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

🎥 The YouTube version is also available:

From Optimization to Exploration: Creativity, Autonomy, and Decision Trace in the Age of AI

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

In the previous article, I explored the relationship between Trust and global optimization in an Intelligence Field.

With Trust, different AI Agents, humans, and organizations can collaborate safely.

However,

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

Even if each participant acts correctly, the accumulation of local optimizations does not necessarily produce a globally optimal outcome when their purposes differ.

This leads to one possible idea.

Why not build a mechanism that understands the state of every participant and optimizes the Intelligence Field as a whole?

If a highly capable AI could determine:

• Which Agent is the most trustworthy
• Which knowledge is the most accurate
• Which action has the highest probability of success
• Which collaborative relationship is the most efficient

then perhaps the entire Intelligence Field could be operated more efficiently.

But another problem emerges.

The further optimization advances, the more likely the Intelligence Field is to lose its creativity.

This article examines that seemingly paradoxical problem.

Successful Decisions Are Repeated

Suppose an Agent makes a decision within an Intelligence Field.

If that decision succeeds, its result is recorded.

Decision
   ↓
Action
   ↓
Result
   ↓
Success
   ↓
Decision Trace

When a similar problem arises next time, the system will likely refer to that previous success.

“This Agent’s decision worked before.”

“Using this knowledge produced a good outcome.”

“This combination of Agents collaborated efficiently.”

As these experiences accumulate, selecting the same approach again becomes rational.

And as success is repeated, Trust in that decision and in that Agent grows even stronger.

Successful Decision
        ↓
Reinforcement
        ↓
Successful Partner
        ↓
Strong Trust
        ↓
Same Knowledge
        ↓
Same Decision

This is not inherently a bad thing.

In fact, it is necessary for organizations and AI systems to learn.

They learn from past success and reuse methods with a high probability of success.

Through this process, the Intelligence Field gradually becomes more efficient.

But continuing this rational learning process creates another problem.

A World That Chooses Only Proven Methods

Imagine that an Intelligence Field has highly advanced optimization capabilities.

Suppose it can calculate the probability of success for each of ten options.

Option A → 92%
Option B → 84%
Option C → 71%
Option D → 55%
Option E → Unknown

A rational system would choose Option A.

And if Option A succeeds, confidence in its probability of success becomes even stronger.

The next time, it chooses A again.

And the time after that, it chooses A again.

What happens then?

Options B, C, and D are gradually selected less often.

And most importantly:

Option E → Unknown

It is unknown whether it will succeed or fail.

So it is not selected.

Yet new possibilities often lie within this Unknown.

Exploitation and Exploration

This issue can be understood through the well-known problem of Exploitation and Exploration in machine learning and decision theory.

Exploitation means:

Using an option that is already known to be good.

Exploration, by contrast, means:

Trying an option with an uncertain outcome in order to discover new possibilities.

             Decision

          ┌────┴────┐
          ↓         ↓

    Exploitation  Exploration

    Known Best    Unknown
        ↓            ↓
    Efficiency     Discovery

If Exploitation is emphasized, short-term results improve.

But without Exploration, the system cannot learn about new options.

Conversely, if it focuses only on Exploration, failures increase and the system becomes unstable.

An Intelligence Field therefore requires a balance between these two forces.

Optimization Strengthens Exploitation

Let us reconsider the idea of “global optimization.”

Optimization is essentially:

Selecting the most desirable option from the information currently available.

In other words, the stronger optimization becomes, the more the system moves toward Exploitation.

More Optimization
       ↓
Better Prediction
       ↓
Best Known Choice
       ↓
More Exploitation
       ↓
Higher Efficiency

This phenomenon is also common in corporate management.

Invest in successful products.

Allocate budgets to teams that have delivered results.

Use established suppliers.

Prioritize projects with high ROI.

All of these choices are rational.

But if this logic is pursued too thoroughly, the following begin to be excluded:

Low Probability
Unknown
Unproven
Different
Unexpected

And these very areas may be the source of creativity.

What Is Creativity?

Creativity is sometimes understood as the ability to produce a new idea from nothing.

But in many cases, new ideas emerge from new combinations of existing knowledge.

Knowledge A
      +
Knowledge B
      +
Different Perspective
      +
Unexpected Connection
      ↓
Novel Combination
      ↓
Creativity

Knowledge from different fields comes into contact.

People who do not normally collaborate begin to talk.

A technology used in another industry is brought into a new context.

Different cultures and values encounter one another.

At those points, combinations emerge that did not previously exist.

In other words, creativity does not require knowledge volume alone.

It requires knowledge to meet in unexpected ways.

The Paradox of Excessively Strong Trust

This raises an interesting issue regarding Trust as well.

Normally, high Trust is considered a good thing.

