Chaos Theory and its application for Theory of generations

Author
Affiliation

Stefan Graser

PhD student, University of Economics and Management in Bratislava, Slovakia

Behavior, attitudes and values of the young and adolescent generations Y and Z have been researched in numerous studies. The attitudes of the two generations in comparison to the previous age cohorts as well as the differences between the two generations have been established and accepted as given within research.

The consequences of the experience of the Corona epidemic - which has not yet been fully overcome - have not yet been fully considered.

In the light of the findings of chaos research, this experience could be interpreted as a bifurcation point that makes the future development of attitudes and behavior of the generation now entering the labor market chaotic, i.e. difficult or impossible to predict. This paper focuses on the situation in the Federal Republic of Germany.

Naturally, the share of generations Y and Z in the German workforce will continue to increase as the Baby Boomers retire and Generation Z gradually finishes their education and studies and enters the labour market.

The concept of generation is used to characterize collective or individual actors in terms of their socio-temporal positioning in a population, a society, a state, a social organization, or a family and to attribute facets of their social identity to them [1].

A generation is an identifiable group that shares birth cohorts and important political and social events in the formative development phase of childhood and adolescence. (source) According to general convention, currently four generations, differentiated from each other, meet on the German labor market, with their respective share of the

German labor force (as of 2020) [2]:

In the following characterization of the generations, special attention should be paid to the common experiences and preconditions which, in addition to the birth cohorts, contribute significantly to the formation of the generations.

It is important to focus on the opportunities of application of chaos theory to the understanding different paths of the development of generations in countries of the world.

The term chaos comes from the Greek and figuratively means “shapeless primeval mass”. In his work “Metamorphoses”, Ovid interpreted chaos as the original, raw and formless mass in which there is only disorder and confusion. And so, the creation mythologies of the most diverse peoples begin with chaos. In today’s usage, the word chaos stands for the absence or non-existence of recognizable order [3]. An important pillar of scientific thinking is the so-called causality: it states that the same initial conditions lead to the same results. However, this formulation is worthless if the mathematically exact conditions cannot be recreated - down to the last decimal place.

One finds that even minimal differences in the initial values lead to large deviations and can lead to completely contradictory results. The meteorologist Edward Lorenz from MIT (Massachusetts Institute of Technology) published a paper on this topic in 1963 that was later to become one of the fundamental works of chaos theory. To save precious computing time, he used intermediate results calculated for weather forecasts as new initial values but used only three instead of six decimal places. The solution now calculated differed fundamentally from the previous results, which was due to the small deviations in the initial conditions. The large impact of the smallest differences in initial conditions became known as the “butterfly effect” [3]: The flap of a butterfly’s wings in India can trigger a hurricane in Europe months later.

We encounter this sensitivity in all dynamic processes such as pendulums, air or water currents, satellites - in other words, everything that moves in a time-dependent manner. Since the initial conditions can never be restored to the last decimal place, the axiom of strong causality (same causes have same effects) has been replaced in the consideration of dynamic systems and processes by weak causality: “Similar causes have similar effects”.

Such a dynamic system can assume three states: 1. the system enters a stable, resting state (e.g., a swinging pendulum). 2. the system enters a stable, moving state (e.g., a pendulum that is pushed); 3. the system becomes unstable; the state variables take on uncontrollable values or approach infinity (e.g., resonance).

In particular, the brief transition from orderly to chaotic behavior could be illuminated by chaos theory. Where are the tipping points from order to chaos to be assumed, where does the predictable range of the development of a system end?

Based on the logistic equation, the supply of a generation of their labor for a specific sector of the economy, e.g. SMEs, is to be examined. It is assumed that the offer is dependent on the offer of the previous year, with multiplication factor a resulting from sub-factors such as employer loyalty, employer attractiveness, recommendation, and others. Factor a has to be equal or higher than 1, otherwise the offer would go to zero. Factor r stands for the migration of employees to other, non-SME employers, or the complete exit from the world of work [4].

In a world of order without chaos, we would expect a higher offer of work supply with a higher multiplier. The model shows that a high multiplier would not lead to a predictable offer of work supply of a generation but will lead to unforeseen results, also lower results. Maybe external factors such as the experience of the Covid-19 pandemic might influence the multiplication factor, so attempts by employers to increase the factor may lead to the opposite result than the desired one [5, 6].

How can we manage a chaotic system? In some respect, dealing with deterministic chaos resembles the situation close to critical points as we already mentioned: small causes may have great effects. Simultaneously this means that the system is influenced easily but cannot be predicted in the long run. It seems important for the functionality of chaotic attractors that they undergo qualitative changes when parameters of the system’s environment vary but slightly; this applies to chaotic dynamics in cognition. To shed more light, the proposed model would have to be refined and expanded - to possibly push the boundaries of the predictable order a little.

References

  1. LÜSCHER, Kurt; LIEGLE, Ludwig; LANGE, Andreas. Bausteine zur Generationenanalyse. DJI-Bulletin: Plus, 2009, 86. Jg., Nr. 2, S. 1-8.
  2. Statistisches Bundesamt (2020). Erwerbstätige nach Altersgruppen. Retrieved from https://www.destatis.de/DE/Themen/Arbeit/Arbeitsmarkt/Erwerbstaetigkeit/FAQ
  3. LOISTL, Otto: Chaostheorie : zur Theorie nichtlinearer dynamischer Systeme / von Otto Loistl und Iro Betz. - 3., erg. Aufl. - München ; Wien: Oldenbourg, 1996.
  4. FERNÁNDEZ DÍAZ, Andres; ESCOT MANGAS, Lorenzo; GRAU CARLES, Pilar. What´ s new and useful about chaos in economic science. 2012, S. 9.
  5. TSCHACHER, W.; BRUNNER, E. J. Organization and self-organization. In: Evolution of dynamical structures in complex systems. Springer, Berlin, Heidelberg, 1992. S. 382-391.
  6. WESTERMAYER, Gerhard. Organisationsdesign 4.0 von AZ. Springer Berlin, 2021.