Nonlinear Dynamics Psychology and Life Sciences最新文献

The present study explores the role of convergent and divergent thinking in learning sciences from the nonlinear dynamical system (NDS) perspective. The participants (N=375) were fifth and sixth graders, aged 11-12, who were taking an introductory course in science. Students' understanding of physical phenomena, such as melting, boiling and evaporation was investigated as a function of four neo-Piagetian constructs via the difference-equation cusp catastrophe model. The nonlinear models where logical thinking acted as the asymmetry factor and field dependence/ independence, convergent thinking and divergent thinking acted as bifurcation factors, were superior, explaining 43-44% of the variance, whereas their linear alternatives explained 0-18%. Empirical evidence regarding the role of the above neo-Piagetian constructs at these early ages is reported for the first time and contributes to theory development within the NDS framework. Further, discussion about the significance of the findings is provided.

To further the understanding of how to build or reduce synchrony in a work team, we examined two principles for defining the optimal condition to produce or limit synchrony: (a) the empath-driver ratio (relative strength of the stronger influencer compared to the receptive strength of any member in the group), and (b) the balance between autocorrelated autonomic arousal (degree to which members' signals are independent of other group members) and the degree of influence that transfers from each group member to other group members. In study 1, we employed a series of computational simulations designed to manipulate the four variables. The results indicated that there is a four-way balance between driver strength, empath strength, autocorrelational and transfer effects among team members. The relationship between the synchronization coefficient and the empath-driver ratio was moderated by whether the group adopted a network structure for group problem solving or command-and-control. In study 2 we analyzed autonomic arousal (electrodermal response) in four teams of five participants playing a first-person shooter computer game. The correlation between the synchronization coefficient and the empath-driver ratio was 0.280 (p < .001) based on 64 pairs of observations. The relationship was moderated by both the network structure and the statistical model that one adopted to analyze dyadic relationships within the group. The implications of these relationships for a growing theory of team synchrony are discussed.

The chaotic structure of air pollution, human health, the evidence of cointegration and causality between the air pollution, transportation, tourism, construction, and manufacturing industries and the evidence of cointegration and causality between the air pollution, transportation, tourism, construction, and manufacturing industries and mortality rate were explored for China, India, and Turkey in the period of 1975-2018 by using four different methods. Firstly, Lyapunov and Shannon tests were applied to determine chaotic dynamics. The maximum Lyapunov test, for the selected variables, found the evidence of chaotic dynamics. Secondly, Fourier ADF and NL tests were applied. Fourier unit root test determined stationary of the variables. Thirdly, bootstrapping autoregressive distributed lag with Fourier transformation (FBARDL) was used to determine the evidence of cointegration between the variables with two different models. FBARDL test determined that the manufacturing, tourism, transport, and construction industries, air pollution, and mortality rate have evidence of cointegration in different two models. Lastly, the causality test with Fourier transformation was used to determine the direction of causality between the variables. Granger causality test determined that there is evidence of one-way causality running from transportation, tourism, construction, and manufacturing industries to air pollution and mortality rates. Accordingly, the results of this paper suggest air pollution and human health have chaotic behaviors. Air pollution has a complex, multi-variable, and multi-coupling system. Air pollutants influencing factors and air pollution itself have adverse effects on human health.

Well documented empirical evidence points to the existence of strong heterogeneity regarding households' savings behavior over the life cycle: individuals endowed with identical inherited wealth, and with similar prospective income earnings and life expectancy, often select antithetical strategies when formulating their consumption-savings intertemporal plans. Underlying this evidence resides the fact that psychology matters, i.e., that economic agents are frequently influenced by their intrinsic beliefs (commanded by genetics and education) and by social and cultural motivations, thus deviating from strict rationality and strict optimal behavior. In this paper, a model of behavioral savings is proposed. In the model, three psychological profiles potentially coexist: individuals can be aligned with the rationality benchmark or, alternatively, they may depart from it by holding optimistic or pessimistic beliefs about future earnings. Each generation of households assumes one of the profiles (rational - optimistic - pessimistic), and new generations form their beliefs by making a constrained assessment of the utility levels attained by the existing generations (namely, they will mimic the behavior of generations which they perceive as being role models). The analysis characterizes the life-cycle implications of assuming each one of the belief profiles and proposes an explanation for aggregate fluctuations in savings and consumption based on the cyclical renewal of beliefs across the mentioned states.

