Organizational Learning through Digital Consensus, Brown bag seminar, 29 Aug 2022, by Junyi Li

1. Why DAOs: Organizational Learning through Digital
Consensus
Junyi Li
August 29, 2022
Department of Information Systems and Analytics
National University of Singapore

2. Outline
1. Introduction
2. Literature
3. Research Design and Methodology
4. Results
5. Conclusion
1

3. Introduction

4. Context
1. “Decentralized autonomous organization (DAOs)” is a concept that shareholders
within a virtual entity have the right to spend the entity’s funds and modify its
code.
2. DAOs may have different constitutional codes and governance protocols, but they
share the consensus-based collaboration mode.
Figure 1: A Voting Example from MakerDAO
2

5. Examples for Consensus-based Collaboration
1. In political science,1 majority rule is the basis for democratic governance [19].
2. In open-source software development:
• Apache Software Foundation uses voting for specific issues (e.g., procedural, code
modifications, and package releases).
3. In online knowledge community
• Decisions on Wikipedia are primarily made by consensus.
4. In crowdfunding, successful launches of projects also reflect the majority view of
the community.2
1In policy making filed, majority rule may harm the interest of minority groups. Yet, in organizational behavior
field, there is no interest conflict between majority and minority group within the same organization.
2Online communities are self-organized and aim to deepen people’s belongingness, expertise, and happiness,
while traditional organizations are not self-organized, aim to get work done (Learn More). However, DAOs break
down these clear boundaries.
3

6. Motivation
1. Information System view
• How to theorize DAO and its value, since it is a typical digital artifact?
2. Organization Science view
• How about consensus-based organizational form?
3. Taken together
• Can digital technology (DT) improve organizational performance, regarding the
emergence of DAO?
4

7. Literature

8. Literature
DT’s value on organizational performance is a typical debate in IT capability literature
[12, 16, 14]:
1. Economic Perspective: cost-revenue trade-off
2. Response-Lag Drivers Perspective: first-mover advantage, IT resources barrier
[20]
3. Resource-based Perspective: sustainable competitive advantage, inability to
imitate [2, 3, 13, 15]
5

9. Literature
Decentralization vs. centralization is also a typical dispute in organizational design
literature [10, 4, 7, 9]:
1. Hierarchy. Hierarchical structures foster conflict resolution, cross-functional
coordination, and high-level supervision.
2. Autonomy. Autonomous structures foster detailed consideration, internal
motivation, and flat governance.
6

10. Research Design and Methodology

11. Genetic Algorithm
Entities in our genetic algorithm
• Reality: a list of bits, e.g., [1, 1, 1, -1, -1, -1] 3
• Individual: tunes operational beliefs
• Superior: tunes strategical policies
Key operations in genetic algorithms
• Payoff: to what extend the solution is close to the reality. Algo.1
• Local search: randomly select one bit in solution to change
(i.e., if Payoff(a) > Payoff(b) , then moving from status a to b.)
3For instance, the decision is to produce a cup of bubble tea. The reality bit could be {“less sugar”, “middle
size”, “less ice”}. Every bit combination has its payoff (e.g., consumer utility).
7

12. Genetic Algorithm
Simplified calculation for problem dimension m = 6, problem complexity s = 3:
• Suppose the reality is {1, 1, 1, 1, 1, 1}.
• Suppose solution s1 is {1, 1, -1 , 1, 1, -1}. Then its payoff is 3 × (0 + 0) = 0.
• Another solution s2 is {1, 1, 1 , 1, 1, -1}. Then its payoff is 3 × (1 + 0) = 3.
8

13. Genetic Algorithm
Modeling the compliance from the lower level to the upper level:
Figure 2: The Confirmation of Individuals’ Beliefs to the Focal Superior’s Policy
9

14. Genetic Algorithm
Modeling the organizational structure:
Figure 3: Hierarchical (left) versus Consensus-Based (right) Structure
10

15. Genetic Algorithm
The difference between our model and March’s (1991) model [11]
1. Consensus modeling
• Rough consensus [17]
• Majority rule [5]
• Incremental consensus
2. Two complexity levels
• Operational complexity
• Strategic complexity
3. Hierarchy modeling
• Personal and limited strategic
scanning [18]
• Providing both constrain and
guideline
11

16. Results

17. Learning Speed
Figure 4: Organizational Performance across Iterations
12

18. Diversity Dynamics
Figure 5: Pair-wise Belief Diversity Dynamics across Iterations
13

19. Learning Speed
Proposition 1
Under static reality, digital consensus is relatively sticky and resist easy change.
1. It takes more time for digital consensus to get converged.
2. It maintain higher belief diveristy even when the crowd get converged.
3. Therefore, DAOs is likely to over-perform hierarchical structure in a long run.
4. However, with limited decision time and static reality, DAOs is not always a good
idea.
14

20. Organizational Performance and Digital Ecodynamics
Over decades, IS strategy field has been pursuing better understanding about how
organizational performance can be enhanced with the backing of digital technology
(DT) in dynamic environments [8, 1, 15, 6].
1. Problem Complexity (i.e., s and t)
2. Environmental Turbulence
3. Token Inequality
15

21. Problem Complexity
Wisdom of Crowd vs. Wisdom of Superior
Proposition 2.1
With low operational complexity, digital consensus-based organizing over-performs
hierarchy.
Proposition 2.2
With low strategical complexity, hierarchy over-performs digital consensus-based
organizing.
16

