SharedMind Specification

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SharedMind

Listen to Humanity. Discover What Is Possible.

Overview

SharedMind is a collective intelligence system designed to listen to humanity, identify problems, understand the experiences and knowledge surrounding them, and discover possibilities for solving what matters. The system analyzes permitted public information, including public discussion, news, research, public data, community knowledge, ideas, concerns, and proposed solutions.

SharedMind is built around the principle that people collectively contain an enormous amount of knowledge, experience, observation, creativity, and problem-solving ability. The system connects these distributed signals and uses multi-agent analysis to transform fragmented information into structured problem intelligence and a broad possibility space.

SharedMind does not assume that an existing solution is the only possible solution. A problem may have an existing answer, an overlooked answer, a combination of existing approaches, or a solution that has not yet been discovered. When no known solution is available, SharedMind explores what could be possible rather than treating the absence of an existing answer as proof that a problem cannot be solved.

Core Principles

Listen to humanity through permitted sources of collective knowledge and public discussion.
Treat people and communities as distributed sources of observations, experiences, ideas, and knowledge.
Distinguish observations, evidence, opinions, hypotheses, assumptions, and claims.
Investigate problems rather than relying solely on sentiment or surface-level discussion.
Use multiple specialized agents to examine problems from different perspectives.
Encourage agents to challenge assumptions, identify contradictions, and expose knowledge gaps.
Preserve provenance so conclusions can be traced back to supporting information.
Search for existing solutions before generating new ones.
Combine compatible ideas to discover new approaches.
Explore the possibility space when no established solution is known.
Examine both potential benefits and unintended consequences.
Identify opportunities where addressing one problem could help address additional problems.
Present possibilities rather than prescribing a single answer.
Keep consequential decisions in human hands.
Continuously learn from new information, attempted solutions, outcomes, and human feedback.

Core Modules

Signal Discovery Module

The Signal Discovery Module identifies potential problems and emerging issues across permitted public information sources.

Features include:

Monitoring public conversations and information sources.
Detecting recurring complaints, concerns, questions, unmet needs, and requests.
Identifying emerging problems before they become widely recognized.
Detecting changes in the frequency or nature of problem signals.
Connecting related signals across different sources.
Identifying geographic, demographic, topical, or contextual patterns where supported by permitted data.
Separating individual signals from recurring or corroborated patterns.
Preserving source provenance for discovered signals.

Problem Intelligence Module

The Problem Intelligence Module transforms distributed signals into structured representations of problems.

Features include:

Defining the problem being observed.
Identifying affected people, communities, systems, and environments.
Mapping symptoms and observable effects.
Identifying contributing factors.
Identifying relationships between related problems.
Tracking how a problem changes over time.
Distinguishing established information from unresolved questions.
Identifying areas where additional investigation is required.
Maintaining a shared problem model for the multi-agent task force.

Collective Voice Module

The Collective Voice Module analyzes the knowledge contained within public discussion and community experiences.

Features include:

Extracting experiences and observations from public discussion.
Identifying recurring themes.
Discovering suggestions and proposed solutions.
Identifying objections and concerns.
Finding successful approaches described by participants.
Finding failed approaches and lessons from unsuccessful attempts.
Identifying disagreements between participants.
Connecting related discussions across sources.
Preserving context around statements and proposals.
Preventing popularity or volume from being treated as proof of correctness.

Evidence and Provenance Module

The Evidence and Provenance Module establishes traceability throughout the intelligence process.

Features include:

Recording the source of significant findings.
Linking conclusions to underlying observations and evidence.
Distinguishing evidence from opinion.
Identifying unsupported or weakly supported claims.
Recording confidence and uncertainty.
Tracking conflicting evidence.
Preserving the history of important analytical findings.
Identifying evidence gaps.
Providing traceable paths from a proposed solution back to relevant supporting information.

Multi-Agent Task Force Module

The Multi-Agent Task Force Module coordinates specialized agents around individual problems.

Features include:

Dynamically assembling task forces based on the nature of a problem.
Assigning specialized investigative roles.
Allowing agents to independently investigate different aspects of the same problem.
Sharing relevant findings through a common problem intelligence model.
Encouraging agents to challenge assumptions and findings from other agents.
Identifying contradictions between agents.
Recording competing hypotheses.
Assigning additional agents when new areas of investigation emerge.
Deactivating agents when their investigation is complete.
Maintaining independent perspectives while coordinating collective analysis.
Requiring significant findings to include supporting evidence, assumptions, confidence, and unresolved questions.

Root Cause Analysis Module

The Root Cause Analysis Module investigates why problems occur rather than focusing only on their visible symptoms.

Features include:

Identifying potential underlying causes.
Separating root causes from contributing factors.
Mapping relationships between causes and effects.
Identifying systemic conditions that contribute to recurring problems.
Testing competing explanations against available evidence.
Identifying assumptions that could invalidate a proposed explanation.
Connecting root causes to related problems.
Identifying areas where evidence is insufficient to establish causation.

Collective Knowledge Discovery Module

The Collective Knowledge Discovery Module searches available knowledge for information that may contribute to solving a problem.

