A Multiplayer Virtual Intelligent System Based on Distributed Virtual Reality

Distributed Virtual Reality (DVR) is a combination of network and virtual reality technology, it could facilitate to construct a uniformly shared Distributed Virtual Environment (DVE) by using network to connect geographically distributed multiplayers. This paper concentrates on the theoretical research and practical development about Multiplayer Virtual Intelligent System (MVIS), and the main contribution could be summarized as two points. (1) Based on the DVR technology, this paper presented some theoretical research on MVIS, including the classification of virtual entities, communication pattern of entities, and the behavioral consistency research. Furthermore, a Multiplayer Earliest Deadline First (MEDF) program was proposed in order to guarantee the consistency of entities. (2) A prototype algorithm experiment system, called Multiplayer Graph-algorithm Intelligent System (MGIS), was designed. MGIS not onlyefficiently solves many problems in traditional computer algorithm teaching, such as high-abstraction, difficulty to understand, and lack of interaction mechanism; but also extends the application of DVR to cultural tourism, because MGIS is developed on the 3D scene of Lushan Mountain, which is one of the notable tourist attractions in China, and was included in the UNESCO World Heritage list in 1996. What i’s more, MGIS illustrates the ability of expression, applicability and generality of the theoretical research about MVIS.

The effects of induced emotions on environmental preferences and behavior: An experimental study

Communication policies employed by policymakers and non-governmental organizations (NGOs) often appeal to the emotions to persuade people to adopt virtuous behavior. The aim of this paper is to study the impact of induced emotions on pro-environmental behavior (PEB). We design a three-stage laboratory experiment. In the first stage, we determine the level of the subjects’ environmental awareness. In the second stage, subjects read scripts that place them in realistic hypothetical scenarios designed to induce specific emotions. We implement a 2 x 2 in-between design by varying both the valence and social dimension of the four emotional states induced: happiness, sadness, pride and shame. In the third stage, subjects play a modified dictator game in which the recipient is an environmental non-governmental organization (ENGO). We show that the emotional states of subjects can influence PEB. In particular, negative emotions significantly reduce the average individual amount of donations made to ENGOs. We also find that the precise impact of the emotional states is more complex and appears to be dependent on individuals’ characteristics and awareness for environmental issues. For instance, in positive emotional states, men donate significantly less than women. In addition, a high level of environmental awareness increases donations in subjects experiencing shame and decreases their likelihood to donate when feeling pride. Also, we observe behavioral consistency for negative emotions and rather compensatory behavior for positive emotions.

Mathematical modeling of risk-taking in bipolar disorder: Reductions in behavioral consistency and substance use history-specific alterations to loss aversion

Bipolar disorder (BD) is associated with excessive pleasure-seeking risk-taking behaviors that often characterize its clinical presentation. However, the mechanisms of risk-taking behavior are not well-understood in BD. Recent data suggest prior substance use disorder (SUD) in BD may represent certain trait-level vulnerabilities for risky behavior. This study examined the mechanisms of risk-taking and the role of SUD in BD via mathematical modeling of behavior on the Balloon Analogue Risk Task (BART). Three groups—18 euthymic BD with prior SUD (BD+), 15 euthymic BD without prior SUD (BD-), and 33 healthy comparisons (HC)—completed the BART. We modeled behavior using 4 competing hierarchal Bayesian models, and model comparison results favored the Exponential-Weight Mean-Variance (EWMV) model, which encompasses and delineates five cognitive components of risk-taking: prior belief, learning rate, risk preference, loss aversion, and behavioral consistency. Both BD groups, regardless of SUD history, showed lower behavioral consistency than HC. BD+ exhibited more pessimistic prior beliefs (relative to BD- and HC) and reduced loss aversion (relative to HC) during risk-taking on the BART. Traditional measures of risk-taking on the BART (adjusted pumps, total points, total pops) detected no group differences. These findings suggest that reduced behavioral consistency is a crucial feature of risky decision-making in BD and that SUD history in BD may signal additional trait vulnerabilities for risky behavior even when mood symptoms and substance use are in remission. This study also underscores the value of using mathematical modeling to understand behavior in research on complex disorders like BD.

Variations in Behavioral and Physiological Traits in Yearling Tibetan Sheep (Ovis aries)

Temperament is a consistent behavioral difference among individuals over time or in different contexts. A comprehensive understanding of temperament and complex behavioral interactions enhances knowledge on animal evolution, welfare, and productivity. However, reports on the development of behavioral consistency over ontogeny are vague. Here, we tested the ontogeny of the temperament and physiological traits of Tibetan sheep (Ovis aries) in three crucial age stages. The mean level of the risk-taking variable increased, while that of the vocalizations variable decreased. The exploration variable was stable over ontogeny. The novelty decreased and the heart rate increased from the juvenile to the adolescent stage but stabilized at the adult stage. The fecal cortisol concentration (CORT) variable was stable at the juvenile and adolescent stages but decreased at the adult stage. Stable correlations were reported for the juvenile and adolescent stages and for the behavioral variables and heart rate. However, some correlations emerged only after maturation, whereas others disappeared over ontogeny. Moreover, CORT was independent of temperament and heart rate at different ages. These results demonstrate that age affects temperament and physiology and their correlations. Hence, developmental aspects should be incorporated into future temperament studies.

