To Know, but Not Understand: David Weinberger on Science and Big Data (2012)
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Re: To Know, but Not Understand: David Weinberger on Science and Big Data (2012)
#2Bottom line:
"With the new database-based science, there is often no moment when the complex becomes simple enough for us to understand it. The model does not reduce to an equation that lets us then throw away the model. You have to run the simulation to see what emerges. For example, a computer model of the movement of people within a confined space who are fleeing from a threat--they are in a panic--shows that putting a column about one meter in front of an exit door, slightly to either side, actually increases the flow of people out the door. Why? There may be a theory or it may simply be an emergent property. We can climb the ladder of complexity from party games to humans with the single intent of getting outside of a burning building, to phenomena with many more people with much more diverse and changing motivations, such as markets. We can model these and perhaps know how they work without understanding them. They are so complex that only our artificial brains can manage the amount of data and the number of interactions involved."
Re: To Know, but Not Understand: David Weinberger on Science and Big Data (2012)
#3At what point do we shift our investment in time and energy from building models like those mentioned in the article to the bigger picture? Maybe it’s just my perception but it doesn’t seem like we have very many people thinking deeply about what models we should build and to what ends. Instead we are just building the models and hoping we can put them to good use afterwards.
For example, what’s the end game for the cellular signaling modeling outlined in the article? It seems like the result isn’t valuable in and of itself, and it can’t be much more than that because the scientist “doesn’t understand it, and doesn’t think any person could.” So we now have an equation that expresses constants within a cell and that’s it. We don’t understand it and we can’t put it to good use. So was that time and effort well spent? Do we just put this work in a drawer so we can pull it out if it could be useful at some point in the future? Is that what we’re doing with all the similar advances in modeling?
There’s nothing wrong with knowledge for knowledge’s sake, but I think we’ve way over indexed on the tools and predictions side of the system. If we continue to constantly create new tools/models/predictions we might find a use for them by chance. It just seems more efficient to focus on what outcomes we really want and then put the models to work in pursuit of those outcomes. Perhaps we focused more on the outcomes in the past because we didn’t have the technological horsepower to constantly churn out new models.
Maybe I’m wrong and there are people working on the big picture. Are there modern day philosophers doing this work? Do they make up a significant portion of the work being done? If not, why?
Re: To Know, but Not Understand: David Weinberger on Science and Big Data (2012)
#4Bottom line: "With the new database-based science, there is often no moment when the complex becomes simple enough for us to understand it. The model does not reduce to an equation that lets us then throw away the model. You have to run the simulation to see what emerges. For example, a computer model of the movement of people within a confined space who are fleeing from a threat--they are in a panic--shows that putt…
> The model does not reduce to an equation that lets us then throw away the model. You have to run the simulation to see what emerges.
This is true of simulation in general, not just data-drive models. E.g., a lot of applied mathematics uses PDE models that don't have closed-form solutions and so you just run a ton of simulations sweeping a parameter space.
> For example, a computer model of the movement of people within a confined space who are fleeing from a threat--they are in a panic--shows that putting a column about one meter in front of an exit door, slightly to either side, actually increases the flow of people out the door.
The crux of this type of science is that you don't know whether the computer simulations are telling you anything about reality. You just have to run real-world experiments and see what happens. And even if the experiment turns out to work, you still don't know for sure that your model was reasonable.
Re: To Know, but Not Understand: David Weinberger on Science and Big Data (2012)
#5From my point of view there are 2 possible ways:
- we simply acknowledge we don't understand something enough and keep looking into it until we do. I mean everything we now understand (at an acceptable level by our standards) has gone through an intermediary phase - see alchemy for example.
- we declare some things (prematurely?) as forever escaping our grasp and accept we may never have a simple model of them.
What bothers me is the 3rd way:
- we don't know why but the computer model gave this result so let's go ahead with putting it into production. We make money, the user/consumer may have a nice experience or die, fingers crossed.
