Analysis Of Individual Behavior Research Articles

Long-term tracking of social structure in groups of rats

Rodents serve as an important model for examining both individual and collective behavior. Dominance within rodent social structures can determine access to critical resources, such as food and mating opportunities. Yet, many aspects of the intricate interplay between individual behaviors and the resulting group social hierarchy, especially its evolution over time, remain unexplored. In this study, we utilized an automated tracking system that continuously monitored groups of male rats for over 250 days to enable an in-depth analysis of individual behavior and the overarching group dynamic. We describe the evolution of social structures within a group and additionally investigate how past behaviors influence the emergence of new social hierarchies when group composition and experimental area changes. Notably, we find that conventional individual and pairwise tests exhibit a weak correlation with group behavior, highlighting their limited accuracy in predicting behavioral outcomes in a collective context. These results emphasize the context-dependence of social behavior as an emergent property of interactions within a group and highlight the need to measure and quantify social behavior in more naturalistic environments.

An approach for two-dimensional convolutional neural networks for hourly passenger boarding demand prediction based on uneven smart-card data

An invaluable resource for understanding passenger boarding patterns and forecasting future travel demand is the tap-on smart-card data. Positive instances, on the other hand—boarding at a given bus stop at a certain time—are less common than negative instances when looking at the smart-card data (or instances) by boarding stops and by time of day. Machine learning algorithms that are used to estimate hourly boarding numbers at a certain location have been shown to be much less accurate when the data is imbalanced. Before using the smart-card data to forecast bus boarding demand, this research tackles the problem of data imbalance in the data. To create fake traveling instances to add into a synthetic training dataset containing more evenly distributed traveling and non-traveling examples, we suggest using deep generative adversarial networks (Deep-GAN). Next, a deep neural network, or DNN, is trained on the synthetic dataset to predict which instances from a given stop in a certain time frame will travel and which ones won't. According to the findings, resolving the data imbalance problem may greatly enhance the predictive model's functionality and make it more accurate in predicting ridership profiles. The suggested strategy may create a synthetic training set with a better similarity so diversity and, therefore, a stronger prediction capability, according to a comparison of the Deep-GAN's performance with other conventional resampling techniques. The study emphasizes the importance of the issue and offers helpful recommendations for enhancing the quality of the data and model performance for individual travel behavior analysis and travel behavior prediction.

Automatic Face Detection of Farm Images Based on an Enhanced Lightweight Deep Learning Model

In the realm of precise management, artificial intelligence has garnered significant attention and adoption, particularly within the domain of smart agriculture. In modern animal husbandry, animal face detection is conducive to individual identification, expression detection and behavior analysis of animals, and this technological advancement holds immense importance in fostering the advancement of intelligent farming practices. In order to solve the challenge of face detection caused by similar appearance features (color, texture, etc.) and no obvious feature differences between the solid-color goats and sheep in the natural environment, this research introduces a novel approach for face detection by combining the capabilities of YOLOv5 and a convolutional block attention module (CBAM). First, datasets of goats and sheep with different angles, scales and densities were constructed. Second, the basic framework of YOLOv5 was used for object detection. To address the obstacle posed by the limited presence of distinguishing features on the faces of goats and sheep, this study aims to overcome the challenge of extracting informative facial characteristics. The CBAM block was introduced to construct the YOLOv5-CBAM model to improve the feature extraction ability. Finally, 2412 images were selected and divided into training set and verification set according to 8:1. The experimental results of this dataset show that the proposed YOLOv5-CBAM model yielded remarkable results with a precision rate of 0.970, a recall rate of 0.890, a mAP@0.5 score of 0.935, an frames per second (FPS) of 140.845, and a model size of 14.680[Formula: see text]MB. In comparison to other approaches such as Faster R-CNN, SSD, YOLOv3, and YOLOv5, the proposed model demonstrated superior performance in some aspects. In addition, it excelled in both lightweight design and overall effectiveness, and it is well-suited for real-time detection of animal faces in real-world farming settings, ensuring efficient identification and monitoring of animals within practical agricultural environments.

