SlideShare a Scribd company logo
Meta-Organizational Learning Through Digital Consensus
Junyi Li1
, Jungpil Hahn1
, Giri Kumar Tayi2
July 19, 2023
1Department of Information Systems and Analytics, National University of Singapore
2School of Business, State University of New York at Albany
Outline
1. Introduction
2. Computational Model
3. Experiments & Results
4. Conclusion
1
Introduction
Emergence of DAOs
• Decentralized Autonomous Organizations (DAOs): making organizational decision
by token-based voting.
• Meta-Oganization: legally autonomous entities [2]
2
Paradoxes in Organizational Design
1. Will DAOs (digital consensus) be an effective mode of decentralized governance?
2. Traditional paradoxes:
• Control vs. Flexibility [3]
• Centralization vs. Decentralization [1]
• Authority vs. Self-Organizing [7]
3
Research Question
How will digital consensus affect organizational design?
1. In what ways does consensus-based governance differ from a traditional one,
namely autonomy, and/or hierarchy (what is)?
2. Under what circumstances does consensus-based governance outperform other
modes (what might be)?
3. What is the underlying mechanism accounting for any performance differences
(how)?
4
Computational Model
March’s Organization†
†March, J. G. (1991). Exploration and Exploitation in Organizational Learning. Organization Science, 2(1),
71–87. 5
Basic Setup: Entities
1. Reality
• m-bits string of 1 and -1. e.g., ⟨1, 1, 1, −1, −1, −1⟩
• Policy: (m/α)-bits aggregated summary of reality
• e.g., Reality: ⟨1, 1, 1, −1, −1, 1⟩
α=3
−
−
−
→ Correct Policy: ⟨1, −1⟩
• α: do managers want to control every detail of their team members’ decisions?
• Policy: 1
α=3
−
−
−
→ Solution: ⟨1, 1, −1⟩
2. Organization
• n individuals divided into groups of size z
• n′
managers (hierarchy); one manager supervise one group
3. Solution
• individual solution is m-bits string corresponding to reality
• manager solution is (m/α)-bits corresponding to policy reality
6
Autonomy
• Individuals learn from higher performing connected peers with probability η
7
Hierarchy
• Individuals learn from higher performing connected peers with probability η
• Top-Down Coordination: individuals’ solutions must be coherent with managers’
policy
8
DAO
• Individuals learn from higher performing connected peers with probability η
• Bottom-Up Coordination: individuals’ solutions must be coherent with policy
consensus
9
Experiments & Results
Experiment 1: Baseline
• Static Reality
• Best Hierarchies as in March’s
model [4]
• Naı̈ve DAOs
• simple majority rule
• uniform token distribution
• full engagement
10
Experiment 1: Baseline
Investigating performance variance adds insights.
• DAOs evolve through a staggered process of polarization and homogenization, as
opposed to autonomies’ continuous polarization and hierarchies’ continuous
homogenization. (What is & How?)
11
Experiment 2: Turbulence
The performance reversal delays as environments become less turbulent (more static).
“Turbulent” ← → “Static”
12
Summary of Baseline
Proposition 1a
In static environments, hierarchies outperform DAOs, and DAOs outperform autonomies.
Proposition 1b
In turbulent environments, DAOs tend to outperform hierarchies and autonomies.
13
Summary of Extensions
Proposition 2
Voting threshold has an inverted U-shaped impact on DAOs’ performance.
Proposition 3
Token asymmetry has a negative impact on DAOs’ performance.
Proposition 4a
Contributor incentive has a positive impact on DAOs’ performance when the inactive
rate is high.
Proposition 4b
Contributor incentive has a negative impact on DAOs’ performance when the inactive
rate is low.
14
Conclusion
Key Takeaways
? Power A vs. Power B
✓ Polarization vs. Homogenization
? Optimize DAOs’ design
✓ By voting threshold, token asymmetry, and contributor incentive
15
Thank you!
Back-Ups
Socialization Tension
• Socialization tension: the self-confined difficulty of accepting any local belief as a
global consensus
Digital Consensus
Digital consensus herein refers to algorithmic coordination rules that are automatically
executed in distributed computer systems and are only modifiable via voting.
• Transparent
• Democratically editable
• Self-executing
March’s Organization Model
Genetic Algorithm
m payoff function in March’s (1991) model [4]:
Φ(x) =
m
X
j=1
δj (1)
where δj = 1 if xj corresponds with reality on dimension j; δj = 0 otherwise.
Belief diversity is measured as follows:
Belief Diversity =
2
mn(n − 1)
1
2
n(n−1)
X
i=1
m
X
j=1
ωij (2)
where ωij = 1 if two chosen individuals in the pair i have different beliefs on dimension
j; ωij = 0 otherwise.
Model Parameters
Parameter Remark Range of parameter Default
m Problem dimension 60, 90, 120, 150 90
n Number of individuals in the organization 280, 350, 420, 490 350
