog owner survey.

Despite availability of video content marketed for dog (Canis familiaris) entertainment, there is little information on dog behaviors when viewing content, nor describing which content is engaging. The aims of this study were to define demographics of dogs that engage with screens, owner observed behaviors, and perceived content interest. A digital survey was distributed to dog owners (03/2022-03/2023). We collected demographics, home environment, owner-rated behaviors, content interest, and interest in 4 presented videos. We compared the representation of dogs from different purebred dog groups (categorized by job/purpose by the American Kennel Club) with the estimated general purebred dog population. Most respondents (total n=1,246) lived in the USA (89%). Median age was 4 years, 54% were purebred, 51% were female. Most (86%, n=1,077) stated their dog watched screen content. Excitement behaviors were often described: 78% of dogs approached the screen, 76% vocalized. Many owners played videos for their dogs when left alone. Dogs most frequently engaged with animal content; dogs were the most popular animal. Age and visual status influenced the frequency of perceived interaction; age and breed influenced content interest. Within purebred dogs that were stated to watch content, there was a relative over-representation of "sporting" and "herding"-type breeds. A dog's age, visual status, and breed type may influence their interest in video content at home. Because many owners reported excitement in their dogs in reaction to screen content, owners may wish to determine whether video content would be suitable for use when their dogs are left alone.

Away from the herd: loneliness as a dysfunction of social alignment.

The tendency of all humans to experience loneliness at some point in their lives implies that it serves an adaptive function. Building on biological theories of herding in animals, according to which collective movement emerges from local interactions that are based on principles of attraction, repulsion and alignment, we propose an approach that synthesizes these principles with theories of loneliness in humans. We present here the 'herding model of loneliness' that extends these principles into the psychological domain. We hold that these principles serve as basic building blocks of human interactions and propose that distorted attraction and repulsion tendencies may lead to inability to align properly with others, which may be a core component in loneliness emergence and perpetuation. We describe a neural model of herding in humans and suggest that loneliness may be associated with altered interactions between the gap/error detection, reward signaling, threat and observation-execution systems. The proposed model offers a framework to predict the behavior of lonely individuals and thus may inform intervention designs for reducing loneliness intensity.

UAV Assisted Livestock Distribution Monitoring and Quantification: A Low-Cost and High-Precision Solution.

Grazing management is one of the most widely practiced land uses globally. Quantifying the spatiotemporal distribution of livestock is critical for effective management of livestock-grassland grazing ecosystem. However, to date, there are few convincing solutions for livestock dynamic monitor and key parameters quantification under actual grazing situations. In this study, we proposed a pragmatic method for quantifying the grazing density (GD) and herding proximities (HP) based on unmanned aerial vehicles (UAVs). We further tested its feasibility at three typical household pastures on the Qinghai-Tibetan Plateau, China. We found that: (1) yak herds grazing followed a rotational grazing pattern spontaneously within the pastures, (2) Dispersion Index of yak herds varied as an M-shaped curve within one day, and it was the lowest in July and August, and (3) the average distance between the yak herd and the campsites in the cold season was significantly shorter than that in the warm season. In this study, we developed a method to characterize the dynamic GD and HP of yak herds precisely and effectively. This method is ideal for studying animal behavior and determining the correlation between the distribution of pastoral livestock and resource usability, delivering critical information for the development of grassland ecosystem and the implementation of sustainable grassland management.

Stay close, but not too close: aerial image analysis reveals patterns of social distancing in seal colonies.

Many species aggregate in dense colonies. Species-specific spatial patterns provide clues about how colonies are shaped by various (a)biotic factors, including predation, temperature regulation or disease transmission. Using aerial imagery, we examined these patterns in colonies on land of two sympatric seal species: the harbour seal and grey seal. Results show that the density of grey seals on land is twice as high as that of harbour seals. Furthermore, the nearest neighbour distance (NND) of harbour seals (median = 1.06 m) is significantly larger than that of grey seals (median = 0.53 m). Avoidance at small distances (i.e. social distancing) was supported by spatial simulation: when the observed seal locations were shuffled slightly, the frequency of the smallest NNDs (0-25 cm) increased, while the most frequently observed NNDs decreased. As harbour seals are more prone to infectious diseases, we hypothesize that the larger NNDs might be a behavioural response to reduce pathogen transmission. The approach presented here can potentially be used as a practical tool to differentiate between harbour and grey seals in remote sensing applications, particularly in low to medium resolution imagery (e.g. satellite imagery), where morphological characteristics alone are insufficient to differentiate between species.

