I found three papers in the same vein with considerably more empirical evidence.
In the paper, provinces and conflicts are modeled to justify Richardson's observation that the proportion of conflict severity in relation to their frequency is described by a power law. In other words, the more space there is between each conflict, the more casualties will result. The models uses a lot of detail. A geographical map is created and conflicts (down to technological advancement, political structural change and resource allocation) are simulated in models with dozens of parameters. More importantly, the level of detail in the paper allows parameters to be set to test historical scenarios to disprove or give further evidence to the model.
The focus of the paper is modeling the change of public opinion in the United States as a way to provide explanation for two phenomenon:
- Polarization of opinion is rare despite being perceived otherwise
- Opinion homogeneity is rare, despite being perceive otherwise.
To model these phenomenon, a network similar to that of the Opinion Dynamics paper from the question is built, with imposed homophily. However, the paper grounds it's parameters in reality (for example, take-off issues that stimulate much discussion are considered rare). In contrast, the Opinion Dynamics paper creates parameters for skepticism and empathy, without giving much consideration for the mechanisms behind these attributes and how they might change over time.
This paper justifies modeling 2D distributed agents using the psychological parameters Influence, Susceptibility and Conformity and their interaction. The model is then validated on American political opinions as they shift over time.
There are papers with empirical basis in the domain of social network models. Typically, this realism is achieved by basing parameters on empirical evidence, rather than simplification of cognitive phenomena.
Reply from author of Social Network papers
The author of two of the papers I cited in the question, Alan Tsang, was kind enough to provide a rebuttal to my scepticism via personal correspondence:
The premise behind both the papers was to examine the effects of a
particular psychological phenomenon by examining it in isolation. We
base our work on more conventional agent models from economics, which
assume completely rational actors. We want to see what happens when
the rational behavior is tweaked to incorporate a behavioral
component. In particular, we are interested in the qualitative
effects that are produced, and the mechanisms by which these are
achieved. An agent based simulation is the ideal way of studying
this, because it allows us to drill down and take measurements that
would be impossible or very difficult to do in an actual community.
So what we are doing is more mathematics and less science. The
results of the papers provide qualitative insights on what are
plausible effects of these behaviors on a larger system. The next
step could certainly be to validate the model against real data, but
the goal of the paper is not to perform "full stack" science. Rather,
we hope it would prove useful component in a more detailed model to be
used on data collect in the wild.
That said, we are also interested in validating our model against real
data as well, but they are difficult to come by. For instance,
Facebook almost certainly has access to data that can be used to
detect homophily in networks, and how they might affect opinions over
time. But the data is very proprietary and, even if we were to get
it, ethical concerns might limit how it can be used. As you've
pointed out in the follow-up post, there are a few papers that examine
political affiliation, and those could potentially be useful ground
truth. But I don't have the background to properly assemble such a
data set from scratch (as it would need both a time series of
opinions, and the underlying network structure). Moreover, there are
sure to be other effects at work as well, which may confound the
analysis. We're giving some thought to gathering data for the last
Canadian election, since strategic voting was so widespread and
successful, but any data we collect will likely be disconnected from
social network structure. Maybe some aggregate network properties
could be inferred (ex: based on region), but it would be a