From Primitive Biochemical Reactions to the Formation of Consciousness: Prof. Yucong Duan's  Evolution Theory of Consciousness (ETC) 

Yucong Duan

Benefactor: Kunguang Wu, Shiming Gong

AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory

DIKWP-AC Artificial Consciousness Standardization Committee

World Conference on Artificial Consciousness  

World Artificial Consciousness  Association CIC

(Email：duanyucong@hotmail.com)

Catalog

Abstract

1 Yucong Duan's Theory of Consciousness (Introduction)

1.1 Basic concepts and definitions

1.2 The beginning of multicellular cooperation

1.3 Differentiation and evolution of cognitive function

1.4 The development of self-awareness

1.5 The application and significance of theory

2 Related theoretical work

3 Comparative analysis of related work

3.1 Professor Yucong Duan's Theory and Integrated Information Theory (IIT)

3.2 Comparison of Global Workspace Theory (GWT)

3.3 Relationship with Complex System Theory

4 Scientific contribution and innovation analysis

5 Possible challenges

6 Yucong Duan Professor's Theoretical Simulation Design

7 Yucong Duan's theoretical abstract reasoning verification

7.1 Simple theoretical derivation and verification

7.2 Probability calculation

7.3 Example Calculation

8 Simulation experiment design

8.1 Simulation design objectives

8.2 Sample Code Summary

8.3 Simulation operation

9 The starting point of biological evolution history demonstration

Conclusion

References

Appendix: Professor Yucong Duan's theoretical simulation design-experimental consciousness PRV2 (Experimenter: Kunguang Wu)

Foreword

1 Simulation plan design

1.1 Simulation target

1.2 Model architecture

1.3 Key parameters and rules

1.4 Simulation execution

1.5 Data analysis

1.6 Purpose and application of simulation

2. Experimental design

2.1 Simulation design objectives

2.2 Programming steps

2.3 Program Code Reference

3 Experimental results

3.1 Experimental data

3.1.1 Initial state

3.1.2 First iteration

3.1.3 The 5th iteration

3.1.4 The 10th iteration

3.1.5 The 15th iteration

3.1.6 The 20th iteration

3.2 Analysis results

Abstract

The origin of life and the formation of consciousness have always been the core issues in scientific research. Professor Yucong Duan's theory of evolution and development of consciousness provides a unique perspective to understand the primitive biochemical reaction of single-celled organisms to the complex cooperation of multi-celled organisms and even the formation of self-consciousness. In this paper, the basic part of this theory is deeply discussed, especially the mechanism of energy exchange between single-celled organisms through biochemical reactions, and how to promote the cooperative behavior between living organisms. Through the detailed analysis of the interaction between single cell and multicellular organisms, this paper constructs a theoretical bridge from the basic biophysical process to the evolution of advanced cognitive function. In addition, this paper also discusses how multicellular organisms gradually form complex organizational structures through cooperative behavior, and how to evolve functional bodies with clear division of labor from these structures, which eventually leads to the emergence of advanced cognitive abilities, including rational cognition and self-awareness. By integrating the latest research in genetics, neuroscience and ecology, this paper provides a new perspective to understand consciousness from a biological point of view, revealing that consciousness is not only the product of biological adaptive evolution, but also the result of complex interaction between environment and society. The purpose of this study is to emphasize the continuity from basic energy exchange to complex social interaction in biological evolution, and challenge the traditional explanation of consciousness formation. By expanding and deepening Professor Yucong Duan's theory, this paper aims to provide a more solid theoretical basis for future scientific research and a new solution to the problems of biological protection and sustainable development in the real world.

1 Yucong Duan's Theory of Consciousness (Introduction)

Professor Yucong Duan put forward a brief theoretical description of the origin of life: in the process of consciousness generation, the original biochemical reaction (unintentional cognitive form) in the interaction between individual organisms such as single cells and the environment is acquisition or energy (food), and in this process, in the process of energy acquisition, there is a biochemical cooperation between multiple individual cells that can be perceived by the original contact with specific food. And each of them has produced a feedback effect that can be perceived at the energy measurement level of saving their own energy. After this effect is regularized, it is assimilated into another form of obtaining energy, forming a combination and cooperation between cells that are in contact with each other, which is not to obtain energy, but to save energy, and then produces the cooperation between multicellular organisms. In the development of multicellular organisms, the functional division in the form of cooperation and the complex cooperation between functional bodies are gradually formed according to similar laws. In the increasingly complex process of these regularities, high-level processing and analysis ability has gradually emerged from low-level processing, and has gradually developed into perceptual cognition (the embryonic form of the subconscious mind). Rational cognition is the cognitive processing ability of symbolizing and storing perceptual cognition by conceptualization and other means to improve efficiency and deal with more complicated problems. The gradual rational cognition rises to the liberation of the independence of physical concrete form carriers, which also produces self-awareness. (See: Professor Yucong Duan's theory that consciousness produces bugs has been published online. )

1.1 Basic concepts and definitions

Concept of biochemical reaction:

Basic concepts:

Biochemical reaction refers to the chemical reaction of single-celled organisms through interaction with the environment, the main purpose of which is to obtain energy (such as food) necessary for survival. These reactions are the basis of life maintenance and development.

Scientific basis:

Single-celled organisms, such as prokaryotic cells and some eukaryotic single-celled organisms, absorb external resources through chemical channels to obtain energy, mainly including glycolysis and photosynthesis. These processes not only support the basic physiological activities of cells, but also provide energy basis for further evolution.

Detailed argumentation:

The core of biochemical activities of living cells lies in the transformation and utilization of energy. For example, through the specific transport protein on the cell membrane, cells can take in external nutrient molecules, such as glucose, and convert it into adenosine triphosphate (ATP) through glycolysis, which is the universal energy currency of cells. The process of ATP production and consumption is the core of cell energy metabolism, which directly affects cell survival, proliferation and functional expression. This process shows how organisms use simple chemical reactions for complex life activities to support their physiological and evolutionary needs.

The concept of unintentional cognitive form:

Basic concepts:

Unintentional cognitive morphology refers to the basic response of primitive organisms to environmental stimuli, which usually does not involve complex information processing or decision-making functions. It is mainly reflected in the intuitive and reflective response to environmental changes.

Scientific basis:

The response of organisms to environmental stimuli can be traced back to the earliest organisms, such as protozoa. These organisms exhibit basic behaviors such as phototaxis or chemotaxis, which are manifested as physiological responses to changes in light or chemical concentration. These behaviors are unconscious, automatically performed by biological neural networks, and do not involve advanced brain processing.

Detailed argumentation:

Single-celled organisms such as protozoa perceive environmental changes through receptors on the cell surface. For example, photoreceptors can detect the intensity and direction of light, and chemoreceptors (such as chemotaxis receptors) can sense the concentration changes of specific chemicals. The signals of these receptors will directly affect the behavior of cells, such as moving to the light source or concentrating on chemical attractants, which is a direct physiological response of organisms to environmental stimuli. This basic environmental perception ability constitutes the most primitive form of cognitive function and is the basis for organisms to make adaptive responses to external changes.

Through this expansion, we not only deepened our understanding of how single-celled organisms interact with the environment through biochemical reactions, but also discussed how these interactions evolved into the original cognitive form of organisms. This provides a scientific basis for understanding how consciousness evolved from a simple biochemical process.

1.2 The beginning of multicellular cooperation

The beginning of multicellular cooperation

In the process of biological evolution, the transformation from single cell to multicellular organism marks a significant increase in biological complexity. One of the fundamental driving forces of this change is the cooperation between cells, especially in energy acquisition and consumption efficiency. The following contents will discuss the initiation of multi-cell cooperation in detail, including physical enclosure and biochemical cooperation and the feedback effect of energy saving.

Physical Surrounding and Biochemical Cooperation

Scientific basis:

In the evolutionary history of organisms, early multicellular organisms such as sponges and algae showed the initial form of group behavior. Through physical contact and simple cooperation mechanism, such as common predation, these creatures formed a preliminary group cooperation. This kind of cooperative behavior is not uncommon in biology, but exists as a common phenomenon, especially in the environment with limited resources.

Detailed argumentation:

Sponges are a typical example of multicellular biological cooperation. These creatures filter food particles in water through a common body structure, forming an efficient collective foraging strategy. The porous structure of sponge allows water to pass through and effectively captures food particles, which not only improves the energy intake efficiency, but also promotes the stability and maintenance of multicellular structure. The complexity and functionality of this structure are difficult for single-celled organisms to achieve, which shows the remarkable advantages of multi-cell cooperation in evolution.

Feedback effect of energy saving

Scientific basis:

Cooperative behavior reduces the energy expenditure of each individual in obtaining food in many biological groups, which is the key factor for the success of social organisms. Through joint action, organisms can use environmental resources more efficiently and reduce energy loss caused by competition.

Detailed argumentation:

For example, by forming a predator group, single-celled organisms can share the risks and costs of resource acquisition. In a resource-rich environment, this behavior may be manifested as simple gathering; When resources are scarce, more complex cooperative strategies may be developed, such as orderly predation behavior and food distribution system. This cooperation not only reduces the energy consumption of finding food, but also improves the survival rate of the whole group by spreading risks. With the passage of time, this cooperation has led to the evolution of organisms from single cells to multicellular structures, because in multicellular bodies, cells can specialize and perform different functions, further improving energy utilization efficiency and adaptability.

The cooperation between physical enclosure and biochemical level and its energy-saving effect played a key role in the evolution of multicellular organisms. This not only explains the driving force of the origin and evolution of multicellular biological forms, but also shows how organisms adapt to environmental pressures through the complexity of internal structure and behavior. Professor Yucong Duan's theory provides a valuable perspective in this respect, which helps us understand the richness and complexity of biodiversity.

Biological experiment and observation of multicellular cooperation;

The beginning of multicellular cooperation

In the study of biological evolution, the initiation of multi-cell cooperation is considered as one of the important changes from simple to complex. The following are two related research directions:

Experimental study: cooperative behavior of multicellular yeast

In the laboratory environment, the researchers simulated the evolution process of multicellular organisms through artificial selection experiments. In these experiments, through continuous selection, yeast cells form aggregates that can settle rapidly, and these aggregates can obtain nutrients more effectively. It is found that this kind of aggregation behavior can develop in a very short evolutionary time, which shows the powerful role of natural selection in promoting the characteristics conducive to group survival. This aggregation behavior not only improves the efficiency of yeast cells to obtain nutrients, but also improves their survival probability in competitive environment.

Observation and research: cooperative network of coral reef ecosystem

In the natural environment, coral reef ecosystem provides an ideal place to observe multicellular cooperation. Coral polyps form a complex ecological structure through symbiotic relationship with algae. Algae provide nutrients for corals, while corals provide carbon dioxide for algae's protection and photosynthesis. This symbiotic relationship shows how to optimize the acquisition of resources and the utilization of energy through cooperation, thus supporting a complex and diverse ecosystem.

Feedback effect of energy saving

The role of cooperative behavior in reducing energy consumption is an important driving force for multicellular evolution. The following is the specific research and demonstration of this effect:

Experimental study: the formation of bacterial biofilm and energy efficiency

Studies have shown that bacteria can cope with external pressure by forming biofilm, such as antibiotic attack or undernourished environment. Bacteria in biofilm show a high degree of cooperation, including the development of common metabolism and defense mechanism. This structure significantly improves the overall energy efficiency of bacterial population, because they can share metabolites and resources, and reduce the energy expenditure of their own battles.

