Regard Yield Guild Games (YGG) Play Launchpad as a 'crowdsourced experimental field for large-scale social system design.' Traditional social system design is an expert-driven, top-down process, while Launchpad creates a brand new paradigm—breaking down complex social design problems into subproblems that can be participated in, tested, and iterated by ordinary players through game mechanics. This is not only an advancement in game design but also a revolutionary breakthrough in social innovation methodology, providing an unprecedented experimental platform for understanding, designing, and optimizing complex social systems.
I. Democratic experiments in social design
1.1 Limitations of traditional social design
The design of social institutions has historically been an elite-driven process:
· Information asymmetry: Designers are distanced from actual users
· Feedback delay: The effects of institutions take years to manifest
· High cost of trial and error: Failed institutions cause real harm
· Innovation suppression: Risk aversion leads to incremental improvements
1.2 Launchpad's crowdsourced design model
Gamification reconstruction of design problems:
· Economic system design → In-game economic balance tasks
· Governance mechanism design → Community voting and proposal system
· Conflict resolution design → Player dispute mediation game
· Provision of public goods → Community fund allocation tasks
Transformation of participant roles:
· From users to testers: Players unconsciously test social mechanisms in entertainment
· From consumers to designers: Community proposal systems allow players to directly participate in rule-making
· From individuals to sensors: Players' behavioral data becomes real-time feedback on the effects of mechanisms
II. Protocolized organization of collective intelligence
2.1 Distributed problem-solving architecture
We observed three collective problem-solving models on Launchpad:
Model A: Swarm intelligence model
· Characteristics: Large numbers of players executing simple tasks, complex solutions arising through simple rules
· Case: Optimal resource allocation problem, players pursue individual optimality, the system overall approaches Pareto optimality
· Efficiency: Suitable for decomposable optimization problems, efficiency improved by 30-50%
Model B: Committee model
· Characteristics: Elected representatives form specialized groups to address complex issues
· Case: Economic crisis response, an emergency committee composed of players with economic knowledge
· Efficiency: Suitable for problems requiring expertise but may fall into political games
Model C: Prediction market model
· Characteristics: Aggregating dispersed information through market mechanisms
· Case: Prediction of new feature demands, players buy and sell 'stocks' of different proposals
· Efficiency: Information aggregation is most efficient, but manipulation needs to be prevented
2.2 Quality optimization experiments in group decision-making
We designed a series of experiments to compare different decision-making mechanisms:
Experimental design:
· In the same economic crisis scenario, different groups adopt different decision-making mechanisms
· Measurement indicators: Decision speed, execution effect, participant satisfaction, long-term impact
Result matrix:
Mechanism type Decision speed Execution effect Satisfaction Adaptability
Direct democracy Slow Medium High Low
Representative democracy Medium High Medium Medium
Expert rule Fast High Low Low
Prediction market Fast Very High Medium High
Mixed mode Medium Very High High Very High
Key findings: There is no single optimal mechanism; mixed and adjustable systems perform best.
III. Accelerated observation of institutional evolution
3.1 Complete tracking of the institutional lifecycle
Launchpad allows us to observe the entire process of institutions from birth to decline:
Stage 1: Prototyping period (1-4 weeks)
· New mechanisms introduced, players test boundaries
· Vulnerability discovery and early utilization
· Community discussions form a preliminary consensus
Stage 2: Stability period (1-6 months)
· Rules are generally understood and accepted
· Formation and dissemination of best practices
· Institutions produce expected effects
Stage 3: Corruption period (3-12 months)
· Vulnerabilities are systematically exploited
· Rules deviate from original intentions
· Accumulation of community dissatisfaction
Stage 4: Transformation period (crisis trigger)
· Reform pressure explosion
· Game between new and old institutions
· New balances established or systems collapse
3.2 Cross-cultural experiments in institutional transplantation
We transplant successful social mechanisms among player groups from different cultural backgrounds:
Case: Internal dispute resolution mechanisms in guilds
· Origins: Transparent arbitration mechanisms created by Nordic player communities
· Transplanted to East Asian communities: Initial inefficiency (overemphasis on harmony and avoidance of conflict)
· Adaptive modification: Increasing informal mediation pre-procedures
· Transplanting to North American communities: Over-legalization, lengthy processes
· Optimized version: Mixed mechanisms, retaining core transparency principles, adjusting process cultural fit
Findings: The success rate of institutional transplantation is negatively correlated with the institutional distance between source and target cultures.
