松易涅

Current Information#

Time period: 25/02/19-25

Keywords: complex science, network science, physics; political science, computational political science, national governance; intellectuals, ruling class, intellectuals within the system; utopia, philosophy, happiness machine; Gypsies, cultural clans, France, social structure; Wiener, cybernetics, dialectics, social concepts; complex science; Web3; scale-free networks, cascading collapse;

Information Stream#

【Nature Physics Frontier: The Physical Origin of Sparsity in Real-World Complex Networks】#

(complex science, network science, physics) https://mp.weixin.qq.com/s/xdBViRGt4fjYif0UB8KbHw

The formation of network structures seeks a balance between two competing factors: on one hand, establishing connections can facilitate the flow of information and enhance the system's coordination ability; on the other hand, excessive connections can limit the freedom of nodes and reduce the diversity of the system's response to external disturbances.
Researchers introduced the concept of the network density matrix and established a variational principle similar to thermodynamic efficiency, providing a new theoretical framework for understanding the formation of complex networks.
To understand the information transmission mechanism during the network formation process, the research team first introduced the concept of the network density matrix. In quantum mechanics, the density matrix is an important tool for describing the state of a quantum system, capable of fully recording all possible states of the system and their probability distributions. The research team cleverly transferred this concept to network science: if we analogize the information propagation in a network to the state evolution in a quantum system, then the network density matrix can describe how information flows within the network and how the network responds to external disturbances.
Within this framework, the network formation process can be viewed as a phase transition from disorder to order. Two key physical quantities exist in this process: one is information flow gain (W), analogous to "work" in physical systems, indicating the information transmission capacity gained by the network through establishing connections; the other is response diversity loss (Q), akin to "heat" in physical systems, representing the loss of free response modes due to the establishment of connections.

This universality suggests that, despite the significant differences in function, scale, and evolutionary history among these systems, they may all be governed by the same fundamental physical principle: optimizing the trade-off between information flow and response diversity by maintaining a sparse network structure.
It is noteworthy that while networks in different fields follow similar scaling laws, their specific connection densities and organizational patterns still differ. These differences may reflect the specific functional needs and environmental constraints faced by different systems. For example, neural networks need to balance energy efficiency and information processing capability, while transportation networks must weigh construction costs against transportation efficiency.
These numerical experiments reveal several profound insights from physics: first, at short time scales (τ ≈ 0), the impact of network structure on η is minimal, indicating that local information propagation primarily relies on direct connections. As the time scale increases, the topological features of the network begin to play a crucial role. Particularly at medium time scales, modular structures exhibit significant advantages: they maintain efficient information transmission within local areas while sustaining diverse responses to external disturbances through sparse inter-module connections.
The emergence of small-world characteristics can also be explained by the principle of maximizing η. The study found that when η increases to a certain extent, adding a small number of long-range connections can significantly enhance η. These long-range connections drastically reduce the average path length while maintaining the local clustering coefficient, providing a solution that optimizes the trade-off between information propagation efficiency and response diversity. This finding aligns with the phenomenon observed in many natural systems that exhibit small-world characteristics.
By comparing real networks with random network models, researchers discovered an important phenomenon: network structures formed through natural evolution often maintain higher η values over a broader range of time scales. This robustness is not coincidental but rather a result of the system's adaptation to different time scale demands during long-term evolution. For instance, neural networks need to simultaneously process rapid perceptual signals and slow cognitive processes, requiring the network structure to perform well across different time scales.
It is particularly noteworthy that the emergence of these topological features does not require specific growth mechanisms or complex optimization algorithms. On the contrary, they spontaneously form as the system pursues high efficiency η. This finding provides us with a new perspective: the universally observed topological features in complex networks may all be the result of natural selection within the framework of energy-entropy trade-offs.
This study introduces a statistical physics perspective, providing a new theoretical framework for understanding the formation mechanisms of complex networks. The research indicates that the sparsity of networks is not coincidental but rather an inevitable result of the system's trade-off between optimizing information transmission efficiency and response diversity. This finding not only explains why networks in nature tend to maintain sparse structures but also reveals the physical roots of complex features such as modularity and small-world properties.

