**Thought experiment 1 – arbitrage opportunities in prediction market**

You’re Mitt Romney, biding your time before riding in on your white horse to win the US republican presidential preselection (bear with me, I’m Australian and don’t know US politics). Anyway, you’ve had your run and you’re not too fussed, but some of the old guard want you back in the fight.

Playing out like a XKCD comic strip ‘Okay’, you scheme. ‘Maybe I can trump Trump at his own game and make a bit of dosh on the election’.

A data-scientist you keep on retainer sometimes talks about LessWrong and other dry things. One day she mentions that decentralised prediction markets are being developed, one of which is Augur. She says one can bet on the outcome of events such as elections.

You’ve made a fair few bucks in your day. You read the odd Investopedia page and a couple of random forum blog posts. And there’s that financial institute you run. Arbitrage opportunity, you think.

You don’t fancy your chance of winning the election. 40% chance, you reckon. So, you bet against yourself. Win the election, lose the bet. Lose the bet, win the election. Losing the election doesn’t mean much to you, losing the bet doesn’t mean much to you, winning the election means a lot of to you and winning the bet doesn’t mean much to you. There ya go. Perhaps if you put

Let’s turn this into a probability weighted decision table (game theory):

Not participating in prediction market:

Cumulative probability weighted value: (0.4*2) + (0.6*-1)=+0.2 value

participating in prediction market::

Cumulative probability weighted value: (0.4*2) + (0.6*-1)=+0.2 value

They’re the same outcome!
Looks like my intuitions were wrong. Unless you value winning more than losing, then placing an additional bet, even in a different form of capital (cash v.s. political capital for instance), then taking on additional risks isn’t an arbitrage opportunity.

For the record, Mitt Romney probably wouldn’t make this mistake, but what does post suggest I know about prediction?

**Thought experiment 2 – insider trading**

Say you’re a C level executive in a publicly listed enterprise. However, for this example you don’t need to be part of a publicly listed organisatiion, but it serves to illustrate my intuitions. Say you have just been briefed by your auditors of massive fraud by a mid level manager that will devastate your company. Ordinarily, you may not know how to safely dump your stocks on the stock exchange because of several reasons, one of which is insider trading.

Now, on a prediction market, the executive could retain their stocks, thus not signalling distrust of the company themselves (which itself is information the company may be legally obliged to disclose since it materially influences share price) but make a bet on a prediction market of impending stock losses, thus hedging (not arbitraging, as demonstrated above) their bets.

**Thought experiment 3 – market efficiency**

I’d expect that prediction opportunities will be most popular where individuals weighted by their capital believe they gave private, market relevant information. For instance, if a prediction opportunity is that Canada’s prime minister says ‘I’m silly’ on his next TV appearance, many people might believe they know him personally well enough that it’s a higher probability that the otherwise absurd sounding proposition sounds. They may give it a 0.2% chance rather than 0.1% chance. However, if you are the prime minister yourself, you may decide to bet on this opportunity and make a quick, easy profit…I’m not sure where I was going with this anymore. But it was something about incentives to misrepresent how much relevant market information one has, and the amount that competitor betters have (people who bet WITH you)

You're an average person.

You don't know what diseases you'll get in the future.

You know people get diseases and certain populations get diseases more than others, enough to say certain things cause diseases.

You're not quite the average person.

You have a strong preference against sickness and a strong belief in your ability to mitigate deleterious circumstances.

You have access to preventative research. You know if you don't work in a coal mine, overtrain when running, and eat healthy, you can stay healthier than those who take those risks.

You know that some disease outcomes are less than predictable, so you want to work towards the available of treatments that fill gaps in the availability of therapeutics. For instance, you might want a treatment for HIV to be developed, in case you become HIV infected, since there is a risk of HIV exposure for almost anyone exposed to unprotected sex, since they won't necessarily know their sexual partners entire serohistory (noologism?)
However, you don't know which diseases you will get. So how do you prioritise?

Perhaps, medical device and pharmaceutical company strategies could be ported to your situation.

Most people, including non-epidemiologist researchers, don't have access to epidemiology data sets.

Most people, don't have the patience to read a book on medical market research

You don't have the funds or connections to employ the world's only specialist in the area of medical market forecasting.

At least he's broken down the field into best practice questions:

In this thread, I would like to invite people to summarize their attitude to Effective Altruism and to summarise their justification for their attitude while identifying the framework or perspective their using.