Observation and research: cooperative hunting of animal groups

In higher organisms, cooperative hunting is an obvious example of energy-saving feedback effect. For example, wolves hunt larger prey through collective cooperation, which enables the whole group to obtain more food resources than when a single wolf hunts alone. This not only improves the success rate of hunting, but also significantly reduces the energy consumption of each wolf in the hunting process. The evolutionary advantage of this behavior is that through collective action, wolves can maximize the efficiency of energy utilization and ensure the effective distribution of food resources in the group, thus supporting the survival and reproduction of a larger community.

Comprehensive analysis of scientific research

From the above experiments and observations, it can be seen that cooperative behavior has significantly improved the survival ability and resource utilization efficiency of organisms, whether in micro-bacterial communities or macro-animal groups. The formation and maintenance of these cooperative behaviors are often closely related to biochemical reactions within organisms, genetic regulation mechanisms and responses to external environmental changes. This supports Professor Yucong Duan's theory that consciousness and advanced cognitive function are adaptive characteristics that organisms gradually evolve to adapt to complex and changeable environments.

Extension of argument: from biological cooperation to consciousness formation

Professor Yucong Duan's theory puts forward that the initial cooperation between single-celled organisms is the basis for the formation of multi-celled organisms and more complex social structures. Furthermore, this cooperative behavior is not limited to the physical and biochemical level, but also includes the information exchange and sharing decision-making process, which are the basic elements of consciousness formation. For example, in animal groups, the emergence of cooperative hunting and social learning behavior shows a basic "collective consciousness" (see: the discussion of group consciousness put forward by Professor Yucong Duan), in which information sharing and collective decision-making are crucial to the survival of the group.

In addition, environmental pressures, such as competition for resources and threats from natural enemies, urge organisms to develop more complex social structures and communication methods, which are the driving forces for the further development of consciousness. Therefore, the evolution of consciousness can be regarded as an advanced form of biological adaptive response to its living environment, which involves complex cognitive processing, emotional response and self-awareness.

Professor Yucong Duan's theory has been supported by modern scientific research, especially in the study of biological cooperative behavior and its influence on survival and energy efficiency. These results not only verify the importance of cooperative behavior in biological evolution, but also reveal how cooperative behavior promotes cognitive function, especially the evolution of consciousness. Future research should further explore how environmental changes affect the evolution of social structure and cognitive function, and how these changes in turn affect the adaptability and survival strategies of organisms. This will provide us with a deeper understanding of how consciousness develops in biological evolution and shows different complexity and functions in many creatures.

1.3 Differentiation and evolution of cognitive function

Development of perceptual cognition

Scientific basis:

In the development of multicellular organisms, the functional differentiation of cells promotes some cells to evolve into specialized sensory cells and nerve cells. The main function of these cells is to receive and process information from the external environment, which marks the formation of the initial nervous system. For example, animal sensory systems, including visual, auditory and tactile systems, are all composed of highly specialized sensory cells, which can convert external stimuli into nerve signals.

Detailed argumentation:

The development of nervous system greatly improves the response speed and efficiency of organisms to environmental changes. For example, sensory cells, such as photoreceptor cells on the retina, can convert light signals into electrical signals, which are quickly transmitted to the brain through nerve fibers. In the brain, these signals are further processed, enabling organisms to instantly recognize the shape, color and movement in the environment. The rapid processing and response of this information is the basis of perceptual cognition, which enables organisms to effectively navigate the environment, avoid danger and catch prey.

The evolution of rational cognition

Scientific basis:

With the increase of biological complexity, especially in vertebrates, the development of brain provides the necessary structural basis for more complex information processing. With the development of the brain, organisms can not only perceive and react, but also conceptualize and symbolize information, thus enhancing their ability to solve complex problems and make advanced decisions. (See: Discussion on the Theory of Consciousness BUG put forward by Professor Yucong Duan)

Detailed argumentation:

The development of the brain, especially the cerebral cortex, enables animals to carry out abstract thinking and predictive planning. The expansion of the prefrontal lobe is particularly important because it involves advanced functions such as judgment, planning and decision-making. For example, in humans and other higher animals, the prefrontal lobe not only participates in complex decision-making processes, but also deals with social behavior and emotional expression. These advanced cognitive functions, including the use of tools, language communication and the establishment of social structure, are the embodiment of rational cognition. These complex functions of the brain allow animals not only to respond to current environmental stimuli, but also to optimize their behavior based on past experience and predictions for the future.

The formation of self-consciousness

On the basis of perceptual and rational cognition, some creatures, especially higher animals, further develop self-awareness. Self-awareness involves individuals' cognition of their own existence and behavior, which is not only a part of the cognitive process, but also the basis of social interaction and cultural development. The formation of self-consciousness is the advanced stage of consciousness evolution, which reflects the deep understanding of biological behavior and environment and its role in social groups.

Through this in-depth analysis and demonstration, we can understand Professor Yucong Duan's theory more comprehensively, especially how cognitive functions differentiate and evolve in multicellular organisms, and how these processes interact with environmental changes and other aspects of biological evolution.

1.4 The development of self-awareness

Cognitive independence

In the evolution of consciousness, the development of self-consciousness marks an advanced stage of biological cognitive ability. The deepening of rational cognition not only enables organisms to respond to specific environmental stimuli, but also enables them to conduct self-reflection and future planning. This cognitive independence means that creatures can not only recognize the outside world, but also recognize their existence as an independent entity. The generation of self-awareness is considered as a key step for organisms to evaluate and correct their own behaviors and make more complex decisions.

Scientific basis

The development of self-awareness is closely related to the complexity of brain structure, especially to the areas of the brain that deal with self-cognition and reflection, such as the prefrontal cortex. Neuroscience research shows that the prefrontal cortex involves advanced cognitive processes, including decision-making, social interaction and self-reflection. In addition, brain regions such as superior temporal sulcus and posterior cingulate cortex are also related to self-related thinking activities, and these regions play a vital role in the formation of self-awareness.

Detailed argumentation

The formation process of self-awareness can be demonstrated in detail through the following key neuroscience foundations:

Neural basis of self-reflection;

Studies have shown that the prefrontal cortex is one of the key areas of self-awareness. It is responsible for dealing with the evaluation and adjustment of individuals' own behavior, so that individuals can shift from the self-centered perspective to a broader environmental cognition. This ability is the basis of animal behavior self-regulation and social adaptability.

Metacognitive ability:

Metacognition, that is, the cognition of one's own cognitive process, is an important part of the development of self-awareness. In humans and some advanced animals, this ability is manifested in the ability to think for yourself. This reflective ability requires the brain to have the ability to process complex information and maintain internal dialogue.

Mirror image recognition test and self-awareness;

Mirror Self-Identification Test (MSR) is used to evaluate whether animals can recognize that the reflection in the mirror is themselves and not other individuals of the same species. Animals that have successfully passed the MSR test (such as gorillas, dolphins and elephants) show that they have a certain degree of self-awareness. These animals show highly developed visual and cognitive processing areas of the brain.

Influence of social and cultural factors:

In human beings, self-awareness is not only influenced by biological factors, but also shaped by cultural and social factors. Language use, cultural customs and social communication all play an important role in the formation of individual self-awareness. For example, how individuals view their role and position in society is regulated and expressed through the cultural framework.

Self-awareness is not only a complex biological phenomenon, but also a product of social and cultural evolution. Understanding the development of self-consciousness is helpful to reveal the multidimensional nature of cognitive evolution and how it evolves in different organisms according to the needs of environment, society and culture.

Evolutionary model of self-consciousness

Biological evolution perspective:

The evolution of self-awareness begins with basic survival needs, such as food acquisition and avoiding predators' intuition. On this basis, more complex social behaviors, such as pairing, parenting and group hunting, require animals to recognize and remember other individuals' behaviors and predict their intentions and reactions. The development of this ability promotes the expansion of the areas in the brain used for social processing and emotional expression.

In higher animals, the further evolution of self-consciousness may be related to more complex social structure and cultural behavior, including the use of tools, the formation of social norms and the transfer of knowledge through tradition and education.

Interaction between environment and culture;

Environmental pressures, such as climate change, changes in food sources and changes in habitats, often force organisms to adapt to new lifestyles, which may include developing new cognitive strategies and social behaviors.

Cultural evolution provides a non-genetic way of adaptation, especially among humans. Cultural factors such as language, education and technology not only affect individual cognitive ability, but also affect the behavior pattern of the whole group through social structure and collective learning.

To fully understand the evolution of self-consciousness, we need to consider how its biological basis and social and cultural factors interact. For example, in human beings, the development of self-awareness is closely related to the evolution of language ability. Language not only changes the way of thinking of human beings, but also greatly expands the possibility of social interaction and cultural inheritance.

1.5 The application and significance of theory

Professor Yucong Duan's theory of evolution and development of consciousness not only provides a coherent path from chemical reaction to biological evolution of complex ideology, but also brings important enlightenment and application value to modern science. The following is the main application and significance of this theory:

Deeply understand the origin and evolution of life

Complete evolutionary path: this theory provides a clear evolutionary path by describing the development from the original biochemical reaction of single-celled organisms to the complex cognitive ability of multi-celled organisms. This meticulous step shows how life becomes more and more complicated on the earth and how these creatures adapt to the ever-changing environment.

Essence of life: By understanding how different organisms develop complex social behaviors and cognitive functions through primitive cooperative mechanisms, scientists can explore the essence of life more deeply. This includes how life responds to natural selection, and how genetic and environmental factors work together to influence the behavior and evolution of organisms.

Enlightenment to modern science

Interdisciplinary study of biology: This theory emphasizes the connection between biology, neuroscience, genetics and environmental science. This interdisciplinary perspective is very important for solving complex biological problems, such as disease treatment, genetic engineering and ecosystem management.

Enlightenment from the Development of Artificial Intelligence

Imitating natural evolution: The theoretical analysis of how cognitive function evolves from simple mechanism to complex processing ability provides an idea of imitating natural evolution for artificial intelligence. For example, by simulating the evolution of biological neural networks, AI developers can design algorithms and systems that can handle more complex tasks.

Dialogue between AI and natural intelligence: This theory also reveals that AI research should pay attention not only to the improvement of computing power, but also to how to make machines learn and adapt independently in a changing environment, similar to the cognitive adaptation process of natural creatures.

The influence of social science and ethics

Evolutionary understanding of social behavior: theory provides a biological basis for understanding the social behavior and cultural evolution of human beings and other social animals. This has a far-reaching impact on social science research, such as social structure, cultural inheritance and behavioral economics.

Bioethics considerations: In the process of in-depth discussion of the essence of life and cognitive evolution, ethical issues must be considered, especially when genetic modification, artificial intelligence development and its potential impact on the environment and society are carried out. Theory provides a framework for evaluating the possible long-term impact of these interventions.

Through the elaboration of these applications and significance, we can see that Professor Yucong Duan's theory not only enhances our understanding of life evolution, but also has a far-reaching impact on the future development direction of many scientific and technological fields.