22. Environmental Turbulence
Proposition 3
Under environmental turbulence, digital consensus-based organizing is associated
with better performance.
Figure 6: Organizational Performance under Turbulence
17

23. Token Asymmetry
Proposition 4
Token asymmetry undermines DAOs’ capability of reaching broad consensus and is
associated with lower performance.
Figure 7: Pair-Wise Belief Diversity across Iterations 18

24. Robustness
1. Using different kinds of payoff functions to model market natures
• Winner-take-all (default)
• Penalty on opposite beliefs
• Multi-winner
2. Changing group size
• There is a critical size such that crowd larger than this size will no longer benefit
from increased diversity and parallel search capability.
3. Changing the problem dimension m.
4. Changing the belief integration rule
• Majority rule (default)
• Tournament rule, only learning from top N agents.
• Inclusiveism rule, resting on 0 unless the portion of dominant element can go over a
predetermined threshold.
Parameters
19

25. Robustness
5. Changing the authority degree
• Absolute power (default) (e.g.,authority = 1.0)
• Benevolent dictatorship (e.g.,authority = 0.4)
• Nominal power (e.g.,authority = 0.05)
6. Changing the digital ecodynamics
• Pareto distribution (default) vs. normal distribution regarding token inequality
• Frequency and intensity regarding the environmental turbulence
7. The validity of hierarchical search
• Organizational performance under 1) high strategical complexity and 2) random
strategic guess
Parameters
20

26. Conclusion

27. Conclusion
Uniqueness Resource Flexibility Resource Perspective 4
1. No one-size-fit-all
• The wisdom of crowd could become the stupidity of masses.
2. Digital consensus has its flexibility advantage under digital ecodynamics.
3. Such an advantage could be impaired by token asymmetry.
4Resources are defined broadly, including physical assets, knowledge, capability, and organizational processes.
21

28. Questions?
Outline

29. Back-ups

30. Differences between DAOs and Traditional Organizations
DAOs
1. Usually flat, and fully democratized.
2. Voting required by members for any
changes to be implemented.
3. Votes tallied, and outcome
implemented automatically without
trusted intermediary.
4. All activity is transparent and fully
public.
Traditional Organization
1. Depending on hierarchical structure,
changes can be demanded from a sole
party, or voting may be offered.
2. If voting allowed, votes are tallied
internally, and outcome of voting must
be handled manually.
3. Requires human handling, or centrally
controlled automation, prone to
manipulation.
4. Activity is typically private.

31. Genetic Algorithm: Payoff
The literature flow of payoff function:
• m payoff function in March (1991)’s work [11]
Φ(x) =
m
X
j=1
δj (1)
where δj = 1 if xj corresponds with reality on dimension j; δj = 0 otherwise.
• m/s payoff function in Fang (2010)’s work [7]
Φ(x) = s(
s
Y
j=1
δj +
2s
Y
j=s+1
δj + · · · +
m
Y
j=m−s+1
δj) (2)
where s adjusts the complexity degree and 1 ≤ s ≤ m.

32. Genetic Algorithm: Payoff
The m/s/t payoff function in this work: real belief have a complexity degree of s, while
real policy has a complexity degree of t. Each payoff follows the same formula mode.
Φ1(x) = s(
s
Y
j=1
δj +
2s
Y
j=s+1
δj + · · · +
m
Y
j=m−s+1
δj) (3)
Φ2(x) = t(
t
Y
j=1
δj +
2t
Y
j=s+1
δj + · · · +
m//s
Y
j=m−s+1
δj) (4)

33. Pareto Distribution and Wealth Inequality
If x is a random variable with a Pareto distribution, then the probability that x is
greater than some value xm is given by:
F(x) = Pr(x > xm) =



(xm
x )α x ⩾ xm
1, x ⩽ xm
(5)
where xm is the minimum possible value of x, and α is a shape parameter.
This distribution is typically used to describe the wealth inequality among individuals
such that a large portion of wealth is owned by a small group of people.

34. Theory Boundary
Basically, one key premise is that block chain can serve as a robust digital
infrastructure.
1. This study doesn’t involve the conflicts coming from the physical-digital interface
(e.g., product/service delivery).
2. This study doesn’t involve algorithmic security issue (e.g., hacker attack) and its
consequence.
3. This study doesn’t involve market inefficiency issue (e.g., turbulence in computing
power supply).

35. Model Parameters
Parameters Remarks Range of parameters
m Problem dimension 60, 90, 120
s Operational complexity of the search problem 1, 2, 3, 4, 5
t Strategic complexity of the search problem 1, 2, 3, 4, 5
n Number of individuals in the organization 100, 200, 300, 400
authority Authority degree of superior 0.2, 0.5, 1.0
asymmetry Wealth or token inequality degree 1, 2, 4, 8
Fenvir The frequency of environmental turbulence 50, 100, 200
Penvir The proportion of environmental turbulence 10, 20, 50, 100

36. References

37. References i
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38. References ii
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39. References iii
[5] P. Chen and S. Redner.
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40. References iv
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41. References v
[11] James G March.
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42. References vi
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43. References vii
[17] Andrew L. Russell.
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War.
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[18] Omar A. El Sawy.
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MIS Quarterly, 9(1):53, March 1985.
[19] Amartya K. Sen.
A Possibility Theorem on Majority Decisions.
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44. References viii
[20] Wade and Hulland.
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