This slide deck is from my talk at the Radical Market's session at CryptoEconomics Security Conference as part of San Francisco Blockchain week in 2018.

1. Engineering, Analysis and Ethics
SFBW: Radical Markets Sessions
Oct 10, 2018
Michael Zargham
@mZargham
Founder & CEO
BlockScience

2. Create interconnected
Communities
of autonomous local actors
within which
global coordination
is
enabled by technology

3. Stateful Economic Systems are Complex
Temporal Dynamics & Closure under Behavioral Adaptation
Tools, Skills, and Domains of knowledge are required for design and
analysis come from a diverse set of fields

4. Systems,
Diverse Domains
& Data

5. Enables

6. Interconnectedness of Technology and Behavior
Interaction Patterns &
Local Behavior of Agents
Software Enforcing low level
immutable “information provenance”
Peer to Peer communication and
computation network
Enables
Requires
Emergent
Global Properties

7. Engineering
(Synthesis)
(Science)
Analysis
&

8. Systems Engineering & Data Science
•Knowledge of System Synthesis “Engineering Design”
and Systems Analysis “Empirical Testing”
•Blockchains serve as high grade sensors and smart
contracts as high precision actuators
•Thus making proper economic system design and
testing mathematically consistent with decentralized
coordination in multi-agent robotics
•As in robotics it is nearly impossible to be expert in
Synthesis or Analysis without complementary expertise
Analysis
Synthesis

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10. Closing the Gap: Generalized Differential Equations
Formalization of design space allows explicit consideration of
both Objectives and Constraints under temporal dynamics and uncertainty
Designed System
(states and mechanism)
Unknown User Strategies
(behavioral models)
ActionsLogic is Known
Prior State Posterior State
System Goals met?
trajectories attracted to
objectives?
system safety: bounded
worst case scenarios?
edge cases ?

11. Modeling & Testing Systems with Agents in the Loop

12. Dev
&
Deploy
Theory
Empiricism Simulation
Scientific Validation and Iteration of Systems

13. Conventional Machine Learning Methods (“Data Science”)
Strengths Weaknesses
Large Pool of easy to apply Model Free Techniques with high
grade open source implementations
Requires Far Less domain Experience to apply
Requires only practical understanding of how algorithms work
Easy to Automate and/or Productize
Resulting models Not Generally Interpretable: “black box”
Requires more, richer data to be useful
Cannot infer events not observed in the passed data
Cannot handle non-periodic temporal dynamics

14. System Model Based Analytics (“System Identification”)
Strengths Weaknesses
Systems with Closure over unknown or noisy subsystems
Can handle Systems with Temporal Dynamics
Results are much more interpretable
Equipped with engineering techniques for modifying systems
to achieve preferred emergent behavior
Requires structured system models
Proper models require more advanced mathematical skills
Proper models require more domain expertise over systems
More Human work overhead to apply properly
Note: ALL “Smart Contract” based economic systems have well defined “system models”
because they characterized by the same elements: (states, action spaces & actions).
Challenge: Only Plant Model is known — Action Policy Models are Unknown

15. Integrated Data Science & Systems Solutions
Generalized System Models can be defined and Auto-regressive techniques can help machine
learn the missing pieces while respecting the models of the known components
Designed System
(states and mechanism)
Unknown User Strategies
(estimated using data)
ActionsLogic is Known
Prior State Posterior State
Auto-Regressive Models
Conventional ML Models
Logic Inside?

16. System Properties: Sensitivity & Stability
Sensitivity Analysis System Identification
Cannot Assume that passed stability implies future stability
Therefore cannot rely solely on passed data to characterize
Non-Ergodicity:
Example from Matlab System Identification Tool Kit
Explanation from Wikipedia

17. Engineering =
{
Capability
Responsibility

18. Responsibility to Be Intentional in our contributions
Decentralized Systems
-Cannot be un-deployed
-Can change the world for better or worse

20. Mindful of “Subjective” Choice of “Objective” Measures
Systems
Choice of Measure is inseparable
from the system itself and its outcomes
?

21. Individuals and Societies Experience Risk Differently
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N.N. Taleb: https://medium.com/incerto/the-logic-of-risk-taking-107bf41029d3

22. Evaluate Fairness of Algorithms ~ Not Outcomes
Abstraction allows design from behind the “Veil of Ignorance”

23. Ethics is Baked into the Engineering Profession
Institute of Electrical and Electronics Engineers: "We, the
members of the IEEE, … do hereby commit ourselves to the
highest ethical and professional conduct and agree:
to accept responsibility in making decisions consistent with
the safety, health and welfare of the public,
and to disclose promptly factors that might endanger the public or
the environment;"[24]

25. Protocol Governance as Adaptive Maintenance
Changing Environment Changing ProtocolsChanging Needs
Changing Behavior Changing Norms

27. Formally Relate Preferences to Choices in General Form
Can incorporate Quadratic Cost
(or more general convex costs here)
Be Intentional about “Subjective”
choice of “Objective” Function

29. Thank you
Web page: https://block.science/
Twitter: @mZargham