Predicting activity occurrence time in smart homes with evolving fuzzy models

In this paper, we propose a linguistic summarization procedure for describing long-term trends of change in human behavior. Our objective consists of defining methods that provide information to elders, caregivers, social workers or even family in an understandable language. We adapt a measure that we defined in previous work on soft cluster partition similarity for comparing behaviors that are adapted over time. From that measure, we are able to produce a time series that numerically describes change in behavior over time. In this article, the resulting time series is partitioned and linguistically summarized depending on a user's (caregiver, social worker, etc.) desired time resolution. Simulated resident behavior is used in order to explore a range of different scenarios and the response of the proposed linguistic summarization process is investigated.

Changes in human behaviors, such as reductions of physical contacts and the adoption of preventive measures, impact the transmission of infectious diseases considerably. Behavioral adaptations may be the result of individuals aiming to protect themselves or mere responses to public containment measures, or a combination of both. What drives autonomous and policy-induced adaptation, how they are related and change over time is insufficiently understood. Here, we develop a framework for more precise analysis of behavioral adaptation, focusing on confluence, interactions and time variance of autonomous and policy-induced adaptation. We carry out an empirical analysis of Germany during the fall of 2020 and beyond. Subsequently, we discuss how behavioral adaptation processes can be better represented in behavioral-epidemiological models. We find that our framework is useful to understand the interplay of autonomous and policy-induced adaptation as a “moving target”. Our empirical analysis suggests that mobility patterns in Germany changed significantly due to both autonomous and policy-induced adaption, with potentially weaker effects over time due to decreasing risk signals, diminishing risk perceptions and an erosion of trust in the government. We find that while a number of simulation and prediction models have made great efforts to represent behavioral adaptation, the interplay of autonomous and policy-induced adaption needs to be better understood to construct convincing counterfactual scenarios for policy analysis. The insights presented here are of interest to modelers and policy makers aiming to understand and account for behaviors during a pandemic response more accurately.

The article quantitatively investigates transformative changes in human behavioral patterns about nature due to using digital forests in relaxation practices. In the era of COVID-19, when physical outings and social interactions have been limited, there has been a growing trend for people to engage in digital interactions with nature. This article aims to quantify the impact of people’s behavior and interaction in the digital space on the change of personal behavioral patterns in the physical natural environment, as well as to analyze the potential effects of digital “bathing in the forest”. Since in work it is advisable to take into account the influence of hidden factors, structural equation modeling was used for the needs of this study. The primary data were the answers of 300 respondents, which were obtained based on the results of a survey of people in the capital region of Japan. A five-point Likert scale was used to evaluate and analyze respondents’ opinions. The internal reliability of the questionnaire scales was tested using Cronbach’s alpha. The work offers an integral indicator for assessing the personal characteristics of individuals, based on which two groups of respondents were formed – extroverts (the index is higher than the median) and introverts (the index is lower than the median). The study analyzed the pathways that lead to changes in human behavior caused by the development of digital natural relaxation methods. The work established a statistically significant relationship between the effect of digital “bathing in the forest” and a change in human behavior (correlation coefficient 0.872). According to the results of structural modeling, it was found that digital “forest bathing” has a significant mediating effect on the promotion of pro-environmental behavior (correlation coefficient is 0.634) and that users who regularly experience nature in cyberspace are more likely to change their behavior concerning nature. It is substantiated that people closer to introversion are more actively involved in the digital space and interact with others, and significantly more often change their behavior towards the environment. This study is a rare contribution demonstrating the potential of enjoying digital “forest bathing” to induce environmentally oriented behavior, even in the physical absence of forests.

This thesis deals with persuasive design. The result of this research was motivated by the quest for understanding the different dimensions or categories of analysis that influence or even determines changes in human behavior through the use of product designs. A transdisciplinary vision was the departure research posture in order to understand the full human behavioral dimensions playing a role in human behavioral changes. This stance recognizes the human behavior as a result of internal forces that shapes the individuals intentionality and external forces that constrain and determines the final behavior. This thesis intent to answer the main question about, which are the conceptual dimensions involved in behavioral change through the use of a product design that persuades people into the new behavior? The hypothesis thus defines the existence of external forces to the product that influence and determines the result of an intended change in human behavior through the use of product design. These external forces are defined as conceptual dimensions that must be taken into account when designing products with the intent of provoking a human behavioral change. The use of the Grounded Theory approach allowed studying a singular phenomenon and generating categories of analysis through theoretical sampling.

