Apparently, one of our studies went viral thanks to this image, which oversimplifies the findings (image not by us, by the way).
I think this is the problem with science communication: people really like simplicity, but science is based on nuances and complexity.

A quick excerpt of today's session

🚨 Join us tomorrow at 13:30 CET for a discussion on incrementality in social simulations 🛠️
Why don’t social simulation models evolve incrementally like in other fields? Should they? Can they?
Bring your ideas, experiences & questions—everyone's voice matters!

https://www.socsimfest.eu/programme-2025/tuesday-20-may-2025/

Dear Funding Agencies,
below is my proposal for your consideration. Please let me know if any parts remain too complex.

Indeed, the problem is not "limited information" but just that we want to simplify something which is clearly not simple. [7/7]

In this article I try to explain more in details this problem,
https://www.nature.com/articles/s44271-024-00084-7
while in this article together with Philip Warncke, we show that even having access to infinite information would not solve the problem.
https://www.ceeol.com/search/article-detail?id=1296671
[6/7]

Furthermore, this "simplification process" (i.e. moving from an opinion to a number) is not unique. So when each person chooses a different method, they also choose a different "simplification process", thus leading to different results. [5/7]

The problem is that many of the things we want to measure are way more complex than we usually think. Things like opinions are not numbers, but are rich concepts that we simplify in various ways to force them into numbers. [4/7]

Thus, it’s no surprise that when people are given the same dataset and research question, they introduce different operationalization (i.e. different measurements of the same "idea") leading to different results. [3/7]

Unfortunately, this is not the case even for many physical quantities; for example, there is no correct way to measure “how big” a building is. Instead, we have multiple alternatives, such as height, volume, area, etc. [2/7]

We often suppose that things like intelligence and opinions not only exist, but can be measured in a “correct” way. So our goal is to reduce the error as much as possible to get closer and closer to such “correct” measurement. [1/7]

Ah, yes, "Avengers infinity job hunt", my favourite movie!

Working with an agent-based model? Start with a vision—but stay flexible!

As your model evolves, check what it’s actually revealing. Your original plan might not hold up, but new and unexpected insights could open better paths forward.
#Modeling #AgentBasedModel #ResearchTips

Academics of the internet, can you please explain how you can make a family if you don’t know where in the galaxy you will be in 2 years? I can’t find any answers in the literature.

Also, here’s ChatGPT's interpretation of this post.

"if it is an interval level, for a score of 3, one should be
able to answer the question: '3 what?' "
https://asmepublications.onlinelibrary.wiley.com/doi/10.1111/j.1365-2929.2004.02012.x

Are we mistaking analogies for empirical truths? In this article, we discuss the tendency to overestimate a model's applicability based on its analogical appeal rather than empirical validation.

https://rofasss.org/2024/05/06/delusional-generality/

Year 2 of testing if Chatgpt can write a joke about agent-based modelling:

"Why don’t agent-based models ever get invited to parties?
Because they always act independently and cause emergent behavior!"

I'm not sure we made any improvements here...

Any good sources or advice for storytelling in a working/professional environment? A lot of the material I find, even when marked as work-related, provides suggestions which are too close to how you'd write a novel, and when somebody tries to put them into practice, it just doesn't work

I mean, most sources only use examples from Ted talks and actors in late-night shows, which is very far from most working environments.

🔬 Researcher Life in a Nutshell 🔬

Me: "I’ll just check this one paper real quick."

Also me, 6 hours later: Has 137 tabs open, joined a new research group, accidentally reinvented the wheel, and now questioning the meaning of life.

We may now end up with something interesting, but way too complex to be succinctly presented in a single article. The solution is to remove everything that is unnecessary for producing the "interesting effect".

Note that I'm not claiming that this is the only way to make ABMs, nor that it is the right way. I'm just saying that this way of thinking works really well for me and I usually recommend it to most of the people I work with; especially if they don't want a hard time during peer-review.