The Pivotal Thought in Consumer Behavior and Its Mathematical-Physical Roots in Complex Systems such as Quantum Many-Body Systems

In our lives, shifts in behavior often occur in an instant, a ‘moment’s thought’ that profoundly influences our actions. The industry is becoming increasingly captivated by the principles underpinning consumer behavior. In reality, these behaviors merely represent the ‘localized centrality’ of the feature spectra in the decision-making space, manifesting outcomes in a steady state.

Regrettably, every instance of consumer behavior is gradually becoming a consequence of capital-driven influences - a migration of the steady point within the decision space’s feature spectra. This trend is becoming increasingly stark, propelled by the efficient advances of AI technology.

Our shopping lists accurately depict our choices for price, category, diet, and travel. Capable data scientists can easily map out our lifestyle needs and habits through our demand for products like low-fat, low-sugar yogurt, and purchase records of other various products. They deliver advertisements based on our media consumption, solidifying our product dependencies.

But are we genuinely making decisions, or has our ‘moment’s thought’ already been preempted by others?

The abundance and diversity of short video pushes might seem to offer us a sense of autonomous choice, but it often cultivates a ‘dependence’ within our consumer behavior - a familiar depiction of the ‘localized centrality’ of the feature spectra and its manifestation as a ‘steady-state solution’.

I am not attempting to devalue the importance of data analysis; rather, I am simply illuminating the reality that numerous advertisements, akin to cigarettes and soda, form part of the “dependency industry,” varying only in the extent to which they stimulate consumer reliance. Pushing further, industries such as alcohol, cigarettes, and gambling could even be categorized under a “Sin Industry.”

Understanding these issues leads us to a choice: The crux is not whether you pursue efficiency, but where you are willing to deploy these efficient technologies.

We can efficiently construct a dependency industry or, alternatively, an anti-dependency industry. I admire XAI for its remarkable choice of the latter, emboldening me to unveil the mathematical-physical principles underlying these behaviors.

Many individuals, during the process of training large models, find our understanding of the mathematical models driving large neural networks to be deficient. Faced with vast random matrices, our comprehension of the evolution of Hamiltonian matrices and the transitions of feature spectra is limited, leading us to rely on idealized tools like the Wigner semi-circle theorem, offering a glimpse into some of its inherent properties. However, these are merely confined, localized semi-realities.

Who says there’s nothing within the zero point of origin? Who asserts that the decision-making space cannot be distorted?

Do not let the profound jargon of physics and mathematics deter you! Witnessing a distorted balloon or soaring soap bubble - those are the physical phenomena in essence. Your decision-making space fundamentally aligns with the essence of these soap bubbles. This is not just a metaphor; rather, it’s the projection of different mapping methods.

New spaces can born from the zero point within a space. This implies that new Hamiltonian matrices can develop within existing ones, directly influencing the generation of another set of eigenvalue spectra from the reference points within the feature spectrum. The evolution of the eigenvalue spectrum signifies changes in steady-state solutions, leading to alterations in people’s behavioral characteristics.

Some scientists might worry that such an overly simplified description could cause misunderstandings and prefer evidence-backed arguments. In that case, let us shift this application scenario to a context they might consider more scientific - the transition from “quantum to classical”. Even though a system’s behavior might appear classical, subtle quantum effects might still lurk within. For instance, even at macroscopic scales, quantum phenomena like high-temperature superconductivity and quantum Hall effects may emerge. This is due to the spawning of a quantum space within the traditional macro space, leading to a quantum effect-representing feature spectrum becoming the dominant one.

In essence, this is the mathematical process underpinning “Quantum Many-Body Systems”.

In the real world, we encounter this embedded spatial construction everywhere. For instance, drug design often requires quantum chemical methods (a many-body problem) to predict interactions between drug molecules and biological targets. The sudden shifts in oil price predictions can also be modeled using this embedded space concept - the key difference lies in the type of information you incorporate into the boundary of the dynamical system.

During this process, we must acknowledge our responsibility. As data scientists and AI practitioners, we cannot merely aim to control and exploit consumer behavior. We need to strive to understand and respect consumers’ needs and rights. Consumers are not mere data points; they are individuals imbued with thoughts, emotions, and free will.

We could leverage AI to construct a series of addictive industries or, alternatively, build a system filled with empathy.

Fortunately, XAI opted for the latter. This decision is a blessing for all of us.

Xiaowen kang.

2023.7.15. 23.32

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