z The group size of the autonomous group 7, 14, 28 7
α The aggregation degree 1, 3, 5 3
η The learning rate of individuals 0.1 ∼ 0.9 0.3
p1 The probability of learning from the code (Hierarchy) 0.1 ∼ 0.9 0.1
p2 The probability of learning by the code (Hierarchy) 0.1 ∼ 0.9 0.9
p3 The probability of individual reconfiguration 0 ∼ 0.5 0
n′ The number of managers (Hierarchy) 40, 50, 60, 70 50
θ The voting threshold to pass a protocol as consensus 0.4 ∼ 0.7 0.5
Ttb The period of environmental turbulence 20 ∼ 100 -
Itb The intensity of environmental turbulence 0, 10%, 20% 0
ρ The shape parameter in Pareto distribution 1, 2, 3 -
γ Incentive degree & reactivation rate 0 ∼ 0.9 0
pi The probability of individuals being inactive in voting 0 ∼ 0.9 0
Repetition The number of simulation runs 500 500
Search Round The iteration of search in one simulation run 300, 500, 1000, 2000 -
Notes: Search rounds are verified by pilot tests. The default values correspond to the usage in the literature.
Baseline & Turbulence: Conclusion
Proposition 1a
In static environments, hierarchies outperform DAOs, and DAOs outperform
autonomies.
Proposition 1b
In turbulent environments, DAOs tend to outperform hierarchies and autonomies.
Experiment 3: Voting Threshold
• Voting threshold has an inverted U-shaped effect on DAOs’ performance.
Experiment 3: Voting Threshold
• Staggered process: polarization before consensus, homogenization after consensus.
• The staggered process can be adjusted by θ.
Experiment 4: Token Asymmetry
• Asymmetry restrains the polarization before consensus formation.
• Causing homogenization into minority views.
Experiment 5: Contributor Incentive
• Due to the intense reward, there will be additional asymmetry, resulting in worse
performance (–).
• Due to reactivation, incentives are beneficial when individuals rarely vote (+).
Interaction Effect: Thresholds * Asymmetry
With more asymmetry, optimal thresholds will increase.
Interaction Effect: Thresholds * Inactivity
With more inactivity, optimal thresholds will decrease.
Sensitivity Analysis
• We conduct sensitivity analyses for non-focus parameters, including the group size
z, the number of individuals n, the number of managers n′, the aggregation level
α, the learning rate η, and the reality dimension m. Qualitative results are
consistent across these parameter values.
• A variety of hierarchies with varying supervision efficiencies are also examined. In
particular, the hierarchies’ performance will linearly decrease with increasing p1 or
decreasing p2, consistent with March’s (1991) model. This means naı̈ve DAOs
certainly outperform some deficient hierarchies.
• With individual reconfiguration rate p3 [8], the performance of DAOs and
hierarchies largely restore to static levels. Yet, autonomies may outperform when
p3 > 0.2, resonating with the literature that autonomous teams excel in intensive
organizational experimentation [5, 6].
Robustness: Supervision (i)
• p1: learning from code, p2: learning by code
• Left side: p2 = 0.9, right side: p1 = 0.1
• These two figures represent two specific segment lines across the entire 3D surface
(p1, p2, Z)
Robustness: Supervision (ii)
Robustness: Learning Rate
Notes: by default η = 0.3
References
References i
[1] Christina Fang, Jeho Lee, and Melissa A. Schilling.
Balancing Exploration and Exploitation Through Structural Design: The
Isolation of Subgroups and Organizational Learning.
Organization Science, 21(3):625–642, June 2010.
[2] Ranjay Gulati, Phanish Puranam, and Michael Tushman.
Meta-Organization Design: Rethinking Design in Interorganizational and
Community Contexts.
Strategic Management Journal, 33(6):571–586, 2012.
References ii
[3] Daniel A. Levinthal and Maciej Workiewicz.
When Two Bosses Are Better Than One: Nearly Decomposable Systems
and Organizational Adaptation.
Organization Science, 29(2):207–224, 2018.
[4] James G. March.
Exploration and Exploitation in Organizational Learning.
Organization Science, 2(1):71–87, February 1991.
[5] Peerasit Patanakul, Jiyao Chen, and Gary S. Lynn.
Autonomous Teams and New Product Development.
Journal of Product Innovation Management, 29(5):734–750, 2012.
References iii
[6] Phanish Puranam, Harbir Singh, and Maurizio Zollo.
Organizing for Innovation: Managing the Coordination-Autonomy Dilemma
in Technology Acquisitions.
Academy of Management Journal, 49(2):263–280, April 2006.
[7] Marlo Raveendran, Phanish Puranam, and Massimo Warglien.
Division of Labor through Self-Selection.
Organization Science, 33(2):810–830, 2022.
References iv
[8] Timo Sturm, Jin Gerlacha, Luisa Pumplun, Neda Mesbah, Felix Peters, Christoph
Tauchert, Ning Nan, and Peter Buxmann.
Coordinating Human and Machine Learning for Effective Organization
Learning.
MIS Quarterly, 45(3):1581–1602, September 2021.