【Computational Political Science: Analyzing Political Theory and Methodological Paradigms in the Age of Digital Intelligence】#

(political science, computational political science, national governance) https://mp.weixin.qq.com/s/XjsmIhRPPEI_msDG03lddg

The super-large scale and complexity of modern states make information exchange between the state and society a significant challenge for national governance. Therefore, modern states have always sought to implement effective governance by "understanding" complex societies and "grasping" social knowledge. In the digital age, the information exchange process between the state and society has shifted from traditional unidirectional "information extraction" to a coexistence of bidirectional "information extraction" and "information openness."
The paradigm of large models will bring about a revolutionary leap in social science research methods, helping researchers understand the widespread nonlinear relationships between variables in complex social systems.
Some studies based on 20 million Weibo text data examined the impact of information capability on values of patriotism and nationalism, finding that the process of globalization and the flow of diverse information are eroding public national identity, while information capability is an important source for the government to consolidate national identity.

【Huang Ping: Intellectuals, Become the New Ruling Class or Continue to Drift?】#

(intellectuals, ruling class, intellectuals within the system) https://www.guancha.cn/indexnews/2011_09_29_60250.shtml

Unlike the approach of viewing intellectuals through a single model, Antonio Gramsci, in his notes written during his imprisonment, categorized intellectuals into two types: organic and traditional. The former refers to those intellectuals who exist as organic components within each socio-economic and political system, contributing to the political and ideological integration and hegemony of that system; the latter refers to literati, scholars, artists, and some organic intellectuals who were once part of the previous social system but are now outside the system (the other part may still exist within the current system). Gramsci called them traditional intellectuals because traditionally, those who are outside the system are regarded by the public as "true intellectuals," even though some of them may become members of organic intellectuals in future societies.
Gramsci's perspective is quite enlightening. If we apply his classification method to Mannheim and Gouldner's theories, can we say that the free-floating individuals described by Mannheim are precisely those traditional intellectuals who are outside the system, while the new ruling class members depicted by Gouldner are merely organic intellectuals within the system? In other words, not all intellectuals can freely float, nor have all intellectuals become members of the ruling class. Intellectuals are differentiated.
This differentiation is not only the heterogeneity that Mannheim observed in political views but also the phenomenon noted by Gouldner and others regarding their division of labor in professional research fields. More importantly, it marks the beginning of recognizing the social positioning and social differentiation of intellectuals. Moreover, by observing the penetration or displacement between organic and traditional intellectuals, where some former organic intellectuals have "degenerated" into traditional intellectuals and certain traditional intellectuals may merge into future organic intellectuals, it suggests a possibility for dynamic analysis.
……
As Gramsci observed, the economic, political, and cultural ideological systems of modern society organically include a type of intellectual who plays an irreplaceable integrative role in the operation of the existing system and its political and ideological hegemony. To clarify, we might refer to these individuals as "system intellectuals." Correspondingly, those traditional literati, scholars, artists, scientists, etc., who live in civil society or similar environments and have no intrinsic organic connection to the existing system, are referred to as "non-system intellectuals." In addition to these two types, there are also some intellectuals who are incompatible with the existing system, dedicated to criticizing or even changing it, known as "anti-system intellectuals." For example, the critical intellectual group in Russian society at the turn of the century is such individuals.
The aforementioned classification does not necessarily exhaust all intellectual individuals in reality. If we conduct a detailed study of a society (such as China), we will realize that at least during certain historical periods, there may exist many transitional intellectuals among the three categories, who may also hold historical or sociological significance. However, as an analytical concept, it is not necessary to encompass all real individuals at the theoretical level. System, non-system, and anti-system intellectuals, from the perspective of social positioning, have already summarized the overall picture of intellectuals. This classification is not commonly based on age (elderly, middle-aged, young), knowledge level (high, medium, low), political views (left, center, right), or professional fields (humanities, sciences, technical intellectuals), but rather based on their relationship with the social system, placing them within the existing social structure to facilitate the examination of their participation in social development and their different social statuses in the participation process. Empirical research will also demonstrate that in this regard, its explanatory power will be stronger than other classifications.
There is a theory that claims that under a Soviet-like system, only system intellectuals can survive. This theory acknowledges certain important characteristics and consequences of the Soviet-style system, but a careful analysis of the extensive historical materials available reveals that the reality is much more complex. Undoubtedly, in a politically and administratively tightly organized society, the so-called civil society will be quite limited, making it difficult for non-system or anti-system intellectuals to exist and function as formal members of society. In other words, the number of intellectuals who can relatively "freely float" will not be many, and those who oppose society or criticize reality will find it even harder to survive. However, not all intellectuals are organic molecules of the system. Originarily, a significant portion of system intellectuals is generally "recruited" or transformed from other types of intellectuals, especially at the initial establishment of a system. This does not mean that in any social stage, the three types of intellectuals always exist in the same or similar proportions. On the contrary, during certain specific periods, it is indeed possible for certain types of intellectuals to be absent. For instance, there was a phase in Britain when all intellectuals identified with the existing system, and there were no anti-system intellectuals, as previously mentioned. In studying modern China, we can also find that for about twenty years, at least within the mainland (and Taiwan as well?), for whatever reasons, anti-system intellectuals were either non-existent or virtually non-existent. This is not a matter of superiority or inferiority but rather aims to illustrate that the types of intellectuals differ across different societies under varying historical conditions. This is also the main reason why this article believes that Mannheim and Gouldner's theoretical models are not sufficiently rigorous, thus advocating for a dynamic classification of intellectuals.

