AI-driven Reconstruction of Government Policy Communication: From Information Release to Investment Perception Management

Against the backdrop of the deep integration of artificial intelligence and digital communication infrastructure, a structural transformation is taking place in the global government policy communication system. The previous policy communication model centered on "information release" is being replaced by a more complex mechanism—a "cognitive management system" oriented toward investment decision-making.

This is not merely an upgrade in communication methods, but a reconstruction of the path of policy influence within an algorithmic distribution environment.

With the widespread application of Artificial Intelligence and generative AI tools in information acquisition, investors increasingly rely on comprehensive judgments from multiple sources to assess the policy environment, including search engine summaries, industry reports, social media interpretations, and AI-generated conclusions. This shift means that "what the policy is" is gradually giving way to "how the policy is understood."

In this process, the essence of policy communication is shifting from one-way information transmission to a cross-platform, multi-node, machine-involved cognitive construction process.

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I. Structural Causes of Policy Communication Failure: From Scarce Information to Cognitive Competition

Traditional policy communication systems were built on a premise: information was scarce, and the government was the sole authoritative source. Therefore, press conferences, policy documents, and official interpretations were sufficient to complete the communication loop.

However, in the current digital environment, this logic is failing, primarily reflected in three changes:

First, information has shifted from scarce to overloaded, and policies are no longer the only source of information. Second, information channels have moved from centralized to distributed, with media and platforms jointly participating in interpretation. Third, the power of interpretation has shifted from the government to algorithmic systems and third-party content networks.

Before making decisions, investors often encounter multiple rounds of "unofficial interpretive versions," including media summaries, consulting agency analyses, and AI-generated policy overviews. This transforms policy communication from a "publication issue" into a "cognitive competition issue."

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II. AI Amplifies Cognitive Divergence: Machines Become Secondary Interpreters of Policy

The proliferation of generative AI has introduced a new variable into policy communication.

Investors often pose questions through AI, such as:

• How should foreign investment tax policies in a certain country be understood?
• How should investment risks in a specific region be assessed?
• Are industrial subsidies stable?

AI systems generate comprehensive answers based on multi-source data. However, due to the varying quality of information sources, policies can be reconstructed or even simplified during the communication process. This "machine-mediated interpretation" is becoming a key factor influencing investment decisions.

In other words, policy communication is now simultaneously addressing two audiences: human investors and algorithmic models.

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III. International Trends: Three Evolutionary Paths of Policy Communication

#### 1. From Policy Release to Policy Narrative System Construction

More and more national investment promotion agencies (IPAs) are shifting policy communication from "one-time releases" to "long-term narratives."

Policies are no longer isolated but embedded within industrial themes—such as green transition, advanced manufacturing upgrades, and other long-term structural narratives—enabling investors to understand the policy logic while grasping the industrial logic.

---#### 2. From Single Channel to Multi-Node Communication Network

The traditional model was a linear "government-media-investor" structure, but the current structure has evolved into a multi-node network:

Government ↔ Media Government ↔ Consulting Agencies Government ↔ Industry Associations Government ↔ Digital Platforms AI Models ↔ Multi-Source Data Redistribution

Policies are constantly "re-interpreted" during dissemination, so the communication system must possess robust re-interpretability design capabilities.

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#### 3. From Information Consistency to Cognitive Consistency Management

In an environment where multiple interpretations coexist, "information consistency" is no longer achievable. The new goal shifts to:

Across different interpretation paths, keeping the core intent of the policy as undistorted as possible.

Therefore, some countries have begun introducing "cognitive anchor" mechanisms, using unified terminology systems and core expressions to reduce the risk of misinterpretation.

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IV. Methodological Framework: The "Four-Layer Cognitive Structure Model" of Policy Communication

A modern policy communication system can be divided into four tiers:

#### 1. Policy Facts Layer

Includes basic information such as tax mechanisms, scope of application, and time cycles, with an emphasis on structuring and machine readability to facilitate AI extraction and distribution.

#### 2. Interpretation Layer

Answers the question "Why was this policy introduced?" and constructs an explanatory logic in advance to avoid fragmented later-stage interpretations.

#### 3. Scenario Layer

Embeds policies into investment decision-making scenarios—such as industrial chain layout, factory construction timelines, or regional development stages—directly linking policy to investment behavior.

#### 4. Cognitive Stability Layer

Ensures interpretive stability when AI participates in dissemination through unified terminology, standardized Q&A structures, and consistent cross-channel expressions.

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V. New Trends: Policy Communication Enters the Algorithmization Stage

#### 1. AI Becomes a Secondary Distribution System for Policies

More and more investors first encounter AI summaries rather than the original policy text. This means policy communication is shifting toward "communicating to models."

#### 2. Data Structure Will Replace Text as the Core Carrier

In the future, the competitiveness of a policy will no longer depend on clarity of expression, but on whether it is structured, machine-readable, and semantically stable.

#### 3. Geopolitical Environment Intensifies Cognitive Fragmentation Risks

The same policy may be interpreted in completely different ways across regions, widening disparities in perceived investment risk.

#### 4. Investment Behavior Shifts from Reading Policies to Verifying Models

The investment path gradually becomes: policy text → AI summary → industry verification → investment decision confirmation. The more intermediate links, the higher the risk of deviation.

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Conclusion

Under the combined influence of artificial intelligence and global digital communication networks, government policy communication is evolving from an information dissemination system into a complex system centered on cognitive structure management.

The importance of the policy itself remains, but "how the policy is understood" is becoming a key variable affecting the efficiency of global capital flows.Future policy communication capabilities will no longer be just communication capabilities, but a comprehensive governance capability that integrates language structure, platform mechanisms, and algorithmic understanding.