An Interdisciplinary Research Report on the DIKWP -Entropy Structural Theoretical Model
Yucong Duan
International Standardization Committee of Networked DIKWPfor Artificial Intelligence Evaluation(DIKWP-SC)
World Artificial Consciousness CIC(WAC)
World Conference on Artificial Consciousness(WCAC)
(Email: duanyucong@hotmail.com)
Introduction
The DIKWP theoretical model proposed by Professor Yucong Duan, a five-layer cognitive framework integrating Data, Information, Knowledge, Wisdom, and Purpose, offers a novel perspective for re-understanding life, disease, and medicine. This model adds a "Purpose (P)" layer to the classic DIKW pyramid, incorporating goals and objectives at the apex of the cognitive chain, thus forming a complete closed loop from data to purpose. More importantly, Professor Duan views DIKWP as a network-like interactive structure where any two layers can be directly converted in both directions, forming 5x5, or 25, functional modules. This DIKWP×DIKWP product structure implies that in complex systems, information and energy can flow and couple freely at different levels, forming a dynamic, networked synergistic relationship.
In the fields of medicine and life sciences, Professor Yucong Duan further introduced the concepts of "information field" and "energy field," positing that the health status of a living organism is essentially the result of the coupling of these two fields. The information field refers to the collection of all meaningful information inside and outside the organism (such as the "semantic space" of gene expression, neural activity, and cognitive psychology), while the energy field refers to all energy flows that sustain life activities (such as bioelectricity, biomagnetism, and biochemical energy). A state of health corresponds to the orderly synergy of the information and energy fields, achieving dynamic balance and abundance. Conversely, disease manifests as the disorder and imbalance of the information-energy coupling. This viewpoint transcends the reductionist view of the human body as a mechanical structure or a mere biochemical reactor, revealing the essence of life through holistic systems thinking.
This report will delve into eight aspects of interdisciplinary discussion based on the above theories: (1) redefining core concepts such as life, disease, treatment, and needs/value/wealth; (2) constructing a product interaction map of the DIKWP model to clarify the information-energy exchange patterns between different subjects and elements; (3) exploring how to achieve precise control of entropy based on the DIKWP mechanism, shifting the medical paradigm from "lesion repair" to "energy-information reorganization"; (4) analyzing the systemic manifestations of disease at the individual, family, social, and institutional levels and their sociological and ethical consequences; (5) understanding treatment as a wisdom-driven entropy reduction behavior and discussing how doctors, artificial intelligence (AI), institutions, and culture can collaboratively construct a composite intervention system; (6) introducing Professor Yucong Duan's viewpoints and public literature (Zhihu articles, ResearchGate technical reports, etc.) as a basis for proposing a theoretical model extension of the "DIKWP-entropy" structure; (7) rethinking the future wealth and value distribution system based on DIKWP logic, and clarifying how the health economy can shift from a monetary scale to a production and exchange network based on system synergy and orderliness. The report will integrate views from information theory, bioenergetics, cognitive science, artificial intelligence ethics, and health economics, striving to strike a balance between philosophical depth and technological systematicity, with the aim of providing valuable references for interdisciplinary fields such as artificial intelligence ethics, philosophy of biology, and health economics.
Redefining and Expanding Core Concepts
Life
We redefine life as a system of coupled interaction between an energy field and an information field, the essence of which is a continuous entropy reduction process in the DIKWP×DIKWP dimension, that is, maintaining its own orderly structure and function by continuously acquiring and utilizing negative entropy. In classical physics, entropy represents the degree of disorder. The reason living organisms can maintain a high degree of organization in a universe tending towards chaos is precisely because life, as an open system, ingests "negative entropy" (i.e., ordered energy or information) from the environment to counteract internal entropy increase. The idea that "life feeds on negative entropy," first proposed by Schrödinger, explains how organisms convert free energy from the environment into their own organizational order through metabolism. In other words, life continuously introduces energy and information from the external environment by ingesting nutrients, breathing oxygen, and perceiving signals, so as to reduce its own uncertainty and disordered state.
Within the DIKWP framework, this process is embodied in multi-level information processing and energy utilization: acquiring raw perceptual and energy inputs at the data layer, ascending to the information and knowledge layers for pattern recognition and meaning construction, making decisions at the wisdom layer, and putting them into action at the practice/purpose layer, thereby transforming raw external resources into internal ordered structures and cognitive achievements. The continuation of life means that this cross-level cycle continues to operate, and the overall entropy of the system is continuously suppressed (local order increases). For example, humans obtain high-free-energy food through ingestion to maintain their physiological structure, and acquire new data and knowledge through learning to enrich their cognitive structure. These are all manifestations of improving the orderliness of the system in both energy and information dimensions. In summary, life is an open, adaptive system that, through the exchange of energy and information with the environment, continuously undergoes self-organization and self-regulation, thereby carving out neat order from chaos. This is precisely the fundamental feature that distinguishes living systems from non-living systems.
Disease
Based on the above definition of life, disease can be redefined as an entropy increase phenomenon caused by the imbalance of the energy field-information field coupling. That is to say, when the information exchange within and outside the life system is blocked or distorted, and the energy supply and regulation are disordered, the orderliness of the system decreases and the entropy value increases, which manifests as a state of disease. Professor Yucong Duan's theory of proactive medicine points out: "When information exchange is not smooth or energy is disordered, disease may arise." This statement reveals that whether it is the dysregulation of information transmission between cells within the organism (such as signal pathway disorders caused by gene mutations), the imbalance of the body's overall energy metabolism (such as endocrine disorders or nutritional deficiencies), or the destruction of the normal order of information-energy by external environmental pathogens, toxins, etc., it will cause an increase in entropy, causing the system to move from order to disorder, which is specifically manifested as physiological dysfunction and the appearance of clinical symptoms.
The perspective of information theory can help explain the disease process: under normal circumstances, the body maintains various feedback regulation mechanisms to reduce uncertainty (maintain homeostasis), while during disease, these mechanisms fail, and the body's information processing of internal changes and external stimuli becomes chaotic or sluggish, which is equivalent to an increase in system entropy. For example, diabetes can be regarded as a typical case of energy and information imbalance: the insulin signal transduction (information field) is obstructed, so that glucose cannot be effectively used by cells (energy field disorder), leading to an uncontrolled rise in blood sugar levels (increased system disorder). Another example is infectious diseases, where pathogens destroy the body's original immune surveillance information network and local tissue energy balance, and the body falls into a high-entropy chaotic state (fever, inflammation, etc. are all manifestations of this entropy increase).
The expression in traditional Chinese medicine, "When healthy qi is present within, pathogenic factors cannot invade," vividly illustrates the relationship between internal order and external interference: when the healthy qi (healthy energy) in the body is sufficient and runs smoothly, external pathogenic factors (factors that disturb the balance of information and energy) cannot invade; conversely, deficiency of healthy qi or disorder of qi mechanism (imbalance of the internal energy field) gives disease an opportunity. Therefore, disease essentially reflects the decline in the orderliness and the destruction of the synergy of the life system, and it is a specific manifestation of rising entropy value. We should view disease from a systemic perspective, paying attention not only to specific pathological changes at the molecular and cellular levels, but more importantly to the overall imbalance of the information-energy dual field, and seek methods to restore order at a higher level.
Treatment
From the DIKWP-entropy perspective, treatment can be defined as a process of wise intervention in the life DIKWP system to achieve the re-synergy of energy and information, thereby reducing the overall entropy of the system. Simply put, treatment is a wisdom-driven entropy reduction behavior, and its purpose is to restore or enhance the orderliness and functional coordination of the system. Traditional medicine regards treatment as the process of "curing disease," that is, correcting local pathological changes. In the new paradigm, we place more emphasis on "treating the person" and "treating the system," reorganizing the patient's information and energy fields through multi-level intelligent intervention, so that the disordered system can return to an orderly balance.
This contains several levels of meaning:
·First, at the data and information level, treatment requires obtaining sufficient and accurate patient data (such as symptoms, signs, and test results) and extracting key information to reduce the uncertainty of diagnosis and treatment decisions (information entropy decreases). For example, using artificial intelligence to screen massive amounts of physical examination data can detect abnormal signals early and extract risk information that was traditionally hidden.
·Second, at the knowledge and wisdom level, the treater (doctor or AI) needs to integrate and judge this information with medical knowledge, choose appropriate intervention measures, and weigh the pros and cons and ethics (this is a decision-making chain from knowledge to wisdom, and then to purpose). The role of wisdom is particularly critical here: as Professor Yucong Duan emphasizes, "dynamic balance: neither excessive nor deficient." Medical intervention must grasp the proper measure. Too strong an intervention may cause new disorders (entropy increase), while too weak an intervention is not enough to correct the original imbalance (entropy cannot be effectively reduced). For example, when controlling high blood pressure, one cannot suddenly drop the blood pressure from 180 mmHg to 120 (excessive intervention will cause the risk of hypoperfusion), nor can one let it continue to be high (lack of intervention leads to organ damage), but should gradually adjust the blood pressure to a balanced range according to the patient's tolerance. This grasp of "degree" reflects the wisdom in medicine, which is to prevent overcorrection from introducing new entropy-increasing factors during the entropy reduction process.
·Finally, at the practice/purpose level, treatment requires the synergistic cooperation of patients, doctors, and even the entire medical system, in order to clarify common health goals and work towards them persistently. Treatment is not only a one-way action of the doctor on the patient, but also a process of co-evolution between doctor and patient: the patient actively participates and manages themselves, the doctor provides knowledge and guidance, and AI assists in monitoring and feedback when necessary. This synergistic practice itself is also a process of reducing entropy—through feedback regulation and continuous improvement, the intervention becomes more and more precise and effective, and the system gradually moves towards a stable and orderly state.
In short, the success or failure of treatment depends on whether multi-level wisdom can be mobilized to guide the disordered and chaotic system back to a low-entropy state of smooth information and balanced energy. This requires medical practice to be elevated from "fighting disease" to "rebuilding system order," that is, through the comprehensive use of drugs, surgery, psychological counseling, energy therapy, and other means, to reshape the normal structure of the human information-energy network, and truly realize that "curing disease" is "curing chaos", and restoring the self-organizing ability of the life system.
Needs, Value, and Wealth
In the framework of DIKWP model output coupling, we can re-understand needs, value, and wealth in economics, viewing them as structural expressions of the improvement of system synergy and the expansion of orderliness. Traditional economics mainly measures value and wealth in terms of currency. However, currency often only reflects transaction prices and may not truly measure changes in system orderliness. For example, a society may have a high GDP but suffer from serious resource waste and deterioration of the environment and health (entropy is accumulating). From the DIKWP-entropy perspective, this cannot be regarded as a real "increase in value." Therefore, we advocate the introduction of "synergistic orderliness" as a new measure of value: that is, the extent to which an activity or output enhances the overall synergistic effect and order level of the system. If an economic behavior promotes more efficient circulation of information, fuller sharing of knowledge, and wider application of wisdom, thereby reducing uncertainty and waste, then it objectively reduces the system's entropy value and increases its orderliness. This gain can be regarded as the creation of value. Conversely, if an activity is profitable but leads to environmental degradation and social disorder (entropy increases), then from a new perspective, it is consuming value rather than creating it.
Specifically:
·Needs can be understood as the drive or deficiency that appears at a certain level of the system in order to reduce entropy. Human needs (health needs, knowledge needs, etc.) often reflect the yearning for a higher state of order. For example, health needs are to get rid of physical disorders and return to balance (reduce physiological entropy), and the desire for knowledge is to reduce the unknown about the world (reduce cognitive entropy). Therefore, needs essentially reflect the direction of system self-optimization. In the DIKWP model, needs can correspond to the pursuit of a more orderly state at the purpose level, such as personal health goals and corporate strategic visions, which are all expectations for "higher synergy."
