Nonlinear Dynamics Psychology and Life Sciences最新文献

It is a commonplace perception that speed of time subjectively experienced by humans significantly differs from chronological (objective) time and shows a great deal of variability. An often cited example is the phenomenon of the time acceleration with age - subjectively, the time passes faster as we get older. While the exact mechanisms behind it are not yet fully established, here we consider three 'soft' (conceptual) mathematical models that might be applicable to the speeding time phenomenon: two proportionality theories widely discussed in the past and the original model that takes into account the novelty of experience effect. The latter is found the most plausible, as not only it satisfactorily describes the decadal subjective time acceleration, but also offers a reasonable explanation of the human life experience accumulation with age.

Theories and studies of corruption typically focus on individual ethics and agency problems in organizations. In this paper, we use concepts from complexity science to propose a process theory that describes how corruption risk emerges from conditions of uncertainty that are intrinsic in social systems and social interactions. We posit that our theory is valid across multiple levels of scale in social systems. We theorize that corruption involves dynamics that emerge when agents in a system take actions that exploit disequilibrium conditions of uncertainty and ethical ambiguity. Further, systemic corruption emerges when agent interactions are amplified locally in ways that create a hidden value sink which we define as a structure that extracts, or 'drains,' resources from the system for the exclusive use of certain agents. For those participating in corruption, the presence of a value sink reduces local uncertainties about access to resources. This dynamic can attract others to join the value sink, allowing it to persist and grow as a dynamical system attractor, eventually challenging broader norms. We close by identifying four distinct types of corruption risk and suggest policy interventions to manage them. Finally, we discuss ways in which our theoretical approach could motivate future research.

This paper is a top-down analysis of the non-protein-coding, canine genome. We demonstrate by use of the y-text-finder method, that the non-protein-coding genome contains lots of hidden y-texts, both short and long, proving that the non-protein-coding genome is the opposite of junk. They are written by means of a y-language of about 28 million y-words separated by stop codons and spelled by nucleotide letters A, C, G, and T. We use the Canis Lupus Familiaris reference genome, Roslin Institute, 2020, from which we select the non-protein-coding part. We show that 70-80 percent of chromosomal y-words are specific for the canine non-protein-coding chromosome, and we show how many y-words any non-protein-coding chromosome shares with any other non-protein-coding chromosome. We demonstrate the peculiar way by which the dog utilizes the nucleotide word-length of y-words to build up its y-language, moreover in a way it shares with the human non-protein-coding genome. In a large table we demonstrate how 18,398 Zipf-qualified y-texts/narratives are distributed over the 40 non-protein-coding chromosomes. 3,812 of these texts/narratives are alpha-qualified and similar in form to human novels. In the last table we compare selected, corresponding characteristics of the human and the canine non-protein-coding genome.

Comparison of some experimental data and deterministic dynamical models of heartbeat show that it is essential to consider stochastic mathematical models. The Zeeman heartbeat model is one of the main heartbeat models whose stochastic dynamics is less studied. Especially, investigating bifurcations in stochastic dynamical models can be useful for identifying abnormal cardiac rhythms. This paper is concerned with two essential features of the two dimensional stochastic Zeeman heartbeat model i.e., stability and bifurcation. To achieve this approach, Taylor expansion, polar coordinate transformation, and stochastic averaging procedure will be used to convert the classical system into an Ito averaging diffusion system. Furthermore, we consider several theorems which provide sufficient conditions of drift and diffusion coefficients to establish stochastic stability, D-bifurcation and phenomenological bifurcation of the model. In the end, numerical simulation plays an important role to show the influences of the noise severity and confirm our theoretical results.

Most scholarly discussions of novelty offer only partial or specified views of what it is or how it can be assessed particular to a discipline or are limited to individual studies. This article investigates novelty with the goal of offering a multi-systemic and generalizable definition and assessment process to be applicable across multiple disciplines. Novelty is proposed as a quality and state, which includes both a qualitative view that integrates subjective experience and a quantitative view that addresses nonlinear dynamical systems. It is also described as a comparative relatedness of space, time, and context. An 'outlier profile' is defined to search for features of the system that are original or unusual, and a 'low-recurrence profile' for features of the system that are unexpected or a surprise. A detailed research strategy is offered for novelty assessment with an example, and is usable across multiple disciplinary contexts in the sciences and humanities for studies of academic and practical use.