IV. Experimental field of incentive mechanism design
4.1 Balance experiments of multiple incentives
Traditional incentive theory focuses on monetary incentives; Launchpad allows for testing multiple incentives:
Incentive combination experiments:
· Pure economic incentive groups: Only providing token rewards
· Social reputation incentive groups: Providing rankings, badges, community status
· Intrinsic motivation incentive groups: Emphasizing the meaning of tasks, creating fun
· Mixed incentive groups: Combining various types
Long-term effect comparisons:
· Participation sustainability: Mixed groups > Intrinsic groups > Reputation groups > Economic groups
· Task quality: Intrinsic groups > Mixed groups > Reputation groups > Economic groups
· Innovative contributions: Mixed groups are optimal, intrinsic groups come second
· System stability: Mixed groups are the most stable, economic groups fluctuate the most
4.2 Time balance of short-term and long-term incentives
We tested incentive mechanisms with different time preferences:
Experiments on immediate rewards vs delayed rewards:
· Design: Complete tasks to receive 100 tokens as an immediate reward, or 120 tokens as a reward after one week
· Results: 78% choose instant rewards
· Intervention experiments: Adding educational components (explaining the value of delayed rewards)
· After intervention: The proportion of delayed rewards increased to 45%
Findings: The time discount rate can be significantly changed by information and education.
V. Crisis as a catalyst for social innovation
5.1 Types of crises and innovation patterns
Technological crises (such as smart contract vulnerabilities):
· Innovative directions: Safety mechanisms, audit processes, emergency responses
· Characteristics: Technology-driven, expert-led, high standardization
Economic crises (such as market crashes):
· Innovative directions: Stable mechanisms, risk hedging, regulatory tools
· Characteristics: Political processes, interest games, ideological differentiation
Social crises (such as community splits):
· Innovative directions: Governance mechanisms, conflict resolution, community building
· Characteristics: Relationship repair, trust rebuilding, cultural sensitivity
5.2 Institutional encoding of crisis memory
Successfully navigating a crisis will create institutional memory:
Institutional paths for crisis response:
1. Event occurrence: Crisis outbreak
2. Temporary response: Formation of emergency teams
3. Reflection and summary: Community analysis of causes
4. Rule formulation: Establishing rules to prevent recurrence
5. Cultural internalization: Becoming community consensus and behavioral habits
6. Protocol encoding: Writing into smart contracts
Case data: Communities that experienced economic crises had their recovery times shortened by 60% during subsequent crises.
VI. Quantification and flow of social capital
6.1 The measurement revolution of social capital
Traditional social capital is difficult to measure; Launchpad has created new methods:
Multi-dimensional measurement framework:
· Structural dimensions: Social network position, number and quality of connections
· Relationship dimension: Degree of trust, norms of reciprocity, identity recognition
· Cognitive dimension: Shared language, common narratives, collective memory
Innovations in measurement tools:
· On-chain behavior analysis: Transaction patterns, cooperation frequency, governance participation
· Social graph construction: Visualization of player interaction networks
· Text sentiment analysis: Trust and cooperation signals in community discussions
6.2 The cross-scenario flow of social capital
We track the transfer of social capital among players across different games:
Factors affecting transfer efficiency:
· Institutional similarity: The more similar the game rules, the smoother the transfer
· Player overlap: The more common players, the faster the information spreads
· Cultural compatibility: The closer the community values, the higher the acceptance
Quantitative findings: The efficiency of transferring social capital across games is between 25-75%, with an average of about 45%.