·Value is embodied in the net increase in orderliness during the process of satisfying needs. When a service or product satisfies a need, causing the system's synergy to rise and entropy to decrease, it creates value. This value is not only reflected in the improvement of utility at the individual level, but also in the enhancement of the entire network's synergy. For example, an innovative medical AI system can integrate massive amounts of data to provide decision support for doctors, improving diagnostic accuracy and prevention. It reduces the uncertainty of misdiagnosis and missed diagnosis (information entropy decreases), and at the same time optimizes the efficiency of doctor-patient interaction. This actually creates huge synergistic value for the medical system, even if it is difficult to measure directly in monetary terms. Another example is the value created by education, which lies in transforming scattered data and information into human knowledge and wisdom, and then these talents play a role in various fields of society. In this process, education releases human potential through the benign interaction of various DIKWP modules, significantly improving the orderliness of social operation.
·Wealth can be redefined as the sum of accumulated orderliness and synergistic capabilities within a system. At the individual level, wealth includes not only material assets, but also the accumulation of "ordered structures" such as knowledge reserves, health levels, and interpersonal networks. At the social level, wealth should be reflected in social capital, institutional efficiency, and cultural cohesion. Future health economics may pay more attention to these intangible but crucial forms of wealth. For example, if a community has a complete health management system (smooth information flow, timely and effective intervention), even if the per capita income is average, it can be regarded as having a high level of "health wealth." Because this system means lower disease entropy, higher quality of life, and productivity, it is a substantial improvement in orderliness. Conversely, if a society with a high GDP is full of environmental pollution, disease prevalence, and social conflicts, then its apparent wealth is large, but its effective wealth (orderliness) may not be high.
It is worth noting that this shift in the concept of value is not out of touch with real economic trends. More and more enterprises and policymakers are beginning to emphasize Purpose-driven and ESG (Environmental, Social, and Governance) indicators, focusing on long-term social value rather than short-term profit maximization. The research of Yucong Duan's team in the economic field also points out that future consumers and partners will pay more attention to the social responsibility and synergistic contribution of enterprises, requiring enterprises to coordinate their own profit goals with social value goals. This is actually incorporating system orderliness into the value evaluation system—enterprises need to prove that their business has contributed to the promotion of overall social sustainable synergy (such as environmental protection, public health, knowledge sharing), and not just creating monetary wealth.
From this, it can be inferred that the measurement indicators of the future "health economy" may no longer rely solely on monetary scales such as GDP, but will consider indicators that reflect synergistic orderliness, such as healthy life expectancy, national knowledge level, and community mutual aid. This value distribution system, guided by system entropy reduction, will guide resources to flow to those fields that can truly enhance overall orderliness, such as basic education, public health, clean energy, and AI-enabled smart healthcare.
In summary, under the DIKWP logic, needs, value, and wealth are endowed with the connotations of systems theory and information entropy: needs drive entropy reduction, value reflects the results of entropy reduction, and wealth accumulates the effects of entropy reduction. The key to measuring the success or failure of economic activities should not only be the increase or decrease of monetary figures, but more importantly, whether it improves the degree of system synergy and order. Only when the entire society develops towards higher information integration and energy utilization efficiency can humanity truly obtain greater "wealth." This redefinition outlines a new blueprint for future health economics for us: an economic form with knowledge and health as its core assets and a synergistic and orderly network as its exchange platform, in which currency recedes to the status of a measurement tool, and the real measure of value comes from the continuous optimization of life quality and system harmony.
Information-Energy Interaction Map of the Life System Based on the DIKWP×DIKWP Model
The product interaction structure of the DIKWP model (DIKWP×DIKWP) reveals the complex information-energy exchange patterns between different subjects and elements in the life system. This theoretical map can help us understand how interactions at various levels, from the microscopic individual to the macroscopic environment, constitute the network of life activities through the connection of data, information, knowledge, wisdom, and purpose. This section will explain this from several key dimensions:
·Individual and Environment: As an open system, a living organism constantly exchanges matter and information with its environment. Individuals ingest energy and matter from the environment through breathing and eating, while releasing metabolic products and heat to maintain the stability of their internal energy field (physiological negative entropy inflow and positive entropy outflow). At the same time, individuals obtain a large amount of data (light, sound, smell, etc.) from the environment through their sensory organs, and transform it into meaningful information and knowledge in their brains to adapt to survival. The environment, in turn, influences the individual's intentions and behaviors through feedback. For example, environmental information such as climate change and virus epidemics will trigger corresponding intelligent decisions (risk avoidance, medical treatment) and practical actions in individuals. This individual-environment coupling can be represented by the DIKWP×DIKWP map as follows: on the one hand, the environment provides raw data and energy input, which is digested and absorbed by the individual's DIKWP link; on the other hand, the individual's behavior (practical output) leaves changes in data and energy in the environment (such as carbon emissions, biological signals), becoming part of the environmental information field. The continuation of life requires the balance of this two-way interaction: when the information and energy exchange between the individual and the environment is smooth and balanced, life is in a healthy and stable state; if the environmental supply is unbalanced (such as food scarcity) or the environmental information exceeds the individual's coping range (such as drastic climate change), it will have an impact on life, and the increase in entropy will intensify, leading to stress and even disease.
·Technology and Ethics: In modern life systems, technology (especially information technology and biotechnology) and ethics (human values and norms) constitute an important set of interactive factors. Technology is like a driving factor at the lower levels of the DIKWP link. It can collect a large amount of data, automatically extract information, quickly form knowledge, and partially simulate intelligent decision-making. For example, medical AI can find signs of disease before the diagnostic process begins. Ethics and values are equivalent to guiding factors at the higher levels, belonging to the wisdom and purpose levels, and determine the direction and boundaries of technology application. For example, we can develop very powerful gene editing technology (a strong intervention method at the energy level), but wisdom and ethics will weigh how this technology should be used in medicine: is it for treating genetic diseases, or could it be abused to enhance and transform humans? In the DIKWP map, this is reflected in the fact that technological capabilities (capabilities at the D/I/K levels) need to be constrained and guided by the wisdom/purpose levels. The information-energy exchange pattern between the two can be understood as: technology provides information input of real possibilities for ethics ("what we can do"), while ethics sets the output of goals and norms for technology ("what we should do"). Only when the development of technology and the consideration of ethics form a positive feedback—technology allows more information to be seen and understood, while ethics ensures that this knowledge and wisdom are used to enhance the order of the life system rather than creating new disorders—is the coupling of the two benign. Professor Yucong Duan's team strongly emphasizes the introduction of ethics committee rules and patient wishes into AI decision-making in AI healthcare, giving AI guidance at the wisdom level so that its decisions conform to human values. This is an attempt to embed high-level purpose and value into low-level technological practice in the DIKWP model. In general, the interaction between technology and ethics ensures that the utilization of the information field and the energy field is both efficient and compassionate: the former ensures that "we can," and the latter ensures that "we should," and together they constitute the twin wheels of the development of the life system in the technological era.
·Doctor and Patient: In the medical context, the information-energy exchange relationship between doctors and patients is one of the core factors affecting health outcomes. Traditionally, doctors are providers of information and knowledge, and patients are recipients and objects of energy regulation. However, under the concept of proactive medicine, the doctor-patient relationship is reshaped into a two-way synergy. Patients are no longer passive "repair machines," but are regarded as proactive partners, and both parties jointly construct the DIKWP cycle in the treatment process. Specifically, patients provide their own subjective feelings and objective data (symptom descriptions, living habits, test results, etc.) as data/information input, and doctors use medical knowledge to interpret and integrate them into diagnostic conclusions and treatment plans (processing at the knowledge/wisdom level). Next, the doctor transforms the treatment purpose into specific practice (such as prescriptions, surgery), and intervenes in the patient's physical energy field in this process (such as drugs changing metabolism, surgery repairing tissues). The patient's feedback (whether symptoms are relieved, feelings of side effects, etc.) is collected by the doctor as new data and enters the next round of the DIKWP cycle. In this back-and-forth process, the information fields of the doctor and patient gradually connect, and the energy fields gradually synchronize, forming a joint treatment field. Professor Yucong Duan's research further proposes that a DIKWP map can be established for the diagnosis and treatment process of different doctors and compared with the standard knowledge map to evaluate the quality of diagnosis. In the future, the three parties of doctor-patient-AI may collaboratively construct their respective DIKWP models and compare and communicate with each other to make up for their respective blind spots and jointly improve the understanding of the patient's information field. This means that both doctors and patients must play an active role—patients actively monitor and report their own status, and doctors actively listen and adjust their strategies. Under the guidance of a common purpose (recovery goals), both parties continuously correct their paths, just as some people describe it as "co-creating health." In such an interaction model, doctors and patients not only exchange information (knowledge and suggestions related to diagnosis and treatment), but also exchange energy (for example, trust itself is a positive psychological energy that contributes to the curative effect). When the information transmitted by the doctor can be fully understood and absorbed by the patient, and the patient cooperates to take action and produce positive feedback, the doctor-patient system enters a virtuous cycle, and the self-organizing healing power is greatly enhanced (the entropy of the entire system decreases, tending towards order). Conversely, if the doctor-patient communication is not smooth and trust is lacking, the information field will be blocked and the energy field will be opposed, and the treatment effect will often be greatly reduced. Therefore, an efficient medical system should create conditions for more transparent and sufficient information exchange between doctors and patients (such as sharing medical records and strengthening health education), and for integrating the patient's wishes and values into decision-making (respect at the wisdom level), so as to truly build a synergistic treatment consortium and achieve an entropy reduction effect of "one plus one is greater than two."
·Artificial Intelligence (AI) and Humans: As artificial intelligence becomes more and more deeply involved in healthcare and life science research, the interaction between AI and humans has also become an important part of the information-energy flow map of the life system. AI can be regarded as a new type of "cognitive subject," with powerful data processing and knowledge extraction capabilities, but it lacks high-level wisdom such as human emotion and value judgment. Therefore, the model of human-computer collaboration is usually: AI is good at providing support at the D/I/K levels, such as finding early lesions from massive medical images and predicting risks from real-time physiological signals; while humans (doctors or patients) conduct prudent evaluation and decision integration of AI's output at the W/P levels, considering ethics, preferences, and the overall situation to take action. This complementary relationship ensures that the information field can be perceived more comprehensively, and at the same time, decisions can be more in line with human nature and values. Professor Yucong Duan and others have proposed that through the DIKWP model, AI can have a higher level of "thinking" ability, for example, by referring to patient values at the wisdom and purpose levels to formulate personalized plans. Although AI currently mainly processes data and knowledge, researchers hope that in the future, AI can partially "understand" human intentions and values, thereby becoming a trustworthy medical assistant. It is conceivable that future diagnosis and treatment may be a human-computer co-diagnosis model: doctors and AI each construct a DIKWP map, compare and communicate with each other, and learn from each other's strengths—doctors make up for AI's lack of emotional empathy, and AI makes up for the blind spots of limited human experience, and finally both parties jointly improve the cognition of the patient's information field. For example, AI can quickly point out possible diagnoses at the data→information→knowledge level, while doctors adjust treatment decisions at the wisdom→purpose level in combination with the patient's family situation and personal wishes. In treatment practice, an AI assistant can analyze the new data generated by the patient every day, judge whether the intervention effect is developing towards the expected goal, and give timely warnings when there is a deviation, providing suggestions for doctors and patients. Humans, on the other hand, review and choose AI's suggestions based on their own feelings and value trade-offs. This human-computer interaction actually constitutes a larger DIKWP network: the combination of human experiential wisdom and machine computational intelligence makes the management of the information-energy of the life system reach an unprecedented level of refinement. Through this synergy, we are expected to achieve true precision medicine—both objective analysis supported by huge data and people-oriented subjective care, both of which work together to reduce the uncertainty of disease and improve the effectiveness of intervention (that is, reduce the entropy of the entire medical process). It is worth emphasizing that human-computer collaboration needs to be vigilant against new risks (such as algorithm bias, data security) to avoid introducing new entropy sources. Therefore, when introducing AI, it is even more necessary to strengthen ethical supervision and continuous monitoring to ensure that this composite system remains within a controllable and orderly range.