More Related Content

Similar to Summer_Workshop_2023.pdf

Research Design and Validity
Research Design and ValidityResearch Design and Validity
Research Design and Validity
Hora Tjitra
 
Introduction to participatory systemic inquiry mongolia
Introduction to participatory systemic inquiry   mongoliaIntroduction to participatory systemic inquiry   mongolia
Introduction to participatory systemic inquiry mongolia
Green Economy Coalition
 
Wcss2010presentation
Wcss2010presentationWcss2010presentation
Wcss2010presentation
yusuke_510
 
Dual Approaches for Integrating Ethics into the Information Systems Curriculum
Dual Approaches for Integrating Ethics into the Information Systems CurriculumDual Approaches for Integrating Ethics into the Information Systems Curriculum
Dual Approaches for Integrating Ethics into the Information Systems Curriculum
ACBSP Global Accreditation
 
Process protocol for virtual team effectiveness
Process protocol for virtual team effectivenessProcess protocol for virtual team effectiveness
Process protocol for virtual team effectiveness
Western Illinois University
 
KnowMe and ShareMe: understanding automatically discovered personality traits...
KnowMe and ShareMe: understanding automatically discovered personality traits...KnowMe and ShareMe: understanding automatically discovered personality traits...
KnowMe and ShareMe: understanding automatically discovered personality traits...
Leon Gou
 
Hcic muller guha davis geyer shami 2015 06-29
Hcic muller guha davis geyer shami 2015 06-29Hcic muller guha davis geyer shami 2015 06-29
Hcic muller guha davis geyer shami 2015 06-29
Michael Muller
 