Initially I prepared an article for a discussion post (that got rather long) and I realised it was from a starkly utilitarian value system with capitalistic economic assumptions. I'm interested in exploring the possibility that I'm unjustly mindkilling EA.

I've posted my write-up as a comment to this thread so it doesn't get more air time than anyone else's summarise and they can be benefit equally from the contrasting views.

I encourage anyone who participates to write up their summary and identify their perspective BEFORE they read the others, so that the contrast can be most plain.

Recently I talked with a guy from Grant Street Group. They make, among other things, software with which local governments can auction their bonds on the Internet.

By making the auction process more transparent and easier to participate in, they enable local governments which need to sell bonds (to build a high school, for instance), to sell those bonds at, say, 7% interest instead of 8%. (At least, that's what he said.)

They have similar software for auctioning liens on property taxes, which also helps local governments raise more money by bringing more buyers to each auction, and probably helps the buyers reduce their risks by giving them more information.

This is a big deal. I think it's potentially more important than any budget argument that's been on the front pages since the 1960s. Yet I only heard of it by chance.

People would rather argue about reducing the budget by eliminating waste, or cutting subsidies to people who don't deserve it, or changing our ideological priorities. Nobody wants to talk about auction mechanics. But fixing the auction mechanics is the easy win. It's so easy that nobody's interested in it. It doesn't buy us fuzzies or let us signal our affiliations. To an individual activist, it's hardly worth doing.

Much of the glamor and attention paid toward Friendly AI is focused on the misty-future event of a super-intelligent general AI, and how we can prevent it from repurposing our atoms to better run Quake 2. Until very recently, that was the full breadth of the field in my mind. I recently realized that dumber, narrow AI is a real thing today, helpfully choosing advertisements for me and running my 401K. As such, making automated programs safe to let loose on the real world is not just a problem to solve as a favor for the people of tomorrow, but something with immediate real-world advantages that has indeed already been going on for quite some time. Veterans in the field surely already understand this, so this post is directed at people like me, with a passing and disinterested understanding of the point of Friendly AI research, and outlines an argument that the field may be useful right now, even if you believe that an evil AI overlord is not on the list of things to worry about in the next 40 years.

Let's look at the stock market. High-Frequency Trading is the practice of using computer programs to make fast trades constantly throughout the day, and accounts for more than half of all equity trades in the US. So, the economy today is already in the hands of a bunch of very narrow AIs buying and selling to each other. And as you may or may not already know, this has already caused problems. In the “2010 Flash Crash”, the Dow Jones suddenly and mysteriously hit a massive plummet only to mostly recover within a few minutes. The reasons for this were of course complicated, but it boiled down to a couple red flags triggering in numerous programs, setting off a cascade of wacky trades.

The long-term damage was not catastrophic to society at large (though I'm sure a couple fortunes were made and lost that day), but it illustrates the need for safety measures as we hand over more and more responsibility and power to processes that require little human input. It might be a blue moon before anyone makes true general AI, but adaptive city traffic-light systems are entirely plausible in upcoming years.

To me, Friendly AI isn't solely about making a human-like intelligence that doesn't hurt us – we need techniques for testing automated programs, predicting how they will act when let loose on the world, and how they'll act when faced with unpredictable situations. Indeed, when framed like that, it looks less like a field for “the singularitarian cultists at LW”, and more like a narrow-but-important specialty in which quite a bit of money might be made.

After all, I want my self-driving car.

(To the actual researchers in FAI – I'm sorry if I'm stretching the field's definition to include more than it does or should. If so, please correct me.)

Control markets are systems where the control of resources and the system is determined by a market, the currency of that market is given out dependent upon how well the system as a whole is doing.

They have been discussed and explored in computer systems for a while with Agorics, Learning Classifier systems and Eric Baum's work being two notable examples (ZCS being the closest LCS to it). These are limited and constrained markets, in that the communication and computational expressiveness of them are limited. However unlimited control markets may be of use in the real world to control an organization in a flexible fashion. For example it might be useful for shareholders  to control a board or possibly the whole company. Even if it isn't compatible with human motivational systems and real world conditions, discussing and thinking about these sorts of systems may enable us to find better organizational structures than our current ones.

Control market can be seen as trying to embed reinforcement learning into an organization.