As a systems engineer, I conduct personal research on topics such as the origin of life, the essence of intelligence, and the nature of society.
Especially with the advancement of biotechnology and AI, and the evolving era of communication based on Web3 or the upcoming Web4, the need to contemplate the positioning of life and AI, the essence of intelligence, and the interaction between technology and society is increasing. This forms the background of my personal research.
In this article, I propose to view complex systems like biology, intelligence, and society through the interaction of agents and objects possessing concepts.
Concept
A concept initially might be a mere thought or idea. Some concepts disappear without holding any meaning or value, but others demonstrate utility within the environment comprised of agents and objects.
Here, I extend the use of the word “concept” to refer to useful connections or structures in environments lacking intelligence. For example, a mechanism maintaining a constant temperature automatically can be seen as an agent with the concept of temperature control, even in the absence of intelligence. There are various phenomena in natural environments that can be treated as agents.
When mechanisms that preserve and reuse concepts operate, useful concepts accumulate in the environment or are replaced by more useful ones.
Such highly useful concepts are called ideals or beliefs in the realms of intelligence and society, where they are considered to have high universality. In the realms of non-intelligent subjects like chemical substances or primitive organisms, these are analyzed and understood as essences or principles.
Thus, in the complex structure of inanimate objects, living beings, intelligence, and society, numerous and diverse concepts arise incidentally, and the useful ones accumulate over time.
Agents and Objects
Objects maintain or change states based on laws.
Agents, possessing purposes or goals, act upon objects to fulfill these aims. Here, the purposes or goals held by agents are termed as concepts.
The objects that interact with agents include physical substances and structures, as well as information and its structures.
Typically, the mechanism of an agent is realized as a system combining hardware and software.
Hardware comprises physical substances and structures, while software consists of information and its structures. Therefore, agents interact with the systems that constitute their own and other agents’ mechanisms.
Furthermore, the essence of an agent, the concept, is also information and its structure. Thus, agents interact with their own and other agents’ concepts.
Interactions within Complex Systems
Let’s decompose and organize the term “interaction” here.
Agents comprehend the state of objects and, reflecting on their concepts, act to maintain or change the state of objects. This is the interaction between agents and objects.
Within complex systems, multiple objects and agents interact with each other. Moreover, substances and information change according to natural laws, even without the influence of agents.
After understanding the state of objects changed by natural laws or agents’ actions, agents again act upon objects in accordance with their concepts. This cycle causes complex systems to evolve over time.
Network of Concepts
The concept held by an agent is also embedded in the object. Thus, within the time evolution of a complex system, concepts themselves also change.
Concepts are the essence of agents. Without concepts, there are only objects following natural laws, and no agents would exist in the system.
Agents have varying levels of ability to align objects with concepts. Abstracting this, a complex system can be viewed as a collection of concepts, a network where concepts indirectly interact and evolve.
Alignment of Concepts and Objects
Focusing on the network of concepts, as the collection of concepts changes, so do the objects within the complex system.
Since agents act upon objects in light of concepts, the higher the agent’s capability, the closer the object aligns with the concept.
In a complex system, objects thus align (alignment) according to the network of concepts.
Of course, if the changes in objects due to natural laws are stronger, there is a divergence between concepts and objects. High capability in agents is necessary for the alignment of objects.
Evolution of Agents
The hardware and software that form an agent are also objects. If there is a concept that realizes agents with higher capabilities, that concept should be strengthened.
This suggests a tendency for the network of concepts to evolve towards enhancing the capabilities of agents over time. This is the evolution of agents due to the network of concepts.
As agents evolve, their ability to align objects becomes stronger than the changes caused by natural laws. Thus, within the complex system, there is an increasing trend for objects to align according to concepts.
Dynamic Changes and Diversity of Concepts
Concepts are not fixed, singular, or absolute entities; they are merely information that changes over time.
However, a collection of concepts that makes it easier for objects to align is reinforced and continues to exist.
The progression of object alignment also affects the network of concepts. If new collections of concepts emerge that can align a broader range of objects more strongly, previous concepts may be supplanted.
Also, objects do not exist uniformly within a complex system. Therefore, in each locality of the system, the collection of local concepts changes according to the characteristics of the group of objects present in that locality.
Thus, the network of concepts is always changing and acquiring a high degree of diversity.
The Ability to Express Dissatisfaction
It might seem that agents need complex mechanisms to act upon objects in alignment with concepts. However, agents can be realized with very simple mechanisms, one of which is the ability to express dissatisfaction.
To bring objects closer to a concept, it’s necessary to understand the nature of the object and predict the impact of the agent’s choices on the future state of the object. However, even randomly selected actions can occasionally bring about a state closer to the ideal. Simple agents aim for this.
However, if they continue to act randomly even after achieving a state that aligns with the concept, they may disrupt that state. Therefore, the minimal requirement for an agent is a mechanism that does nothing when the object is in a state aligning with the concept, and acts upon the object in any way, even randomly, when there is a misalignment.
Such minimally capable agents, bearing various concepts and appearing diversely, will evolve through natural selection and learning mechanisms within the network of concepts.
Evolution from Minimally Capable Agents
In the evolution of the concept network, minimally capable agents that initially just randomly manipulate objects as an expression of dissatisfaction will gradually evolve.
They might evolve to perform fixed actions instead of random ones, act in ways that bring objects closer to the concept, and vary the magnitude of their actions based on the degree of discrepancy with the concept.
As they become more advanced, they might respond appropriately to various patterns of discrepancy, predict future scenarios for control, and even dominate or collaborate with other agents for greater impact.
The Example of Neurons
One of the technologies for realizing artificial intelligence, neural networks, consists of neurons connected in a network.
Each neuron is a mechanism corresponding to an agent with minimal capabilities.
A neuron receives multiple signals as inputs and produces a single output signal. If we view neurons as agents, the input signals represent the state of the object, and the output signal represents the action on the object.
Inputs that lead to a larger output can be interpreted as having a greater discrepancy with the concept. Inputs that result in zero output align with the concept.
The response of a neuron can be expressed with relatively simple mathematical equations. Thus, the concept held by the neuron, as an agent, can be expressed in mathematical terms.
A neural network formed by connected neurons is, therefore, a network of agents. Focusing on concepts, a neural network is a network of concepts.
Since individual concepts can be expressed mathematically, the network of concepts formed by a neural network can also be expressed in mathematical terms.
In Conclusion
The network of concepts (Concept) has similarities to the Theory of Forms proposed by the ancient Greek philosopher Plato. While Plato assumed the existence of immutable Ideas, concepts in the network of concepts change.
However, if evolution stagnates at a point, concepts might appear constant during that period.
Objects represent reality, and the essence of agents is to bring reality closer to concepts. This aspect aligns with the Theory of Forms.
The network of concepts is a system model and analytical framework for understanding complex systems like biology, intelligence, and society.
By analyzing complex systems in accordance with this framework, I believe it’s possible to deepen our understanding of such systems.
Also, in my personal research on the origin of life, applying the framework of the network of concepts enables more profound contemplation.
Furthermore, in considering the impact of technology on society, I realized that agents powered by AI are key in the era of Web4. Based on the network of concepts, the essence of AI agents is also concepts. What kinds of concept-bearing AI agents we implement and utilize in society will become central to discussions in the era of Web4.
