One of my backgrounds is in stats and economics. I've done some work with what I would term similar models...
It's not that I don't think they can't be useful, like all models, but the paper greatly simplifies and brushes over some VERY complex problems and issues with them.
Let's focus on some:
1The paper says the underlying rules governing such systems can be easily translates into computer language and understood. I assure you they cannot and as the model gets bigger, like software it gets harder and harder. If your model is so complex it can accurately describe the things about social systems that fool simple human cognitive systems, the question arises how did YOU discover those rules without the model, and how do you debug something that has effects that are far removed and non intuitive.
The decision as to what the foundations of the model are and what is important are often confounded and become political: fundamentally when it comes to the nature of these complex systems, we don't have a consensus reality, so someone has to make a call, and am the effects that stop you being able to understand complex social systems also feed into who usually makes that call.
And finally, as with all predictive future models and forecasting, often conjoined in this case because of the chaotic and nonlinear effects, how do you judge the falsafiability or fit/error of your model and implementation.
Let's say your model says that humans get wiped out in 50 years, or your policy, which everyone in the room loves, looks like a total failure. Then is the model right? Have the assumptions that gone into it been correct? Is the implementation correct?
So what you'll tend to get in practice are iterative design choices that result in models that confirm the assumptions and beliefs of the most powerful people in the room: because how well the model confirms to their beliefs is the judge of the model fitness, and you quickly devolve into a political echo chamber where modelling becomes marketing/self-confirming...