Objective – Relative – Subjective: the ORS model

There are numerous emerging theories regarding the role of learning and observers in defining our reality, yet many of these are not readily testable through scientific methods. The goal here is to draw inspiration from cellular automata experiments to design robust simulation models that can be executed effectively. By running these simulations, we aim to generate and analyze results, comparing them with observable phenomena in the real world. This approach allows us to bridge theoretical concepts with practical observations, potentially offering empirical support or new insights into how learning processes may influence our understanding of reality.

The Objective-Relative-Subjective (ORS) model represents an approach in the simulation of complex physical systems, drawing inspiration from the foundational principles observed in cellular automata experiments. Cellular automata, simple models in computational theory, demonstrate how intricate patterns emerge from basic rules applied to cells on a grid. Similarly, the ORS model employs learning agents to explore the nuanced interactions between quantum physics and general relativity, two of the pillars of modern physics.

The significance of incorporating learning and observers in theoretical models is a burgeoning area of interest within physics and cognitive science. Emerging theories propose that the act of observation itself might influence the outcomes in a quantum system, potentially shaping the very fabric of reality. This notion aligns with quantum theory’s observer effect, where the state of a particle can be altered by the act of measurement. The scientific challenge lies in designing experiments or simulations that can test these theories under controlled conditions, a non-trivial endeavor given the complexities involved.

The motivation behind the ORS model stems from this very challenge. Traditional cellular automata provide a robust framework for simulating systems that evolve according to set rules, offering insights into pattern formation and system dynamics that parallel real-world phenomena. By leveraging a similar approach, the ORS model aims to create a testable, simulated environment where theories of quantum mechanics and general relativity can coexist. Such simulations are vital, not only for verifying theoretical predictions but also for exploring how different layers of reality might interact when subjected to the dual lens of these fundamental theories.

Scientific objectives of the ORS Model

The primary scientific objective of the ORS model is to simulate a multi-layered reality where distinct layers are governed by different physical laws, reflecting the complex structure of the universe. At the most external, the "objective" layer, quantum mechanics dominates, providing a stochastic, probabilistic foundation reminiscent of the quantum world's unpredictable nature. This layer forms the raw input for the model’s learning agents, who strive to decode and internalize these quantum phenomena.

?In the middle, or "relative" layer, the focus shifts to general relativity. Here, the global learner—a sophisticated AI algorithm—synthesizes the information gathered from the objective layer to construct a coherent model of reality. This layer effectively acts as a translator, rendering the probabilistic quantum events into the deterministic concepts familiar in classical physics. It represents an internal, emergent view of the universe, bridged between the unpredictability of quantum mechanics and the structured predictions of relativity.

At the heart of the ORS model is the global learner, a hierarchical assembly of learning agents, each layer nested within the other. This structure is designed to reflect the nested complexities of the universe, where each layer of understanding contributes to and refines the overall model. The global learner's role is critical as it attempts to integrate and reconcile the principles of quantum mechanics with those of general relativity, aiming to produce a unified view of reality from these seemingly incompatible theories. This not only tests the flexibility and adaptability of learning systems but also mirrors the scientific quest to find common ground in the theories that describe the universe at its most fundamental level.

Structure of the ORS model

The ORS Model is structured into three distinct layers, each serving a unique purpose in the simulation of physical realities. These layers are designed to interact with one another, allowing for the emergence of complex behaviors and insights into the foundational theories of physics.

Objective layer (Outer reality): At the outermost layer of the ORS model lies the domain of quantum physics, ruling with its intrinsic probabilistic and often counterintuitive principles. This layer acts as the primary input for the learning agents, offering raw quantum data that embody the uncertainty and dualistic nature of quantum mechanics. The learning agents at this level are tasked with deciphering these quantum signals, attempting to make sense of phenomena such as superposition and entanglement without the direct influence of classical physics. This foundation is crucial as it sets the stage for all subsequent layers of interpretation and model building within the ORS framework.

