Robert Axtell\nComputational Social Science Program\,\nDe
partment of Computational and Data Sciences\nGeorge Mason University
\, Fairfax\, VA

\n

\n

Up until quit
e recently the theory of the firm within economics was data poor. We knew
little about the empirical structure of firms outside of narrow samples (e
.g.\, manufacturing firms in some specific region\, small start-up firms i
n some sector\, or the < 10K firms that are publicly owned and traded). To
day we have data on all 6 million firms that have employees and the other
24 million that do not have employees. Some 120 million people work for th
ese businesses in the U.S. private sector. After reviewing the very peculi
ar data on firm sizes\, ages\, growth rates\, job tenure\, wages paid\, an
d so on\, I will go on to describe a parsimonious model in which 120 milli
on software agents arrange themselves into 6 million groups having most of
the properties of U.S. firms. Several model variations will be discussed.
These data and models constitute the main content my new book\, “Dynamics
of Firms: Data\, Theories\, and Models\,” due out from MIT Press next yea
r.

Over the last few years\, social media (e.g. T
witter\, Flickr\, YouTube\, etc.) have become an integral part of the mode
rn information and communication landscape. Through social media\, individ
uals\, groups\, organizations\, and even states can now acquire\, probe\,
and deliver information\, as well as shape and reshape public opinion. At
the same time\, social media content is increasingly related to physical g
eographical locations. Fueled by advances in of Web 2.0\, mobile computing
\, and spatially-aware technologies (i.e. GPS enabled smartphones)\, socia
l media can provide a unique opportunity to observe and study the flow of
information in both cyber and physical spaces. Employing a geographically
data-driven analysis approach allows us not only to track how information
spreads\, but also to derive information about real-world events and happe
nings. We call this geospatially-driven approach “GeoSocial Media Analysis
.” In this presentation I will review some of the key trends and develop
ments that has enabled the rise of geosocial media\, and shaped their char
acteristics. I will then move on to describe examples of various geosocial
analysis methods in the context of different application areas (e.g. natu
ral disasters\, protests\, international relations). Looking ahead\, I wil
l conclude the presentation with some of the emerging challenges and needs
in the analysis of geosocial media data.

Superconductivity has been one of the
main problems in condensed matter physics a hundred years after its disco
very. Many results of this intensive research made it to textbooks hinting
at its fundamental importance besides promising applications. In the last
few decades\, new superconducting materials were found that were beyond t
he realm of conventional phonon based superconductors described by Bardeen
-Cooper-Schriffer (BCS) theory. This new class of superconductors found a
wide ground in particle physics due the presence of exotic symmetries that
can be described by models akin to the Lorentz invariant field theories.<
/p>\n

In this talk\, I will be focusing on a re
cent member of this family of exotic superconductors Weyl superconductors
and related Majorana fermions and discuss possible experimental realizatio
ns of these materials.

The need for mining massive and complex data has become para
mount in areas like education\, web mining\, social network analysis\, sec
urity\, and a variety of scientific pursuits. However many traditional sup
ervised and unsupervised learning algorithms break down when applied in bi
g data scenarios: this is known as the “big data problem”. Among other con
cerns\, big data presents serious scalability difficulties for these algor
ithms.

\n

In this talk I will discuss the big data problem and soluti
ons in the context of complex data I have worked on in recent years\, name
ly\, social networks\, text\, and educational data. I will also present a
new method for distributed machine learning which directly tackles key pro
blems posing challenges to successful and scalable mining of big data. Our
method is scalable\, general-purpose with regard to the machine learning
algorithm\, and easily adaptable to a variety of heterogeneous grid or clo
ud computing scenarios.

Kenneth De Jong\nDepartment of Computer Science and Office of Research Computing
\nGeorge Mason University\nFairfax\, VA

\n

October 19\, 2015\, 4:30 p.m.\nExploratory Hall\, Room 3301
p>\n

\nInterest in co‐evolutionary algorithms w
as triggered in part with Hillis’ 1990 paper describing his success in usi
ng one to evolve sorting networks. Since then there have been heightened e
xpectations for using this nature‐inspired technique to improve on the ran
ge and power of evolutionary algorithms for solving difficult computation
problems. However\, after more than two decades of exploring this promise\
, the results have been somewhat mixed. In this talk I summarize the pro
gress made and the lessons learned with a goal of understanding how they a
re best used and identify a variety of interesting open issues that need t
o be explored in order to make further progress in this area.

The progression of Alzheimer’s disease has b
een linked to the aggregation of Aβ peptides. Current theories suggest tha
t Aβ interactions with cellular membranes results in its neurodegenerative
effect\, although little is known of the molecular mechanism. To probe th
is mechanism\, we performed replica-exchange molecular dynamics simulation
s of Aβ peptides binding to a model membrane represented by a lipid bilaye
r.

\n

In this talk I will describe our investigation of the conformat
ional change of Aβ peptides induced by the bilayer\, the Aβ binding mechan
ism\, and the impact of Aβ on bilayer structure. Our results find a correl
ation between the Aβ cytotoxicity and the aggregation of Aβ peptides.

Silica is one of the most-abundant natural compounds and
a major component of the Earth’s crust and mantle. Its various high-press
ure forms make it an often-used study subject for scientists interested in
the transition between different chemical phases under extreme conditions
\, such as those mimicking the deep Earth. Compressing single crystal coes
ite SiO2 under hydrostatic pressures of 26~53 gigapascal at room temperatu
re\, we discover a new polymorphic phase transition mechanism of coesite t
o post-stishovite\, by means of single-crystal synchrotron x-ray diffracti
on experiment and first-principles computational modeling.

\n

The tra
nsition features the formation of multiple previously unknown triclinic ph
ases of SiO2 on the transition pathway as structural intermediates. Coexis
tence of the low-symmetry phases results in extensive splitting of the ori
ginal coesite X-ray diffraction peaks that appear as dramatic peak broaden
ing and weakening\, resembling an amorphous material. This work provides n
ew insights into the structural transition of SiO2 crystal under high pres
sures\, and clarifies the issue of the pressure-induced amorphization of c
oesite\, which has often been cited as an archetypal example of the amorph
ization phenomena in general.

Non
radiative Auger recombination is an energy dissipation mechanism in which
the recombination energy of an electron-hole pair\, instead of being emitt
ed as a photon\, is transferred to another charge carrier and is eventuall
y lost as heat. If efficient enough\, the Auger process can considerably r
educe the radiative efficiency of semiconductor materials\, therefore comp
licating their application as light emitters. As a consequence of quantum
confinement\, in nanoscale semiconductor systems such as quantum wells or
quantum dots\, the Auger recombination is greatly enhanced and is able to
interfere with the radiative recombination. Here we present a theoretical
study of the nonradiative Auger recombination processes in III-V quantum w
ell light-emitting diodes (LEDs). The performance of these LEDs is known t
o steadily decrease with increasing driving current and the origin of this
efficiency droop phenomenon is still debatable. Our calculations show tha
t the Auger recombination can be efficient enough in these materials and t
hat it can be the source of the efficiency droop. Further\, we propose a s
trategy to suppress the confinement-enhanced Auger recombination by contro
llable modification of the confining potential shape. We apply this approa
ch to both polar and nonpolar variants of the III-V quantum well LEDs and
we demonstrate an appreciable suppression of the Auger recombination and c
onsequent improvement of light-generation efficiency in these materials.
p>\n

Heart disease is th
e leading cause of death in the developed world and is being recognized as
and increasing problem in the the developing world. A fatal cardiac arrhy
thmia is often the underlying cause of death. One class of arrhythmia rise
s from defects in calcium dynamics in the heart. Calcium plays a major rol
e on heart function linking the electrical depolarization of the heart wit
h contraction. We use an integrative multiscale model to explore how defec
ts on the level of molecular function can lead to arrhythmia in the heart.

\n

These simulations cross many orders of magnitude on the spatial a
nd temporal scales to provide insight into mechanisms of cardiac arrhythmi
a. To make this problem tractable we have employed novel computational alg
orithms and leverage modern GPU computing techniques. The insights gained
by these studies provide advanced our understanding of arrhythmia such as
how an arrhythmia is initiated\, how genetic defects can contribute to arr
hythmia\, and how do changes to structure and gene expression during disea
se increase the likelihood of arrhythmia.

