CYBERNETIC IMMUNITY
Cybernetic 3 (Cyb3) reimagines the human immune system not as a machine or mere feedback loop, but as an active participant in Levitating Organisational Resonance (LOR). LOR represents the dynamic coherence between an organism and its environment, where information plays an important role in shaping interactions and responses.
The immune system is a self-regulating, adaptive network shaped by dynamic oscillations, meaningful feedback, and a continuous exchange of information. It recalibrates in response to both external and internal stimuli, thereby enhancing resilience and supporting systemic stability. This system embodies a living resonance that intertwines molecules and cells, the biological and relational, the personal and social, the material and informational, as well as between the body and “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 are 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 human agency. 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 entity shaped by biological knowledge, memory, and history. However, immunity is not solely a biological function, it is deeply influenced by social, technological, psychological, and informational factors. Socially, health outcomes are shaped by access to resources, and immunity co-created through education, cultural practices, trust, and public policy. Technologically, medical innovations such as mRNA vaccines and antibiotics enhance immune responses by strengthening the body’s capacity to detect and combat pathogens, while digital platforms mediate and amplify human decisions in health care. Psychologically, stress plays a significant role in immune function, as immune regulation is closely tied to emotion, perception, and behavior. From an informational perspective, signals drive immune responses, and meaning shapes action. Feedback in this context reflects human engagement, an ongoing dynamic process that integrates doing, sensing, and adapting.
Immunity as a multilayered system can be modeled by assigning hypothetical probabilities (p). Here is one example;
The immune system’s response in a complex environment receives signals from biological memory (p = 0,3), social context (0,15), technological input (e.g.vaccines, p = 0.25), psychological state (0,20), and informational signals (0,10). These informational channels each contribute a signal with varying predictability. The entropy can be calculated with:
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 reflects the level of uncertainty across the immune-informational system. A lower entropy would mean more predictable input (better immune coordination) and a higher entropy suggests noisy unclear signals (possibly an immune dysregulation or failure). In this integrated Cyb3 framework, the immune system exemplifies a complex interplay of several dimensions, highlighting the necessity of human agency and contextual understanding.