Property-level adaptation to pluvial flooding: An analysis of individual behaviour and risk communication material

Integrated risk management requires all stakeholders to work together proactively. Residents of floodplains can participate by implementing property-level adaptive measures. Risk communication materials can motivate those households to do so. Research on these materials is limited. Therefore, we systematically assessed freely available German risk communication materials in terms of their recommendations and how their content aligns with behavioural theories. We compare these results with data from surveyed households affected by urban flooding (N = 1,352) on their attitudes towards flood adaptation and the adaptation measures implemented.209 risk communication materials were reviewed. Adaptation options (mostly evasion or resistance strategies) were communicated in 93%, the hazard itself in 78%, the local hazard situation in 48%, and responsibilities in risk management in 54% of the risk communication materials. These aspects were rarely broken down for the reader by, for example, presenting the damage that can be expected or by presenting measures with the expected costs or their response efficacy. However, these details commonly increase the adaptive behaviour of residents according to commonly used behavioural theories.Survey data indicated that households feel able (82%) and responsible (41%) for implementing adaptive measures. However, many households indicated that measures are not effective (41%), too expensive (34%), and that there are not enough support programs (87%). We recommend that future risk communication materials focus more on communicating the efficacy and costs of adaptive measures and highlight funding opportunities. Our mixed method approach highlighted mismatches in information needed and provided.

A clustering-based differential privacy protection algorithm for weighted social networks.

Weighted social networks play a crucial role in various fields such as social media analysis, healthcare, and recommendation systems. However, with their widespread application and privacy issues have become increasingly prominent, including concerns related to sensitive information leakage, individual behavior analysis, and privacy attacks. Despite traditional differential privacy protection algorithms being able to protect privacy for edges with sensitive information, directly adding noise to edge weights may result in excessive noise, thereby reducing data utility. To address these challenges, we proposed a privacy protection algorithm for weighted social networks called DCDP. The algorithm combines the density clustering algorithm OPTICS to partition the weighted social network into multiple sub-clusters and adds noise to different sub-clusters at random sampling frequencies. To enhance the balance of privacy protection, we designed a novel privacy parameter calculation method. Through theoretical derivation and experimentation, the DCDP algorithm demonstrated its capability to achieve differential privacy protection for weighted social networks while effectively maintaining data accuracy. Compared to traditional privacy protection algorithms, the DCDP algorithm reduced the average relative error by approximately 20% and increases the proportion of unchanged shortest paths by about 10%. In summary, we aimed to address privacy issues in weighted social networks, providing an effective method to protect user-sensitive information while ensuring the accuracy and utility of data analysis.

The influence of the media on shaping the moral image of students

Relevance. The issue of student moral values is a significant problem for both the discipline of sociology and society. The existence of values supports social cohesion, ensures appropriate behavior, and creates a common ethos through media in today's world. The shaping of youth morality through the media is linked to the development of culture in different ways while forming a fundamental part of the set of principles, values, beliefs, and rules of society.Purpose. The research aims to analyze the factors of the impact of media tools on the behavior of students in the society of the Republic of Kazakhstan.Methodology. To achieve the research objectives, the following methods were used: descriptive, interview, and comparative.Results. The results of the research determined that the relationship between media environment and youth audience behavior goes beyond the analysis and intervention of individual behavior, conditioning an inclusive and interdisciplinary framework for explaining and predicting social effects. The media influence the educational phenomenon and the socialization process because they are part of the processes of group interaction and the identity construction of young people. Television, social media, and the internet have an important influence on student behavior, but questions remain as to the conditions under which this influence is manifested. The ability of the media to influence the cultural practices and behavior of young students is enhanced by the structure and control of the information sources that produce media content. This is reinforced in postmodern contexts using state and corporate communication forms that disseminate information. Information sources act as systems with expedient cultural practices, where the roles and actions of participants are fixed and predictable.Conclusions. The result is an interconnected system of economic, social, and interpersonal circumstances that can influence student behavior and perspectives. The practical significance lies in the use of the research findings by academics, as well as specialists in journalism, media, and pedagogy.