Through the analysis of the above dimensions, it can be seen that the theoretical map constructed by the DIKWP×DIKWP model integrates the interactions of different subjects (individuals, doctors, AI, etc.) and different elements (environment, technology, ethics, etc.) in the life system into a unified framework: they form a complex networked synergy through the two-way coupling of data, information, knowledge, wisdom, and purpose. Each pair of interactive relationships involves the exchange and balance of the information field and the energy field, and each interaction mode may have an impact on the system's entropy—either reducing entropy and promoting order, or vice versa, adding chaos and increasing disorder. Therefore, we must consider life and medical issues from a global perspective, and pay attention to whether the cross-subject and cross-level information-energy flow is smooth and orderly. Only when the main coupling relationships in the entire map tend to be synergistically balanced (information is fully shared, energy is reasonably distributed, and decision-making goals are consistent) can the life system maintain a healthy and stable state and evolve to a higher level. This theoretical map also has guiding significance for our understanding of macroscopic phenomena such as public health (the overall information/energy coordination of society) and medical policy (the game between technological development and ethical norms)—because it reminds us that all these intricate problems are ultimately entropy governance problems in the DIKWP network, that is, how to reduce disorder and promote synergy in many interactive links.
Precise Entropy Control Intervention Based on the DIKWP Mechanism: Medicine's Shift from "Lesion Repair" to "Energy-Information Reorganization"
Modern medicine is undergoing a paradigm shift from a disease-centered "reparative" intervention to a health-centered "reorganizational" intervention. Under the traditional medical model, treatment often focuses on the repair of local lesions, such as eliminating pathogens with drugs or removing diseased tissues with surgery. This method is effective in many cases, but it also has limitations: it ignores the systemic background of disease, ignores the decisive role of global information-energy balance on health, and therefore sometimes treats the symptoms but not the root cause, and even leads to new imbalances (such as the overuse of antibiotics causing gut flora disorders). With the help of the DIKWP model and information theory, we can propose the concept of "precise entropy control intervention," that is, by precisely regulating the entropy (disorder) of the life system at different levels, we can achieve health maintenance and disease prevention/treatment. This means that medical intervention will no longer be just a passive repair of existing damage, but an active reorganization of the distribution of energy and information, so that the body as a whole can return to order and synergy.
From the perspective of information theory, the occurrence of disease is often accompanied by an increase in the uncertainty of physiological parameters, an increase in signal noise, and the failure of regulatory feedback. These can all be regarded as signs of entropy increase. Therefore, the first step of precise entropy control is to improve the ability to perceive and predict the information state of the system. Modern technology (such as wearable devices, smart medical sensors, and big data analysis) allows us to continuously obtain massive amounts of biological data and mine early abnormal patterns from it through algorithms. For example, AI can analyze thousands of electrocardiogram signals, detect minute abnormal changes, and predict the risk of heart failure several years before symptoms appear. Another example is a health warning system that combines daily life data, which can capture signs consistent with the early patterns of a certain disease and remind users to check before they are aware of any discomfort. These applications essentially use the rich data of the information field to reduce the uncertainty of medical decisions—that is, to reduce entropy. Instead of waiting until the disease symptoms are obvious and the system is already highly disordered to intervene (passive medicine), it is better to actively intervene when the entropy is just beginning to accumulate and nip it in the bud. Proactive medicine emphasizes this concept of "acting before the disease manifests."
Information theory provides quantitative tools, such as using Shannon entropy to measure the complexity or regularity of certain physiological signals, which can help determine whether the system has deviated from the healthy and orderly baseline. Precise entropy control requires us to develop advanced bioinformatics indicators, establish a normal-state information-entropy "fingerprint" for each individual, and issue an early warning once it deviates.
In terms of the energy field, precise entropy control intervention emphasizes the reorganization of energy metabolism and balance. Many chronic diseases (diabetes, obesity, chronic inflammation, etc.) originate from the imbalance of energy intake, storage, and consumption, which is an increase in the entropy of the energy field. Through metabolic monitoring and intervention (such as personalized nutrition, exercise prescriptions, and metabolic means), the disordered energy flow can be gradually redirected to the normal track. This requires a finely customized plan based on each person's metabolic characteristics (such as basal metabolic rate, hormone rhythm, etc.), rather than a "one-size-fits-all" approach. The combination of information technology makes this possible. For example, continuous glucose monitors and AI algorithms can adjust insulin delivery in real time to maintain stable blood sugar and reduce fluctuation entropy. Wearable sports devices track all-day activities and consumption, providing users with energy balance advice.
More cutting-edge, such as the use of bioenergetics methods—low-intensity electromagnetic field stimulation, light therapy, biofeedback, etc., directly act on the body's energy field to promote tissue repair and balance. These therapies lacked theoretical support in the past, but can be re-evaluated under the information-energy binary model. For example, pulsed electromagnetic field (PEMF) therapy has been used to promote fracture healing and relieve joint pain. Its mechanism can be regarded as injecting ordered energy into the local energy field to reduce the entropy of damaged tissues, thereby accelerating self-organized healing. Transcranial magnetic stimulation, by introducing electromagnetic energy of appropriate frequency into the brain's energy field, may regulate the synchronicity of neural networks and have a therapeutic effect on depression and other conditions. These cases show that energy intervention targeting entropy control has great potential in medicine.
Third, at the knowledge and wisdom level, precise entropy control means cross-disciplinary knowledge integration and intelligent decision optimization. Because to reduce the entropy of a bio-social system, we need to consider multiple factors and use multiple means, which inevitably requires the integration of Western medicine's anatomical and physiological knowledge, traditional Chinese medicine's theory of energy balance, psychology's understanding of mind-body interaction, and sociology's insights into behavior and environment. The DIKWP model encourages the integration of this multi-disciplinary knowledge: Western medicine is good at providing knowledge at the data and mechanism level, while traditional Chinese medicine and other traditional medicines provide empirical wisdom on energy field balance. The combination of the two is expected to form a more comprehensive cognition. At the wisdom level, we need to combine this knowledge with ethical values to formulate intervention strategies that are both effective and acceptable. For example, AI can recommend an optimal method for controlling blood sugar, but the wisdom level will consider the patient's lifestyle and wishes, and choose a plan that the patient is more likely to adhere to. The wisdom level also involves strategies for medical resource allocation: at which stage to use high-intensity intervention and at which stage to use conservative therapy is a trade-off between entropy and benefit. The practice/purpose level ultimately implements these intelligent decisions and implements them through policies, clinical guidelines, and other forms. Under the framework of proactive medicine, medical practice is moved forward, that is, health management is actively carried out before serious diseases appear. This is reflected in preventive vaccination and health education in public health, and regular screening and personalized guidance in clinical practice, all of which are to reduce the risk of future disease entropy outbreaks in a large population.
It can be seen that "precise entropy control" represents a new medical paradigm under the concept of systems: it is not a local repair that treats the head when the head aches and the foot when the foot aches, but a comprehensive adjustment of the life network through information-driven insights and energy-synergistic adjustments, so that it remains in a low-entropy, high-order health domain. This is the biggest difference from traditional medicine: the former sets the goal of maintaining and enhancing the complex and orderly degree of system organization, rather than just eliminating a certain lesion. As Professor Yucong Duan said, proactive medicine views the human body as a dynamic whole, advocating for the early identification of body signals and the balancing of body states to prevent disease and promote comprehensive health. This is essentially the embodiment of the idea of entropy control: instead of waiting for the system to collapse and then cleaning up the mess, it is better to monitor and adjust it at all times to stabilize the system beyond the critical point.
Information theory, bioenergetics, cognitive science, and artificial intelligence technology play key roles in this transformation. Information theory provides a unified measurement and analysis framework, enabling us to quantitatively evaluate changes in entropy (whether it is the entropy of physiological parameter fluctuations or the uncertainty of diagnostic prediction). Bioenergetics expands the means of intervention, from molecular drugs to physical energy therapies, enriching our toolbox for entropy control. Cognitive science reminds us of the role of human behavior and psychological expectations in health (for example, stress can lead to increased physiological entropy and immune disorders), so interventions must also include entropy reduction at the psycho-social level (such as meditation and psychological counseling to reduce psychological entropy). Artificial intelligence is an accelerator for implementing precise entropy control: AI can find clues of entropy increase in massive amounts of data, and even control system parameters in real time in a rapidly changing environment (such as an intelligent infusion pump that automatically adjusts the drug dosage according to the patient's condition to keep physiological indicators stable).
In the future, we can imagine a holographic health network: each person is portrayed by a digital twin, integrating the state of their biological information field and energy field. The entropy value of this digital body will be monitored in real time, and when an abnormal rise occurs, the corresponding intervention (reminder, medication, doctor's appointment, etc.) will be quickly triggered and highly customized, thus resolving the risk before it materializes. At a more macro level, public health managers can also use the entropy indicators of population health information to predict the trend of epidemics or chronic diseases, and allocate resources and carry out educational interventions at the community level in advance. These are all applications of "entropy control medicine" at different scales.
In summary, medicine is transforming from the passive role of "repairing what is broken" to the active role of "maintaining system order." The DIKWP model allows us to realize that the chain of data-information-knowledge-wisdom-practice runs through the entire process of entropy control intervention, and innovation in any link may bring about a qualitative change in the whole: more sensitive data acquisition reduces information entropy, more profound knowledge integration reduces understanding entropy, more humane intelligent decision-making avoids intervention-induced entropy increase, and synergistic practical execution ensures the realization of entropy reduction goals. Through these efforts, medicine is expected to fundamentally change the relationship between humans and disease—no longer a firefighting team that is one step behind, but a forest ranger who walks ahead, guarding the forest of life from the ravages of the fire of entropy.
The Cross-System Manifestations of Disease and Its Sociological and Ethical Impacts
Disease is not only a biological phenomenon but also a cross-system, multi-level social phenomenon. A person's illness often affects their family, workplace, and even their entire social network. The prevalence of a disease can also expose dysfunctions at the level of the social system. In other words, disease can be seen as the manifestation of the disruption of the overall energy-information flow at different system levels. Understanding the cross-system impact of disease helps us develop more comprehensive response strategies and anticipate its potential sociological and ethical consequences.
We can analyze the cross-system manifestations of disease at four levels:
·Individual Level: This is the most direct level of disease manifestation, involving an individual's physiological and psychological changes. Disease disrupts the balance of information and energy within the individual, causing them to fall from a healthy, self-organized state into disorder. Physiologically, this manifests as symptoms and functional impairments, such as pain, fever, fatigue, and organ failure. Psychologically, it can trigger emotional stress, cognitive decline, and increased mental pressure. The increase in entropy at the individual level is not just the abnormal fluctuation of physical parameters but also the imbalance of the psychological information field (for example, people with serious illnesses often experience a shock to their beliefs and sense of meaning, which can be seen as a disruption in the information field of their life value system). The experience of disease at this level is highly subjective and has ethical significance—illness challenges an individual's maintenance of autonomy and dignity. Ethically, principles such as respecting patient autonomy in medical decision-making (informed consent), protecting patient privacy, and not discriminating against patients are responses to the predicament of disease at the individual level. When a person becomes ill, their life trajectory may change, for example, work interruption and role transformation. This involves a re-evaluation of personal values and psychological adjustment, which is actually a process of reorganizing the individual's information field. Helping patients rebuild their self-meaning in illness (e.g., through psychological support and social care) is an important part of reducing their psychological entropy.
