Tag Archives: complexity

Best Combos Are Robust

I’ve been thinking a lot lately about what a future world of ems would be like, and in doing so I’ve been naturally drawn to a simple common intuitive way to deal with complexity: form best estimates on each variable one at a time, and then adjust each best estimate to take into account the others, until one has a reasonably coherent baseline combination: a set of variable values that each seem reasonable given the others.

I’ve gotten a lot of informal complaints that this approach is badly overconfident, unscientific, and just plain ignorant. Don’t I know that any particular forecasted combo is very unlikely to be realized? Well yes I do know this. But I don’t think critics realize how robust and widely used is this best combo approach.

For example, this is the main approach historians use studying ancient societies. A historian estimating Roman Empire copper trade will typically rely on the best estimates by other experts on Roman population, mine locations, trade routes, travel time, crime rates, lifespans, climate, wages, copper use in jewelry, etc. While such estimates are sometimes based on relatively direct clues about those parameters, historians usually rely more on consistency with other parameter estimates. While they usually acknowledge their uncertainty, and sometimes identify coherent sets of alternative values for small sets of variables, historians mostly build best estimates on the other historians’ best estimates.

As another example, the scheduling of very complex projects, as in construction, is usually done via reference to “baseline schedules,” which specify a best estimate start time, duration, and resource use for each part. While uncertainties are often given for each part, and sophisticated algorithms can take complex uncertainty dependencies into account in constructing this schedule (more here), most attention still focuses on that single best combination schedule.

As a third example, even when people go to all the trouble to set up a full formal joint probability distribution over a complex space, as in a complex Bayesian network, and so would seem to have the least need to crudely avoid complexity by focusing on just one joint state, they still quite commonly want to compute the “most probable explanation”, i.e., that single most likely joint state.

We also robustly use best tentative combinations when solving puzzles like Sudoku, crossword, or jigsaw. In fact, it is hard to think of realistic complex decision or inference problems full of interdependencies where we don’t rely heavily on a few current best guess baseline combinations. Since I’m not willing to believe that we are so badly mistaken in all these areas as to heavily rely on a terribly mistaken method, I have to believe it is a reasonable and robust method. I don’t see why I should hesitate to apply it to future forecasting.

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Does complexity bias biotechnology towards doing damage?

A few months ago I attended the Singularity Summit in Australia. One of the presenters was Randal Koene (videos here), who spoke about technological progress towards whole brain emulation, and some of the impacts this advance would have.

Many enthusiasts – including Robin Hanson on this blog – hope to use mind uploading to extend their own lives. Mind uploading is an alternative to more standard ‘biological’ methods for preventing ageing proposed by others such as Aubrey de Gray of the Methuselah Foundation. Randal believes that proponents of using medicine to extend lives underestimate the difficulty of what they are attempting to do. The reason is that evolution has led to a large number of complex and interconnected molecular pathways which cause our bodies to age and decay. Stopping one pathway won’t extend your life by much, because another will simply cause your death soon after. Controlling contagious diseases extended our lives, but not for very long, because we ran up against cancer and heart disease. Unless some ‘master ageing switch’ turns up, suspending ageing will require discovering, unpacking and intervening in dozens of things that the body does. Throwing out the body, and taking the brain onto a computer, though extremely difficult, might still be the easier option.

This got me thinking about whether biotechnology can be expected to help or hurt us overall. My impression is that the practical impact of biotechnology on our lives has been much less than most enthusiasts expected. I was drawn into a genetics major at university out of enthusiasm for ideas like ‘golden rice’ and ‘designer babies’, but progress towards actually implementing these technologies is remarkably slow. Pulling apart the many kludges evolution has thrown into existing organisms is difficult. Manipulating them to reliably get the change you want, without screwing up something else you need, even more so.

Unfortunately, while making organisms work better is enormously challenging, damaging them is pretty easy. For a human to work, a lot needs to go right. For a human to fail, not much needs to go wrong. As a rule, fiddling with a complex system is a lot more likely to ruin it than improve it. As a result, a simple organism like the influenza virus can totally screw us up, even though killing its host offers it no particular evolutionary advantage:

Few pathogens known to man are as dangerous as the H5N1 avian influenza virus. Of the 600 reported cases of people infected, almost 60 per cent have died. The virus is considered so dangerous in the UK and Canada that research can only be performed in the highest biosafety level laboratory, a so-called BSL-4 lab. If the virus were to become readily transmissible from one person to another (it is readily transmissible between birds but not humans) it could cause a catastrophic global pandemic that would substantially reduce the world’s population.

The 1918 Spanish flu pandemic was caused by a virus that killed less than 2 per cent of its victims, yet went on to kill 50m worldwide. A highly pathogenic H5N1 virus that was as easily transmitted between humans could kill hundreds of millions more.

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