TeamQuest Advisors/Marci Schnapp - Overview
TeamQuest Advisors/Marci Schnapp - OverviewTeamQuest Advisors/Marci Schnapp - Overview
TeamQuest Advisors/Marci Schnapp - Overview
Marci Schnapp, Founder TeamQuest Advisors
 
Dr. Claude Tanoe
Dr. Claude TanoeDr. Claude Tanoe
Dr. Claude Tanoe
ANGdoc
 
The Formation of Job Referral Networks: Evidence from a Field Experiment in U...
The Formation of Job Referral Networks: Evidence from a Field Experiment in U...The Formation of Job Referral Networks: Evidence from a Field Experiment in U...
The Formation of Job Referral Networks: Evidence from a Field Experiment in U...
essp2
 
System Thinking - Affect on Decision Making
System Thinking - Affect on Decision MakingSystem Thinking - Affect on Decision Making
System Thinking - Affect on Decision Making
Muhammad Awais
 
Complexity in Ambiguous Problem Solution Search: Group Dynamics, Search Tac...
Complexity in Ambiguous Problem Solution Search:   Group Dynamics, Search Tac...Complexity in Ambiguous Problem Solution Search:   Group Dynamics, Search Tac...
Complexity in Ambiguous Problem Solution Search: Group Dynamics, Search Tac...
Dr. Elliot Bendoly
 
Organizational development research
Organizational development researchOrganizational development research
Organizational development research
Ahsan Ali
 
Summary of Breakout Session from RSSE'12
Summary of Breakout Session from RSSE'12Summary of Breakout Session from RSSE'12
Summary of Breakout Session from RSSE'12
Dennis Pagano
 
Engineering design of an environmental management system: A trans-disciplinar...
Engineering design of an environmental management system: A trans-disciplinar...Engineering design of an environmental management system: A trans-disciplinar...
Engineering design of an environmental management system: A trans-disciplinar...
Henk (Jan) Roodt
 
How to conduct a social network analysis: A tool for empowering teams and wor...
How to conduct a social network analysis: A tool for empowering teams and wor...How to conduct a social network analysis: A tool for empowering teams and wor...
How to conduct a social network analysis: A tool for empowering teams and wor...
Jeromy Anglim
 
KASIRYE NICHOLAS
KASIRYE NICHOLASKASIRYE NICHOLAS
KASIRYE NICHOLAS
Kasirye Nicholas
 
Group Selection Grid AWeSoMe07
Group Selection Grid AWeSoMe07Group Selection Grid AWeSoMe07
Group Selection Grid AWeSoMe07
guest078724
 
From data to endgame for dissertation and theses: what does it take
From data to endgame for dissertation and theses: what does it takeFrom data to endgame for dissertation and theses: what does it take
From data to endgame for dissertation and theses: what does it take
DoctoralNet Limited
 
20142014_20142015_20142115
20142014_20142015_2014211520142014_20142015_20142115
20142014_20142015_20142115
Divita Madaan
 

Similar to Summer_Workshop_2023.pdf (20)

Research Design and Validity
Research Design and ValidityResearch Design and Validity
Research Design and Validity
 
Introduction to participatory systemic inquiry mongolia
Introduction to participatory systemic inquiry   mongoliaIntroduction to participatory systemic inquiry   mongolia
Introduction to participatory systemic inquiry mongolia
 
Wcss2010presentation
Wcss2010presentationWcss2010presentation
Wcss2010presentation
 
Dual Approaches for Integrating Ethics into the Information Systems Curriculum
Dual Approaches for Integrating Ethics into the Information Systems CurriculumDual Approaches for Integrating Ethics into the Information Systems Curriculum
Dual Approaches for Integrating Ethics into the Information Systems Curriculum
 
Process protocol for virtual team effectiveness
Process protocol for virtual team effectivenessProcess protocol for virtual team effectiveness
Process protocol for virtual team effectiveness
 
KnowMe and ShareMe: understanding automatically discovered personality traits...
KnowMe and ShareMe: understanding automatically discovered personality traits...KnowMe and ShareMe: understanding automatically discovered personality traits...
KnowMe and ShareMe: understanding automatically discovered personality traits...
 