In this talk
I will present a new method for determining efficient spatial distributio
ns of police patrols in a metropolitan region. A mathematical model called
the police patrol area covering (PPAC) model is formulated and solved. Th
e method uses inputs of police geography (divisions\, sectors\, beats\, an
d response areas) within a geographic information systems (GIS) framework\
, analyzes that data through an optimal covering model formulation\, and p
rovides alternative optimal solutions for presentation to decision makers.
The goals of this research are to increase the level of police service in
the face of variation in both the demand for police services and the avai
lability of police resources. Examples of the inputs from – and outputs to
– GIS are provided through a pilot study of the city of Dallas\, Texas.
p>\n

Di
ssertation Defense

\n

Candidate: Robert Reznik\nTitle: Electr
ic Field Effects On Single And Small Networks of Neurons

\n

Abstract:

\n

An electric field can polarize a neuron\, esp
ecially a neuron with elongated dendrites\, and thus modify its excitabili
ty. In this dissertation\, we use computational models\, experimental data
\, and analysis to investigate the effects on neural excitability due to e
xternally applied static electric fields and the extracellular currents ge
nerated by a spiking neuron. We apply our results to individual and small
networks of neurons. This work has potential implications in the areas of
neural prosthetics and therapies to treat a number of neurological conditi
ons such as Depression and Epilepsy. This work also contributes to our und
erstanding of the effects of electric fields endogenous to the brain. We f
irst address the effects of polarization induced by non-weak electric fiel
ds on elongated neurons with active dendrites. We find that our model agre
es with experimental observation for both weak and strong polarization. Fo
r intermediate polarization\, we identify novel behavior that should be am
enable to experimental verification.

\n

Through analysis and modeling
\, we determine the underlying mechanisms for the observed behavior. Curre
nts that are negligible at weak polarizations and associated with the spik
ing or bursting phase of a neuron play an important role in the response o
f a resting neuron to either injected or synaptically generated stimulus.
For weak polarizations\, small differences in model parameters such as ext
racellular potassium and the rate of current injection yield proportionall
y small differences in the trajectory of the state variables of the neuron
. However\, as polarization strength increases the trajectories begin to d
iverge falling into either a sublinear or superlinear response categories
with respect to polarization.

\n

We identify the relative strengths o
f the hyperpolarizing and depolarizing active dendrite currents as a predi
ctor of polarization-dependent excitability. In the second part of the dis
sertation we look at localized ephaptic effects due to a single spiking ne
uron on spike propagation in a chain of synaptically connected neurons. Mo
deling the extracellular currents using a resistive lattice we observe a n
on-monotonic relationship between excitability and extracellular resistanc
e. Furthermore\, this surprising result is evident for a range of resistan
ces that fall within those estimated using experimentally measured paramet
ers. We define three mechanisms of the localized ephaptic effects\; source
loading\, synaptic coupling\, and non-synaptic membrane currents. Through
computational experiment and analysis\, we are able to analyze these effe
cts as a function of the time between the ephaptic polarization and synapt
ic input as well as extracellular resistance.

Neutrons can penetrate deeply into materials.
They can sense the position of atomic nuclei through strong interaction an
d magnetic moments through dipole-dipole interaction. The scattering of ne
utrons by both these mechanisms is well understood\, so theories need not
be concerned about the modeling or simulating the scattering process. Inst
ead\, the investigations can focus on the computation and experimental det
ermination of the intrinsic positions of nuclei and spin-correlation funct
ions. This enables neutron scattering as a unique tool to determine crysta
l structures\, nanostructures\, spin dynamics and phonon dispersions in so
lids. In this talk\, I will focus on the applications of small angle neu
tron scattering to determine the orientational ordering of nanostructures
in MgO nanorods\, TiO2 nanowires and gamma-Fe2O3 nanoparticle dispersions.
I will also discuss how small neutron angle scattering from nanostructu
res is modeled and calculated.

Each year\, natural and anthropogenic crises disrupt the
lives of millions of people. Local\, national and international crisis res
ponse systems struggle to cope with urgent needs during and immediately af
ter a crisis. The challenges multiply as population grows\, density of urb
an areas increases\, and coastal areas become more vulnerable to rising se
a levels. A typical crisis scenario requires coordinating many diverse pla
yers\, including local\, national and international military\, other gover
nmental\, and non-governmental organizations. Often\, no single entity is
in charge of the response\, making coordination even more difficult. There
is an urgent need for better ways to allocate resources\, maintain situat
ion awareness\, and reallocate resources as the situation changes. Multi-s
ource information fusion is vital to effective resource allocation and sit
uation awareness. Some of the greatest inefficiencies in crisis response
stem from the inability to exchange information between systems designed
for different purposes and operating under different ownership. Too often\
, information integration and fusion must be performed manually. Greater a
utomation could support more timely\, better coordinated responses in situ
ations where time is of the essence. The greatest need is not for low-leve
l fusion of sensor reports to classify and track individual objects\, but
for high-level fusion to characterize complex situations and support plann
ing of effective responses. This talk describes challenges of high-level
fusion for crisis management\, proposes a technical framework for address
ing high-level fusion\, and discusses how effectively addressing high-leve
l fusion challenges can improve efficiency of crisis response. We illustra
te our ideas with a case study involving a humanitarian relief operation i
n a flooding scenario.

\n

Refreshments will be served at 4:15 PM.

\n

———————————————————————-\nFind the schedule at http://www.cmasc
.gmu.edu/seminars.htm

This talk will present two related top
ics. The first is a discussion of image operators\, which is a developing
mathematical notation that coalesces modern computer scripting with precis
e descriptions of image processing algorithms. The second is an exploratio
n of pulse image processing which is a tool set based on discoveries of th
e mammalian visual cortex. These processes are capable of extracting coupl
ed shapes and textures from images that are the foundations for many analy
sis algorithms. The inherent nature of these algorithms are capable of ret
rieving traditional features as well as image signatures which are invaria
nt to position\, rotation and limited scale. Several applications of pulse
image processing will be reviewed to demonstrate the efficacy of this app
roach.

\n

Refreshments will be served at 4:15 PM.\n————————————
———————————-\nFind the schedule at http://www.cmasc.gmu.edu/seminars
.htm

Wireless spectrum is becoming
a scarce resource largely due to the static allocation of licensed spectr
um by regulatory bodies\, resulting in a high degree of underutilization i
n large portions of the spectrum. One approach to alleviating spectrum sca
rcity involves the use of cognitive radios that are capable of sensing the
wireless environment and dynamically accessing unused portions of the spe
ctrum\, while avoiding harmful interference to licensed users of a spectru
m band. Dynamic spectrum access is promising\, but there remain technologi
cal challenges in spectrum sensing and resource allocation\, as well as sp
ectrum policy issues\, which need to be addressed for it to be widely depl
oyed. In this talk\, we will discuss models and algorithms for perform
ing spectrum sensing in time\, space\, and frequency. Spectrum sensing ess
entially amounts to characterizing primary users in terms of temporal dyna
mics\, spatial location and power usage\, and center frequency and bandwid
th usage. Such characterization must also take into account wireless propa
gation effects and impairments. We will discuss the application of a class
of hidden Markov models to characterizing the temporal dynamics of primar
y users\, as well as their spatial and frequency domain properties.

The solidification of binary and multicomponent alloys occurs in
many industrial and geophysical systems. Often in these systems instabili
ties of the solid/liquid interface lead to dendritic regions that separate
completely solid regions from the liquid region. These so-called mushy la
yers have important consequences for heat and solute transfer between the
solid and liquid regions. In this talk we outline mathematical models an
d their connection to laboratory experiments that help understand the phys
ical processes occurring in these systems. Novel fluid dynamical phenomena
that occur in mushy layers will be discussed for binary and ternary syste
ms.

In this talk we will describe t
wo projects in the neural engineering lab\, both focusing on neural interf
aces. One project is in collaboration with the FDA and aims at demonstrati
ng the safety in retinal implant devices. We will discuss how we model an
implantable electrode in terms of electric circuits. The second project fo
cuses on neural cell cultures and we will present results on how to interf
ace with networks over time and train them to respond to electrical stimul
i. We intend to discuss how that impacts our understanding of the mechanis
ms mediating memory and learning in the brain. Some of the other project
s in the lab are described in our site: http://neural.bioengineering.gmu.e
du.

In this talk I will focus on two mechanical effects rela
ted to fluid adsorption by porous materials: adsorption-induced deformatio
n and change of elastic properties of fluids during adsorption. The develo
pment of quantitative theories of these effects will provide the opportuni
ty to employ them for new sensing technologies and new approaches for the
characterization of nanoporous materials. Adsorption-induced deformation i
s a change of shape or volume of a porous sample during adsorption. It cou
ld be either expansion or contraction\, and while the former is well under
stood\, the latter is still puzzling due to the apparent contradiction wit
h the Gibbs adsorption equation. I will show how this apparent contradicti
on can be resolved by relating the strain of the solid to the change of th
e surface stress due to adsorption. I will present the results of the surf
ace stresses calculations based on ab initio molecular dynamics simulation
s. In the second part of the talk I will focus on the effects of nanoconfi
nement on the mechanical properties of the adsorbed fluid. Recent ultrason
ic experiments have shown that the elastic modulus of argon adsorbed in na
noporous glass noticeably differs from the elastic modulus of bulk liquid
argon. I will present the results of a molecular modeling study\, which ex
plains these experimental observations and sheds light on possibilities of
ultrasonic investigation of nanoporous materials.