Analysis of Individual Behavior Through a Psychoanalytical Approach: A Literature Review

This literature review explores the analysis of individual behavior using a psychoanalytical approach, focusing on critical writings that elaborate on the concepts proposed by Sigmund Freud and related thinkers. Through synthesis and analysis of various articles, this research aims to provide an in-depth understanding of how the psychoanalytical approach is employed to delve into the complexity of individual behavior. The findings of this literature review reveal the evolution of psychoanalytical concepts from Freudian theory to contemporary contributions in psychology. There is a detailed exploration of how the id, Ego, and superego play crucial roles in shaping individual behavior. Additionally, the study highlights the use of the psychoanalytical approach in addressing aspects such as internal conflicts, defense mechanisms, and the impact of past experiences. In the context of the literature review, it is evident that the analysis of individual behavior through the psychoanalytical approach provides profound insights into psychological aspects and significantly contributes to understanding the variations in human behavior. This review presents a comprehensive overview of the development of psychoanalytical theory and how this approach continues to be relevant in understanding individual behavior. The practical implications of this review are also discussed, emphasizing its relevance in clinical settings, education, and personal development

110 Evaluation of Computer Vision to Analyze Beef Cattle Feeding Behavior

Abstract Leveraging computer vision (CV) methods to study pen-fed cattle feeding behavior presents several advantages, including monitoring animal health and identifying feed efficient animals. This research employed a region-based convolutional neural network (RCNN) combined with the common objects in context (COCO) dataset for automatic livestock recognition using CV techniques in experimental feedlot pens. Thirty Angus-influenced steers were allocated in one pen with four automated feed intake systems (AFIS; Vytelle SENSE). CV data were recorded during daylight hours using a webcam (Microsoft LifeCam Cinema) with a resolution of 1280x720 pixels at ten frames per second connected to a video surveillance camera software (Contaware, Switzerland). The CV dataset obtained by the cameras, Figure 1, was benchmarked with feeding behavior data obtained from observed annotations (OA) and AFIS. The CV model utilized was a Mask-RCNN with pre-trained weights on the COCO dataset. The Mask RCNN algorithm can identify and locate multiple objects, objects of different scales, and overlapping objects within an image. The COCO dataset is a large and diverse set of annotated images that can be used for training and evaluating object detection models. The fully trained Mask-RCNN model takes each video frame as input and outputs the pixel segmentation of each object detected. For the CV analyses, each animal bounding box was associated with a score threshold, and each pixel within was assigned a probability threshold; both ranging from 0 to 100. The higher the score and probability thresholds are, the fewer the boxes and pixels will be marked as the animal boxing shape, resulting in less feeding detection from the CV system. In contrast, a low score and probability thresholds produce many predictions, including false detections, which might be misleading. Therefore, a dataset analysis was conducted to optimize the best threshold combination. We evaluated the accuracy and precision of the CV model compared with OA and AFIS by analyzing a dataset section of feeding events of one feed bunk using a 0.5 score and 0.5 probability thresholds. We obtained the values for the true positive (TP), false positive (FP), true negative (TN), and false negative (FN) to calculate the accuracy = [TP+TN]/[TP+TN+FP+FN], and precision = [TP]/[TP+FP], Table 1. As a result, the CV model achieved 95.39% and 99.82% accuracy and 93.60% and 99.90% precision when compared with OA and AFIS, respectively. Our findings showcase the promising capabilities of employing the Mask-RCNN algorithm and COCO datasets for detecting beef cattle feeding behavior in feedlot pens. This approach holds significant value for the cattle industry by enabling precise monitoring and analysis of individual animal behavior within feedlot pens, facilitating early detection of behavioral abnormalities.

Perceived Effectiveness of COVID-19 Preventive Practices and Behavioral Intention: Survey of a Representative Adult Sample in the United States.

