One of the narratives in Russian propaganda refers to the tragic events in Odesa in 2014, when pro-Russian and pro-Ukrainian groups clashed on the streets. The unrest, fueled by provocations, escalated into violence, culminating in a fire at the Trade Unions House and multiple casualties. Propaganda dehumanizes the pro-Ukrainian side, ignoring the chaotic and unpredictable nature of mass social events.
This project explores the "butterfly effect," a concept introduced by mathematician Edward Lorenz, which describes how minor initial differences in chaotic systems lead to unpredictable outcomes. Almost all processes in the universe — from the motion of galaxies to human interactions — are chaotic, meaning that even an imperceptible variation in starting conditions can lead to drastic divergence.
The installation visualizes two chaotic systems running in parallel. At the beginning of each cycle, a new set of starting parameters is generated randomly using a thermal noise source, and these values are identical for both systems —except for a minuscule, randomly assigned difference of 10⁻¹² in one parameter. This tiny variation eventually causes the systems to unpredictably diverge.
When their accumulated difference reaches a threshold equal to the number of hours since 6:00 AM on February 24, 2022 (the moment of Russia's first missile strikes on Ukraine), the system resets and initiates a new cycle with a freshly generated set of synchronized parameters and another minor variation. These systems symbolize alternate realities where even an insignificant deviation can lead to disorder.
Their divergence feeds into a generative neural network trained on tranquil landscapes of Odesa and photo archives of the 2014 events. The result is a continuous stream of morphing abstract landscapes —serene when the systems remain close, but increasingly unsettling as chaos unfolds.
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Two generators continuously transmit conditionally random sequences of values by solving Chua's differential equations, which describe a chaotic system. These values illustrate the flow of events in two independent systems. The initial values are set by the program, but over time, they become chaotic and unpredictable.
The main program receives input values from the two generators, processes them, calculates the difference, and routes the data to the neural network modules and the tuning fork system. It also generates dynamic graphs and attractors in two-dimensional space, visualizing them on two displays, and assigns sound values to each event flow.
The neural network generates video sequences based on the incoming difference values between the two systems—the greater the difference, the more tragic the events depicted on the screen. The network has been trained on thousands of real images of Ukrainian and Russian cities during periods of both peace and war.
Chua attractors are complex mathematical objects that emerge in systems described by Chua's equation. These structures may appear random at first glance but actually follow specific patterns. Unlike familiar systems such as a pendulum or a clock, which can be predicted, chaotic systems like Chua attractors exhibit intricate and often non-repeating trajectories.On the screen, attractors for both systems are visualized in parallel, along with their corresponding values.
Graphs are used to visualize the dynamics of the two systems over time. In the early stages, the graphs are nearly identical, "coexisting in harmony" and generating similar values. However, as chaos within the systems grows, the difference between the values increases. When this difference reaches a critical point, a crisis occurs—indicated by the graphs turning red as a visual warning.
The power supply ensures the continuous operation of all modules. It connects to a standard 220V network and converts the voltage to the values required by the modules.
