- Edited
Regarding the topics for further research,
I recently wrote a (math) paper, can be found here, that gives some answers/insight into a few of the future research topics:
Are there scenarios in which dynamic RBS parameters prove more effective than static ones?
Yes - making RBS parameters more dynamic with respect to statistically predictable MEV trading volume, which accounts for over 75% of all OHM trade volume and the majority of large swaps. This makes MEV bots a far greater market mover than humans.
Creation of a more complete model that can capture the impact of OHM illiquidity as a consequence of features like OHM Bonds and lending.
The paper specifically models a topological model on the historical sushiswap LP dataset for OHM and lending pools such as Rari and Vesta. Although just at the beginning of the era of OHM lending, it's very clear already that there is a significant relationship between liquidations and MEV trading volume.
MEV volume represents the majority of trading volume as well as the largest swap sizes implies that MEV bots are a non-trivial actor within the OHM ecosystem implies efficiency can be increased by understanding and catering towards this large subset of OHM users. Liquidation volume is even greater because 100% is driven by MEV bots. Humans don't liquidate anything.
It doesn't appear that there were any considerations in the report for MEV and its possible impacts on RBS over time. Why isn't this topic included as an additional area of further research?