Decision Output Types (DOT)
MetaPoll can serve two fundamentally different purposes in collective decision systems. Understanding these distinct Decision Output Types ("DOTs") helps us deploy them effectively for different coordination challenges.
1. Enforceable Execution (DOT1)
The first and perhaps most unique use case transforms collective preferences directly into executable actions. In this format, MetaPoll functions as a parametric control system for automated or semi-automated processes:
MetaPoll results map directly to parameter variables in function calls
Option rankings determine parameter values
Execution happens automatically through smart contracts, APIs, or agent systems
Enforceable Execution Data creates what we might call a "preference-executable interface" - a bridge between what people want and what systems do.
The Cockpit Analogy
To understand this DOT, consider an aircraft cockpit. Traditional governance is like having a single pilot (or small crew) operating all flight controls. MetaPoll transforms this into a collectively operated cockpit where:
Community preferences directly adjust control surfaces
Different preference layers map to different control mechanisms
Aggregation functions create coherent control signals from distributed inputs
These controls can operate at different levels of abstraction AKA semantic precision:
Low-level controls - Direct manipulation of specific parameters (e.g., "set interest rate to 3.7%")
Mid-level controls - Operational directives (e.g., "allocate more resources to security than features")
High-level controls - Strategic goals (e.g., "prioritize long-term stability over short-term growth")
The hierarchical structure of MetaPoll accommodates these different semantic precision levels naturally, allowing communities to find the right balance between direct control and delegation.
Scaling Beyond Individual Control
What makes this approach truly unique is its ability to scale beyond the limitations of individual decision-makers. Traditional governance systems face an inherent tension:
Higher chance to act coherently
Can be incoherent and slow
Lack representativeness
Improved representation
MetaPoll resolves this tension through structured preference aggregation. A MetaPoll can integrate input from millions or billions of participants while still producing coherent, actionable directives. It achieves this by:
Structuring the decision space hierarchically
Aggregating preferences in ways that reveal explicit trade off patterns
Focusing on relative priorities rather than binary choices
Maintaining ongoing preference streams rather than one-off decisions
MetaPoll offers something exciting: a mechanism for coherent collective control at planetary scale. Even when participants disagree substantially, the priority structure ensures the most widely supported objectives receive attention first.
2. Sentiment Information (DOT2)
The second DOT is less direct but potentially just as valuable. Here, MetaPoll functions as a sophisticated information system that reveal collective preferences without directly executing them:
MetaPoll aggregates and structures community sentiment
Results inform decision-makers but don't automatically execute
Organizations retain decision authority while gaining deeper insight
This creates a rich preference exploration tool without the commitment to automatic execution.
Applications of Sentiment MetaPolls
This format has wide-ranging applications across various domains:
Product development: Companies can understand customer priorities for features, aesthetics, and functionality, increasing likelihood of product market fit
Scientific research: Funding bodies can map public interest in different research directions
Content creation: Media producers can align with audience values and preferences
Political representation: Officials can better understand constituent priorities
Market research: Businesses can detect emerging trends and preference shifts and respond proactively rather than in dangerously reactive ways
The Autonomy Balance
Decision Output Types strike an important balance in autonomy between community preference expressers and organizations. The community expresses detailed, structured preferences, but the organization retains implementation authority and responsibility.
This separation:
Acknowledges the expertise gap between preference-having and implementation
Respects organizational autonomy while improving accountability
Creates information flows without micromanagement
The historical significance of this development shouldn't be understated. We're seeing the emergence of governance technology that potentially resolves longstanding tensions between direct democracy (maximizing participation) and representative systems (enabling expertise and coherence).
MetaPoll suggest a path to a new generation of decision systems that can be simultaneously more participatory and more effective than anything previously developed.
How to decide between DOT1 or DOT2?
When designing a MetaPoll, the choice of Decision Output Type fundamentally shapes the relationship between collective preference and system behavior.
DOT1 (Enforceable Execution)
DOT1 makes sense when you have well-defined actions that map cleanly to preferences - setting a parameter, allocating resources between fixed options, or triggering predefined functions.
The key requirement is that the preference space and action space must be tightly coupled; you cannot execute what you cannot clearly specify.
This works well for operational decisions where the community has sufficient context and the consequences are reversible or bounded.
DOT2 (Sentiment Information)
DOT2 becomes valuable in exploratory contexts where the mapping between preference and action remains unclear, where implementation requires specialized knowledge the crowd lacks, or where legal/practical constraints prevent direct execution.
It's particularly useful for strategic questions, creative direction, or any domain where you want to understand collective priorities without committing to automatic implementation. DOT2 can work with either semantic precision style - abstract options help gauge general sentiment, while concrete options can reveal specific preferences even when direct execution isn't feasible.
The choice often reduces to a simple question: is this a decision the crowd can meaningfully make, or merely one they can meaningfully have preferences about?
Rule of thumb
When voters can directly specify executable parameters → use DOT1.
When you need interpretation, expertise, or flexibility → use DOT2.
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