Using existing models of behavioral health promotion, specifically the Extended Parallel Process Model, previous research has identified factors that may impact engagement in preventive health behaviors during the COVID-19 pandemic such as perceived threat, perceived susceptibility to the threat, perceived severity, and perceived efficacy. This study aims to examine the role of perceived effectiveness of COVID-19 preventive behaviors, perceived susceptibility, perceived threat, and perceived severity of COVID-19 in participants' intentions to engage in Centers for Disease Control (CDC)-recommended individual health behaviors in the first year of the pandemic. In October 2020, a representative sample of 506 US adults completed a web-based survey through the RAND American Life Panel. The study primarily found that participants who perceived that CDC-recommended health practices were effective had stronger intentions to engage in those practices. The second strongest correlate was participants' perceived severity of COVID-19 across the United States. Perceived effectiveness of recommended practices accounted for the largest variance in behavioral intention. However, analysis of individual behaviors indicated a mismatch in the behaviors perceived to be the most effective (avoiding sick people and mask-wearing) and those participants indicated intention to engage in (throwing away used tissues, avoiding sick people, and coughing into their elbows) in the next 30 days. The authors recommend tailoring public health messaging to address the perceived threat of COVID-19 and self-efficacy. Thus, health promotion efforts should emphasize the effectiveness of CDC-recommended practices while highlighting the pandemic's severity. Additionally, rebuilding trust in public health messaging and messengers is necessary to increase perceived self-efficacy. As the COVID-19 pandemic continues, health messaging must continue to promote and build trust in CDC-recommended health practices and educate regarding the efficacy of vaccination and other preventive behaviors.

SynthEco - A multi-layered digital ecosystem for analysing complex human behaviour in context

Introduction & BackgroundHuman behaviour is multi-faceted and complex, with different dimensions interacting and impacting each other and individuals operating in an environmental context. In order to understand this behaviour better, the combination of data from different sources is useful to uncover some of those interactions and complexities. We present a multi-layered digital ecosystem based on a data platform providing statistically representative synthetic population derived from census data at different geo-spatial granularity, which we call SynthEco. This platform is enriched with individual data stemming from cohorts and cross-sectional surveys and geo-scanning of different layers of socio-environmental actors and conditions to create a complex digital ecosystem.
 Objectives & ApproachThe objective of SynthEco is to allow for the analysis of behaviour, as well as health and wellbeing outcomes, through the integration of cohort and cross-sectional data into a geospatially anchored synthetic population embedded into environmental data which is forming the backdrop. We demonstrate the use of this platform on the example of Montreal, Canada. The synthetic population is first generated from census data using iterative proportional fitting, which allows for the creation of a population data set that is artificial yet statistically representative for a given geospatial granularity, such as a city. Each individual household is assigned a geospatial location, which allows for the consideration of their surrounding environment including enterprises or institutions such as schools, hospitals and the local food environment. Through fuzzy matching and statistical extrapolation, different cohort and cross-sectional survey data are then merged to individual records, in order to describe them in more detail. This includes health, as well as financial wellbeing or social environment descriptors.
 Relevance to Digital FootprintsThere are two important points made through the presented work in relation to Digital Footprints data: the first is the technical approach to merging multiple datasets describing different dimensions of interacting human characteristics and behaviour by anchoring them into a synthetic population through fuzzy record matching. The second is the consideration of a spatial dimension when describing human behaviour. This is especially important when describing behaviour within local environments, such as the interaction with local food outlets.
 ResultsRecent work in this context includes an analysis of the food environment in Montreal, Canada. It introduces a way of utilising the synthetic population to predict the healthfulness of their local environment in terms of healthy food outlets, as well as providing a platform for the analysis of food environment surveillance and intervention simulations. For this purpose, the healthfulness of different census tract regions in Montreal is calculated to identify food deserts, food swamps, as well as healthy areas as defined through the Modified Retail Food Environment Index. We test different machine learning approaches to then predict these healthfulness scores using census variables from the synthetic population in their respective census tract, achieving accuracy scores of around 0.53 to 0.60. This demonstrates that census data has some limited predictive power in explaining the healthiness of the local food environment, which could be especially relevant for situations in which no information on the retailers is available to local policy makers. Future work can extend this approach to also include further data describing the population, stemming from the integrated cohorts and survey data, which could improve the prediction accuracy or help in identifying areas of concern.
 Conclusions & ImplicationsThe presented SynthEco platform views individuals as agents nested within modular systems of systems, trying to capture both internal systems and processes as well as environmental ones within which individuals are operating. The platform thus enables the application of computational systems modelling for the analysis of individual human behaviour in contexts. As demonstrated through the example of using SynthEco in the context of healthier food environments, the approach is especially relevant to practitioners and policy makers interested in local intervention strategies and identifying areas for targeted policy related to different dimensions of health and wellbeing.