Herding behavior and government policy responses: Evidence from COVID-19 effect.

The purpose of this study is to investigate the impact of a sudden shock from the COVID-19 epidemic on the behavioral bias of investors in the stock market of Iran as a developing country. The study also examines whether the government response to the COVID-19 pandemic can reduce investor herding behavior. We have used the Cross-sectional absolute deviation (CSAD) to measure securities dispersion from market returns. The studied period includes the cross-sectional data of the top 50 companies listed on the stock exchange during 2381 working days of the market (from March 1, 2012, to March 1, 2022). Furthermore, we use the semi-parametric estimator of the quantile regression for the data on the Iranian government response during the COVID-19 epidemic taken from the Oxford COVID-19 Government Response Tracker (OxCGRT). The main findings are in order. First, results show that the COVID-19 pandemic caused the formation of herding behavior aggravated by market volatility. Second, we document that the government response stringency index is unsuccessful in reducing investor herding behavior in the Iranian stock market. Finally, given the evidence that herding behavior, as a form of behavioral distortion, can drive security prices away from equilibrium values supported by fundamentals and cause price bubbles, our findings have important implications for policymakers and investors to mitigate herding effects and mis valuations.

Assessment of thermal changes in water buffalo mobilized from the paddock and transported by short journeys.

Evaluating the welfare of buffaloes during transport is key to obtaining and commercializing high-quality meat products; however, effective assessments require recognizing several stressors that activate physiological mechanisms that can have repercussions on the health and productive performance of species. The aim of this study was to evaluate the surface temperatures of different body and head regions in this species during events prior, and posterior, to transport for short periods; that is, from paddock to loading. The second goal was to determine the level of correlation between thermal windows. This study used infrared thermography (IRT) to evaluate the surface temperature of 624 water buffaloes (Buffalypso breed) during 12 short trips (average duration = 2 h ± 20 min) by focusing on 11 regions of the body (Regio corporis), in the head regions (Regiones capitis) the face regions (Regiones faciei), Orbital region (Regio orbitalis) with special attention to structures such as the lacrimal caruncle, periocular area and lower eyelid (Regio palpebralis inferior); nasal region (Regio nasalis) with special attention to nostril thermal window; and regions of the skull (Regiones cranii) such as auricular region (Regio auricularis) with special attention to auditory canal and frontal-parietal region (Regio frontalis-parietalis) and trunk region (Truncus regionis) such as thoracic and abdominal regions, regions of the vertebral column (Columna vertebralis) with the thoracic vertebral region (Regio vertebralis thoracis) and lumbar region (Regio lumbalis); and regions of the pelvis limb (Regiones membri pelvini). Recordings were made during seven phases: paddock (P1), herding (P2), corral (P3), chute handling (P4), shipping (P5), pre- (P6), and post-transport (P7). A total of 48,048 readings were obtained from 11 thermal windows. The results showed that the surface temperatures of the windows increased by as much as 5°C during P2, P3, P5, P6, and P7 compared to P1 and P4 (p < 0.0001). Differences of at least 1°C were also observed between thermal windows in the craniofacial, lateral corporal, and peripheral zones (p < 0.0001). Finally, a strong positive correlation (r = 0.9, p < 0.0001) was found between the thermal windows. These findings lead to the conclusion that the surface temperature of the craniofacial and corporal regions of buffaloes transported for short periods varied in relation to the phase of mobilization (from paddock to post-transport), likely as a response to stressful factors, since herding and loading increased the thermal values in each window. The second conclusion is that there are strong positive correlations between central and peripheral thermal windows.

Intelligent design: stablecoins (in)stability and collateral during market turbulence.