Human activity has led to a steady degradation of the environment over the past decades, pushing the ecosystem to the brink of collapse. It is now clear that a dramatic change in behavior is needed to avoid system collapse. Economics has been striving to devise approaches to bring about this change. While the application of rational choice models saw the solution primarily in incentivization, the effectiveness of this was cast into doubt by findings from early behavioral economics. The realization that heuristics affect human thinking and acting led to the extension of the economic policy repertoire by including nudges. While hard rules, incentives, and nudges can change human behavior in specific situations, recent findings suggest that they are not sufficient to bring about the necessary change. Current behavioral economics research sheds light on the role of the mechanisms underlying human behavior. Factors scrutinized are categories, identities, narratives, worldviews, and values, which can be united under the collective term mental models. Through these mental models it is possible to explain not only linear decision processes as is conventional, but also dynamic ones. These dynamic processes consider factors that have been highlighted as important in other behavioral sciences, such as habits and positive spillovers. In this chapter, we present a conceptual framework that describes the analysis of human behavior including mental models. The framework combines insights from behavioral economics with those from other behavioral sciences to explore far-reaching changes in human behavior and thus provide a scientific basis for the necessary transformation.

Analyzing and predicting the high frequency trading (HFT) financial data stream is very challenging due to the fast arrival times and large amount of the data samples. Aiming at solving this problem, an online evolving fuzzy rule-based prediction model is proposed in this paper. Because this prediction model is based on evolving fuzzy rule-based systems and a novel, simpler form of data density, it can autonomously learn from the live data stream, automatically build/remove its rules and recursively update the parameters. This model responds quickly to all unpredictable sudden changes of financial data and re-adjusts itself to follow the new data pattern. Experimental results show the excellent prediction performance of the proposed approach with real financial data stream regardless of quick shifts of data patterns and frequent appearances of abnormal data samples.

There is a growing recognition among wildlife managers that focusing management on wildlife often provides a temporary fix to human–wildlife conflicts, whereas changing human behavior can provide long-term solutions. Human dimensions research of wildlife conflicts frequently focuses on stakeholders' characteristics, problem identification, and acceptability of management, and less frequently on human behavior and evaluation of management actions to change that behavior. Consequently, little information exists to assess overall success of management. We draw on our experience studying human–bear conflicts, and argue for more human dimensions studies that focus on change in human behavior to measure management success. We call for help from social scientists to conduct applied experiments utilizing two methods, direct observation and self-reported data, to measure change in behavior. We are optimistic these approaches will help fill the managers' tool box and lead to better integration of human dimensions into human–wildlife conflict management.

Digital phenotyping and the COVID-19 pandemic: Capturing behavioral change in patients with psychiatric disorders

The formation of a healthy lifestyle of youth is one of the main objectives of Ukraine's youth policy. The strategy of forming the population's attitude to a healthy way of life is an important aspect of social prevention. The use of social marketing as an approach to the formation and implementation of social prevention strategies significantly increases the effectiveness of preventive measures. Social marketing focuses on the social context of decision-making and the use of theoretical methods to analyse the mechanisms of interaction in the public space. Current theoretical approaches to the explanation of human behaviour are the basis for the development of strategies aimed at the formation of a healthy way of life. These strategies are based on theories that explain changes in human behaviour, such as theories of deliberate action, planned behaviour, the theory of social learning, and others. Applying social marketing strategies involves the social environment and consumers' orientations analysis, the audience analysis and audience segmentation strategy, etc. Relevant theoretical concepts provide interpretation on behavioural decisions and behavioural patterns changes. Correspondingly social marketing utilizes variety of research approaches into the programs’ efficiency and their advanced testing, analysis of channels for developing distribution systems and advertising campaigns. Some commercial marketing techniques are applicable for the social marketing purpose. Among others are analysis of the target audience, identifying goals of desired behavioural changes, adaptation of messages and adaptation of strategies such as branding to facilitate the adoption and maintenance of safe behaviour in the health sector. The basic principles of social marketing can be seen as the messages targeted on the behaviour relevant to a healthy lifestyle should be different at different stages of program’ planning and implementation. Social marketing is used for the development and implementation of programs that contribute to a change in behaviour to socially beneficial, especially in the area of public health.

We use mathematical models to assess the impact of behavioral changes in response to an emerging epidemic. Evaluating the quantitative and qualitative impact of public health interventions on the spread of infectious diseases is a crucial public health objective. The recent avian influenza (H5N1) outbreaks and the 2009 H1N1 pandemic have raised significant global concerns about the emergence of a deadly influenza virus causing a pandemic of catastrophic proportions. Mitigation strategies based on behavior changes are some of the only options available in the early stages of an emerging epidemic when vaccines are unlikely to be available and there are only limited stockpiles of antiviral medications. Mathematical models that capture these behavior changes can quantify the relative impact of different mitigation strategies, such as closing schools, in slowing the spread of an infectious disease. Including behavior changes in mathematical models increases complexity and is often left out of the analysis. We present a simple differential equation model which allows for people changing their behavior to decrease their probability of infection. We also describe a large-scale agent-based model that can be used to analyze the impact of isolation scenarios such as school closures and fear-based home isolation during a pandemic. The agent-based model captures realistic individual-level mixing patterns and coordinated reactive changes in human behavior in order to better predict the transmission dynamics of an epidemic. Both models confirm that changes in behavior can be effective in reducing the spread of disease. For example, our model predicts that if school closures are implemented for the duration of the pandemic, the clinical attack rate could be reduced by more than 50%. We also verify that when interventions are stopped too soon, a second wave of infection can occur.