2 Related theoretical work

Related research work

From the primitive biochemical reaction of organisms to the formation of self-consciousness: Professor Yucong Duan's theory of consciousness evolution and development: Professor Yucong Duan's theory puts forward a unique perspective, connecting the physical basis of life with the advanced form of consciousness, and exploring the relationship between the origin of life and the generation of consciousness. The following contents will analyze and demonstrate Professor Duan's theory on the origin of life and the evolution of consciousness in detail, and support its scientific basis through relevant academic literature.

Theoretical core point of view:

Basic concepts of biophysical interaction

Professor Yucong Duan's theory starts from the primitive biochemical reactions between single-celled organisms and the environment, which are the most basic forms of life activities. These primitive reactions not only include obtaining energy by chemical means (such as sugar decomposition and ATP synthesis), but also involve physical and chemical cooperation between single cells to save energy, such as improving environmental adaptability and resource utilization efficiency by forming biofilm or symbiont.

Generation of consciousness and biological evolution

Professor Duan believes that the generation of consciousness is closely related to the evolution of organisms. From the simplest perceptual cognition (that is, the direct physiological response to external stimuli) to the complex rational cognition (that is, information processing, memory and decision-making), the evolution of consciousness can be regarded as the result of biological gradual adaptation to environmental challenges. In this process, the emergence of multicellular organisms is a key node, because it marks the transition from single to collective cooperation, which further promotes functional differentiation and complication of nervous system.

Key documents supporting the theory:

Biochemical reaction and primary cognitive form

Alberts et al. (2015) and Lodish et al. (2016) provided basic knowledge of biochemical activities of single-celled organisms, including energy acquisition and cell signal transmission. These processes provide a detailed explanation at the molecular and cellular levels for Professor Duan's initial biochemical reaction and unintentional cognitive form of life.

Margulis (1998) showed that organisms increase survival rate through symbiosis and cooperation, which supported Professor Duan's view on the origin of multicellular biological cooperation.

Multicellular cooperation and energy saving

Maynard Smith & Szathmáry (1995) described a major turning point in life, such as the transition from single cell to multi-cell, which provided an evolutionary background for the cooperation between multi-cell organisms and conformed to Professor Duan's theory.

De Waal (2016) discussed the social cooperation phenomenon in animal behavior, which can be regarded as a complex evolution form of multi-cell cooperative behavior, further proving the evolutionary advantages of energy saving through cooperation.

Differentiation and evolution of cognitive function

The neuroscience foundation of Kandel et al. (2012) provides evidence for Professor Duan about the differentiation of nerve cells and the evolution of perceptual cognition. With the development of nervous system, organisms can process more complex information and respond to the environment more effectively.

Minsky (1986) and Ramachandran (2011) revealed the evolution of cognition and self-awareness in biology, and supported Professor Duan's evolutionary path from perceptual cognition to rational cognition.

Development of self-awareness

Tononi's information theory and consciousness theory (2012) provide a framework for understanding the physical and chemical basis of consciousness. These theories are consistent with Professor Duan's view that the independence of cognition and the development of self-consciousness are related to the increase of brain complexity.

Penrose (1989) proposed the connection between consciousness and physical laws, which provided a profound physical and philosophical basis for understanding the immaterial origin of consciousness.

With the support of these documents, Professor Yucong Duan's theory not only found the basis in different fields of biology, but also showed its far-reaching influence in a wider scientific field. These references strengthen the scientific and practical nature of his theory and provide a solid foundation for further research and exploration. (See the appendix for more references. )

3 Comparative analysis of related work

Professor Yucong Duan's consciousness theory mainly discusses the evolution process from single-celled organisms to multi-celled organisms with complex cognitive functions, and how consciousness is formed and developed in this process. In order to better understand and evaluate this theory, we can compare it with several other famous consciousness theories, including the integrated information theory (IIT) in neurobiology, the Global Workspace Theory, GWT) in psychology and philosophy, and the complex system theory.

3.1 Professor Yucong Duan's Theory and Integrated Information Theory (IIT)

In exploring the essence and origin of consciousness, Professor Yucong Duan's theory and Integrated Information Theory (IIT) provide two completely different perspectives. These two theories provide rich theoretical basis and research direction in the field of consciousness research, but there are significant differences in their focus and methods.

Integrated information theory (IIT)

Theoretical proposition:

IIT was put forward by Giulio Tononi, which advocates that consciousness can be quantified and is the product of the degree of information integration within the system. The existence and level of consciousness can be measured by the quantitative index "φ value" (Phi). A high φ value indicates that the information inside the system is highly integrated and reflects a higher level of consciousness.

Key features:

IIT focuses on analyzing and understanding the connection mode of the internal structure of the nervous system and how to integrate information. According to the theory, if a system can form more internal interactions among its components, then its consciousness level will be higher.

Professor Yucong Duan's Theory

Theoretical proposition:

Professor Yucong Duan's theory emphasizes that consciousness is an adaptive feature that gradually evolves with the interaction between organisms and the environment. This theory puts forward that the development of consciousness is closely related to the survival strategy and environmental adaptability of organisms, and is gradually formed in the process of biological evolution.

Key features:

The theory focuses on the evolution of organisms from single cells to multicellular organisms, especially how to develop complex information processing ability and social behavior in this process. Professor Duan's theory not only discusses the biochemical and physiological mechanisms inside organisms, but also includes how these organisms optimize their behavior and structure through social interaction.

Comparison and discussion

Different theoretical emphases:

IIT focuses on the structure and function of the nervous system, especially how information is integrated inside the brain; Professor Yucong Duan's theory discusses the whole evolution process of organisms and the interaction in the ecological environment more widely.

Method and application:

IIT tries to explain and predict the state of consciousness by providing quantitative tools, which makes it have important applications in neuroscience and clinical research. Professor Duan's theory is more inclined to explain the evolutionary background of biodiversity and complexity, which is of great value for understanding the long-term process of biological adaptive behavior and environmental adaptation.

Potential bonding points:

Although the two theories are different in research paradigm and focus, they both emphasize the importance of internal interaction of the system. Professor Duan's theory can provide a supplementary explanation for the biological basis of consciousness formation in IIT from the perspective of evolution. On the contrary, IIT can provide a more detailed biophysical basis for the part of Professor Duan's theory about the evolution of neural networks and information processing.

Comparative analysis of Professor Yucong Duan's theory and Integrated Information Theory (IIT), the following is a detailed table, showing the differences and connections between the two theories in terms of propositions, key features, research focus, application fields and potential integration points:

From this table, we can clearly see that although there are obvious differences between the starting points and methods of the two theories, they are potentially complementary in exploring the formation and function of consciousness. Professor Yucong Duan's theory provides a macroscopic evolutionary perspective, while the integrated information theory provides a quantitative analysis method from a microscopic neuroscience perspective. Combining these two theories can not only deepen our understanding of the nature of consciousness, but also promote the development of related scientific fields.

3.2 Comparison of Global Workspace Theory (GWT)

In the process of further exploring the origin and development of consciousness, Global Workspace Theory (GWT) provides a perspective of neuroscience, which contrasts with Professor Yucong Duan's theory of consciousness evolution in several key aspects. The following is a detailed comparison of the two theories, aiming at a more comprehensive understanding of the complexity and diversity of consciousness.

Global workspace theory (GWT)

Theoretical proposition:

GWT was put forward by Bernard Baars, who advocated that consciousness is a global information processing place in the brain. Through this workspace, different neural networks can broadcast information to the whole brain, thus becoming the content of consciousness.

Key features:

GWT emphasizes the temporality and dynamics of consciousness. Consciousness is regarded as a temporary integration of information, which is processed and updated in the global workspace of the brain.

The theory focuses on how all regions of the brain share and process information through the connection of neural networks, and the interaction of these regions constitutes the physical basis of consciousness.

Professor Yucong Duan's Theory of Consciousness Evolution

Theoretical proposition:

Professor Yucong Duan pointed out that the generation of consciousness is a long-term evolutionary process, starting from the most basic biochemical reaction of organisms, and gradually developing into complex social interaction and advanced cognitive function through multiple stages.

Key features:

Theory focuses on how organisms use their biological structures and functions to adapt to the environment, and how these adaptations promote the evolution of cognition and consciousness. Consciousness is regarded as an adaptive result in the process of biological evolution, which is closely related to the survival and reproduction strategies of organisms.

The theory discusses the development process from simple unintentional cognitive form to complex social interaction and rational cognition, which involves the gradual complexity of biological structure and the evolution of social behavior.

Supplement and comparison of theories

Although these two theories are different in discussing the generation and function of consciousness, they both provide a valuable framework for understanding how consciousness works and develops.

Supplementary perspective:

GWT provides an immediate neural basis for consciousness, while Professor Yucong Duan's theory provides a macro perspective of how consciousness forms in the process of evolution. Combining these two theories, we can form a more comprehensive consciousness model, which includes not only the immediate neural mechanism of consciousness, but also the evolutionary history and biological basis of consciousness.

Theoretical complementarity:

In Professor Yucong Duan's theory, the evolution of consciousness is an adaptive process to environmental changes, which can explain why different organisms have different cognitive abilities. GWT, on the other hand, explains how these cognitive abilities are integrated in real time and affect behaviors and decisions in individuals.

The global integration mechanism of neural network borrowed from GWT can be used to further explore the neural basis of cognitive function of multicellular organisms in Professor Duan's theory.

Through in-depth comparison and analysis of these two theories, we can not only better understand the structure and function of consciousness, but also gain insight into the complex process of how consciousness evolves and develops in different organisms.

The prospect of integrating and developing consciousness theory

Fusion of neuroscience and evolutionary biology

Combining the global workspace theory with Professor Yucong Duan's theory of consciousness evolution will help to establish a more comprehensive theoretical framework. This fusion can not only explain the function and mechanism of consciousness from the perspective of neuroscience, but also explore how consciousness can help organisms adapt to complex and changeable environments from the perspective of evolutionary biology.

Experimental design: experiments can be designed to observe and analyze the changes of brain activity of different organisms in the face of environmental pressure. For example, functional magnetic resonance imaging (fMRI) is used to observe the brain activity patterns of animals in complex social interactions.

Cross-species research: study how the nervous system of different species supports their social and cognitive functions, and compare the relationship between these functions and the survival strategies of species.

Deepen the application field of theory

The further development of the theory can also be applied to the fields of artificial intelligence and machine learning, especially in the development of algorithms and systems that can simulate human or animal cognitive processes.

Artificial intelligence simulation: Based on this fusion theory, a new algorithm is developed, which enables the artificial intelligence system to better simulate the decision-making process of human beings or animals, especially when dealing with social interaction and environmental adaptation decisions.

Evolutionary algorithm of machine learning: Using biological evolutionary strategy optimization algorithm, the machine learning model can adaptively optimize its parameters and improve its performance in dynamic environment.

The comparison and integration between the global workspace theory and Professor Yucong Duan's theory of consciousness evolution provides a unique perspective to understand the essence and function of consciousness. By combining the detailed mechanism of neuroscience with the macro perspective of evolutionary biology, we can not only deeply understand the origin and development of consciousness, but also explore how consciousness affects the adaptive behavior of organisms. This comprehensive perspective provides a rich theoretical basis and broad application prospects for future research, especially in the rapidly developing field of artificial intelligence, the application potential of this theory is particularly important.

