Nonlinear Dynamics Psychology and Life Sciences最新文献

We consider a stylized model of competition between two firms who provide a local service, for instance coffee-shops or hamburger chains. These firms are characterised by their quality of service, with one firm being high quality and the other being low quality. Quality impacts both the fixed and variable costs of the firms. The firms compete for customers in two areas, which are characterised by a different customer density. Firms decide in which area(s) to locate, and what price to charge. A firm entering both areas must charge the same price in both, i.e., price-discrimination is not allowed. We analyse the impact of cost levels and quality and density differences on the resulting market structure, prices, profits, customer surplus and social welfare. We show how the balance between fixed and variable cost determine the competitive conditions ranging from highly competitive markets to local monopolies under the same regulatory environment. Furthermore, in some areas with multiple equilibria the profitability of the firms is highly dependent on which of the possible equilibria is realised. The results can help explain some of the patterns observed in the location of chain outlets.

Change is ubiquitous in the study of organizations. Organizational change is characterized by multiple perspectives, both conceptually and methodologically. Computational modeling efforts are not the exception. In this work, we aim to provide an analysis of computational modeling approaches to organizational change. For that, we first review published works that directly connect to developing knowledge in organizational change from a computational lens. Second, we offer an account of unexplored topics in computational organizational change. Last, we highlight the potentialities of computer simulation models based on agent interactions in regard to how they could contribute to the understanding of central issues in this organizational research subfield.

Since its inception the science of physiology, like many other non-physical disciplines, has been guided in its development by the mechanical models of physics. That strategy has proven to be extraordinarily successful, even surviving the introduction of fractals into the modeling strategy. That is until quite recently. The true complexity of physiologic networks has been revealed with the development and implementation of ever more sensitive sensors and mathematically sophisticated data processing techniques. These developments have led to a divergence of the modeling strategies appropriate for the physical sciences from those for the life sciences. Therefore, we review how far the fractal concept has taken us into the non-mechanical interpretation of physiology. What emerges in this brief review of fractal physiology is the increasing importance of criticality, the cooperative nature of networks in physiologic behavior, and the importance of the fractional calculus in characterizing the dynamics of living systems. We draw some further inferences from the review and speculate as to what research directions might be most productive for continuing future success.

We discuss a computational model that describes stabilization of percept choices under intermittent viewing of an ambiguous visual stimulus at long stimulus intervals. Let T_off and T_on be the time that the stimulus is off and on, respectively. The behavior was studied by direct numerical simulation in a grid of (T_off, T_on) values in a 2007 paper of Noest, van Ee, Nijs, and van Wezel. They found that both alternating and repetitive sequences of percepts can appear stably, sometimes even for the same values of T_off and T_on. Longer T_off, however, always leads to a situation where, after transients, only repetitive sequences of percepts exist. We incorporate T_off and T_on explicitly as bifurcation parameters of an extended mathematical model of the perceptual choices. We elucidate the bifurcations of periodic orbits responsible for switching between alternating and repetitive sequences. We show that the stability borders of the alternating and repeating sequences in the (T_off, T_on) -parameter plane consist of curves of limit point and period-doubling bifurcations of periodic orbits. The stability regions overlap, resulting in a wedge with bistability of both sequences. We conclude by comparing our modeling results with the experimental results obtained by Noest, van Ee, Nijs, and van Wezel.

Gaze behavior represents a complex phenomenon in social inter-action. We focus here on dyadic face-to-face interaction during naturally occurring verbal exchanges, where shared attention can be operationalized by joint gazes and eye contact. A multi-step methodology for the analysis of eye synchrony is presented, exemplified by a single case. The dynamics of face-to-face interaction allows estimating the degree of interlocutors' synchrony. While there is growing evidence for interpersonal synchrony of various behavioral and physiological signals, eye synchrony has not yet been studied outside the laboratory. The method presented is based on time series of gaze behavior acquired by mobile eye tracking devices. We applied windowed cross-correlations to the data and used surrogate testing to attain effect sizes even for single interactions (Surrogate Synchrony, SUSY). SUSY thus integrates nomo-thetic with idiographic research goals: The nomothetic interest is to test hypotheses that gaze behavior may be generally synchronized and linked with psychological variables. The idiographic aspect is that effect sizes can be determined even in single-case studies owing to the surrogate analyses, which supports qualitative research. Results of the exemplary dataset suggested that proof-of-concept of this approach was attained. We describe what prerequisites are needed of a setting and technical setup for use in future studies of psychotherapy, counseling, negotiations, or work-related interactions.

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.