# Automated Regression Market Maker (ARMM)

## Overview

The ARMM mechanism could have huge implications for energy credit markets, and open a new world of possibilities by tying the technology of automated market makers to real world variable assets represented on-chain. This enables efficient price discovery for highly complex arrangements of attributes, such as (for energy credits) the type of energy generation, geographic location, etc.

An Automated Regression Market Maker (ARMM) is a market-level price discovery mechanism. It is an automated agent deployed within an ARM and is capable of measuring the state of the market, making direct trades, and leveraging the mathematical similarities between machine learning (ML) models and [Automated Market Makers (AMMs)](https://www.cs.cmu.edu/~sandholm/automatedMarketMakersThatEnableNewSettings.AMMA-11.pdf). It differs from price discovery mechanisms such as AMMs in that they work when one fungible asset is being traded for another. ARMMs can be used when the assets being traded on a market share some similarities, but they are not all the same.

<figure><img src="/files/2Vqf4N2EC78UBieiTLr6" alt=""><figcaption><p>Source : BlockScience</p></figcaption></figure>

An illustration of multiple attributes or parameters that could exist on the supply and demand sides of renewable energy credit markets. The top (green boxes) represent RECs and the bottom (blue) represent CORCs. ARMMs can distill private preference signals into public price signals while tracking underlying signal changes. An ARMM can “learn” to make more accurate pricing predictions and provide automated matching of the supply and demand sides of the market.


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