Through further experiments and theoretical research, we expect to reveal more secrets about the evolution of consciousness and how to use this knowledge to solve problems in the real world, such as improving the decision-making ability and adaptability of artificial intelligence systems. This will open up a new road for scientific research and technological development and bring far-reaching social and technological influence.

In order to clearly compare Global Workspace Theory (GWT) with Professor Yucong Duan's Theory of Consciousness Evolution, the following is a tabular presentation, which compares their main concepts, key features, research focus and potential applications:

The global workspace theory provides a neuroscience framework for understanding the immediate process of consciousness, while Professor Yucong Duan's theory provides a biological framework for understanding how consciousness forms and develops in the process of biological evolution. Although the two theories have different emphases, they both try to explain the key problems of how consciousness is produced and functions in organisms.

Through this comparison, we can see the possibility and potential of integrating these two theories, which may jointly provide us with a more comprehensive way to understand consciousness. For example, in the field of artificial intelligence, we can combine the neural mechanism understanding of GWT and the adaptive evolution process in Professor Duan's theory to develop a machine learning system that can adapt itself to complex environments and perform advanced cognitive processing.

In addition, through in-depth discussion and integration of these two theories, scientists and researchers can explore the multi-dimensional characteristics of consciousness more effectively, which will promote further research and development in cognitive science, neuroscience and evolutionary biology.

3.3 Relationship with Complex System Theory

Complex system theory

Theoretical proposition: Complex system theory emphasizes that the overall behavior of the system is produced by the interaction of multiple components in the system. This theory holds that local interaction can produce unpredictable global behavior in the system, which is a key perspective to explore the phenomenon from simple to complex.

Key features: Complex system theory mainly studies how to emerge order and organizational structure from the complexity of the system, emphasizing the search for potential regularity and structure in a seemingly chaotic environment. For example, ecosystems, economic systems and social systems are all regarded as complex systems, which form highly organized structures through simple rules and interactions among individuals.

Professor Yucong Duan's Theory

Theoretical proposition: Professor Yucong Duan's theory puts forward that consciousness is a phenomenon that gradually evolves with the increase of biological complexity. The emergence and development of consciousness is regarded as the natural adaptation of organisms to environmental pressure, which reflects the response of organisms to complex environmental challenges in the process of evolution.

Key features: From the perspective of evolutionary biology, Professor Duan discussed how the biological structure and function gradually became complicated from single-celled organisms to multicellular organisms, and then to organisms with advanced cognitive abilities, which led to the emergence of cognitive functions and ultimate consciousness. This gradual complication involves the evolution from the basic survival mechanism to the complex social and cultural behavior patterns.

The connection and contrast between theories

Contact:

Systematic perspective: Both theories adopt a systematic perspective to explain complex phenomena. Complex system theory explains global behavior by analyzing local interaction, while Professor Duan's theory explains the evolution of biological cognitive ability through the interaction between biological individuals.

Evolution from simple to complex: both of them focus on how to evolve more complex and advanced systems or behaviors from simpler structures or functions.

Contrast:

Research focus: Complex system theory is more extensive, not limited to biology, and covers all types of systems (such as social, economic and technical systems). Professor Duan's theory focuses on biological evolution and the development of consciousness.

Scope of application: Complex system theory is widely used in explaining system dynamics and predicting system behavior, while Professor Duan's theory focuses on biology and cognitive science more specifically.

Professor Yucong Duan's theory provides a profound insight into biological evolution in understanding the evolution of consciousness, which complements the theory of complex systems. By combining the two perspectives, we can understand the complexity of life system more comprehensively, especially in the evolution of cognitive function and the formation of consciousness. This interdisciplinary integration provides a more complete and systematic understanding framework for studying the development of consciousness in the process of biological evolution.

The following table shows the connection and contrast between Professor Yucong Duan's consciousness evolution theory and complex system theory, which helps to understand the similarities and differences between them more clearly.

Although the research focus and application scope of the two theories are different, they both emphasize the evolution from simple to complex and how local interaction leads to the formation of global phenomena. By comparison, we can see that Professor Duan's theory not only provides profound insights in the field of biology, but also its emphasis on environmental adaptability and evolutionary continuity echoes the explanation of system behavior in complex system theory, providing us with a new perspective to understand the evolution of life and consciousness across disciplines.

The following table shows the innovation of Professor Yucong Duan's theory in the field of consciousness research and its connections and differences with other theories.

labour

Professor Yucong Duan's theory provides a brand-new perspective, which directly relates the development of consciousness with the energy management and ecological adaptation of living things. According to this theory, the formation and evolution of consciousness is the result of living things interacting with the environment more effectively and improving the success rate of survival and reproduction. This view highlights the actual function and role of consciousness in biological evolution, and provides the possibility of understanding consciousness from a biological point of view.

Integrated Information Theory (IIT): It provides a scientific model from the perspective of the structure and function of the nervous system. This model attempts to quantify the degree of integration of information inside the brain and explain the existence and change of consciousness. The innovation of this theory lies in providing a concrete method to quantify consciousness, which provides a new tool for scientific research and possible clinical application.

Global Workspace Theory (GWT): It emphasizes that consciousness is a global information processing process involving multiple brain regions, in which information can be extracted when necessary and used for decision-making and behavior guidance. The core contribution of GWT lies in its description of consciousness as the center of information processing and decision-making, which provides a framework for understanding how the brain processes and integrates information.

Complex system theory: it spans many disciplines and is used to explain how the interaction of simple components produces complex system behaviors. This provides a point of view in understanding consciousness, that is, consciousness may be the complex result of a large number of simple neural activity interactions, which helps us to understand the structure and function of consciousness from the system level.

4 Scientific contribution and innovation analysis

Professor Yucong Duan's theory provides a remarkable innovative perspective and far-reaching scientific contribution in the scientific exploration of the origin of life and the development of consciousness. This theory combines the concepts of biology, evolutionary chemistry, neuroscience and psychology to form an interdisciplinary theoretical framework. The following is a detailed demonstration of his theoretical innovation and scientific contribution:

Innovation

Exploring the origin of consciousness from the perspective of evolution;

Most existing consciousness theories mainly focus on psychology and neuroscience, emphasizing the role of brain structure and function in consciousness generation. Professor Yucong Duan's theory starts from the perspective of biological evolution, and puts forward that the development of consciousness is the result of adaptive evolution, which provides a broader biological background for understanding consciousness.

Elaborate the evolution process of consciousness from single cell to multicellular organism;

Professor Duan's theory not only discusses how multicellular organisms develop complex cognitive functions, but also traces back to single-celled organisms, and discusses how they gradually develop more advanced information processing capabilities through simple biochemical reactions and preliminary cooperative behaviors. This discussion on the evolution process from single cell to multi-cell is relatively rare in the study of the origin of consciousness.

Put forward energy acquisition and saving as the driving force of consciousness evolution;

In biology, the effective use of energy is the key factor for organisms to adapt to the environment and evolution. Professor Yucong Duan applied this concept to the evolution of consciousness, and proposed that in order to obtain and save energy more effectively, organisms gradually evolved complex cognitive and conscious structures. This provides a new explanation for the origin of consciousness from the perspective of energy dynamics.

Scientific contribution

Provide a new theoretical framework for the biological basis of consciousness;

By linking the development of consciousness with biological energy management and environmental adaptability, Professor Duan's theory provides a new perspective for the biological basis of consciousness. This framework helps scientists understand that consciousness is not only the product of neural activity, but also a wide range of biological adaptability characteristics.

Promote the in-depth study of the origin and evolution of life;

Professor Yucong Duan's theory emphasizes the evolution path from the most basic single-celled organism to the complex multi-celled organism. This has promoted the in-depth study of key turning points in life evolution, such as the transition from single-celled organisms to multicellular organisms, and how these turning points have contributed to the emergence of complex behaviors and cognitive functions.

Provides a bridge for interdisciplinary research:

By combining the theories and methods of evolutionary biology, neuroscience and psychology, Professor Duan's theory has built a platform for interdisciplinary research. This not only helps to promote the cooperation between disciplines, but also promotes a more comprehensive and in-depth understanding of the complex phenomenon of consciousness.

Provides new tools and methods for consciousness research:

This theory urges researchers to use new experimental designs and methods to explore the biological basis and evolution of consciousness. For example, comparative genomics can be used to study the differences in the development of consciousness of different organisms, or behavioral and physiological methods can be used to study how consciousness affects the energy management of organisms.

Professor Yucong Duan's theory of consciousness provides an innovative perspective and method in academic field, and puts forward a new theoretical and practical framework for the scientific research of consciousness and its position in evolutionary biology. These contributions have important theoretical and applied value in academic circles.

5 Possible challenges

Professor Yucong Duan's theory provides an innovative perspective in the field of the origin and evolution of consciousness, combining various theoretical frameworks of biology, evolutionary chemistry and cognitive science. However, like all scientific theories, it also has some possible problems and limitations. These problems and limitations mainly include the following aspects:

Difficulty of experimental verification

Problem description: Professor Yucong Duan's theory is largely based on the macroscopic history and theoretical deduction of biological evolution, and these processes are often difficult to observe or reproduce directly under experimental conditions. The evolution of consciousness, especially in the study of non-human organisms, has great methodological challenges.

Impact: The lack of direct experimental support may lead to some theoretical assumptions being questioned in the scientific community.

Oversimplify complex processes

Problem description: The theory may oversimplify the evolution process from single cell to multicellular organism with complex cognitive ability. In fact, the development of consciousness and cognition is complicated by many factors such as heredity, environment and random variation.

Impact: This simplification may lead to the neglect of other important factors, such as genetic heterogeneity, environmental adaptability and the contribution of biodiversity to the development of consciousness.

The universality of theory

Problem description: Professor Yucong Duan's theory tries to apply a set of evolutionary principles to the development of consciousness of all living things, which may not be enough to explain the unique development of consciousness complexity of specific species (such as human beings).

Impact: The universality of the theory may limit its application in explaining specific biological cognitive and consciousness abnormalities (such as neurodegenerative diseases).

Dependence on the definition of consciousness

Description of the problem: Theory depends on the definition and quantification of consciousness to a great extent, but the essence of consciousness is still an unsolved mystery in philosophy and science.

(See: Professor Yucong Duan's definition of artificial intelligence and artificial consciousness DIKWP:

The interaction of AI mainly relies on the DIK (data, information, knowledge), while artificial consciousness (AC) introduces processing of intelligence (W) and purpose (P):

AI interaction: The interaction of AI systems is mainly DIK * DIK or DIKW * DIKW, such as automated decision support systems, which respond based on available data, information, and knowledge.

AC interaction: The interaction of artificial consciousness is DIKWP * DIKWP, which not only covers data, information, knowledge, and wisdom, but also includes purpose interaction. This means that the AC system can understand and internalize human purposes, and make independent judgments and decisions based on this foundation. )

Influence: If the definition of consciousness is not widely accepted or understood, the relevant assumptions and conclusions of the theory may be limited.

Ignore cultural and social factors.

