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Trent McConaghy
@trentmc0
Ocean | BigchainDB
Tokens,
Complex Systems,
and Nature
On the internet,
no one knows
you’re a dog(e)
On the internet of things,
nobody knows you’re a
toaster
But what is this?
Robot? Plant?
What do you call
a forest that
owns itself?
Can a wind farm own itself?
How?
Blockchain
(and ways to frame it)
“A 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
..
“Database with blue ocean benefits”
• Decentralized
• Immutable
• Assets
STORE OF VALUE
Bitcoin, zcash
FILE SYSTEM
IPFS/FileCoin, Swarm
DATABASE
BigchainDB, OrbitDB
BIZ LOGIC
Ethereum, Dfinity
HIGH PERF.
COMPUTE
TrueBit, Golem, iExec
STATE
PolkaDot
DATA
TCP/IP
VALUE
Interledger,
Cosmos
COMMUNICATIONSPROCESSINGSTORAGE
“Emerging Decentralized Stack”
“Trust machine”
because it minimizes
trust needed to
operate.
It’s more socially
scalable. (Ref Szabos)
Bitcoin incentivizes security = hash rate = electricity
Result: > USA by mid 2019!
Get people to do stuff
By rewarding with tokens
“Incentive Machine”
“Public Utility Network”
Self-sustaining, anti-fragile
A computational process that
• runs autonomously,
• on decentralized infrastructure,
• with resource manipulation.
“DAO: Decentralized Autonomous Organization”
It’s code that can own stuff!
Aka “good computer virus”
“Life Form”
-Ralph Merkle
“Bitcoin is the first example of a new form of life.”
“It lives and breathes on the internet. It lives because it can pay
people to keep it alive. It lives because it performs a useful
service that people will pay it to perform. … It can’t be stopped.
It can’t even be interrupted. If nuclear war destroyed half of
our planet, it would continue to live, uncorrupted.”
AI
(and ways to frame it)
“Replicates human cognitive
behavior” [Turing test]
“Can do tasks that only a
human could previously do”
“Can do a task at
speed/ accuracy/
capacity not
possible by a
human.”
“A set of tools”
“Sufficiently a mystery,
Not yet a technology”
• Classification
• Regression
• Knowledge extraction
• Optimization
• Creative / Structural design
• …
“Embodied agents” (AGI)
Evolutionary Algorithms
& Token Design
Token design is hard. Easy to flail. Easy to fail.
Realization: Tokenized Ecosystems
Are a Lot Like 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
We can approach token design
as EA design.
Steps in EA Design
1. Formulate the problem. Objectives,
constraints, design space.
2. Try an existing EA solver. If needed, try
different problem formulations or solvers.
3. Design new solver?
1. Formulation of optimization problem
Objectives & constraints in a design space
2. Try an existing EA solver. Does it converge?
3. Design new EA solver
Example of a Successful Outcome
Steps in Token Ecosystem Design
1. Formulate the problem. Objectives,
constraints, design space.
2. Try an existing building block. If needed, try
different formulations or EA solvers.
3. Design new building block?
1. Formulate the Problem: [ex. Ocean]
Obj:
• Maximize supply of relevant data
Constraints = checklist:
• For priced data, is there incentive for
supplying more? Referring? Spam
prevention?
• For free data, “” ?
• Does the token give higher marginal
value to users vs. hodlers?
• Are people incentivized to run keepers?
• Is it simple? Is onboarding low-friction?
Who are stakeholders?
What do they want?
Objectives &
constraints
2. Try Existing Patterns
1. Curation
2. Proofs of human or compute work
3. Identity
4. Reputation
5. Governance / software updates
6. Third-party arbitration
7. …
2.1 Patterns for Curation
•Binary membership: Token Curated Registry (TCR)
•Discrete-valued membership: Layered TCR (like ALPS!)
