Understanding Walking Behavior: Its Benefits and Barriers

Health survey demonstrates that 5.3 million people each year experienced a premature death due to physical inactivity (Lee et al., 2012). Data from Department of Health (2004) revealed that in the United Kingdom more than 60% of adult males and 75% of adult females did not perform enough physical activity. Hence, to minimize this problem, currently, health practitioners are trying to encourage people to be more physically active, especially by promoting several types of exercise, including walking (Marshall et al., 2009; Hallal et al., 2012). Regular walking is one of the essential predictors for long-term physical and mental health benefit. Some recent studies mention that there are lots of advantages if adults can maintain their regular walking (Gunnell, Knuiman, Divitini, & Cormie, 2014; Morgan, Tobar, & Synder, 2010; Roe & Aspinall, 2011; Shiue, 2015; Nagai et al., 2011). Regular walking minimum 10,000 steps each day can burn as much as 400 calories so that it may help overweight or obesity people to reduce their weight (NHS, 2014). However, most of the people perceive walking as one form of transport rather than exercise; therefore, this reason discourages them to walk sufficiently for healthy life purpose (Darker et al., 2007)

Machine Understanding of Human Behavior

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior

Understanding Child Behavior and Misbehavior

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Understanding employees’ intrapreneurial behavior: a case study

Purpose – The purpose of this paper is to provide a deeper insight into the organizational factors and personal motivations of intrapreneurs that may foster intrapreneurial behaviors of employees in a new technology-based firm (NTBF). Design/methodology/approach – The paper takes a qualitative approach to explore organizational and individual antecedents of employees’ intrapreneurial behavior. A single case study was conducted on the basis of semi-structured interviews with the founders and top managers of the firm and with intrapreneurial employees. Findings – Results show that intrapreneurial projects may arise in firms whose top managers support corporate entrepreneurship (CE) in a non-active manner. Intrapreneurial behaviors of employees can emerge despite the lack of time and limited resources available for undertaking projects. Moreover, work discretion and mutual confidence and the quality of the relationship between employees and top managers are the most valued factors for intrapreneurs. Practical implications – Based on the intrapreneurial projects studied, this paper helps to contextualize intrapreneurs’ perception of organizational support and the personal motivations for leading projects within an NTBF. Originality/value – Traditionally, the literature has mainly focused on the top-down implementation of entrepreneurial projects within large firms. This paper contributes to the understanding of the combination of firm- and individual-level factors that facilitate intrapreneurial behaviors of employees. It also illustrates the contextual conditions and the firms’ orientation on CE within an NTBF

Understanding the recent behavior of M2

A discussion of the unanticipated weakness in the M2 monetary aggregate in recent years, suggesting that the shortfall may be largely attributable to the restructuring of the thrift industry, and an explanation of why economic models predicting M2 growth have had difficulty tracking this weakness.Money supply ; Savings and loan associations

Understanding the Heavy Tailed Dynamics in Human Behavior

The recent availability of electronic datasets containing large volumes of communication data has made it possible to study human behavior on a larger scale than ever before. From this, it has been discovered that across a diverse range of data sets, the inter-event times between consecutive communication events obey heavy tailed power law dynamics. Explaining this has proved controversial, and two distinct hypotheses have emerged. The first holds that these power laws are fundamental, and arise from the mechanisms such as priority queuing that humans use to schedule tasks. The second holds that they are a statistical artifact which only occur in aggregated data when features such as circadian rhythms and burstiness are ignored. We use a large social media data set to test these hypotheses, and find that although models that incorporate circadian rhythms and burstiness do explain part of the observed heavy tails, there is residual unexplained heavy tail behavior which suggests a more fundamental cause. Based on this, we develop a new quantitative model of human behavior which improves on existing approaches, and gives insight into the mechanisms underlying human interactions.Comment: 9 pages in Physical Review E, 201

Understanding recurrent crime as system-immanent collective behavior

Containing the spreading of crime is a major challenge for society. Yet, since thousands of years, no effective strategy has been found to overcome crime. To the contrary, empirical evidence shows that crime is recurrent, a fact that is not captured well by rational choice theories of crime. According to these, strong enough punishment should prevent crime from happening. To gain a better understanding of the relationship between crime and punishment, we consider that the latter requires prior discovery of illicit behavior and study a spatial version of the inspection game. Simulations reveal the spontaneous emergence of cyclic dominance between ''criminals'', ''inspectors'', and ''ordinary people'' as a consequence of spatial interactions. Such cycles dominate the evolutionary process, in particular when the temptation to commit crime or the cost of inspection are low or moderate. Yet, there are also critical parameter values beyond which cycles cease to exist and the population is dominated either by a stable mixture of criminals and inspectors or one of these two strategies alone. Both continuous and discontinuous phase transitions to different final states are possible, indicating that successful strategies to contain crime can be very much counter-intuitive and complex. Our results demonstrate that spatial interactions are crucial for the evolutionary outcome of the inspection game, and they also reveal why criminal behavior is likely to be recurrent rather than evolving towards an equilibrium with monotonous parameter dependencies.Comment: 9 two-column pages, 5 figures; accepted for publication in PLoS ON

Understanding the recent behavior of U.S. inflation

One of the most surprising features of the long current expansion has been the decline in price inflation through the late 1990s. Some observers interpret the decline as evidence of a permanent change in the relationship between inflation and economic growth. But an analysis based on a standard forecasting model suggests that conventional economic factors_most notably, a decrease in import prices_can account for the low inflation rates in recent years.Inflation (Finance) ; Imports

An understanding of influence on human behavior

We describe a candid model for learning, why and how learning transpires. We investigate the original as well as the leading conditions of the learning process. We provide an insight into the realm of beliefs and their formation, their interaction and influence with the actor’s environment. In addition, we provide to our terms (and terminology) real definitions, thus differentiating between nominal and real definitions. Having this approach, the same terminology can be employed by other models, theories or frameworks without creating ‘expert language’ barriers. Moreover, we provide an understanding of the influence that learning in general has on human behavior.conceptual conglomerate, learning, learning process, human behavior.