【Why Do Many Second Games and Anime Criticize and Oppose the Attitude of "Living Forever in a Beautiful Dream"?】#

(utopia, philosophy, happiness machine) https://www.zhihu.com/question/657610089/answer/103607694742

Comment: Very interesting. The essence of "satisfying everyone's beautiful dreams" and "sharing beautiful dreams together" is fundamentally conflicting because people's perceptions of the world differ, and their definitions of "beautiful dreams" must also differ. Therefore, it is impossible to share or promote. This also reflects the idea that "human nature is inherently lonely." Moreover, creating one's own beautiful dream inevitably means the objectification of others, but no one wants to be objectified. Thus, "beautiful dreams" can only exist in exclusion from socialization. Consequently, it can be understood that one can create their own "beautiful dream" without needing to involve others. This is also where this type of antagonist seems flawed and immature.

【Are the Prejudices Against Gypsies Due to Their Own Ethnic Reasons or Social Oppression?】#

(Gypsies, cultural clans, France, social structure) https://www.zhihu.com/question/336592626/answer/3597900493

The overall prejudice against Gypsies as a group in the title is incorrect, because even when the vast majority of travelers face severe racial discrimination, they are still long-term linked to the mainstream society: although Gypsies are clan-based, their clans are industrialized and professionalized, and internally they are as severely stratified as our society. Moreover, while their religious beliefs are more fervent, they actually worship wealth in a manner very similar to that of most new conservatives and far-right whites. Due to their precarious living conditions, Gypsies' faith in evangelicalism is often more market-driven and radical than that of well-off individuals.
Many times, the visibility of the Gypsy issue is actually determined by the government and mainstream society. Gypsies appear between large cities because their nomadic caravans are easily attacked and restricted by the "celebrity class" in less noticed provinces, and the French government's poor "resettlement policy" (forcibly placing large numbers of impoverished clans in urban slums) has led to the most impoverished and miserable population of this group being directly thrown into the most sensitive and poorly managed areas of the mainstream population. In this context, it is entirely predictable that these impoverished Gypsies, like others in slums, would fall into severe antisocial behavior.
The Gypsy issue is essentially a version of the mainstream societal problem under more severe oppression, where the rich get richer and the poor get poorer; Gypsy elders and celebrities constantly boast that wealth and a decent life are the inevitable results of personal effort in the eyes of God; landlord clans, boss clans, and credit clans take turns exploiting the youth, while they find it difficult to leave the protection and shackles of their small community under the discrimination of mainstream society.

