Adaptive Visualization Usage, Personalization, and Collective Visual Defaults¶

Summary¶

MAP and DAHN visualization behavior is inherently adaptive.

Visualization is not treated as a fixed rendering layer attached statically to a type. Instead, visualization behavior emerges from:

local personalization
repeated interaction patterns
salience signaling gestures
affinity signaling through navigation and embedding
contextual usage patterns
collective aggregation within shared spaces

This document captures a conceptual model for:

personalized visualization state
visualization usage metrics
salience and affinity signaling
adaptive visual defaults
collective visualization evolution
sovereignty-preserving aggregation

This pattern is closely related to descriptor usage and schema evolution, but it concerns visualization behavior rather than schema structure.

1. Core Principle¶

Visualization in MAP is not purely declarative.

It is adaptive, contextual, and socially shaped.

The same holon type may be visualized differently:

by different agents
in different spaces
in different execution contexts
for different purposes
at different levels of detail
with different salience priorities

Visualization therefore becomes:

a living behavioral layer over the ontology.

2. Personalization as First-Class State¶

Core idea¶

Each agent may personalize how holons are visualized.

This includes choices such as:

which visualizer to use for a holon type
which value visualizer to use for a property type
which collection visualizer to use
which relationships are expanded by default
which properties are hidden
which properties are emphasized
which sections are collapsed
ordering and layout decisions
embedding preferences
preferred navigation paths
preferred detail levels

These choices are not incidental UI state.

They are durable expressions of local salience and meaning.

Personalization persistence¶

The system therefore needs a place to persist visualization choices so that future interactions can reuse them.

For example:

When an agent revisits a holon type, the system should bias toward previously preferred visual arrangements.

This implies some form of:

VisualizerUsage
VisualizationPreference
VisualizationContext
VisualizerSelection
VisualizationProfile

or related concepts.

3. Visualization Gestures as Semantic Signals¶

Core idea¶

Visualization interactions are not merely interface events.

They are semantic signals.

They communicate:

salience
affinity
attention
importance
workflow relevance
conceptual grouping
contextual usefulness

This means interaction telemetry is not simply analytics data. It becomes part of the adaptive intelligence layer of the MAP.

4. Salience Signals¶

Property salience¶

Gestures such as:

moving properties upward in a layout
pinning properties
expanding properties
repeatedly accessing properties
revealing hidden properties
suppressing properties
resizing visual emphasis

all act as salience indicators.

These suggest:

This property is important in this context.

Possible derived metrics:

property display frequency
property expansion frequency
property reorder frequency
average visual prominence
hide frequency
reveal frequency
interaction dwell time
edit frequency
sort priority frequency

Relationship salience¶

Relationship navigation is also a salience signal.

For example:

repeatedly traversing a relationship
expanding a relationship inline
pinning a relationship view
embedding target holons
visualizing relationship collections

suggests that:

This relationship is operationally important.

Possible derived metrics:

traversal frequency
inline expansion frequency
embed frequency
relationship dwell time
relationship reuse frequency
relationship persistence frequency

5. Affinity Signals¶

Relationship affinity¶

Frequent traversal between two holon types suggests affinity.

For example:

When viewing holons of type T1, users frequently navigate relationship R to holons of type T2.

This indicates:

conceptual association
workflow coupling
operational relevance
cognitive adjacency

Affinity is stronger than simple salience.

Salience means:

This element matters.

Affinity means:

These elements meaningfully belong together.

Embedding as affinity signaling¶

Embedding gestures are especially strong affinity indicators.

For example:

embedding a target holon inside another holon's visualization
repeatedly visualizing two holon types together
preserving side-by-side layouts
nesting collections within parent views

suggests:

These structures are cognitively and operationally coupled.

Embedding therefore acts as a strong affinity signal between:

holon types
relationship types
property groups
visualization contexts

6. Visualization Usage Records¶

Core idea¶

Just as descriptor usage captures schema interaction patterns, visualization usage records capture visualization interaction patterns.

These records become the natural place to accumulate:

personalization state
salience metrics
affinity metrics
layout preferences
visualization context usage
adaptive rendering signals

Possible concepts:

VisualizerUsage
VisualizationUsage
VisualizationPreference
VisualizationAffinity
VisualizationContextUsage

The exact decomposition remains open.

7. Suggested Conceptual Model¶

VisualizationUsage¶

Captures how a holon type or descriptor is visualized within a particular context.

Because this usage relationship is intentionally descriptor-agnostic, the visualized descriptor target should use the v2.0 generic descriptor root rather than the removed TypeDescriptor node.

Possible properties:

Property	Purpose
`viewer_ref`	Agent or viewer
`space_ref`	Space where usage occurred
`descriptor_ref`	Descriptor being visualized
`visualizer_ref`	Visualizer selected
`usage_count`	Number of uses
`last_used_at`	Most recent use
`context_signature`	Contextual usage key
`salience_profile`	Derived salience vector
`affinity_profile`	Derived affinity vector

Possible relationships:

Relationship	Target
`UsesVisualizer`	`Visualizer`
`VisualizesDescriptor`	`DescriptorRoot`
`HasPropertyVisualizationMetric`	`PropertyVisualizationMetric`
`HasRelationshipVisualizationMetric`	`RelationshipVisualizationMetric`
`HasEmbeddingAffinityMetric`	`EmbeddingAffinityMetric`

8. Property Visualization Metrics¶

Purpose¶

Captures visualization-specific interaction patterns for properties.