In the analysis of time series data, roughness is sometimes seen as a distinct feature of fractality. This paper seeks to distinguish it from other aspects of that construct (self-affinity and long-range memory processes) and it examines the reliability of the roughness measures currently available, i.e., Gneiting et al.'s (2010) fractal dimension and Marmelat et al.'s (2012) relative roughness. The response of these estimators is evaluated to simulations at varying levels of persistence, as specified by the Hurst exponent, and to the presence or absence of short-range ARMA processes. Four empirical time series datasets are subjected to roughness estimation: the flow of the river Nile, daily recordings of the number of births to teens in the state of Texas, daily school attendance rates at an urban middle school, and unemployment figures provided by the US Department of Labor. Results from the simulation study indicate that persistence levels are faithfully reproduced by both estimation techniques, which also show the (dis)attenuating effects of the short-range dependencies. Analysis of the empirical data indicates that the fractal dimension works best for non-stationary data, while relative roughness is more suitable for stationary data. In the simulations as well as the empirical situation, both estimations reliably identify randomness, and are therefore recommended as goodness of fit measures when time series are analyzed.

The present study investigated whether fractal dynamics can be observed during single-leg standing on a slackline. We also examined whether the temporal structure differs with skill level. To address these questions, we compared single-leg standing performance between novices (N=5) and experts (N=5) in terms of fractal dynamics in both ankles (i.e., stance and swing legs), center of mass, and head acceleration time series using detrended fluctuation analysis. Participants were required to perform single-leg standing on a slackline. To collect motion data while slacklining, we used a three-dimensional motion capture system and obtained time-series data on the position. We conducted detrended fluctuation analysis on the original acceleration time series and random shuffled time series to examine the fractal dynamics in each body part's fluctuation. Results suggest that experts showed persistent temporal structure in the swinging leg, center of mass, and head fluctuations in the horizontal direction, while that of novices did not differ from random fluctuations. These findings revealed that experts performing a single-leg standing task on a slackline show fractal dynamics. This might reflect their flexible or adaptive exploratory behavior in the performer-environment system and contribute to the dynamic stability of whole-body dynamic balancing.

How does novelty arise? While modern scholarly investigations show that new complex system paths arise due to dissipative structures post-bifurcation, few consider the subjectivity of the observer and fewer describe what can be deemed as truly novel in light of a causal chain of deterministic events. By investigating the 'problem of novelty' (i.e., how something can come from nothing) and adding a subjective appraisal process for a novelty threshold as per complex systems, this paper offers an alternative view of the birthplace of novelty. The findings reveal that novelty arises in a breach of causal normality described as a causal 'breakthrough,' and in a nonlinear 'transition zone' post-bifurcation between disordering and ordering, based on quantitative and qualitative criteria. The article offers a subjective approach to nonlinear dynamical self-organization considering both 'outliers' and 'low-recurrence' in a spatio-temporal perspective to determine what separates novelty from 'newness.' Four 'preconditions of novelty' (i.e., tension, competition, instability, and diversity) are also presented to clarify favourable conditions for novelty generation.

Issues pertaining to the organization and efficiency of the labor market and to the dynamics of employment and unemployment have always been at the forefront of the concerns of economists. Typically, such issues are approached through the analysis of the conflicting interests of a representative worker and of a representative firm, each of which intending to maximize the corresponding intertemporal objective function. Much of the research undertaken on this subject neglects the fact that each person is unique and endowed with different personality traits that influence their educational attainment, their ability to access jobs, their productivity while employed, and also their willingness to support, through social welfare mechanisms, those who become unemployed. In this research, we propose a simulation model to approach the dynamics of the labor market. The model conceives an economy populated by a large number of individuals who, over their life cycles, acquire education, search for a job, receive a wage while employed, and access an unemployment benefit while out of work. Because individuals are endowed with different personalities, they experience different degrees of professional success over their life cycles. Such reasoning leads to a labor market aggregate outcome characterized by emergent phenomena, out-of-equilibrium, path dependence, and other features that are characteristic of a complex evolving system. In the proposed setting, the personality of individuals is shaped by taking into account the big five personality traits of psychological analysis, namely openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism.

Previous research identified the stability of wrist position as a performance indicator in a static basketball dribbling task performance under different experimental conditions since professionals displayed higher stability values than amateurs. We hypothesized that the trajectories of this cyclical task may be different between amateurs and professionals under downward peripheral vision occlusion and auditory occlusion. A modified version of the Procrustes analysis was used to quantify the dissimilarity between wrist trajectories along time. Results showed that peripheral vision occlusion caused dissimilarity in amateurs' dribbling trajectories almost four times larger than professionals'; however, auditory occlusion did not affect neither amateur nor professionals' performance. There were no cumulative effects on performance when the individual was submitted to both occlusions simultaneously.