Similarly to
the probability distribution of energy in physics\, the probability distri
bution of money among the agents in a closed economic system is also expec
ted to follow the exponential Boltzmann-Gibbs law\, as a consequence of en
tropy maximization. Analysis of empirical data shows that income distribut
ions in the USA\, European Union\, and other countries exhibit a well-defi
ned two-class structure. The majority of the population (about 97%) belong
s to the lower class characterized by the exponential (“thermal”) distribu
tion. The upper class (about 3% of the population) is characterized by the
Pareto power-law (“superthermal”) distribution\, and its share of the tot
al income expands and contracts dramatically during booms and busts in fin
ancial markets. Globally\, data analysis of energy consumption per capit
a around the world shows decreasing inequality in the last 30 years and co
nvergence toward the exponential probability distribution\, in agreement w
ith the maximal entropy principle. Similar results are found for the globa
l probability distribution of CO2 emissions per capita. All papers are ava
ilable at http://physics.umd.edu/~yakovenk/econophysics/. For recent cover
age in Science magazine\, see http://www.sciencemag.org/content/344/6186/8
28

Current technology trends\, su
ch as smart phones\, mobile devices\, stationary sensors and satellites\,
coupled with a new user mentality of voluntarily sharing information gener
ates a huge volume of geo-spatial and geo-temporal data that might be usef
ul for many applications. Indeed\, the increasing volume of geo-spatial da
ta from heterogeneous sources is an example of Big Data. In this talk I wi
ll present effective and efficient solutions to problems related to reliab
le spatial pattern analysis and mining of uncertain spatial and spatio-tem
poral data. These techniques can substantially improve the quality of deci
sion making\, minimize risk\, and unearth valuable insights from data that
would otherwise remain hidden. Use of uncertain data presents a two-fold
challenge: 1) identifying potential solutions and assigning a probability
to each solution such that the user is confident about the results\, and 2
) enabling fast computations such that the user obtains results in a reaso
nable time\, even for large data sets.

Th
e Churchillian quote “Never\, in the field of human conflict\, was so much
owed by so many to so few”\, encapsulates perfectly the heroics of Royal
Air Force (RAF) Fighter Command (FC) during the Battle of Britain. Despite
the undoubted heroics\, questions remain about how FC employed the ‘so fe
w’. In particular\, the question as to whether FC should have employed the
‘Big Wing’ tactics\, as per 12 Group\, or implement the smaller wings as
per 11 Group\, remains a source of much debate. In this paper\, I create a
n agent based model (ABM) simulation of the Battle of Britain\, which prov
ides valuable in- sight into the key components that influenced the loss r
ates of both sides. It provides mixed support for the tactics employed by
11 Group\, as the model identified numerous variables that impacted the su
ccess or otherwise of the British.

Atomic and molecular physics was an
early beneficiary of the development of electronic computation. Luckily\,
most of the interactions between electrons and nuclei are well understood
and it appears that all that remains is to “turn the crank”. In retrospec
t\, this was not that simple. These are complex many-body systems and in o
rder to overcome the exponential scaling of these computations with the nu
mber of particles\, clever algorithms and efficient codes needed to be dev
eloped. In this talk I will describe a number of the important development
s that have taken place over the past four decades and how they have impac
ted our qualitative and quantitative understanding of scattering processes
and the interaction of radiation with matter.

Robert Axtell\, Computational and Social Science Professor\nDepartment of Computational and Data Sciences\nGeorge Mason Un
iversity

\n

Friday\, September 16\, 3:00 p
.m.\nCenter for Social Complexity Suite\n3rd Floor\, Research
Hall

\n

Beyond Gibrat’s Law: On th
e Growth of Firms One Worker at a Time

\n

The stochastic theory of firm growth\, originating with Gibr
at\, is not a theory of firm interactions but rather a model of an individ
ual firm subject to exogenous shocks. Mathematically\, it is well-known th
at Gibrat’s formulation of firm growth is a kind of random walk of log(fir
m size) and produces lognormal distributions of sizes that are not station
ary. Thus the theory has to be supplemented with additional features\, as
was done by Simon and others\, in order to yield empirically-observed firm
sizes\, i.e.\, a Pareto distribution with exponent near unity\, the so-ca
lled Zipf distribution. Here we show that there are multiple problems asso
ciated with these variations on Gibrat’s law\, having to do with fluctuati
ons in firm sizes being too volatile to be meaningful economically\, with
the Pareto exponents produced not agreeing with empirical values generally
\, and the inability to aggregate from a single firm to a population of fi
rms without further specifications that are unrealistic. We propose a diff
erent kind of stochastic growth specification that couples the firms in an
economy through labor flows and leads naturally to the Zipf distribution.
We will argue that it is a kind of anti-Gibrat process insofar as it nega
tively couples firms—each time one grows\, another shrinks. This alternati
ve theory has a natural interpretation in terms of labor economics as job-
to-job flows\, which are a large and persistent feature of modern economie
s. We conclude by applying these arguments to other social phenomena that
have been modeled with Gibrat’s law\, including membership in voluntary or
ganizations\, sizes of cities\, and the connectivity of networks.

The behavior of e
lectrons in materials underpins key materials properties\, which means tha
t computing and understanding the electronic structure of various material
s systems is of vital importance. These computations are\, nowadays\, ofte
n performed using density functional theory (DFT)\, a first-principles met
hodology that is an important tool in the computational materials scientis
t’s toolbox. One of the materials properties that is accessible via DFT is
magnetism\, and DFT can be used to study the magnetic interaction between
electrons (called the exchange interaction) in a material. This involves
constructing models such as the Heisenberg model and mapping DFT calculati
ons onto it\, which allows one to understand how tuning different features
impacts important parameters such as the critical temperature and the sta
bility of magnetic ground state. This approach to studying magnetic materi
als is of particular appeal in the spin electronics field\, where the enco
ding and processing information using the magnetic states of electrons is
of central importance. In this talk I will: 1) introduce the basic concept
s of computational materials science using DFT in an accessible manner\, a
nd 2) present calculations on two different materials where I used DFT in
conjunction with modeling to analyze the magnetic interactions. The first
presented material will be the dilute magnetic semiconductor (Ba\, K)(Zn\,
Mn)2As2\, which exhibits ferromagnetism when a small amount of manganese
and potassium are substituted into the material\, and where changing the r
elative quantity of potassium influences the strength of the magnetic inte
ractions. The second presented material is MnAu2\, a magnetic metal that h
as a cork-screw noncollinear magnetic ground state which can be tuned in i
ntriguing ways using pressure and chemical substitution. Using modeling in
combination with DFT\, I will show how we are able to understand the natu
re of the microscopic magnetic interactions in each material and that the
microscopic mechanisms driving the the magnetic interactions in both compo
unds is the same. These results can then be used to resolve several experi
mental questions\, one of which had gone unaddressed for several decades.<
/p>\n

High-speed computerized trading\, often called “high-frequenc
y trading” (HFT)\, has increased dramatically in financial markets over th
e last decade. In the US and Europe\, it now accounts for nearly one-half
of all trades. Although evidence suggests that HFT increases market effic
iency\, there are concerns it also adds to market instability\, especially
during times of stress. Here\, I show that HFT synchronizes prices in fi
nancial markets\, making the prices of related securities change contempor
aneously. With a model\, I demonstrate how price synchronization leads to
efficiency gains: prices are more accurate and transaction costs are redu
ced. During times of stress\, however\, localized errors quickly propagat
e through the financial system if safeguards are not in place. This resea
rch highlights an important role that HFT plays in markets and helps answe
r several puzzling questions that previously seemed difficult to explain:
why HFT is so prevalent\, why HFT concentrates in certain securities and l
argely ignores others\, and finally\, how HFT can lower transaction costs
yet still make profits.

Emanuel A. Lazar\nLaboratory for
Research on the Structure of Matter\nSchool of Engineering and Appl
ied Science\nUniversity of Pennsylvania\nPhiladelphia\, PA

\n

Describing how atoms are arranged in real and simulated materials is
a very natural problem that arises in numerous computational materials sci
ence applications. However\, aside from perfect crystals\, insightful yet
tractable descriptions of local arrangements of atoms can be tricky to dev
elop. We consider several conventional order-parameter methods for descr
ibing local structure and highlight their theoretical and practical limita
tions. We then introduce a topological approach more naturally suited fo
r structure analysis and highlight its versatility and robustness. In part
icular\, the Voronoi tessellation method can aid in the study of materials
at high temperatures\, close to melting\, without uncontrolled modificati
on of raw data. Applications to the study of grain boundary evolution and
melting will be briefly presented.

ABSTRACT: This talk will focus on two recently funded projects. The first is to a
nswer how people would react to at nuclear WMD (weapon of mass destruction
) event in a mega-city and a regional area. The task is to characterize th
e population’s behavior over the hours\, days\, and few weeks following th
e event. This basic research project began during the summer and will invo
lve two faculty and two Graduate Research Assistants (GRAs) for 3 years. T
he project is being supported by the Defense Threat Reduction Agency (DTRA
). The second project is an effort to develop advanced engineering languag
e\, symbols\, and visualization to improve the systems engineering of comp
lex and increasingly autonomous systems. This project involves incorporati
ng concepts including beliefs and intents as part of the specifications an
d development process for systems that are becoming more complex and auton
omous\, such as drones operating in the national air space. The project in
volves one faculty member and one GRA for one year and is sponsored by the
National Aeronautic and Space Administration (NASA) grant to LMI (Logisti
cs Management Inc.). These two project are part of the Center for Social C
omplexity within the Krasnow Institute for Advanced Study.

