Find Direction by Imagining Your Own Digital Twin

• Her digital twin tries five different antidepressants to find what’s best.
• Her digital twin spends a week in Paris following five different itineraries, to see which vacation plan she’d enjoy most.
• Her digital twin eats five different lunches to see how they affect her afternoon energy levels.

2. Which Imagined Experiments Could You Actually Try Without a Digital Twin?

Look at your list and notice which you could already test. You might be surprised how many are more feasible than you initially thought.

• Erin realizes the lunches experiment would be easy to try. She’d need to think about her grocery shopping list a bit more than usual, but it wouldn’t require much more than that.
• For her upcoming vacation, she devises a creative way to simulate an experiment. She could ask artificial intelligence (AI) to generate Paris itinerary options and create a rating system for her and her husband to rank them by. It’s not quite the same as a digital twin taking the trips, but she thinks they know themselves well enough that they could differentiate between concrete options if given them.

3. What Real Life Friction Points Is the Digital Twin Solving?

Now look at the experiments that seem impossible. Digital twins solve friction points that would occur with real life experiments. For example, in real life you can’t usually run unlimited simulations, so the twin solves the problem of needing to prioritize. For Erin, clearly she couldn’t try five different antidepressants. But even trying one alternative to her current medication seems difficult.

Erin wonders: How could she make a multi-month plan with her doctor that would need multiple appointments, when he always seems to be rushing?

Sometimes identifying tangible roadblocks makes their solutions obvious. Other times the solutions aren’t as easily apparent. For someone like Erin, when she has a concrete problem to solve, she’s very confident that if something were important enough to her, she’d be able to figure out a way to make it possible.

When we’re clear about what friction points the digital twin idea solves, we can sometimes solve them another way.

4. When You Put Your Digital Twin Through Different Scenarios, How Are You Measuring the “Best”?

When anyone runs an experiment, there are two main parts: what you manipulate (and for how long) and how you measure the outcomes. When a scientist does this, it forces them to think about what they’re optimizing for and any trade-offs.

Imagining experiments you’d do with a digital twin can be clarifying in the same way.

• If your digital twin tests how different lunches affect afternoon energy, do you mean energy at 1 p.m., 3 p.m., or 5 p.m.? And, where does enjoying what you eat come into it?
• When your digital twin goes to Paris, what makes an itinerary “best”? Avoiding jet lag? Having peak moments? Returning home relaxed? Minimizing squabbles with your partner?
• If your digital twin tries different antidepressants, is the “best” one determined by which improves mood fastest, has the fewest side effects, or helps you sleep better?

Thinking this through can reveal what outcomes matter most to you, highlight unnoticed patterns, and clarify trade-offs you might otherwise miss.

5. If You Had Clear Data, How Would You Apply It?

Imagine your digital twin revealed that a vacation with more downtime would work better than your usual go-go-go approach.

How would you actually adapt your behavior and plans based on that?

This step is easily forgotten. Even in the simplest scenarios, like learning which lunches make you feel better, friction that needs to be worked through can arise in implementation.

Try imagining: If my digital twin gave me X information or Y information, what would I do with that?

Apply Your Analytical and Managerial Skills to Yourself

We’ve only scratched the surface of the digital twin concept here. If you’re interested in exploring further, you might think about the cumulative effects of actions, responsiveness to real-time events and feedback (e.g., what might cause you to adjust your best lunch plan?), or when randomness and uncertainty could be important (like how the best Paris plan might depend on that week’s weather).

The key point of this article is that we often have analytical skills that we don’t apply to our questions about ourselves. The digital twin framework, even when only used as a thought experiment, can help us do that. Just like Erin, the right tools can help you discover how to leverage systematic thinking skills you already possess and point them at yourself.