I’m sad that I missed your talk in Rotterdam. I want to briefly flag a concern I have with advocating ‘systems thinking’ or ‘a complex systems approach’. While the promise is always nice, I think you need to deliver on the promise right away, since otherwise you risk just making a point that is unfalsifiable or somewhat of an applause light (no one will exclaim “we don’t need complexity to describe complex phenomena!”) .
- Use a model from complexity science and show that it explains something otherwise left unexplained or show that it outperforms some other model on a relevant feature. -You’ll probably want to make use of (1) Agent Based Modelling, (2) Network Models, (3) Statistical Physics and common models like Ising, Hard Spheres, Lennard Jones potentials etc, (4) Dynamical System Analysis (5) Bifurcation Analysis or (6) Cellular Automata. -You can find a good introduction to most of these here https://www.dbooks.org/introduction-to-the-modeling-and-analysis-of-complex-systems-1942341091/ -Using these methods also demystifies the whole concept of “complexity” a little bit, and makes it more mundane (though you can never get enough of the Ising Model :D)
So yeah, endorse your message, but please make it testable and quantitative soon!
Martijn, your comment points me to something I’ve noticed around communicating ‘systems thinking’ and a complexity mindset with some EAs. Gideon points to a more fundamental ontological difference between those who tend to focus on that which is predictable (measurable and quantifieable) and those who pay attention to shifting patterns that seem contextual and more nebulous.
I read your comment as an invitation to translate across different ontologies—to explain the nebulous concretely, to explain the unpredictable in predictable terms. I personally haven’t found success in my attempts, and I’d love to hear more about how you communicate around complexity.
I’ve most often found success in pointing out parts of one’s experience that feel unknown and then getting mutually curious about the successful strategies one might use to navigate. To invite one into a place where their existing tools aren’t working anymore and there is real curiosity to try a different approach. When I’ve tried speaking about complexity in the abstract or as applied to something that people see as ‘potentially predictable’, the deeper sense of complexity tends to be missed—often getting translated into “that’s a cool tool, but aren’t you just describing a more accurate way of modeling?”
The comment below about embracing a pluralistic approach seems to provide a path forward that doesn’t rely on translation though… lots of interesting ideas in this comment section already.
Hey Gideon,
I’m sad that I missed your talk in Rotterdam. I want to briefly flag a concern I have with advocating ‘systems thinking’ or ‘a complex systems approach’. While the promise is always nice, I think you need to deliver on the promise right away, since otherwise you risk just making a point that is unfalsifiable or somewhat of an applause light (no one will exclaim “we don’t need complexity to describe complex phenomena!”) .
- Use a model from complexity science and show that it explains something otherwise left unexplained or show that it outperforms some other model on a relevant feature.
-You’ll probably want to make use of (1) Agent Based Modelling, (2) Network Models, (3) Statistical Physics and common models like Ising, Hard Spheres, Lennard Jones potentials etc, (4) Dynamical System Analysis (5) Bifurcation Analysis or (6) Cellular Automata.
-You can find a good introduction to most of these here https://www.dbooks.org/introduction-to-the-modeling-and-analysis-of-complex-systems-1942341091/
-Using these methods also demystifies the whole concept of “complexity” a little bit, and makes it more mundane (though you can never get enough of the Ising Model :D)
So yeah, endorse your message, but please make it testable and quantitative soon!
Martijn, your comment points me to something I’ve noticed around communicating ‘systems thinking’ and a complexity mindset with some EAs. Gideon points to a more fundamental ontological difference between those who tend to focus on that which is predictable (measurable and quantifieable) and those who pay attention to shifting patterns that seem contextual and more nebulous.
I read your comment as an invitation to translate across different ontologies—to explain the nebulous concretely, to explain the unpredictable in predictable terms. I personally haven’t found success in my attempts, and I’d love to hear more about how you communicate around complexity.
I’ve most often found success in pointing out parts of one’s experience that feel unknown and then getting mutually curious about the successful strategies one might use to navigate. To invite one into a place where their existing tools aren’t working anymore and there is real curiosity to try a different approach. When I’ve tried speaking about complexity in the abstract or as applied to something that people see as ‘potentially predictable’, the deeper sense of complexity tends to be missed—often getting translated into “that’s a cool tool, but aren’t you just describing a more accurate way of modeling?”
The comment below about embracing a pluralistic approach seems to provide a path forward that doesn’t rely on translation though… lots of interesting ideas in this comment section already.