Technology of chaos measurement and non-linear dynamics in social sciences

Baština

Halmi Aleksandar A.¹ 

[1] Sveučilište u Zadru, Odjel za turizam i komunikacijske znanosti, Zadar, Hrvatska;

关键词: chaos;    non-linear dynamics;    graphic and spectral analysis;    logistic equation;    lyapunov exponents;   

DOI  :  

来源: DOAJ

【 摘 要 】

The ultimate goal of scientific research is the attempt to discover and predict future flows of social phenomena. However, predicting the future is not at all easy. Many phenomena in nature and society are described by researchers with linear models, but these phenomena are in fact non-linear. Many are dynamic systems, in which processes are susceptible to changes in time, are non-linear and can behave improperly. These irregularities can so increase that the behaviour of the dynamic system becomes completely unpredictable and so spontaneously goes into chaos. The scientific discipline, which will be discussed in this paper, is called non-linear dynamics. In this context, it is necessary to point out in particular a specific group of techniques that deal with the study of linear and non-linear time series, since they are of utmost importance for forecasting the future developmental flows of a social system. Modern technological technology has a rich arsenal of various analytical procedures that serve this purpose. For each case of forecasting the behaviour of a system, it is important that the empirical values of the variable are predicted to be chronologically built over a period of time. The data obtained in this way constitute a specific time series or series, and a group of statistical techniques for analysing that time series is called a time series analysis that can include deterministic but also non-deterministic data. For the analysis of linear data in the statistical methodology, special techniques have been developed that allow analysis of the time components. The classic model provides a convincing explanation of the four basic components of the time series, as well as their interrelations. These are: 1. Trend, 2. Seasonal variations, 3. cyclic component, and 4. Residual component. However, in a further discussion, we will not deal with this.

【 授权许可】

Unknown