Problem description: Professor Yucong Duan's theory mainly focuses on biological and physiological factors, and may not fully consider the role of cultural and social factors in the development of human consciousness.

Influence: This may lead to the limitation of theory in explaining the role of consciousness in human social behavior and cultural evolution.

Solutions and suggestions

In view of these problems and limitations, it is suggested that future research should:

Strengthen interdisciplinary cooperation: combine knowledge in genetics, ecology, neuroscience and social science to explore the development of consciousness more comprehensively.

Develop new experimental and observation techniques: use modern science and technology, such as brain imaging and genetic engineering, to study the biological basis and evolution process of consciousness more directly.

Refined theoretical model: while maintaining the theoretical framework, the special situation of consciousness evolution under different biological species and environmental conditions is studied in more detail.

Integration of culture and social research: integrating human society and cultural factors into theory, especially studying the role of culture and society in the formation of human consciousness complexity. In this way, we can understand that consciousness is not only the product of biological evolution, but also the result of the interaction between human society and culture.

Using artificial intelligence and computational models for reference: Using artificial intelligence and computational models to simulate the evolution of consciousness can provide new tools for understanding the mechanism of complex cognitive functions. These models can also be used to test various predictions of the theory, thus verifying its scientificity.

Strengthen the dialogue between philosophy and science: Because the study of consciousness involves deep philosophical problems, it is suggested to strengthen the dialogue between philosophers and scientists to ensure that the philosophical assumptions of the theory are consistent with scientific discoveries. This kind of interdisciplinary dialogue is helpful to deepen the understanding of the essence of consciousness and promote the in-depth development of theory.

Through these directions and suggestions, Professor Yucong Duan's theory can not only be strengthened and improved scientifically, but also be combined with other disciplines more effectively, providing a more comprehensive and profound understanding for exploring the consciousness of human beings and other creatures. The development and application of this theory will help to promote the research frontier in related scientific fields, and also provide theoretical basis and new research direction for related technologies and application fields.

6 Yucong Duan Professor's Theoretical Simulation Design

The simulation of Professor Yucong Duan's theory can be carried out in several different ways, depending on the specific aspects that he hopes to explore. Because this theory covers the evolution process from single cell to multicellular organism and how ideology develops in these processes, we can choose to use computational models to simulate these evolution processes. The following is a simulation plan based on theoretical points:

Simulation plan design

Simulation target:

Simulate how single-celled organisms form cooperation through basic biochemical reactions, and then evolve into multicellular organisms.

Simulate how these multicellular structures gradually develop complex cognitive functions, including basic perceptual cognition and more advanced rational cognition.

Model architecture:

Agent-Based Model (ABM): Select an agent-based model to simulate how each organism (single cell or cell group) interacts and evolves according to simple rules. Each individual can make simple "decisions" according to his environment (for example, pursuing nutrients or avoiding harmful environment).

Key parameters and rules:

Energy acquisition: individuals must find and consume resources (such as food) to survive and reproduce. The distribution of resources can be random or patterned, simulating different environmental conditions.

Collaborate and form a multicellular structure: When individuals approach, they can choose to form a permanent or temporary group structure to share resources or defense.

Evolution of perceptual and rational cognition: With the passage of time, according to the complexity of the environment and the pressure of selection, these groups can develop more complex information processing mechanisms, such as signal transmission systems, and may eventually evolve similar structures of neurons.

Simulation execution:

Use computational simulation software (such as Netlogo, Python with mesa for agent-based modeling) to realize this model.

Initialize an environment containing a large number of single-cell agents, which act and interact according to preset rules.

Run the simulation to observe how the group structure and cognitive function change under different initial conditions and evolution rules.

Data analysis:

Analyze the frequency and stability of the formation of multicellular structures and how their complexity grows with time.

Observe and record the interaction patterns between agents and how these patterns evolve with the pressure of selection.

Detect any signs of advanced cognitive function in the simulation, such as changes in learning behavior or decision-making mode.

Purpose and application of simulation

This simulation can help us understand how organisms evolve complex multicellular structures and cognitive functions through simple biochemical mechanisms under different environmental pressures and interaction modes between organisms. In addition, this simulation can also be used to test various hypotheses in Professor Yucong Duan's theory, verify the robustness and predictive ability of the theory, and provide guidance for further experimental design and theoretical development.

7 Yucong Duan's theoretical abstract reasoning verification

7.1 Simple theoretical derivation and verification

1. Model setting:

Suppose there are n single-celled organisms, and each single cell has a certain probability p of discovering resources and a probability q of cooperating with other cells.

2. The probability of a single cell discovering resources:

The probability of each single cell finding resources at each time step is p, and the probability of not finding resources is 1-p.

3. The probability of cooperation:

Suppose that each single cell has a probability of Q to cooperate with any other cell. With the increase of cell number, the probability of cooperation with at least one other cell is 1-(1-q)N-1.

4. Formation of multicellular structure:

If cells cooperate, they can form a stable multi-thin chest structure, and the formation probability of this structure is related to the number of cells and the cooperation probability. We can simplify the assumption that the probability of forming a multicellular structure is a function of cooperative cells, such as

P(multi-cell)=1-(1-q)k, where k is the number of cooperative cells.

5. The evolution of cognitive ability:

The development of cognitive ability is related to the stability and complexity of multicellular structure. We can set a value t, when the number of cells in the structure or the level of cooperation exceeds this threshold, the structure develops more advanced cognitive functions.

7.2 Probability calculation

1. Cell cooperation model:

We assume that each cell cooperates with other cells independently, and the probability of cooperation is Q.

The probability that a cell cooperates with at least one other cell (at least one cooperation occurs) is 1-(1-q)N-1.

2. The probability of multicellular structure development:

To simplify the model, we assume that when a cell cooperates with more than or equal to k cells, they can form a multicellular structure. The probability of structure formation is 1-(1-q) k.

To calculate the stable cooperation probability of five or more cells, let's assume k=5.

3. Concrete probability calculation of structure formation:

Using the above cooperation model, we can calculate the probability of forming multicellular structures under different N and Q.

7.3 Example Calculation

Suppose N=50 and q=0.1:

Probability of at least one cooperation:

1-(1-0.1)49≈0.994

This means that there is almost always at least one cooperation among 50 cells.

Probability of five or more cells cooperating:

1-(1-0.1)5≈0.40951

This means that there is a probability of about 40.951% that five or more cells can cooperate stably.

Exploration of different n and q

In order to fully understand the influence of different situations, we can calculate and compare the probability of reaching the threshold t under different combinations of n and q. For example, if we increase the cooperation probability q or increase the total number of cells n, the probability of cooperation is expected to increase, thus increasing the possibility of forming a complex cognitive structure.

When the cooperation probability q is higher than q=0.2:

1-(1-0.2)5≈0.67232

This means that when the cooperation probability increases, the probability of stable cooperation of five or more cells increases significantly.

Through this analysis, we can see that if the number of cells is fixed, increasing the cooperation probability q of a single cell will significantly increase the probability of forming a multi-cell structure, which may lead to the development of higher cognitive ability. This analysis provides a quantitative verification of Professor Yucong Duan's theory and shows how different parameter settings affect the evolution of cognitive ability of multicellular organisms under theoretical assumptions.

8 Simulation experiment design

To create a specific simulation experiment to explore the biological evolution and consciousness development in Professor Yucong Duan's theory, we can design an individual-based model (ABM). You can use Python with the Mesa library to build this model. Mesa is a powerful library dedicated to building complex agent models.

8.1 Simulation design objectives

Simulated environment: create an environment containing nutrients (resources), which can be generated at a fixed location or randomly distributed.

Single-celled agent: Each agent represents a single-celled organism and has the ability to find and consume resources to obtain energy.

Cooperation mechanism: when agents are close to each other, they can choose to form a cooperative relationship, share resources or act together.

Multicellular formation: Agents can form a more stable multicellular structure based on certain rules (such as adjacency and resource richness).

Cognitive evolution: agents in simulation should be able to gradually develop more complex behaviors, such as determining group actions through signal transmission.

Programming steps

Install the necessary libraries:

Install Mesa: use the pip install mesa command to install in Python environment.

Set model parameters:

Define space size, resource distribution, initial number of agents, etc.

Define agents and environments:

Write code to define the behavior of single-cell agent, such as moving, finding resources and consuming resources.

How do design agents evaluate the decision logic that forms a multi-cell structure?

Running and observing the model:

Design a visual interface to observe the running state of the model, and Mesa provides a visual tool based on browser.

Observe and record the behavior pattern and evolution of agents.

Analysis results:

Evaluate the formation frequency and stability of multicellular structures.

Analyze the evolution of cognitive function, such as the change of decision-making ability.

8.2 Sample Code Summary

python

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from mesa import Agent, Model

from mesa.space import MultiGrid

from mesa.time import RandomActivation

from mesa.datacollection import DataCollector

class Cell(Agent):

"""defines a single-cell agent, which has the ability to find and consume resources."""

def __init__(self, unique_id, model):

super().__init__(unique_id, model)

Self.energy = 10 # initial energy

def step(self):

# Define the behavior of a single cell: moving, looking for resources, and possible cooperative behavior.

self.move()

self.eat()

self.cooperate()

def move(self):

# Define Move Logic

possible_steps = self.model.grid.get_neighborhood(

self.pos,

moore=True,

include_center=False)

new_position = self.random.choice(possible_steps)

self.model.grid.move_agent(self, new_position)

def eat(self):

# Define the logic of obtaining resources

cell_contents = self.model.grid.get_cell_list_contents([self.pos])

for content in cell_contents:

if isinstance(content, Resource):

self.energy += 1

content.amount -= 1

def cooperate(self):

# Define collaboration logic

pass

class Resource(Agent):

"""Resources, single cells can consume resources to gain energy." ""

def __init__(self, unique_id, model, amount):

super().__init__(unique_id, model)

self.amount = amount

class CellularModel(Model):

"""Model definition, including single-cell agent and environment." ""

def __init__(self, N, width, height):

self.num_agents = N

self.grid = MultiGrid(width, height, True)

self.schedule = RandomActivation(self)

# Create a Single Cell Agent

for i in range(self.num_agents):

a = Cell(i, self)

self.schedule.add(a)

# Randomly place single cells

x = self.random.randrange(self.grid.width)

y = self.random.randrange(self.grid.height)

self.grid.place_agent(a, (x, y))

# Create resources

for (content, x, y) in self.grid.coord_iter():

Amount = self.random.randint (1,5) # The amount of resources is random.

resource = Resource((x, y), self, amount)

self.grid.place_agent(resource, (x, y))

def step(self):

"""Do a model iteration" ""

self.schedule.step()

# Start the model

empty_model = CellularModel(50, 10, 10)

for i in range(20):

empty_model.step()

The above code is a simple framework, which shows how to build a simulation model based on Mesa to explore how a single cell evolves into a complex multicellular organism through behavior and cooperation. In practical application, the specific behaviors and rules of the model can be further improved to further study and verify Professor Yucong Duan's theory.

8.3 Simulation operation

To run the model:

Step 1 install Python

Make sure that Python is installed on your computer. You can download and install the latest version from Python's official website.

