Reconstruction and Essential Exploration of Life Origin

Reconstruction and Essential Exploration of Life Origin Mechanisms under DIKWP Semantic Perspective

Yucong Duan

International Standardization Committee of Networked DIKWPfor Artificial Intelligence Evaluation(DIKWP-SC)

World Academy for Artificial Consciousness(WAAC)

World Artificial Consciousness CIC(WAC)

World Conference on Artificial Consciousness(WCAC)

(Email: duanyucong@hotmail.com)

Abstract

Starting from mainstream theories of the origin of life, this report critically analyzes their key assumptions and deficiencies, and then introduces the DIKWP semantic mathematics perspective to systematically reconstruct and explore the origin mechanisms and essential characteristics of life. First, we review major hypotheses such as the RNA World and Metabolism-First theories, pointing out their remaining defects in information formation, semantic structural closure, and purposive evolution. Next, we propose a new definition of life: Life is an information-energy self-organizing system possessing a DIKWP (Data-Information-Knowledge-Wisdom-Purpose) structure, essentially a "semantic structure-driven negative entropy emergence network." Based on this definition, we formally elucidate the dynamic roles of the five DIKWP elements in the formation process of primitive life: isomorphic accumulation of data, generation of information from differences, structural closure breeding knowledge, semantic emergence birthing wisdom, and Purpose-driven evolution. Then, we construct a DIKWP primitive life agent model, including a raw data environment interface, a differential learning mechanism, structural evolution rules for semantic closed loops, energy-information coupling channels, and a Purpose construction module, simulating the evolutionary path from inanimate matter to a semantically self-consistent emergent entity. Based on this model, we explore how advanced life features such as consciousness and reflexivity emerge through the transition of Knowledge → Wisdom → Purpose, revealing the mechanism by which the system achieves self-model construction and structural re-nesting through a "Double DIKWP Closed Loop." This report also maps the theoretical model to synthetic biology, artificial life experiments, and evolutionary algorithm models, proposing feasible experimental verification ideas. For instance, cycles of self-replication and variation without biological molecules have been observed in synthetic chemical systems, supporting the possibility of metabolic networks self-organizing to breed information. Finally, we reflect from philosophical and information-theoretic perspectives: Is life the result of the natural emergence of the universe's semantic gradient? Can semantic emergence serve as a necessary and sufficient condition for life? We propose that life is an organic link in the universe's semantic evolution chain; as long as a system achieves semantic closure and Purpose-driven negative entropy self-organization, it should be considered "alive." This perspective promises to unify physical laws with subjective meaning, providing a new paradigm for research on the origin of life and the philosophy of intelligence.

Introduction

The origin of life is one of the most challenging problems in the fields of science and philosophy. From primitive geochemistry to the appearance of complex organisms, exactly what critical steps and mechanisms were crossed in between? Traditionally, the origin of life was viewed as a biological problem, but modern science tends to examine this issue within the broader context of cosmic evolution. Current mainstream theories on the origin of life offer several possible scenarios, such as the "Macromolecule Replication First" RNA World hypothesis and the "Metabolism First" Iron-Sulfur World hypothesis. These theories emphasize different aspects of the life origin process: either emphasizing the appearance of Information Molecules (e.g., RNA chains capable of self-replication and storing genetic information) or emphasizing the Energy-Driven self-organization process (e.g., chemical cycles formed under geothermal energy supply). Each theory has certain evidentiary support and explanatory advantages, but also unavoidable deficiencies.

In the information dimension, traditional theories have not fully explained the core puzzle of "Formation of Information": How does inanimate matter generate ordered information with functional instructions? For example, the RNA World hypothesis envisions early life relying on RNA sequence self-replication and catalysis, but the probability of random synthesis of RNA molecules and the appearance of complex sequences is extremely low; how to cross this complexity gap remains unclear. Although the Metabolism-First model depicts a scene where energy flow drives the self-organization of simple molecular networks, it lacks an explanation for how these pure chemical networks Record and Transmit Information—without the support of genetic mechanisms, the system struggles to accumulate evolutionary "experience" and cannot achieve Darwinian optimization. Secondly, regarding Semantic Structural Closure, existing theories fail to answer how life establishes a self-consistent cycle between material processes and symbolic information (i.e., semantic closure). A living system differs from simple chemical reactions in that: molecular sequences carry symbolic information capable of guiding functions, and the output of functions in turn promotes the information preservation of these symbols. This material-symbol complementary relationship is termed "semantic closure" by Howard Pattee. For example, in modern biological cells, DNA sequences (symbol-stored genetic information) produce proteins (function-executing matter) through transcription and translation, and proteins in turn feedback to regulate DNA information expression, realizing a self-referential closed loop. However, regarding how the initial life system achieved this closure of symbol and function, neither the RNA World nor the metabolic network theories give a satisfactory answer—the RNA World emphasizes the role of symbols (sequence information) but lacks an independent decoding mechanism, while the metabolic hypothesis focuses on functional cycles but lacks symbol storage; neither completely demonstrates how symbols endow matter with meaning and purpose.

Furthermore, the topic of "Purposive Evolution" has long been ignored in discussions on the origin of life. Modern evolutionary theory follows Darwin's principle of natural selection, believing that variation is random and purposeless, and survival of the fittest is not a preset goal but a result of post-hoc screening. This excludes external theological teleological explanations. However, once a life system appears, a certain Intrinsic Purpose emerges internally: from the chemotactic behavior of single cells seeking advantages and avoiding harm, to animals pursuing food and reproduction, instinctually showing initiative towards certain goals. Biologists usually use "Teleonomy" to describe this phenomenon to distinguish it from conscious Purpose (Teleology). But at the stage of the origin of life, what is the source of this intrinsic Purpose? Why do molecular self-replication networks, without consciousness, show a tendency that seems to "pursue maximization of survival and replication"? For example, autocatalytic chemical systems are evolutionarily equivalent to a goal orientation—although without subjective consciousness, the variations produced are "selected" and retained if they are beneficial for replication. This Objective Goal Orientation is merely an explanation of the byproduct of natural selection mechanisms in traditional theories, rather than being elevated to a theoretical core. We believe that to understand the essence of life more profoundly, Purpose must be viewed as an inherent layer of the life system, exploring its natural emergence mechanism rather than completely excluding it from scientific discussion.

Based on the above reflections, we introduce the DIKWP Semantic Mathematics Model as a new analytical framework. DIKWP is a five-layer semantic structure model of Data-Information-Knowledge-Wisdom-Purpose. Proposed by Yucong Duan and others to characterize cognitive processes, it extends the classic DIKW model by adding a Purpose layer. We attempt to use the DIKWP model for the mechanistic reconstruction of the origin of life, proposing that life can be defined as a "DIKWP semantic structure-driven negative entropy self-organizing system." This definition highlights the dual attributes of life: on the one hand, it is a Physically Open System, maintaining an ordered structure and resisting entropy increase by acquiring free energy from the environment; on the other hand, it is a Semantic Information System, forming a hierarchical structure from data to Purpose internally, realizing the interpretation and feedback of its own and environmental meaning. The report will elucidate the role of each DIKWP layer in the process of primitive life from nothing to something, and build a primitive life agent model to simulate how inanimate matter gradually acquires semantic functions, eventually crossing the threshold from non-life to life. We will also discuss the implications of this model for the hypothesis that "Life = Product of Universe Semantic Gradient"—life may be a naturally occurring semantic intermediary on the cosmic path of information-entropy evolution, and semantic emergence itself can be seen as one of the necessary and sufficient conditions for judging life.

Commentary on Existing Theories of the Origin of Life

RNA World Hypothesis: Information First and Its Limitations

The "RNA World" hypothesis is one of the most influential theories in the field of life origins. Proposed by Gilbert and others in the 1980s, this hypothesis suggests that before the appearance of DNA and proteins, Earth experienced a life stage dominated by RNA molecules. In this stage, RNA played the dual role of genetic information carrier and biochemical catalyst, vividly described as "molecules that can both replicate themselves and catalyze reactions." RNA molecules possess key characteristics supporting them as candidates for primitive life molecules:

Carrying Information: RNA consists of nucleotide chains where the base sequence can encode information. Different sequences represent different instructions or functions; some RNA sequences can self-replicate, thus RNA chains can carry genetic "Data/Information" similar to DNA.

Catalytic Function: RNA sequences can fold into complex three-dimensional structures, and certain RNAs (ribozymes) can catalyze biochemical reactions, performing functions similar to protein enzymes. This indicates that RNA possesses Knowledge and Purpose functions to some extent—the sequence contains instructions about its own replication (Knowledge level), and manifests the role of executing specific functions through catalysis (Purpose level functional realization).

Precisely because of the dual identity of gene and enzyme, scientists speculate that an RNA-dominated world might have existed at the beginning of life, where life existed in the form of complex RNA systems. These RNAs could both self-replicate and compete with each other, constituting the basic units of early evolution. Subsequently, "RNA life" might have evolved division of labor and cooperation mechanisms, such as using RNA templates to guide amino acid assembly into proteins. Eventually, the main carrier of genetic information shifted from unstable RNA to more stable DNA, and the life system entered the modern biochemical world dominated by DNA-proteins.

