fix typos (#38)

Author: omahs

tokenomics/README.md

@@ -11,6 +11,6 @@ In order to test variants of the GNOT/Gno.land fee equation, a number of scripts
   - Fee data will be used to weight the GNOT fee simulations, resulting in a more realistic distribution. Wei Ethereum data is then converted to Gwei to better align with GNOT notation.
   - Gas limit, gas used, the percentage used out of the max, median wei price, and median gwei price are all scrapped and added to a CSV. The percentage of gas used each block will be used to weight the GNOT fee equation's network congestion metric during simulations. 
 
-- Once the base Gno.land fee equation is finalized, a Monte Carlo simulation will be created to test a number of Gno.land network conditions. This includes testing against exploits such as spam attacks, various levels of network congestion, etc. Individual parameters within the fee equation such as CPU cycles required, bytes stored, and the threshold at which fee cost begins increasing expotentially (to combat exploits) will also be tested. 
+- Once the base Gno.land fee equation is finalized, a Monte Carlo simulation will be created to test a number of Gno.land network conditions. This includes testing against exploits such as spam attacks, various levels of network congestion, etc. Individual parameters within the fee equation such as CPU cycles required, bytes stored, and the threshold at which fee cost begins increasing exponentially (to combat exploits) will also be tested. 
 
-If for any reason the Monte Carlo simulation does not provide adoquete insight, a seperate Cartesian Product simulation may be created to brute force additional results. By virtue of testing every possible parameter input against every other parameter input, a Cartesian Product sim can further substantiate any findings as necessary (trading efficiency for thoroughness). 
+If for any reason the Monte Carlo simulation does not provide adequate insight, a separate Cartesian Product simulation may be created to brute force additional results. By virtue of testing every possible parameter input against every other parameter input, a Cartesian Product sim can further substantiate any findings as necessary (trading efficiency for thoroughness).