Step 2 install Mesa

Open your command line interface (CMD or PowerShell on Windows, Terminal on macOS or Linux), and then enter the following command to install the Mesa library:

bash

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pip install mesa

3. Create a model file

Create a new Python file (for example, cellular_model.py) using a text editor (such as Notepad++, Sublime Text, or Visual Studio Code).

Copy and paste the Python code provided above into this file.

Step 4 run the model

In the command line interface, switch to the directory containing your Python file.

Run the following command to start the model:

bash

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python cellular_model.py

5. Observation results

Depending on the design of the model, you may need to add some code to print or visualize the results of each step. For example, you can modify the step method in the CellularModel class to print out the status information of the current model, or summarize the behavior of the model after the loop ends.

Example: Printing Model Information

You can add the following code to the step method of the model to observe the results of the model operation:

python

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print(f'Step {i+1}')

for agent in self.schedule.agents:

if isinstance(agent, Cell):

print(f'Cell {agent.unique_id} at {agent.pos} with energy {agent.energy}')

This will print out the position and energy state of each cell after each time step.

9 The starting point of biological evolution history demonstration

In order to explore the consistency between Professor Yucong Duan's theory of consciousness evolution and the evolution history of life on earth, we need to refer to the evolution timeline of life on earth, the evolution process of life from low to high, and the influence of environmental factors (such as ice age) on evolution. This analysis can help to verify the validity of the theory and reveal possible limitations.

The approximate time axis of biological evolution on earth

The appearance of prokaryotes (about 3.8 billion years ago);

The earliest life forms on the earth are unicellular microorganisms, mainly prokaryotes, such as bacteria.

The appearance of eukaryotes (about 2 billion years ago);

Eukaryotic cells have nuclear membranes and complex cell structures, which are the basis for the emergence of multicellular organisms.

The emergence of multicellular organisms (about 1 billion years ago);

The emergence of multicellular organisms marks a significant increase in biological complexity and can form more complex organizational structures and functions.

Animal differentiation and development (CAMBRIAN, about 540 million years ago);

During the CAMBRIAN explosion, a large number of animal classes appeared, and the diversity and complexity of animal life forms increased sharply.

Land plants and the appearance of animals (about 450 million years ago);

Organisms began to expand from water to land, which had a far-reaching impact on ecosystem and biological evolution.

Evolution of mammals and birds (about 200 million years ago to 65 million years ago);

This period marks the development of advanced nervous system and social behavior, especially in the diversification of mammals after the extinction of dinosaurs.

Analysis of evolution speed and stage

The speed and stage of evolution are usually influenced by many factors, including environmental changes, geographical isolation, inter-species competition and genetic variation of organisms themselves. Especially under the influence of environmental pressure such as ice age, biological evolution will show an accelerated trend. For example:

The ice age (the last one started about 26,000 years ago) caused organisms to adapt to a colder environment, pushed some species to migrate to warmer regions, or developed more complex social structures and behavior patterns to ensure their survival.

Conformity discussion

Professor Yucong Duan's theory claims that consciousness is a characteristic that organisms gradually evolve in order to interact with the environment more effectively, which is consistent with the general trend of biological evolution. From single cell to multi-cell, and then to organisms with complex social behaviors, every evolutionary step may be an adaptation to environmental challenges. Especially in extreme environmental conditions, such as the ice age, the evolution of biological social structure and behavior patterns may be an adaptation to the cold climate.

Limitation analysis

Although Professor Yucong Duan's theory explains how consciousness evolves as an adaptive feature to some extent, it may simplify the complexity of the development and evolution of biological consciousness too much. Next, we will analyze the limitations of this theory and explore possible complementary directions.

Limitation analysis

Genetic and molecular biological factors:

Professor Yucong Duan's theory emphasizes the influence of the interaction between environment and biology on the evolution of consciousness, but may not fully consider the role of genetic variation and molecular biological mechanism in the development of consciousness. The development of consciousness may not only be the result of environmental adaptation, but also be strongly influenced by genetic factors, such as gene mutation and gene recombination.

Individual development and neuroplasticity;

Biological consciousness and cognitive ability are closely related to the neuroplasticity of its brain, which includes individual learning experience and development stage. The evolution of consciousness may not only be a long-term process at the species level, but also include the adaptation and learning of individuals to complex environments in their lifetime.

The role of cultural and social evolution:

In human beings and some social animals, culture and social structure have a significant impact on the development of consciousness. Social learning, cultural inheritance and language use play a key role in the evolution of human consciousness, which may not be fully discussed in Professor Duan's theory.

Supplementary direction

Integrated genetics research:

In order to fully understand the evolution of consciousness, the theory can integrate the research results of genetics and epigenetics, and investigate how specific genes affect neural development and cognitive ability, and how these genes are naturally selected in species.

Consider the details of neural development:

Theory can further explore how the brain structure and function evolve in different organisms, especially how the brain supports more complex information processing and decision-making ability.

Interdisciplinary approach:

Combining the methods of ecology, behavior, sociology and anthropology can provide a more comprehensive framework to understand how consciousness develops not only through biological evolution, but also through social and cultural evolution.

Professor Yucong Duan's theory provides a valuable framework for understanding how consciousness, as an adaptive feature, develops in the process of biological evolution. However, the evolution of consciousness is a complex and multidimensional process, involving biology, ecology, genetics, society and culture. Future research needs to adopt a more comprehensive approach to explore how these factors work together in the development and evolution of consciousness. This interdisciplinary approach can not only enhance the depth and breadth of the theory, but also help us better understand the nature of the complex phenomenon of consciousness.

Conclusion

Professor Yucong Duan's theory of consciousness puts forward the development path from the origin of life to complex ideology, covering the evolution from single cell to multicellular organism and the hierarchical evolution of consciousness function. This theory not only tries to explain how organisms transition from simple biochemical reactions to life forms with complex cognition and self-awareness, but also reveals the biological and neuroscience mechanisms of each stage in this process. The following is a comprehensive analysis and summary of Professor Yucong Duan's theory:

Origin of Life and Multicellular Cooperation

Professor Yucong Duan's theory begins with the most basic biochemical reactions of organisms, which involve how single-celled organisms obtain energy through simple chemical processes. The biological activities at this stage are very basic, but it lays the foundation for the development of complex forms of life. With the adaptation of organisms to the environment and the increase of survival challenges, single-celled organisms began to show initial group cooperative behavior, such as common predation through physical encirclement, which not only improved the efficiency of energy acquisition, but also reduced the energy consumption of a single organism.

Differentiation and evolution of cognitive function

With the complexity of biological structure, especially the emergence of multicellular organisms, the cooperation between cells has gradually evolved into more specialized functional differentiation. The formation of nerve cells marks the initial establishment of the nervous system, enabling organisms to deal with more complex environmental information. Perceptual cognition, that is, direct perception and response to environmental stimuli, has become the basic way for organisms to respond to external changes. On this basis, organisms gradually develop rational cognition, which involves the conceptualization and symbolization of information, as well as the memory of past experiences and the reasoning of future possibilities.

Development of self-awareness

The further deepening of rational cognition leads to the emergence of self-awareness. Self-awareness is not only a passive response to the environment, but also includes the evaluation of one's own behavior and self-awareness. This improvement of cognitive independence is an advanced stage of consciousness evolution, which indicates that organisms have turned from a simple survival mechanism to a more complex social interaction and cultural behavior model.

Significance and application of theory

Professor Yucong Duan's theory not only provides us with a framework to understand the development of consciousness from a biological point of view, but also may provide enlightenment to related technical fields, such as artificial intelligence and machine learning. By simulating the development process of biological consciousness, we may be able to design a more efficient and adaptable intelligent system.

In addition, this theory also emphasizes the interaction between biological evolution and environmental factors, reminding us that consciousness is not only the nerve activity within the organism, but also the result of the continuous interaction between the organism and its environment. This is of great significance for understanding the complexity of human society and culture.

Professor Yucong Duan's consciousness theory provides us with a comprehensive perspective from molecule to mind and from single cell to society, which helps us to deeply understand the essence of life and consciousness. The far-reaching influence of this theory may continue to inspire future scientific research and technological innovation.

References

[1] Alberts, B., et al. (2015). Molecular Biology of the Cell. 6th ed. Garland Science.

[2] Dawkins, R. (1989). The Selfish Gene. Oxford University Press.

[3] De Waal, F. B. M. (2016). Are We Smart Enough to Know How Smart Animals Are? W. W. Norton & Company.

[4] Gould, S. J. (2002). The Structure of Evolutionary Theory. Belknap Press.

[5] Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S., Hudspeth, A. J. (2012). Principles of Neural Science. 5th ed. McGraw-Hill.

[6] Koch, C. (2004). The Quest for Consciousness: A Neurobiological Approach. Roberts and Company Publishers.

[7] Lodish, H., et al. (2016). Molecular Cell Biology. 8th ed. W. H. Freeman.

[8] Margulis, L. (1998). Symbiotic Planet: A New Look at Evolution. Basic Books.

[9] Maynard Smith, J., Szathmáry, E. (1995). The Major Transitions in Evolution. Freeman.

[10] Minsky, M. (1986). The Society of Mind. Simon & Schuster.

[11] Monod, J. (1971). Chance and Necessity: An Essay on the Natural Philosophy of Modern Biology. Vintage Books.

[12] Penrose, R. (1989). The Emperor's New Mind: Concerning Computers, Minds and The Laws of Physics. Oxford University Press.

[13] Ramachandran, V. S. (2011). The Tell-Tale Brain: A Neuroscientist's Quest for What Makes Us Human. W. W. Norton & Company.

[14] Sagan, C. (1977). The Dragons of Eden: Speculations on the Evolution of Human Intelligence. Random House.

[15] Shubin, N. (2008). Your Inner Fish: A Journey into the 3.5-Billion-Year History of the Human Body. Pantheon.

[16] Tononi, G. (2012). Phi: A Voyage from the Brain to the Soul. Pantheon.

[17] Varela, F. J., Thompson, E., Rosch, E. (1991). The Embodied Mind: Cognitive Science and Human Experience. MIT Press.

[18] Watson, J. D., Crick, F. H. C. (1953). "Molecular Structure of Nucleic Acids: A Structure for Deoxyribose Nucleic Acid". Nature, 171, 737-738.

[19] Wilson, E. O. (1975). Sociobiology: The New Synthesis. Belknap Press.

[20] Zimmer, C. (2013). Evolution: Making Sense of Life. Roberts and Company Publishers.

[21] Knoll, Andrew H. (2011). "The multiple origins of complex multicellularity." Annual Review of Earth and Planetary Sciences, 39, pp. 217-239.

[22] King, Nicole. (2004). "The unicellular ancestry of animal development." Developmental Cell, 7(3), pp. 313-325.

[23] Buss, Leo W. (1987). The Evolution of Individuality. Princeton University Press.

[24] Grosberg, Rick K., and Richard R. Strathmann. (2007). "The evolution of multicellularity: A minor major transition?" Annual Review of Ecology, Evolution, and Systematics, 38, pp. 621-654.

[25] Michod, Richard E. (2000). Darwinian Dynamics: Evolutionary Transitions in Fitness and Individuality. Princeton University Press.