•Continuous-valued membership: Curation Markets
•Hierarchical membership: each label gets a TCR
•Work tied to membership: Curated Proofs Market
Key Question 1 2 3 4 5
For priced data: incentive for supplying more? Referring? ✖ ≈ ✔ ≈ ≈
For priced data: good spam prevention? ≈ ✔ ✔ ✔ ✔
For free data: incentive for supplying more? Referring? ✖ ≈ ✖ ✔ ✔
For free data: good spam prevention? ≈ ✔ ≈ ✔ ≈
Does token give higher marginal value to users of the
network, vs external investors? Eg Does return on capital
increase as stake increases?
✔ ✔ ✔ ✔ ✔
Are people incentivized to run keepers? ≈ ≈ ✔ ✔ ✔
It simple? Is onboarding low-friction? Where possible, do we
use incentives/crypto rather than legal recourse?
✔ ✔ ≈ ≈ ✔
2. Try existing patterns: evaluate on objectives &
constraints. [Ex Ocean: None passed…]
Key Question 1 2 3 4 5 6
For priced data: incentive for supplying more? Referring? ✖ ≈ ✔ ≈ ≈ ✔
For priced data: good spam prevention? ≈ ✔ ✔ ✔ ✔ ✔
For free data: incentive for supplying more? Referring? ✖ ≈ ✖ ✔ ✔ ✔
For free data: good spam prevention? ≈ ✔ ≈ ✔ ≈ ✔
Does token give higher marginal value to users of the
network, vs external investors? Eg Does return on capital
increase as stake increases?
✔ ✔ ✔ ✔ ✔ ✔
Are people incentivized to run keepers? ≈ ≈ ✔ ✔ ✔ ✔
It simple? Is onboarding low-friction? Where possible, do we
use incentives/crypto rather than legal recourse?
✔ ✔ ≈ ≈ ✔ ✔
3. Try new patterns: evaluate on objectives &
constraints. [Ex Ocean: pass!]
Simulation of Tokenized Ecosystems?
• Q: How do we design computer chips? ($50M+ at stake)
• A: Simulator + CAD tools
• Q: How are we currently designing tokenized
ecosystems? ($1B+ at stake)
• A: By the seat of our pants!
• Which means we might be getting it all wrong!
What we (desperately) need:
1. Simulators: agent-based systems [Incentivai, ..]
2. CAD tools: for token design
Design of Tokenized Ecosystems
From Mechanism Design to Token Engineering
Analysis: Synthesis:
Game theory Mechanism Design
Optimization Design
Practical
constraints
Engineering theory,
practice and tools
+ responsibility
Token Engineering for Analysis & Synthesis
AI * Blockchain:
AI DAOs
“An AI running on decentralized processing substrate”
<or>
“A DAO running with AI algorithms”
Definition of AI DAO
The ArtDAO
1. Run AI art engine to generate new image,
using GP or deep learning
2. Sell image on a marketplace, for crypto.
3. Repeat!
1. Run AI art engine to generate new image,
using GP or deep learning
2. Sell image on a marketplace, for crypto.
3. Repeat!
<Over time, it accumulates wealth, for itself.>
The ArtDAO
1. Run AI art engine to generate new image, using
GP or deep learning
2. Sell image on a marketplace, for crypto.
3. Repeat!
<Over time, it accumulates wealth, for itself.>
<It could even self-adapt: genetic programming>
The ArtDAO
AI DAO Arch 1: AI at the Center
AI DAO Arch 2: AI at the Edges
AI DAO Arch 3: Swarm Intelligence
Many dumb agents with emergent AI complexity
Angles to Making AI DAOs
• DAO → AI DAO. Start with DAO, add AI.
• AI → AI DAO. Start with AI, add DAO.
• SaaS → DAO → AI DAO. SaaS to DAO, add AI
• Physical service → AI DAO
AI DAOs
When Moon
🚀🚀
Evolving the ArtDAO
Market
Art work
Code
Level to adapt at
How to
adapt
Human-based adapt at the code level.
Here, humans put in new smart contract
code (and related code in 3rd party
services), to improve ArtDAO’s ability to
generate art and amass wealth.