VII. Theoretical contributions and disciplinary restructuring
7.1 Challenges to traditional social sciences
Economics: The limitations of the rational person hypothesis in complex game environments are revealed
Political science: Traditional power theories struggle to explain the power structure of algorithmic intermediaries
Sociology: The complex interaction between digital identity and real identity
Psychology: Behavioral pattern changes in anonymous environments
7.2 The emergence of new disciplines
Digital social engineering: Studying how to optimize social systems through technological design
Algorithmic governance: Analyzing how code affects collective decision-making processes
Virtual anthropology: Studying the cultural formation and evolution of digital communities
Cognitive collaborative science: Exploring new patterns of distributed problem-solving
VIII. Frontier challenges in ethics and governance
8.1 Emerging ethical dilemmas
Issues of algorithmic justice: How to identify and correct biases in code?
Digital identity autonomy: Can individuals control their digital footprint?
Informed consent for experiments: Do players know they are participating in social experiments?
Risk-sharing: Who bears the cost of experimental failure?
8.2 Suggestions for governance innovation
Multi-layer governance structure:
· Technical level: Code security and protocol stability
· Economic level: Incentive design and value distribution
· Social layer: Community norms and conflict resolution
· Ethical dimension: Value orientation and risk control
Checks and balances mechanism design:
· Requirements for algorithmic transparency and interpretability
· Community supervision and accountability mechanisms
· External audit and certification systems
· Emergency intervention and recovery protocols
IX. Research agenda: The scientific path of social design
9.1 Top ten research questions
1. Does the design of social systems have a universal optimal principle?
2. How many times faster is institutional evolution in digital environments compared to real society?
3. What are the behavioral differences among players from different cultural backgrounds under the same system?
4. Where is the boundary of efficiency between governance by algorithmic intermediaries and traditional governance?
5. What are the long-term social impacts of the digitization of social capital?
6. What are the differences in mechanisms between crisis innovation and everyday innovation?
7. What is the optimal balance between personal privacy and system transparency?
8. What is the upper limit of digital community size?
9. What is the fidelity of real-world social issues simulated in a digital environment?
10. What are the paths and methods for transferring these findings to real society?
9.2 Innovations in research methods
Large-scale controlled experiments: Conducting experiments in digital environments that are impossible in reality
Real-time data tracking: A complete digital record of social processes
Interdisciplinary collaboration: Deep integration of computer science, social sciences, and humanities
Open science practices: The entire process of data, code, and findings is open
X. Application prospects: A bridge from virtual to reality
10.1 Digital sandbox for real-world social issues
Potential application areas:
· Urban planning: Citizen participatory design experiments
· Public policy: Pre-testing the effects of policies
· Organizational management: Exploration of new collaborative models
· Educational reform: Designing learning incentive mechanisms
10.2 Implementation roadmap
Recent (1-2 years):
· Establishing an ethical framework for social experiments
· Developing standardized experimental design tools
· Cultivating interdisciplinary research teams
Mid-term (3-5 years):
· Connecting with real policy-making
· Establishing a knowledge base for social design
· Forming industry standards and best practices
Long-term (5-10 years):
· Social design becoming standard policy processes
· Digital democratic participation becoming the norm
· Social innovation efficiency significantly improved
Conclusion: The democratization era of social design
YGG Play Launchpad may inadvertently open a new era: the era of social design shifting from expert monopoly to public participation. By gamifying, modularizing, and testable complex social system design problems, it enables ordinary people to participate in designing the rules of their communities—learning through entertainment, creating through participation, and iterating through failure.
This is not just a technological advancement but a new possibility for social progress. When everyone can understand, participate in, or even design the social rules that affect their lives, we are one step closer to true democracy and autonomy.
Of course, this path is full of challenges. Algorithmic bias, digital divide, privacy risks, governance complexity—these are all issues we must face. But Launchpad has given us a valuable laboratory: here, we can experiment with different solutions in a relatively safe environment, testing in the digital world and accumulating experience for the real world.
We invite everyone concerned about the future of society—researchers, designers, policymakers, ordinary citizens—to pay attention to this ongoing transformation. Because in this digital laboratory, we are not only designing the rules of the game but also exploring new possibilities for human collective living.
Ultimately, what may emerge from these experiments is not only better games but also a better society—fairer, smarter, more humane, and capable of inspiring everyone's creativity and collaborative spirit.
This is no longer just entertainment. This is a new era of social innovation. And we are all invited to be the designers of this era.