[26] 段玉聪(Yucong Duan). (2024). DIKWP与语义生物学：拓展跨学科的知识领域(DIKWP and Semantic Biology: Expanding Interdisciplinary Knowledge Areas). DOI: 10.13140/RG.2.2.27474.32962. https://www.researchgate.net/publication/377416091_DIKWP_and_Semantic_Biology_Exp anding_Interdisciplinary_Knowledge_Areas

[27] 段玉聪(Yucong Duan). (2024). 语义物理化学(Semantic Physical Chemistry). DOI: 10.13140/RG.2.2.21261.51684. https://www.researchgate.net/publication/377439785_Semantic_Physical_Chemistry.

[28] 段玉聪(Yucong Duan). (2024). DIKWP与语义哲学(DIKWP and Semantic Philosophy). DOI: 10.13140/RG.2.2.34185.21606. https://www.researchgate.net/publication/377416120_DIKWP_and_Semantic_Philosophy

[29] 段玉聪(Yucong Duan). (2024). 语义认知学：连接人类思维与计算机智能的未来(Semantic Cognition: Connecting the Human Mind to the Future of Computer Intelligence). DOI: 10.13140/RG.2.2.29152.05129. https://www.researchgate.net/publication/377416321_Semantic_Cognition_Connecting_the_Human_Mind_to_the_Future_of_Computer_Intelligence

[30] 段玉聪(Yucong Duan). (2024). 语义物理：理论与应用(Semantic Physics: Theory and Applications). DOI: 10.13140/RG.2.2.11653.93927. https://www.researchgate.net/publication/377401736_Semantic_Physics_Theory_and_Applications

[31] 段玉聪(Yucong Duan). (2024). DIKWP与语义心理学(Semantic Psychology and DIKWP). DOI: 10.13140/RG.2.2.12928.61449.

[32] 段玉聪(Yucong Duan). (2024). DIKWP与语义认知学(DIKWP and Semantic Cognition). DOI: 10.13140/RG.2.2.14052.55680.  https://www.researchgate.net/publication/377415901_DIKWP_and_Semantic_Cognition.

[33] 段玉聪(Yucong Duan). (2024). 语义物理与创新发展(Semantic Physics and Innovation Development). DOI: 10.13140/RG.2.2.19085.72167. https://www.researchgate.net/publication/377416222_Semantic_Physics_and_Innovation_Development

[34] 段玉聪(Yucong Duan). (2024). 直觉的本质与意识理论的交互关系(The Essence of Intuition and Its Interaction with theory of Consciousness). DOI: 10.13140/RG.2.2.16556.85127. https://www.researchgate.net/publication/378315211_The_Essence_of_Intuition_and_Its_Interaction_with_theory_of_Consciousness

[35] 段玉聪(Yucong Duan). (2024). 意识中的“BUG”：探索抽象语义的本质(Understanding the Essence of "BUG" in Consciousness: A Journey into the Abstraction of Semantic Wholeness). DOI: 10.13140/RG.2.2.29978.62409. https://www.researchgate.net/publication/378315372_Understanding_the_Essence_of_BUG_in_Consciousness_A_Journey_into_the_Abstraction_of_Semantic_Wholeness

[36] 段玉聪(Yucong Duan). (2024). 个人和集体的人造意识(Individual and Collective Artificial Consciousness). DOI: 10.13140/RG.2.2.20274.38082. https://www.researchgate.net/publication/378302882_Individual_and_Collective_Artificial_Consciousness

[37] 段玉聪(Yucong Duan). (2024). 人工意识系统的存在性探究：从个体到群体层面的视角(The Existence of Artificial Consciousness Systems: A Perspective from Group Consciousness). DOI: 10.13140/RG.2.2.28662.98889. https://www.researchgate.net/publication/378302893_The_Existence_of_Artificial_Consciousness_Systems_A_Perspective_from_Collective_Consciousness

[38] 段玉聪(Yucong Duan). (2024). 意识与潜意识：处理能力的有限性与BUG的错觉(Consciousness and Subconsciousness: from Limitation of Processing to the Illusion of BUG). DOI: 10.13140/RG.2.2.13563.49447. https://www.researchgate.net/publication/378303461_Consciousness_and_Subconsciousness_from_Limitation_of_Processing_to_the_Illusion_of_BUG

[39] 段玉聪(Yucong Duan). (2024). 如果人是一个文字接龙机器，意识不过是BUG(If Human is a Word Solitaire Machine, Consciousness is Just a Bug). DOI: 10.13140/RG.2.2.13563.49447. https://www.researchgate.net/publication/378303461_Consciousness_and_Subconsciousness_from_Limitation_of_Processing_to_the_Illusion_of_BUG

[40] 段玉聪(Yucong Duan). (2024). 超越达尔文：技术、社会与意识进化中的新适应性(Beyond Darwin: New Adaptations in the Evolution of Technology, Society, and Consciousness). DOI: 10.13140/RG.2.2.29265.92001. https://www.researchgate.net/publication/378290072_Beyond_Darwin_New_Adaptations_in_the_Evolution_of_Technology_Society_and_Consciousness

[41] 段玉聪(Yucong Duan). (2023). DIKWP 人工意识芯片的设计与应用(DIKWP Artificial Consciousness Chip Design and Application). DOI: 10.13140/RG.2.2.14306.50881. https://www.researchgate.net/publication/376982029_DIKWP_Artificial_Consciousness_Chip_Design_and_Application

[42] 段玉聪(Yucong Duan). (2024). DIKWP体系与语义数学结合构建传染病防治指标体系(DIKWP System Combined with Semantic Mathematics to Construct an Indicator System for Infectious Disease Prevention and Control). DOI: 10.13140/RG.2.2.12374.83521. https://www.researchgate.net/publication/377416103_DIKWP_System_Combined_with_Semantic_Mathematics_to_Construct_an_Indicator_System_for_Infectious_Disease_Prevention_and_Control

[43] 段玉聪(Yucong Duan). (2024). 语义数学与 DIKWP 模型（本质计算与推理、存在计算与推理以及意图计算与推理）(Semantic Mathematics and DIKWP Model (Essence Computation and Reasoning, Existence Computation and Reasoning, and Purpose Computation and Reasoning)). DOI: 10.13140/RG.2.2.24323.68648. 377239628_Semantic_Mathematics_and_DIKWP_Model_Essence_Computation_and_Reasoning_Existence_Computation_and_Reasoning_and_Purpose_Computation_and_Reasoning

[44] 段玉聪(Yucong Duan). (2024). 从主观到客观的语义数学重构（存在计算与推理、本质计算与推理、意图计算与推理）(Semantic Mathematics Reconstruction from Subjectivity to Objectivity (Existence Computation and Reasoning, Essence Computing and Reasoning, Purpose Computing and Reasoning)). DOI: 10.13140/RG.2.2.32469.81120. https://www.researchgate.net/publication/377158883_Semantic_Mathematics_Reconstruction_from_Subjectivity_to_Objectivity_Existence_Computation_and_Reasoning_Essence_Computing_and_Reasoning_Purpose_Computing_and_Reasoning

[45] 段玉聪(Yucong Duan). (2024). DIKWP与语义数学在车票订购案例中的应用(DIKWP and Semantic Mathematics in the Case of Ticket Ordering). DOI: 10.13140/RG.2.2.35422.20800. https://www.researchgate.net/publication/377085570_DIKWP_and_Semantic_Mathematics_in_the_Case_of_Ticket_Ordering

[46] 段玉聪(Yucong Duan). (2024). DIKWP与语义数学分析《论语》“君子和而不同，小人同而不和”(DIKWP and Semantic Mathematical Analysis The Confluent Analects Gentleman is harmonious but different, while petty people are the same but not harmonious). DOI: 10.13140/RG.2.2.28711.32165. https://www.researchgate.net/publication/377085455_DIKWP_and_Semantic_Mathematical_Analysis_The_Confluent_Analects_Gentleman_is_harmonious_but_different_while_petty_people_are_the_same_but_not_harmonious

[47] 段玉聪(Yucong Duan). (2024). Collatz Conjecture的语义数学探索(Collatz Conjecture's Semantic Mathematics Exploration). DOI: 10.13140/RG.2.2.28517.99041. https://www.researchgate.net/publication/377239567_Collatz_Conjecture's_Semantic_Mathematics_Exploration

Appendix: Professor Yucong Duan's theoretical simulation design-experimental consciousness PRV2 (Experimenter: Kunguang Wu)

Foreword

Simulating Professor Yucong Duan's theory can be carried out in several different ways, depending on the specific aspects we want to explore. Because this theory covers the evolution process from single cell to multicellular organism and how ideology develops in these processes, we can choose to use computational models to simulate these evolution processes.

1 Simulation plan design

1.1 Simulation target

Simulate how single-celled organisms form cooperation through basic biochemical reactions, and then evolve into multicellular organisms. Simulate how these multicellular structures gradually develop complex cognitive functions, including basic perceptual cognition and more advanced rational cognition.

1.2 Model architecture

Agent-Based Model (ABM): Select an agent-based model to simulate how each organism (single cell or cell group) interacts and evolves according to simple rules. Each individual can make simple "decisions" according to his environment (for example, pursuing nutrients or avoiding harmful environment).

1.3 Key parameters and rules

Energy acquisition: individuals must find and consume resources (such as food) to survive and reproduce. The distribution of resources can be random or patterned, simulating different environmental conditions.

Collaborate and form a multicellular structure: When individuals approach, they can choose to form a permanent or temporary group structure to share resources or defense.

Evolution of perceptual and rational cognition: With the passage of time, according to the complexity of the environment and the pressure of selection, these groups can develop more complex information processing mechanisms, such as signal transmission systems, and may eventually evolve similar structures of neurons.

1.4 Simulation execution

Use computational simulation software (such as Netlogo, Python with mesa for agent-based modeling) to realize this model. Initialize an environment containing a large number of single-cell agents, which act and interact according to preset rules. Run the simulation to observe how the group structure and cognitive function change under different initial conditions and evolution rules.

1.5 Data analysis

Analyze the frequency and stability of the formation of multicellular structures and how their complexity grows with time.

Observe and record the interaction patterns between agents and how these patterns evolve with the pressure of selection. Detect any signs of advanced cognitive function in the simulation, such as changes in learning behavior or decision-making mode.

1.6 Purpose and application of simulation

This simulation can help us understand how organisms evolve complex multicellular structures and cognitive functions through simple biochemical mechanisms under different environmental pressures and interaction modes between organisms. In addition, this simulation can also be used to test various hypotheses in Professor Yucong Duan's theory, verify the robustness and predictive ability of the theory, and provide guidance for further experimental design and theoretical development.

2. Experimental design

2.1 Simulation design objectives

Simulated environment: create an environment containing nutrients (resources), which can be generated at a fixed location or randomly distributed. At present, a 10*10 grid environment is created, and 20 single-cell agents are randomly placed.

Single-celled agent: Each agent represents a single-celled organism and has the ability to find and consume resources to obtain energy. We assume that each single-celled organism moves one unit at a time and its position is random, that is, the single-celled agent can choose one of the eight directions at most to move, and its consumption capacity is fixed, and its initial energy is 10 units.

Cooperation mechanism: when agents are close to each other, they can choose to form a cooperative relationship, share resources or act together. The assumption of cooperation is that the current energy of a cell is not enough to maintain the next movement to find resources, so cooperation is performed by default. When cooperation occurs, the cellular energy of both sides merges, and when multiple cells cooperate, they are regarded as a single cell agent.