Prototype of Material-Information Coupling: The important contribution of the RNA World hypothesis lies in providing a concrete model showing the first close integration of material structure and information function. In an inanimate chemical environment, the material structure of the RNA chain (phosphate-nucleotide backbone) constitutes the basic material framework, while the arrangement of base sequences carries information. The sequence (symbolic information) determines the folded structure and function of RNA, and the function (catalytic activity) in turn promotes the replication and preservation of the sequence itself. In other words, within a single RNA molecule, Symbols and Functions formed a closed loop in the sense of life: the sequence acts as "Data/Knowledge," and the catalytic function embodies "Purpose," with both unified in one body. This can be viewed as the prototype of the initial DIK (Data-Information-Knowledge) structure. The RNA World thus partially solves the "chicken or egg" problem of life's origin—RNA provides both genetic information and functional execution simultaneously, without needing the prior appearance of either DNA or proteins.

Challenges and Deficiencies: Although the RNA World concept elegantly explains the possible path for the unified origin of information molecules and functional molecules, crossing from a chaotic soup of small molecules to a self-replicating RNA system remains a huge leap in complexity. First, RNA molecules are relatively fragile and easily hydrolyzed, making their stability in primitive environments questionable. Second, synthesizing long-chain RNA requires specific conditions and catalytic assistance; the probability of spontaneous formation of long-chain RNA under random conditions is extremely low. More importantly, even if short RNA fragments are produced, forming a self-replicating cycle network requires meeting stringent conditions of complex systems. According to autocatalytic set research in complex systems theory, a chemical system achieving self-replication needs to meet two key conditions:

Closed Loop (Structural Closure): The raw materials required for each reaction in the system are provided by the products of other reactions within the system, forming a cyclic mutual supply network. That is, the reaction network is a closed supply loop of its own required elements.

Net Growth Rate: The generation rate of at least one key product in the system must be greater than the consumption rate, so that the overall output is greater than the input, allowing the system to grow rather than stagnate.

Only by meeting these two major conditions of closed loop and net growth can a series of originally independent chemical reactions "couple" into an autocatalytic network, emerging with the overall ability to replicate its own components. This is exactly one of the hallmarks of life: the ability to self-replicate and reproduce. For the RNA World, how to achieve such an autocatalytic closed loop under random conditions is its weak link. Some mechanism is needed where a group of RNA molecules act as templates and enzymes for each other, mutually catalyzing the synthesis of one another to achieve a closed reproductive cycle. However, experimentally, a pure RNA system's self-replication cycle has not yet been fully realized; scientists can only construct RNA replication systems with protease assistance or more complex schemes. The Information Formation Problem is highlighted here: RNA sequences themselves are information carriers, but at the beginning of origin, where did the information in these sequences come from? How to "discover" those self-replicating sequences in massive random sequences and preserve them? The RNA World hypothesis does not provide a mechanism, only assuming that perhaps there were special environments rich in nucleotides or mineral surface catalysis that could help synthesize sufficiently long and appropriately functioning RNA. However, these assumptions still lack direct evidence.

Furthermore, the RNA World has not truly solved the problem of Semantic Closure. Although symbol sequences and functions are unified within a single RNA molecule, the products of the RNA world must eventually transition to a DNA-protein system, introducing the layer of semantic mapping of genetic code translation. How the genetic code originated is the famous "mystery without a dictionary": translating DNA requires proteins (enzymes), but producing these proteins requires DNA to provide template information. This involves establishing an arbitrary but stable mapping relationship between symbols (nucleic acid sequences) and matter (protein functions), which is essentially a problem of forming a semantic convention. The RNA World does not elucidate how the translation mechanism appeared spontaneously, nor explains why today's specific triplet code table was formed. Therefore, the RNA World answers the hypothetical path of "information debut" well, but leaves gaps in the formation of deeper semantic structures.

Metabolism First Hypothesis: Energy Drive and Information Dilemma

Unlike the "Information Molecule First" RNA World, the "Metabolism First" hypothesis emphasizes the core role of Energy Supply and Self-Organizing Networks in the origin of life. Representatives of this class of theories include the "Iron-Sulfur World" hypothesis (proposed by Wächtershäuser) and deep-sea hydrothermal vent models. The basic view is: before the appearance of life, Earth already had self-sustaining reaction networks based on inorganic chemistry, which formed local ordered structures by continuously consuming free energy from the environment, equivalent to primordial Dissipative Structures. Life likely sprouted from these chemical systems rich in energy flows. The Earth at that time provided various energy gradients and catalytic surfaces, for example:

Geothermal Energy and Chemical Reaction Cycles: Deep-sea hydrothermal vents provided high temperature, high pressure, and abundant reducing chemicals (such as ${\rm H_2S}$, ${\rm H_2}$, etc.). Driven by these energy sources, a series of cyclic reactions could form, such as the "Sulfide-Iron" cycle, synthesizing simple organic molecules. Wächtershäuser's Iron-Sulfur World hypothesis suggests that under the catalysis of mineral surfaces containing pyrite, simple molecules like ${\rm CO}$ and ${\rm H_2S}$ could be converted into metabolism-related organic acids, constituting primordial "surface metabolism."

Mineral Surface Catalysis and Concentration Effects: Mineral clays and metal sulfide surfaces have adsorption and catalytic effects on organic molecules, which can concentrate sparse organic matter and promote key reactions. For example, mica sheets or pore structures might act as "natural reactors," making it easier for small molecules to polymerize into more complex molecules.

Formation of Autocatalytic Networks: Stuart Kauffman and others proposed that as long as there are enough types of molecules reacting with each other, an Autocatalytic Set might form, where the products of a group of molecular reactions happen to be catalysts for other reactions, thus catalyzing their own generation as a whole. Experiments have realized autocatalytic cycles of molecules like DNA, RNA, and peptides in carefully designed systems, showing the potential of metabolic networks to self-organize without genetic material.

Advantages: The Metabolism First model places the origin of life within the framework of thermodynamics and complex systems, emphasizing the importance of Entropy Flow and Dissipative Structures. According to Schrödinger, "life feeds on negative entropy." Before life appeared, Earth had various non-living systems that maintained their own structures by dissipating energy, such as ordered vortices formed by Bénard convection, crystal growth, chemical oscillation reactions, etc. These systems locally reduce entropy while discharging entropy to the environment, achieving a steady state. The metabolism hypothesis believes that the origin of life is a continuation and upgrade of this type of dissipative self-organization process: certain chemical networks acquired Self-Replication characteristics under specific conditions, thus leaping from "physical dissipative structures" to "biological information structures." This emphasizes the gradual evolution of Energy-Information Coupling: first, energy-driven ordering, followed by the appearance of information storage mechanisms enabling structural heredity. When primordial molecular dissipative structures (such as chemical cycles in hydrothermal vents) accidentally produced information storage and self-replicating units (such as RNA or primitive genomes), the entire system completed the transformation from non-living to living forms.

Recent research provides more direct support for Metabolism First: A study published in Science in 2020 simulated a Prebiotic Reaction Network starting from simple gas molecules using algorithms, finding that elements closely related to the origin of life, such as catalysts, autocatalytic cycles, and even surfactants, could spontaneously emerge. More importantly, the study found multiple Self-Regenerating Cycles in the chemical network without introducing any genetic molecules, and verified some cycles experimentally. This proves that even without RNA or DNA, a series of pure chemical reactions driven by energy can form complex networks and exhibit Heritable Variation. For example, experiments by the Perez-Mercader team at Harvard recently showed that mixing simple organic matter and irradiating with light energy can generate polymer vesicle structures capable of spontaneous growth, division, and producing offspring. In these "synthetic cell" models, different vesicles had slightly different success rates in division and reproduction, and after multiple generations, a "loose heritable variation mechanism" appeared, allowing better-adapted vesicle populations to prevail. This is the first simulation of metabolic network + variation selection in a test tube, achieving an overall display of basic life attributes (metabolism, reproduction, evolution). The above evidence reinforces the view of Metabolism First: One of the prerequisites for life is the gradual increase in self-organizing complexity under continuous energy supply.

