So essentially, we've now reached the end of the course. But before I leave you,
I wanted to speculate on three trends and necessities of agent-based modelling
that I see are vitally important as we move forward with agent-based modelling.
And this is my particular take, these are things I'm passionately interested in,
but it's also capitalizing on trends we're seeing in other areas of computational
sciences, especially computational social sciences and biological-socio-ecological
sciences. And one of them is the automatic generation of agent rules. Now, this is
something I talked about in 9.1, right? We have this huge amount of big data
that's now being collected about what humans and entities are doing around the
world, right? We have sensors on everything, we have the internet of things
exploding left and right. We have social data, we have app data, we have data about
the phones in everybody's pockets, right? And the question is, can we use that
in some way to define rules automatically that capture the essence of human actions
in those spaces, right? Like define how humans move through the world, define how
humans interact with the internet of things, right? Or define how the internet
of things interacts with itself, right, as cars become more and more automated.
Of course, these rules also need to be validated, right? And one of the ways we
can do the validation is by building up these rules, making predictions
with them seeing these predictions carried out.
Now why would it be interesting to do this at an agent level, right?
If we have all this data, why can't we just use the data, right?
But without the agent rules that predict individual-level behavior,
we don't really have the ability to assess what would happen if we were to change
the incentive structure for one particular individual in that space.
Now causal state modelling gives us one example of this.
But we could use many others, you could use decision trees,
you could use the associative rules, right.
You could use classifier systems. There are a lot of methods
out there that allow you to do this.
And with all these sources of data, big data,
administrative data is not new, but the ability to process it in large amounts is,
natural language data, text to speech data,
social data, app data, really being able to
capitalize on this will powerfully change the way
agent-based modelling is perceived in the world around us, right.
And give us a much more powerful tool kit to address some of those situations.
Now, of course, one of the big problems with some of this
data and one of the problems that people often complain about, right is that
trace data is essentially just digital exhaust.
It is data that doesn't tell you anything.
So we need ways to validate those models
against real-world data and calibrate
those models in order to show that they're actually working well, right.
We need rigorous guidelines, I believe, to follow,
to show that our models, the agent-based models, have been validated appropriately.
I often think of this as a statistics-like suite of tests, right.
Statistics is very good. The discipline of statistics is very good.
at saying, "If you have data looks like this and your outputs look like this,
then these are the various tests that you need to apply," right.
And in some cases, all we would be doing is
basically discovering which of those appropriate statistical tests, that had
already existed, would be most likely to apply in that particular situation, right.
And then, what that means is that lends a lot of credence and credibility to
the idea that this model I created and run through these tests to compare to
empirical data that I've seen have now then, have helped me to validate and
increase my confidence in this model.
Now, of course, being able to calibrate our models
in order to increase that level of validation would also be useful.
So making tools like BehaviorSearch, which we discussed earlier, easier to use
so users can calibrate models automatically against
real-world data would be a very powerful step forward in this space.
Finally, and this kind of stems off of both of those other thoughts about
the future, if we could build models automatically... if we could construct
them from these vast amounts of data that
are coming in - and we could continuously validate them in some automatic way
where we have a suite of tests that we know will tell us whether or not
the model is behaving accurately, then we
theoretically could build a tool which will automatically construct a model
on the basis of streaming data, right?
and by that I mean it will be pulling down... for instance...
stock-ticker data, Twitter data, whatever
it needs, and continually making a model of, say, the socioeconomic status
of the country as a whole, for instance, or something like that.
Or maybe it's pulling in data from the internet of things, that looks at
sensors that are attached to all of the street lights in a particular city
that detects the traffic flow, and then making continual predictions
about whether or not there's going to be
traffic jams in certain areas
so that policy makers and city managers can, in real time, change signage
in order to have people move around traffic jams.
If that was the case... if we had this ability, this tool to build these
frameworks in real time that were predictive, well, not predictive,
but able to forecast and explore scenarios into the future,
we could then use this to support real-time decision-making.
So those are three areas - the use of streaming data, big data
and the ability to automatically create rules from big data,
and this validation and calibration and improvement, that I think are
critical for the future of agent-based modelling.
Like I said, that's it. Unit 9, we talked about big data ABM, we talked about
design guidelines. We talked about the uses of ABM for communication
and education. We talked about advanced programming constructs like map and
reduce and run and run-result task.
We talked about participatory simulations, systems dynamic modelling
then we talked about extensions and the future of agent-based modelling.
You'll see the Unit 9 slides, and you'll see the Unit 9 tests coming up shortly.
But I want to thank everyone for participating in this course.
I hope you enjoyed it. I enjoyed teaching it for sure,
and I've really enjoyed a lot of
the discussions that we've been having
in the forums, and through the YouTube office hours, and things like that.
And I hope that you are able to gain something out of this.
Please, stay in touch as you go forward
and use agent-based modelling in your
own work or for your own fun, for that matter.