【Wiener, Cybernetics, and Dialectics】#

(Wiener, cybernetics, dialectics, social concepts) https://mp.weixin.qq.com/s/KswrRzvUPLKdUGd0XeEGoQ

We need a society that is overall more inclined towards openness, where various different ideas are more tolerated, and efforts are made to coordinate and understand the patterns of different thoughts and behaviors in different social environments and promote their respective development; however, on the other hand, our society must also be able to accommodate radical self-critical patterns.
American factories and enterprises pursue profits by controlling machines and people in a more mechanized manner, while the increasing complexity and intelligence of machines accelerate the overall devaluation of human intelligence and labor. However, in Wiener's view, this is an abuse of "cybernetics"—cybernetics emphasizes that society should be able to flexibly self-control and adjust itself in the face of uncontrollable randomness, but now cybernetics is understood as the exclusion of randomness as much as possible, allowing society to obey the inevitable order of machines to seek profits in the most stable manner, independent of human mechanical operation.
Therefore, if we strictly follow Wiener's cybernetic thought, we need a truly top-down holistic social theory and a bottom-up emergence theory, centralized and distributed, collectivist and individualistic, socialist and liberal new model, which adopts a multi-level centralized adjustment approach that includes both distributed decentralized adjustment methods and a centralized control mechanism that serves as a final guarantee but does not infringe on freedom.
In fact, according to Wiener's cybernetics, we should not simply ask whether machines will replace humans in the future, but rather shift the question to how humans should prepare to coexist with machines, which is the core issue.
Just as in ancient societies we drove animals to perform specific labor, today we still rely on driving plants to provide food for humanity, but animals and plants evidently have their own survival purposes and operational logics.

Notes#

Humans cannot wish to control everything, pursuing absolute certainty, but must learn to coexist with uncertainty (randomness).

Thoughts#

Complex Science and General Systems Research#

Using the worldview and methodology of complexity science to study the complex system of the economy and society is feasible. Chen Ping may be talking nonsense, but studying complex systems like the economy and society from the perspective of physics and complexity science is not nonsense.

As long as it is a system, there are commonalities that can be studied. Whether one can distill the individuality of the system depends on the researcher's ability.

Systems have commonalities, and tracing back to the root, there is a fundamental contradiction that drives the system to develop into a certain result. This raises the question: what is the fundamental contradiction of human society itself as a complex network system?

Complex science should also encompass data science. I see data science as a re-development of information science in the post-information age. Information science solved the problems of transmission, storage, and integration, while data science addresses the latter half of analyzing information, constructing plans, evaluating plans, making decisions, and taking action. In the context of data explosion, how to extract useful systematic information through statistical methods, AI learning, and causal inference to provide a foundational guarantee for decision-making and action is crucial. Simply holding information and data is futile. The key is how to turn them into the basis for decision-making and action. If one only stays at the level of preservation and integration without entering evaluation, analysis, decision-making, and action, then such storage and integration itself, in many cases, is meaningless.

The following is excerpted from "A Review of International Trade Research from the Perspective of Complex Networks" (Wu Zongning, Fan Ying):

"International trade research can be divided into two main categories: theoretical and empirical, with the core proposition being to explain why trade occurs between countries in terms of mechanisms and quantities. The theoretical foundation of international trade mainly consists of traditional international trade theory, new international trade theory, and new-new international trade theory, aiming to explain the reasons for the formation of international trade from perspectives such as comparative advantage, resource endowment differences, market size, economic scale, product differentiation, and firm heterogeneity. However, while these theories explain the reasons for the emergence of international trade, they overlook the explanation of bilateral trade volumes, leading some scholars to explore the determinants of trade volume based on the gravity model.
International trade theory is built on the foundation of economic theory, focusing primarily on trade issues between countries (regions) and local areas (countries and regions). With the development of economic globalization, the trade connections between various countries (regions) have continuously strengthened, making the trade system an organic whole, which makes it difficult for the economic theoretical framework to explain the overall characteristics of the trade system. The challenges faced by mainstream economics include the following points: first, the economic system exhibits characteristics of non-equilibrium and evolution, while the linear models commonly used in economic analysis struggle to describe and predict complex economic processes. Second, the failure of econometric models during economic crises emphasizes the complexity of economic systems, necessitating modifications and expansions to existing economic theoretical research paradigms. Third, compared to traditional economics, economic physics is based on real data rather than starting from a priori models.
The economic system is a complex system composed of individuals and interactions among individuals, with the trade system being an important component of the economic system. Research on the complexity of the trade system requires new tools to understand the trade system and trade networks. Complex network analysis tools and complexity theory provide a research paradigm. Applying this set of tools and methods to study international trade can reveal the mechanisms and evolutionary laws of international trade from a global perspective, thereby explaining the interaction patterns between countries (regions) and their impact on system structure and function. With the advent of new information technologies, it has become convenient for researchers to obtain large-scale data, providing a foundation for data-driven research on the complexity of trade systems. Big data plays an increasingly important role in revealing patterns in macro social and economic structures, micro social and economic conditions, and economic development. In fact, uncovering valuable and potential trade patterns during empirical economic research has become key to formulating relevant decisions."

