**2. The resume of chaos theory**

In 1961, the meteorologist Edward Lorenz was the first to introduce the chaos theory. This theory tried to find a form of uniformity from seemingly random data [27]. Lorenz discovered this theory accidentally. He was looking for a reason why the weather was unpredictable. He used computer assistance and 12 formulation models. He created a program which could not predict the weather but can illustrate what the weather will be like if its starting point is known. One time Lorenz wanted to see the results of the weather model's sequence.

He started from the middle, not from the beginning. For simplicity, Lorenz entered a value consisting of three decimal numbers (0.506), while the number of the sequence was 0.506127. Because the rounding was correct, then the pattern formed by the two numbers should be similar, but it turned out that the design which appeared was more and more different from before. Based on this discovery, Lorenz re-experimented, this time using a simpler model with only three formulations. The result of the data, when displayed, again appeared to be random, but when the data were entered in graphical form, a phenomenon called the butterfly effect was created. A small difference at the starting point (only 0.000127 difference) changed the overall pattern.

The chaos theory refers to the tendency of dynamic, non-linear systems toward disorder or chaos, sometimes behaving unexpectedly, but always deterministically [27]. This theory also refers to the underlying linkages, which exist in random events, which are calculated from the initial conditions [1, 28, 29]. Chaos science focuses on hidden patterns, nuances, sensitivity to things, and rules about how something that cannot be predicted leads to human behaviors.

This theory is not only applied to exact sciences but also social ones, such as the social sciences, psychology, finance, decision making, management and behavioral or information systems. McBride [29] was the first researcher to use a framework based on the chaos theory in the field of information systems. This framework consisted of interaction domains, initial conditions, foreign attractors, events and choices, peak clutter, bifurcation, looping, and connectivity. The focus of this interpretive model was on the value of building descriptions of information systems' interactions in organizations.

Levy [19] applied the chaos theory when making theoretical frameworks to understand the dynamics of the industrial evolution and the complex interactions among industry players. An industry can be conceptualized and modeled as a

complex and dynamic system, which shows both uncertainty and underlying order. Levy created a simulation model to illustrate the interactions between computer manufacturers, their suppliers, and their markets. The simulation's results showed how managers might underestimate the costs of international production. He concluded that, by understanding that any industry is a complex system, managers could improve their decision making and find innovative solutions.

Meanwhile, Ayers [28] mentioned that, in the field of psychology, the concept of chaos had been explored extensively. This concept is primarily in the area of psychoanalysis, on a symbolic level. Outside of psychoanalysis, the chaos theory has been applied to a variety of clinical subjects to varying degrees. Still, almost all of its applications appear metaphorical (although one cannot always make this statement explicitly). This theory was also used for psychological processes through the practical application of chaos methodology, e.g. [30, 31]. It showed that, even though the application of metaphors is useful in providing appropriate ways of looking at psychological disorders, the successful application of future psychopathological changes depends on whether it is validated by practical work demonstrating chaos in the associated psychological phenomena.

Moreover, Radu et al. [32] presented the application of chaos theory in management. Also, they explained the positive and negative sides of this theory in a company's current strategic management, in organizational change projects or the management of highly dynamic projects. Furthermore, Klioutchnikov et al. [33] explained that the chaos theory is very suitable for understanding financial perspectives because several circumstances determine the behavior of the financial markets, which are relative to the needs, and internal and external reasons can cause those circumstances to arise. They tried to clarify several points related to the possibility of using chaos theory in finance. Its mechanism of implementation in finance was in macro- and micro-processes. This mechanism also used specific methods and instruments, such as fractal and stochastic processes and predictions.

The latest work by Sauermann [34] involved chaos theorems drawn from the social choice theory and used to investigate the relationship between the indeterminacy of majority rule leads and voting cycles and to make democratic decisions. The study's results contradicted Riker's interpretation of the chaos theorems' implications. This core exhibited less attraction than generally assumed. Then, an empty core is not associated with majority rule's increased instability. Instead, conflicting preferences lead to more instability irrespective of the existence of an equilibrium.
