Cybernetic Immunity
Cybernetic 3 (Cyb3) views the human immune system as more than a machine or a simple feedback loop. It treats the immune system as an active part of Levitating Organisational Resonance (LOR), which is the ongoing harmony between an organism and its environment. In this view, information shapes how organisms and their environments interact and respond. The immune system is a self-regulating and adaptive network shaped by constant change, helpful feedback, and a steady flow of information. It responds to signals from both inside and outside the body, which helps it stay strong and balanced. This system connects molecules and cells, biology and relationships, personal and social factors, physical and informational elements, and even the body and what some call the “soul”.
Traditional analogies between the immune system and cybersecurity tend to reduce the immune response to a mechanistic framework. For instance, while the immune system detects pathogens, cybersecurity detects malware/threats. Antibodies and T-cells can be likened to antivirus software and firewalls. Immune memory parallels threat databases; inflammation serves as a system alert; autoimmune diseases represent false positives; and immunodeficiency reflects security gaps. However, this mechanistic view overlooks the deeper, more nuanced interactions that define both systems. By shifting from systems as machines to systems as living resonances shaped by human action, we can better understand the LOR of the immune system and cybersecurity. The immune system functions as a conversation between the inner self and the outer world, just as cybersecurity embodies the coherence between users, systems, and social contexts. Subtle immune tuning through sleep and nutrition is like whispers, while the shouts of aggressive immune responses mirror alternative firewalls or lockdowns in cybersecurity. The observer becomes a participant through lifestyle choices, access to healthcare, and relationships, shaping security through behavior.
In this context, information is not merely a passive entity; it is a dynamic force that creates order only when acted upon or structured by humans. While information is not energy, it shapes the world powerfully through understanding, communication, and intentional design. The degree of order, alternatively, the degree of uncertainty in a system can be quantified using Shannon’s entropy. In information theory, Shannon entropy measures the unpredictability of information content. When humans engage with information, they reduce entropy and increase the informational order of a system or organization.
The immune system is a self-regulating system shaped by biology, memory, and history. But immunity is not just biological; it is also shaped by social, technological, psychological, and informational factors. Socially, health depends on access to resources, and immunity is built through education, culture, trust, and public policy. Technological advances like mRNA vaccines and antibiotics help the immune system by making it better at finding and fighting pathogens, while digital tools influence health care decisions. Psychologically, stress affects how the immune system works, since emotions, perceptions, and behaviors are closely linked to immune regulation. From an informational view, signals guide immune responses, and meaning influences actions. Feedback here shows human involvement, as people keep doing, sensing, and adapting.
We can model immunity as a system with many layers by assigning a probability (p) to each part. Here is an example:
In a complex environment, the immune system’s response gets signals from biological memory (p = 0.3), social context (0.15), technological input like vaccines (p = 0.25), psychological state (0.20), and informational signals (0.10). Each of these channels sends signals with different levels of predictability. We can calculate the entropy using:
H = - ∑ p * log2 * p
p = probability of each possible state or message
H = entropy in bits
H = - (0,30 * log2 *0,30 + 0,15 * log2 *15 + 0,25 * log2 0,25 + 0.20 * log2 * 0.20 + 0,10 * log2 *0,10) = 2,227 bits
The entropy H of 2.227 bits shows the level of uncertainty in the immune-informational system. Lower entropy means the input is more predictable and the immune system is better coordinated. Higher entropy means the input is less predictable and the immune system is less coordinated.
Noisy, unclear signals may point to immune dysregulation or failure. In the Cyb3 framework, the immune system shows how different factors interact, highlighting the importance of human involvement and understanding the context..