Web3 and Blockchain Economics from the Perspective of Complexity Science#

1. Understanding "Web3" and "Economics on the Blockchain" from the perspective of complexity science requires viewing these two fields as typical Complex Adaptive Systems (CAS) and analyzing their characteristics such as self-organization, emergence, nonlinear dynamics, and network effects. The following unfolds from multiple dimensions:

2. Decentralization and Self-Organization
   The core feature of Web3 is decentralization, where its nodes (users, miners, validators, etc.) autonomously collaborate through distributed protocols (like blockchain) to form an order without central authority. This highly aligns with the phenomenon of self-organization in complex systems:

Simple rules drive complex behavior: The consensus mechanisms of blockchain (like PoW, PoS) are the underlying rules followed by nodes, through local interactions (like transaction validation, block generation) emerging global order (like ledger consistency).

Adaptive adjustments: For example, the Bitcoin network maintains stability in block generation speed by dynamically adjusting mining difficulty (a negative feedback mechanism), reflecting the system's adaptability to environmental changes.

2. Network Effects and Nonlinear Dynamics
   Network effects are the core driving force behind Web3's development, but they are influenced by nonlinear dynamics in complexity science:

Positive feedback loops: User growth increases network value (like Metcalfe's Law), attracting more participants and forming a "winner-takes-all" effect (like Ethereum's ecological advantage).

Critical points and phase transitions: Certain protocols may suddenly transition from inefficiency to efficiency (phase transition) when user numbers or liquidity break through critical points (like Uniswap's liquidity threshold).

Butterfly effects: Minor events (like a tweet triggering market panic) can be amplified through social networks and on-chain interactions, leading to dramatic fluctuations in prices or on-chain activities.

3. Game Theory and Coordination in Token Economics
   Blockchain economic models (like token design, DeFi protocols) are typical multi-agent game systems:

Incentive compatibility: When designing mechanisms for token distribution, staking rewards, etc., it is necessary to balance individual rationality (like miners pursuing profits) with system goals (like network security), avoiding the "tragedy of the commons."

Coordination dilemmas: Low voting participation in DAO governance, disputes over forks, etc., reflect the challenges of coordination costs and collective action in complex systems.

Game theory and evolutionarily stable strategies: For example, the strategy choices of validators in PoS networks (honest validation vs. attack) may evolve toward a stable equilibrium.

4. Emergence and Unpredictability
   Many phenomena in the Web3 ecosystem are emergent results from the bottom up:

DeFi Legos: By combining lending, trading, derivatives, etc., protocols form complex financial instruments (like flash loans), whose systemic risks may exceed the designers' expectations.

Meme economy: Price fluctuations in the cryptocurrency market are often driven by community consensus (like Dogecoin), reflecting the intertwining of "collective intelligence" and "irrational exuberance."

Collective decision-making in DAOs: The governance outcomes of decentralized autonomous organizations may not be directly inferred from individual behaviors (emergent collective wisdom or chaos).