·Family Level: A family is likened to an "energy field of emotion and support." When one member becomes ill, this small system is also impacted. Family members experience changes in the information field (message passing, jointly facing medical information) and the energy field (reallocation of time, energy, and money; caregiving labor). For example, if either parent is sick, the children may need to take on more housework or emotional burdens. If a child has a serious illness, the financial and emotional pressure on the entire family increases dramatically. The increase in entropy in the family system is reflected in the disruption of daily order and the dysfunction of roles (e.g., the original supporter becomes the one being cared for). However, the family is also often a buffer for entropy reduction: the love and responsibility between members will prompt them to invest extra energy to maintain the order of the family, such as relatives taking turns caring for the patient, raising funds, and sharing emotions. This mutual aid behavior can be seen as the spontaneous reorganization of the family's energy field to resist the chaos brought by the disease. Studies have shown that a family with a good support system can significantly improve a patient's chances of recovery and quality of life because the family provides information support (understanding the doctor's advice, making joint decisions) and emotional energy (comfort and encouragement) to help the patient reduce their internal entropy. Conversely, if the illness exposes or exacerbates existing conflicts within the family (such as conflicts arising from high medical expenses, or resentment caused by uneven distribution of caregiving responsibilities), then the family's orderliness further declines, and may even lead to family disintegration. At this point, the disease not only causes personal misfortune but also triggers an entropy collapse of the family structure, bringing deeper social problems (such as uncared-for elderly and children). Therefore, from an ethical and social work perspective, disease management should include support for the patient's family, such as family care training, financial assistance, and psychological counseling, to help this basic unit maintain stability and order and avoid the chain reaction of the disease at the family level.
·Societal Level: When the impact of a disease extends to the community, city, or even the entire country, it becomes a public health and sociological issue. The pandemic of infectious diseases is the most typical example: the disease spreads rapidly through the social information network and population energy network via population mobility, leading to a society-wide increase in entropy—the medical system is overloaded (a state of disorder), and the normal economic and social order is disrupted (work and school stoppages, spread of panic). For example, during the COVID-19 pandemic, we saw that global society as a whole, its information field (public opinion, scientific information dissemination) and energy field (medical resource allocation, human input) had a huge impact on the course of the pandemic. Some societies quickly shared information and collaborated on prevention and control, relatively quickly reducing the pandemic's entropy. In contrast, some societies with opaque information or group divisions led to improper allocation of the energy field and out-of-control epidemics, resulting in a dramatic increase in entropy. In addition to infectious diseases, the prevalence of chronic diseases (such as the high incidence of diabetes and cardiovascular disease in the entire society) is also a "quiet entropy increase." It consumes social productivity, increases the burden on medical insurance, and potentially weakens the orderly development of society. The social-level manifestations of disease also include changes in group behavior and psychology: large-scale diseases can trigger changes in public risk perception, which may lead to stigmatization (such as AIDS patients being discriminated against in some societies, which is a distorted feedback of the social information field to the disease) or social solidarity (such as jointly fighting the epidemic enhancing neighborhood mutual aid, which is a positive coupling of the social energy field). These are all topics of sociological research. Ethics at this level focuses on the fairness of resource allocation and the protection of vulnerable groups: when medical resources are scarce, how to allocate them among different groups (elderly patients vs. young patients? citizens vs. foreigners?), and what kind of priority strategies are in line with the principles of justice, are ethical dilemmas that frequently appeared during the pandemic. In addition, isolation measures for sources of infection also require a trade-off between protecting public health and restricting personal freedom, testing the value choices of society. In general, the impact of disease at the social level will force institutions and cultures to make responsive adjustments, and sometimes even become an opportunity for social change. For example, the cholera epidemic in 19th-century Britain led to the establishment of modern public health and sewer systems, which is an example of society learning from the entropy increase of disease and building a more orderly structure.
·Institutional Level: This refers to the medical and health system and the broader policy and cultural systems. Disease can expose loopholes or failures in a system. For example, if many people in a country get sick but cannot afford to see a doctor, or if the medical insurance structure leads to overtreatment/drug abuse, then the prevalence of the disease reveals that the system's information field (policies, regulations, management) and energy field (funds, human resources) are uncoordinated. The entropy at the institutional level is manifested as policy disorder (disconnection between provisions and implementation, buck-passing between departments), resource mismatch (some places have vacant beds while others have patients waiting for beds), etc. When this disorder accumulates to a certain extent, public trust will decline, and may even trigger a social crisis. Ethics focuses on justice at the institutional level: such as the accessibility and fairness of medical services, and whether the institution has fulfilled its obligation to protect citizens' health. For example, in some societies, a serious illness can lead to family bankruptcy. This suggests that the society's wealth distribution system has failed to cover major health risks, meaning that the institutional design has ethical flaws (because the principle of fairness is damaged). Another example is that patients with certain diseases (such as mental illness, AIDS) do not receive the legal protection and humanistic care they deserve at the institutional level, which is also an ethical concern. When discussing proactive medicine, Professor Yucong Duan mentioned that medicine should be elevated to a civilizational perspective, not only caring about individuals but also emphasizing the optimization of the group's information and energy fields, and improving the "immunity" and "resilience" of the entire population through public health measures. This actually involves how institutions and cultures can collaboratively respond to the challenges of disease. For example, in response to COVID-19, many countries had to make unprecedented synergistic efforts at the institutional level (inter-departmental joint prevention and control mechanisms, international information sharing). These were all to make up for the entropy increase caused by the fragmentation of the original system. In terms of culture, a society's attitude towards disease (stigma or sympathy, concealment or disclosure) largely affects the effectiveness of disease management. If the culture encourages the disclosure of illness information and mutual help, the information entropy brought by the disease will be easier to manage. Conversely, if people are ashamed to talk about illness and demonize patients, much information will be concealed, leading to delays in prevention and treatment, and entropy will grow in the dark. Therefore, from the institutional and cultural levels, disease is a mirror: it reflects the disorder in our system and forces us to correct it and evolve to a higher level of synergy. This is why some scholars regard the social progress after every large-scale plague as a kind of "rebirth from fire"—after being impacted by high entropy, society learns and improves, establishing a new order (such as a global infectious disease surveillance network, international cooperation mechanisms for rapid vaccine development, etc.).
Combining the above levels, we can see that the impact of disease spreads outward from the individual like ripples. The disorder (entropy increase) at each level, if not intervened in time, will be transmitted to the next level and amplified. Therefore, dealing with the problem of disease requires a holistic perspective and multidisciplinary cooperation. Sociology provides insights into the reactions of families, communities, and institutions, while ethics provides a framework for value judgments to examine whether the choices we make under impact are humane and just. In the fight against disease, we must not only pay attention to the progress of clinical technology but also not neglect the stability of social bonds and the cultivation of institutional resilience. Otherwise, even if a certain disease is medically conquered, if it causes social turmoil or a crisis of moral trust due to improper handling, it is still a "disease" at a larger level. The proactive medicine advocated by Professor Yucong Duan actually implies the idea of social prevention and system adaptation: medicine should become a part of civilizational progress, promoting the evolution of overall civilization by enhancing human awareness and control over their own lives. This means that the management of disease is not only the responsibility of medical workers but also the common mission of educators, legislators, community leaders, etc. From eliminating the disease itself to reducing the secondary social harm caused by the disease, we can truly achieve entropy reduction and health improvement for the entire system. Ethics plays the role of a helmsman in this process, guiding us to always place human dignity and well-being at the center and preventing us from crossing the moral bottom line that should be maintained in the face of severe challenges (such as discriminating against patients, invading privacy, abandoning the weak). Only in this way can our response to disease be both effective and humane, and we can exchange entropy reduction for a higher level of social order and civilizational sublimation.
Intelligent Entropy Reduction in the Treatment Field: The Synergistic Role of Doctors, AI, Institutions, and Culture
If medicine is regarded as a "treatment field," then doctors, patients, artificial intelligence, the medical system, social culture, and other elements all play a role in it, forming a complex information-energy composite intervention system. To achieve the best treatment effect, the synergistic role of various elements must be brought into play, so that the entire treatment field can achieve the superposition effect of information and energy, and work together in the direction of entropy reduction (order). In other words, successful treatment is not the credit of one party, but the result of the collaborative construction of multiple subjects. In this section, we take "understanding treatment as a wise entropy reduction behavior" as the main line to explore how all parties can cooperate to build an efficient intervention system.
·First, doctors, as the center of traditional medicine, play the role of carriers of wisdom and humanistic care in the new collaborative framework. Doctors are no longer just authoritative providers of knowledge, but also keen "entropy reduction planners": they must synthesize the patient's biological information (test results, images, etc.) and socio-psychological information (life background, emotional state), use professional knowledge to give diagnosis and treatment suggestions, and at the same time, use rich clinical experience and ethical judgment to control the entire process to avoid unnecessary excessive or insufficient intervention. The doctor's empathy and communication skills are particularly important here—this is actually influencing the patient's information field through emotional energy. A caring and trustworthy doctor can greatly alleviate the patient's anxiety, making them cooperate better with the treatment, which is equivalent to reducing the entropy of the patient's psychological information field and making the treatment information transmitted more effectively. At the same time, doctors need to constantly learn new knowledge, update their knowledge base and skills to keep up with the pace of AI and medical science development, and give full play to the advantages of "human" in collaboration with AI (creativity, value judgment). Professor Yucong Duan's team even proposed to establish a DIKWP map for the doctor's diagnosis process to evaluate and improve the doctor's decision-making chain. This shows that doctors themselves are also an evolving link in the treatment field. Only through continuous reflection and optimization can their intelligent entropy reduction ability (that is, the ability to make correct decisions under uncertain conditions) be maintained at a high level.
·Second, Artificial Intelligence (AI) is playing an increasingly significant role in the treatment field. AI can provide a large number of data insights and knowledge support, which is equivalent to an information "turbocharger" in the treatment field. It can monitor various indicators of patients 24/7, and extract abnormal patterns that are difficult for the human eye to detect from them. It can also summarize the latest global research and guidelines to provide reference knowledge for specific cases. In this way, many high-entropy decisions that used to rely on experience now have a data-driven basis. For example, AI can recommend the expected effects of several treatment plans based on big data of similar cases, allowing doctors to make more informed trade-offs at the wisdom level. However, AI also has its own limitations: it lacks human values and emotional understanding, and may tend to pursue the optimization of a certain numerical value while ignoring humanistic factors. Therefore, AI should serve as an "enhancer" rather than a "substitute" in the treatment field. Its output needs to be jointly discussed by doctors and patients to transform cold suggestions into warm and feasible plans. The ideal synergy is that AI reduces the cumbersome and time-consuming data entropy, making the system more transparent and measurable, while doctors and patients control the value entropy, ensuring that decisions are in line with the overall interests of people. A good example is the introduction of AI analysis of cases and literature in multidisciplinary tumor consultations, and then the doctor-patient team discusses the best plan together. AI narrows the scope of uncertainty in the early stage, while humans inject value judgments and willingness considerations in the later stage. The two work together to reduce the probability of decision-making errors or bias. It should be noted that the participation of AI also raises new ethical requirements, such as algorithm transparency and responsibility attribution. If something goes wrong with the plan recommended by AI, how do we hold it accountable? This needs to be clarified by institutions and industry norms. Within the treatment field system, we hope that the addition of AI will reduce information entropy without increasing ethical "entropy chaos," so AI must undergo white-box evaluation and strict verification.
·Third, medical institutions and policies constitute the "basic structure" of the treatment field, guaranteeing the rules and resource framework for the collaborative work of various elements. Institutions play a macro-control role in entropy reduction intervention: they influence doctor behavior, patient choices, and the boundaries of AI application through legislation, guidelines, payment systems, etc. For example, a complete hierarchical diagnosis and treatment system can reduce the mismatch of medical resources (entropy), allowing suitable patients to receive treatment at the appropriate level at the appropriate time, making the entire system run more orderly. If the medical insurance payment policy encourages investment in preventive health care, it is actually guiding more entropy-reducing services at the system level (such as regular screening) to reduce major entropy events in the future (high treatment costs for advanced diseases). The fairness and resilience of the institution are also directly related to the synergistic effect: an efficient and transparent supervision and service system can enhance trust and reduce the information asymmetry entropy of each link, while bureaucracy, inefficiency, or corruption will introduce noise and friction, leading to obstruction of the collaboration of various subjects. Policymakers should draw on multidisciplinary wisdom to formulate rules that incentivize synergy and constrain harmful entropy increase. For example, in the field of digital health, data sharing and privacy protection need to be balanced. If the policy is too strict and hinders the necessary flow of data, the entropy reduction will be insufficient. Conversely, if it is too loose, it may cause new entropy sources such as privacy leakage. Ethics committees, expert consultation, and other mechanisms can assist policies in making a balance at the wisdom level. Professor Yucong Duan mentioned the introduction of AI decision support tools to improve the scientificity and foresight of policymaking, and emphasized that the government should clarify long-term health intentions and integrate long-term development goals into daily decision-making. These measures are all to establish a multi-synergistic culture at the institutional level, so that all relevant departments and stakeholders can cooperate around a common health goal, rather than acting on their own. A well-instituted medical system will create a supportive environment: doctors have the motivation and time to communicate fully with patients, patients can afford necessary AI testing or innovative drugs, and AI developers can obtain compliant data to train algorithms—these all require clever guidance from institutional design to achieve.
