> For the complete documentation index, see [llms.txt](https://web3-growth-agent-wga.gitbook.io/whitepaper_ver2.0_en/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://web3-growth-agent-wga.gitbook.io/whitepaper_ver2.0_en/.-wga-solution-overview-and-structure2/1.-functional-scope-of-wga.md).

# 1. Functional Scope of WGA

WGA is a decentralized data processing network designed to transform behavioral data into verifiable analytical assets. WGA does not collect data; it processes existing behavioral data. WGA does not execute advertising; it determines the validity of behaviors. WGA is not a marketing tool; it is a processing architecture that generates tradable outcomes for the analytics market.

The operations of WGA are categorized into three core functions:

First, the mitigation of data distortion. The system utilizes AI to isolate bots, automation, redundant participation, and distorted patterns driven by incentive seeking. The objective is not arbitrary deletion, but the differentiation between authentic usage signals and patterns focused solely on reward acquisition.

Second, the interpretation of behavior into semantic units. Rather than providing simple click counts or conversion rates, the system deconstructs behavior into metrics such as interest intensity, recurrence, engagement depth, attrition points, and diffusion structures. These interpretations are structured into standardized Key Performance Indicators (KPIs).

Third, the anchoring of results in a comparable format. The system records the criteria used for analysis and specifies which data points were excluded. This ensures that the same standards can be reapplied, results can be compared across datasets, and data remains re-interpretable over time.

WGA does not invent new analytical techniques. Instead, it reconstructs the foundational premise the reliability of input data upon which existing market analytical techniques depend.