The relationship between social life and emotions. Adam Ferguson and sociology.

Adam Ferguson has a leading position among those who have developed a sociological interpretation of modernity that dismiss metaphysics without following the echoes of rationalism. Ferguson outlines a vision of social life that correlates the analysis of individual behavior to the study of social context and institutions. Consistently with this approach, the Scottish scholar emphasizes the multidimensionality of human beings without forgetting the non-rational features of social behavior. This essay aims to discuss Ferguson's thought with a special attention to the importance of the emotions in social life, so as to enhance the contribution of classical sociology to the analysis of the emotionality. Ferguson, in fact, argues that emotions have a leading role in shaping the behaviors and values of individuals. Developed in the context of Scottish Enlightenment, Ferguson's sociology shows how the study of modern society can be reconciled with a reasonable as well as emotional approach to social life.

Deep Learning-Based Cow Tail Detection and Tracking for Precision Livestock Farming

Cow tail detection and tracking in videos provide valuable information for individual identification, calving process, behavior analysis, and body condition monitoring. Although deep learning-based detection methods have demonstrated good performance, many of them have high complexity and still require an improvement in video object detection by reducing the computation time and false positives. To fully exploit the interframe information and achieve a real-time online detection, the optimized cow tail detection and tracking method is proposed based on an improved single shot multibox detector (SSD) and Kalman filter. Here, our improved SSD-integrated DenseNet and Inception-v4 reduce detection information loss and network parameters. Then, an improved window function-based Kalman filter and Hungarian are adopted to remove error detections and enhance the cow tail tracking accuracy. Experiments on our acquired rear-view videos show that the proposed approach achieved fast tail detection with an accuracy of 96.97%, a speed of 96 fps, a smaller model size of 25 MB, and higher position accuracy with an average 6.45 pixels deviation. The proposed approach outperformed the region-based R-CNN models and other tracking methods (e.g., particle filter), which provides a new solution to automatic cow detection and tracking in smart livestock farming.

Differentiated, rather than shared, strategies for time-coordinated action in social and non-social domains in autistic individuals

Autism spectrum disorder (ASD) is a neurodevelopmental condition with a highly heterogeneous adult phenotype that includes social and non-social behavioral characteristics. The link between the characteristics assignable to the different domains remains unresolved. One possibility is that social and non-social behaviors in autism are modulated by a common underlying deficit. However, here we report evidence supporting an alternative concept that is individual-centered rather than deficit-centered. Individuals are assumed to have a distinctive style in the strategies they adopt to perform social and non-social tasks with these styles presumably being structured differently between autistic individuals and typically-developed (TD) individuals. We tested this hypothesis for the execution of time-coordinated (synchronized) actions. Participants performed (i) a social task that required synchronized gaze and pointing actions to interact with another person, and (ii) a non-social task that required finger-tapping actions synchronized to periodic stimuli at different time-scales and sensory modalities. In both tasks, synchronization behavior differed between ASD and TD groups. However, a principal component analysis of individual behaviors across tasks revealed associations between social and non-social features for the TD persons but such cross–domain associations were strikingly absent for autistic individuals. The highly differentiated strategies between domains in ASD are inconsistent with a general synchronization deficit and instead highlight the individualized developmental heterogeneity in the acquisition of domain-specific behaviors. We propose a cognitive model to help disentangle individual-centered from deficit-centered effects in other domains. Our findings reinforce the importance to identify individually differentiated phenotypes to personalize autism therapies.

Analysis of Individual Behaviour based on Status Quo Bias

Status quo bias describes a behavioural tendency which occurs disproportionately often when individuals make their decisions, individuals under this influence will prefer to select a previously chosen alternative or an option which maintains the status quo. This paper discusses the mechanism and the factors influencing the status quo bias. We found that the mechanism could possibly be interpreted by the three psychological principles: loss aversion, regret theory, and anchoring effect. Then this paper discusses factors that may influence the status quo bias which include emotions and satisfaction of the decision-maker as well as the number of alternatives. The results show that the status quo bias will occur only when the individual is satisfied with the current situation, and they will tend to choose the status quo option when facing more choices. This research could be used to better analyse individuals and organisations when they make decisions in wider areas.