Hcic muller guha davis geyer shami 2015 06-29
Hcic muller guha davis geyer shami 2015 06-29Hcic muller guha davis geyer shami 2015 06-29
Hcic muller guha davis geyer shami 2015 06-29
 
TeamQuest Advisors/Marci Schnapp - Overview
TeamQuest Advisors/Marci Schnapp - OverviewTeamQuest Advisors/Marci Schnapp - Overview
TeamQuest Advisors/Marci Schnapp - Overview
 
Dr. Claude Tanoe
Dr. Claude TanoeDr. Claude Tanoe
Dr. Claude Tanoe
 
The Formation of Job Referral Networks: Evidence from a Field Experiment in U...
The Formation of Job Referral Networks: Evidence from a Field Experiment in U...The Formation of Job Referral Networks: Evidence from a Field Experiment in U...
The Formation of Job Referral Networks: Evidence from a Field Experiment in U...
 
System Thinking - Affect on Decision Making
System Thinking - Affect on Decision MakingSystem Thinking - Affect on Decision Making
System Thinking - Affect on Decision Making
 
Complexity in Ambiguous Problem Solution Search: Group Dynamics, Search Tac...
Complexity in Ambiguous Problem Solution Search:   Group Dynamics, Search Tac...Complexity in Ambiguous Problem Solution Search:   Group Dynamics, Search Tac...
Complexity in Ambiguous Problem Solution Search: Group Dynamics, Search Tac...
 
Organizational development research
Organizational development researchOrganizational development research
Organizational development research
 
Summary of Breakout Session from RSSE'12
Summary of Breakout Session from RSSE'12Summary of Breakout Session from RSSE'12
Summary of Breakout Session from RSSE'12
 
Engineering design of an environmental management system: A trans-disciplinar...
Engineering design of an environmental management system: A trans-disciplinar...Engineering design of an environmental management system: A trans-disciplinar...
Engineering design of an environmental management system: A trans-disciplinar...
 
How to conduct a social network analysis: A tool for empowering teams and wor...
How to conduct a social network analysis: A tool for empowering teams and wor...How to conduct a social network analysis: A tool for empowering teams and wor...
How to conduct a social network analysis: A tool for empowering teams and wor...
 
KASIRYE NICHOLAS
KASIRYE NICHOLASKASIRYE NICHOLAS
KASIRYE NICHOLAS
 
Group Selection Grid AWeSoMe07
Group Selection Grid AWeSoMe07Group Selection Grid AWeSoMe07
Group Selection Grid AWeSoMe07
 
From data to endgame for dissertation and theses: what does it take
From data to endgame for dissertation and theses: what does it takeFrom data to endgame for dissertation and theses: what does it take
From data to endgame for dissertation and theses: what does it take
 
20142014_20142015_20142115
20142014_20142015_2014211520142014_20142015_20142115
20142014_20142015_20142115
 

Recently uploaded

Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdfUni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems S.M.S.A.
 
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalizationFull-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
Zilliz
 
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Neo4j
 
Mariano G Tinti - Decoding SpaceX
Mariano G Tinti - Decoding SpaceXMariano G Tinti - Decoding SpaceX
Mariano G Tinti - Decoding SpaceX
Mariano Tinti
 
GenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizationsGenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizations
kumardaparthi1024
 
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Neo4j
 
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 202420240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
Matthew Sinclair
 
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAUHCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
panagenda
 
Microsoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdfMicrosoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdf
Uni Systems S.M.S.A.
 