Evolving the ArtDAO
Market
Art work
Code
Level to adapt at
How to
adapt
Auto adapt at the market level.
It creates more of what humans buy, and
less of what humans don’t buy.
Evolving the ArtDAO
Market
Art work
Code
Level to adapt at
How to
adapt
Auto adapt at the art-work level.
Here, a human influences the creation of an
artifact. For example, it presents four variants of
a work, and a human clicks on a favorite. After
10 or 50 iterations, it will have a piece that the
human likes, and purchases.
Evolving the ArtDAO
Auto adapt at the code level.
Here, the ArtDAO modifies its own code, in hopes of improving.
• It creates a copy of itself, changes that copy’s code just a little bit, and gives a tiny bit of
resources to that new copy.
• If that new copy is bad, it will simply run out of resources and be ignored.
• But if that new copy is truly an improvement, the market will reward it, and it will be able
to amass resources and split more on its own.
• Over time, ArtDAO will spawn more children, and grandchildren, and the ones that do well
will continue to spread. We end up with a mini-army of AI DAOs for art.
• If buyers are DAOs too, it’s a network of DAOs, leading to swarm intelligence
Market
Art work
Code
Level to adapt at
How to
adapt
Giving Personhood to an AI DAO
With Today’s Laws (!)
Self-driving,
self-owning cars
Self-driving,
self-owning trucks
Self-owning
roads
Self-owning
wind farms
Self-owning
grid
Machines → Nature:
“Plantoid”
Nature → Machines:
Self-owning forest “Terra0”
Ever-higher levels of integration
From beasts → ecosystems
Connected via IoT / M2M
Nature 2.0?
Bio + machines
in symbiosis, evolving.
From “simulated evolution”
To simply “evolution” ?
Conclusion
In Nature 2.0,
no one knows
you’re a forest
In Nature 2.0,
no one knows
you’re a grid
Nature is the ultimate complex system.
Nature 1.0 is seeds & soil. Evolving.
Nature 2.0 adds silicon & steel. Evolving.
@trentmc0Trent McConaghy h/t Jan-Peter Doomernik

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Tokens, Complex Systems, and Nature

  • 1. Trent McConaghy @trentmc0 Ocean | BigchainDB Tokens, Complex Systems, and Nature
  • 2. On the internet, no one knows you’re a dog(e)
  • 3. On the internet of things, nobody knows you’re a toaster
  • 4. But what is this? Robot? Plant?
  • 5. What do you call a forest that owns itself?
  • 6. Can a wind farm own itself? How?
  • 8. “A 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 ..
  • 9. “Database with blue ocean benefits” • Decentralized • Immutable • Assets
  • 10. STORE OF VALUE Bitcoin, zcash FILE SYSTEM IPFS/FileCoin, Swarm DATABASE BigchainDB, OrbitDB BIZ LOGIC Ethereum, Dfinity HIGH PERF. COMPUTE TrueBit, Golem, iExec STATE PolkaDot DATA TCP/IP VALUE Interledger, Cosmos COMMUNICATIONSPROCESSINGSTORAGE “Emerging Decentralized Stack”
  • 11. “Trust machine” because it minimizes trust needed to operate. It’s more socially scalable. (Ref Szabos)
  • 12. Bitcoin incentivizes security = hash rate = electricity Result: > USA by mid 2019! Get people to do stuff By rewarding with tokens “Incentive Machine”
  • 14. A computational process that • runs autonomously, • on decentralized infrastructure, • with resource manipulation. “DAO: Decentralized Autonomous Organization” It’s code that can own stuff! Aka “good computer virus”
  • 15. “Life Form” -Ralph Merkle “Bitcoin is the first example of a new form of life.” “It lives and breathes on the internet. It lives because it can pay people to keep it alive. It lives because it performs a useful service that people will pay it to perform. … It can’t be stopped. It can’t even be interrupted. If nuclear war destroyed half of our planet, it would continue to live, uncorrupted.”