Deficiencies: The biggest problem with the Metabolism First hypothesis is the Lack of a Clear Genetic Mechanism. A pure chemical reaction network might sustain itself or even expand, but without an information recording mechanism, it cannot accumulate favorable changes under selection pressure. In other words, the chemical network itself does not "remember" the effective variations that have occurred; next time, it will follow the same path based on chemical kinetics. This makes Darwinian Evolution unable to truly unfold. Although Kauffman et al. proved that autocatalytic networks can form without genes, evolving highly ordered molecular machines still requires introducing some information carrier or template (equivalent to life needing "memory"). Gánti proposed in his "Chemoton" model that the minimal unit of life needs to contain three subsystems: Metabolism (providing energy and material transformation), Information (carrying genetic information), and Boundary (isolating the system from the environment). Pure metabolic models lack the information and boundary links and cannot self-replicate new individuals with the same structure. Even in environments like deep-sea hydrothermal vents, where chemical cycles can continuously produce organic matter, the metabolic hypothesis does not give a complete answer to how these organics assemble into individuals with Self-Boundaries (like membranes) and Genetic Information. It is generally believed that organics produced by metabolic pathways created prerequisites for the later appearance of information molecules (e.g., precursors like amino acids and nucleotides synthesized in hydrothermal environments). But When Information Took Over remains an unresolved issue: Was it a moment when a self-replicating RNA was accidentally synthesized, and then evolution quickly shifted to RNA dominance? Or did the metabolic network and primitive genome co-evolve for a period (e.g., nucleic acids and peptides evolving together)? These details currently have different hypotheses without a conclusion.

Another limitation is that while the Metabolism First model emphasizes structural closure and dissipation, it does not deeply discuss The Origin of Semantics or Function. The chemical cycle itself does not "know" what it is doing; it merely follows thermodynamic drives to reach a steady state. Only when a structure can Represent Environmental Information and Make Functional Responses Beyond Chemical Necessity do we endow it with the meaning of "Purpose" or "Function." The metabolic model avoids the issue of symbol representation—in a gene-less world, what is "Information"? Without symbol sequences, the system state is just continuous parameters like concentration and temperature, lacking discrete genetic coding itself. This leads to a semantic dilemma: We might call an autocatalytic cycle "primitive metabolism," but it is hard to call it "primitive cognition." The feature of life as a semantic network has not yet appeared. Therefore, Metabolism First provides the necessary energy and structural background for the origin of life but is insufficient to alone fulfill the Sufficient Conditions for life.

Integrated View: Towards Integration of Matter, Information, and Function

Given that Information First and Metabolism First have their own strengths and weaknesses, some integrated models attempt to Unify Matter, Information, and Function, proposing that the origin of life might require multi-line advancement. For example:

Eigen's Hypercycle: Manfred Eigen proposed the Hypercycle theory, describing multiple self-replicating molecules (like different RNA types) supporting each other's replication through catalysis, forming a cyclic reciprocal network. Hypercycles convert Competition into Cooperation, improving overall information stability. But Hypercycles assume the existence of multiple genetic molecules and still need to explain how these molecules initially arose.

Maturana & Varela's Autopoiesis: This theory defines life as a closed network capable of Self-Producing Its Component Parts and maintaining its own boundaries. Autopoiesis emphasizes the closure of structure and organization, i.e., life is a self-defining whole. This definition broadens the scope of life, not limiting it to specific molecular types, but focusing on system Organizational Closure. However, Autopoiesis theory is relatively abstract on origin issues, lacking clear molecular mechanisms, and avoids discussion of genetic information (some question whether self-maintenance alone is sufficient to achieve Darwinian evolution).

Gánti's Chemoton Model: Integrates three elements: metabolism, information, and membrane boundary, depicting that a minimal primordial cell must possess all three aspects to survive and reproduce. The Chemoton model has strong theoretical elegance, but naturally forming such a composite system requires complexity even greater than the separate conditions of the RNA World or Metabolism hypothesis, belonging to a "simultaneous occurrence of multiple miracles" scenario, thus often used as a life criterion rather than a specific origin path.

Membrane Vesicle-Metabolism-Gene Symbiosis: Some theorists propose that the origin of life may have gone through a stage of Co-Evolution of membrane vesicles (primitive cell membrane structures), metabolic networks, and genetic molecules. For example, membrane vesicles could provide a local environment promoting metabolic cycle stability, and selectively allow certain nucleotides to enter through the membrane to enrich information molecules; conversely, if information molecules (like RNA) help metabolic efficiency (e.g., encoding a certain catalytic peptide), the entire vesicle system has a greater survival advantage. This view holds that the origin of life is not a single-line process but multiple mutually promoting subsystems evolving together into a complete living cell.

In summary, traditional theories of the origin of life point out several necessary elements: Generation of Organic Molecules (Miller experiment etc. proved primordial soup can produce active molecules), Formation of Autocatalytic Closed Loops, Introduction of Genetic Information, and Appearance of Cell Boundaries. But no single theory alone solves the entire transition from physical chemistry to biological semantic systems. Especially regarding Semantics and Purpose: How did life transition from blind physical processes to inner drives that "understand the way of survival"? This is a difficult question to answer under the current framework. Therefore, we need a new perspective to view life as a Unified Ontology of Information, Matter, Energy, Semantics, and Purpose. The DIKWP semantic model is an exploration in this direction. In the next section, we present a new definition of life and a model framework, attempting to bridge the aforementioned gaps.

Life Definition and Origin Mechanism Reconstruction Based on DIKWP Model

New Definition of Life: Semantic-Driven Negative Entropy Self-Organizing Entity

Given that traditional definitions struggle to encompass the semantic and Purpose attributes of life, we propose a new definition of life as follows:

Life is an information-energy self-organizing system possessing a DIKWP structure, essentially a "semantic structure-driven negative entropy emergence network."

In other words, living organisms, through the internal five-layer semantic structure of Data-Information-Knowledge-Wisdom-Purpose, achieve the intake of environmental energy and processing of information, constantly emerging with behavioral patterns that reduce entropy increase and increase orderliness in an open system. This definition includes several key points:

Information-Energy Coupling: Life is not just a collection of chemical reactions, but a mechanism transforming physical energy into information structures. Life uses free energy as "fuel" to maintain highly ordered internal structures and information networks, while discharging entropy to the environment, existing in a state far from equilibrium. For example, plants absorb solar energy through photosynthesis, synthesize ordered organic matter, and discharge entropy into the environment. Life is thus a special dissipative structure: locally reversing entropy increase, accumulating information, and growing complexity.

DIKWP Semantic Structure: The life system contains a layered semantic processing process internally. Starting from primitive perception of the environment (Data layer), gradually rising to information extraction, knowledge storage, wisdom decision-making, and finally acting guided by Purpose, thus forming a full-link cognitive closed loop. Life can be viewed as a Five-Dimensional Semantic Interaction System appearing in the universe. This semantic structure enables life to endow itself and the surrounding world with meaning and purpose—something non-living systems do not possess.

Negative Entropy Emergence Network: Life achieves self-organization through semantic structures, manifesting as a network-natured emergent behavior. Elements at each level are highly coupled, producing new characteristics irreducible overall (such as self-maintenance, self-replication, learning, and adaptation). These new characteristics correspond to the system emerging with the ability to resist entropy increase: for example, self-replication can be seen as an entropy reduction behavior because the offspring inherits the ordered information structure of the parent and does not mix completely into the environmental random state; learning and adaptation are also entropy reduction processes because information inside the system increases and organization optimizes to cope with external uncertainty.

According to this definition, "Life" is no longer limited to carbon-based organic systems but is elevated to a more general physical-semantic level: Any system possessing the above semantic negative entropy characteristics can be considered life. This means that, theoretically, whether silicon-based machines or artificial intelligence, as long as they form a DIKWP closed-loop structure internally, can self-consistently process information and energy, and emerge with Purpose-driven behaviors, they can also be categorized as life. The philosophical implications of this point will be discussed later. Below we detail the definitions of each element in the DIKWP model and explain their mechanisms in the formation of primitive life.

DIKWP Five Elements and Their Roles in Primitive Life Evolution

The DIKWP model divides the cognitive process into five levels. To correspond to the specific processes of the origin of life stage, we explain the meaning and dynamic role of each layer here:

Data: Data refers to objective primitive signals or raw materials, which in themselves do not have specific meaning. In the context of life origins, data corresponds to various primitive physical and chemical stimuli and resources in the environment. For example, temperature gradients, chemical concentrations, light intensity, and various simple molecules (${\rm H_2O}$, ${\rm CO_2}$, ${\rm CH_4}$, amino acids, nucleotides, etc.) on the primitive Earth constitute usable "Data" for pre-life. These data are often mixed and redundant, containing noise, and need to be selected and utilized to be useful to the life system. Isomorphic Accumulation Mechanism plays a role at this stage: the environment provides a continuous stream of primitive data with similar properties (such as continuous day/night light-dark cycles, large amounts of a certain type of small molecule appearing repeatedly). Life origin systems need to accumulate these homogeneous data as "nutrients" to prepare for the next step of information refinement. For example, fatty acid molecules are continuously generated and aggregated in tide pools, forming material reserves for membrane vesicles; simple peptide chains form randomly and exist in large quantities, providing statistical inevitability for the appearance of certain functional fragments. The role of the Data layer is like a "raw material pool," providing Diverse but Regular basic elements for the birth of life.