For Artificial Intelligence (AI) to reach its int
ended goals\, it must mitigate fears of singularity and illustrate its ben
efits to the human race. Many researchers in this field believe that conte
xt is a gateway to many of these positive aspects of intelligence. The imp
ortance of data analytics and context in many recent software research stu
dies have been rising significantly. Injecting context awareness into data
models has facilitated multiple facets of modeling\, increasing user sati
sfaction\, and increased the level of intelligence in software. Context ha
s been a major part of traditional AI\, lately however\, it is being deplo
yed to a wider spectrum of research topics through analytics such as human
cognition\, user interfaces design and development\, the internet of thin
gs\, the cloud\, software usability and many others. In this talk\, a ne
w context-aware method for emergency applications is introduced. An emerge
ncy is a situation that poses an immediate risk to life\, health\, propert
y or environment. There is an obvious gap in applying context to such crit
ical cases\; this talk will show that context awareness and intelligent us
er interfaces can play an important role with notifications and improve hu
man reactions during such unfortunate and unforeseen incidents.

From Networks to Recovery:\nEffects of soc
ial networks on community resiliency\nin the face of flooding disast
ers

\n

Friday\, October 7\, 3:00
p.m.\nCenter for Social Complexity Suite\n3rd Floor\, Research
Hall

\n

ABSTRACT: The value of social capital and social networks ha
s increasingly been identified as a critical factor for adaptive capacity
in disaster recovery and community resilience. Inherent in an individual’s
social ties are available information\, resources and behaviors that can
be accessed in times of need. Simon Szreter and Michael Woolcock (2004) pr
oposed social capital framework within social networks that have been foun
d to impact disaster recovery differently. Bonding ties provide access to
immediate relief and aid\, and bridging and bracing/linking ties provide p
athways to longer-term survival resources such as recovery information and
resource mobilization. An agent-based model of a community recovering fro
m flood is proposed to test the utility of these different social network
forms and community groups in a period of disaster recovery.

Mining Social Data for Public Behaviors to Improve Absorptive<
br />\nCapacity of Organizations for Decision Support

\n

Friday\, October 14\, 3:00 p.m.\nCenter for Social C
omplexity Suite\n3rd Floor\, Research Hall

\n

ABSTRACT: Social Media or Web 2.0\, one source of Big Data\, h
as completely revolutionized information sourcing\, management\, and proce
ssing. The opportunity to understand and exploit such unprecedented level
of human interaction data can help public sector organizations improve the
ir processes and decision making\, such as for disaster management in buil
ding resilient smart cities. The data challenges of large scale volume of
public data\, velocity of content generation\, sparsity of data behaviors\
, variety of language complexity and diversity of community demographics i
n this online interaction data present an exciting venue for improving sci
ence of public behavior in mediated communication. For instance\, mining i
ntentional behaviors of resource seeking on social media helps disaster re
sponse organizations better coordinate resources\, given the mounting cost
of response estimated to be 271 billion USD/yr by 2025.

Hemant Purohit
is an interdisciplinary\, computational social science researcher and an
assistant professor of Information Sciences and Technology at GMU. He was
one of the ITU Young Innovator 2014 for UN’s ICT agency\, for winning a gl
obal challenge on Open Source Technologies for Disaster Management\, as we
ll as one of the eight international fellows of USAID\, Google and ICT4Pea
ce foundation at a key humanitarian technology event of CrisisMappers\, IC
CM-2013 at UN Nairobi. He has presented several talks\, lectures\, and tutorials on soci
al computing at prestigious venues including AAAI and SIAM conferences as
well as published at and served as reviewer for peer-reviewed conferences
and journals including ICWSM\, WWW\, Journal of CSCW\, and ACM TIST. More
about Hemant: http://ist.gmu.edu/~hpurohit

We propose a new percolation model in which reachability (a genera
lization of connectivity) between any pair of nodes is defined on the basi
s of the relative increase in distance between nodes before and after perc
olation removal or some other path lengthening process. Reachability is we
ll justified for real-world networks where\, contrary to microscopic syste
ms\, the ability to explore all possible paths of any length between nodes
is not achievable. If path lenghtining exceeds an externally imposed frac
tion tau\, which reflects the specifics of the problem\, node pairs are no
longer reachable. This concept induces a new form of phase transition\, w
hich we call Limited Path Percolation (LPP). We find that LPP induces in m
any cases first-order phase transitions in contrast to percolation\, which
is second order. Also\, LPP predicts that networks are more fragile than
what is suggested by conventional percolation models\, where effectively t
au is considered infinite (any path length increase is acceptable). In n
etworks\, we find that Erdös-Rényi graphs and networks with steep power-la
w distributions of connectivity exhibit a range of critically reachable cl
usters between the new reachability transition and the conventional percol
ation transition. Very heavy tailed power law distributions of connectivit
y are generally robust to the introduction of the reachability concept and
behave as in conventional percolation. The models and results above\, as
well as some implications for areas such as epidemiology\, will be discuss
ed.

In an effort to mode
l terrorist (ISIS/ISIL/DAESH) social media networks\, a foundational model
of dynamic social networks is necessary. Over the last decade a concerted
effort has been made from the various lines of inquiry concerned with soc
ial networks to describe this model\, but many would argue that a comprehe
nsive framework to describe changing\, fluctuating social networks continu
es to elude us.

\n

In this session\, I propose the beginning and esse
ntial elements of a dynamic\, stochastic\, undirected\, mathematically tra
ctable model built through the use of an agent-based model utilizing simpl
e rules. In other words\, I propose a generating function for dynamic soci
al networks.

\n

The model proposed preserves the fundamental elements
of random network generation and is in line with the early network paradi
gm described by Erdos\, Renyi and Gilbert in the 1950s\, but produces skew
ed and heavy tailed degree distributions in line with more recent network
models such as the Barabasi-Albert and Watts-Strogatz Models.

\n

I pr
opose that the emergence of scale-free and small world properties of a net
work can be produced using simple randomized rules. I also propose that pa
rtial conditional dependence\, or the relaxation of the dyadic independenc
e property championed by the statistical branch of network research is not
entirely necessary for the generation of real world networks.

\n

Fin
ally\, I propose a new modeling element in the form of a coupled set of ti
me-based rules\, useful in the modeling and simulation of agent-based mode
ls that rely on social networks and furthermore argue its importance and s
howcase its implications.

This Friday seminar will deal with a few of the major issu
es involved with creating and running high-performance agent-based models.
With modern computing hardware creating large scale agent models is much
easier. Furthermore\, running large scale designs of experiments are now
possible\, also. However\, what about very large scale agent-based model
s\, ones where the number of agents reaches 100 million or more? And what
of dealing with designs of experiments that have 10^50 points? Even mode
rn hardware and software can be quickly overwhelmed by the scale of an age
nt-based model or experiments using them. Under these circumstances the s
cience of high performance computing becomes especially important. This t
alk will introduce a few of these important concepts from moving past time
stepping to discrete event schedulers\, to the finer points of threading
a model on a shared memory machine or computing cluster. Many examples fr
om practical\, real world\, problems will be used to highlight the utility
of these concepts.\n

In this talk I will give an overview of of scient
ific data mining (SDM) followed by a few examples of SDM applications to t
he analysis of tropical cyclones (TCs)\, focusing on their intensity chang
es. Because rapidly intensifying (RI) tropical cyclones are the major erro
r sources in TC intensity forecasting\, association rules facilitate the R
I process by mining for sets of conditions that have strong interactions w
ith rapidly intensifying TCs. The technique of association rules explores
associations among multiple conditions in a simple manner identifying a pr
edictor set with fewer factors but improved RI probabilities. Furthermore\
, in searching the “optimal” RI condition combinations\, a peculiar condit
ion combination was identified that gives a very high RI probability. Such
combination can be considered as a sufficient condition for RI that almos
t guarantees that a RI will take place. Applications of classification tec
hniques to the intensity forecasting will also be discussed. Several drawb
acks and future directions for SDM with the TC intensity change problem wi
ll be discussed at the end of the talk.

In job markets\, some p
airs of firms frequently see workers changing jobs between them\, while at
the same time other firm pairs see little or no worker movement between t
hem. This has led to the introduction of the so-called Labor-Flow Network\
, a firm-to-firm network that acts as the scaffolding for job mobility. In
this talk\, I present the first model for the dynamics of the workforce i
n the labor flow network with the help from data for two countries (Finlan
d and Mexico) where large scale microdata is available for individuals and
their employers. The model correctly provides a new way to predict firm-s
ize distributions based on microscopic mechanisms\, and may in the future
allow for the tracking of employment and even tax evasion in some circumst
ances.