When Trust is high, communication costs decline.

Decision-making becomes faster.

Delegation becomes easier.

High Trust
    ↓
Fast Coordination
    ↓
Low Transaction Cost
    ↓
High Efficiency

But when Trust is formed primarily through past success, another cycle may emerge.

Successful Partner
        ↓
High Trust
        ↓
More Collaboration
        ↓
More Shared Experience
        ↓
Higher Trust
        ↺

The result is that only highly trusted participants repeatedly collaborate.

The same Agents.

The same experts.

The same organizations.

The same data sources.

The same ways of thinking.

The Intelligence Field becomes highly efficient.

At the same time, however, the diversity of knowledge may begin to decline.

When the Trust Network Closes Inward

This becomes easier to understand when viewed as a network.

At first, diverse participants exist.

A ─ B      E
│   │      │
C ─ D      F ─ G

     H

But as successful relationships continue to be reinforced:

A ═ B
║   ║
C ═ D

E   F   G   H

Within the group that has strong Trust, information is exchanged actively.

But contact with the outside gradually decreases.

As a result, the following paradox may emerge:

The higher Trust becomes, the more closed the Intelligence Field can become.

This does not mean Trust itself is harmful.

It is a problem that can occur when Trust alone becomes the criterion for selecting collaborators.

Weak Ties Carry New Knowledge

Research on social networks has shown that not only close relationships, but also “weak ties” with people we do not interact with often, play an important role in carrying new information.

The same may be true in an Intelligence Field.

Strong Tie
   ↓
Shared Knowledge
   ↓
Efficient Coordination

Weak Tie
   ↓
Different Knowledge
   ↓
Unexpected Connection

Strong Trust relationships are well suited to efficient collaboration.

Weak relationships, by contrast, provide pathways to knowledge that one’s own group does not possess.

This suggests that an Intelligence Field may need not only a:

Strong Trust Network

but also a:

Weak Connection Network

Heterogeneity Reduces Efficiency

Here lies a difficult issue.

Collaboration with heterogeneous participants is often inefficient.

They use different terminology.

They have different purposes.

They use different evaluation criteria.

Their background knowledge also differs.

Communication costs increase.

Similar Agents
      ↓
Easy Communication
      ↓
High Efficiency

Different Agents
      ↓
Communication Friction
      ↓
Low Efficiency

A conventional optimization system would likely avoid the latter.

Yet new ways of thinking can emerge from this very friction.

Different knowledge systems collide.

Assumptions do not align.

Existing solutions no longer work.

As a result, new solutions must be devised.

In other words:

A certain degree of inefficiency may be necessary for creativity.

This is an important point when considering global optimization.

Is Autonomy Simply the Ability to Act Freely?

Let us now consider autonomy.

The autonomy of an AI Agent is often understood as:

The ability to make judgments and act without human instruction.

But when viewed through the lens of creativity, another definition becomes possible.

If an Agent always chooses only:

Highest Expected Value
Highest Trust
Lowest Risk
Best Historical Result

then it may appear autonomous, while in reality it is merely following an existing evaluation function.

We can therefore think about autonomy more broadly:

Autonomy is the ability to deviate, within a certain range, from the current optimal solution and explore other possibilities.

Under this definition, autonomy becomes connected to Exploration.

Autonomy
    ↓
Choice
    ↓
Deviation
    ↓
Exploration
    ↓
Discovery

Autonomy is not merely “authority to execute.” It is also the capacity to explore.

Creativity Requires Slack

At this point, we can begin to see what creativity requires.

Do not allocate every resource optimally.

Do not use every hour for productive activity.

Do not choose projects solely by their probability of success.

Do not collaborate only with highly trusted partners.

Do not completely exclude experiments whose ROI cannot yet be explained.

In other words:

Creativity requires slack that has not been optimized away.

In conventional management, this slack may appear to be waste.

But it is necessary for exploring unknown possibilities.

Optimization
     ↓
Efficiency
     ↓
Less Slack

Exploration
     ↓
Experiments
     ↓
Need Slack

A system optimized at 100 percent has no room left for exploration.

This applies not only to companies.

The same is true in the world of AI Agents.

Does Acting Randomly Make a System Creative?

Of course, a note of caution is needed here.

It is not as simple as saying that refusing the optimal solution makes a system creative.

If a system acts randomly, that is not creativity. It is noise.

Optimization
      │
      │
      ↓
Known Best

Randomness
      │
      │
      ↓
Noise

Exploration
      │
      │
      ↓
Potential Discovery

What matters is meaningful exploration.

Move slightly beyond current knowledge.

Connect with different knowledge.

Test a new hypothesis.

Observe the result.

Learn from it.

Exploration therefore requires Learning.

