Friday, May 24, 2013

SFI Talks on YouTube

Via Twitter (@SFI_News), I have just come come across some excellent talks that took place at the Santa Fe Institute and thought they were worth sharing. At this time their YouTube channel has 95 videos ranging across complexity science such as the Emergence of Complex Societies and Cities, Scaling and Sustainability.

In another video which is relevant to some of the work we are doing at the Department of Computational Social ScienceLeysia Palen talks about "How Social Media Might Help You Survive the Next Big Disaster."

The SFI YouTube channel is really worth checking out.

Friday, May 10, 2013

Tweets from President Obama's inauguration 2013-01-21

Following on from a previous post on agent-based modeling and elections. Here we show geo-located tweets during the day of President Obama's inauguration 2013-01-21.

If you want to explore what people are currently saying about President Obama check out our Geosocial Gauge Website.

Screen shot of Geo social Gauge. Clockwise from top left: Location of tweets, basic sentiment of tweets (green positive, red: negative and gray: neutral), most active countries tweeting and a word cloud of the most popular words in the tweets.

Employment Growth through Labor Flow Network

    Omar Guerrero and Robert Axtell from the Department of Computational Social Science at GMU have recently published a paper in PLoS ONE entitled "Employment Growth through Labor Flow Networks." The work uses "newly available micro-data and the ability to work with large-scale, complex networks computationally, to study labor dynamics." Below is the abstract from the paper:

    It is conventional in labor economics to treat all workers who are seeking new jobs as belonging to a labor pool, and all firms that have job vacancies as an employer pool, and then match workers to jobs. Here we develop a new approach to study labor and firm dynamics. By combining the emerging science of networks with newly available employment micro-data, comprehensive at the level of whole countries, we are able to broadly characterize the process through which workers move between firms. Specifically, for each firm in an economy as a node in a graph, we draw edges between firms if a worker has migrated between them, possibly with a spell of unemployment in between. An economy's overall graph of firm-worker interactions is an object we call the labor flow network (LFN). This is the first study that characterizes a LFN for an entire economy. We explore the properties of this network, including its topology, its community structure, and its relationship to economic variables. It is shown that LFNs can be useful in identifying firms with high growth potential. We relate LFNs to other notions of high performance firms. Specifically, it is shown that fewer than 10% of firms account for nearly 90% of all employment growth. We conclude with a model in which empirically-salient LFNs emerge from the interaction of heterogeneous adaptive agents in a decentralized labor market.

      Communities of firms.

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      Thursday, May 09, 2013

      ABM & Elections

      Ever wondered if agent-based models have been applied to look at elections? I recently came across a nice little NetLogo model by Michael Laver which is part of the book "Party competition: an agent based model" (2012).

      This simple model allows users to explore the 2012 US presidential election campaign, Just like the election itself the model has two phases. 1) the  primary contest between the  Republican challengers.  2) The winner of the Republican primary then goes head to head with the Democratic incumbent.