Ants’ Personality and Its Dependence on Foraging Styles: Research Perspectives

The paper is devoted to analyzing consistent individual differences in behavior, also known as “personalities,” in the context of a vital ant task—the detection and transportation of food. I am trying to elucidate the extent to which collective cognition is individual-based and whether a single individual’s actions can suffice to direct the entire colony or colony units. The review analyzes personalities in various insects with different life cycles and provides new insights into the role of individuals in directing group actions in ants. Although it is widely accepted that, in eusocial insects, colony personality emerges from the workers’ personalities, there are only a few examples of investigations of personality at the individual level. The central question of the review is how the distribution of behavioral types and cognitive responsibilities within ant colonies depends on a species’ foraging style. In the context of how workers’ behavioral traits display during foraging, a crucial question is what makes an ant a scout that discovers a new food source and mobilizes its nestmates. In mass recruiting, tandem-running, and even in group-recruiting species displaying leadership, the division of labor between scouts and recruits appears to be ephemeral. There is only little, if any, evidence of ants’ careers and behavioral consistency as leaders. Personal traits characterize groups of individuals at the colony level but not performers of functional roles during foraging. The leader-scouting seems to be the only known system that is based on a consistent personal difference between scouting and foraging individuals.

EXPRESS: Virtual Reality in New Product Development: Insights from Pre-Launch Sales Forecasting for Durables

This investigation examines how consumer durable goods producers can leverage virtual reality for new product development (NPD). First, the authors develop a pre-launch sales forecasting approach with two key features: virtual reality and an extended macro-flow model. To assess its effectiveness, the authors collect data from 631 potential buyers of two real-world innovations. The results reveal that the new approach yields highly accurate pre-launch forecasts across the two field studies: Compared to the actual sales data tracked after the product launches, the prediction errors for the aggregated first-year sales are only 1.9% (Study 1a, original pre-launch sales forecast), 0.0% (Study 1b, forecast with actual advertisement spending), and 20.0% (Study 1b, original pre-launch forecast). Moreover, the average the mean absolute percentage error (MAPE) for the monthly sales is only 23% across both studies. Second, to understand the mechanisms of virtual reality, the authors conducted a controlled laboratory experiment. The findings reveal that virtual reality fosters behavioral consistency between participants’ information search, preferences, and buying behavior. Moreover, virtual reality enhances participants’ perceptions related to presence and vividness, but not their perceptions related to alternative theoretical perspectives. Finally, the authors provide recommendations for when and how managers can use virtual reality in NPD.

Impact of Dataset Size on the Signature-Based Calibration of a Hydrological Model

Many calibrated hydrological models are inconsistent with the behavioral functions of catchments and do not fully represent the catchments’ underlying processes despite their seemingly adequate performance, if measured by traditional statistical error metrics. Using such metrics for calibration is hindered if only short-term data are available. This study investigated the influence of varying lengths of streamflow observation records on model calibration and evaluated the usefulness of a signature-based calibration approach in conceptual rainfall-runoff model calibration. Scenarios of continuous short-period observations were used to emulate poorly gauged catchments. Two approaches were employed to calibrate the HBV model for the Brue catchment in the UK. The first approach used single-objective optimization to maximize Nash–Sutcliffe efficiency (NSE) as a goodness-of-fit measure. The second approach involved multiobjective optimization based on maximizing the scores of 11 signature indices, as well as maximizing NSE. In addition, a diagnostic model evaluation approach was used to evaluate both model performance and behavioral consistency. The results showed that the HBV model was successfully calibrated using short-term datasets with a lower limit of approximately four months of data (10% FRD model). One formulation of the multiobjective signature-based optimization approach yielded the highest performance and hydrological consistency among all parameterization algorithms. The diagnostic model evaluation enabled the selection of consistent models reflecting catchment behavior and allowed an accurate detection of deficiencies in other models. It can be argued that signature-based calibration can be employed for building adequate models even in data-poor situations.

Behavioral consistency in the digital age

Efforts to infer personality from digital footprints have focused on behavioral stability at the trait level without considering situational dependency. We repeat Shoda, Mischel, and Wright’s (1994) classic study of intraindividual consistency with data on 28,692 days of smartphone usage by 780 people. Using per app measures of ‘pickup’ frequency and usage duration, we found that profiles of daily smartphone usage were significantly more consistent when taken from the same user than from different users (d > 1.46). Random forest models trained on 6 days of behavior identified each of the 780 users in test data with 35.8% / 38.5% (pickup / duration) accuracy. This increased to 73.5% / 75.3% when success was taken as the user appearing in the top 10 predictions (i.e., top 1%). Thus, situation-dependent stability in behavior is present in our digital lives and its uniqueness provides both opportunities and risks to privacy.

A CSP-Based Approach for Managing the Dynamic Reconfiguration of Software Architecture

Software applications are composed of a set of interconnected software components running on different machines. Most of these applications have a dynamic nature and need to reconfigure structure and behavior at run-time. The dynamic reconfiguration of software is a problem that must be dealt with. Reconfiguring this kind of applications is a complicated task and risks to take software at an undesirable situation. In this paper, the authors present a solution whose objective is to provide a complete support for reconfiguring and formally verifying consistency of dynamic updates of software before performing them. The aim is to provide highly available systems with the ability to safely modify their structure and behavior at run-time. The proposed approach is based mainly on the use of the meta-model concept for reconfiguration structural checking, and the CSP language, refinement technique, and the FDR model checking tool for the verification of reconfiguration behavioral consistency. The authors have also developed a tool prototype that validates and implements their proposals.