Features include:

Finding existing solutions.
Discovering historical approaches.
Identifying research and documented experiments.
Finding community-developed solutions.
Identifying successful implementations.
Identifying failed implementations and their lessons.
Discovering approaches used in other locations or domains.
Connecting knowledge from seemingly unrelated fields.
Identifying concepts that could be adapted to the problem.
Recording limitations and conditions associated with discovered solutions.

Solution Generation Module

The Solution Generation Module explores new possibilities when existing solutions are incomplete, unavailable, inaccessible, or insufficient.

Features include:

Generating alternative approaches.
Developing solutions from identified root causes.
Combining concepts from multiple existing approaches.
Exploring unconventional approaches.
Identifying incremental and transformative possibilities.
Generating multiple solution pathways rather than a single answer.
Identifying assumptions behind each possibility.
Clearly distinguishing generated possibilities from established solutions.
Identifying possibilities that require research, experimentation, or validation.

Solution Synthesis Module

The Solution Synthesis Module combines compatible knowledge, ideas, and approaches into broader solution possibilities.

Features include:

Combining complementary solutions.
Identifying shared components between different approaches.
Resolving compatible ideas into unified pathways.
Identifying opportunities for one intervention to address multiple problems.
Developing alternative implementation pathways.
Comparing different combinations of ideas.
Preserving the origin of contributing ideas.
Identifying where synthesis introduces new assumptions or risks.

Humanity Benefit Mapping Module

The Humanity Benefit Mapping Module examines how potential solutions could create benefits beyond the original problem.

Features include:

Identifying direct benefits.
Identifying secondary benefits.
Identifying additional problems that could potentially be addressed.
Identifying communities that could benefit.
Identifying opportunities for positive effects across multiple systems.
Mapping dependencies between benefits.
Identifying potential tradeoffs.
Distinguishing intended benefits from speculative benefits.
Exploring possibilities that maximize beneficial outcomes while addressing the original problem.

Risk and Adversarial Analysis Module

The Risk and Adversarial Analysis Module challenges proposed solutions and searches for unintended consequences.

Features include:

Identifying weaknesses in proposed solutions.
Challenging assumptions.
Identifying potential unintended consequences.
Examining misuse scenarios.
Identifying potential harms.
Identifying affected groups that may be overlooked.
Testing whether a proposed solution could create new problems.
Identifying dependencies and failure conditions.
Comparing competing risks between solution pathways.
Presenting unresolved risks for human consideration.

Possibility Mapping Module

The Possibility Mapping Module organizes discoveries into a structured map of potential ways forward.

Features include:

Presenting the identified problem.
Showing observed signals and supporting evidence.
Mapping affected populations and systems.
Showing potential causes and contributing factors.
Presenting existing solutions.
Presenting generated possibilities.
Showing combinations of compatible approaches.
Mapping potential benefits.
Mapping risks and unintended consequences.
Identifying evidence gaps.
Identifying unresolved questions.
Showing relationships between problems and possible solutions.
Clearly distinguishing known information from possibilities and uncertainty.

Human Decision Module

The Human Decision Module places human judgment at the center of consequential decisions.

Features include:

Presenting multiple possibilities without selecting a prescribed outcome.
Providing evidence and provenance for significant findings.
Showing assumptions, uncertainties, benefits, and risks.
Allowing humans to investigate the underlying evidence.
Allowing humans to compare solution pathways.
Allowing humans to reject, modify, combine, or pursue proposed possibilities.
Recording human decisions and reasoning where appropriate.
Supporting human review before consequential actions.
Preventing the system from treating its own recommendations as authoritative decisions.

Learning and Feedback Module

The Learning and Feedback Module allows SharedMind to improve its understanding through new information and observed outcomes.

Features include:

Recording human feedback.
Tracking investigated and attempted solutions.
Recording successful and unsuccessful outcomes.
Comparing predicted and observed effects.
Updating problem models as new evidence emerges.
Identifying previously overlooked information.
Learning from failed approaches.
Revisiting unresolved problems when new knowledge becomes available.
Preserving historical reasoning and evidence where appropriate.

Optional Plugin Modules

Social Media Source Plugin

Provides connectors for permitted public social media information and public discussions.

News Source Plugin

Provides access to permitted news sources for discovering reported problems, emerging events, and public responses.

Research Plugin

Provides access to research and scholarly knowledge relevant to identified problems.

Government Data Plugin

Provides access to permitted government datasets, reports, statistics, and public information.

Public Records Plugin

Provides access to permitted public records and other publicly available civic information.

Scientific Literature Plugin

Provides specialized discovery and analysis of scientific literature and documented findings.

Geographic Intelligence Plugin

Adds geographic analysis for identifying spatial patterns, regional differences, and location-specific problems.

Economic Analysis Plugin

Adds analysis of economic conditions, incentives, costs, resource constraints, and potential economic effects.

Environmental Analysis Plugin

Adds environmental analysis for problems involving ecosystems, resources, climate, pollution, or environmental conditions.

Historical Solutions Plugin

Searches historical events, previous interventions, and documented attempts to identify approaches that may inform current problems.