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practicesNational Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
Quotidiano Piemontese
 
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and MilvusBuilding Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
Zilliz
 
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing DaysClimate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Kari Kakkonen
 
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUHCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
panagenda
 
RESUME BUILDER APPLICATION Project for students
RESUME BUILDER APPLICATION Project for studentsRESUME BUILDER APPLICATION Project for students
RESUME BUILDER APPLICATION Project for students
KAMESHS29
 
Serial Arm Control in Real Time Presentation
Serial Arm Control in Real Time PresentationSerial Arm Control in Real Time Presentation
Serial Arm Control in Real Time Presentation
tolgahangng
 
How to use Firebase Data Connect For Flutter
How to use Firebase Data Connect For FlutterHow to use Firebase Data Connect For Flutter
How to use Firebase Data Connect For Flutter
Daiki Mogmet Ito
 
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Neo4j
 
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Neo4j
 
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FMEEssentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
Safe Software
 
Artificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopmentArtificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopment
Octavian Nadolu
 

Recently uploaded (20)

Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdfUni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdf
 
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalizationFull-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
 
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
 
Mariano G Tinti - Decoding SpaceX
Mariano G Tinti - Decoding SpaceXMariano G Tinti - Decoding SpaceX
Mariano G Tinti - Decoding SpaceX
 
GenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizationsGenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizations
 
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
 
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 202420240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
 
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAUHCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
 
Microsoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdfMicrosoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdf
 
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practicesNational Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
 
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and MilvusBuilding Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
 
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing DaysClimate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
 
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUHCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
 
RESUME BUILDER APPLICATION Project for students
RESUME BUILDER APPLICATION Project for studentsRESUME BUILDER APPLICATION Project for students
RESUME BUILDER APPLICATION Project for students
 
Serial Arm Control in Real Time Presentation
Serial Arm Control in Real Time PresentationSerial Arm Control in Real Time Presentation
Serial Arm Control in Real Time Presentation
 
How to use Firebase Data Connect For Flutter
How to use Firebase Data Connect For FlutterHow to use Firebase Data Connect For Flutter
How to use Firebase Data Connect For Flutter
 
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
 
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
 
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FMEEssentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
 
Artificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopmentArtificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopment
 

