Author Archives: Christopher Fariss

A Dynamic Ordinal Item Response Theory Model with Application to Human Rights Data

by Chris Fariss

Keith Schnakenberg and I continue work on a paper in which we develop a measure the unobservable level of respect for human rights. I blogged about it earlier here. In the new version of the paper we build upon existing insights in the Bayesian measurement literature to develop a dynamic ordinal item-response (DO-IRT) model. We then assess the validity of the estimates obtained from the latent variable generated from the DO-IRT model with the estimates from an ordinal item-response (O-IRT) model and the original additive human rights scales. Below is a plot of the estimates of the latent physical integrity variable (see the paper for a similar plot of latent empowerment variable):

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R function for sorting NAs and data in each row of a Matrix

by Chris Fariss

# —– naSortMatrix function —————————— #
# —– by Chris Fariss ———————————— #
# Takes a data frame or matrix as an argument
# and returns another matrix after moving any NAs
# within each matrix row to the right of the data
# in that row without changing the order of the
# data. Also calculates the time taken to complete
# the sort and saves it as a global variable:
# naSortMatrix.time
# Just copy and paste this during an R session to
# use the function
# ——————————————————– #
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New Papers in the Physics and Society section at

By Chris Fariss

Evolution of Coordination in Social Networks: A Numerical Study

Coordination games are important to explain efficient and desirable social behavior. Here we study these games by extensive numerical simulation on networked social structures using an evolutionary approach. We show that local network effects may promote selection of efficient equilibria in both pure and general coordination games and may explain social polarization. These results are put into perspective with respect to known theoretical results. The main insight we obtain is that clustering, and especially community structure in social networks has a positive role in promoting socially efficient outcomes.

More below:

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Identifying influential spreaders in complex networks

By Chris Fariss

Check out this interesting new paper posted at “Identifying influential spreaders in complex networks” that attempts to model the individuals within a network that spread information the most efficiently.

Here is the abstract:

Networks portray a multitude of interactions through which people meet, ideas are spread, and infectious diseases propagate within a society. Identifying the most efficient “spreaders” in a network is an important step to optimize the use of available resources and ensure the more efficient spread of information. Here we show that, in contrast to common belief, the most influential spreaders in a social network do not correspond to the best connected people or to the most central people (high betweenness centrality). Instead, we find: (i) The most efficient spreaders are those located within the core of the network as identified by the k-shell decomposition analysis. (ii) When multiple spreaders are considered simultaneously, the distance between them becomes the crucial parameter that determines the extend of the spreading. Furthermore, we find that– in the case of infections that do not confer immunity on recovered individuals– the infection persists in the high k-shell layers of the network under conditions where hubs may not be able to preserve the infection. Our analysis provides a plausible route for an optimal design of efficient dissemination strategies.


By Chris Fariss

I found the following post at Chris Blattman‘s blog; however, the original post is from yet another blog, The Frontal Cortex.  Is this cross-blogging or blog-crossing?  Anyway, I think the experiments will be of interest to our group.  Enjoy.

For the most part, self-control is seen as an individual trait, a measure of personal discipline. If you lack self-control, then it’s your own fault, a character flaw built into the brain.

However, according to a new study by Michelle vanDellen, a psychologist at the University of Georgia, self-control contains a large social component; the ability to resist temptation is contagious. The paper consists of five clever studies, each of which demonstrates the influence of our peer group on our self-control decisions.

For instance, in one study 71 undergraduates watched a stranger exert self-control by choosing a carrot instead of a cookie, while others watched people eat the cookie instead of the carrot. That’s all that happened: the volunteers had no other interaction with the eaters. Nevertheless, the performance of the subjects was significantly altered on a subsequent test of self-control. People who watched the carrot-eaters had more discipline than those who watched the cookie-eaters.

Be sure to check out the many other interesting posts at Chris Blattman‘s blog.  As for The Frontal Cortex, this was my first visit to the site but it might be worth exploring a bit more.

Slime Mold Leaves Urban Planners Unemployed

By Chris Fariss

The abstract of a recently published report in Science:

Transport networks are ubiquitous in both social and biological systems. Robust network performance involves a complex trade-off involving cost, transport efficiency, and fault tolerance. Biological networks have been honed by many cycles of evolutionary selection pressure and are likely to yield reasonable solutions to such combinatorial optimization problems. Furthermore, they develop without centralized control and may represent a readily scalable solution for growing networks in general. We show that the slime mold Physarum polycephalum forms networks with comparable efficiency, fault tolerance, and cost to those of real-world infrastructure networks—in this case, the Tokyo rail system. The core mechanisms needed for adaptive network formation can be captured in a biologically inspired mathematical model that may be useful to guide network construction in other domains.

Atsushi Tero, Seiji Takagi, Tetsu Saigusa, Kentaro Ito, Dan P. Bebber, Mark D. Fricker, Kenji Yumiki, Ryo Kobayashi, Toshiyuki Nakagaki. 2010. “Rules for Biologically Inspired Adaptive Network DesignScience Vol. 327. no. 5964, pp. 439 – 442 DOI: 10.1126/science.1177894

Human Rights as a Latent Variable

By Chris Fariss

Keith Schnakenberg and I are working on a paper in which we measure the unobservable level of respect for human rights.  We use the same Bayesian model that Shawn Treier and Simon Jackman use to measure the latent level of democracy in their 2008 paper that was published in the American Journal of Political Science.   As with the construction of GRE scores, the ordinal item-response (IRT) model explicitly models the measurement error that results when different component indicators are aggregated together.

Posterior densities for item discrimination parameters for individual physical integrity rights (300 draws). The item discrimination parameter represent the degree to which the item discriminates between states' along the latent human rights variable. Greater values along the x-axis signify greater discrimination by the item.

The data we use to estimate the IRT model is available from the CIRI Human Rights Data Project.  The model allows us to generate point estimates and credible intervals for the latent variable of interest.

Latent variables estimates for all 192 countries in the CIRI dataset in the year 2007. Blue dots are point estimates (posterior means) and red lines are 95% credible intervals.

For those interested, Simon Jackman has posted several slide-shows on his website that demonstrate the IRT model in action.  In our paper we also demonstrate a simple way to include the uncertainty from the estimates in models that include such a measure as an independent variable.  You can download a copy of our paper at SSRN (we will upload the paper soon).  For now, here is the current version of our abstract:

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