I understand why price manipulators fail to make the market look the way they want, and instead make it more accurate.

What I don’t understand is why insider traders, with access to true relevant information that the other traders cannot get merely by further research, aren’t “neutralized” by the same mechanism.

That is: Assume a shallow consideration of a given question suggests a probability of 60%, more extensive research turns up data implying a more accurate 65%, and one insider has special data implying an even more accurate 64%. Further assume that the other traders besides the insider can’t turn up the insider’s special data with even extensive research — the only way they can learn what the insider knows, if at all, is through the insider.

As I understand the article, if a manipulator tries to mess with the market, or if the insider tries to profit from the special data, in either case the market will settle on 65%.

What can the insider do to prove they’re not a manipulator masquerading as an insider? If nothing, then how can the insider move the market to 64% when the manipulator can’t move it to 66%?

How does the market distinguish between a trader who’s betting against market consensus because she knows something the other traders don’t, versus a trader who’s betting against market consensus because she wants the other traders to think she knows something they don’t?

Bull. The cost of the fuel is incentive to economize on its use. There is no good evidence that atmospheric carbon dioxide is a negative externality, and thus NO REASON to “economize” on its production.

My beef is that in a response to a critique of decision markets in the context of a specific case ( global warming), you respond with a paper that makes assumptions that are not even close to the reality of the situation.

Also, I am quite familiar with academic modeling. I just have very little respect for the way it is done.

I am not against models in general. Nor am I against math, or making simplified assumptions, or for using approximations due to to time constraints. I work in software, and I often write documents to my co-workers about how I think a system should be engineered. I will use simplifications and approximations. As you say, an exact spec would be completely unwieldy. But these documents are nothing like an academic paper. Your goal is essentially an engineering goal – you are trying to examine whether a certain design will have the desired outcome. Thus I hold you to an engineering standard.

Here is how I would alter your paper to make it a convincing engineering design document:

a) First, start with the assumptions and explain and justify every non-obvious assumption you make. For instance, your assumption of “a commonly known strength of desire to manipulate” needs to be justified if your paper is to be at all convincing. Garbage in, garbage out, if your assumptions are not correct or not justified, nothing else in your model matters.

b) Second, solve for the average case and the worst case, not the best case. For a model, you do not need exact numbers. Simplifications are fine. But you should attempt to use ball park figures for the average case. An “unlimited budget” is not an average case. Pick a typical securities market, and use that as a ballpark guess. Proving that your model works in a best case is a useless exercise.

c) Third, it’s much more important to have ball park figures for all the assumptions, than in depth analysis of one aspect of the system. You do the opposite. You painstakingly write out the math, using the most advanced techniques possible, for a very narrow set of assumptions. Unfortunately, because the assumptions are so narrow, the paper is not at all convincing for any real world scenario. If you are time constrained (as we all are) It’s almost always better to do back of the envelope math for a very broad set of assumptions. Then if there is one particular area where precise math is needed to make an accurate judgment, you can add more advanced math as needed (although most of the time it turns out the back of the envelope estimates are good enough, your paper would be just as convincing if you simply cut out pages 4 through 11).

Your paper passes muster in academia. But if your communicating on a blog frequented by engineers, you’ll need to do a lot better. Come up with a simplified model that actually makes reasonable estimations of budgets, noise in determining the asset value, knowledge about bias, etc.. Also add in a reasonable proposal for how the winner of the bet gets decided. You don’t need to be “exactly realistic”. Just get it in the right ballpark. As it is, the assumptions in your paper are so unrealistic as to make any further discussion impossible.