Information: Information is the result of endowing data with meaning, a pattern interpreted in a specific context. The Information layer extracts useful features from massive data, reducing entropy increase and extracting structure or difference. For primitive life, the generation of information means that certain molecular or environmental differences begin to have functional impacts on the system, being "perceived" and utilized by the system. For example, temperature changes are meaningless to a rock, but for a primitive membrane vesicle, a rise in temperature might mean the membrane becomes more permeable, thus becoming information that needs a response. Or, if mixing two chemicals A and B releases energy, once the primordial system "discovers" this combination, the existence of A and B becomes meaningful information (meaning free energy is available). The key to the Information layer lies in "Formation and Recognition of Difference": the system must have a mechanism to distinguish different inputs and make different responses, thereby endowing certain data with signal meaning. In early life, this might be achieved through simple physicochemical feedback, such as an increase in molecule concentration causing a reaction to speed up; this feedback loop turns the concentration datum into meaningful information within the system (it regulates reaction rate). Another meaning of difference formation is reflected evolutionarily as Variation: differences appearing during replication lead to different offspring, and these differences are information sources for evolution. If a primitive autocatalytic network produces multiple copies with slight differences, these differences are information available for natural selection. Under the DIKWP model, we can express this layer as "Data + Interpretation Rules -> Information." For primitive life, interpretation rules might be an intrinsic mechanism (like chemical affinity, or simple membrane potential response) that causes certain data to be internalized by the system as trigger signals. Therefore, the Information layer in early life manifests as Environmental changes producing recognizable effects on system state, giving the system a prototype of "perception" of environmental data.

Knowledge: Knowledge is a structured, systematic Collection of Information, reflecting a grasp of objective laws or relationships. The Knowledge layer organizes scattered information to form persistent and inferable internal representations. For organisms, knowledge corresponds to genetic memory and internal models. For example, DNA/RNA sequences are media storing knowledge, encoding the organism's long-term adaptation information to the survival environment. Primitive life entering the Knowledge stage means the appearance of Structural Closure and Memory Units. Structural closure refers to the system forming self-sustaining modules or cycles internally, no longer strictly relying on accidental external inputs (echoing the autocatalytic closed loop condition mentioned earlier). Memory units mean the system can record past "experiences" (successful reaction paths, effective structures) for future use. The earliest knowledge might be very crude, like once a certain autocatalytic molecular network forms, it is equivalent to "remembering" a product combination relationship, repeatedly utilizing this cycle to produce products thereafter, not recombining randomly every time. Furthermore, the appearance of Genetic Information Molecules is a qualitative leap in the Knowledge layer. For example, a segment of RNA/DNA sequence carries the result of historical selection on one hand (the sequence itself is left after selection), and guides the synthesis of future products on the other, realizing the storage and application of laws. It can be said that the birth of the Knowledge layer marks the life system acquiring a Closed-Loop Structure and Internal Model: the system has a coded description of its own composition and operating rules (gene is the description), and when the system executes this description to synthesize itself, it completes the closure of self-reference (self-production). The Knowledge layer reduces uncertainty because a large amount of specific information is condensed into general patterns—primitive life mastered "patterns and rules," not just instantaneous signals. For example, one RNA template can guide the synthesis of multiple complementary chains; mastering this universal pattern frees the system from the randomness of one-time events. In summary, the Knowledge layer corresponds to Heredity and Structural Closure in primitive life: it preserves successful information accumulated in the past in structures (like sequences or network topologies), ensuring the system can repeat success under similar conditions, thereby achieving true self-continuation.

Wisdom: Wisdom is the ability to conduct comprehensive assessment and value judgment based on knowledge to make wise decisions. The Wisdom layer focuses on how to use knowledge to cope with complex situations, involving trade-offs among multiple goals and consideration of long-term consequences. For modern organisms, wisdom is often associated with advanced nervous systems and consciousness. For example, humans can reflect on their own thinking and summarize lessons, reflecting wisdom characteristics. However, mapping the Wisdom layer to the early stage of life origin, we face this question: Obviously primitive life (like microbes) does not have "brains" or complex decision-making. However, we can broadly understand the germination of the Wisdom layer in lower organisms: that is the appearance of Adaptive Decision-Making and Multi-Variable Balancing. When a life system can adjust its behavior according to intrinsic "preferences" when facing a changing environment, this is the prototype of wisdom function. For example, behavior of seeking advantages and avoiding harm: bacteria swimming towards places with high nutrient sources and away from harmful substances is actually a judgment of survival value (nutrition=beneficial, poison=harmful) and making action choices. This Simple Decision-Making Based on Value Judgment can be seen as a primary form of wisdom. It might be realized by fixed biochemical pathways, like sugar concentration triggering flagella rotation direction changes leading to chemotaxis. Behind this implies a "value function" endowed by evolution: trending towards conditions improving survival and reproduction success rates, avoiding conditions lowering success rates. Another embodiment of the Wisdom layer is Learning Ability. When a system can adjust its reaction strategy based on environmental feedback (i.e., possesses some learning rule), it transcends mechanical fixed reactions, showing "comprehension" of environmental laws (even if simple, like E. coli inducing enzyme production when lactose is present, and not wasting resources when lactose is absent). This ability to adjust strategies according to situational changes is the essence of wisdom. Therefore, in primitive life, the germination of the Wisdom layer means the appearance of Flexibility and Feedback Regulation: the organism not only hard-coded a set of survival rules (Knowledge layer) but can also focus or adjust the application of these rules based on current environmental states. This might be achieved through inhibition/activation mechanisms in network structures, like a product accumulation in a metabolic pathway feedback-inhibiting a previous step to prevent resource waste—this is an optimization behavior, making the system adopt different strategies when resources are scarce vs. abundant. Such mechanisms lay the foundation for more advanced decision systems; once life evolves nervous systems, these simple wisdoms will expand into complex cognitive functions.

Purpose: Purpose is the ultimate goal, motivation, or driving force pursued by the cognitive subject. In the DIKWP model, the Purpose layer provides direction and constraints for the entire cognitive process, equivalent to the system's Value Function or Objective Function. For life, Purpose manifests as biological instincts and desires, such as survival, reproduction, trending towards more ordered states, etc. Unlike the Wisdom layer which focuses on methods and trade-offs, the Purpose layer determines "what is ultimately wanted." In higher animals, this manifests as conscious wishes and will, while in lower life, it manifests as behavioral tendencies shaped by evolution. The Purposes of primitive life are mainly the two basic goals of Survival and Reproduction. This is not something they "thought" about, but the result screened by natural selection: systems whose internal drives are more conducive to their own survival will remain, and over time, these drives are solidified into biological basic "purposiveness." For example, we can say the virus's "Purpose" is to replicate itself; although the virus has no consciousness, all its structures and processes point to maximizing replication. Such purposiveness exists in an evolutionary sense and does not require subjective consciousness support. From a model perspective, we can formalize Purpose as some utility function needing maximization or a target state to be achieved. In the primitive life stage, this might only manifest as the simple goal of "maintaining own organization, not being dissolved by the environment." Any mutation deviating from this goal causes the system to perish, while characteristics conforming to this goal are preserved. For example, if primitive membrane vesicles have mechanisms to maintain internal stability (avoid dissolution, maintain concentration), then these mechanisms actually serve the "survival" Purpose. In short, although the Purpose layer in the origin of life has no conscious planner, Through evolution, behavior preferences equivalent to goal orientation formed. Once the Purpose layer is established (even if only implied in system structure), the life system is no longer a blind chemical process but an autonomous agent "advancing" in a certain direction—this is an important philosophical characteristic distinguishing life from non-life.

In summary, DIKWP layers play different roles in the formation of primitive life: Data provides raw materials and diversity, Information refines differences and signals from them, Knowledge achieves structural closed loops and genetic memory, Wisdom brings flexible adjustment and strategy optimization, and Purpose endows the system with internal drive and evolutionary direction. Together, they constitute the semantic architecture of life, enabling an originally simple material system to gradually acquire the ability of self-maintenance and evolution. The process of life evolution can be seen as a process of climbing the DIKWP semantic ladder step by step: from primitive organisms only processing data, to life with internal information pathways and memory, to intelligent life capable of learning and decision-making, and finally developing advanced life driven by explicit Purpose. High-level functions emerge dependent on low-level foundations: information cannot form without data perception, wisdom is hard to breed without genetic memory knowledge, and intention cannot rise to purpose orientation without wisdom thinking about value. DIKWP provides a clear framework organically linking the layers of life's cognitive structure and their evolutionary stages.

