# Solvers Network

The Solvers Network is a decentralized collective of actors and computational entities focused on optimizing the swapping and purchasing routes for users' orders. The solvers are incentivized by a competitive model that rewards successful determination and implementation of the optimal order paths.

A Solver is an individual actor or computational agent whose primary role is to determine the most efficient pathway for executing a user's order. Solvers compete against each other to find the optimal routing based on a number of parameters (explained in detail in the prioritization section). A solver’s calculation and process script will be available on Grix Protocol’s public repository.

To incentivize the work of calculating and submitting the best solution for the order flow, the solver is rewarded by the protocol per successful routing with its solution.

Here is the overview of the solvers’ incentives:

For every order signed by the user, there are two pricing benchmarks: the cheapest single protocol price (named **market price**), the naive routing model with multi protocols (named **expected price**). When a user signs a transaction, the benchmark price is the expected price. Now it’s the solvers' job to get a better price (**proposed price**) than the benchmark so that the user’s **actual price** can only get a better price than it previously signed. The solver’s incentives come as a fixed fee and as a percentage of the difference between the expected and actual prices.

This incentive mechanism ensures that solvers are motivated to provide the best possible solution for the end user. For a solver, there are many ways to get a better price than the expected price, like: optimizing the amount purchased per protocol, swapping payment tokens for a better price, using the Secondary Options Pool or limit orders submitted.

In some high volatility cases, there might be a difference between the proposed price (that the solver calculated) and the actual price that the user pays, so that the user pays more than the expected price. This situation is generally known as slippage tolerance that is set by the user. In this case the solver’s percentage incentive is zero but it still gets paid from the fixed fee paid by the user.

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