Wizard: Unsupervised goats tracking algorithm

Computer vision is an interesting tool for animal behavior monitoring, mainly because it limits animal handling and it can be used to record various traits using only one sensor. From previous studies, this technic has shown to be suitable for various species and behavior. However it remains challenging to collect individual information, i.e. not only to detect animals and behavior on the video frames, but also to identify them. Animal identification is a prerequisite to gather individual information in order to characterize individuals and compare them. A common solution to this problem, known as multiple objects tracking, consists in detecting the animals on each video frame, and then associate detections to a unique animal ID. Association of detections between two consecutive frames are generally made to maintain coherence of the detection locations and appearances. To extract appearance information, a common solution is to use a convolutional neural network (CNN), trained on a large dataset before running the tracking algorithm. For farmed animals, designing such network is challenging as far as large training dataset are still lacking. In this article, we proposed an innovative solution, where the CNN used to extract appearance information is parameterized using offline unsupervised training. The algorithm, named Wizard, was evaluated for the purpose of goats monitoring in outdoor conditions. 17 annotated videos were used, for a total of 4H30, with various number of animals on the video (from 3 to 8) and different level of color differences between animals. First, the ability of the algorithm to track the detected animals was evaluated. When animals were detected, the algorithm found the correct animal ID in 94.82% of the frames. When tracking and detection were evaluated together, we found that Wizard found the correct animal ID in 86.18% of the video length. In situations where the animal detection rate could be high, Wizard seems to be a suitable solution for individual behavior analysis experiments based on computer vision.

Application of Deep Learning-Based Object Detection Techniques in Fish Aquaculture: A Review

Automated monitoring and analysis of fish’s growth status and behaviors can help scientific aquaculture management and reduce severe losses due to diseases or overfeeding. With developments in machine vision and deep learning (DL) techniques, DL-based object detection techniques have been extensively applied in aquaculture with the advantage of simultaneously classifying and localizing fish of interest in images. This study reviews the relevant research status of DL-based object detection techniques in fish counting, body length measurement, and individual behavior analysis in aquaculture. The research status is summarized from two aspects: image and video analysis. Moreover, the relevant technical details of DL-based object detection techniques applied to aquaculture are also summarized, including the dataset, image preprocessing methods, typical DL-based object detection algorithms, and evaluation metrics. Finally, the challenges and potential trends of DL-based object detection techniques in aquaculture are concluded and discussed. The review shows that generic DL-based object detection architectures have played important roles in aquaculture.

Increased duration of laser environmental enrichment increased broiler physical activity and pen-wide movement without altering tibia measurements

Inactivity and leg disorders negatively impact broiler welfare. Enrichment designed to increase barn complexity may encourage physical exercise. The study aim was to implement a second-generation laser enrichment device, previously shown to increase broiler activity, for extended periods of time and to measure behavior and tibia quality. A total of 1,360 Ross 708 broilers in 40 pens of 34 were assigned to laser enrichment or control (no laser enrichment) for 49 d. Seventy focal birds were randomly selected on d 0 for individual behavior analysis. Laser-enriched birds were exposed to 6-min laser periods 4 times daily. A 3-min novel object test was performed on all pens and tonic immobility was induced on 1 bird/pen on wk 1 and 6. Focal bird time budget and walking distance and pen-wide laser-following behavior and movement were obtained during laser periods d 0 to 8 and 1 d/wk through wk 7. Right tibias were collected from focal birds on d 49 for dual energy x-ray absorptiometry and bone breaking analysis. Time spent active during laser periods was increased on d 3, 6, and 8 and wk 2 to 3 in laser-enriched vs. control focal birds (P=0.04). Time at the feeder was increased in laser-enriched focal birds on d 0, 3 to 4, and 8 and wk 2 and 4 (P < 0.01). Distance walked during laser periods was increased in laser-enriched focal birds on d 1, 3 to 5, 8, and wk 2 compared to the control (P < 0.01). Pen-wide movement was increased on d 0, 2, 4 to 8, and wk 1 to 5 and 7 in laser-enriched vs. control birds (P < 0.01). More laser-enriched broilers were within 25 cm of the novel object at 1 min 30 s than the control (P=0.03), and latency to approach the novel object was reduced in both treatments at wk 6 compared to wk 1 (P < 0.01). Tonic immobility duration was increased by 123 s on wk 6 compared to wk 1, regardless of treatment (P < 0.01). Daily laser enrichment for extended periods increased bird activity without inducing fearfulness or altering tibia measures.

