Ascription is an Anathema to any Enthusiasm

This just makes me sick. The report linked to below followed the money back to figure out who was funding the campaign to repeal the estate tax.

…the families identified in this report and their companies’ political action committees have, since 1999, made at least $27.7 million in contributions to candidates and federally focused political committees, largely to unregulated Section 527 committees.
…
Collectively, these super-wealthy families have a net worth of at least $185.5 billion. They include 23 billionaires, each of whom is listed in the Forbes 400. They stand to save $71.6 billion if their repeal campaign succeeds.

Nice return on investment.

Health Care Coverage for Uninsured Americans ($34 to $69 billion).

More here (pdf).

Meanwhile in California:

The top 3 percent of the returns, those with incomes exceeding $200,000, paid about 60 percent of all state taxes. “What happens to the top 1 percent is of great interest to the Department of Finance,”

As we can see the feeling is mutual.

Jane Jacobs has passed away.

I’ve often sorted people into two groups. Those for whom the fact that the minimum width of a sidewalk is 12 feet seems obvious v.s. those who don’t get it. Jane Jacobs was in the first group. We will miss her.

Since I got some positive reenforcement for my posting on Nagios I’ll go ahead and point to this posting on screen; which I use as well. I use screen on headless servers, a lot.

Screen is much lighter than what I used to do. I’d run an X server on the headless machines with an in memory virtual display device. On that server I’d run a XEmacs to which I would typically connect using gnuclient. Occationally I’d connect to the X server using VNC. The nice thing about this approach is you can run a rich visual display of the server’s status in the memory resident X server and pop it up on demand.

HA! I see that Justin Mason has some screen tricks as well.

Another fun item from Chandler Howell’s blog about how people manage the risk. People try to get what they percieve to be the right amount of risk into their lives, but they do this on really really lousy data. So there are all kinds of breakdowns.

For example you get unfortunate scenarios where actors suit up in safety equipment, this makes them feel safer so they take more risks and after all is said and done the accident rates go up. Bummer!

I’ve written about how Jane Jacobs offers a model for why Toronto overtook Montreal as the largest city in Canada. After the second world war Toronto was young and niave with a large appetite for risk; while Montreal was more mature and wise. To Toronto’s benefit and Montreal’s distress the decades after the second world war were a particularly good time to take risks and a bad time to be risk adverse.

I’ve also written about how limited liablity is a delightful scheme to shape the risk so that corporations will take more of it. All based on a social/political calculation that risk taking is a public good that we ought to encourage.

What I hadn’t apprecated previously is how this kind of thinking is entriely scale free. Consider the fetish for testing in many of the fads about software development. The tests are like safety equipment, they encourage greater risk taking. Who knows if the total result is a safer development process?

Michael points out a nice article by Chandler Howell discussing ways that sellers can game eBay’s second chance mechnisms to assure they get the best price.

First off recall how an eBay auction works. The seller puts up his vintage pezz dispencer and the buyers put forward various amounts they are willing to pay. For example; $10, $15, $100. Ebay, in it’s role as market maker, computes the winning bid. In this case the guy who bid $100 gets it; but he only pays a small increment over the second highest bid.

That’s a nice example of the problem that frustrates all sellers. The buyers hide what they are willing to pay. If the seller in this example had known that the highest bid was $100 then he could have hired a shill to bid $99 and practically no risk.

In the liturature about pricing this is called the “hidden price” problem. I love that term because the first time encountered it I was reading about hotel room pricing and the paper went on an on about the problem of the hidden price and i thought the price in question was the minimum price the hotel would be willing to rent the room for; but no it meant the maximum price I would be willing to pay for the room.

EBay’s second chance program can be used to solve the hidden problem for the sellers. The nominal purpose of second chance is to allow sellers to sell their item when the buyer fails to follow thru on the purchase. For example he wins, but he never sends the money. This is probably reasonably common since buyer’s remorse is a natural part of the sales cycle. But second chance solves the hidden price problem by allowing the buyer to introduce a shill with no risk that the shill will have to actually buy the product.

The seller sets up his auction to assure he can do second chance - he pays a bit extra for this and does some extra work. For example he has to do a buy it now auction and then quickly have his shill make a low bid so the buy it now option is removed from sight. He then waits till the auction is about to end and has his shill make a very high bid. The shill wins the auction for slightly more than the value of the top real bidder. At this point the buyer knows the maximum bid of the high bidder. Latter he pretends the shill failed to pay and he offers it to the high bidder for that price.

Of course from the market maker’s point of view, i.e. eBay, the problem they are trying to address is that some percentage of auctions don’t work out. Buyer’s remorse is only one reason why a buyer might decide to fail to complete a transaction. Incompetence of either the buyer or the seller is a common reason. There is a marvalous term in the auction liturature called “winner’s curse” that points out that from a statistical point of view auctions are a bizzare way to discover the value of a item. Instead of getting the average of a large number of valuations the action coughs up the most outragous valuation. Buyers who win at an auction sometimes realize that and back out because they now know nobody on in the whole world of eBay things that pezz dispenser is worth what they just bid.

The seller like that curse. Using a shill and second chance can assure the winner is maximally cursed.