The core feature of tokenized ecosystems, aka public blockchains, is getting people to do stuff. In this talk, I give more structure to this idea using a framing from optimization literature, and more precisely, evolutionary algorithms (EAs). I give examples of this approach using Bitcoin and Ocean Protocol as examples. Link to video: https://www.youtube.com/watch?v=Sm8j0u5NuGQ

1. Token Design as
Optimization Design
Trent McConaghy
@trentmc0

2. Outline
• Blockchains as trust machines
• Blockchains as incentive machines
• Case study: Bitcoin
• Towards a token design practice
• Case Study: Ocean Protocol
• Getting objective function right really matters

3. Blockchains as
Trust Machines

4. Blockchain data structure = chain of blocks
-Block = list of transactions, where tx = “create asset” or
“transfer asset” action, digitally signed
-Chain = linked list, where links are hashes
Header
Tx1
Tx2
Tx3
..
Header
Tx1
Tx2
Tx3
..
Header
Tx1
Tx2
Tx3
..

5. Blockchains as Distributed DBs, with 3 new characteristics
• Decentralized: via Byzantine fault tolerant (BFT) consensus
• Immutable: do undo a tx, need to undo each block in chain
• Assets: digital signature on every transaction. Create, transfer.
Alice
Mongo
DB

6. From Permissioned  Permissionless Blockchain
• Permissioned: to be a server node, need to be on an approved list
• A classical BFT setting
• “1 public key = 1 vote”
• Permissionless: anyone can join as server node
• Need BFT and Sybil tolerance (to “attack of the clones”)
• E.g. via “1 electron = 1 vote”

7. “Trust machine”
because it minimizes
trust needed to
operate.
It’s more socially
scalable. (Ref Szabos)

8. Blockchains as
Incentive Machines

9. What’s the amazing thing about blockchains?
•Decentralized?
•Immutability?
•Assets
•One more…

10. Blockchain Superpower:
Get people to do stuff
By rewarding with tokens

11. “I think I've been in the top 5% of my age
cohort all my life in understanding the power
of incentives, and all my life I've
underestimated it.
Never a year passes that I don't get some
surprise that pushes my limit a little farther.”
-Charlie Munger

12. Economic Incentive for Bitcoin
Objective: Maximize security of network
• Where “security” = compute power
• Therefore, super expensive to roll back changes to the transaction log

13. Economic Incentive for Bitcoin
Objective: Maximize security of network
• Where “security” = compute power
• Therefore, super expensive to roll back changes to the transaction log
E(Ri) α Hi * T
E() = expected
value
# tokens (BTC)
dispensed each
block
block
rewards
hash power of actor
= contribution to
“security”

14. E(Ri) α Hi * T
Bitcoin Token Release Schedule
F(H, t) = 1 - (0.5t/10) = % tokens released after t years
• Schedule is fixed in advance
• 4 years for 50% of tokens released = half-life “the halvening”

15. Result of Bitcoin maximizing security?
Maximizing energy usage! > USA by mid 2019!

16. Convergence of Bitcoin (Hash rate vs time)

17. Towards a
Token Design Practice

18. Formulation of an optimization problem

19. Canonical formulation of optimization problem
0 or more objectives, inequality constraints, and equality constraints

20. Apply or design an optimization algorithm
that’s appropriate to the optimization problem

21. Convergence of the optimization algorithm
against the objective function (and constraints)
If it doesn’t converge or converge well enough: try new algorithm

22. Design of Tokenized ecosystem
as Design of EAs (Evolutionary Algorithms)
What Tokenized ecosystem Evolutionary Algorithm
Goals Block reward function
E.g. “Maximize hash rate”
Objective function
E.g. “Minimize error”
Measurement
& test
Proof
E.g. “Proof of Work”
Evaluate fitness
E.g. “Simulate circuit”
System agents Miners & token holders (humans)
In a network
Individuals (computer agents)
In a population
System clock Block reward interval Generation
Incentives &
Disincentives
You can’t control human,
Just reward: give tokens
And punish: slash stake
You can’t control individual,
Just reward: reproduce
And punish: kill