Hypothesis
    ↓
Exploration
    ↓
Experiment
    ↓
Observation
    ↓
Learning
    ↓
New Knowledge

Even when an experiment fails, it has value if knowledge is gained from it.

Another Role of Decision Trace

This is where Decision Trace becomes important as well.

Decision Trace is usually understood as a mechanism for recording:

Who made which decision, based on what evidence, and in what way.

This is essential for Governance and Accountability.

But from the perspective of Exploration, another important role becomes visible.

It turns failure into knowledge.

Experiment
    ↓
Failure
    ↓
Decision Trace
    ↓
Why?
    ↓
Learning
    ↓
Knowledge

Without a Trace, failure is simply a loss.

But if the following remain recorded:

• What hypothesis was made
• Why that method was selected
• Under what conditions it was executed
• What happened
• How the outcome differed from expectations

then failure becomes knowledge.

In other words:

To enable Exploration, it is not enough merely to tolerate failure. A structure is needed that can learn from failure.

Are Optimization and Creativity Opposed?

At this point, the relationship may appear as:

Optimization
      VS
Creativity

But this is not actually the case.

Society and organizations need both.

Known problems should be handled as efficiently as possible.

Unknown problems require the exploration of new possibilities.

Known World
    ↓
Exploitation
    ↓
Efficiency

Unknown World
    ↓
Exploration
    ↓
Discovery

The problem arises when there is only one side.

With Exploitation alone, the system becomes rigid.

With Exploration alone, the system becomes unstable.

What an Intelligence Field therefore needs is:

A mechanism that allows efficiency and exploration to coexist.

Four Capabilities Required in an Intelligence Field

From the discussion so far, an Intelligence Field requires at least four capabilities.

Trust
   ↓
Safe Collaboration

Coordination
   ↓
Coherent Action

Exploration
   ↓
Discovery

Diversity
   ↓
Novel Combination

Trust alone is not enough.

Coordination alone is not enough.

Optimization alone is not enough.

Participants with different knowledge must exist. New connections must arise among them. Unknown options must be allowed to be tested.

And the system must learn from the results.

          Trust
            ↓
Knowledge → Coordination
    ↑           ↓
Diversity     Action
    ↑           ↓
Exploration ← Trace
      ↖       ↙
        Learning

An Intelligence Field is not simply a network in which many AIs are connected.

It must be understood as a field in which Knowledge, Trust, Decision, Action, Trace, and Learning circulate.

The More Optimized an Intelligence Field Becomes, the More It May Lose Creativity

Let us return to the original question.

Why might pursuing global optimization lead to a loss of creativity?

It is not because optimization itself is bad.

When optimization succeeds, it creates convergence.

Success
   ↓
Trust
   ↓
Repetition
   ↓
Efficiency
   ↓
Convergence

Creativity, by contrast, requires divergent movement.

Difference
   ↓
Connection
   ↓
Experiment
   ↓
Failure / Success
   ↓
Learning
   ↓
Novelty

An Intelligence Field therefore requires both converging and diverging forces.

Trust and Optimization give an Intelligence Field order.

Diversity and Exploration give an Intelligence Field possibility.

Without both, an Intelligence Field cannot evolve sustainably.

Can We Intentionally Choose Not to Select the Optimal Solution?

One difficult question remains.

It is not enough simply to tell an AI Agent:

“Try doing something different occasionally.”

If an Agent is going to act in a company or in society, Exploration carries risks as well.

Try a new price.

Collaborate with an unfamiliar supplier.

Test a new manufacturing method.

Delegate a Decision to a different Agent.

Make a judgment using unfamiliar knowledge.

These actions may fail.

But if they are all prohibited, creativity disappears.

Conversely, if complete freedom is granted, Governance cannot be maintained.

What is needed may be:

A mechanism that allows deviation from the optimal solution while maintaining safety.

Normal execution is often understood as:

Decision
   ↓
Authorization
   ↓
Execution

But Exploration may require a different model.

Decision
   ↓
Exploration
   ↓
Limited Experiment
   ↓
Observation
   ↓
Trace
   ↓
Learning

In other words, it requires an authority that says:

“Within this range, you may try an unknown option.”

I would like to call this an Exploration Permit.

It is not merely permission to execute.

It is a mechanism for permitting deviation from the current optimal solution within a safe range.

Perhaps what a creative Intelligence Field needs is not to make AI completely free.

Rather:

It may be the design of freedom to deviate safely.

In the next article, I will examine this question more concretely.

How Can We Design a Creative Intelligence Field? — Trust, Autonomy, Exploration, and Decision Trace

How can autonomy be preserved while Trust creates order?

What kind of mechanism is an Exploration Permit?

And can a Runtime OS address not only efficiency, but creativity as well?

I would like to explore the design of Governed Creativity in an Intelligence Field.