Re: To Know, but Not Understand: David Weinberger on Science and Big Data (2012)
#6Bottom line: "With the new database-based science, there is often no moment when the complex becomes simple enough for us to understand it. The model does not reduce to an equation that lets us then throw away the model. You have to run the simulation to see what emerges. For example, a computer model of the movement of people within a confined space who are fleeing from a threat--they are in a panic--shows that putt…
> The model does not reduce to an equation that lets us then throw away the model. You have to run the simulation to see what emerges. This is true of simulation in general, not just data-drive models. E.g., a lot of applied mathematics uses PDE models that don't have closed-form solutions and so you just run a ton of simulations sweeping a parameter space. > For example, a computer model of the movement of people wi…
So you are suggesting we need to do a double blind test, by means of throwing a molotov cocktail at a few people gatherings?
Re: To Know, but Not Understand: David Weinberger on Science and Big Data (2012)
#7Earlier quoted context omitted.
> The model does not reduce to an equation that lets us then throw away the model. You have to run the simulation to see what emerges. This is true of simulation in general, not just data-drive models. E.g., a lot of applied mathematics uses PDE models that don't have closed-form solutions and so you just run a ton of simulations sweeping a parameter space. > For example, a computer model of the movement of people wi…
So you are suggesting we need to do a double blind test, by means of throwing a molotov cocktail at a few people gatherings?
There are probably slightly more ethical ways of designing that study...
Re: To Know, but Not Understand: David Weinberger on Science and Big Data (2012)
#8Earlier quoted context omitted.
> The model does not reduce to an equation that lets us then throw away the model. You have to run the simulation to see what emerges. This is true of simulation in general, not just data-drive models. E.g., a lot of applied mathematics uses PDE models that don't have closed-form solutions and so you just run a ton of simulations sweeping a parameter space. > For example, a computer model of the movement of people wi…
So you are suggesting we need to do a double blind test, by means of throwing a molotov cocktail at a few people gatherings?
Double blind so that neither the thrower of recipients of said cocktail doesn't know wether it is real or placebo? It's the only way to know if they are panicking because of a bottle or a fireball...
Re: To Know, but Not Understand: David Weinberger on Science and Big Data (2012)
#9At what point do we shift our investment in time and energy from building models like those mentioned in the article to the bigger picture? Maybe it’s just my perception but it doesn’t seem like we have very many people thinking deeply about what models we should build and to what ends. Instead we are just building the models and hoping we can put them to good use afterwards. For example, what’s the end game for the…
> For example, what’s the end game for the cellular signaling modeling outlined in the article?
I think the article meant to refer to systems biology (as in a new field). It's not exactly a single 'model' rather a methodology as far as I know. Also the 'end game' IMO in bioinformatics the goal is mostly to discover new knowledge rather than having a 'production ready' model. Through the 'big data' science one could uncover hidden biological effects, new mechanism and new insights etc. Each of these big data modelling exercise is really to push the biology forward to a deeper level. In a way it can be comparable to astrophysics (is it a coincidence that many people working in bioinformatics have an astrophysics background)?
Re: To Know, but Not Understand: David Weinberger on Science and Big Data (2012)
#10At what point do we shift our investment in time and energy from building models like those mentioned in the article to the bigger picture? Maybe it’s just my perception but it doesn’t seem like we have very many people thinking deeply about what models we should build and to what ends. Instead we are just building the models and hoping we can put them to good use afterwards. For example, what’s the end game for the…
> what’s the end game for the cellular signaling modeling outlined in the article?
Pharma.
Most of the modeling work that people do is fairly well motivated. Going from models to working technology is indeed a huge leap, but everything starts with the basic scientific understanding.
> Maybe I’m wrong and there are people working on the big picture.
You can usually find the "big picture" behind a paper by reading the recent grant applications from the PI who funded the research (or the funding lines explicitly mentioned in the paper, if any).