Mental Models For Crypto Investors

This writing is aimed at investors participating in the crypto asset-class. Mental models are nothing but systems you set up for yourself on how you think about certain things. They act as filters for decision making, which is why it becomes important to inculcate them into your investment framework. I target models that would better inform you on making decisions before plunging into an investment in the space, with a focus on fundamentals.

  1. A model on tokenholding: Similar to considering promoter shareholding in public companies. This is for the consideration of incentive alignment and to understand the consolidation of power.
  2. A model on composability: How composable a protocol is in comparison to competitors, and barriers if built on vertically/horizontally different layers.
  3. A model on protocol fatness: Size of actual value accrual from what underlies the asset.
  4. A model on foundational layer infrastructure: Even if the investment being considered is on the application layer, considering its base layer’s characteristics is important.
  5. A model on asset economics: The demand/supply dynamics + its control over the asset’s characteristics.
  6. A model on project activity: If a project requires constant development or not, If the project is being built by a single team or a community of teams, how much mind power is involved, how much tangible activity takes place, etc.
  7. A model on censorship: How resistant is a project/asset to external interference.
  8. A model on timeframes: Short-term, medium-term, long-term.
  9. A model on network effects: How far along is a project in terms of barriers to exit + community involvement.
  10. A model for relative valuation: What other projects/assets can said investment be compared to.
  11. A model for absolute valuation: What financial, fundamental and economic factors do you consider while valuing the asset.
  12. A model on asset categorisation: How you classify assets with different functions, characteristics, value drivers, etc.
  13. A model on value drivers: For example, the value of utility tokens is linked to its underlying utility/platform.
  14. A model on liquidity: Difficulty in entering and exiting said investment.
October 22, 2020