Summer_Workshop_2023.pdf

  • 1. Meta-Organizational Learning Through Digital Consensus Junyi Li1 , Jungpil Hahn1 , Giri Kumar Tayi2 July 19, 2023 1Department of Information Systems and Analytics, National University of Singapore 2School of Business, State University of New York at Albany
  • 2. Outline 1. Introduction 2. Computational Model 3. Experiments & Results 4. Conclusion 1
  • 4. Emergence of DAOs • Decentralized Autonomous Organizations (DAOs): making organizational decision by token-based voting. • Meta-Oganization: legally autonomous entities [2] 2
  • 5. Paradoxes in Organizational Design 1. Will DAOs (digital consensus) be an effective mode of decentralized governance? 2. Traditional paradoxes: • Control vs. Flexibility [3] • Centralization vs. Decentralization [1] • Authority vs. Self-Organizing [7] 3
  • 6. Research Question How will digital consensus affect organizational design? 1. In what ways does consensus-based governance differ from a traditional one, namely autonomy, and/or hierarchy (what is)? 2. Under what circumstances does consensus-based governance outperform other modes (what might be)? 3. What is the underlying mechanism accounting for any performance differences (how)? 4
  • 8. March’s Organization† †March, J. G. (1991). Exploration and Exploitation in Organizational Learning. Organization Science, 2(1), 71–87. 5
  • 9. Basic Setup: Entities 1. Reality • m-bits string of 1 and -1. e.g., ⟨1, 1, 1, −1, −1, −1⟩ • Policy: (m/α)-bits aggregated summary of reality • e.g., Reality: ⟨1, 1, 1, −1, −1, 1⟩ α=3 − − − → Correct Policy: ⟨1, −1⟩ • α: do managers want to control every detail of their team members’ decisions? • Policy: 1 α=3 − − − → Solution: ⟨1, 1, −1⟩ 2. Organization • n individuals divided into groups of size z • n′ managers (hierarchy); one manager supervise one group 3. Solution • individual solution is m-bits string corresponding to reality • manager solution is (m/α)-bits corresponding to policy reality 6
  • 10. Autonomy • Individuals learn from higher performing connected peers with probability η 7
  • 11. Hierarchy • Individuals learn from higher performing connected peers with probability η • Top-Down Coordination: individuals’ solutions must be coherent with managers’ policy 8
  • 12. DAO • Individuals learn from higher performing connected peers with probability η • Bottom-Up Coordination: individuals’ solutions must be coherent with policy consensus 9
  • 14. Experiment 1: Baseline • Static Reality • Best Hierarchies as in March’s model [4] • Naı̈ve DAOs • simple majority rule • uniform token distribution • full engagement 10
  • 15. Experiment 1: Baseline Investigating performance variance adds insights. • DAOs evolve through a staggered process of polarization and homogenization, as opposed to autonomies’ continuous polarization and hierarchies’ continuous homogenization. (What is & How?) 11
  • 16. Experiment 2: Turbulence The performance reversal delays as environments become less turbulent (more static). “Turbulent” ← → “Static” 12
  • 17. Summary of Baseline Proposition 1a In static environments, hierarchies outperform DAOs, and DAOs outperform autonomies. Proposition 1b In turbulent environments, DAOs tend to outperform hierarchies and autonomies. 13
  • 18. Summary of Extensions Proposition 2 Voting threshold has an inverted U-shaped impact on DAOs’ performance. Proposition 3 Token asymmetry has a negative impact on DAOs’ performance. Proposition 4a Contributor incentive has a positive impact on DAOs’ performance when the inactive rate is high. Proposition 4b Contributor incentive has a negative impact on DAOs’ performance when the inactive rate is low. 14
  • 20. Key Takeaways ? Power A vs. Power B ✓ Polarization vs. Homogenization ? Optimize DAOs’ design ✓ By voting threshold, token asymmetry, and contributor incentive 15
  • 23. Socialization Tension • Socialization tension: the self-confined difficulty of accepting any local belief as a global consensus
  • 24. Digital Consensus Digital consensus herein refers to algorithmic coordination rules that are automatically executed in distributed computer systems and are only modifiable via voting. • Transparent • Democratically editable • Self-executing
  • 26. Genetic Algorithm m payoff function in March’s (1991) model [4]: Φ(x) = m X j=1 δj (1) where δj = 1 if xj corresponds with reality on dimension j; δj = 0 otherwise. Belief diversity is measured as follows: Belief Diversity = 2 mn(n − 1) 1 2 n(n−1) X i=1 m X j=1 ωij (2) where ωij = 1 if two chosen individuals in the pair i have different beliefs on dimension j; ωij = 0 otherwise.