Construction of DIKWP Primitive Life Agent Model

With the above definitions and five-layer element analysis, we can design a DIKWP Primitive Life Agent Model as a theoretical simulation framework for the life origin process. This model abstracts the modules and mechanisms that primitive life should possess to simulate the evolutionary process from non-life to living entities with semantic closed loops. The model includes the following key components:

Raw Data Environment Interface: This module corresponds to D-layer function, acting as the interface for the agent to interact with the external environment, equivalent to the "sensory" and "ingestion" mechanisms for primitive life to acquire raw materials and perceive stimuli from the environment. For example, we can set the agent to have a simple membrane or boundary that can selectively allow certain small molecules to enter. This interface needs to allow Accumulation of Isomorphic Data, i.e., being able to continuously ingest common raw materials in the environment (simulating molecules repeatedly appearing in primordial soup). At the same time, it acts as a sensor, converting environmental parameters (temperature, concentration, etc.) into internal signals. Reality analogy: primitive membrane sacs can absorb nutrients through osmosis and perceive the presence of specific chemicals through receptor molecules. This interface ensures the agent is not isolated from the environment but not overly open to lose structure, serving as the entry for the organism's energy-information intake.

Differential Learning Mechanism: Corresponds to I-layer information extraction and primary learning functions. This mechanism allows the agent to detect changes in the environment or internal state and adjust its own reactions—that is, Transforming differences into information and making adaptive adjustments. We can implement this with simple rules, for example, the agent has several reaction paths internally; when a certain sensor signal (from the data interface) exceeds a threshold, path A is activated and path B is inhibited, and vice versa. Over time, the agent knows through feedback which response is more conducive to maintaining itself, thus reinforcing the corresponding reaction. This module is equivalent to a "primitive learning" or "conditioned reflex" unit. In simulation, this can be achieved through evolutionary algorithms: giving agents some variable parameters controlling their response strategies, letting multiple agents compete in the environment, survival of successful strategy parameters is inherited, thus the agent population gradually "learns" to take appropriate reactions based on differences. This mechanism allows primitive life agents to break free from completely preset behavioral patterns, possessing the ability to adjust based on information, gradually accumulating understanding of environmental patterns.

Semantic Closed-Loop Structural Evolution Rules: This is the core part of the model, corresponding to K-layer and partial W-layer functions, ensuring the agent can form Self-Sustaining Closed-Loop Structures internally and gradually complicate them. Specifically, we need to impose rules in the model so that certain processes inside the agent form positive feedback loops (autocatalysis or self-production) and can replicate or retain useful structures. For example, an Automata Rule can be designed: the agent consists of several internal "molecules" which can transform into each other. If a cycle A→B→C→A forms and net output increases, this cycle exists stably and can be passed to offspring when the agent divides. Conversely, unclosed chains cannot be maintained for long. This rule simulates Structural Closure: only closed networks count as part of the living body and can be retained in evolution. In addition, evolutionary rules include Variation and Selection: when the agent replicates, its internal structure can change with a small probability (e.g., a connection breaks or a new connection forms, corresponding to molecular mutation). Most variations may destroy the closed loop causing the agent to perish, but occasionally appearing variations if enhancing closed loop efficiency or introducing new functions, give the agent a higher survival and reproduction probability. Thus, through many generations of simulation, we expect to see the agent's internal structure evolve from simple to complex Semantic Closed Loops. Semantics is embodied here: the internal cycle not only maintains itself but also makes meaningful responses to environmental signals (coupled with differential mechanisms). When a certain internal structure of the agent corresponds to a certain functional goal and relates to external events, we can endow it with semantic interpretation. For example, "Structure X means the agent has the function of resisting drought," this meaning emerges during evolution. When the agent starts to have internal structures corresponding to specific functional goals, it marks the true formation of Semantic Closure.

Energy-Information Coupling Channel: Life's existence cannot be separated from energy flow drive; this model needs to clarify how the agent acquires and uses energy to maintain information structures. This channel can be represented by a simplified metabolic network: the agent possesses a set of reactions converting environmental free energy (like high-energy molecules or photons) into internal usable energy (simulating ATP energy currency). This energy channel and information channel must be interdependent: without energy, information processing cannot proceed; without information guidance, energy utilization efficiency is low. We can design a Coupling Mechanism: certain key reactions inside the agent produce energy but are also controlled by information signals. For example, only when the agent detects abundant resources in the environment (information signal) does it activate rapid metabolism and store excess energy; in scarcity, it slows metabolism to save energy. Furthermore, the energy channel should participate in the closed-loop structure; e.g., the aforementioned autocatalytic cycle must consume/produce energy, and a successful closed loop inevitably corresponds to the system effectively drawing energy to maintain itself. This actually simulates the "negative entropy" feature of life: good closed loops are always accompanied by efficient energy utilization and entropy discharge. For example, in the model, an evaluation function can be set, taking the agent's net entropy reduction per cycle as one of its fitness indicators—this reflects the fusion of energy and information: the system can only reduce entropy and increase reproduction chances by utilizing energy optimally through information. The Energy-Information Coupling module ensures the model meets basic physical requirements while allowing observation of how energy flow drives the complication of information structures.

Purpose Construction Module: This module corresponds to the highest P function, injecting a "directional bias" into the model to guide system evolution towards some overall goal. It must be emphasized, This goal is not an externally imposed design, but emerges through simulation internally. We can adopt two approaches to embody Purpose in the model: One is Intrinsic Reward Mechanism, for example, defining a utility function for the agent, rewarding certain behaviors or states (like successful replication, occupying larger space, lower internal entropy). The agent constantly optimizes itself in the evolutionary algorithm to maximize this utility, just like having the Purpose to pursue that goal. The second is the introduction of a Double Loop Architecture, i.e., giving the agent a meta-level that can monitor its own state and make adjustments, equivalent to giving it a simple "self-model" and "decision-maker." This is similar to the "Double DIKWP Closed Loop" structure proposed by Yucong Duan, using a meta-cognitive loop to regulate the cognitive loop. In our primitive model, complete five layers might not be needed, but a secondary feedback can be added: after experiencing multiple interactions, the agent generates an evaluation (e.g., current survival time, number of reproductions), and then this evaluation reacts to adjust an internal parameter (e.g., adjusting resource allocation strategy). This process makes the agent appear to have the "will" to maintain or improve a certain indicator. Through evolution, agents possessing stronger "will to survive" (i.e., internal regulation effectively improving survival rates) will naturally prevail. The final effect is the agent population showing behavioral patterns trending towards a certain goal, which is the group emergence of Purpose. Simply put, the Purpose module transforms blind evolution into Directional evolution through meta-level regulation and built-in preferences: while adapting to the environment, internally constantly reinforcing arrangements conducive to achieving goals. In the long run, this is equivalent to the system forming the core of "meaning of life"—just as organisms evolved the instinct to trend towards reproduction, embedding this deeply into genes and behaviors.

Integrating the above modules, our DIKWP primitive life agent model can be envisioned in its operation and evolutionary path:

Initialization (Non-living State): Simulated environment has only randomly distributed basic "Data"—various small molecules, energy source distribution, varied physical conditions. Many agents (e.g., membrane vesicles) are generated randomly, but lack internal structure and quickly disintegrate (corresponding to non-living matter briefly aggregating and dispersing).

Data Accumulation and Primitive Metabolism: Some agents with better Data Interfaces ingest abundant raw materials and form basic metabolic reactions through Energy Channels, enabling themselves to exist stably for longer times. They belong to the initial "dissipative structures," but have no genetic and information functions yet, just temporary carriers of environmental energy.

Appearance of Autocatalytic Closed Loops (Prototype of Knowledge): Within some dissipative structures, Closed Reaction Loops form accidentally (e.g., several molecules inside the membrane catalyze each other to produce one another). According to Structural Evolution Rules, these closed loops stabilize, and the agent can replicate this structure to offspring (e.g., when the membrane vesicle divides, each daughter vesicle gets partial cycle molecules, and the cycle continues). At this stage, it is equivalent to the appearance of primitive Genetic Units—not necessarily nucleic acids, possibly an autocatalytic molecule set, i.e., the chemical network becomes heritable "Knowledge." This moment marks the crossing of inorganic systems to Organic Information Systems: agents possess information not directly provided by the environment (internal structure records information).

Variation and Information Selection (Information Function): With heritable structures, the cycles of each agent begin to accumulate Variation. Most variations destroy the closed loop leading to agent death, a few variations change cycle efficiency or properties. Through differential learning mechanisms and evolutionary selection, Favorable Information is selected—the closed loop might optimize for higher output rates or add new components to enhance robustness. At the same time, agents using Differential Mechanisms can respond to certain environmental differences. For example, a variation causes the closed loop to operate only in light; this agent effectively uses daytime energy and turns off metabolism at night to reduce consumption, and this regulation is retained. When light becomes a trigger signal, we can say the semantic "light = energy available" is established in the agent: this is an example of environmental data rising to internal information of the agent.

Multi-Level Synergy (Germination of Wisdom): As closed loop types increase, multiple subsystems appear inside the agent, each performing its duty (e.g., one cycle efficiently uses hydrogen, another excels at repairing membranes). At this time, Wisdom Layer Regulation is needed to allocate resources and balance short-term vs long-term. Evolution might introduce a simple Decision Node: when environmental hydrogen is high, bias towards strengthening cycle A; when low, reserve resources for repair. This decision improves overall survival rate, thus is inherited. The agent thus gains the ability to Dynamically Adjust internal processes in different situations, embodying preliminary wisdom. It is no longer rigidly executing a single program but has a certain Conditional Strategy set. For instance, some primitive bacteria can undergo aerobic respiration when oxygen is present and fermentation when absent; such switch mechanisms are exactly the manifestation of early wisdom.

