DTM 2.0 — From Decision Trace to Meaning Trace

Chinoba — Runtime Society and Coordination Systems
https://chinoba.org

When we first proposed the Decision Trace Model (DTM), our goal was simple.

We wanted to record the decision-making processes of both humans and AI, making them explainable, observable, and reusable.

The original structure was:

Event
↓
Signal
↓
Decision
↓
Boundary
↓
Human
↓
Execution
↓
Log

This structure is still useful.

However, after observing real-world systems and organizations, something became clear.

What truly matters is not merely:

What decision was made.

What matters is:

Why that decision had meaning.

Decision Alone Cannot Explain Everything

Different people often make different decisions even when presented with the same information.

The same AI model can produce very different outcomes in different organizations.

Why?

Because before a decision exists, there is already a process of meaning formation.

Decisions do not emerge from nowhere.

Behind every decision are:

• What is needed (Need)
• What is being pursued (Goal)
• What constraints exist (Constraint)
• What is missing (Lack)

These factors shape how signals are interpreted.

The same signal can have completely different meanings depending on the context in which it is perceived.

Deacon: Meaning Emerges from Absence

Terrence Deacon argues that meaning does not arise from information itself.

Rather, meaning emerges from what is absent.

Living systems act because something is lacking.

Organizations behave in the same way.

There are problems to solve.

There are gaps between the current state and desired goals.

There are risks.

There are futures we wish to realize.

In other words, meaning emerges from:

Lack.

Decision-making itself is a process of responding to absence.

DTM 2.0

If this is true, then Decision Trace should contain more than a sequence of decisions.

It should include the process through which meaning is generated.

The original DTM was:

Event
↓
Signal
↓
Decision
↓
Boundary
↓
Human
↓
Execution
↓
Log

DTM 2.0 expands this into:

Need
Goal
Constraint
Lack
↓
Event
↓
Signal
↓
Interpretation
↓
Decision
↓
Boundary
↓
Human Gate
↓
Execution
↓
Outcome
↓
Learning

The key addition here is:

Interpretation.

Signals themselves do not possess meaning.

Meaning emerges through the relationship between signals and goals, needs, and constraints.

Meaning Trace Model (MTM)

This expanded structure may no longer be best described as a Decision Trace.

Perhaps it should be called:

Meaning Trace Model (MTM)

Need
↓
Goal
↓
Constraint
↓
Lack
↓
Event
↓
Signal
↓
Interpretation
↓
Decision
↓
Execution
↓
Outcome
↓
Learning

What we want to record is not simply:

“What decision was made?”

Instead, we want to capture:

• Why was the situation interpreted that way?
• Why was that particular decision selected?
• Which constraints mattered most?
• Which values were prioritized?
• What was ultimately learned?

The center of gravity shifts from

Decision Trace

to

Meaning Trace.

Context and Boundary Also Matter

Real decisions never exist in isolation.

They are embedded within:

• Organizations
• People
• Rules
• Society
• Trust relationships

Therefore, DTM 2.0 introduces additional dimensions:

Context
Goal
Constraint
Boundary
Trust
Human Gate

The overall structure becomes:

Need
↓
Goal
↓
Constraint
↓
Context
↓
Lack
↓
Event
↓
Signal
↓
Interpretation
↓
Decision
↓
Boundary
↓
Human Gate
↓
Execution
↓
Outcome
↓
Feedback
↓
Learning
↓
Knowledge

Knowledge then influences future needs and contexts.

This is no longer a linear process.

It is a cycle.

DTM 2.0 Approaches a Model of Life

Interestingly, this structure begins to resemble life itself.

Living systems repeatedly perform:

Need
↓
Perception
↓
Interpretation
↓
Decision
↓
Action
↓
Outcome
↓
Learning

Intelligence is not simply about possessing knowledge.

Intelligence is the ability to:

• Generate meaning
• Act
• Learn
• Maintain oneself

DTM 2.0 evolves beyond a model of decision records.

It becomes a model for the continuous cycle of meaning generation and learning.

From DTM 2.0 to Runtime Society

In the knowledge society, information was the central asset.

With Decision Trace, the focus shifted to decisions.

But in the AI era, even decisions themselves are not the ultimate concern.

What matters is:

How meaning is formed, shared, learned, and accumulated as trust.

The true object of the AI era is not Decision.

It is Meaning.

And DTM 2.0 evolves from the Decision Trace Model into the Meaning Trace Model.

From there, it connects naturally to:

• Knowledge Infrastructure
• Trust Infrastructure
• Runtime Society

Ultimately, this perspective allows us to see AI not merely as a generator of knowledge, but as a living system capable of generating meaning, learning, and co-evolving with humans and organizations.

Perhaps this is the foundation of the next generation of intelligent systems.

Chinoba

Chinoba

Intelligence as Relationship

Research Platform

founded by

Masao Watanabe

AI Systems Architecture
Decision Trace
Human–AI Coordination
Algorithmic Governance