23. Design of Tokenized Ecosystems
= Mechanism Design
Analysis: Synthesis:
Game theory Mechanism Design
Optimization Design
Practical
constraints

24. Other labels
for design of tokenized
ecosystems:
Mechanism Design
Tokenomics
Crypto-economics
Financial cryptography
Token engineering
Incentive engineering
Economics
Finance
Electrical
Engineering
Control
systems
Cybernetics
Computer
science
Distributed
systems
Behavioral
psychology
Game
theory
Optimization
Related fields:
Complex
systems
AI

25. Agent-based Systems for Token Simulation
• Q: How do we design computer chips?
• A: Simulator + CAD tools
• Q: How are we currently designing tokenized
ecosystems?
• A: By the seat of our pants!
What we (desperately) need:
1. Simulators: agent-based systems
2. CAD tools: for token design
(Alas, we must be patient…)

26. Application to A Future
Tokenized Ecosystem

27. 1000x more data
The Unreasonable Effectiveness of Data

28. Mo’ data
(and mo’ compute)
Mo’ accuracy
Mo’ $

29. Here’s
your
personal
data

31. A new data economy
Have lotsa AI
(1000 AI startups)
Have lotsa data
(1000 enterprises)
DM DM DM DM
DM DM DM DM
Ocean

32. Ocean goal: maximize supply of relevant data
Token rewards if: supply data, and curate it

33. Economic Incentive for Ocean
Objective: Maximize supply of relevant data
• This means: reward curating data + making it available
• Where “curating” = betting on data. Reward taste-making.
E(Rij) α log10(Sij) * log10(Dj) * T *Ri
Expected
reward for user
i on dataset j
Dj = proofed popularity
= # times made dataset
available
Sij = predicted popularity
= user’s curation market
stake in dataset j
# tokens
during
interval

34. From AI data to AI services
Motivations:
• Privacy, so compute on-premise or decentralized
• Data is heavy, so compute on-premise
• Link in emerging decentralized AI compute
Objective function: Maximize supply of relevant services
=reward curating services + proving that it was delivered
E(Rij) α log10(Sij) * log10(Dj) * T *Ri
proofed popularity
of service
predicted popularity
of service

35. Ocean is a network of curated services. An AI services pipeline.
Availability Consumption Privacy GovernanceProduction
commons
Inter-
Operability
Discovery
*Note: logos shown are examples and do not imply partnerships or integrations

36. Self-driving cars: fewer accidents, more mobility

37. >100x more data for health
care research

38. Erode the data silos

39. Erode the data silos

40. Erode the data silos

41. Why Getting The Objective
Function Right
Really Matters

42. The Paperclip Maximizer (Nick Bostrom, 2003)
Suppose we have an AI whose only goal is to make as many
paper clips as possible. The AI will realize quickly that it would be
much better if there were no humans because humans might
decide to switch it off. Because if humans do so, there would be
fewer paper clips. Also, human bodies contain a lot of atoms that
could be made into paper clips. The future that the AI would be
trying to gear towards would be one in which there were a lot
of paper clips but no humans.

43. The Paperclip Maximizer (Nick Bostrom, 2003)

44. Blockchains as Life (Merkle)
-Ralph Merkle, “DAOs, Democracy and Governance”, May 2016

45. Blockchains as Life (Merkle)
-Ralph Merkle, “DAOs, Democracy and Governance”, May 2016

46. Result of Bitcoin maximizing security?
Maximizing energy usage! > USA by mid 2019!
A life form optimizing maniacally, but for energy
(i.e. the thing we fight wars over)

47. Conclusion

48. Trent McConaghy
@trentmc0
• Blockchains as trust machines
• Blockchains as incentive machines: you can get people to do stuff!
• Case study: Bitcoin
• Towards a token design practice, using optimization & more
• Case Study: Ocean Protocol
• Getting objective function right really matters
Conclusion