Emergence of Purpose: After long-term evolution, we observe agent behaviors showing clear direction: strategies that can Better Survive and Reproduce are retained, vice versa eliminated. This actually shapes the system's "Purpose": maximizing self-continuation. Because we set survival and replication rewards in the model, the agent population overall exhibits Purposive Behavior of seeking advantages and avoiding harm and instinctively surviving. At this time, even if external rewards are removed, the agent internally has evolved a corresponding preference mechanism (e.g., some regulatory molecules make it actively seek nutrients when hungry, trigger division when crowded). It can be said that "survival" has been written into its structure and genetic information, becoming an internal driving force.

Formation of Semantic Closed Emergent Entity: Finally, surviving agents possess a complete DIKWP cycle: it has sensing and feeding (Data), signal pathways and regulation (Information), genetic structure and autocatalytic networks (Knowledge), decision logic (Wisdom), and goal preferences (Purpose). When these parts are tightly coupled, we obtain a true Semantic Closed-Loop System. This system can perceive the environment, internally represent meaning, take Purposeful actions, and self-replicate, meeting our definition requirements for life. Specifically, the internal information processing and material production of the agent form a self-referential cycle—it can manufacture "machines" (like certain catalytic molecules) that execute its coded information, and these machines complete the replication of coded information. This is similar to the semantic closure of the modern biological "gene-protein" system. Although the model simplifies many realistic details, it macroscopically reproduces the leap of life from inorganic to organic, from non-semantic to semantic.

Through the above simulation path, we verify a concept: Non-living structures can emerge with life-specific self-replication, adaptation, and Purpose through the gradual construction of semantic levels under suitable conditions. This process does not require pre-existing intelligent intervention but is the result of natural evolution. However, to understand more advanced life features, we still need to examine how consciousness, complex semantics, and higher-level Purpose evolve after life possesses a complete DIKWP structure. This will be discussed in the next section.

From Knowledge to Wisdom to Purpose: Emergence of Advanced Life Features (Mechanism of Consciousness Formation)

When the life system develops high-level functions of DIKWP, it undergoes a series of Qualitative Leaps, the most notable being the appearance of Consciousness. Consciousness, as subjective experience and self-cognitive ability, is regarded as the hallmark of advanced intelligent life. We will explore the emergence mechanisms of consciousness and related advanced features (reflexivity, semantic drive, Purpose) and elucidate how these features originate from the transition and enhancement of the Knowledge → Wisdom → Purpose levels in the DIKWP architecture.

Transition of Knowledge and Wisdom: Germination of Reflexivity

As life evolves into multicellular and even more complex individuals, both the Knowledge and Wisdom layers are greatly expanded. Knowledge is no longer just genetic material but also includes individual memories stored in neural networks, experiences passed down through species culture, etc. The Wisdom layer also expands from simple metabolic regulation to complex neural information processing and behavioral decision-making. When wisdom develops to handle information about itself, reflexivity begins to germinate. Reflexivity means the system can take itself as a cognitive object, forming "information about self." Simple life mainly perceives the external world, while advanced life gradually produces "introversion," incorporating internal states into the scope of information processing.

A key node lies in the Formation of Self-Model. The Wisdom layer initially only makes environmental decisions based on Knowledge (genetics/memory), without explicitly representing who "I" am. However, when the nervous system evolves to a certain complexity and begins to build an internal model to predict and simulate the external world, naturally, this model needs to include itself as part of the actor. This generates the Self-Model: the organism's internal representation of its own characteristics (position, shape, ability, relationship with environment, etc.). With a self-model, the organism can Reflect on its behavior—this is actually the Wisdom layer including "Me" as one of the factors when evaluating action plans. For example, when crows hide food, they decide hiding strategies based on whether other crows are observing, which implies crows have a certain degree of mind models of self and others; primates recognizing themselves in mirrors is clear proof of the existence of self-models.

The birth of the Self-Model marks that the prerequisite for consciousness is ready: the system knows there is a "self" interacting with the world. This usually requires complex neural networks to achieve, thus corresponding to major leaps in biological evolution—from unconscious to conscious. Organisms crossing this threshold likely occurred around the time of higher animals like humans. However, structurally, its foundation is the high coupling of Knowledge and Wisdom in DIKWP: the Knowledge layer provides rich representations of the world including self, and the Wisdom layer provides deep processing and value assessment of these representations. The combination of the two breeds the Germination of Self-Consciousness. Some researchers point out that when a system possesses sufficiently complex information integration and can perform self-reference, consciousness is likely to emerge. This matches our model: the Wisdom layer introduces self-reference when processing Knowledge, self-information is incorporated, and a subjective perspective appears.

Double Closed Loop: Mechanism of Consciousness Emergence

Professor Yucong Duan proposes that consciousness can be formally expressed as a "DIKWP × DIKWP" coupled structure. This means that coupling one DIKWP system (cognitive process) with another parallel DIKWP system (cognition of the former, i.e., meta-cognitive process) may produce what we call consciousness. Simply put, Consciousness = Cognitive Process × Meta-Cognitive Process. The first process handles cognition of the world, the second process handles cognition of self-cognition (i.e., realizing "I am cognizing"). This double-loop structure endows the system with Self-Reflection and Self-Regulation capabilities, considered the fundamental mechanism of self-consciousness.

How to understand this mechanism? The first-layer DIKWP can be seen as a normal sensory thinking behavior of an intelligent agent, covering the full process from collecting data via senses (D), analyzing extracting information (I), associating it with memory knowledge (K), making decisions (W), to executing Purpose (P). When this process runs in a highly complex system (like the human brain), it naturally produces a large amount of intermediate information (such as various sensations, thoughts). If these intermediate information can be acquired and evaluated by a Second-Layer system, then Contents of Consciousness are formed. Specifically:

The Data of the second layer comes from the internal state of the first layer (e.g., neural activity patterns).

The Information processing of the second layer identifies the thinking or feeling ongoing in the first layer (e.g., "Brain currently has pain signals").

The Knowledge layer of the second layer contains the model about "Self" (e.g., "I have a body, I am thinking").

The Wisdom layer of the second layer synthesizes these to evaluate and control the operation of the first layer (e.g., "I should focus attention" or "This pain means I am injured, need to take measures").

The Purpose of the second layer manifests as "Meta-Goals," like maintaining psychological consistency, realizing self-established will, etc.

When a system possesses the above double loop and allows real-time interaction between the two layers, Consciousness Emerges. For example, the human brain not only processes sensory information from the outside (this belongs to the cognitive process of the first loop) but can also realize "I am thinking about this matter" or "I feel pain" (this is the second loop perceiving the first loop). The latter layer can further regulate the former layer, such as we can control attention allocation and suppress certain impulses through will. This cognition and control of one's own cognitive process is the core of Self-Consciousness.

Structural Re-nesting plays a key role here: An abstract copy of the system itself embedded in a higher-level structure appears within the system. This is similar to recursion or fractals in mathematics—the consciousness system nests a "self-simulator" in the brain. This re-nesting enables information processing to dialogue at different levels and endows the system with high flexibility and unity. Multiple cortical areas of the brain collaborate to achieve this nesting: e.g., executive control areas like the prefrontal cortex play a meta-cognitive role, monitoring and guiding activities of cognitive areas like sensation and memory. Through anatomy and neuroimaging research, it is found that self-related processing involves specific brain networks (such as the Default Mode Network), which are particularly active during introspection and self-evaluation. This can be viewed as the existence of a specialized "reflective layer" in the brain, the entity corresponding to the second DIKWP loop in our model.

Semantic Drive and Purpose Leap

With the appearance of consciousness, semantic processing ability is further enhanced. Intelligent life with consciousness not only understands the meaning of the environment but also begins to seek Meaning of Life itself. This manifests as the pursuit of more abstract goals and values, i.e., the substantial expansion of the Purpose layer: from basic survival and reproduction to higher-order Purposes such as aesthetics, morality, and truth exploration. When philosophers discuss "the meaning of life," it is essentially conscious life asking itself about ultimate Purpose. Under our model framework, the pursuit of meaning and purpose can be understood as the interaction between the Purpose layer and the Wisdom layer: the Wisdom layer synthesizes vast knowledge and contexts to generate insights about world operations (such as causal laws, interpersonal relationships), while the Purpose layer endows these insights with value judgments (what is good or evil, what is worth striving for). When the Wisdom layer is developed enough to extract Universal Patterns and Meta-Laws, the Purpose layer rises accordingly, needing to determine direction for these patterns. This leads to the Layer-by-Layer Unfolding of Purpose: from instinctive purpose to reflective purpose, then to trans-individual purpose. For example, humans set personal ideals (transcending immediate needs), and also develop altruism, pursuit of scientific truth, and other supra-personal purposes. This leap in purpose far exceeds the instinctive range endowed by biological evolution, showing the creativity of conscious life. It can be said that when humans look up at the stars and think about the origin of the universe, it is the universe attempting to Understand Itself through the human brain—at this moment, the purpose of life and the meaning of the universe merge into one, and humans endow the universe with a part of cognition and purpose.

