Can Trust Alone Deliver Global Optimization in an Intelligence Field?

🎥 The YouTube version is also available:

Can Trust Alone Deliver Global Optimization? | Intelligence Fields, Coordination, Global Adaptation

Books: Trust Infrastructure Practical Guide: AI-Era Trust Infrastructure Design with Decision Trace, Knowledge Flow, and Trust Engine

As AI agents become more capable, we are moving toward a world in which problems are not resolved by one enormous AI making every decision. Instead, multiple AI systems, people, organizations, and systems will work together to solve them.

I have been thinking of an environment in which many intelligent actors interact in this way as an Intelligence Field.

Within an intelligence field, each actor has different knowledge, capabilities, objectives, and authority.

AI Agent
Human
Organization
System
Knowledge
        ↓
Intelligence Field

Trust is what becomes essential when these different actors collaborate.

Can we trust the other party’s capabilities?

May we use the information it holds?

How far should we accept its judgment?

To what extent may we delegate execution to that actor?

Trust Infrastructure can be understood as the foundation that enables different actors to collaborate safely while answering these questions.

But an important question arises here.

If actors that trust one another collaborate, will the system as a whole truly function well?

More specifically:

Can collaboration based on Trust alone achieve global optimization?

The answer is: not necessarily.

Trust is a necessary condition for collaboration, but not a sufficient condition for global optimization.

In this article, I would like to examine this question.

What Trust Solves

When multiple actors collaborate, the first requirement is to determine whether the other party can be trusted.

For example, imagine that one AI agent receives information from another agent. At a minimum, it must be able to assess the following:

• Who is this agent?
• Is it authorized to provide this information?
• Where did the information come from?
• How reliable is it?
• May we entrust the next process to this agent?
• Who is responsible if something goes wrong?

Without answers to these questions, safe collaboration cannot be established.

This is why Trust Infrastructure is needed.

Conceptually, the relationship looks like this:

Identity
   ↓
Capability
   ↓
Authority
   ↓
Trust
   ↓
Delegation
   ↓
Coordination

Only after Trust has been established can we say:

“This information may be used.”

“This judgment may be accepted within a defined scope.”

“This process may be delegated to this agent.”

Trust is therefore a critically important foundation for a multi-agent society.

But what Trust guarantees is the possibility of collaboration. It does not necessarily guarantee an outcome that is desirable for the whole.

Even When Everyone Is Right, the Whole May Not Be Right

Consider a company.

Within a company, different departments pursue different aims.

Sales wants to increase revenue.

Manufacturing wants to improve production efficiency.

Finance wants to improve profit margins and cash flow.

Customer service wants to improve customer satisfaction.

If each department is supported by an AI agent, the structure may look like this:

Sales Agent
→ Maximize Revenue

Manufacturing Agent
→ Maximize Efficiency

Finance Agent
→ Maximize Profitability

Customer Agent
→ Maximize Customer Satisfaction

None of these agents is wrong. Each is making sound decisions within its own role.

Let us also assume that the agents trust one another and exchange information accurately. The problem still remains.

A Sales Agent may propose substantial discounts to maximize revenue.

A Customer Agent may greatly relax return conditions to improve customer satisfaction.

A Manufacturing Agent may seek to reduce product variety in order to improve production efficiency.

A Finance Agent may demand cost reductions to increase profit margins.

Each decision is locally rational. But there is no guarantee that their combined effect will be optimal for the company as a whole.

Local Optimum and Global Optimum

This problem can be understood through the distinction between a Local Optimum and a Global Optimum.

Sales
  ↓
Local Optimum

Manufacturing
  ↓
Local Optimum

Finance
  ↓
Local Optimum

Customer Service
  ↓
Local Optimum

        ↓

Global Optimum ?

Even if every actor optimizes its own objective function, the sum of those optimizations does not necessarily become a global optimum.

In fact, organizations often experience the opposite: every department meets its KPIs, yet company-wide performance does not improve. Each system becomes more efficient, yet the overall operation becomes more complex. Each agent makes rational decisions, yet the organization as a whole takes contradictory actions.

This is not a problem unique to AI. It has long existed in human organizations.

But as AI agents begin to act autonomously, it becomes more significant. In human organizations, when contradictions arise, people can call a meeting, managers can coordinate, and exceptions can be handled. In a world where many AI agents make autonomous decisions at high speed, conflicts between local optima also accelerate.

Would More Trust Solve the Problem?

At this point, we may think: “Wouldn’t it be enough to increase Trust among the agents?”

But that still does not solve the fundamental problem, because Trust and Optimization are different issues.

Trust answers the question:

With whom can we collaborate?

Optimization, by contrast, seeks to answer:

What should we aim for?

These questions may appear similar, but they are fundamentally different.

Trust
↓
Can we collaborate?

Optimization
↓
What should we optimize?

Even trusted actors can conflict if their objectives differ. Conversely, even when objectives are aligned, collaboration is impossible if the actors cannot trust one another.

