# Efficient

Consensus protocols - basically the way how servers define the truth about shared data - determine to a large part the performance and energy consumption of a blockchain system and also how much influence validators (or miners, or pools) have on the ordering or censoring of transactions. 

ARTIS ∑2 uses a new and novel consensus protocol [published in 2016 by Andrew Miller et al.](https://eprint.iacr.org/2016/199.pdf) called Honey Badger BFT (HBBFT) consensus. It is built into the [OpenEthereum](https://github.com/openethereum) client (formerly known as Parity client), currently maintained by Gnosis. 

All validators work in a cooperative way together to form new blocks through a threshold signature scheme, where once 2/3 of all validators have signed the block, the outcome is revealed and also finalized. 

Due to the cooperative and leaderless nature of the Honey Badger BFT (HBBFT) consensus the energy consumption of ARTIS ∑2 is only:

| Energy (Wh) / Transaction | Human Radiation (100 Watt) | Relativ to Visa |
| ------------------------- | -------------------------- | --------------- |
| 0.0035                    | 0.1 sec.                   | >500x better    |

Furthermore, the use of threshold signatures among the validators effectively mitigates the risk of front running and censorship on the protocol layer. This doesn't just reduce fraud, it also reduces automated load on the system, when it comes to financial transactions. 

{% hint style="success" %}
ARTIS ∑2 will be one of the most efficient blockchain system on the market.
{% endhint %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.artis.eco/artis-2.0-whitepaper/challenges-and-solutions/efficient.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.