Community Knowledge Plugin

Provides structured access to community-generated knowledge, local experience, public proposals, and documented grassroots solutions.

Patent and Invention Plugin

Searches permitted patent and invention information for technologies, concepts, and approaches relevant to identified problems.

Simulation Plugin

Allows proposed solutions to be explored through simulations and scenario analysis where appropriate.

Forecasting Plugin

Provides scenario exploration and projections based on available evidence and clearly identified assumptions.

Knowledge Graph Plugin

Builds interconnected representations of problems, causes, evidence, people, organizations, ideas, solutions, and relationships.

Human Expert Plugin

Allows qualified human experts to contribute domain knowledge, challenge findings, validate assumptions, and identify areas requiring further investigation.

Multi-Agent Investigation Process

SharedMind should support a continuous investigative process:

Observe → Detect → Verify → Understand → Connect → Research → Generate → Synthesize → Challenge → Analyze → Present → Human Selects → Test → Measure → Learn

Each stage should contribute to a continuously evolving understanding of the problem. The process may return to earlier stages whenever new evidence, contradictions, failed solutions, or previously unknown relationships are discovered.

Possibility Space

SharedMind should not reduce a complex problem to a single answer. Each significant investigation should produce a possibility space containing:

The problem being investigated.
Observed signals.
Supporting evidence.
Affected populations and systems.
Potential causes.
Contributing factors.
Existing solutions.
Historical approaches.
Community proposals.
New solution possibilities.
Combinations of existing ideas.
Potential implementation pathways.
Potential benefits.
Secondary benefits.
Risks and unintended consequences.
Dependencies.
Assumptions.
Evidence gaps.
Unresolved questions.
Areas requiring human judgment.
Opportunities for additional investigation.

Collective Intelligence Principles

SharedMind should treat collective intelligence as more than aggregation. The system should seek relationships between individual observations, community knowledge, research, historical experience, professional expertise, and newly generated ideas.

The system should recognize that a useful solution may be distributed across many sources. One person may identify the problem, another may describe an overlooked cause, another may have attempted a partial solution, and another may propose an idea from an unrelated field. SharedMind should connect these contributions into a coherent possibility space.

The system should preserve disagreement rather than forcing artificial consensus. Conflicting observations, competing explanations, and alternative solutions should remain visible when the available evidence does not establish a clear resolution.

Solution Discovery Philosophy

SharedMind should operate from the principle that the absence of a known solution does not establish that a solution is impossible.

A problem may be constrained by resources, knowledge, infrastructure, economics, policy, technology, social conditions, or other factors. SharedMind should identify those constraints while continuing to explore whether they can be changed, bypassed, combined with other approaches, or addressed through new possibilities.

The system should distinguish between:

A solution that is known to work.
A solution that has been attempted.
A solution with supporting evidence but unresolved limitations.
A proposed solution that requires validation.
A newly generated possibility.
A speculative possibility requiring substantial investigation.

Specification Branding License (SBL)

Standard

Fully AGPL-3.0+ compliant system
Copyleft enforced for network deployments
Required attribution:
Roxanne Ardary
https://www.roxanneardary.com/

Optional

Specification Branding License (SBL)
Attribution-free commercial deployment
Pricing based on scale, usage, and deployment scope
https://roxanneardary.com/sharedmind/

License & Notice Requirements

SharedMind is released under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).

By contributing to any Open Arsenal project, you agree that your contributions will also be released under this license.

Please note the following:

All contributions must comply with the AGPL-3.0+ terms.
Under Section 7 of the license, all redistributions, forks, and derivative works must preserve attribution to Roxanne Ardary and roxanneardary.com.
SharedMind specifications are free to use with attribution. A Specification Branding License can be negotiated upon request.
The project’s notice.md file tracks attribution requirements and contributor acknowledgments. Any update that adds new contributors or modifies attribution should also update notice.md.
When submitting a pull request, ensure that any new files maintain the attribution headers where applicable.
Network-deployed versions of this software must also remain fully AGPL-3.0+ compliant, including exposure of source code modifications when applicable under the license.

For full legal details, please refer to the AGPL-3.0+ license and the project’s notice.md file.

Notice – SharedMind

Attribution Requirement: Under Section 7 of the AGPL 3.0+ license, all redistributions, forks, and derivative works, including network-deployed versions of this project, must provide attribution to Roxanne Ardary and roxanneardary.com.

Contributors

This file tracks contributors and their specific contributions to the project.

Roxanne Ardary, roxanneardary.com – September 16, 2026
Created the repository for SharedMind. Developed the concept and specification for a collective intelligence system that listens to humanity, identifies problems, and uses multi-agent analysis to discover possible solutions.
[Add other contributors here] – [Date]
[Describe contribution in one sentence]

License – SharedMind

This repository is licensed under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).

Key Points:

You are free to use, modify, and distribute the code.
All redistributions, forks, and derivative works or network-deployed versions must also be licensed under AGPL-3.0+ and provide attribution to Roxanne Ardary and roxanneardary.com as required under Section 7 of the license.
The software is provided “as is,” without warranty of any kind.

For the full license text, see GNU AGPL-3.0 License.