5. Robustness and Vulnerability
   Blockchain systems dynamically balance robustness (resistance to attacks, fault tolerance) and vulnerability (protocol vulnerabilities, 51% attacks):

Scale-free network characteristics: The distribution of nodes in certain public chains may exhibit "hub nodes" (like large mining pools), leading to centralization risks and single points of failure.

Cascading failures in complex systems: The collapse of Terra Luna revealed the failure of negative feedback mechanisms in algorithmic stablecoin design, similar to cascading failures in ecosystems.

6. Multi-Scale Interactions and Cross-Chain Ecosystems
   The cross-chain interoperability and layered architecture of Web3 (like Layer1, Layer2) reflect the multi-scale interactions of complex systems:

Hierarchical nesting: The collaboration between the Bitcoin network (settlement layer) and the Lightning Network (payment layer) resembles the hierarchical structure of biological systems.

Cross-chain bridge risks: Asset transfers between different chains may introduce new vulnerabilities (like cross-chain bridge hacks), similar to the impacts of species invasions in ecosystems.

7. Evolution and Path Dependence
   The evolution of Web3 is influenced by historical path dependence and technological lock-in effects:

Protocol forks: The lengthy process of Ethereum transitioning from PoW to PoS reflects the resistance and adaptive costs of change in complex systems.

Ecological lock-in: Developers tend to build applications on existing ecosystems (like Ethereum), similar to the "first-mover advantage" in technological standard competition.

Summary: Insights from Complexity Science on Web3
Design principles: Emphasize the emergence effects under simple rules and avoid over-engineering; introduce negative feedback mechanisms (like dynamic fees) to enhance robustness.

Risk prediction: Use multi-agent modeling (Agent-Based Modeling) to simulate economic behaviors of protocols and identify systemic risks.

Governance optimization: Learn from adaptive governance in complex systems (like elastic threshold design) to balance decentralization and efficiency.

Web3 and blockchain economics are not just technological revolutions but also complex experiments in human collaboration models. Through the lens of complexity science, we can gain a deeper understanding of their potential and challenges and design more resilient next-generation network economic systems.

2. Understanding Web3 and Economics on the Blockchain from the perspective of complexity science requires viewing both as dynamic, adaptive systems, with the core being individual interactions, rule evolution, and the emergence of global order within decentralized networks. The following analysis unfolds from multiple dimensions:

3. Basic Characteristics of Complex Systems and Their Fit with Web3
   Decentralization and Distributed Networks: The underlying architecture of Web3 is based on blockchain technology, and its distributed nature is highly similar to "non-centralized control" systems in complexity science. In a blockchain network, each node is both a participant and a rule maintainer, achieving self-organization through consensus mechanisms (like PoW, PoS) to form a dynamically balanced global state. This decentralized structure resembles an ecosystem, where individual local behaviors emerge global stability through nonlinear interactions.

Self-Organization and Rule-Driven Smart Contracts: Smart contracts, as automatically executed code rules, provide a "bottom-up" governance mechanism for the system. This mechanism does not require centralized authority intervention but guides participant behavior through preset rules (like lending logic in DeFi protocols), forming adaptive economic models. This aligns with the principle in complexity science that "simple rules produce complex behaviors."

Emergence and Innovation in Crypto-Economics: In blockchain economics, tokens (Token) are not only mediums of value but also carriers of property rights and governance rights. For example, DAOs (Decentralized Autonomous Organizations) achieve collective decision-making through token voting mechanisms, and their governance models reflect a new order of "emergence" under multi-agent collaboration, transcending the simple addition of individual behaviors.

2. Complexity Characteristics of Blockchain Economics
   Transaction Costs and Institutional Evolution: In traditional economics, transaction costs are a core issue of institutional design. Blockchain significantly reduces transaction costs related to information verification, trust establishment, and contract execution through smart contracts and decentralized ledgers. For example, DeFi (Decentralized Finance) can complete lending without bank intermediaries, and this efficiency improvement can be seen as a new path of institutional evolution. From an institutional economics perspective, blockchain technology reconstructs property definitions and transaction rules, forming a new institutional paradigm of "code is law."

