Tag: analysis

Night Life Clusters

Why would a lamp shop want to be located next to a whole lot of competition? Wouldn’t it be better to be the only lamp shop in the area? No, because clustering allows them to specialize. Sure, you’ll lose some customers because they can just go next door when you don’t have a lamp they want. But you’ll also gain customers from other stores. Having a big cluster means that when folks in the know want a lamp, they’ll head to your district; with such high traffic, the spillover effects more than make up for the disadvantages of not having a captive audience. As with lamps, so with bars.

how the east village became party district

The city as idea incubator

New York excels at creating those eclectic networks. Subcultures and small businesses generate ideas and skills that inevitably diffuse through society, influencing other groups. As the sociologist Claude Fischer put it in an influential essay on subcultures published in 1975, “The larger the town, the more likely it is to contain, in meaningful numbers and unity, drug addicts, radicals, intellectuals, ‘swingers’, health-food faddists, or whatever; and the more likely they are to influence (as well as offend) the conventional center of the society.”

Arrow’s Theorem

What Arrow showed is that group choice (aggregation) is not like individual choice. Suppose that a person is rational and that we observe their choices. After some time we will come to understand their choices in terms of their underlying preferences (assume stability–this is a thought experiment). We will be able to say, “Ah, I see what this person wants. I understand now why they are choosing in the way that they do. If I were them, I would choose in the same way.”

this is why we can’t have nice things: even rational actors lead to absurd group choices.
2022-03-24:

When people make mistakes, they usually try to make better decisions subsequently. To do this, you have to acknowledge that you made a wrong choice. Next, you have to examine the process by which you made the choice, in order to theorize about what would have produced a better outcome. The next time you face a similar decision, you try to correct your decision-making process.

People can experience bad outcomes when they vote. Your preferred candidate or policy could lose. Or your side could win and produce bad results. But chances are, you will not go through an error-correction process. Very rarely will a voter say, “I made a mistake. What went wrong? I need to review how I made my choice, so that I do things differently the next time.”

There are 2 reasons that voters do not engage in error correction. One reason is that 1 person’s vote almost never affects the outcome of an election. It does not pay to invest effort in figuring out what went wrong and trying to correct it. Another reason is that political outcomes are more complex than personal outcomes.

Peak Data Approaches?

Without radical innovation in our physical network infrastructure—that is, improvements in the key physical properties of transmission fibers and the optical amplifiers that we rely on to transmit data over long distances—we face what has been widely referred to as a “capacity crunch” that could severely constrain future Internet growth, as well as having social and political ramifications.

Comics with problems

Meet GABBY THE ANTI-PEDOPHILE PUPPET! She knows when you are sleeping and she knows when you’re awake. Here’s a great pamphlet on avoiding molestation and abduction in local bathrooms, gas stations and from ice cream vendors. It’s Gabby the Puppet in THE RIDDLE OF THE FRIENDLY STRANGER. A 1960s giveaway from Marathon Oil Company.

Hello Weighted Sort

a very nice improvement to make web analytics useful. that is, if you use a competent analytics package.

We have a very long tail of data in web analytics. 10s of 1000s of rows of keywords in the Search Report (even for this small blog!). 100s and 100s of referring urls and campaigns and page names and so on and so forth. Yet because we are humans we tend to look at just the top 10 or 20 rows to try and find insights. The problem? The top 10 of anything rarely changes (except in rare circumstances like a sale or on a pure content – think news – site). Hence I have persistently evangelized the need for true Analysis Ninjas to move beyond the top 10 rows of data to find insights.

Forking is a Feature

THE ONE TRUE VERSION
Most importantly, the new culture of ubiquitous forking can have profound impacts on lots of other categories of software. There have been recent rumblings that participation in Wikipedia editing has plateaued, or even begun to decline. Aside from the (frankly, absurd) idea that “everything’s already been documented!” one of the best ways for Wikipedia to reinvigorate itself, and to break away from the stultifying and arcane editing discussions that are its worst feature, could be to embrace the idea that there’s not One True Version of every Wikipedia article.

A new-generation Wikipedia based on Git-style technologies could allow there to be not just one Ocelot article per language, but an infinite number of them, each of which could be easily mixed and merged into your own preferred version. Wikipedia already technically has similar abilities on the back end, of course, but the software’s cultural bias is still towards producing a definitive consensus version instead of seeing multiple variations as beneficial.

There are plenty of other cultural predecessors for the idea of forking, all demonstrating that moving away from the need for a forced consensus can be great for innovation, while also reducing social tensions. Our work on ThinkUp at Expert Labs has seen a tremendous increase in programmers participating, without any of the usual flame wars or antagonism that frequently pop up on open source mailing lists. Some part of that is attributable to the cultural infrastructure GitHub provides for participation.

Moving forward, there are a lot more lessons we can learn if we build our social tools with the assumption that no one version of any document, app, or narrative needs to be the definitive one. We might even make our software, and our communities, more inclusive if we embrace the forking ourselves.

or more accurately, easy forking and easy merging is. the author goes off the rails towards the end saying that forking wikipedia would be good.