Possible metrics:

Metric	Meaning
display frequency	How often property is shown
hide frequency	How often property is hidden
reorder frequency	How often property is repositioned
expansion frequency	How often expanded
edit frequency	How often edited
pin frequency	How often pinned
visual prominence score	Aggregate salience score
contextual relevance score	Relevance in specific contexts

These metrics become signals for:

default layouts
adaptive forms
progressive disclosure
intelligent summarization
compact vs expanded rendering
context-aware visualization

9. Relationship Visualization Metrics¶

Purpose¶

Captures visualization and navigation patterns involving relationships.

Possible metrics:

Metric	Meaning
traversal frequency	How often traversed
inline expansion frequency	How often expanded inline
embed frequency	How often targets embedded
co-navigation frequency	Frequently traversed with other relationships
persistence frequency	How often kept open
affinity score	Strength of association

These metrics support:

adaptive navigation
predictive expansion
graph visualization weighting
context-aware relationship ordering
embedded visualization suggestions

10. Visualization Affinity¶

Core idea¶

Visualization affinity reflects the tendency for structures to be experienced together.

This may arise from:

repeated co-visualization
repeated embedding
repeated navigation
repeated simultaneous expansion
repeated contextual grouping

Possible affinity relationships:

Source	Relationship	Target
`HolonType`	`HasVisualizationAffinity`	`HolonType`
`RelationshipType`	`HasVisualizationAffinity`	`RelationshipType`
`PropertyDescriptor`	`HasVisualizationAffinity`	`PropertyDescriptor`

These affinities can later influence:

layout recommendations
graph clustering
embedded views
adaptive dashboards
contextual summarization
predictive navigation

11. Contextual Visualization¶

Visualization is context-sensitive¶

Visualization preferences likely depend on more than:

holon type
visualizer type

Additional contextual factors may eventually matter:

current workflow
active dance
device form factor
interaction mode
urgency
collaboration state
role
trust context
cognitive load
current task
time horizon
editing vs browsing mode

The architecture should therefore avoid assuming that visualization preference is globally fixed.

A likely pattern is:

visualization preference = descriptor + context signature

The exact structure of the context signature remains open.

12. Personalization vs Collective Defaults¶

Personalization¶

Each agent should retain sovereignty over personal visualization choices.

This includes:

visualizer selection
property ordering
hidden fields
preferred expansions
preferred layouts
embedding preferences
interaction density
navigation preferences

These choices directly shape the local experience.

Collective influence¶

At the same time:

Personalization choices may contribute signals toward collective defaults.

This creates a feedback loop:

Individuals personalize views.
Personalizations generate salience and affinity metrics.
Metrics aggregate within a community context.
Shared defaults emerge.
Individuals may still override them locally.

This allows the system to evolve adaptive defaults without imposing rigid centralized behavior.

13. Sovereignty and Aggregation¶

Important concern¶

Agents or spaces may not want raw interaction telemetry sent to a centralized stewarding space.

This is important for:

sovereignty
privacy
autonomy
cultural differentiation
political independence
trust

Therefore:

Visualization aggregation should likely be community-scoped rather than globally centralized.

Community-scoped aggregation¶

Instead of sending metrics to the stewarding space for a descriptor, usage aggregation may occur within:

intentional communities
bioregional spaces
communities of practice
organizational spaces
affinity groups
collaborative networks

This produces:

community-adapted defaults rather than universal defaults.

This feels significantly more MAP-aligned than globally centralized UI optimization.

Consequence¶

Different communities may evolve:

different default visualizers
different salience assumptions
different relationship emphases
different embedding conventions
different navigation defaults
different cognitive maps

without fragmenting the underlying ontology.

14. Relationship to Descriptor Usage¶

Visualization usage is related to descriptor usage, but they should remain distinct.

Descriptor usage concerns:

schema interaction
retrieval behavior
hydration behavior
traversal behavior
runtime optimization

Visualization usage concerns:

perception
cognition
salience
layout
interaction
presentation
embedding
navigation experience

The two systems may inform each other, but they are not the same.

For example:

frequently traversed relationships may suggest visualization affinity
frequently displayed properties may influence prefetch policy
visualization salience may inform retrieval salience
retrieval metrics may inform adaptive rendering

But the conceptual separation remains useful.

15. Possible Architectural Layers¶

Layer 1 — Personal Visualization State¶

Local durable preferences:

visualizer choices
layout choices
hidden/revealed state
ordering
embedding choices

Layer 2 — Visualization Usage Metrics¶

Behavioral telemetry:

salience
affinity
traversal patterns
interaction patterns
co-visualization patterns

Layer 3 — Community Aggregation¶

Community-level adaptation:

shared defaults
common layouts
common embeddings
common expansions
common navigation patterns

Layer 4 — Adaptive Visualization Engine¶

Runtime adaptation:

predictive expansion
contextual rendering
progressive disclosure
adaptive dashboards
recommendation systems
personalized defaults

16. Conceptual Framing¶

The deeper pattern emerging here is:

Visualization is not merely presentation.
Visualization behavior is a form of collective sensemaking.

Or:

Salience is expressed through interaction.

Or:

Repeated visualization gestures become votes about meaning.

Or:

Personalization becomes collective adaptation without sacrificing sovereignty.

This gives MAP and DAHN a path toward:

deeply adaptive interfaces
context-aware rendering
sovereignty-preserving personalization
community-shaped defaults
semantically meaningful interaction metrics
evolving cognitive landscapes
participatory interface evolution

without collapsing into centralized behavioral optimization.