The short answer is not much.
There's lots to pick from, so we could focus on stuff that matters to the common man, or put in other words, practical stuff. A couple of examples come to mind -- the weather, which would include tornados and hurricanes, earthquakes, asteroid collisions, Super Bowl, when and how you'll die, ... by now you get the point. Not much.
Let's look at the weather. We have a gazillion satellites, and terrabytes of memory and terraflops of computer speed. But it doesn't help. The best we can do is forecast today's weather, and even then chances are better than 50/50 that something a bit different happens. And notice that as one predicts further into the future, the predictions really become quite useless. Why is that? Given that we have our hands on a ton of data, and know the physics of each individual component of the weather, we still fall dismally short. The same thing applies to earthquakes and asteroids, and to almost anything else that bumps into something else.
There are two problems: we live in a universe that is full of non-linear interactions, and these interactions fall prey to chaotic behavior. In the first case, we simplify non-linear interactions into linear ones, so that we can more easily make a guesstimate. In the second case we have Chaos theory -- basically tells us that a series of simple interactions becomes essentially impossible to predict after a time because of immeasurable fluctuations in how objects interact. Immeasurable -- means we can NEVER be able to measure these tiny interactions. Let's say we have a bag of popcorn about to start popping, and we ask ourselves, "which kernel is the tenth one to pop?" Good luck with that. Yet all the data is there...heat distribution, location of all kernels, size and water content of each, etc. We may be able to predict which pops first, perhaps even which goes off second. But by then, things have rearranged inside the bag. The process by which they have shifted position is governed by Chaos Theory. Along with the shift comes a shift in heat distribution and which kernels are exposed to the most heat.
Weather forecasting simplifies the various forces at work into models of turbulence. Try forecasting the shape of the smoke from a campfire...same thing. Turbulence is a fine example of chaos. By now, I think you get the idea.
Do we give up then? Not exactly...chaos theory includes an invisible attractor...ie, an over-riding formula by which all things in a system behave. So, if one determined the nature of this formula, one might be able to make a decent prediction. The rub is that divining such a formula is staggeringly difficult, especially as more complex systems may have more than one such attractor.
Sit back and enjoy the uncertainty.
There's lots to pick from, so we could focus on stuff that matters to the common man, or put in other words, practical stuff. A couple of examples come to mind -- the weather, which would include tornados and hurricanes, earthquakes, asteroid collisions, Super Bowl, when and how you'll die, ... by now you get the point. Not much.
Let's look at the weather. We have a gazillion satellites, and terrabytes of memory and terraflops of computer speed. But it doesn't help. The best we can do is forecast today's weather, and even then chances are better than 50/50 that something a bit different happens. And notice that as one predicts further into the future, the predictions really become quite useless. Why is that? Given that we have our hands on a ton of data, and know the physics of each individual component of the weather, we still fall dismally short. The same thing applies to earthquakes and asteroids, and to almost anything else that bumps into something else.
There are two problems: we live in a universe that is full of non-linear interactions, and these interactions fall prey to chaotic behavior. In the first case, we simplify non-linear interactions into linear ones, so that we can more easily make a guesstimate. In the second case we have Chaos theory -- basically tells us that a series of simple interactions becomes essentially impossible to predict after a time because of immeasurable fluctuations in how objects interact. Immeasurable -- means we can NEVER be able to measure these tiny interactions. Let's say we have a bag of popcorn about to start popping, and we ask ourselves, "which kernel is the tenth one to pop?" Good luck with that. Yet all the data is there...heat distribution, location of all kernels, size and water content of each, etc. We may be able to predict which pops first, perhaps even which goes off second. But by then, things have rearranged inside the bag. The process by which they have shifted position is governed by Chaos Theory. Along with the shift comes a shift in heat distribution and which kernels are exposed to the most heat.
Weather forecasting simplifies the various forces at work into models of turbulence. Try forecasting the shape of the smoke from a campfire...same thing. Turbulence is a fine example of chaos. By now, I think you get the idea.
Do we give up then? Not exactly...chaos theory includes an invisible attractor...ie, an over-riding formula by which all things in a system behave. So, if one determined the nature of this formula, one might be able to make a decent prediction. The rub is that divining such a formula is staggeringly difficult, especially as more complex systems may have more than one such attractor.
Sit back and enjoy the uncertainty.
