Uncovering Tellor's Decentralized Consumer Price Index (CPI)

In the presentation below, Brenda Loya, Co-Founder and CEO at Tellor leads a presentation on building a decentralized Consumer Price Index (CPI) and how the blockchain could introduce transparency and responsibility into government policies.

Below is a glossary of key concepts mentioned during Brenda’s talk, intended as a supplement to her video presentation.

ABOUT TELLOR

Tellor is a blockchain oracle protocol that puts off-chain data onchain in a way that is decentralized, permissionless, and censorship-resistant. Tellor was built to handle any data type and ensures that data can be provided and checked by anyone. Tellor was launched in 2019 and its native token, $TRB, is used for payment, data reporter staking, and dispute compensation.

DECENTRALISED NETWORKS

While oracles focus on the methods of bringing off-chain data onchain, the next frontier would be improving off-chain data with open decentralized networks and the transparency they inherently offer. These networks, characterized by their decentralized nature, distribute control and information across a network of participants or nodes, reducing reliance on a central authority. Decentralized networks not only foster greater security but also ensure a higher degree of trust and openness in the validation and verification processes of off-chain data.

CONSUMER PRICE INDEX (CPI)

The Consumer Price Index (CPI) is a crucial economic indicator that reflects inflation or deflation in an economy by measuring changes in the average prices of specific goods and services consumed by households over time. It is calculated using a weighted average approach where a representative sample of goods and services is selected and each item is assigned a weight based on its share of total consumer spending. Price data for these items are collected regularly and then weighted and combined to produce the CPI.

The CPI provides insights into the level of inflation, which is crucial for monetary policy and fiscal planning. Central banks, governments, and businesses also use CPI data to adjust interest rates, pensions and tax brackets, and wages. However, calculating the CPI can be complex due to changing consumer preferences, technological advancements, and the inclusion of new goods and services.

The CPI has also faced three long-standing criticisms over the years:

1. Substitution

2. Delays in including new things

3. Unmeasured or poorly measured changes in quality

   
   

However, a more recently identified and significant issue, surpassing the problems of data collection and quality adjustments, is centralization and the lack of transparency. This is where open, decentralized networks can make a big difference by bringing about major improvements to the index. Specific details of the US CPI and how it is being calculated can be found here.

OUTLIER DEFINITION

An outlier is a data point that significantly deviates from the overall pattern of a dataset. In the context of oracle data reporting, an outlier refers to a data point that significantly differs from the rest of the dataset. It can be an unusually high or low value that stands out when compared to the majority of the data. Identifying outliers in Oracle data reporting is important for ensuring the accuracy and reliability of reports and analyses. Outliers can sometimes indicate errors in data entry, or measurement issues, or they may represent meaningful insights or anomalies that need further investigation. Managing outliers is a common practice in data analysis to ensure the quality and validity of the reported information.

OUTLIER DETECTION

Outlier detection refers to the process of identifying and managing data points that deviate significantly from the expected values reported by data providers. It involves employing statistical methods, machine learning algorithms, or consensus mechanisms to prevent these outliers from disproportionately influencing the final aggregated data.

Striking the right balance between filtering out genuine outliers and preserving accurate data can be challenging as overly aggressive outlier detection algorithms might incorrectly identify valid data as outliers. Median aggregation and threshold-based checks are some of the common outlier detection mechanisms.

DATA AGGREGATION

Data aggregation is the process of collecting data from various sources to generate precise information for integration into smart contracts and blockchain systems. The techniques employed for aggregation include averaging, median computation, and weighted voting. The choice of aggregation method depends on the specific application and underlying protocol design.

In oracle networks, data aggregation is a necessary step to bridging off-chain data sources and on-chain executions while retaining the accuracy and credibility of the data.

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