From Truth to Usefulness: How Change Fitness Turns Ideas into Adaptive Power
In a world where disruption is the norm, leaders are drowning in ideas. Some are brilliant, some are flawed, and many are untested. The challenge isn’t just finding the truth — it’s knowing which truths will actually help you adapt, survive, and thrive.
That’s where three powerful lenses meet:
Karl Popper’s falsifiability — the gold standard for testing whether an idea could be true.
Hazen & Wong’s functional information — a way of understanding how systems evolve by keeping the configurations that work.
Change Fitness — the human and organisational capacity to act on, embed, and compound those learnings.
Together, they form a bridge from epistemic truth to functional usefulness.
Step 1: Popper — Sorting the Testable from the Untestable
Popper’s insight was simple but radical: a theory is scientific only if it can, in principle, be proven wrong. This “falsifiability” filter is the first gate any idea must pass through.
In leadership terms, this means:
Framing strategies, policies, or role designs as hypotheses.
Defining kill criteria before you start.
Running tests that could genuinely disprove your assumption.
Example: A founder believes, “Our ideal customers will pay 20% more for a premium service tier.” Popper’s lens says: make it testable. Offer the tier to a subset of customers, track uptake, and be ready to drop it if the data says no.
Step 2: Hazen & Wong — Building the Pool of “What Works”
Hazen & Wong’s Law of Increasing Functional Information says that complex systems evolve when they:
Generate many different configurations.
Test them under real conditions.
Keep the ones that perform the desired function.
In business, “configurations” might be:
A new pricing model.
A redesigned leadership role.
A different team structure.
A marketing channel you’ve never tried.
Every time you run a test and keep what works, you add to your organisation’s functional information — the bank of know‑how that improves your ability to achieve your goals.
Example: A restaurant experiments with three new delivery packaging designs. One keeps food hotter for longer and gets rave reviews. That design becomes part of the standard operating procedure — functional information gained.
Step 3: Change Fitness — The Human Engine That Makes It Stick
Here’s the catch: knowing an idea is testable and even knowing it works doesn’t mean it will be used. That’s where Change Fitness comes in.
Change Fitness is made up of seven interdependent “muscles”:
| Element | What It Enables |
|---|---|
| Motivation | Energy to try new configurations. |
| Agency | Belief you can influence the outcome. |
| Trust | Safety to share dissent and data. |
| Balance | Emotional steadiness under uncertainty. |
| Vision | Clarity of purpose so changes don’t threaten identity. |
| Insight | Skill in spotting patterns and designing good tests. |
| Empowering Beliefs | Narratives that make being wrong safe. |
If any one of these is weak, it throttles your ability to generate, test, and retain functional information.
From Truth to Usefulness: The Flow
Popper filters for testable ideas.
Hazen & Wong explain how tested ideas add to the “what works” pool.
Change Fitness determines whether you can adopt, apply, and keep those ideas.
Five Factors That Determine Functional Usefulness
The functional usefulness of information depends on:
1. Context Fit
Does the information address the right function in this environment?
Is the goal clear? (“Increase repeat bookings by 15% in 6 months.”)
Does it match market realities and resource limits?
Can it scale beyond a one‑off?
Example: A cruise operator learns that themed events boost bookings in big cities. But in a small coastal town, the limiting factor is tourist volume, not event appeal — so the idea has low functional usefulness there.
2. Actionability
Can it be operationalised quickly and cheaply?
Can it be run as a reversible micro‑experiment?
Are success metrics obvious?
Does it fit into existing decision rhythms?
Example: A retail manager hears about a complex AI inventory system. It’s promising, but requires a year‑long rollout. A simpler reorder‑point tweak can be tested in a week — higher functional usefulness in the short term.
3. Readiness Alignment
Does it match the Change Fitness profile of the people who must act on it?
High Motivation? They’ll try bold tests.
Low Trust? They’ll hide bad results.
Low Balance? They’ll abandon the test at the first wobble.
Example: A team with low Trust won’t share that a new sales script is bombing. The information exists, but it’s not functionally useful because it never enters the selection process.
4. Information Quality
Is it high‑grade “ore” or noisy scrap?
Specific to the function.
Distilled — low noise, high signal.
Context‑validated — tested under real conditions.
Retained — embedded into processes, not just remembered by one person.
Example: A vague “customers like personal service” insight is low‑grade. “Customers who get a follow‑up call within 24 hours are 30% more likely to reorder” is high‑grade functional information.
5. Retention & Compounding
Will it stick and build over time?
Is it codified into SOPs, checklists, or rituals?
Is it shared across the system?
Does it connect to previous learnings to create richer maps?
Example: A hotel discovers that greeting guests by name increases satisfaction scores. They script it into check‑in training and track results — the learning compounds as new staff join.
Putting It All Together: A Worked Example
Imagine you’re coaching the owner of a dinner‑cruise business. He’s hands‑on, reluctant to delegate, and wary of spending on marketing.
Popper: Frame a testable hypothesis — “A $100 targeted ad will generate at least 10 extra bookings in two weeks.”
Hazen & Wong: This is a new configuration (marketing channel) to be tested under real conditions.
Change Fitness:
Motivation: Low — frame it as profit protection.
Agency: High — he controls the spend.
Trust: Low — start with a reversible, low‑risk test.
Balance: Medium — keep the time frame short to avoid stress.
Vision: Absent — link the test to his goal of fuller cruises.
Insight: Low — help design the test and metrics.
Empowering Beliefs: Weak — celebrate the learning, not just the win.
If the ad works, it’s retained as functional information. If it doesn’t, the learning still adds to the “what works” map — perhaps “Facebook ads don’t convert for weekday cruises” — and the next configuration can be tested.
Why This Matters
In a volatile world, the winners aren’t those with the most ideas, but those with:
The discipline to test them (Popper).
The systems to keep what works (Hazen & Wong).
The fitness to act on and embed the learning (Change Fitness).
When you combine these, you create a self‑reinforcing loop:
More configurations tested.
More functional information gained.
Greater adaptive capacity.
Higher readiness for the next change.
Truth is necessary, but not sufficient. Functional usefulness is truth that’s been tested, adopted, and embedded — and Change Fitness is the human engine that makes that possible.