I love text consoles. The more I can do without moving a mouse or opening a new window, the better. So, when I saw XKCD’s command-line interface, I grabbed the code and started to build new features into it, as my kind of browser window to a cyber world of text.

I want to tell you about my console-based time-management system, the entertainment system, the LambdaMOO world, the integration with my fledgling single-stream analysis toolbox. But the first step was to clean out the password-protected stuff, and expose the console code for anyone who wants it.

So here it is! Feel free to play around on the public version, http://ift.tt/1PEsTtI, or clone the repository for your own.

Here are the major changes from the original XKCD code by Chromacode:

• Multiple “shells”: I currently just have the Javascript and XKCD-Shell ones exposed. Javascript gives you a developer-style javascript console (but buggy). You can switch between the two by typing x: and j:.
• A bookmark system: ln URL NAME makes a new bookmark; ls lists the available bookmarks, and cd NAME opens a bookmark.
• A login/registration system: Different users can have different bookmarks (and other stuff). Leave ‘login:’ blank the first time to create a new account.
• Some new commands, but the only one I’m sure I left in is scholar [search terms] for a Google Scholar search.

Share, expand, and enjoy!

In the spirit of Labor Day, I did a little research into Labor issues. I wanted to explore how much time people spent either at or in transit to work. Ever since the recession, it seems like we are asked to work longer and harder than ever before. I’m thinking particularly of my software colleagues who put in 60 hour weeks as a matter of course, and I wanted to know if it’s true across sectors. Has the relentless drive for efficiency in the US economy taken us back to the limit of work-life balance?

I headed to the IPUMS USA database and collected everything I could find on the real cost of work.

When you look at average family working hours (that is, including averaged with spouses for couples), there’s been a huge shift, from an average of 20-25 hours/week to 35-40. If those numbers seem low, note that this is divided across the entire year, including vacation days, and includes many people who are underemployed.

The graph below shows the shift, and that it’s not driven by specifically employees or the self-employed. The grey bands show one standard deviation, with a huge range that is even larger for the self-employed.

So who has been caught up in this shift? Everyone, but some industries and occupations have seen their relative quality of life-balance shift quite a bit. The graph below shows a point for every occupation-and-industry combination that represents more than .1% of my sample.

In 1960, you were best off as a manager in mining or construction; and worst as a laborer in the financial sector. While that laborer position has gotten much worse, it has been superseded in hours by at least two jobs: working in the military, and the manager position in mining that once looked so good. My friends in software are under the star symbols, putting in a few more hours than the average. Some of the laboring classes are doing relatively well, but still have 5 more hours of work a week than they did 40 years ago.

We are, all of us, more laborers now than we were 60 years ago. We struggle in our few remaining hours to maintain our lives, our relationships, and our humanity. The Capital class is living large, because the rest of us have little left to live.

The research behind the Risky Business report will be released as a fully remastered book, tomorrow, August 11!  This was a huge collaborative effort, led by Trevor Houser, Solomon Hsiang, and Robert Kopp, and coauthored with nine others, including me:

From the publisher’s website:

Climate change threatens the economy of the United States in myriad ways, including increased flooding and storm damage, altered crop yields, lost labor productivity, higher crime, reshaped public-health patterns, and strained energy systems, among many other effects. Combining the latest climate models, state-of-the-art econometric research on human responses to climate, and cutting-edge private-sector risk-assessment tools, Economic Risks of Climate Change: An American Prospectus crafts a game-changing profile of the economic risks of climate change in the United States.

The book combines an exciting new approach to solidly ground results in data with an extensive overview of the world of climate change impacts. Take a look!

Hello everyone, I am here to do a little guest blogging today. Instead of some useful empirical tools or interesting analysis, I want to take you on a short tour through of the murkier aspects of economic theory: anticipation. The very idea of the ubiquitous Nash Equilibrium is rooted in anticipation. Much of behavioral economics is focused on determining how people anticipate one another’s actions. While economists have a pretty decent handle on how people will anticipate and act in repeated games (the same game played over and over) and small games with a few different decisions, not as much work has been put into studying long games with complex history dependence. To use an analogy, economists have done a lot of work on games that look like poker but much less work on games that look like chess.

One of the fundamental problems is finding a long form game that has enough mathematical coherence and deep structure to allow the game to be solved analytically. Economists like analytical solutions when they are available, but it is rare to find an interesting game that can be solved by pen and paper.

Brute force simulation can be helpful. Simply simulating all possible outcomes and using a technique called backwards induction, we can solve the game in a Nash Equilibrium sense, but this approach has drawbacks. First, the technique is limited. Even with a wonderful computer and a lot of time, there are some games that simply cannot be solved in human time due to their complexity. More importantly, any solutions that are derived are not realistic. The average person does not have the ability to perform the same computations as a super computer. On the other hand, people are not as simple as the mechanical actions of a physics inspired model.

James and I have been working on a game of strategic network formation which effectively illustrates all these problems. The model takes 2 parameters (the number of nodes and the cost of making new connections) and uses them to strategically construct a network in a decentralized way. The rules are extremely simple and almost completely linear, but the complexities of backwards induction make it impossible to solve by hand for a network of any significant size (some modifications can be added which shrink the state space to the point where the game can be solved). Backwards induction doesn’t work for large networks, since the number of possible outcomes grows at a rate of (roughly) but what we can see is intriguing. The results seem to follow a pattern, but they are not predictable.

Each region of a different color represents a different network (colors selected based on network properties). The y-axis is discrete number of nudes in the network. The x axis is a continuous cost parameter. Compare where the color changes as the cost parameter is varied across the different numbers of nodes. As you can see, switch points tend to be somewhat similar across network scales, but they are not completely consistent.

Currently we are exploring a number of options; I personally think that agent-based modeling is going to be the key to tackling this type of problem (and those that are even less tractable) in the future. Agent based models and genetic algorithms have the potential to be more realistic and more tractable than any more traditional solution.