Snark aside, I'm definitely interested in whether or not this field will have impacts on future policy. In order to be useful I feel it would have to actively avoid simplifying assumptions that have trapped similar fields.
Regarding academic study, sociology is so partisan you need not worry about them using computer science and/or logic to uncover hidden truths in the near future.
In the private sector however, companies such as Premise and Palantir are already eons ahead of academia, but their insights and data are proprietary.
This is my field, too. I am a Ph.D. candidate in computational social science, working on a computational model of social belief systems.
I'm quite happy to see this on HN. Like many other disciplines, our field is blossoming with the rise of cheap, high performance computing!
If your involved in this field and on HN, please reach out to me at @generativist on twitter. I'm in the process of setting up a community for CSS work, and will be soliciting feedback soon!
Can you give more details (accessible to programmers rather than social scientists) how that'd work, what do you actually model and aim to find out? Sounds pretty interesting.
I'll be posting some things to HN soon. My writing style is very accessible, to anyone. I always shoot for an "explain it to your grandmother"-level of writing.
Also, my entire dissertation is Python/Cython/Jupyter with a prebuilt docker container, so, it's also low-friction to play with. But, complicated by dissertation rules for sharing pre-publication (i.e. "is this definitely your work, because it's on github and you accepted PRs?").
Can you talk about what software packages, perhaps data structures and algorithms you use? Any shortcomings / low hanging fruit? I see the domain as an invaluable tool for a few applications... any particularly strong ones you can see?
The important thing here is the objective : there are different methods and tools to reach that objective.
Primarily, the objective of Computational Social Science is to understand human behavior but with computational methods. Traditionally, social science was done offline with surveys. But, with the increase in social information on the Internet could we use that data to know more about humans?
How? There are various methods on how one could do so - network theory, statistics, text mining etc. are some tools that are useful to achieve the objective.
Re policy, it's used e.g. in environmental resource management (regional level), anti-terrorism (networks, opinion dynamics), urban planning, disease control.
Policy isn't based alone on it, of course. But it's used as an ingredient. Often the models aren't good enough (lack of data and quantifiable causal relationships) to gain an exact image of the modeled system, but they provide a general system understanding (system behavior under different [intervention] scenarios) that is often educational and gives insights that then need to be empirically validated.
A barrier to increase its use in policy-making is the "science-policy gap" between researchers and decision-makers. Either they don't understand the utility of model results and dismiss them or they trust them too much--both is not good. So researchers had to come up with ways to communicate results, often include decision-makers (and other stakeholders) in the modeling process.
Depending on how you classify him (as an economist or a sociologist), Thomas Schelling's work on residential segregation has likely informed policy over the last decades.
Recently a nice animation of his dynamic models of segregation processes was posted here: http://ncase.me/polygons/
I mean, the field has inspired how Facebook determines to serve up news feed content so, in a way, it has driven policy by way of influencing democracy.
Since you're in the UK, look at [1] and perhaps check topics that interest you and follow the names at [2]. Then we have the European Social Simulation Association [3] with Summer Schools and annual conferences.
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Snark aside, I'm definitely interested in whether or not this field will have impacts on future policy. In order to be useful I feel it would have to actively avoid simplifying assumptions that have trapped similar fields.