  • 27. Model Parameters Parameter Remark Range of parameter Default m Problem dimension 60, 90, 120, 150 90 n Number of individuals in the organization 280, 350, 420, 490 350 z The group size of the autonomous group 7, 14, 28 7 α The aggregation degree 1, 3, 5 3 η The learning rate of individuals 0.1 ∼ 0.9 0.3 p1 The probability of learning from the code (Hierarchy) 0.1 ∼ 0.9 0.1 p2 The probability of learning by the code (Hierarchy) 0.1 ∼ 0.9 0.9 p3 The probability of individual reconfiguration 0 ∼ 0.5 0 n′ The number of managers (Hierarchy) 40, 50, 60, 70 50 θ The voting threshold to pass a protocol as consensus 0.4 ∼ 0.7 0.5 Ttb The period of environmental turbulence 20 ∼ 100 - Itb The intensity of environmental turbulence 0, 10%, 20% 0 ρ The shape parameter in Pareto distribution 1, 2, 3 - γ Incentive degree & reactivation rate 0 ∼ 0.9 0 pi The probability of individuals being inactive in voting 0 ∼ 0.9 0 Repetition The number of simulation runs 500 500 Search Round The iteration of search in one simulation run 300, 500, 1000, 2000 - Notes: Search rounds are verified by pilot tests. The default values correspond to the usage in the literature.
  • 28. Baseline & Turbulence: Conclusion Proposition 1a In static environments, hierarchies outperform DAOs, and DAOs outperform autonomies. Proposition 1b In turbulent environments, DAOs tend to outperform hierarchies and autonomies.
  • 29. Experiment 3: Voting Threshold • Voting threshold has an inverted U-shaped effect on DAOs’ performance.
  • 30. Experiment 3: Voting Threshold • Staggered process: polarization before consensus, homogenization after consensus. • The staggered process can be adjusted by θ.
  • 31. Experiment 4: Token Asymmetry • Asymmetry restrains the polarization before consensus formation. • Causing homogenization into minority views.
  • 32. Experiment 5: Contributor Incentive • Due to the intense reward, there will be additional asymmetry, resulting in worse performance (–). • Due to reactivation, incentives are beneficial when individuals rarely vote (+).
  • 33. Interaction Effect: Thresholds * Asymmetry With more asymmetry, optimal thresholds will increase.
  • 34. Interaction Effect: Thresholds * Inactivity With more inactivity, optimal thresholds will decrease.
  • 35. Sensitivity Analysis • We conduct sensitivity analyses for non-focus parameters, including the group size z, the number of individuals n, the number of managers n′, the aggregation level α, the learning rate η, and the reality dimension m. Qualitative results are consistent across these parameter values. • A variety of hierarchies with varying supervision efficiencies are also examined. In particular, the hierarchies’ performance will linearly decrease with increasing p1 or decreasing p2, consistent with March’s (1991) model. This means naı̈ve DAOs certainly outperform some deficient hierarchies. • With individual reconfiguration rate p3 [8], the performance of DAOs and hierarchies largely restore to static levels. Yet, autonomies may outperform when p3 > 0.2, resonating with the literature that autonomous teams excel in intensive organizational experimentation [5, 6].
  • 36. Robustness: Supervision (i) • p1: learning from code, p2: learning by code • Left side: p2 = 0.9, right side: p1 = 0.1 • These two figures represent two specific segment lines across the entire 3D surface (p1, p2, Z)
  • 38. Robustness: Learning Rate Notes: by default η = 0.3
  • 40. References i [1] Christina Fang, Jeho Lee, and Melissa A. Schilling. Balancing Exploration and Exploitation Through Structural Design: The Isolation of Subgroups and Organizational Learning. Organization Science, 21(3):625–642, June 2010. [2] Ranjay Gulati, Phanish Puranam, and Michael Tushman. Meta-Organization Design: Rethinking Design in Interorganizational and Community Contexts. Strategic Management Journal, 33(6):571–586, 2012.
  • 41. References ii [3] Daniel A. Levinthal and Maciej Workiewicz. When Two Bosses Are Better Than One: Nearly Decomposable Systems and Organizational Adaptation. Organization Science, 29(2):207–224, 2018. [4] James G. March. Exploration and Exploitation in Organizational Learning. Organization Science, 2(1):71–87, February 1991. [5] Peerasit Patanakul, Jiyao Chen, and Gary S. Lynn. Autonomous Teams and New Product Development. Journal of Product Innovation Management, 29(5):734–750, 2012.
  • 42. References iii [6] Phanish Puranam, Harbir Singh, and Maurizio Zollo. Organizing for Innovation: Managing the Coordination-Autonomy Dilemma in Technology Acquisitions. Academy of Management Journal, 49(2):263–280, April 2006. [7] Marlo Raveendran, Phanish Puranam, and Massimo Warglien. Division of Labor through Self-Selection. Organization Science, 33(2):810–830, 2022.
  • 43. References iv [8] Timo Sturm, Jin Gerlacha, Luisa Pumplun, Neda Mesbah, Felix Peters, Christoph Tauchert, Ning Nan, and Peter Buxmann. Coordinating Human and Machine Learning for Effective Organization Learning. MIS Quarterly, 45(3):1581–1602, September 2021.