A lot of attention has been paid to wealth inequality since 2008-9
. Many theories contend that regulations favorable to the wealthy and pern
icious market effects might be to blame. However\, we are interested to wh
at degree natural characteristics of agents\, like their preferences for c
areer or money\, their ability to observe opportunities\, and their level
of ambition in seeking out new opportunities\, are components of inequalit
y. We create a simple agent-based model to investigate the potential effec
ts of natural characteristics on wealth inequality. We find that these kin
ds of natural characteristics are likely components of wealth inequality\,
though their effects are relatively small\, where most of the inequality
we see is due to a natural growth process.

\n

The Impact of I
nvestor Networks\nMatthe
w Oldham\, CSS PhD Student

\n

In this session\, I will be demonstrati
ng how investor networks and the dividends from firms impact the behavior
of financial markets. The model I will demonstrate will highlight the spec
ial properties of a scale-free network and how management can inflate thei
r stock price.

Agent-based models (ABMs) and “big data” sets both contain / ge
nerate large volumes of disparate information about individuals. In the c
ase of ABMs\, the models generate all information for all agents about wha
t an agent did\, where it went\, which other agents it interacted with\, a
nd its reasons for choosing each action. In the case of “big data”\, ther
e can be large disparate data sets in individuals\, sometimes containing t
oo much information and sometimes containing sparse or missing information
\, but still allowing inferences on what an individual did over time. Typ
ical approaches of selecting a few numeric variables and averaging them an
d/or tracking their results over time can lose significant details on the
individual. This talk will discuss approaches for taking the raw data fro
m ABMs or other data sources and generating narrative-form “life histories
” of individuals as a method for making sense of individual behavior. Als
o discussed will be methods for identifying which individuals are “interes
ting” — worth the time of a researcher to read through the agent’s history
and or dig deeper into data.

Establishing robust quantit
ative metrics which allow decision makers to determine the amount of risk
in a system with extreme loss events is a problem of interest in many scie
ntific fields. One of the fundamental metrics which is universally accepte
d in all fields of risk management is the quantity known as Value-at-Risk
(VaR). A subfield of risk management\, modern Operational Risk Management
(ORM)\, closely investigates methodologies on robustly estimating VaR\, “R
obust Estimation of VaR.” Currently\, academic researchers and industry pr
actitioners are actively looking at ways to make this estimate more statis
tically robust and accurate with minimal assumption requirements.

\n

In this talk I will present two new quantitative approaches for estimating
VaR that are agnostic regarding the relationship between frequency and se
verity: (1) Data Partition of Frequency and Severity (DPFS) using K-means
to estimate VaR\; (2) Distribution based partitioning (DBP) of frequency a
nd severity using copulas. Verification is conducted on five simulated sce
nario datasets while validation is conducted on five publicly available da
tasets from four different domains: –US Financial Indices data of Standa
rd & Poor’s 500 and Dow Jones Industrial Average\; –Chemical Loss spills
as tracked by the US National Coast Guard\; –Australian automobile acci
dents\; –US hurricane data. It is observed that previous VaR calculation
s inaccurately estimate the VaR for 80% of the cases in simulated data and
60% of the cases in real-world data studies while new methodologies attai
ns accurate VaR estimates which are within the 95% confidence interval bou
nds for both simulated and real-world data.

Mass shootings unfold quickly and are rarely foresee
n by victims. Increasingly\, training is provided to increase chances of s
urviving active shooter scenarios\, usually emphasizing\, “Run\, Hide\, Fi
ght.” Evidence from prior mass shootings suggests that casualties may be l
imited should the shooter encounter unarmed resistance prior to the arriva
l of law enforcement officers (LEOs). An agent-based model (ABM) explored
the potential for limiting casualties should a small proportion of potenti
al victims swarm a gunman\, as occurred on a train from Amsterdam to Paris
in 2015. Results suggest that even with a miniscule probability of overco
ming a shooter\, fighters may save lives but put themselves at increased r
isk. While not intended to prescribe a course of action\, the model sugges
ts the potential for a reduction in casualties in active shooter scenarios
.

Polymers and other soft ma
terials have an important role as a coating for nanoscale building blocks
like metallic nanoparticles (NPs) and nanorods. This coating mediates inte
ractions between these building blocks and their environment. Atomistic mo
lecular dynamics (MD) simulations are ideal for examining the role of chem
istry and atomic interactions at the sub-nanometer scale\, for example in
the interactions between a NP and a solvent\, or between pairs of NPs. Unf
ortunately\, atomistic MD simulations are limited to lengths of order 50 n
m and times of order 50 ns. The time scale limitation precludes modeling n
anoscale self-assembly\, and limits dynamic simulations to extremely high
rates of deformation or thermalforcing. These simulations are also limited
to sizes that represent a small number of NPs\, making it impossible to m
odel large assembled structures.

\n

Faced with these limitations\, we
have developed coarse-grained (CG) models of polyethylene\, a simple poly
mer used to coat NPs and nanorods. These models have enabled simulations o
f bulk polymer melts that overcome the limits of atomistic MD by providing
a computational speedup of greater than 104 while retaining fundamental d
etails at the sub-nanometer scale. These details produce the viscoelastic
properties and semi-crystalline behavior that are intrinsic to polyethylen
e and that are missed by generic CG models. When applied to a NP coating t
he CG models capture the coating morphology\, indicating the value of usin
g these CG models in nanoscale applications.

\n<
p style='line-height: 18pt\;text-align: left' align='center'>This talk wil
l focus on the specifics of how you connect R to NetLogo. The open source
agent based modeling platform NetLogo is used to create many agent based m
odels. R is a powerful open source programming language that offers a wide
array of tools for cleaning\, analyzing\, modeling and visualizing data.
The first example will focus on how to get started using the well known Sc
helling segregation model from the models library in NetLogo. The talk wil
l progress with examples of how I have used these tools to analyze and und
erstand my own agent based models. Topics covered will include data import
(agents\, networks)\, data cleaning\, running parameter sweeps\, repeatab
ility and replication\, as well as ways to integrate code with data to hel
p with repeatability and replication.\n

The talk will be followed by a Q&A session
along with light refreshments.

Many applie
d processes generate complex microstructures or patterns which are hard to
quantify due to the lack of any underlying regular structure. These patte
rns may evolve with time or include some element of stochasticity. The res
ulting variations in the detail structure frequently force one to concentr
ate on rougher geometric features. From a mathematical point of view\, sev
eral notions from algebraic topology suggest themselves as natural quantif
ication tools in such a setting. In this talk I will describe some of th
ese tools\, in particular homology and persistent homology\, and how they
can be efficiently computed using open source software. I will also presen
t some applications motivated by materials science problems.

Attribution of responsibility and blame are important
topics in political science especially as individuals tend to think of pol
itical issues in terms of questions of responsibility\, and as blame carri
es far more weight in voting behavior than that of credit. However\, surpr
isingly\, there is a paucity of studies on the attribution of responsibili
ty and blame in the field of disaster research.

\n

The Flint water cr
isis is a story of government failure at all levels. By studying microblog
posts about it\, we understand how citizens assign responsibility and bla
me regarding such a man-made disaster online. We form hypotheses based on
social scientific theories in disaster research and then operationalize th
em on unobtrusive\, observational social media data. In particular\, we in
vestigate the following phenomena: the source for blame\; the partisan pre
disposition\; the concerned geographies\; and the contagion of complaining
.

\n

This paper adds to the sociology of disasters research by exploi
ting a new\, rarely used data source (the social web)\, and by employing n
ew computational methods (such as sentiment analysis and retrospective coh
ort study design) on this new form of data. In this regard\, this work sho
uld be seen as the first step toward drawing more challenging inferences o
n the sociology of disasters from “big social data”.

The Origin of Agriculture in the Peiligang Cultur
e: An Agent-based Modeling Approach

\n

The emergence of
agriculture played an important role in human history as it allowed peopl
e to move from a nomadic to a sedentary life. This not only provided abund
ant food\, but also sufficient numbers of non-cultivating specialists\, wh
ich are necessary conditions for the rise of civilization. However\, quest
ions about how and why agriculture originated have remained controversial.
This paper explores the origin hypotheses of agriculture\, using the cano
nical theory of social complexity as a framework to the transition from hu
nter-gatherer to agricultural societies in the region of the Peiligang cul
ture in China based on existing literature\, and develops an agent-based m
odel to simulate the transition process. The model assumes that the combin
ation of population growth and gaining knowledge on plants drove the trans
ition to agriculture. Results show that based on the basic hypotheses and
assumptions\, the model is able to generate the key phases that are identi
cal with the existing literatures.