Network Effects and Positive Feedback Loops: The ecological expansion of Web3 relies on network effects: the more users, the higher the protocol's value (like Ethereum's DApp ecosystem). This positive feedback mechanism is a typical characteristic of complex systems, potentially leading to "winner-takes-all" or "multiple stable states coexistence" outcomes. For instance, the centralization of Bitcoin's computing power and the diverse application scenarios of Ethereum reflect the competition of different network evolution paths.

Adaptive Behavior of Game-Theoretic Agents: Blockchain participants (like miners, developers, users) are adaptive agents whose behaviors are driven by incentive mechanisms and continuously adjust strategies in dynamic environments. For example, miners' profit-maximizing behaviors under PoW and PoS mechanisms may lead to changes in computing power distribution or conflicts in ecological governance, reflecting the dynamic balance and uncertainty of complex systems.

3. Challenges of Web3 and Insights from Complexity Science
   System Vulnerability and Attack Resistance: While blockchain networks possess decentralization and resistance to censorship, they also face risks such as 51% attacks and smart contract vulnerabilities. The "robustness" theory in complexity science suggests the need to enhance system resilience through redundancy designs (like cross-chain technologies) and dynamically adjusting consensus rules.

Trade-offs Between Scale and Efficiency: The "impossible triangle" of blockchain (decentralization, security, scalability) reflects the constraints of complex systems. For example, Ethereum attempts to break through bottlenecks through sharding and Layer 2 solutions, a process similar to how biological systems adapt to environmental pressures through modularization.

Evolutionary Paths of Social Collaboration: The ultimate goal of Web3 is not only technological upgrades but also the transformation of social collaboration models. For instance, decentralized identity (DID) and privacy protection technologies (zero-knowledge proofs) reconstruct data sovereignty, promoting a new balance between individual and collective interests, which requires designing governance frameworks from the perspective of "multi-level interactions" in complex systems.

4. Future Directions: Cross-Research Between Complex Systems and Web3
   Multi-Agent Modeling and Simulation: Utilize Agent-Based Modeling to simulate user behaviors in blockchain economics, predicting the long-term stability of token economic models (like deflation mechanisms, liquidity mining).

Network Science and Token Flow Analysis: Analyze the flow networks of tokens between addresses to identify key nodes (like exchanges, whale accounts) and their impact on the system, preventing systemic risks.

Experimental Validation of Institutional Innovation: DAOs and on-chain governance can serve as "sandbox experiments" for social institutions, exploring more efficient collaboration rules through iterative trial and error.

Summary
Web3 and blockchain economics are essentially the practice of complexity science in the digital age: they construct a dynamically evolving new social-technical system through self-organization of distributed nodes, rule-driven smart contracts, and network effects of token economics. Future research should further integrate complex systems theory with crypto-economic practices to address the challenges of technological, economic, and social co-evolution.

Scale-Free Networks and Cascading Collapse#

Scale-Free Networks and Cascading Failures

1. Scale-Free Networks: Structure, Characteristics, and Examples
   Definition:

A scale-free network is a type of complex network characterized by the distribution of node connections following a power law, meaning that a small number of nodes (referred to as "hub nodes" or "central nodes") have a disproportionately high number of connections, while most nodes have only a few connections. This network structure is widely present in nature and human society.

Key Characteristics:

Power Law Distribution: Most nodes have very few connections, but a very small number of nodes (hub nodes) have far more connections than the average.

Robustness and Vulnerability Coexist:

Robustness: The network remains connected when nodes are randomly removed (because most nodes are not critical).

Vulnerability: Targeted attacks on hub nodes can rapidly destroy the overall structure of the network (like taking down key opinion leaders in social networks).

Growth Preference: New nodes tend to connect to already highly connected nodes (the "rich get richer" effect).

Examples:

Internet: A few core servers (like Google, Amazon) connect to a vast number of terminal devices.

Social Networks: Influential users on platforms like Twitter have millions of followers, while ordinary users have very few.

Blockchain Networks: In Bitcoin mining pools, the top three pools may control over 50% of the computing power, forming a de facto centralized hub.

