But that's always been the case. Nixon started to use the term in 1969 just as his campaign was working with top advisor to build more interactive systems to measure and predict the elections per riding. A bit theme of the book is how to appreciate the gap between a simulation and reality, something old and new.
I mean it's debatable that it's an optimization problem but it's been tried. One thread in the book is the reaction the Limits of Growth. That model extrapolated trends, very abstractly, to predict the future. A successor, the Latin American World Model or the Bariloche Model, tried to imagine an ideal world and optimize for it. Very different approaches and a good question to ask when you hear someone talking about computer simulations.
There is such an enduring link between democracy, elections and math. What I interesting was the focus on trying to model politics correctly rather than "better" with computers. I dug up some good games and programs from the early days that I have to put online sometime.
People sure tried. But really the book traces how that happened. I think it became easier to imagine politics working like a computer than with people, like how computer reasoning and rational choice became an ideal to measure other political thought.
I was tempted with Show HN, but it also seems more like demos, and this is more a link to an open access book so I thought it was more reading material.
In the rush to cash in on the generative artificial intelligence gold rush, one possible outcome of AI’s future rarely gets discussed: what if the technology never works well enough to replace your co-workers, companies fail to use AI well or most AI startups simply fail?