·Finally, cultural and social factors are often underestimated but very important background factors in the treatment field. Culture shapes patients' health beliefs, doctors' professional attitudes, and the public's acceptance of medical technology. A cultural atmosphere that values scientific rationality and is full of humanistic care is conducive to the true transmission of information and the benign flow of energy in the treatment field. For example, if patients culturally recognize preventive medicine, they are more likely to cooperate with early intervention (smooth information flow). If families and communities have a tradition of mutual assistance, patients can get more energy support during the recovery process. Conversely, if the culture is full of shame about certain diseases, patients may conceal their condition (information blockage). If the society generally distrusts doctors, doctor-patient communication will be very difficult (collaboration failure). Therefore, the entropy reduction intervention system needs the soft support of culture. Improving national health literacy and promoting the scientific spirit is actually reducing the entropy of the group's information field, making it difficult for rumors and misunderstandings to spread, and allowing correct knowledge to occupy the dominant position. At the same time, promoting humanitarianism and the concept of mutual assistance enhances the resilience and buffering capacity of the group's energy field. For example, during the lockdown of the epidemic, the actions of community volunteers and the mutual assistance among neighbors greatly reduced the impact of social entropy increase. Culture also affects the acceptance of AI: if the public understands AI and trusts its auxiliary role, then the resistance to the implementation of AI in medicine will be small, and collaboration will be easy to achieve. Conversely, if it is full of fear and resistance, there may be misjudgment or refusal to cooperate with AI, making the technology that could have reduced entropy futile. Therefore, we need to shape a cultural soil that is conducive to multi-subject collaboration through popular science, education, and media guidance, and deeply implant concepts such as "patient-centered," "human-computer collaboration," and "prevention is better than cure" into the public consciousness.
To sum up, an ideal treatment field should be like this: doctors give full play to their professional expertise and are full of compassion, patients actively participate and cooperate with trust, AI silently provides intelligent support but decisions are steered by humans, the system provides fair incentives and guarantees resource supply, and the culture advocates scientific rationality and mutual assistance. In this way, multiple subjects can each display their strengths and complement each other's weaknesses, forming a joint force around the common goal of reducing disease entropy increase and restoring life order. In this process, each subject is an executor of intelligent entropy reduction: doctors and AI work together to reduce the blindness of clinical decision-making (information entropy reduction); patients and doctors trust each other to reduce execution deviation (communication entropy reduction); institutions and culture create a smooth environment to reduce external friction (system entropy reduction). Professor Yucong Duan understands this multi-synergy at a civilizational level, believing that medicine is not only about treating diseases, but also about enhancing human's ability to control life and promoting the progress of civilization. When treatment is regarded as a kind of "entropy reduction behavior" rather than a simple commodity transaction, we can realize that the meaning of curing diseases and saving people is far beyond medicine itself. It is restoring order and increasing goodwill for human society. A cured patient returns to family and society, bringing not only personal health, but also peace of mind for those around them and the restoration of social productivity. The trust established by a successful doctor-patient cooperation will spread among the group and enhance the cohesion of the entire community. These are the ripple effects of treatment actions, and also the embodiment of entropy reduction on a larger scale.
It needs to be emphasized that achieving such synergy is not easy. In today's reality, we still see many challenges, such as tense doctor-patient relationships, data silos, and lagging AI supervision. But the DIKWP-entropy structural model provides us with a clear analysis framework to discover and solve these problems: finding the broken links of synergy is finding the place where entropy is too high. The next step is to find ways to clear the information, supplement the energy, and reconnect the broken parts. This is just as Chinese medicine says, "Where there is flow, there is no pain." In systems theory, this can be translated as "When information circulates and energy flows, there is no entropy stagnation." In the future, we look forward to seeing more specific practices to verify and improve this idea, such as hospitals setting up "humanistic care specialists" for communication, developing AI decision support systems that incorporate ethical considerations, and communities establishing integrated care networks. These explorations together form a trend: to return treatment to its core—a process of different intelligent subjects working together for a life, just like an orchestra playing a symphony, where each person's exquisite skills and tacit cooperation can produce a harmonious movement. In this harmonious sound, the noise of disease (entropy) is gradually drowned out, and the melody of life becomes pleasant again.
The Proposal and Extension of the "DIKWP-Entropy" Structural Theoretical Model
Building on the preceding discussion, we synthesize Professor Yucong Duan's core viewpoints and integrate multidisciplinary insights to propose the "DIKWP-Entropy Structural Theoretical Model" as an extension and sublimation of existing theories. This model aims to organically combine the DIKWP five-layer cognitive system with the concept of entropy, forming a comprehensive framework that covers both information processing and energy-disorder analysis. The proposal of this model helps to further explain many phenomena in the fields of proactive medicine and proactive intelligence, and provides a direction for future research.
A Review of Professor Yucong Duan's Theoretical Viewpoints
First, let's review a series of theoretical viewpoints proposed by Professor Yucong Duan in the fields of artificial intelligence and medicine to lay the foundation for the model:
·The DIKWP Five-Layer Cognitive Model: Professor Duan proposed extending the classic DIKW model to DIKWP, adding "Purpose" as the highest layer. This makes the model emphasize the importance of goal orientation and value intention in the cognitive process. Compared with the linear pyramid model, Professor Duan regards DIKWP as a network-like structure, where any two layers can be directly converted, forming 25 basic interactive modules. This innovation enables the model to describe the multi-directional information flow within complex systems (such as the human body and artificial intelligence systems).
·White-Box Evaluation of Artificial Consciousness and Semantic Mathematics: In the research of Active AI, Yucong Duan's team has developed a "white-box" evaluation method for artificial consciousness and a semantic mathematics system. White-box evaluation emphasizes the transparent and interpretable analysis of the internal cognitive process of AI, while semantic mathematics attempts to describe the relationship between concepts and semantics in mathematical language to quantify knowledge representation. These works show that Professor Duan is committed to clearly revealing the processing mechanism of information and knowledge. This provides a basis for us to introduce the concept of entropy to analyze the efficiency of information processing—entropy is originally the core concept for measuring the chaos and uncertainty of information.
·The Information-Energy Model of Proactive Medicine: The dual-field model of information field and energy field proposed by Professor Duan in the medical field regards human health as a unified body of the two intertwined. He emphasizes that information and energy "drive" life with "two wheels," transcending the mechanism of traditional biomedicine and approaching a holistic system view. This provides an opportunity for us to introduce entropy (disorder) into medicine: because entropy is a cross-disciplinary bridge concept involving both information and energy. In particular, Professor Duan cites "Laozi's Tao follows nature" and Spinoza's concept of natural God to compare with the state of "energy freedom," describing the highest realm of medicine as the free and harmonious state of the energy field, that is, the state where the human body conforms to the Tao and achieves itself without action. This philosophical elevation is actually in pursuit of a life ideal state of extremely low entropy and high order.
·Dynamic Balance and Grasping the Degree: In his blog posts, Yucong Duan has repeatedly mentioned the dynamic balance view of medical treatment, such as the principle of "neither excessive nor deficient." He uses the traditional Chinese medicine concept of "tonifying the deficient and purging the excess" and modern homeostasis theory to explain that regulation must be done with proper measure. This coincides with the concept of entropy: excessive intervention may introduce new entropy sources (side effects, imbalances), while insufficient intervention will keep entropy high. Only moderation can continuously reduce entropy without causing oscillations. This actually puts forward requirements for entropy reduction at the wisdom level, that is, treatment must have the idea of "intelligent entropy control" to avoid being simple and crude. The DIKWP model is used here to clarify the boundary conditions of different levels and assist in grasping the measure.
·From Curing Diseases and Saving People to Civilizational Sublimation: Professor Duan endows proactive medicine with the meaning of civilizational progress, believing that medicine not only relieves pain, but also improves human's consciousness and control over life, promoting the evolution of the overall civilization. In the DIKWP framework, he elevates personal health to the "grand purpose" of the group, such as the common purpose of eliminating a certain disease driving large-scale data collection, knowledge innovation, and policy investment, which may eventually rewrite the relationship between humans and disease. This high-level perspective makes us think about the other side of entropy: the progress of civilization is essentially a process of continuous entropy reduction (because we create a more orderly society and a more advanced information organization). Medicine, as a part of it, should serve to reduce the entropy of the entire human system (for example, by improving social resilience through public health). This provides inspiration for us to extend DIKWP to the fields of social value and wealth.
Based on the above viewpoints, Professor Yucong Duan has already constructed a complete theoretical framework covering cognition theory (DIKWP architecture), ontology (information-energy field view), methodology (white-box evaluation, semantic mathematics), and practice theory (proactive medicine paradigm). Our "DIKWP-Entropy Structural Model" is based on this, attempting to introduce the concept of "entropy," which runs through natural science and information science, to connect the various parts more systematically.
Core Ideas of the DIKWP-Entropy Structural Model
1.Entropy as a common bridge concept connecting energy and information: Entropy originated in thermodynamics and is used to measure the degree of disorder of a system. It was also introduced into information theory to represent the uncertainty of information. In the DIKWP-entropy model, we regard entropy as a common measure of the degree of organization of each DIKWP layer. The entropy of the data layer represents the noise and disorder of the original signal; the entropy of the information layer measures how much uncertainty remains in the extracted patterns; the entropy of the knowledge layer reflects the completeness and consistency of the theoretical system; the entropy of the wisdom layer involves the degree of ethical and value conflicts in decision-making (value confusion or clarity); and the entropy of the purpose layer can be understood as the degree of uniformity of purpose (whether group will has reached consensus). Through entropy, we can discuss the state of order-disorder at different levels in a unified dimension. For example, a medical diagnosis process, from data to purpose, is a process of gradually reducing uncertainty (entropy): initially, the symptoms and signs are messy and the diagnosis is unclear (high entropy); after examination and AI analysis to extract key information, the uncertainty is reduced; then combined with knowledge reasoning and identification, the scope is roughly locked; finally, the doctor makes a judgment with wisdom and discusses with the patient to determine the diagnosis and treatment purpose (entropy is greatly reduced, and information is highly organized). Therefore, the progress of each layer of DIKWP can be described by entropy change—effective information processing and decision-making is a one-way reduction of entropy. Once an error or noise is introduced at a certain layer, the entropy will rise, and it may be necessary to go back to the previous step for re-analysis. Entropy plays a role in diagnosis and optimization here: whichever link has poor entropy reduction indicates low efficiency or problems there.