Voting behavior in one-shot and iterative multiple referenda

We consider a set of voters making a collective decision via simultaneous vote on two binary issues. Voters’ preferences are captured by payoffs assigned to combinations of outcomes for each issue and they can be nonseparable: a voter’s preference over an issue might be dependent on the other issue. When the collective decision in this context is reached by voting on both issues at the same time, multiple election paradoxes may arise, as studied extensively in the theoretical literature. In this paper we pursue an experimental approach and investigate the impact of iterative voting, in which groups deliberate by repeating the voting process until a final outcome is reached. Our results from experiments run in the lab show that voters tend to have an optimistic rather than a pessimistic behaviour when casting a vote on a non-separable issue and that iterated voting may in fact improve the social outcome. We provide the first comprehensive empirical analysis of individual and collective behavior in the multiple referendum setting.

A general equilibrium analysis of individual choice behavior on alternative fuel vehicles

This study empirically analyzed the effect of the distribution of alternative fuel vehicles (AFVs) that are expected to reduce CO2 emissions and improve the national economy. The effect of individual vehicle choices, determined using a discrete choice (DC) model, on the overall economy was quantified using a computable general equilibrium (CGE) model. After integrating the two models, the DC model captured more elastic changes in the price-attribute levels by endogenously reflecting the results of the CGE model. Furthermore, the CGE model reflected the technical specification in the DC model to identify the effect of AFVs proliferation in detail. The results show that replacing ICEVs with AFVs decreased CO2 emissions in the transportation sector, but emissions from other increased production in other industries offset this reduction. AFVs are set to be mainstream in the market by 2050 and, with improved technological performance and infrastructure, fuel economic growth. Expecting that increased AFV demand will reduce CO2 emissions without the support of renewable energy generation and electrolysis of hydrogen production may be too optimistic.

Analysis of Competencies, Individual Behavior and Trainee Satisfaction in Human Reseource Development Center Regional Bukittinggi

This study aims to analyze the competence ofparticipants in theera of the covid-19 pandemic at the Bukittinggi Regional Human Resources Development Center (PPSDM Pusat Pengembangan Sumber Daya Manusia Regional Bukittinggi), analyze the individual behavior of trainees in the era of the covid-19 pandemic at the Bukittinggi Regional PPSDM, analyze the satisfaction of trainees in the era of the covid-19 pandemic at the Bukittinggi Regional PPSDM. This type of research is quantitative with descriptive methods and is carried out at the Bukittinggi Regional PPSDM. The data collection technique spread the questionnaire with a population of 120 trainees and validation tests using softwere Statistical Product and Service Solution (SPSS) version 24. The results of this study illustrate 1) Kompetension got the highest mean score of 4.675 and TCR of 93.5% is already very good. 2) Individual behavior, getting the highest mean value of 4.325 and TCR of 86.5% is good. 3) The e-learning method is a statement with the lowest value, namely with a mean value of 3.26 and TCR of 65.167% knowledge which is still low. 4) Customer satisfaction is a statement with the lowest value, namely with a mean value of 3.26 and TCR of 65.167% knowledge which is still low. 4) Customer satisfaction is a statement with the lowest value of 3.26 and TCR of 65.167% knowledge which is still low. 4) Customer satisfaction is a statement with the lowest value of 3.26 and TCR of 65.167% knowledge which is still low. got the highest score of the mean of 4.65 and the TCR of 93% with a good category. It can be concluded that of the 4 variables, 3 of them with a high category are good while the low category is in the knowledge variable of e-learning learning methods.