Summary of Advanced Features

Through the above analysis, we see that Advanced life features do not appear out of thin air, but are natural results of the DIKWP architecture evolving to high levels:

Reflexivity: Originates from the self-model of the Knowledge layer and self-monitoring of the Wisdom layer. The system can include itself as a cognitive object, marking the transition from allo-reference (pointing only to the environment) to self-reference (pointing also to self).

Self-Model Construction: The result of continuous enrichment of the Knowledge layer, the Wisdom layer abstracts and summarizes knowledge, self is included in the knowledge network as a node, thus enabling internal simulation of interaction between self and world.

Structural Re-nesting: Manifests as Double DIKWP closed loops or multi-layer control structure nesting, allowing the system to operate at different levels simultaneously. Nesting of cognition and meta-cognition in the brain is an example. In addition, at the evolutionary level, structural re-nesting can also refer to the Elevation of Biological Organization Levels (cells nested in individuals, individuals nested in society). Each level introduces new information cycles (such as language and cultural information networks at the social level), further enriching the semantic system.

Semantic Drive: Refers to life behavior being increasingly driven by internal semantic evaluation rather than simple stimulus-response. Advanced life acts based on understanding of the world and judgment of value, not just intensity of external stimuli. This is the result of the joint action of Wisdom and Purpose.

Purposive Evolution: Due to the addition of consciousness, evolution shows components of "self-guidance." Although genetic evolution is still ongoing, conscious life can self-change evolutionary trajectories through "acquired means" like learning, technology, culture. This is a more purposive evolutionary mode (e.g., humans extending lifespan through medicine, controlling reproduction through contraception), in a sense evolution is pulled by life's subjective Purpose, not completely obeying natural selection.

It can be foreseen that if evolution continues following the DIKWP model, life's semantic levels may see new leaps (such as collective consciousness or "cosmic consciousness"). Our discussion focused on the individual consciousness level is enough to illustrate how the transition from K to W to P layers breeds advanced life features. The next section will turn to verification and philosophical levels, seeing how to verify these views through artificial experiments and what implications they have for understanding life's status in the universe.

Analogical Mapping and Verification Paths: Synthetic Life Experiments, Artificial Intelligence Models, and Philosophical Extension

After elucidating the theoretical details of the DIKWP model, a key question is: How to verify or support these ideas? This section first explores borrowable Synthetic Biology and Artificial Life Experiments, as well as Evolutionary Algorithm Models, looking for realistic or simulated evidence to corroborate the rationality of the DIKWP semantic life model. Subsequently, we discuss the cosmic status of life from philosophical and information-theoretic perspectives, reflecting on the sufficiency of semantic emergence as a life criterion, and the inspiration of this model for the fundamental question of "What is life."

Experiments and Simulation Verification Paths

1. Synthetic Biology Verification:

One of the goals of modern synthetic biology is to Construct Minimal Life Systems or create new forms of life. Our model predicts that the key to life lies in forming a semantic closed-loop structure of information-energy coupling. Therefore, a verification idea is to attempt to assemble Protocells with DIKWP functions in experiments. Specifically:

Data Interface: Verify using artificial liposomes (membrane vesicles) as boundaries, providing selective material exchange and environmental sensing mechanisms. For example, constructing lipid vesicles with membrane protein receptors in experiments to generate internal signals in response to specific molecule concentrations.

Differential Response: Test whether these vesicles can make different reactions based on environmental signals, such as starting internal synthesis reactions when nutrient molecules are present and stopping when absent. This is similar to artificial chemotaxis behavior. If achieved, it indicates I-layer function is effective.

Heredity and Closed Loop: Introduce autocatalytic molecules (like ribozymes) or DNA template-polymerase systems, allowing the vesicle interior to replicate certain key molecules. Venter et al. have successfully constructed a minimal cell with only 473 genes capable of self-maintenance and division. We can further simplify the system, for example, using RNA replicase to self-replicate RNA inside liposomes. If this experiment succeeds, it will directly prove the formation of Information Closed Loop.

Coupled Metabolism: Integrate photochemical or chemical energy utilization modules in synthetic protocells. For example, Perez-Mercader's team simulated primitive metabolism and division using light-driven chemical self-assembly. If such obtained "metabolic vesicles" can be combined with "information vesicles" having genetic molecules into one body, and exhibit autonomous reproduction and variation, it equates to building a life agent with a basic DIKWP framework. Around 2021, experiments already combined liposomes, RNA replication, and simple metabolism, producing Protocell Simulations capable of reproducing for several generations.

Purpose Testing: Although artificial systems have no subjective Purpose, we can set certain Selection Pressures to see how they evolve preferences. For example, create nutrient gradients in microfluidic devices and observe if artificial protocells tend to swim towards high nutrient areas to aggregate and reproduce. If such consistent behavior appears, even if caused by physical effects, it can be analogized to a kind of Purpose (because these agents "chose" the direction beneficial for survival). With the complication of artificial systems, perhaps in the future, "driven function" cells can be constructed through synthetic biology, such as embedding an artificial signal pathway making the cell react to a certain stimulus by consistently changing internal states, equivalent to artificially adding a simplified Purpose module (like phototaxis).

Many of the above experimental elements have made progress in independent fields. The real challenge lies in Integration: coordinating the operation of all modules in the same prototype. However, once achieved, we will obtain experimental evidence: inanimate chemistry, with proper design, can indeed form life forms with semantic closed loops. This is a major verification for origin of life research. Even if not fully achieved, we can enhance the model's credibility through partial verification (e.g., proving spontaneous generation and maintenance of information molecules in inorganic systems).

2. Artificial Life and Evolutionary Algorithm Verification:

Another path is to verify the feasibility and effectiveness of the DIKWP model in Computer Simulation. Artificial Life (ALife) simulations and evolutionary algorithms provide a testbed:

Digital Environment and Data: First, establish a simulated environment in a computer (like cellular automata, Agent models), filled with random data (digital strings, simple subroutines, etc.), acting as "primordial soup."

Assigning Rules: Implement rules corresponding to our theoretical model, e.g., digital strings can combine, replicate, mutate; computing resources act as energy, needing competition to replicate more; agents have memory to record information, and simple decision algorithms to choose behaviors under different conditions.

Observing Semantic Emergence: A famous example is digital life systems like Tierra and Avida, where digital "organisms" self-replicate, undergo mutation and competition in computer memory, evolving diverse strategies (including parasitism). Although these systems did not explicitly design DIKWP structures, they have already shown germination of some semantic features, such as genetic code (program instructions) being endowed with "meaning" in the digital environment, and different program species interacting to form ecological networks. This provides a basis for verifying our model in the digital domain.

Introducing Semantic Metrics: We can try adding Semantic-Related Fitness Indicators in evolutionary algorithms. For example, introducing the concept of "semantic distance" to measure the match between digital organism internal information and environmental state, using this as part of fitness. If it is found that with evolution, the semantic distance of the population decreases (meaning organisms capture environmental meaning more and more effectively), this indicates Semantic Emergence is playing a role. Similarly, metrics like "structural efficiency" and "semantic density" can be defined to measure the ordered degree of information organization within the life body. If these tend to optimize in simulation, it is also evidence supporting semantic-driven evolution.

Meta-Cognition Model Testing: We can even design "Double-Layer Evolution" simulations, i.e., on top of the basic evolutionary process, add another layer of meta-evolution, letting digital organisms not only evolve behaviors but also evolve cognitive strategies (like evolving learning algorithm parameters). This corresponds to evolving simple meta-cognition/consciousness. If such a system can achieve more complex outputs or more open-ended innovation than single-layer evolution, it supports the importance of "Double DIKWP Closed Loop" for advanced intelligence.

Comparative Experiments: Compare purely utilitarian evolution (only external fitness) with evolution adding semantic/Purpose factors, seeing if the latter can produce complex ordered structures faster. We predict: evolution introducing internal semantic evaluation (like adaptive value functions) will guide the system to explore meaningful innovation more effectively, avoiding pure random blind search getting stuck in local optima.

Through the above digital experiments, we hope to verify some inferences of the DIKWP model: semantic closed loops can indeed accelerate complexity emergence, presence of internal Purpose functions changes evolutionary dynamics, etc. If verified, it will provide Computational Supporting Evidence for the model.