An intelligence field therefore contains both problems:

Trust Problem
Who can collaborate?

        +

Coordination Problem
How should they collaborate?

        +

Optimization Problem
What should the whole system pursue?

Trust Infrastructure alone cannot answer this final question.

Who Is Included in “the Whole”?

There is an even more difficult issue: what does “the whole” mean when we speak of global optimization?

If we define the company alone as the whole, maximizing profit may seem rational. But if we include customers, that is not enough. If we include employees, working conditions must also be considered. If we include business partners, the sustainability of the supply chain becomes relevant. And if we include society, the environment and public value must also be taken into account.

Agent
  ↓
Team
  ↓
Company
  ↓
Supply Chain
  ↓
Industry
  ↓
Society

In other words, the meaning of “optimal” changes each time we expand the scope.

This is crucial.

Global optimization cannot be defined without first defining the boundary of the whole.

And that boundary itself is not necessarily fixed. In an intelligence field, different companies, AI systems, people, public institutions, and communities may connect dynamically. Under such conditions, the very idea of optimizing the whole with one fixed objective function becomes difficult.

Can an Intelligence Field Be Optimized Centrally?

This leads to the next idea: why not centralize all information and place an AI at the center that can see the entire system?

Agent A ─┐
Agent B ─┤
Agent C ─┼→ Global Optimization AI
Agent D ─┤
Human  ──┘
              ↓
        Global Decision

The central AI would understand the objectives, resources, constraints, and risks of each agent, and calculate the decision that is optimal for the system as a whole.

At first glance, this appears rational. Where a problem is bounded, objective functions are clear, necessary information is available, and environmental change is limited, this approach can indeed be effective.

But the more an intelligence field approaches the complexity of the real world, the harder the problem becomes.

Can a central AI truly know all necessary information?

Can the different interests of different companies be integrated into a single objective function?

Can privacy and corporate secrets all be handed over to the center?

Who defines what is “optimal” for people and organizations with different values?

And above all:

Should every actor be required to accept the optimum defined by a central AI?

This is not merely a question of AI architecture. It is a question of governance.

Is Global Optimization Something We Calculate?

This brings us to a more fundamental question.

Perhaps we have treated global optimization too much as a problem of:

Observe
   ↓
Calculate
   ↓
Optimal Solution
   ↓
Execute

In real organizations and societies, the environment constantly changes.

New technologies emerge. Customer values change. Competitive conditions shift. New agents join. Knowledge that was correct yesterday can become outdated today.

Does a fixed global optimum really exist in such a world?

Perhaps what an intelligence field needs is not the ability to calculate the right answer once, but the ability to continually revise itself as a whole by observing the results of interactions.

Decision
   ↓
Action
   ↓
Result
   ↓
Trace
   ↓
Observation
   ↓
Learning
   ↓
New Decision
   ↺

This also changes the meaning of the Decision Trace.

A Decision Trace is not simply an audit log. It records who made a judgment, on what basis, how that judgment was made, and what happened as a result. It can then become information from which the intelligence field as a whole learns.

Global optimization, then, may not be a static state imposed from the center. It may be a dynamic state continually formed through the interaction and feedback of many actors.

What We Need to Consider After Trust

To summarize, Trust Infrastructure is extremely important.

Without Trust, different agents, people, and organizations cannot collaborate safely.

However:

Trust
≠
Global Optimization

More precisely, we need to think in terms of a larger structure:

Trust
   ↓
Collaboration
   ↓
Coordination
   ↓
Action
   ↓
Feedback
   ↓
Adaptation

Trust is the starting point. But Trust alone does not ensure that an intelligence field will move in a desirable direction.

That is why, when designing an intelligence field, we need to consider not only whom to trust, but also how to coordinate actors with different objectives, and how the whole learns from the results.

Should We Put a “Global Optimization AI” at the Center?

One final question remains.

The accumulation of local optima does not create a global optimum. Trust alone does not solve the problem. So should we place a “Global Optimization AI” at the center of the intelligence field to coordinate everything?

But if a central AI always makes the optimal decision and every actor follows it, a different problem emerges.

Inefficient options are eliminated.

Actions with a low probability of success are no longer selected.

Agents, knowledge, and methods that succeeded in the past are repeatedly reused.

The system gradually becomes more efficient.

But at the same time:

It may stop exploring unknown possibilities.

Here, another issue emerges—one distinct from global optimization:

Creativity.

Are optimizing the whole and creating new possibilities really directed toward the same goal?

Or might a more optimized intelligence field be more likely to lose its creativity?

Next time, I will explore this question:

Why Does Creativity Diminish as We Pursue Global Optimization? — The Problem of Exploitation and Exploration

Why does an intelligence field that has learned to collaborate through Trust need mechanisms that deliberately avoid always choosing the optimal answer?

From there, we can examine the relationship between autonomy and creativity.