Robert Axtell\, Professor\nComputational Social Science\n
Department of Computational and Data Sciences

\n

Foundations of Agent Computing
for Economics and Finance

\n

Friday\, February 3\, 2017\nCenter for Social Complexity S
uite\n3rd Floor\, Research Hall

\n

\n

In many ways economics a
nd finance are fields for which the strengths of agent computing should le
ad researchers to become ‘early adopters\,’ at least in comparison to less
quantitative social sciences. While this has happened to some extent\, it
is also true that methodological norms are strong in economics and financ
e\, meaning that innovations will always face resistance. In this talk I w
ill preview the ideas from a paper\, co-authored with J. Doyne Farmer of t
he University of Oxford’s Complexity Economics Programme\, geared toward e
nticing economists toward agent-based modeling. I will contrast the proces
s-based\, procedural explanations of social phenomena that result from age
nt computing with the substantive but often static explanations that are m
ore conventional in economic theory. The role of agents who learn will be
highlighted. Next\, the ability of agent computing to use all available ha
rdware resources—the ‘small compile time\, large run time’ character of AB
Ms–will be surveyed and compared with standard numerical economics. Then w
e ask whether having better\, more user friendly software could substantia
lly increase the rate of adoption of agents among economists. Finally\, we
suggest that technologies for systematic parallelization of agent models
might go far as a ‘killer app’ insofar as it would permit the creation of
large-scale models applicable to a wide variety of policy problems. For ea
ch of these arguments an illustrative example will be provided. I will con
clude with a brief discussion of bottlenecks that appear to be limiting th
e progress of agent computing in economics and finance at the present time
.

B
ig data are ubiquitous and graph-based datasets are especially interesting
with broader impacts in social networks\, biological networks\, and cyber
security. In this talk\, I will discuss a number of our recent projects (E
nterprise SC’15\, iBFS SIGMOD’16\, G-Store SC’16\, and Graphene FAST’17) a
nd present our efforts on designing and developing high-performance graph
algorithms and systems. In particular\, I will discuss several novel techn
iques on addressing the computational and I/O challenges in graph computin
g. Furthermore\, I will present our ongoing work on utilizing these graph
systems for understanding and analyzing complex network data.

\n

Dr.
Howie Huang is an Associate Professor in Department of Electrical and Comp
uter Engineering at the George Washington University. His research interes
ts are in the areas of computer systems and architecture\, including cloud
computing\, big data\, high-performance computing and storage systems. Hi
s work on big graph analytics has ranked highly on both the Graph500 and G
reen Graph500 benchmarks. Dr. Huang is a recipient of the NSF CAREER Award
\, Comcast Technology Research and Development Fund\, NVIDIA Academic Part
nership Award\, IBM Real Time Innovation Faculty Award\, and GWU School of
Engineering and Applied Science Outstanding Young Researcher Award. His p
rojects won the Best Poster Award at PACT’11\, ACM Undergraduate Student R
esearch Competition at SC’12\, and a finalist for the Best Student Paper A
ward at SC’11. He completed his Ph.D. in the Department of Computer Scienc
e at the University of Virginia.

Adverse selection is the phenomenon of “rigged trades” c
reated by asymmetric information between buyers and sellers. This becomes
significant in the case of health insurance\, where a buyer may know more
about his or her health than the insurer. The concern is that widespread a
dverse selection may lead to a “death spiral”\, where premiums become too
costly for healthy people to afford\, and the only subscribers left are un
healthy people requiring costly health care. Both before and after the pas
sing of the Patient Protection and Affordable Care Act (PPACA\, also known
as “Obamacare”) in 2013\, the concern for adverse selection has been asse
ssed by a number of different methods of economic modeling\, most notably
game theory models\, econometrics\, and microsimulation. Kevin Comer utili
zes agent-based modeling to assess the emergence and effects of adverse se
lection on a simulated individual health insurance market\, and to test th
e parameters of already existing policy (coverage and individual mandates\
, risk corridors\, medical loss ratios\, and health insurance exchanges) t
o assess their long-term feasibility.

\n

About the presenter: Kevin Comer is a Seni
or Simulation Modeling Engineer at MITRE Corporation. He received his B.S
in Systems Engineering and Economics from the University of Virginia\, and
his M.S. in Operations Research from George Mason University. He is curre
ntly a Computational and Social Science Ph.D. Candidate in the Department
of Computational and Data Sciences at George Mason University. Kevin is sc
heduled to defend his dissertation in Spring 2017.

The emergence of agriculture played an important role in human history as
it allowed people to move from a nomadic (i.e. hunter-gather) to a sedenta
ry (i.e. agricultural) lifestyle. This shift in lifestyle not only provide
d abundant food\, but also sufficient numbers of non-cultivating specialis
ts\, which are necessary conditions for the rise of a civilization. Howeve
r\, questions about how and why agriculture originated have remained contr
oversial. This paper explores the origin hypotheses of agriculture\, using
the canonical theory of social complexity as a framework to study the tra
nsition from hunter-gatherer to agricultural societies in the region of th
e Peiligang in China based on existing literature\, and develops an agent-
based model to simulate the transition process. The model assumes that a c
ombination of population growth and gaining knowledge on plants drove the
transition from hunter-gatherer to agriculture. Results show that based on
the basic hypotheses and assumptions\, the model is able to generate the
key phases that are identical with the existing literature on such a trans
action.

The recent string of events in the Middle East\, dubbed as Arab Spring t
ranscended rapidly. There was no mechanism to predict them or their outcom
e. While there are a few models that forecast rebellion\, most of them do
not take into account the ability of different factors\, such as emotional
threshold\, of both the citizens and military and their the ability to be
influenced by vision of what is going around the agent geographically\, a
long with the influence of media/communication channels\, to form a realm
of influence and affect the actions of the agents simultaneously. This pap
er explores an agent-based model whose agents react based on economic and
emotional levels and a rebellion ensues. Once the rebellion has begun\, th
ere are several other factors in this agent-based model that decide the ou
tcome of the rebellion including agents being killed\, their geographic vi
sion\, their inclement towards news/media\, being influenced by current ev
ents\, and also their personality type of A or B\; all these factors combi
ned together affect the dynamics of the unanticipated revolution. The resu
lts of the model are rendered in a short duration of time\, as one would e
xpect of revolutions\, except for those that plunder into a civil war stat
e. The model could be used as one of many components for forecasting futur
e rebellions that have a combination of factors present\, as those discuss
ed this paper.

We investigate the interplay bet
ween technological change and macroeconomic dynamics in an agent-based mod
el of the formation of production networks. On the one hand\, production n
etworks form the structure that determines economic dynamics in the short
run. On the other hand\, their evolution reflects the long-term impacts of
competition and innovation on the economy. We account for process innovat
ion via increasing variety in the input mix and hence increasing connectiv
ity in the net- work. In turn\, product innovation induces a direct growth
of the firm’s productivity and the potential destruction of links. The in
terplay between both processes generate complex technological dynamics in
which phases of process and product innovation successively dominate. The
model reproduces a wealth of stylized facts about industrial dynamics and
technological progress\, in particular the persistence of heterogeneity am
ong firms and Wright’s law for the growth of productivity within a technol
ogical paradigm. We illustrate the potential of the model for the analysis
of industrial policy via a preliminary set of policy experiments in which
we investigate the impact on innovators’ success of feed-in tariffs and o
f priority market access.

Agent-based modeling was lacking in the Python community un
til Mesa. Mesa is an open-source\, liberally licensed\, agent-based modeli
ng framework built in Python 3. It allows users to quickly build models by
providing a series of reusable components off of which to build. This inc
ludes things like agents\, space\, and time. Mesa is also flexible and dec
oupled for greater efficiency. It has a back-end that handles the model pr
ocessing and a browser-based front-end that handles the visualization. It
also allows for custom visualizations to be added\, as well. Lastly\, user
s can use the Python ecosystem for analyzing data with ease with tools lik
e Jupyter notebooks and Pandas. The goal of Mesa is to provide a Python al
ternative to other agent-based modeling frameworks such as Netlogo and Mas
on.

The livelihoods of Chinese rural households have been undergoing a transf
ormation amid rapid urbanization. Though participation in the urban econom
y has led to improved rural living standards\, rural income has consistent
ly lagged behind urban income\, and a broader prosperity gap persists betw
een urban and rural areas. Meanwhile\, increasing non farm income is assoc
iated with the decline of agriculture\, especially in those regions with r
elatively high industrial development. In this seminar I present an agent-
based model developed to explore future rural development. The model repre
sents land-use and livelihood decisions of farmer households in the Poyang
Lake area\, informed by household surveys and interviews. The model exper
iments aid our understanding about (1) the potential effects of three subs
idy policies in villages with different farmland endowments and at differe
nt stages of urbanization\, and (2) the resilience of rural development un
der plausible environmental and economic shocks. I discuss how policy may
need to differentiate across locations and adapt in the near future to eff
ectively promote rural development amid social and environmental changes.