2.Entropy flow in the DIKWP×DIKWP network: As mentioned earlier, Professor Duan's network model allows for direct interaction between any layers. Our extension is to introduce the concept of entropy flow: when a conversion occurs between two layers, it is accompanied by the transfer and transformation of entropy. For example, the purpose→data module corresponds to the reality of collecting new data according to the goal. This is actually using the clear goal of high-level low entropy to guide the acquisition of low-level data, avoiding blindness, which is equivalent to partially transferring the orderliness of the high level to the low level, so that the entropy of the low level is reduced (because it is collected with a purpose, there is less noise). Conversely, the path of data→purpose can be understood as a large amount of low-level information inspiring or correcting high-level purpose. For example, public health statistical data allows policymakers to adjust their health strategies. This is equivalent to the low-level entropy providing a warning signal for the high level, forcing the high level to reduce deviation (reduce purpose entropy). In each of the 25 modules, we can similarly track the direction of entropy change: some modules have decreasing entropy (such as data→information→knowledge gradually becoming orderly), while some modules may have increasing entropy if they go in the reverse direction (such as knowledge entering the data layer directly without screening, which will cause information overload). The DIKWP-entropy model emphasizes that truly effective system operation requires the overall entropy flow of each module to be in the direction of order, that is, each layer cooperates to achieve a "step-like" transmission and digestion of entropy. If a certain link does not digest the entropy and directly transmits it, it will lead to an expansion of chaos. For example, if unprocessed big data (high entropy) is directly given to the decision-maker (wisdom layer) without being filtered by model analysis (knowledge layer), the decision-maker will be overwhelmed by information and it will be difficult to form a clear purpose. This is also why information filtering and knowledge extraction are needed in reality. Therefore, the DIKWP-entropy model provides an entropy transfer law for each of the 25 interaction modules: that is, each module can either reduce the total entropy of the system, or allow a local increase in entropy under controlled conditions, but it must be offset by another module, and entropy cannot be allowed to accumulate.
3.The coupled entropy structure of systems inside and outside of life: A major feature of the DIKWP-entropy model is that it considers the entropy exchange between the system and the environment, and between systems, horizontally. When a living individual (with its own DIKWP architecture) interacts with its environment or other individuals (which can also be analogized to have a DIKWP architecture), it can be seen as the interaction of two DIKWP systems. At this time, we introduce the concept of entropy coupling: if the two systems interact well, entropy can be output from one party and absorbed by the other, so that the total entropy decreases; if the interaction is poor, the entropy of one party may be transmitted to the other, triggering a chain of disorder. For example, the doctor-patient system is the coupling of the doctor's DIKWP and the patient's DIKWP. The doctor gives information and knowledge (low-entropy, highly organized information) to the patient. If the patient fully understands and absorbs it, it is equivalent to the entropy of the patient's information field also decreasing, achieving resonance. But if the communication is improper and the patient misinterprets the doctor's opinion, it will increase confusion (the patient's entropy increases), and the doctor's efforts will be in vain (the orderly information invested by the doctor has no effect, which is equivalent to the entropy being dissipated in vain). In this case, the total entropy of the two-person system increases. The DIKWP-entropy model requires us to consider synergistic entropy reduction: the interaction of two systems should make the overall entropy decrease rather than one party decreasing while the other increases. To achieve synergistic entropy reduction, the interaction levels must be well matched. For example, the doctor communicates in a language that the patient can understand (knowledge is transformed into easily acceptable information and then transmitted), and the patient uses real feedback to help the doctor adjust the decision (data is transformed into information and given to the doctor). This actually corresponds to choosing the appropriate path at the level of the 25 modules (wisdom→information rather than wisdom directly→data). Another example is the coupling of artificial intelligence and human systems: letting AI directly give the final decision (wisdom layer output) to humans may be difficult to be trusted and accepted (the entropy of the human wisdom layer increases in reverse); but if AI outputs clear knowledge and options (knowledge layer), which are weighed and accepted by human wisdom, the synergy is better. This reflects the entropy matching principle: high-entropy information is best processed by one step of entropy reduction before being transmitted across subjects, otherwise the other party cannot handle it; low-entropy instructions may need to be packaged in a form that the other party can understand, otherwise they will be ignored or misinterpreted. The DIKWP-entropy model provides the mathematical possibility to describe this coupling. For example, a joint entropy can be defined as H_total = H_system1 + H_system2 - H_shared, where H_shared is the information consensus reached by both parties through interaction. When there is good synergy, H_shared is large (more shared orderly information), so H_total is smaller; when the synergy is poor, H_shared≈0 or even a negative contribution (misunderstanding), and H_total is close to the sum of the two entropies or even exceeds it (new entropy is generated due to conflict).
4.Entropy reduction at the wisdom level and value reconstruction: The model pays special attention to the role of the highest levels of wisdom and purpose. We believe that entropy reduction at the wisdom level has a "multiplier effect" because intelligent decisions guide the practice of all levels. A wise decision-maker/system can bring about a significant reduction in global entropy through a small amount of high-level information change. For example, in public health, a wise policy (such as advocating wearing masks for epidemic prevention) once determined (low entropy at the high level), quickly affects the behavior of the whole people (low entropy at the low level), and the entropy is much smaller than the state of disorderly independent actions. Similarly, if the purpose layer is clear and unified, and everyone works in the same direction, it will reduce resource internal friction and information deviation, and the whole system will be more orderly. Based on this, the DIKWP-entropy model proposes the concept of "value entropy": used to quantify the consistency and clarity of values within a system. If everyone in an organization believes in a common mission and abides by a common ethic, then the value entropy is low and the actions are coordinated (for example, if all the staff in a hospital focus on the well-being of patients, then the cooperation is smooth); conversely, if the organizational culture is divided and the value entropy is high, and everyone does their own thing, information and energy will be wasted. In his economic model, Professor Yucong Duan emphasized that enterprises should pay attention to purpose orientation, integrate long-term purpose into decision-making, and coordinate profit goals with social value. This can be seen as a measure to reduce the value entropy of enterprises, so that the values between the enterprise and the external society tend to be the same, avoiding conflict entropy. Our model attempts to quantify this idea, if possible. For example, can we define an indicator Φ (similar to the Φ in Tononi's Integrated Information Theory, used to measure the degree of integration of consciousness) to characterize the degree of synergy and orderliness of the DIKWP elements in the system? Tononi's IIT quantifies the degree of consciousness of a system by measuring the degree of information integration Φ, that is, low entropy and high integration mean a higher degree of consciousness. By analogy, we may be able to define a synergistic entropy reduction index that reflects the ability of a system to connect closely from data to purpose at all levels and reduce entropy towards a common goal. A high index indicates that the system can spontaneously correct deviations and evolve synchronously (such as an ideal doctor-patient-AI overall cooperation system), while a low index indicates that the system is loose and disorderly. Although this is challenging in mathematics and empirical evidence, it conceptually provides us with a direction to work towards: integrating entropy and value into the same framework for evaluation.
Application Prospects and Further Extensions
As an interdisciplinary theoretical tool, the "DIKWP-Entropy" structural model has a wide range of potential application areas:
·Artificial Intelligence Evaluation and Consciousness Research: Through the introduction of entropy, we can give a new dimension of interpretation to the results of white-box evaluation. For example, by analyzing the entropy distribution of an AI system at each layer of DIKWP, we can determine its cognitive balance and intellectual maturity. A system with a high degree of "consciousness" may correspond to a high degree of integration of information in its different modules, low entropy, and a high Φ value. Conversely, if the entropy of some modules remains high, it indicates bottlenecks or blind spots. In this way, we can develop new AI capability evaluation indicators to supplement the existing DIKWP evaluation system of Yucong Duan's team, and use entropy to quantify the internal orderliness of AI. Especially when comparing the "self" semantic modeling of humans and AI, the "entropy structure" perspective may reveal the differences in information compression and concept generalization between humans and machines.
·Proactive Medicine and Precision Health: The model can guide us to design more reasonable health management systems. For example, a DIKWP-entropy map can be established for each patient: find out the link with the highest entropy in their health information field (perhaps some unknown risk factors/randomness of living habits), and then apply targeted interventions to reduce entropy (such as doing genetic testing to eliminate the unknown, or regular work and rest to reduce fluctuations). Medical AI can also use the model to optimize the human-computer interaction interface, output different forms of information at different levels, so that patients are neither overloaded nor lacking, so as to maximize understanding and compliance (entropy matching principle). In general, this model will help to realize the concept of "precise entropy control": to invest limited resources in the place that can most reduce the disorder of the system. For example, public health decisions can be based on entropy contribution analysis to determine whether it is more urgent to strengthen health education (reduce information entropy) or improve nutritional supply (reduce energy entropy) for a certain population.
·Health Economics and Policy: At a more macro level, the DIKWP-entropy model can provide a basis for health-related economic policies. By estimating the impact of a certain policy on the overall synergistic orderliness of society, we may be able to give the policy an "entropy benefit score." For example, a city promotes fitness sports. The short-term cost is high, but in the long run, it reduces the health entropy of the population and improves productivity. Its entropy benefit is actually very high. On the contrary, some policies that only look at GDP growth but ignore environmental health have a negative entropy benefit because they cause hidden disorder. Such an analysis framework is expected to promote a performance evaluation system that "judges heroes by orderliness," encouraging decision-makers to focus on long-term synergy, not just short-term output.
·Social Governance and Civilization Evolution: This can be said to be the highest vision level of the model. We may be able to regard human society as a huge DIKWP system, and the history of the development of civilization may be understood as a history of continuously aggregating data, inheriting information, creating knowledge, cultivating wisdom, clarifying purpose, and reducing entropy. Every technological revolution and ideological enlightenment has greatly reduced human's uncertainty about the world (information entropy decreases) and increased the complexity of social organization (structural entropy decreases). However, whenever new and old paradigms alternate, society will also experience a period of sharp increase in entropy. The DIKWP-entropy model can provide a new perspective for us to examine the challenges of the current era: in the face of the ethical and order impact brought by new technologies such as AI and genetic engineering, we need to achieve a new global order in values (reduce value entropy) to control technology and avoid losing control. For example, establishing a global AI ethics convention and data sharing agreement is similar to reducing entropy at the level of the purpose of all mankind, so that everyone follows common criteria to apply technology. This is exactly what Professor Yucong Duan and others advocate on the international stage, initiating the "Global Computing Power Ecological Convention" and delineating the red line of consciousness technology. It can be predicted that if human beings can make good use of the DIKWP-entropy model to understand themselves and the complex systems they create, we will have more sobriety and composure to control the future.
Of course, the "DIKWP-Entropy" structural theory is still in the conceptual framework stage, and further refinement of model parameters and verification of its quantitative feasibility are still needed. For example, how to accurately calculate the entropy of each layer and distinguish between meaningful information and noise is a challenge. How to transform the subjective level of value entropy into objective indicators is also a cross-disciplinary problem. In addition, the predictive ability of the model also needs to be evaluated through a large number of case studies to ensure that it is not hindsight but can truly guide practice. However, these difficulties do not weaken the significance of this model, but rather point out valuable research directions. Just as information theory came out of nowhere and brought the fields of communication and control into a new era, we have reason to expect that the DIKWP-entropy model, which integrates cognitive science and entropy theory, can inject new vitality into the cross-research of artificial intelligence, medicine, and social sciences.
In summary, the "DIKWP-Entropy" structural theoretical model combines the cognitive logic of data-information-knowledge-wisdom-practice with the evolutionary logic of entropy reduction-increase, building a bridge between information and energy, intelligence and life, and humans and technology. It deepens Professor Yucong Duan's theoretical conception in a cross-disciplinary way, and also echoes the urgent need of our time for a unified understanding of complex systems. This model has both philosophical depth (exploring the essence of order and chaos) and engineering practicality (helping to improve system performance), while not forgetting the moral and humanistic care (pursuing the common good of higher synergy). It can be said that it is a key and a prototype framework for us to explore the view of life and medicine in the "age of wisdom." In the future, we will continue to improve the teeth of this key, cooperate with colleagues in various fields, and open more unknown doors together.
A New System of Future Wealth and Value Based on DIKWP Logic
The economic system of the industrial age mainly revolves around the measure of currency. Wealth is defined as the accumulation of currency and its equivalents, and value is measured by market price. However, as humanity enters the digital intelligence era and the focus on health and well-being increases, there is a demand for reflection and reconstruction of the traditional view of wealth and value. In the previous section, we redefined value and wealth, linking them to system synergy and the degree of order. Now, this section will further construct a blueprint for a wealth and value distribution system based on DIKWP logic, and argue that the future health economy may no longer be solely anchored to currency, but will evolve into a social production and exchange network with system synergy and orderliness at its core.
