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Sourcery · 2 min · Oct 26, 2026
What people do can differ sharply from what they say
Joon Sung Park explains that stable traits can be predictable, but stated intentions often miss the friction involved in acting on them. His gym example shows why survey answers and real behavior need different models.
Joon Sung Park is cofounder and CEO of Simile.
Molly O'Shea, Host · Founder, Sourcery
Ex-Upfront Ventures investor; has interviewed Alex Karp and Palmer Luckey.
About this segment
Joon Sung Park explains that stable traits can be predictable, but stated intentions often miss the friction involved in acting on them. His gym example shows why survey answers and real behavior need different models.
From “The $2B AI Startup Simulating Human Behavior with 85% Accuracy”, Oct 26, 2026. Watch the full episode
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“the activation energy for me to actually go to the gym is actually way higher than what I would expect.”
Takeaways
- Risk tolerance is one trait that can remain stable over time.
- Survey answers often follow a normal distribution.
- Taking action can require much more activation energy than expected.
In this episode
Full episode- 8:52Simile tested digital twins against real people2:17
11:22What people do can differ sharply from what they say2:22
- 33:31An EV message risked hurting the whole category2:23Up next
- 5 more moments in the full episode
Transcript
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
- 11:26
is human behavior predictable? In the training data, we actually do inject a lot of behavioral knowledge. It's not just about what they say, but it's also really about what they do. And there's a lot of irrational behaviors in human tendencies. So a lot of things that we study in economic behavior game, randomized control trials, they all try to cover those. At the same time, there are certain things in people that's really not known to change.
- 11:55
Your risk tolerance, as I briefly mentioned, it's not really known to change in a person. So once we are able to learn that, then this actually becomes a trait that becomes very predictable. So it really is about understanding the gap between what people think they are and what they actually do in their life. And really the modeling challenge here actually is really learning that translational layer.
- 12:20
How do you take observed behaviors or attitudes into their fundamental tendencies? And what is in that gap? Is it time? Is it events around you? How do you determine that gap that ultimately creates the outcome? So the part that is actually very consistent in our experimentation is these two functions are actually governed by a different function or a different sort of shape of the curve. what people say in survey actually often just follows a normal distribution right so when I say you know am I likely to go to the gym this week I likely say with some normal distribution curve you know yes maybe you know seven point scale I likely say five to six every week now do I actually go to gym that week the activation energy for me to actually go to the gym is actually way higher than what I would expect. So that looks much more like an exponential curve where it has
- 13:24
a much clearer drop-off in terms of what kind of activation we need to actually get to that state of, oh, I'm actually going to exercise this week. So those are the kind of different functions that we need to learn. And that's where often the gap is. Again, I have so many questions about this.