How does stablecoin design affect market behavior during turbulent periods? Stablecoins attempt to maintain a "stable" peg to the US dollar, but do so with widely varying structural designs. The spectacular collapse of the TerraUSD (UST) stablecoin and the linked Terra (LUNA) token in May 2022 precipitated a series of reactions across major stablecoins, with some experiencing a fall in value and others gaining value. Using a Baba, Engle, Kraft and Kroner (1990) (BEKK) model, we examine the reaction to this exogenous shock and find significant contagion effects from the UST collapse, likely partially due to herding behavior among traders. We test the varying reactions among stablecoins and find that stablecoin design differences affect the direction, magnitude, and duration of the response to shocks. We discuss the implications for stablecoin developers, exchanges, traders, and regulators.

Mechanisms of group-hunting in vertebrates.

Group-hunting is ubiquitous across animal taxa and has received considerable attention in the context of its functions. By contrast much less is known about the mechanisms by which grouping predators hunt their prey. This is primarily due to a lack of experimental manipulation alongside logistical difficulties quantifying the behaviour of multiple predators at high spatiotemporal resolution as they search, select, and capture wild prey. However, the use of new remote-sensing technologies and a broadening of the focal taxa beyond apex predators provides researchers with a great opportunity to discern accurately how multiple predators hunt together and not just whether doing so provides hunters with a per capita benefit. We incorporate many ideas from collective behaviour and locomotion throughout this review to make testable predictions for future researchers and pay particular attention to the role that computer simulation can play in a feedback loop with empirical data collection. Our review of the literature showed that the breadth of predator:prey size ratios among the taxa that can be considered to hunt as a group is very large (<100 to >102 ). We therefore synthesised the literature with respect to these predator:prey ratios and found that they promoted different hunting mechanisms. Additionally, these different hunting mechanisms are also related to particular stages of the hunt (search, selection, capture) and thus we structure our review in accordance with these two factors (stage of the hunt and predator:prey size ratio). We identify several novel group-hunting mechanisms which are largely untested, particularly under field conditions, and we also highlight a range of potential study organisms that are amenable to experimental testing of these mechanisms in connection with tracking technology. We believe that a combination of new hypotheses, study systems and methodological approaches should help push the field of group-hunting in new directions.

Rising Temperature and the Spatiotemporal Patterns of Foot and Mouth Disease of Livestock in Mongolia.

BACKGROUND: Climate change is projected to have cascading effects on the environment and thereby trigger effects on animal health, human health and wellbeing. Foot and Mouth Disease (FMD) is a highly contagious disease affecting cloven-hoofed animals that has had dramatic socioeconomic impacts on nomadic pastoralist communities who are increasingly vulnerable to environmental degradation and climate change. FMD outbreaks are occurring more frequently in Mongolia and the effects of climatic change, such as more droughts, increasing temperature, and changing snow fall patterns, are also becoming more obvious. METHODS: In this study we use spatiotemporal mapping and regression analyses to explore trends and associations between climate variables and FMD outbreaks across Mongolia from 2010 to 2020. RESULTS: We found that the number of days with temperature above 80 °F in a province in a given year was associated with having a FMD outbreak. None of the other climate variables were associated with FMD outbreaks at the provincial level. CONCLUSION: Given the projected increase in warming temperatures across Mongolia, there is a need to further explore the association between rising temperatures and FMD outbreaks to prevent FMD from having cascading impacts on nomadic herder communities. Mitigating approaches for herders to use to reduce the impact of rising number of hot days on FMD spread needs to be devised and governments in countries with nomadic herding communities should enact climate adaptation policies for them.

Improvised herding: Mapping biobehavioral mechanisms that underlie group efficacy during improvised social interaction.

Improvisation is a natural occurring phenomenon that is central to social interaction. Yet, improvisation is an understudied area in group processes and intergroup relations. Here we build on theory and research about human herding to study the contributions of improvisation on group efficacy and its biobehavioral underpinnings. We employed a novel multimodal approach and integrative method when observing face-to-face interactions-51 triads (total N = 153) drummed together in spontaneous-free improvisations as a group, while their electrodermal activity was monitored simultaneously with their second-by-second rhythmic coordination on a shared electronic drum machine. Our results show that three hypothesized factors of human herding-physiological synchrony, behavioral coordination, and emotional contagion-predict a sense of group efficacy in its group members. These findings are some of the first to show herding at three levels (physiological, behavioral, and mental) in a single study and lay a basis for understanding the role of improvisation in social interaction.