Relative layer (Middle reality): The relative layer serves as a pivotal bridge within the ORS model. Here, the global learner takes the stochastic data from the objective layer and begins the process of translation into the more deterministic language of general relativity. This middle ground is where quantum uncertainty is woven into a coherent spacetime fabric, adhering to the principles of gravity and large-scale structure as dictated by general relativity. It's in this layer that the probabilistic nature of quantum mechanics is reconciled with the deterministic worldview of classical physics, showcasing the model’s capacity to integrate these two fundamental but traditionally incompatible theories.

Subjective layer (Inner realities): Nested within the global learner are various local learners, each with its own subjective reality. These subjective realities are not isolated but are instead deeply influenced by both the objective inputs and the relative interpretations. Each local learner develops a unique perspective based on their interactions with the data, contributing to the overall knowledge and refinement of the global model. This layer highlights the individual variability and adaptability of learning agents, reflecting the diverse possible interpretations and integrations of quantum and relativistic principles.

Experimental goals and implications

The ORS model has several ambitious goals, each aimed at enhancing our understanding of the universe through advanced simulations.

Goals of simulations: The primary goal of conducting simulations within the ORS model is to observe emergent phenomena that can be analogues to those found in the actual universe. By simulating the interactions between quantum mechanics and general relativity, the model seeks to identify and predict novel phenomena that might not be readily observable in traditional experiments. These simulations are crucial for testing the validity and effectiveness of the ORS model in replicating and explaining complex physical realities.

Potential insights: One of the most significant potential insights from the ORS model relates to understanding complex physical phenomena such as gravity and spacetime curvature. By observing how these phenomena emerge from quantum-level interactions in the simulations, researchers can gain valuable clues about the underlying principles of the universe. Additionally, these insights could lead to a better understanding of black holes, quantum gravity, and the early moments of the Big Bang where traditional physics theories currently fall short. Through these simulations, the ORS model not only tests the boundaries of current scientific knowledge but also pushes the envelope in integrating complex systems into a unified understanding of reality.

The collapse of the wave function as a switch from the O to the R representation

There are several equivalent representations of an object in mathematics. For example, you can represent an object in cartesian, polar, cylindrical, spherical, or elliptic coordinates. You can represent a wave as a Fourier series in time-domain representation, frequency-domain representation, phase-space representation, wavelet representation, etc. Some calculations are easier in one representation, while other calculations are easier in the other representation. For example, you can start your calculation in cartesian coordinates, then at some point, switch to polar coordinates because the next calculations are easier in polar coordinates. This change of representation is very common in scientific calculation.

In the ORS model, we can test the assumption that the collapse of the wave function is a change from the O (fields) representation to the R (particles) representation. From the viewpoint of the global learner, when a measurement is made, it can only make sense of the results of that measurement by using its prior knowledge. That prior knowledge is stored in the R (particles) representation. The global learner has, therefore, no other choice than to switch from the O to the R representation after the measurement to compute the implications of the results of the measurement. It gives the impression that the wave has collapsed into a particle, but what really happened is that the global learner has switched to the particle representation to make sense of the results of the measurement and incorporate them into its already existing body of knowledge.

Conclusion

The ORS model represents a pioneering framework that integrates quantum mechanics and general relativity through the lens of advanced simulation and artificial intelligence. By constructing a multi-layered reality where each layer operates under different physical laws, the ORS model allows for a dynamic exploration of how these fundamental theories might interact and influence one another. The use of AI and machine learning within the ORS model is particularly significant. These technologies enable the model to simulate complex interactions that are otherwise too complicated to manage through traditional analytical methods. AI helps bridge the gap between quantum mechanics and general relativity, offering a unique platform where theoretical predictions can be tested, and new physics phenomena can be explored.

References:

Wolfram, S. (1984). Computation theory of cellular automata. Communications in mathematical physics, 96(1), 15-57.