Local vs. Global Control

Photo via smoothgroover22 on flickr

As a kid, I was fascinated by the idea of self-driving cars. This may be the very definition of a nerdy child, but I remember sitting awake one night, thinking about how self-driving cars might work. All of my schemes involved a centralized computer that would be aware of where every car was, and would calculate ahead of time when collisions might occur, and make minor adjustments to vechicle speeds to avoid them.

However, the actual self-driving cars which are coming are very different from what I envisioned. Instead of operating via a centralized "hive mind", each car operates autonomously and locally, without any direct knowledge about other cars. Instead of a centralized knowledge about the whole system, they each gather their own information about their local environment, and operate in a decentralized way, with only local knowledge of their portion of the system.

There are some reasons for this, some of which can point toward general principles for when systems are better as decentralized and when they are better centralized:

• There are important aspects of the environment which are unknown. A centralized system doesn't know about cyclists or children running into the road, or debris, etc.
• Centralized systems create a single point of failure. If there is a bug in the system, all cars are affected. A bug in a single car (or even a single model) is much less catastrophic
• Centralized systems are less adaptable. If you decide that you don't want to go to that restaurant after all, and you'd rather just go to Wendy's, with a local system, your car simply adjusts its route accordingly. With a centralized system, an adjustment to one car propagates through the system, and hundreds or thousands of route adjustments need to be calculated and applied (similarly every time someone starts a new route or there is an accident, etc.). These are complex optimizations problems which can be approximated by local systems without all of the overhead.

I think that the eventual system of transportation will be a mix of local and centralized. As I understand it, current cars have access to centralized maps, and I can imagine that the richness of the sort of data that can be centralized will grow: information about when lights will change, information about traffic jams, even information about the destination of other cars in the area. However, for the reasons outlined above, I think that these will be information systems, not decision systems. They will always be accompanied by a rich understanding of the local situation, with the actual decision making done at that level.

Creating LaTeX PDF from included file

I learned a neat trick today. One of those things that once you learn it, you wonder how/why you were so long without it.

I've been working on revising my thesis to try to publish it as a few papers. Because it's such a long paper, I have a 'thesis.tex' file, which is basically a bunch of include statements, to include each of the chapters. As I'm editing it, I open each chapter in a tab in vim, and until today, whenever I wanted to recompile the PDF, I would move to the 'thesis.tex' tab and recompile from there.

So, today I found out that you can set up a .tex file as a "main" file, so that when you '\ll' to compile your .tex code, it will compile that file, instead of the file you're currently looking at.

 Instructions:
http://vim-latex.sourceforge.net/documentation/latex-suite/latex-project.html#latex-project-example

Importing Edgelists into RSiena

So, importing data into RSiena is a bit of a pain. The GUI has some support for importing Pajek files, for example, but I've been working mostly from the command line, and with .R files, which are what the manual covers.

For my current project, I have CSV files in a very common edgelist format, something like -

sourceID,receiverID,weight,wave 

I think it should be simple to import these into RSiena, but it isn't.

RSiena accepts either adjacency matrices - which are matrices with a 0 or 1 in each spot, for each node - or sparse matrices. These are similar to edgelists, but they have to be in the dgTMatrix class. As you can tell by reading the documentation, it's not exactly obvious how to get the data into that format.

I started by trying the Matrix() function, then I found the sparseMatrix() function. I realized that weight didn't matter, so I simply ignored the weight column. This creates a sparse matrix of the type "ngCMatrix", which is a "pattern matrix", and can't be coerced to a dgTMatrix.

So, eventually, I ended up creating a new weight column, with everything set to 1, and reset to 1 if there are duplicate entries in the data.

My current code is below:

 edgeListToAdj <- function(x, waveID){   
     # Remove entries who are not connect to anyone (NomineeID == 0), and not the   
     # current wave   
     tempNet <- x[x$NomineeID > 0 & x$NomineeID <= nodeCount & x$Wave == waveID,]   
     # Create a binary column for weights (since RSiena doesn't use weights).   
     tempNet$Weight <- 1   
     # Convert network obejct to adjacency matrix   
     adjacencyMat <- sparseMatrix(tempNet$NomineeID, tempNet$RespondentID, x=tempNet$Weight,   dims=c(nodeCount,nodeCount))   
     # If any items appear more than once, re-binarize them.   
     # Yes, binarize is a real word.   
     adjacencyMat[adjacencyMat > 1] <- 1   
     # Convert to a dgTMatrix, since this is what RSiena expects   
     return(as(adjacencyMat, "dgTMatrix"))   
 }   
 createNetwork <- function(fileName, numWaves) {  
     print(fileName)  
     # Convert CSV file to data frame  
     netDF <- as.data.frame(read.csv(fileName))  
     # Create an array of adjacency networks  
     net <- lapply(1:numWaves, function(x) edgeListToAdj(netDF, x))  
     # Change this into an RSiena network  
     RSienaObj <- sienaDependent(net)  
     return(RSienaObj)  
 }  

Thesis Accepted

My thesis is defended, edited, and accepted.

It's online at
https://www.academia.edu/7544796/ONLINE_NATURALIZATION_EVOLVING_ROLES_IN_ONLINE_KNOWLEDGE_PRODUCTION_COMMUNITIES

I'm pretty proud of how it turned out. I didn't get all of the results I was hoping for, but that's research.

Thesis Defended!

I haven't kept up on this blog as much as I should have, but my thesis is done and defended!

An Analysis of Interactions on the Boston Subway

I made my first ever visit to Boston this past week, and while I was there, I was able to put together a small research project. On a subway ride, I carefully noted each of the interactions between the 50 riders in the train car I was in, during a 15 minute ride. Edge weight represents number of minutes spent talking to each other.

Nodes are colored based on degree centrality, sized based on eigenvector centrality, and spaced with Fruchterman-Reingold in Gephi.

So far, the research is merely descriptive, but I think some real insights could be made through running an actor-oriented model.

:)

.vimrc and Dropbox

If you have used vim for very long, you have almost certainly made some modifications to your .vimrc file - this is the file that stores configurations for how vim does things like tabs, syntax highlighting, etc.

If you use more than one computer, I highly recommend keeping your .vimrc file on the cloud. It's incredibly simple, and provides for a consistent experience across computers.

This Stack Overflow post gives simple instructions on how to do this.