Multicellular formation: Agents can form a more stable multicellular structure based on certain rules (such as adjacency and resource richness). When cooperating into a multicellular organism, it is more efficient to obtain resources. By default, a single resource is 1 unit, and a single cell can only obtain 10 units of energy by default. When cooperative cells acquire resources, the acquired single resources are converted into energy as follows:

Where X represents the number of cooperative cells, and E represents the energy finally obtained.

Cognitive evolution: when multiple cells cooperate, because of the improvement of energy utilization efficiency, it is set here that the resources consumed when they move are equal to those consumed by a single cell.

2.2 Programming steps

Install the necessary libraries:

Install Mesa: use the pip install mesa command to install in Python environment.

Set model parameters:

Define space size, resource distribution, initial number of agents, etc.

Define agents and environments:

Write code to define the behavior of single-cell agent, such as moving, finding resources and consuming resources.

How do design agents evaluate the decision logic that forms a multi-cell structure?

2.3 Program Code Reference

from mesa import Agent, Model

from mesa.space import MultiGrid

from mesa.time import RandomActivation

from mesa.datacollection import DataCollector

from mesa.visualization.modules import CanvasGrid

from mesa.visualization.ModularVisualization import ModularServer

import pandas as pd

class Cell(Agent):

"""defines a single-cell agent, which has the ability to find and consume resources." ""

    def __init__(self, unique_id, model):

        super().__init__(unique_id, model)

        self.is_dead = False

Self._energy = 10 # initial energy

Self. cooperative _ num = 1 # Initial individual quantity

        self.unique_ids = [unique_id]

    @property

    def energy(self):

        return self._energy

    @energy.setter

    def energy(self, value):

        self._energy = value

        if value < 0:

            self.is_dead = True

    def step(self):

# Define the behavior of a single cell: moving, looking for resources, and possible cooperative behavior.

        if not self.is_dead:

            self.seek_cooperate()

            self.eat()

            self.move()

    def set_dead(self):

        self.is_dead = True

    def move(self):

# Define Move Logic

        possible_steps = self.model.grid.get_neighborhood(

            self.pos,

            moore=True,

            include_center=False)

        new_position = self.select_better_poi(possible_steps)

        if new_position is None:

            new_position = self.random.choice(possible_steps)

        self.move_cost()

        self.model.grid.move_agent(self, new_position)

    def select_better_poi(self, possible_steps):

        poi_list = []

        for (x, y) in possible_steps:

            resource_contents = self.model.grid.get_cell_list_contents([self.pos])

            for content in resource_contents:

                if isinstance(content, Resource) and content.amount > 0:

                    poi_list.append((x,y))

        if len(poi_list) > 0:

            return self.random.choice(poi_list)

        else:

            return None

    def eat(self):

# Define the logic of obtaining resources

        resource_contents = self.model.grid.get_cell_list_contents([self.pos])

        for content in resource_contents:

            if isinstance(content, Resource):

                if content.amount > 0 and (not self.is_dead):

                    self.energy = 5 + 5 * self.cooperate_num

                    content.amount -= 1

    def set_coop(self, num):

        self.cooperate_num = num

    def set_ids(self, ids):

        self.unique_ids = ids

    def move_cost(self):

# Define Mobile Consumption

        self.energy -= 5

    def seek_cooperate(self):

# Define the logic of seeking cooperation. If the current individual or population can't have enough energy to maintain it, it must be added.

        if self.energy <=  self.count_move_cost():

            self.cooperate()

        else:

            pass

    def cooperate(self):

# Define the cooperation logic, that is, the small total population is integrated into the large population by default, and then the small population is defined as death.

Cell _ contents = self.model.grid.get _ cell _ list _ contents ([self.pos]) # Get the instance of the current coordinate cell.

        max_num = 0

        count_coop = 0

        count_eng = 0

        content_instant = None

        list_content_cell = []

        list_id = []

        for content in cell_contents:

            if isinstance(content, Cell):

                if content.is_dead:

                    continue

                else:

                    count_eng += content.energy

                    list_id.extend(content.unique_ids)

                    # print(list_id)

                    if content.cooperate_num > max_num:

# If the population currently traversed is the largest, then merge.

                        content_instant = content

# Merge previous members first

                        count_coop = count_coop + max_num

# Re-assign value

                        max_num = content.cooperate_num

                    else:

                        count_coop  = content.cooperate_num + count_coop

                        list_content_cell.append(content)

        if content_instant is not None:

            if max_num + count_coop > 0:

                content_instant.set_coop(max_num + count_coop)

                content_instant.energy = count_eng

                content_instant.set_ids(list_id)

            for sub_cell in list_content_cell:

                sub_cell.set_dead()

    def count_move_cost(self):

        if self.cooperate_num >=5:

            return 5

        else:

            return self.cooperate_num

class Resource(Agent):

"""Resources, single cells can consume resources to gain energy." ""

    def __init__(self, unique_id, model, amount):

        super().__init__(unique_id, model)

        self.amount = amount

class CellularModel(Model):

"""Model definition, including single-cell agent and environment." ""

    def __init__(self, N, width, height):

        self.num_agents = N

        self.grid = MultiGrid(width, height, True)

        self.schedule = RandomActivation(self)

# Create a Single Cell Agent

        for i in range(self.num_agents):

            a = Cell(i, self)

            self.schedule.add(a)

# Randomly place single cells

            x = self.random.randrange(self.grid.width)

            y = self.random.randrange(self.grid.height)

            self.grid.place_agent(a, (x, y))

# Create resources

        for (content, (x, y)) in self.grid.coord_iter():

Amount = self.random.randint (1,5) # The amount of resources is random.

            resource = Resource((x, y), self, amount)

            self.grid.place_agent(resource, (x, y))

# Initialize the distribution of resources:

        ids = []

        pos = []

        eng = []

        cooperation = []

        for agent in self.schedule.agents:

            if isinstance(agent, Resource):

                print(f'Cell {agent.unique_id} at {agent.pos} with amount {agent.amount}')

# Initialize the distribution of population:

        for agent in self.schedule.agents:

            if isinstance(agent, Cell):

                if not agent.is_dead:

                    ids.append(agent.unique_ids)

                    pos.append(agent.pos)

                    eng.append(agent.energy)

                    cooperation.append(agent.cooperate_num)

                    data_dict = {

Cell id': ids,

Current coordinate', pos,

Current remaining energy', eng,

Number of cooperations': cooperation

                    }

                    df = pd.DataFrame(data_dict)

# Save DataFrame as CSV

                    df.to_excel('Step0 output.xlsx', index=False)

                    # print(f'Cell {agent.unique_ids} at {agent.pos} with energy {agent.energy} and cooperation num {agent.cooperate_num}')

                else:

                    # print(f'Dead Cell {agent.unique_ids} at {agent.pos}')

                    pass

    def step(self):

"""Do a model iteration" ""

        self.schedule.step()

        print(f'Step {i+1}')

        ids = []

        pos = []

        eng = []

        cooperation = []

        for agent in self.schedule.agents:

            if isinstance(agent, Cell):

                if not agent.is_dead:

                    ids.append(agent.unique_ids)

                    pos.append(agent.pos)

                    eng.append(agent.energy)

                    cooperation.append(agent.cooperate_num)

                    # print(f'Cell {agent.unique_ids} at {agent.pos} with energy {agent.energy} and cooperation num {agent.cooperate_num}')

        data_dict = {

Cell id': ids,

Current coordinate', pos,

Current remaining energy', eng,

Number of cooperations': cooperation

        }

        df = pd.DataFrame(data_dict)

# Save DataFrame as CSV

        df.to_excel(f'Step {i+1} output.xlsx', index=False)

# Start the model

empty_model = CellularModel(50, 10, 10)

for i in range(20):

    empty_model.step()

3 Experimental results

Experimental hypothesis:

The environmental resources of 50 cells are limited, that is, the resources are non-renewable.

The premise of cell cooperation is that the energy stored in the current cell can not meet the needs of the next activity.

Aft that cells cooperate, a small numb of cooperative cells or single cells will be merged into a large numb of multi-cell cooperative clusters and the stored energy will be merged.

By default, cells move towards the direction with resources. If there are no resources or multiple resources, they are randomly selected.

In the initial state, each cell stores 10 units of energy.

3.1 Experimental data

In this section, the experiment will carry out 20 iterations and record the initial state and the results after the first, fifth, tenth, fifteenth and twentieth iterations under the premise of limited living environment resources of cells.

3.1.1 Initial state

3.1.2 First iteration

3.1.3 The 5th iteration

3.1.4 The 10th iteration

3.1.5 The 15th iteration

3.1.6 The 20th iteration

3.2 Analysis results

Initial state

All cells have 10 units of energy in the initial state, and they are independent and have not formed a cooperative relationship. Every cell is in the initial stage of resource search and survival, with a fair environment and no obvious competitive advantage.

The first iteration

After the first iteration, we can see that the energy of some cells has dropped to 5 units, and the number of cooperation of all cells is still 1, indicating that in the initial exploration of limited resources, cells have not yet started significant cooperative behavior, and each cell has tried to obtain resources independently.

The fifth iteration

In the fifth iteration, the state data presented by cells began to show signs of cooperation, and cell combinations with energy of 10 units and cooperation number of 2 or 3 appeared, such as [22, 10] and [29, 32, 45]. This shows that some cells begin to share resources through cooperation, which improves survival efficiency and reduces energy consumption.

The 10th iteration

In the tenth iteration, the cooperation phenomenon became more obvious, and four-cell cooperators such as [26, 46, 6, 15] appeared, with the energy maintained at a high level (10 or 20 units) and the number of cooperations reached 4. This shows the advantages of cooperative cell groups in resource acquisition, and they can make more efficient use of environmental resources.

The 15th iteration

By the 15th iteration, cooperative cell populations such as [33, 14, 29, 32, 45], [35, 41, 5] and [26, 46, 6, 15] showed significant survival advantages, with cooperative numbers reaching 5, 3 and 4 respectively, and the energy level was relatively high. This confirms the positive effect of cooperation mechanism on survival in the environment of scarce resources, and the cooperative cells not only survive, but also have a good energy state.

The 20th iteration

Finally, by the 20th iteration, most of the independent cells failed to survive because of energy exhaustion, and only the cooperative cell groups [33, 14, 29, 32, 45] remained active, and the number of cooperations was still 5. Although the energy was reduced (from 25 to 15 units), they still maintained their viability.

Analysis and summary

The experimental data clearly show that under the condition of limited resources, cells can significantly improve the survival probability and resource utilization efficiency through cooperation. Cooperation not only increases the opportunities for individuals to obtain resources, but also reduces the energy consumption of individuals through resource sharing, thus improving the overall viability. This verifies the hypothesis in Professor Yucong Duan's theory that cells form cooperation through basic biochemical reactions and then evolve into multicellular organisms. The simulation results further emphasize the importance of cooperative mechanism for biological evolution under specific environmental pressure, especially in the face of resource competition and environmental challenges. This simulation not only provides a new perspective for understanding biological evolution, but also provides an empirical basis for future experimental design and theoretical deepening.