Reality Is a Terrible Place to Test an Assumption

Practicing for a speech, recital, or sporting event can feel repetitive, difficult, and largely invisible. No one applauds the rehearsal.

But practice builds the muscle memory, confidence, and judgment we rely on when our actions finally matter.

A game or exercise is the practice space between our plans or assumptions and reality. It allows us to get as close as we reasonably can to the real world, given the time, computing power, and data available, so that we can plan, practice, and make decisions safely.

The point is not to predict the future perfectly. It is to expose uncertainty early, test choices, and improve the odds that a real-world decision will be the right one.

Figure 1. Exercises connect assumptions to reality through practice, feedback, and refinement.

A Practice Space for Better Decisions

Every plan begins with assumptions: what we believe the environment will look like, how other people will act, what information will be available, and which decisions will produce the desired result. Reality eventually tests those assumptions, but waiting for reality can be costly, dangerous, or irreversible.

A game or exercise creates a controlled bridge between the two. Players can make decisions, observe consequences, receive feedback, and adjust their thinking before acting in the real world. The closer the exercise reflects the parts of reality that matter, the more useful the learning becomes.

The Three Main Components of an Exercise

Decrease risk by increasing certainty that a decision is a correct one.

1. A process to define, observe, and record objectives

First, the exercise needs a process that connects what you want to achieve with what can actually be observed in the simulated environment.

This may be as simple as worksheets, facilitator notes, and after-action observations, or as sophisticated as a real-time digital dashboard. The key is to capture evidence, not just impressions.

Key outcome: Measurable objectives and captured data.

2. A world that represents reality

The exercise world is a purposeful representation of reality.

It may be abstract like a board game, where complexity is reduced so the situation remains playable, but real data is still used to make the experience useful. It may also be highly realistic like a flight simulator, where physical detail and timing matter. As games become more strategic, the world generally becomes more abstract.

Key outcome: A believable, data-backed environment to explore.

3. Mechanics, rules, and experts that produce feedback

Mechanics and rules determine how the simulated world responds to player actions.

Experts and models assess whether decisions were effective and explain why. The system should be loaded with enough useful data to provide context, consequences, and guidance without overwhelming the people who need to learn from it.

Key outcome: Meaningful feedback on decisions and outcomes.

How the Feedback Loop Works

At the START…

• Define the goal: What real-world challenge are you looking to solve for, and what fictious scenario or world will help present this event in a safe way.

In WORLD…

• Player action(s): A player makes a decision or takes an action.
• World feedback: The consequences play out in the simulated environment, guided by facilitators or controllers.
• Expert feedback: Experts as evaluators and models interpret the result and add context.

BACK to REALITY…

• Refine and adjust: Once out of the world, apply what was learned to improve the real-world decision.

This loop may be repeated several times. Each round gives participants a chance to challenge assumptions, compare alternatives, and strengthen their understanding. The exercise does not remove uncertainty, but it makes uncertainty more visible and manageable.

Use People When Human Judgment Matters

People are central when the purpose of the exercise is to understand judgment, collaboration, communication, negotiation, or adaptation. Human players bring creativity, bias, emotion, and surprise, which are often exactly what the exercise needs to reveal.

But not every problem requires human participation. When the primary goal is calculation, optimization, or repeated testing, a model without direct human interaction may be faster and more appropriate.

A simple rule applies:

• If you do not need humans to generate the learning, use a model.
• If human decisions are the subject of the learning, put people in the loop in a game or exercise.

This process provides two forms of feedback: feedback from the simulated world and feedback from experts. Together, these help participants revise the assumptions and decisions they will carry into the real world.

The value of scenario-based events is not that they prove a decision to be correct. The value is that they increase confidence, reveal weaknesses, and reduce avoidable risk before the stakes become real.

The Goal: Reduce Risk Before Reality Arrives

Practice does not eliminate uncertainty. It gives us a safer place to confront it. And yes, practice takes planning, commitment, and time.

Reality will eventually test every plan. The goal is to make sure it is not the first to do so.