  • 16. AI (and ways to frame it)
  • 18. “Can do tasks that only a human could previously do”
  • 19. “Can do a task at speed/ accuracy/ capacity not possible by a human.”
  • 20. “A set of tools” “Sufficiently a mystery, Not yet a technology” • Classification • Regression • Knowledge extraction • Optimization • Creative / Structural design • …
  • 23. Token design is hard. Easy to flail. Easy to fail.
  • 24. Realization: Tokenized Ecosystems Are a Lot Like 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
  • 25. We can approach token design as EA design.
  • 26. Steps in EA Design 1. Formulate the problem. Objectives, constraints, design space. 2. Try an existing EA solver. If needed, try different problem formulations or solvers. 3. Design new solver?
  • 27. 1. Formulation of optimization problem Objectives & constraints in a design space
  • 28. 2. Try an existing EA solver. Does it converge?
  • 29. 3. Design new EA solver
  • 30. Example of a Successful Outcome
  • 31. Steps in Token Ecosystem Design 1. Formulate the problem. Objectives, constraints, design space. 2. Try an existing building block. If needed, try different formulations or EA solvers. 3. Design new building block?
  • 32. 1. Formulate the Problem: [ex. Ocean] Obj: • Maximize supply of relevant data Constraints = checklist: • For priced data, is there incentive for supplying more? Referring? Spam prevention? • For free data, “” ? • Does the token give higher marginal value to users vs. hodlers? • Are people incentivized to run keepers? • Is it simple? Is onboarding low-friction? Who are stakeholders? What do they want? Objectives & constraints
  • 33. 2. Try Existing Patterns 1. Curation 2. Proofs of human or compute work 3. Identity 4. Reputation 5. Governance / software updates 6. Third-party arbitration 7. …
  • 34. 2.1 Patterns for Curation •Binary membership: Token Curated Registry (TCR) •Discrete-valued membership: Layered TCR (like ALPS!) •Continuous-valued membership: Curation Markets •Hierarchical membership: each label gets a TCR •Work tied to membership: Curated Proofs Market
  • 35. Key Question 1 2 3 4 5 For priced data: incentive for supplying more? Referring? ✖ ≈ ✔ ≈ ≈ For priced data: good spam prevention? ≈ ✔ ✔ ✔ ✔ For free data: incentive for supplying more? Referring? ✖ ≈ ✖ ✔ ✔ For free data: good spam prevention? ≈ ✔ ≈ ✔ ≈ Does token give higher marginal value to users of the network, vs external investors? Eg Does return on capital increase as stake increases? ✔ ✔ ✔ ✔ ✔ Are people incentivized to run keepers? ≈ ≈ ✔ ✔ ✔ It simple? Is onboarding low-friction? Where possible, do we use incentives/crypto rather than legal recourse? ✔ ✔ ≈ ≈ ✔ 2. Try existing patterns: evaluate on objectives & constraints. [Ex Ocean: None passed…]
  • 36. Key Question 1 2 3 4 5 6 For priced data: incentive for supplying more? Referring? ✖ ≈ ✔ ≈ ≈ ✔ For priced data: good spam prevention? ≈ ✔ ✔ ✔ ✔ ✔ For free data: incentive for supplying more? Referring? ✖ ≈ ✖ ✔ ✔ ✔ For free data: good spam prevention? ≈ ✔ ≈ ✔ ≈ ✔ Does token give higher marginal value to users of the network, vs external investors? Eg Does return on capital increase as stake increases? ✔ ✔ ✔ ✔ ✔ ✔ Are people incentivized to run keepers? ≈ ≈ ✔ ✔ ✔ ✔ It simple? Is onboarding low-friction? Where possible, do we use incentives/crypto rather than legal recourse? ✔ ✔ ≈ ≈ ✔ ✔ 3. Try new patterns: evaluate on objectives & constraints. [Ex Ocean: pass!]