The diffusion process in networks is studied with the objective of identifying the dynamics and for predicting the behavior of network entities. Social media plays an important role in people's lives. Diffusion processes, as one of the most important branches of social media analysis, have their presence in various domains such as information spreading, diffusion of innovation, idea dissemination, and product acceptance to identify user's pattern and their behavior in social media networks. Users are not limited to one social network and are engaged in multiple social media such as Twitter, Instagram, Telegram, and Facebook. This fact has created new phenomena in social network analysis, called multiplex network analysis. Thus, the scope of diffusion process analysis has been transferred from single layer networks to multiplex networks. Diffusion process analysis can be studied at both infrastructure-level and diffusion-level; at infrastructure-level, the structural network's properties such as clustering coefficient and degree centrality are being studied; and in diffusion-level the diffusion network's properties such as diffusion depth and seed nodes are being studied. On the other hand, a reliable analysis requires complete information on both infrastructure and diffusion networks. However, complete data is not accessible forever, this fact is due to some limitations such as crawling big data, gathering social media policies, and user privacy. Incomplete data can lead to poor analysis, so in this work we, first of all, investigate the impact of missing data in both infrastructure and diffusion networks, the impact of random and non-random missing infrastructure data on nine diffusion network's properties such as number of infected nodes, number of infected edges, diffusion length and number of seed nodes. Secondly, based on the multiplex diffusion tree, we introduce a new model named as MLC-tree for an incomplete diffusion network. Finally, we evaluate our model on both synthetic and real social networks; these results show that the MLC-tree can decrease the relative error more than 50 percent while missing 20 to 80 percent of complete data.

Proprioceptive based interpersonal communication, playing a crucial role in cooperative motor tasks, needs further investigation. This study aimed to explore the interpersonal coordination of dyads cooperating to stand up in balance on a slackline through the study of inter and intrapersonal synergies. With this purpose, acceleration time series of the slackline as well as of both legs and the center of mass of slackliners were recorded. The Uncontrolled Manifold was used to evaluate inter and intrapersonal synergies, and afterwards, the Hierarchical Cluster Analysis was performed to detect hypothetical embedded organization of synergies. Furthermore, the kinematic variability of the synergetic elements was studied through the Detrended Fluctuation Analysis to find potential stabilizing roles among slackliners. Inter and intrapersonal synergies were identified with a higher hierarchical dominance of the former. Interpersonal stabilizing roles were demonstrated among slackliners, revealing greater kinematic control of free leg and the center of mass in those slackliners with more training experience and higher task performance. This exploratory study of interpersonal coordination found that there was an embedded organization between inter and intrapersonal synergies in which stabilizing roles emerged. Dyads established a dominantly proprioceptive dialogue to form a co-adaptive whole and cope with an unstable environment.

Artistic technique and scientific discovery are superficially contradictory, but are unified at their core as products of creativity. I have always embraced both disciplines, but at some point during my academic journey, I lost my imagination and ability to dream creatively. For over a decade, I have remained passionate about perfecting a way to retrieve this intrinsic component of myself, and nurture it in my students and peers. We must think anew and act anew - bridge the gulf between the disciplines of art and science - if we are to rescue creativity in scientific discovery, and retain many of the bright minds that burn out and even leave academia in science. Surrealism and fractals in nature are such bridges, and offer a space for early career scientists to embrace the full breadth and cultural capacity of science. Here, I present a retrospective review of artworks I have produced over nearly two decades, in considering how they reflect my mindset and relationship with science at various stages of my academic journey.

We study the knowledge acquisition process in a teaching-learning scenario that takes place within the classroom. We explore two complementary approaches, which include classroom observations and student surveys, and the formulation of theoretical models through the use of statistical physics tools. We develop an analytical model and a set of dynamics agent-based models that allow us to understand global behaviors, as well as to follow individual trajec-tories in the knowledge acquisition process. As a proxy of the final achievements of the students we use their final grade, allowing us to assess the validity of our approach. Our models, supported by observations and surveys, reproduce fairly well the process of acquiring knowledge of the students. This work sheds light on the internal dynamics of the classroom and allows us to understand some global aspects of the teaching-learning process.

We build on Nonlinear Dynamic Systems (NDS) theory to examine if team performance change across a complete performance cycle is nonlinear, and if such change is related with team processes change over time. Participants were 214 teams enrolled in one management competition. The hypotheses were tested using nonlinear regressions and catastrophe modeling. The results of the nonlinear regression model support the hypothesis that change in team performance over time follows a cusp catastrophe distribution, R2Cusp = .93, F(5, 1065) = 16889.82, p < .001; and that team processes do function as asymmetry (transition and action processes) and bifurcation (interpersonal processes) factors. The results also suggest that the cusp catastrophe model (R2 = .68) explains team performance better than the linear (R2 = .05) and logistic models (R2 = .07). This study reiterates the importance of incorporating the NDS perspective within the teamwork literature to leverage our knowledge about the way teams perform over time.

In this paper, we will study two independent economies in a country (national, regional and urban), where the dynamics of fluctuations in each economy is described by Keynes's mathematical business cycle model. This is an interaction of two economies which include trade and competition. In the resulting system that consists of two independent economic entities, we show that fluctuations can emerge as two possible types of economic indicators (synchronous and antiphase) when the peaks and downturns of business activities in each of the economies are completely synchronized or on the contrary when the rise of one economy is accompanied by a recession (antiphase cycles). Our aim is to examine the stability question of solutions of the cognate mathematical model. Our analysis of the mathematical model will render methods of the theory of dynamical systems, such as the method of integral manifolds and the Poincare normal forms. This approach will provide a sufficient analysis of the dynamics of solutions of a system of differential equations, which is used as a mathematical model. Asymptotic formulas will be obtained for solutions that depict economic cycles.