Fifteen years of conflict have sho
wn severe limitations in the United States’ ability to influence foreign p
opulations in pursuit of national objectives. Intertwined within these cha
llenges is the difficulty the U.S. Intelligence Enterprise has in integrat
ing recent research to more effectively analyze foreign populations and su
pport decision makers. The introduction of a Complex Adaptive Systems base
d meta-framework for intelligence analysts\, supported by an agent based m
odel can reduce the cost of analysts learning and applying new research. A
s a first attempt we adopt the Army’s current framework Intelligence Prepa
ration of the Battlefield (IPB) and begin to formulate one possible meta-p
erspective Complex Intelligence Preparation of the Battlefield. Complex IP
B has the potential to be a constantly improving model that integrates new
and emerging theories from economics\, communication\, politics\, demogra
phy\, game theory and social network analysis to analyze the emergence and
contagion of civil conflict in local populations. Complex IPB can assist
in identifying regions of potential instability before escalation. Finally
\, a quasi-global sensitivity analysis identifies effective and efficient
policy levers in the face of limited resources.

The CSS seminar f
ormat for Friday\, April 21 will be an “Open Mic” session for CSS PhD stud
ents to present their research ideas to their peers prior to starting thei
r projects. Peer feedback in encouraged at this event

\n

Some points for presenters to consider are:

\n

\n

Do
you have a paper that is ‘stalled’ and in need of some help to push it to
the finish line?

\n

Is one of your models producing interesting re
sults but also doing wacky things?

\n

Do you have exciting results
but can’t figure out how to visualize/display them?

\n

Do you need
advice on how to calibrate/estimate your theoretical model with data?

Social
desirability bias is a tendency people have to lie about their opinions if
they perceive they will be judged or rejected.
span>We present an Opinion Dynamics model in which agents
may not be truthful about their opinions when the
y interact with their social circle. We model two
processes through which agents influence one anoth
er: an online anonymous process in which agents ca
n interact with anyone and do not fear social rejection\, and a face-to-fa
ce process where they interact only with friends a
nd may feel compelled to conform. In a political s
etting\, this would apply to a race in which one o
f the candidates bears a social stigma and therefo
re some agents are reluctant to voice support for him or her. The results<
/span> that these nonlinear and asymmetrical processes
span>will have on the overall electorate are not obvious\
, and are well-suited to an agent-based study.

\n

We hypothe
size that this model will produce a “poll bias” of the kind we saw in the
2016 Presidential election — i.e.\, a significant<
/span> difference between the number of agents who say th
ey will vote for a candidate and the number who actually do so on <
span class='s1'>election day. We present an analys
is of this “Hidden Trump model” and describe the way in which poll bias de
pends on the strength of t
he various interaction processes.

Abstract: In January of 2017\,
Forbes Magazine listed the top technical job skills showing the highest g
rowth in demand from 2011 to 2015. The number three position\, with 1\,581
% growth\, was Tableau\, a software solution that helps people see and und
erstand their data.

\n

Tableau offers free licenses for academic rese
arch.

\n

In this session\, Paul Albert will:

\n

\n

Provide a
hands-on overview of Tableau to show how it can help people do more with
their data

\n

Show examples of Tableau data visualizations relevant
to the CSS world

\n

Discuss how Tableau might be able to alter par
adigms for sharing academic findings

\n

Discuss free resources avai
lable to learn more about Tableau

\n

\n

Paul recently retired fr
om Tableau and has started graduate studies with the GMU Art History progr
am. His initial focus is on applying quantitative analysis and social theo
ry to art markets. His secondary focus is on exploring how products like T
ableau can be used to support new ways of presenting academic research and
findings.

\n

While at Tableau\, Paul coordinated and conducted over
60 training events for over 1\,100 participants. Paul was also one of four
finalists\, out of a field of over 200 contestants\, in the annual Tablea
u “Viz Wiz” data visualization contest for 2016.

The Mercat
us Center at George Mason University recently launched QuantGov\, an open-
source policy analytics platform designed to facilitate policy-relevant re
search. QuantGov deploys text analysis and machine-learning algorithms t
o identify the latent governance indicators buried in policy documents\, s
uch as legislation or administrative code. QuantGov grew out of the RegDat
a project\, which was designed to capture novel metrics in regulations tha
t would advance our understanding of the United States’ federal regulatory
process in ways that were previously infeasible. QuantGov is the next ge
neration in that project’s evolution.

ABSTRA
CT: This talk will be a review of calibration methods for classifiers th
at make probabilistic predictions on a scale 0 to 1. It is known that cert
ain classification methods\, such as Naïve Bayes or Random Forest make bia
sed predictions that to not match the true posterior probabilities. By ca
librating the predictions made by classifiers the true probability of the
predicted class can be determined. This type of calibration can be crucial
for real-world decision making problems in medicine\, business\, marketin
g\, and finance. In this talk I will focus on applications in marketing.
p>\n

\n

Part two: Marketing yourself f
or future careers outside of academia

\n

It is kn
own that the number of jobs in academia is not rising as fast as the numbe
r of PhD’s graduating. Currently a new career option is available to these
PhDs\, the Data Scientist. But how does one make the transition out of ac
ademia to this hot new field? I will discuss strategies for marketing your
self as well as tools necessary to be successful in your transition.

\n

\n

Dr. Oscar Olmedo is an alumnus of George Ma
son University who studied physics in undergrad (2004)\, Computational Sci
ences and Informatics Masters (2007)\, and Computational Sciences and Info
rmatics PhD (2011) with a concentration in solar physics under Dr. Jie Zha
ng. After graduating in 2011\, Dr. Olmedo went on to NRL as an NRC fellow
for two years\, and briefly worked at NASA Goddard for a few months in 201
3 before moving to Syntasa\, a startup focusing on ecommerce/marketing ana
lytics. In 2015\, he moved to CACI to work on cyber security research as a
DARPA contractor.

William G. Kennedy\, Research Assistant Professor\nCen
ter for Social Complexity

\n

Chara
cterizing the reaction of the population of NYC to a nuclear WMD<
/p>\n

Friday\, September 1\, 3:00 p.m.
\nCenter for Social Complexity Suite\n3rd Floor\, Research Hall

\n

ABSTRACT: This talk will review the status of our multi-year project
to characterize the reaction of the population of a US megacity to a nucl
ear WMD event. Our approach is to develop an agent-based model of the New
York City area\, with agents representing each of the 20-25 million people
\, and establish a baseline of normal behaviors before exploring the popul
ation’s reactions to small (5-10Kt) nuclear weapon explosions. In our firs
t year\, we explored understanding a large population’s reaction to a nucl
ear WMD event with four major activities: (1) reviewing existing social th
eories and reports of disaster behavior\, (2) collecting data and modeling
the infrastructure of a mega-city and surrounding region\, (3) generating
synthetic population\, and (4) developing an agent-based model of all the
individuals in the region. The review of social science theories and data
on individual/group behavior during disasters led to the publication of a
case study (the Flint River drinking water crisis) and preparation of two
review papers. For the New York City mega-city and surrounding area\, we
collected spatial\, demographic\, and workforce data from several sources
and devised methods and algorithms to make the data useful for our simulat
ion. Using Python\, we processed road data and created one connected netwo
rk forming the transportation layer of the model. Using demographic data a
nd our own heuristics\, again in Python\, we synthesized individuals\, the
ir households\, their associated schools and workplaces and finally their
social networks. Other datasets were utilized so that children attend near
by schools or daycare constrained with actual capacities and people are em
ployed in workplaces located nearby matching workforce data. Finally\, we
began modeling individuals’ movement in three counties\, two rural countie
s and one in the heart of Manhattan. I will start with a discussion of the
effects of a nuclear WMD event and then discuss the details our work and
our future plans.

Claudio Cioffi-
Revilla\, Professor\nComputational Social Science\nDepartment
of Computational and Data Sciences\nDirector\, Center for Social Com
plexity\nGeorge Mason University

\n

Computational Modeling of Terrorism

\n

Monday\, September 11\, 4:30-5:45\nExploratory Hall\,
Room 3301

\n

\n

Computational social scientists have investiga
ted terrorism for decades\, but only recently has the field advanced to cr
eating the first testable formal theories. This talk will review some impo
rtant background and present recent advances in agent-based modeling of te
rrorism\, based on radicalization theory and research. Enduring challenges
will also be covered\, as opportunities for research projects\, theses\,
and dissertations.

\n

Dr. Cioffi-Revilla is a Professor of Computatio
nal Social Science\, founding and former Chair of the Department of Comput
ational Social Science\, and founding and current Director of the Mason Ce
nter for Social Complexity at George Mason University. He holds two doctor
al degrees in Political Science and International Relations and his areas
of special interest include quantitative\, mathematical\, and simulation m
odels applied to complex human and social systems.

Th
e Quest for Living Beta: Investigating the Implication of Shareholder Netw
orks

\n

Friday\, September 15\, 3:00 p.m.\nCenter for Social Complex
ity Suite\n3rd Floor\, Research Hall

\n

The behavior of
financial markets has\, and continues\, to frustrate investors and academi
cs. With the advent of new approaches\, including complex systems and netw
ork analysis\, the search for an explanation as to how and why markets beh
avior as they do has greatly expanded\, and moved away from the tradition
neoclassical approaches that have been beholden to the Efficient Market Hy
pothesis.