  • 37. Simulation of Tokenized Ecosystems? • Q: How do we design computer chips? ($50M+ at stake) • A: Simulator + CAD tools • Q: How are we currently designing tokenized ecosystems? ($1B+ at stake) • A: By the seat of our pants! • Which means we might be getting it all wrong! What we (desperately) need: 1. Simulators: agent-based systems [Incentivai, ..] 2. CAD tools: for token design
  • 38. Design of Tokenized Ecosystems From Mechanism Design to Token Engineering Analysis: Synthesis: Game theory Mechanism Design Optimization Design Practical constraints Engineering theory, practice and tools + responsibility Token Engineering for Analysis & Synthesis
  • 40. “An AI running on decentralized processing substrate” <or> “A DAO running with AI algorithms” Definition of AI DAO
  • 41. The ArtDAO 1. Run AI art engine to generate new image, using GP or deep learning 2. Sell image on a marketplace, for crypto. 3. Repeat!
  • 42. 1. Run AI art engine to generate new image, using GP or deep learning 2. Sell image on a marketplace, for crypto. 3. Repeat! <Over time, it accumulates wealth, for itself.> The ArtDAO
  • 43. 1. Run AI art engine to generate new image, using GP or deep learning 2. Sell image on a marketplace, for crypto. 3. Repeat! <Over time, it accumulates wealth, for itself.> <It could even self-adapt: genetic programming> The ArtDAO
  • 44. AI DAO Arch 1: AI at the Center
  • 45. AI DAO Arch 2: AI at the Edges
  • 46. AI DAO Arch 3: Swarm Intelligence Many dumb agents with emergent AI complexity
  • 47. Angles to Making AI DAOs • DAO → AI DAO. Start with DAO, add AI. • AI → AI DAO. Start with AI, add DAO. • SaaS → DAO → AI DAO. SaaS to DAO, add AI • Physical service → AI DAO
  • 49. Evolving the ArtDAO Market Art work Code Level to adapt at How to adapt Human-based adapt at the code level. Here, humans put in new smart contract code (and related code in 3rd party services), to improve ArtDAO’s ability to generate art and amass wealth.
  • 50. Evolving the ArtDAO Market Art work Code Level to adapt at How to adapt Auto adapt at the market level. It creates more of what humans buy, and less of what humans don’t buy.
  • 51. Evolving the ArtDAO Market Art work Code Level to adapt at How to adapt Auto adapt at the art-work level. Here, a human influences the creation of an artifact. For example, it presents four variants of a work, and a human clicks on a favorite. After 10 or 50 iterations, it will have a piece that the human likes, and purchases.
  • 52. Evolving the ArtDAO Auto adapt at the code level. Here, the ArtDAO modifies its own code, in hopes of improving. • It creates a copy of itself, changes that copy’s code just a little bit, and gives a tiny bit of resources to that new copy. • If that new copy is bad, it will simply run out of resources and be ignored. • But if that new copy is truly an improvement, the market will reward it, and it will be able to amass resources and split more on its own. • Over time, ArtDAO will spawn more children, and grandchildren, and the ones that do well will continue to spread. We end up with a mini-army of AI DAOs for art. • If buyers are DAOs too, it’s a network of DAOs, leading to swarm intelligence Market Art work Code Level to adapt at How to adapt
  • 53. Giving Personhood to an AI DAO With Today’s Laws (!)
  • 60. Nature → Machines: Self-owning forest “Terra0”
  • 61. Ever-higher levels of integration From beasts → ecosystems Connected via IoT / M2M
  • 62. Nature 2.0? Bio + machines in symbiosis, evolving. From “simulated evolution” To simply “evolution” ?
  • 64. In Nature 2.0, no one knows you’re a forest
  • 65. In Nature 2.0, no one knows you’re a grid
  • 66. Nature is the ultimate complex system. Nature 1.0 is seeds & soil. Evolving. Nature 2.0 adds silicon & steel. Evolving. @trentmc0Trent McConaghy h/t Jan-Peter Doomernik