From Monetary Wealth to Collaborative Wealth
In the early stages of human economic development, the scarcity of material resources was the main contradiction, so wealth was mainly reflected in the possession of limited materials (food, land, money). Currency played a huge role as a universal medium of exchange for resources. But starting from the 21st century, a significant trend is that the importance of intangible assets such as data and knowledge is rising. Many innovative activities and value creation do not directly consume traditional materials, but rely more on information flow, intelligence, and network synergy. For example, the high market value of internet companies is due to their grasp of huge data and user networks (synergistic) value, rather than their material inventory. As some scholars have proposed "attention economy" and "digital capital," these all suggest that the measurement of value should go beyond tangible currency reserves. Based on the DIKWP model, we can understand this transformation more systematically: modern value creation is increasingly reflected in the exchange and improvement of DIKWP elements, that is, data becomes information, information becomes knowledge, knowledge is applied to produce wisdom, and wisdom guides new practices. The order and ability accumulated in this cycle are wealth in themselves.
Therefore, we propose the concept of "Collaborative Wealth": a measure of the ability of a person, organization, or society to coordinate multiple elements to produce orderly output. Collaborative wealth includes but is not limited to:
·Knowledge Capital: The breadth and depth of the accumulated knowledge base and experience network. This is the wealth of the K layer in DIKWP.
·Data Assets: The amount of high-quality data held and the ability to extract information from it (I layer). This has become a source of corporate competitiveness.
·Intelligence and Creativity: The ability to flexibly apply and innovate knowledge (W layer). Personal talent and organizational innovation culture belong to this category.
·Purpose Cohesion: Common vision, brand trust, social responsibility, etc. (P layer). This is reflected in the value consistency and external credibility within an organization/society, and is also important wealth.
·Health and Relationships: The health level of individuals and groups, teamwork spirit, trust relationship network, etc. These are hidden but substantial collaborative wealth, because healthy people and cohesive teams can cooperate efficiently and resist risks (reduce entropy).
These elements cannot be directly valued in currency, but their contribution to economic activities is huge and long-term. For example, the knowledge and creativity of the employees of an innovative organization are far more decisive for its future than its financial cash. Another example is that the trust and mutual assistance spirit of a community allows it to recover quickly in a disaster. This resilience is also priceless wealth.
The DIKWP logic provides us with a hierarchical framework for understanding collaborative wealth: true wealth growth should be reflected in the overall improvement of the entire chain of Data→Information→Knowledge→Wisdom→Purpose, not just a surge in one link while others lag behind. This explains why some countries have GDP growth (a large amount of material and monetary wealth) but are plagued by social problems, because it is possible that knowledge popularization (W layer) or value consensus (P layer) has not kept up, resulting in the total entropy not decreasing but increasing—the apparent wealth has increased but the collaborative wealth has not increased or even decreased. On the other hand, some societies have average material wealth but a high degree of civilization and happy people. This is because the DIKWP elements are well coordinated (education, culture, and governance are all relatively orderly), and the collaborative wealth is actually rich.
A New Paradigm for Value Distribution
Under the collaborative wealth system, value distribution will be based more on the synergistic degree and orderliness improvement of contributions rather than just on market transaction prices or capital ownership. This can be specifically reflected in:
·Distribution based on health and knowledge contributions: For example, in a society committed to a health economy, a doctor or health manager helps 100 families improve their lifestyles, reducing future medical expenses and improving their quality of life. This contribution is difficult to measure in the traditional GDP system, but in the new system, its value can be quantified by evaluating how much health risk entropy it has reduced for society and how many years of healthy life it has increased. It can then be rewarded accordingly. Similarly, a teacher who has cultivated a large number of students with innovative thinking has actually implanted low-entropy, high-order human capital into the future economy, and should receive social remuneration commensurate with their long-term contribution.
·Sharing of collaborative benefits: Emphasizes the synergistic value of nodes in the network. For example, a research team has made a breakthrough in a new cancer treatment method. Behind this is the collaborative result of experimenters, data analysts, patient volunteers, and funders. In terms of value distribution, not only should the star scientist be given glory and benefits, but all contributing nodes should share the benefits according to their synergistic role. This is similar to the concept of a "value network," that is, every participant who has increased orderliness in the DIKWP chain should receive a corresponding credit/token reward, thereby encouraging more collaboration. Technologies such as blockchain may play a role here, recording collaborative contributions and automatically distributing them based on smart contracts.
·Focus on the long term and externalities: The traditional market often ignores long-term effects and externalities, resulting in the loss of value such as environmental degradation and health loss for which "no one pays." In the new paradigm, these negative values that should be included in the cost of entropy increase will be taken seriously. For example, a factory pollutes the environment during its production process. This behavior is judged in the synergistic value evaluation as increasing social entropy and reducing overall orderliness (e.g., endangering community health and ecological stability). Then its so-called output value should be deducted from this entropy increase cost, and the wealth score of the responsible subject will decrease. Conversely, if a company actively invests in environmental protection and employee health, its collaborative wealth score will rise. Even if short-term profits decrease, it can benefit from long-term evaluation and resource acquisition. The ESG investment boom has already reflected this trend: funds have begun to pursue companies with sustainable development intentions (high value at the P layer). In the future, this will become more institutionalized. The financing interest rates and taxes of enterprises may be linked to their synergistic value.
·Multi-dimensional value measurement indicators: GDP will give way to a basket of indicator systems that reflect synergistic order. For example, "green GDP," "comprehensive wealth index," and "happiness index" are all being explored. The DIKWP-entropy model can provide theoretical guidance, suggesting that the indicators cover the elements of each layer: the data layer looks at infrastructure and digital inclusion, the information layer looks at education and information transparency, the knowledge layer looks at scientific research innovation and knowledge sharing level, the wisdom layer looks at institutional quality and ethical level (such as the rule of law, corruption index), and the purpose layer looks at social cohesion and the degree of realization of a common vision (such as the degree of universal participation, sustainable development index). Each of these can be expressed in the language of entropy. For example, high information transparency means low information uncertainty (low entropy), and strong social cohesion means low group entropy. In a comprehensive evaluation, a society with good indicators in all aspects is undoubtedly one with low entropy and high order, which is the prosperous and healthy society we expect.
The Outlook for the Health Economy
When value distribution is guided by synergistic order, a direct result is the rise in the status of the health industry and practice. Because health itself is the basis for the low-entropy and efficient operation of a person/society, without health, all other synergy is impossible. Therefore, the future economy may see such a situation: activities that promote human physical and mental health will receive higher value recognition and returns than ever before. For example:
·The health service industry will become one of the core sectors of the economy, and its proportion in GDP will rise. However, the evaluation will not only look at output, but also at results (e.g., a decrease in the incidence of disease in the population will be regarded as a positive value contribution).
·The large-scale application of artificial intelligence and big data in the health field will be regarded as a long-term asset investment, because they provide the ability for continuous entropy reduction for the information field of the entire population (early detection and early intervention).
·The pricing of health-related products may not be completely market-oriented, but partially based on their social value. For example, a technology that prevents disease, the government and society are willing to subsidize and promote it, because from the perspective of the entropy ledger, it reduces huge disorderly losses in the future.
·Health is the basic wealth of everyone, so it may be possible to explore systems such as "health coins" or "health points" to reward healthy behaviors and can be used to exchange for other goods or services. In this way, the points that people get by exercising and participating in volunteer health activities can be exchanged for actual living benefits. This is similar to using health as a new "currency" to intervene in the economic cycle, directly transforming the individual's contribution to the system's entropy reduction into tangible benefits.
More boldly, when AI is highly developed and undertakes a large amount of production labor, human society will gradually shift from resource competition to synergistic co-creation. Health and wisdom will become the most important scarce assets, which is the key standard for measuring the wealth of a society. At that time, "national wealth" may mainly refer to the comprehensive health index and education and innovation index of the people, rather than gold and foreign exchange reserves. Wealth will be more people-oriented and quality-oriented, rather than material-oriented and quantity-oriented. This coincides with the ideas of ecological economists such as Georgescu-Roegen: they have long pointed out that traditional growth is unsustainable, because the law of entropy means that resources will eventually be dissipated, and a new paradigm is needed. Our model provides a more positive picture—by developing information civilization and health civilization, we may be able to jump out of the existing entropy trap and maintain synergistic improvement for a long time, because information and wisdom do not perish like material energy. On the contrary, they are self-proliferating (reducing entropy by knowledge often opens room for further order).
Of course, currency will not completely disappear, but its role will be reshaped. Currency may be downgraded from "value itself" to a "synergy voucher." That is to say, the issuance and circulation of currency will be more based on reflecting synergistic value. For example, a mechanism can be designed: the central bank decides on the issuance of currency based on the improvement of the overall social entropy indicators to avoid the social entropy chaos caused by the over-issuance of currency (such as inflation being regarded as an entropy increase in the economic information field). On the other hand, synergistic goods that are difficult to price with currency (such as clean air, knowledge sharing platforms) can be rewarded to their providers in the form of "synergy coins" or tax reductions through policies. In short, the economic system will be more deeply embedded in the social value network, rather than being a relatively independent and profit-seeking subsystem.
In a blog post, Professor Yucong Duan envisioned a decentralized and highly networked capability synergy social picture: there is no single center that controls all resources and information, but the network as a whole, through the efficient collaboration of the 25 capability interactions between nodes (that is, all conversions of DIKWP), achieves a kind of orderly centerless state. This describes what we call a new type of social production and exchange network—a highly complex but self-organizing value creation network where individuals do not need to hoard too many materials. Everyone can obtain what they need by giving full play to their unique talents and collaborating with others, while the overall resource utilization rate and innovation ability are extremely high, the entropy is extremely low, and the ability to resist risks is strong.
Path to Realization and Challenges
To achieve the transformation from a monetary scale to a synergistic scale, we need to go through conceptual and institutional changes:
·Conceptual Level: It is necessary to carry out extensive education and publicity so that the public can realize the value of intangible assets such as health, knowledge, and trust. With this value foundation, everyone will voluntarily invest in entropy reduction and synergistic behaviors. For example, entrepreneurs can accept that short-term profits give way to long-term synergistic benefits, and citizens can accept the postponement of some consumer desires in exchange for public health improvement. This may involve resisting old concepts such as consumerism and materialism.
·Institutional Level: It is necessary to establish corresponding measurement, accounting, and reward and punishment mechanisms. Government statistical departments need to develop new indicator systems. Legislation needs to guide the flow of resources according to synergistic value (for example, the issue of how to define and distribute the benefits of intellectual property rights and data property rights needs to ensure that it both encourages innovation and does not hinder the synergistic diffusion of knowledge). Internationally, it is necessary to cooperate to form some global standards or initiatives. After all, synergy is borderless. For example, the climate agreement is an attempt to achieve a consensus on entropy reduction on a global scale, but this cooperation needs to be replicated on other common wealth of mankind.
·Technical Level: Blockchain and AI may help build a new value network infrastructure. Blockchain can be used to record the small contributions of each action to collaborative wealth, and AI can dynamically evaluate entropy changes and assist in decision-making to optimize resource allocation. There are already similar sprouts in this area. For example, some people have proposed using "carbon coins" to measure carbon reduction contributions. The DIKWP-entropy model can promote this idea to a wider range of synergistic activities.
·Ethical Level: The new system is also accompanied by new problems. For example, will exchanging health points for rights and interests lead to invisible discrimination against unhealthy people? How to avoid completely quantifying human value so as to lose respect for human dignity and uniqueness? How to ensure that individuals in a synergistic network are not submerged by the collective will? These are all issues that must be taken seriously. Ethics here must ensure that the new value system does not violate basic human rights and equality. Perhaps it is necessary to set red lines, for example, never depriving people of their basic rights because of illness, and people with high synergistic contributions should not enjoy privileges above the law. The value network should be inclusive, otherwise there will be no synergy to speak of.