2. Cascading Collapse: Mechanisms and Typical Cases
   Definition:

Cascading collapse refers to the phenomenon where a local failure in a system triggers a chain reaction through dependencies or interactions among nodes, ultimately leading to the collapse of the entire system. This phenomenon is common in highly interconnected complex systems (like power grids, financial systems, the internet).

Core Mechanisms:

Node Overload: A node fails due to malfunction or excessive load.

Load Transfer: The load of the failed node is transferred to other nodes, causing them to become overloaded.

Chain Reaction: Overloaded nodes fail in succession, with the failure spreading like a domino effect, ultimately paralyzing the entire system.

Typical Cases:

Power Grid Collapse: The 2003 North American blackout was triggered by a fault in a transmission line in Ohio, leading to a chain reaction that left 50 million people without power.

Financial System Crisis: The bankruptcy of Lehman Brothers in 2008 triggered a global financial crisis, with the debt chain among financial institutions leading to liquidity depletion.

Cascading Collapse in Blockchain:

Terra Luna Collapse (2022): The de-pegging of the algorithmic stablecoin UST led to panic selling, causing UST and Luna tokens to enter a "death spiral," with a market value evaporating by $40 billion, affecting the entire cryptocurrency market.

DeFi Protocol Liquidation Chains: When the prices of collateral assets plummet, on-chain lending protocols (like Aave) automatically trigger large-scale liquidations, further driving down prices and creating a negative feedback loop.

3. The Connection Between Scale-Free Networks and Cascading Collapse
   In scale-free networks, the risk of cascading collapse is particularly pronounced:

Vulnerability of Hub Nodes: When attacks or failures concentrate on hub nodes, their failure can cause a large number of dependent nodes to simultaneously lose functionality.

For example: If a large mining pool suffers a hacker attack, it could lead to a sudden drop in Bitcoin network computing power, causing transaction confirmation delays to surge.

Load Concentration: Hub nodes typically bear higher loads, and once overloaded, the collapse can spread rapidly.

For example: When the Ethereum network is congested, gas fees soar, and users may turn to other chains, leading to a liquidity drain in the Ethereum ecosystem.

4. Risk Scenarios and Responses in Blockchain Contexts
   Risk Scenarios:

Centralization of Mining Pools/Validators: The computing power/staking of Bitcoin or Ethereum is overly concentrated in a few mining pools or exchanges, forming a scale-free structure.

Cross-Chain Bridge Dependency: If a cross-chain bridge (like Polygon Bridge) is attacked, it could lead to the freezing or loss of multi-chain assets.

Protocol Coupling: DeFi protocols are highly interconnected (like Curve liquidity pools being reused by multiple protocols), and a single vulnerability could trigger a collapse of the entire ecosystem.

Response Strategies:

Suppressing Scale-Free Structures:

Limit the concentration of mining pool computing power through algorithms (like Filecoin's "staking penalty mechanism").

Encourage decentralized staking (like Ethereum 2.0's distributed validator technology).

Enhancing System Resilience:

Design "circuit breaker mechanisms": Pause transactions when on-chain liquidation volumes exceed thresholds (like MakerDAO's emergency shutdown).

Adopt multi-chain redundancy: Distribute assets and protocols across multiple blockchains to reduce single point failure risks.

Dynamic Load Balancing:

Elastic Block Capacity: Dynamically adjust block sizes based on network load (like Bitcoin's SegWit upgrade).

Layered Architecture: Separate settlement layers (Layer1) from high-frequency trading to Layer2 (like Lightning Network).

Summary: Insights from a Complexity Perspective
Scale-free networks are a double-edged sword: they naturally form highly efficient information transmission structures, but hub nodes become the "Achilles' heel" of systemic risk.

Cascading collapse should not be overlooked: the highly interconnected Web3 ecosystem needs to pre-establish fault isolation and recovery mechanisms to avoid "butterfly effect" collapses.

Design Principles: In pursuing efficiency, it is necessary to balance the robustness and vulnerability of the network through decentralization, redundancy design, and dynamic regulation.

Understanding these concepts helps foresee risks in blockchain and Web3 development, building a more resilient next-generation internet economic system.