Humans, who were once able to wait for news of loved ones for a long time, are now anxious to receive information within minutes and seconds. Technology may contribute to this change in human nature, as technology and social media have become an integral part of human life. While their appropriate use can certainly be beneficial, studies and research indicate that excessive and inappropriate use has brought disadvantages to many aspects of human life. Excessive use has not only led to a kind of addiction in many people but has also unnaturally altered human behavior. Is it possible that technology and social media are causing a decline not only in human behavior but also in patience and overall human capability? The main objective of this article is to analyze the changes in human behavior and their effects caused by mobile phones and social media, and to provide suggestions on focusing on their proper use. The article is based on facts obtained from the study and review of secondary materials. Although this explanatory and analytical article has been prepared using this methodology, it does not include facts and data from primary sources. It is believed that the article will be useful for those interested in the use of mobile phones and social media and their effects.

A number of postmodern practitioners have turned to theorists such as Foucault, Derrida, and Wittgenstein to inform their intervention efforts. Yet it may be difficult for the average practitioner, or educator teaching HBSE, to make the connection between these theorists and human behavior. Phenomenology, as a theory of ontology, serves as afundamental theory of the postmodern paradigm. As such, phenomenological concepts such as existence and essence, presence and absence, and distinctness and vagueness offer much in illustrating the link between postmodern theories of meaningmaking and intervention efforts seeking change in human behavior.

Schistosomiasis, a neglected tropical disease caused by parasitic trematodes of the genus \textit{Schistosoma}, affects millions of people in tropical and subtropical regions lacking access to clean water and proper hygiene. With its impact on health and well-being, the World Health Organization aspires to eliminate schistosomiasis by 2030. This work addresses the challenge of effective control in endemic areas by integrating diffusion in each sub-population using reaction-diffusion equations. The proposed model includes treated individuals who have undergone massive drug administration and a time-dependent function that models the change in human behavior. We present a Partial Differential Equation (PDE) model of schistosomiasis spread that incorporates population movement and human behavior change. Mathematical analysis explores the system's dynamics according to the infection threshold $R_0$, shedding light on the disease's behavior. Sensitivity analysis is used to identify the key parameters affecting disease spread. Numerical simulations under different scenarios elucidate the impact of human behavior on disease dynamics. This research contributes to a deeper understanding of schistosomiasis transmission and provides insights into control strategies.

Understanding and predicting mobility are essential for the design and evaluation of future mobile edge caching and networking. Consequently, research on human mobility prediction has drawn significant attention in the last decade. Employing information-theoretic concepts and machine learning methods, earlier research has shown evidence that human behavior can be highly predictable. Whether high predictability manifests itself for different modes of device usage, across spatial and temporal dimensions is still debatable. Despite existing studies, more investigations are needed to capture intrinsic mobility characteristics constraining predictability, to explore more dimensions (e.g. device types) and spatiotemporal granularities, especially with the change in human behavior and technology. We investigate practical predictability of next location visitation across three different dimensions: device type, spatial granularity and temporal spans using an extensive longitudinal dataset, with fine spatial granularity (AP level) covering 16 months. The study reveals device type as an important factor affecting predictability. Ultra-portable devices such as smartphones have "on-the-go" mode of usage (and hence dubbed "Flutes"), whereas laptops are "sit-to-use" (dubbed "Cellos"). The goal of this study is to investigate practical prediction mechanisms to quantify predictability as an aspect of human mobility modeling, across time, space and device types. We apply our systematic analysis to wireless traces from a large university campus. We compare several algorithms using varying degrees of temporal and spatial granularity for the two modes of devices; Flutes vs. Cellos. Through our analysis, we quantify how the mobility of Flutes is less predictable than the mobility of Cellos. In addition, this pattern is consistent across various spatio-temporal granularities, and for different methods (Markov chains, neural networks/deep learning, entropy-based estimators). This work substantiates the importance of predictability as an essential aspect of human mobility, with direct application in predictive caching, user behavior modeling and mobility simulations.

Advances in computer science prompt the creation of new economic models. One of them in urban life is Sharing Economy. This new economic model receives high attentions and has been applied to a wide range of fields in recent years. We argue that sharing economy, and its applications, may be taken as a way for researchers to understand human behavior, and thus this paper targets a growing issue on the relationship between sharing economy and its impacts on human behavior. It especially looks at the change of behaviors while the sharing economy comes into our daily lives. The Airbnb, one of the most significant examples of sharing economy, is taken as a starting point of this survey, and results are revealed that young generation tends to accept this new economic experience as well as willing to be involved, not only as traveler but host, in this new phenomenon.