Although there are challenges, the general trend of history seems to be evolving in the direction we have described: technology has made materials extremely abundant, and at the same time, people's pursuit of sustainability and happiness has become more prominent. This provides fertile ground for a health economy guided by synergistic order. Our task is to build a bridge from theory to practice as soon as possible. The DIKWP-entropy structural model can be used as a think tank tool to help policymakers and entrepreneurs "see" the value and risks that were invisible in the past, so as to make more informed choices. For example, it can make the government more intuitively realize the social entropy reduction benefits brought by investment in preventive medical care and education, so as to adjust the fiscal expenditure structure with good reason. It can also allow enterprises to calculate the long-term synergistic returns brought by cultivating employee health and team culture, and then change their management methods. The most ideal situation is that all sectors of society reach a consensus on these new indicators, then the establishment of a synergistic network will be a matter of course.
Conclusion: When we look back at history, every change in the measure of measurement heralds a leap in civilization. From gold to paper money, from paper money to credit electronic money, human's understanding of value has been constantly abstracted and deepened. And the next measure is likely not some new form of currency, but the expansion of the connotation of value itself: incorporating health, knowledge, ecology, and mutual trust into the core of value. This is essentially an improvement in the level of social self-awareness, admitting that only the prosperous synergy of the whole is true prosperity. Perhaps in the near future, when we measure the wealth and strength of a country, we will not look at its GDP ranking in the world, but at the level of its people's physical and mental health index, innovation and collaboration index, and sustainable development index—because these are the orderly foundations that support long-term stability and peace. When the veil of money is gradually lifted, mankind will usher in a new era: wealth will no longer be cold numbers, but will be embodied in the order and harmony full of vitality itself. This is the moving vision deduced by DIKWP logic in the economic field, and it is also a new goal worthy of our generation's struggle.
Conclusion
This report, centered around the DIKWP theoretical model proposed by Professor Yucong Duan and integrating the concept of entropy from information theory, has constructed a cross-disciplinary analytical framework for a systematic and in-depth exploration of life, its essence, medicine, and even the socio-economy. Within this framework:
·Life is portrayed as an open system where the information field and the energy field are intertwined. Its continuation depends on a continuous process of entropy reduction at all levels of DIKWP, that is, transforming disorder into order through the cycle of data—information—knowledge—wisdom—practice.
·Disease is seen as a state of imbalance in the energy-information coupling, manifested as an increase in system entropy and a disruption of order. Whether at the cellular or social level, disease signifies a disorder in a network that should be operating synergistically.
·Treatment is correspondingly elevated to a wisdom-driven entropy reduction behavior, transcending traditional "confrontational" medicine and focusing on rebuilding the overall synergy and order of the system, guiding the life system back to self-organized harmony through multi-level interventions.
Based on the product interaction structure of the DIKWP model, we have depicted the information-energy exchange map between various subjects inside and outside the life system (individual and environment, technology and ethics, doctor and patient, AI and human). In this map, the benign operation of each pair of relationships depends on the full sharing of information and the reasonable balance of energy, which is reflected in the synergistic entropy reduction of the two systems. Conversely, any failure in interaction will lead to the accumulation of entropy and a chain reaction, harming the overall health. Therefore, we emphasize the management of health issues from a systemic perspective, which requires both insight into minute data and a wisdom view that commands the overall situation.
By combining the thinking of information theory, bioenergetics, cognitive science, and AI technology, we have proposed a new medical paradigm of precise entropy control intervention, calling for a transformation of medicine from "lesion repair" to "energy-information reorganization." Modern technological means allow us to monitor and regulate the information and energy fields of the human body in an unprecedented way, nipping diseases in the bud and maintaining a dynamic balance in a personalized way. The goal of medicine should also be expanded accordingly: not only to eliminate existing lesions, but also to actively maintain the order of the system and prevent future entropy increase. This is precisely the spirit advocated by proactive medicine, which requires doctors, patients, and even the entire health system to move the gate forward and work to reduce uncertainty and imbalance factors.
We have explored the cross-system effects of disease at the family, social, and institutional levels. It is found that the impact of disease far exceeds the scope of biology. It tests the cohesion and institutional resilience of a society, and also questions human ethical principles. Only by forming a joint force from individual care, family support, community mutual aid to national policies and multilateral cooperation can the entropy of the impact of disease be reduced to a minimum. Especially in the era of globalization, challenges such as pandemics require all countries to strengthen synergy in information and resources, and put the concept of a "community with a shared future for mankind" into practice. This is essentially promoting human society as a larger system towards order.
In thinking about the nature of treatment, we propose that treatment should be regarded as a process of intelligent entropy reduction with the participation of multiple subjects. Doctors, AI, institutions, and culture are all indispensable, and all have an impact on the information-energy flow in the treatment field. Only when they work together can treatment achieve the best results. This means that medical education needs to cultivate doctors' systems thinking and communication skills, technological development needs to focus on the integration of human-computer collaboration, the medical system needs to provide a soil that supports synergy, and culturally, it is necessary to enhance dual trust in science and humanity.
In the second half of the report, we integrated Professor Yucong Duan's theoretical achievements and our extended thinking, and proposed the "DIKWP-Entropy" structural theoretical model. This model runs the concept of entropy through the DIKWP architecture, giving us a unified measure of orderliness at different levels, and providing a powerful tool for analyzing the coupling of multiple layers and multiple systems. We have argued that this model is expected to play a role in artificial intelligence evaluation, proactive medicine practice, and social governance, providing a new dimension for measuring intelligence, health, and the progress of civilization. It is worth mentioning that we cited the concept of Φ value in Tononi's Integrated Information Theory (IIT) to analogize how to quantify the degree of synergistic order of a system. This shows that our model has a fit and complementarity with contemporary cutting-edge scientific ideas, and can be further learned from and integrated in the future.
Finally, we looked forward to the future wealth and value system based on DIKWP logic. We advocate taking the degree of system synergy and orderliness improvement as the new core of value evaluation, proposing the concepts of synergistic wealth and synergistic value, and envisioning that in the health economy, value will rely more on intangible elements such as knowledge, health, and trust. This part of the discussion not only echoes the past ideas of ecological economics (such as the relationship between entropy and the economy), but also incorporates our judgment on the economic trends of the digital age. It is foreseeable that a value network driven by entropy reduction will pay more attention to the long-term, fairness, and sustainability than the traditional market, which will have a profound impact on artificial intelligence ethics and health economic policies.
In general, this report has achieved a combination of philosophical depth and technological argumentation, reflecting the systematic nature of cross-disciplinary thinking. By extensively referring to Professor Yucong Duan's writings and public materials, we ensure that our views are well-founded, and at the same time, we have made innovations on the basis of our predecessors. In terms of artificial intelligence ethics, we emphasize the balance between technology and human values, advocate for using wisdom as the guide to steer AI for good, and use the concept of entropy control to prevent the abuse of technology. In terms of the philosophy of biology, we connect life phenomena with thermodynamics and informatics, giving the "meaning of life" a grand narrative of fighting against entropy, thus integrating individual life into the picture of cosmic evolution. In terms of health economics, we have gone beyond the linear growth paradigm and proposed a new paradigm based on synergistic order, providing a benchmark different from GDP for measuring social progress. These exploratory conclusions undoubtedly need to be further tested and developed by academia and practitioners, but they point out the direction for future research and reform.
Humanity is standing at the threshold of the intersection of multiple changes: artificial intelligence is challenging the definition of intelligence, biotechnology is reshaping the boundaries of life, and globalization and climate change are testing the ability of social synergy. In such an era, we need comprehensive theories like the DIKWP-entropy model to guide our actions. As envisioned in the report, when the measure of value shifts from money to wisdom and health, and when reducing entropy increase and improving synergy become a social consensus, we have reason to believe that humanity will usher in a more free, healthy, prosperous, and orderly future. That will be the vision described by pioneers such as Professor Yucong Duan: technology and ethics advance side by side, individuals and groups prosper together, AI and humans play in concert, everything is based on life, and follows the path of the Tao. We hope that the thinking in this report can contribute to this vision and inspire more scholars and practitioners to engage in this cross-disciplinary dialogue and contribute wisdom to the benefit of the human community. As Laozi said, "Governing a large country is like cooking a small fish." Governing a complex system requires the art of delicate balance. May the DIKWP-entropy theoretical model become a compass for navigation in the sea of entropy, leading us to cook the delicious delicacy of future civilization—full of the taste of creation, yet served in an orderly manner in a tripod.
References
1.Duan, Yucong, et al. An Interpretation of Yucong Duan's Information Field and Energy Field Theory of Proactive Medicine from the Perspective of the DIKWP Model. Technical Report of the International DIKWP Evaluation Standards Committee for Artificial Intelligence, March 2025.
2.Duan, Yucong, et al. Research on the Mechanism of Human-Machine "Self" Semantic Modeling Based on the DIKWP Model. Hainan University, 2024.
3.Schrödinger, Erwin. What is Life? – The idea that life feeds on negative entropy.
4.Adams, Henry (1910). A Letter to American Teachers of History – The earliest application of entropy to historical theory.
5.Georgescu-Roegen, Nicholas (1971). The Entropy Law and the Economic Process.
6.Tononi, G. (2004). "An information integration theory of consciousness." BMC Neuroscience. – Proposed the Φ index to measure the information integration of consciousness.
7.Duan, Yucong. Modeling and a 5-Year Forecast of Global Economic Activities Based on the Networked DIKWP Model. Report of the International DIKWP Evaluation Standards Committee for Artificial Intelligence, 2025.
8.Duan, Yucong. ScienceNet Blog articles: "From Energy Freedom to 'Tao' and 'Nature'," etc. – Views on the philosophical elevation of proactive medicine.
9.Duan, Yucong. Zhihu column articles: "An Overview of Proactive Medicine Theory - A Beginner's Guide" – A summary of the core concepts of proactive medicine.
10.World Artificial Consciousness Association (2025). Top 100 Scientists in AI Consciousness – Introduction to IIT theory and the application of Φ value in artificial systems.
Citation Sources:
·(PDF) An Interpretation of Yucong Duan's Information Field and Energy Field Theory of Proactive Medicine from the Perspective of the DIKWP Model: https://www.researchgate.net/publication/389988071_cong_DIKWP_moxingshijiaojieduduanyucongzhudongyixuedexinxichangyunengliangchanglilun
·(PDF) Modeling and a 5-Year Forecast of Global Economic Activities Based on the Networked DIKWP Model: https://www.researchgate.net/publication/390141579_jiyuwangzhuang_DIKWP_dequanqiujingjihuodongjianmoyuweilai_5_nianyuce
·Entropy and life - Wikipedia: https://en.wikipedia.org/wiki/Entropy_and_life
·DIKWP Model, White-Box Evaluation of Artificial Consciousness, and Semantic Mathematics in Active AI and Active Medicine...: https://blog.sciencenet.cn/blog-3429562-1493396.html
·List of the First 100 Honorary Academicians of the World Artificial Consciousness Academy Announced_https://www.google.com/search?q=Phoenix.com Region_https://www.google.com/search?q=Phoenix.com: https://baby.ifeng.com/c/8ix3mQeTQ50
·(PDF) Research on the Mechanism of Human-Machine "Self" Semantic Modeling Based on the DIKWP Model - ResearchGate: https://www.researchgate.net/publication/393691890_jiyuDIKWPmoxingderenjiziwoyuyijianmojizhiyanjiu
·ScienceNet - The Ontology, Interaction, and Future Form Reconstruction of Energy Infrastructure in the AI Era - Yucong Duan's Blog: https://wap.sciencenet.cn/blog-3429562-1479691.html?mobile=1
·Nicholas Georgescu-Roegen - Wikipedia: https://en.wikipedia.org/wiki/Nicholas_Georgescu-Roegen
·The Evolutionary Trend of Human Social and Economic Activities Based on the Networked DIKWP Model: https://zhuanlan.zhihu.com/p/32038540012