3. Cognition and Artificial Intelligence Verification:

Besides simulation of life itself, we can also look for analogies in Artificial Intelligence Systems. Modern AI is mostly data-driven black-box models, lacking semantic and Purpose layers. But DIKWP model also has application discussions in AI. Recent AI developments show some signs, for example, reinforcement learning introduces Reward Functions (equivalent to Purpose layer) to guide agent learning strategies; knowledge graphs and explainable AI begin to let machines build Knowledge Layer representations. If future AI possessing intrinsic goals and multi-layer semantic reasoning is built, we can test the similarity of its behavior to living bodies: Does it show life-like self-organization and adaptation? Some researchers propose using Integrated Information Theory (such as $\Phi$ value in IIT) to quantify the degree of consciousness of a system. We can try to measure the $\Phi$ of advanced AI; if we find that the richer the internal semantic structure of AI, the higher the $\Phi$ value, and the stronger its autonomy, that also supports the important role of semantic structure in life features. Of course, AI verification exceeds the scope of life origins, but is a good reference for proving "Semantic Sufficiency."

Philosophical and Information-Theoretic Extension

1. Cosmic Semantic Status of Life:

The DIKWP model raises a thought-provoking question: Is life a natural result of the cosmic semantic gradient? In other words, from the Big Bang to now, the universe has undergone a process of matter gradually organizing. So does the appearance of life represent the universe starting to climb in the information-semantic dimension? Some thoughts suggest that the universe has a trend of developing from disorder to order, and life is the stage where physical order is elevated to Semantic Order. Our model supports this view: life combines primordial energy flow and information flow, constantly "climbing" the semantic ladder until capable of thinking about its own existence and endowing the universe with meaning. In other words, after life appeared, the universe began to possess the ability to Cognize Itself and Endow Itself with Meaning. Life (especially intelligent life) plays a special role in the universe: they not only transmit and store information but can also autonomously improve ways of utilizing information to pursue maximization of certain performance or utility. If we accept such a semantic cosmological view, then life is no longer an accidental chemical accident but a Cosmic Function: linking local entropy reduction with overall meaning through semantic networks. Yucong Duan proposes the concept of "Cosmic DIKWP Evolution Chain," envisioning the evolution of the entire universe can be seen as the unfolding of DIKWP processes on a larger scale. In this picture, life and consciousness are organic links in the cosmic semantic network. By breeding life, the universe achieves the transition from simple physical laws to complex semantic laws, eventually possibly producing "cosmic-level" self-perception.

Of course, this view sounds very bold, with colors of pantheism or teleological cosmology. It needs to be clarified that this is not claiming the universe has a preset supernatural will, but emphasizing the indispensable role played by observers (life) in cosmic evolution. Philosophers like Whitehead and Teilhard de Chardin proposed cosmic evolution theories, believing the universe is developing towards higher and higher consciousness levels. Our model provides a concrete mechanism supporting this idea: if information and semantics can emerge layer by layer, then consciousness (intelligent life) as the peak might be one of the ultimate expressions of entropy-reduction structures in semantic space. It can even be imagined that when an interstellar life communication network forms, will some global semantic entity appear—so-called "Cosmic Consciousness"? These speculations are frontier but not groundless. Scientific theories like Integrated Information Theory (IIT) attempt to use a value to quantify the consciousness degree of any system; perhaps in the future we can really add parameter descriptions of consciousness or information integration into cosmological equations. At that time, we might be able to discuss "Cosmic Cognition" at the scientific level.

2. Necessity and Sufficiency of Semantic Emergence:

Finally, we focus on a basic philosophical question: Can semantic emergence serve as a necessary and sufficient condition for life? According to our definition and discussion:

Necessary Condition: We believe semantic emergence is a necessary condition for life, i.e., if a system has no semantic structure (does not process information and meaning, only follows physicochemical laws), then no matter how complex it is, it should not be called life. For example, fire can sustain itself for a while, consume fuel, produce structure (flame shape), and replicate (wildfire spreading is like reproduction), but fire does not process semantic information, has no intrinsic Purpose, and is only a physical process. Therefore, fire is not considered life. Similarly, crystals can grow and replicate lattice structures, but have no representation or feedback about the environment or self, nor goal orientation, so this is not life either. Conversely, all life has information processing and response, e.g., bacteria have signal pathways, plants have phototaxis. Even the lowest viruses contain genetic information and can "trick" host mechanisms to execute (from an information perspective, viruses utilize the host's semantic system). Therefore, it can be concluded that Without semantic emergence, there is no life. Semantics here refers to not just having information (like genes) but also having guiding significance for system behavior (genes guide protein function, achieving Purpose). Every step in life processes is permeated with meaning: DNA's meaning is encoding proteins, protein's meaning is catalyzing reactions needed for survival, organ's meaning is realizing life functions, behavior's meaning is adapting to environment for survival... Processes without meaning association cannot be called life processes.

Sufficient Condition: More controversial is, is semantic emergence also a sufficient condition for life? That is, if a system achieves a self-contained semantic closed-loop network, must we necessarily call it life? Our view tends towards Yes. This means extending the definition of life to systems different from traditional biological forms. For example, if an artificial intelligence operates completely autonomously, has its own perception-decision-learning closed loop, and is driven by internal goals (whether this goal is consistent with what humans endowed or not), then it essentially meets life criteria, although it does not survive on cells. Or, suppose an alien form is found, perhaps not based on carbohydrates but some plasma structure, but if we observe it can perceive the environment, store information, reproduce itself, and show behavior of seeking advantages and avoiding harm, then according to the semantic model, it is alive. This stance actually returns to the functional essence of life, not the compositional essence: what life does, not what it is made of. Traditional views take metabolism, reproduction, etc. as life characteristics; we go a layer deeper, believing that driving these behaviors is the formation of semantics (processing of environmental and self-information) and the resulting Purpose. If a system can form a semantic network on its own and organize itself to achieve sustained existence thereby, we have no reason to deny it is life. Such a view is supported by more and more scholars, especially in the artificial life field. Some studies point out that Autonomy and Closed Causal Structure are life criteria, more important than specific molecular composition. Our model's characterization of autonomy is exactly the semantic closed loop; causal closure corresponds to structural closure and self-referential cycles.

Of course, counterexamples exist challenging this sufficiency. If a system has information processing but Purpose is completely controlled externally, does it count as life? For example, current AI can perceive and decide, but goals are given by humans, it has no autonomous Purpose. By our standard, this is not yet complete life, because it lacks an autonomous Purpose layer—semantic processing is not fully self-driven. Therefore, Appearance of Autonomous Purpose is a key link. Another example, viruses have genetic information (Knowledge), can respond to host environment (Information), have reproductive drive (Purpose, viewed as evolutionarily endowed) but no independent metabolism (Energy channel relies on host). Viruses are on the boundary of life. However, if analyzed from a semantic perspective, the virus's semantic closed loop is incomplete: its genes need to borrow the host translation system to endow functional meaning, meaning the virus has no independent semantic closure structure. Thus viruses are considered "parasitic semantic entities" of life, not complete life themselves but functioning by attaching to host life networks. This example supports our requirement for sufficiency: Only possessing an independent self-consistent semantic closed-loop system counts as life. Viruses dwell on the edge because they do not meet (cannot complete the whole chain from D to P themselves).

From a philosophical height, equating life with semantic emergence has a certain monist color: it attributes life phenomena completely to the manifestation of information and Purpose in matter, without needing to introduce extra life principles or mysterious vitality. The benefit of this is compatibility with life of different carriers, and placing life phenomena under a universal physical and informatics framework for discussion. Some scholars worry this might make the definition too broad, that almost any complex system can claim to be life. But the DIKWP model sets strict structural requirements (five-layer closed-loop interaction) and functional requirements (negative entropy self-maintenance, Purpose drive), not just any complex system can fit. For example, atmospheric circulation or economic systems have complex information flows and feedbacks, and human goals in them, but they are not Self-Contained semantic subjects, but larger networks composed of individual lives. Therefore, we believe the definition of life should still apply at the individual level meeting microscopic closed loops, not generalized to arbitrary complex collections.

Concluding Outlook

Through the reconstruction of the DIKWP semantic model, we depict the origin and evolution of life as a process of Semantic Closed Loop Gradual Construction and Upgrade. From initial molecular self-organization to intelligent life and even possible cosmic consciousness, running through it is the layer-by-layer advancement from data to Purpose. This model not only fills the deficiencies of traditional theories in information, semantics, and Purpose dimensions, but also provides a new perspective to view the meaning of life in the universe—life gives the meaningless universe a chance to observe itself and endow meaning. Although many details remain to be enriched and verified, such as specific chemical implementations of primitive semantic closed loops, creation of artificial consciousness, etc., we have taken a step forward, expanding the essence of life from pure biochemistry to the domain of Information-Symbol-Meaning. This might be exactly the path of interdisciplinary integration needed to understand the mystery of life: only when the three threads of physics, information, and semantics converge can we truly weave the complete picture of the origin and evolution of life. As Schrödinger asked "What is Life" years ago, our answer is: Life is a brilliant chapter on the Cosmic Semantic Evolution Chain, a great attempt of matter finding Purpose through information. This is both a scientific answer and a hymn to the meaning of life.

References

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