\n

The complex system approach utilizes a number of a conce
pts in an attempt to understand stock market returns including\; imitation
\, herding\, self-organized co-operativity\, and positive feedbacks\, with
many of these features captured by network analysis. In addition\, with t
he meteoric rises of network science has come the realization that the beh
avior of a system can vary greatly depending on the network structure (the
topology) of a system\, thus providing further impetus for the use of net
work analysis in terms of financial markets.

\n

My presentation will detail my recent resea
rch of the US Institutional shareholder networks over the period of 2007-1
0\, a period which includes the beginning of the Global Financial Crisis.
The research utilized an extensive dataset provided from the Thomson Reute
rs 13f database\, to undertake a temporal analysis of the networks formed
between US institutional investors and the stocks in the S&P 500. The anal
ysis makes use of both projected and bipartite networks and uncovers numer
ous insights regarding relationships between the market in general\, stock
s and their shareholders. In addition\, I will illustrate how the findings
can be used in conjunction with an agent-based model to uncover the worki
ngs of the stock market.

Dr. Simpson is a seasoned researcher in
the areas of machine learning\, deep learning\, and cybersecurity. He has
served as Principal Investigator and Data Scientist on several DARPA progr
ams with a focus on machine learning applications to data fusion\, predict
ion\, and unsupervised anomaly detection problems. He holds a Ph.D. in Ele
ctrical Engineering from North Carolina State University where he develope
d novel receivers and algorithms for undersea communications.

Friday\, September 22\, 3:00 p.m.\nCenter
for Social Complexity Suite\n3rd Floor\, Research Hall

\n

\n

While great advances in modeling
have been made\, one of the greatest challenges we face is that of underst
anding human behavior and how people perceive and behave in physical space
s. Can new sources of data (i.e. “big data”) be used to explore the connec
tions between people and places?

\n

In this presentation\, I will review the current state of art of model
ing geographical systems. I will highlight the challenges and opportunit
ies through a series of examples that new data can be used to better under
stand and simulate how individuals behave within geographical systems.

ABSTRACT: This presentation will detail novel analytic methods for proces
sing time series data at scale using techniques drawn from digital signal
processing and document matching. These methods can be applied to detect c
oordinated and automated cyber activity\, to match patterns present in tim
e series data\, and to fuse together disparate datasets.

\n

Dr. Deaso
n holds a BS in Applied Mathematics from the University of Virginia\, a MA
in Mathematics with an emphasis in Real Analysis and Probability Theory f
rom UC Berkeley\, and a PhD in Economics from the University of Maryland\,
College Park. Dr. Deason has 10 years of experience in mathematical model
ing and data science\, spanning employment as a Professor of Mathematics\,
an Economist\, and a Data Scientist. Dr. Deason’s past experience include
s developing dynamic stochastic models within a game theoretic framework t
o explore the effects of trade policy uncertainty as well as estimating em
pirical models to explain various phenomena. More recently\, Dr. Deason ha
s developed multiple algorithms for detecting and classifying periodic and
coordinated behavior in a variety of contexts on large data sets as part
of DARPA’s Network Defense Program.

Robert Axtell\, PhD\nComputational Social Science
Program\, Department of Computational and Data Sciences\,\nCollege o
f Science\nDepartment of Economics\, College of Humanities and Socia
l Sciences\nKrasnow Institute for Advanced Study\nGeorge Mason
University

\n

Co-Director\nComputat
ional Public Policy Lab\nKrasnow Institute for Advanced Study and Sc
har School of Policy and Government

Abstract: Usi
ng data on the entire population of American firms I will first show that
the distribution of firm ages is approximately stationary\, with small ‘de
fects’ arising at the start of last decade’s Financial Crisis now propagat
ing through the distribution. From these data I will derive the distributi
on of U.S. firm lifetimes and demonstrate that it has a specific structure
that conforms to economists’ intuitions about new (and small) firms havin
g higher failure probabilities than older (and larger) firms. I will then
demonstrate that the large body of statistical theory on ‘survival analysi
s’ is directly applicable to firms. Specifically\, I will focus on firm ha
zard functions and empirically show that for the U.S. this is a power law
over a wide range of ages\, ostensibly a new finding\, declining with age.
This permits computation of the expected remaining lifetime of firms as a
function of their age\, an INCREASING function\, implying that American f
irms gain longevity as they get older\, a very non-biological type of agin
g. Conditioning on firm size produces further results. Specifically\, usin
g the Cox ‘proportional hazards’ specification\, the reduction in failure
probability associated with larger size is quantified. At the end I will d
emonstrate that an ABM of firm dynamics can be calibrated to reproduce all
of these features of U.S. firms.

\n

Rob Axtell earned an inte
rdisciplinary Ph.D. degree at Carnegie Mellon University\, where he studie
d computing\, social science\, and public policy. His teaching and researc
h involves computational and mathematical modeling of social and economic
processes. Specifically\, he works at the intersection of multi-agent syst
ems computer science and the social sciences\, building so-called agent-ba
sed models for a variety of market and non-market phenomena.

\n

His r
esearch has been published in the leading scientific journals\, including
Science and the Proceedings of the National Academy of Sciences<
/i>\, USA\, and reprised in Nature\, and has appeared in top
disciplinary journals (e.g.\, American Economic Review\, Comput
ational and Mathematical Organization Theory\, Economic Journal
)\, in general interest journals (e.g.\, PLOS One) and in specialty
journals (e.g.\, Journal of Regulatory Economics\, Technology F
orecasting and Social Change.) His research has been supported by Amer
ican philanthropies (e.g.\, John D. and Catherine T. MacArthur Foundation\
, Institute for New Economic Thinking) and government organizations (e.g.\
, National Science Foundation\, Department of Defense\, Small Business Adm
inistration\, Office of Naval Research\, Environmental Protection Agency).
Stories about his research have appeared in major magazines (e.g.\, Ec
onomist\, Atlantic Monthly\, Scientific American\, Ne
w Yorker\, Discover\, Wired\, New Scientist\,
Technology Review\, Forbes\, Harvard Business Review\, <
i>Science News\, Chronicle of Higher Education\, Byte\,
Le Temps Strategique) and newspapers (e.g.\, Wall St. Journal\, Washington Post\, Los Angeles Times\, Boston Globe\, Detroit Free Press\, Financial Times). He is co-author o
f Growing Artificial Societies: Social Science from the Bottom Up (
MIT Press) with J.M. Epstein\, widely cited as an example of how to apply
modern computing to the analysis of social and economic phenomena.

ABSTRACT: Smart data are essential when faced with massive-sca
le data collections. “Smart” refers to data that are tagged or indexed wit
h meaning-filled metadata that carry information about the semantic meanin
g of the data\, its applications\, use cases\, content\, context\, and mor
e. Such meta-tags enable efficient and effective discovery\, description\,
and delivery of the right data at the right time\, both to humans and to
automatic processes.

\n

Dr. Borne advises and consults with numerous
organizations\, agencies\, and partners in the use of data and analytics f
or discovery\, decision support\, and innovation. Previously\, he was Prof
essor at George Mason University (GMU) for 12 years in the CSI and CDS pro
grams\, where he did research\, taught\, and advised students in data scie
nce. Prior to that\, Dr. Borne spent nearly 20 years supporting data syste
ms activities on NASA space science research programs\, including a role a
s NASA’s Data Archive Project Scientist for the Hubble Space Telescope.

Mea
sles is one of the leading causes of death among young children. In many d
eveloped countries with high measles\, mumps\, and rubella (MMR) vaccine c
overage\, measles outbreaks still happen each year. Social media has been
one of the dominant information sources to gain vaccination knowledge and
thus has also been the focus of the “anti-vaccine movement”. This talk is
about two of my recent research projects on this topic. The first one will
be introduced briefly\, which is an agent-based model demonstrating how a
small amount of online anti-vaccine sentiment could have the power of inc
reasing the probability of measles outbreaks significantly. This research
inspired me to investigate details of communicative pattern of “anti-vacci
ners” by analyzing a large twitter dataset (660892 tweets) after the Calif
ornia Disneyland measles outbreak in 2015. This second research has two ma
in parts: first\, in order to identify “anti-vacciners”\, I used supervise
d learning to label each tweet as either positive\, neutral\, or negative
opinion towards vaccination. The linear support vector machine model shows
good performance on this dataset with an accuracy score of 72% on test da
ta. Second\, Louvain’s method for community detection of the retweet netwo
rk shows the common pattern of social media communities\; i.e.\, overall f
ragmented but with a few large communities. By investigating the opinion d
istribution in big communities\, however\, I discovered that they are high
ly overlapped\, especially within “anti-vacciners”\, meaning that they hav
e more frequent communication within their own opinion group than with oth
ers. What’s useful for health communication strategies is to look further
into the brokers–those who stand between two or more communities. At the e
nd of the talk\, I will address details of analyzing the brokerage as well
.

ABSTRACT: I will present a variety of aty
pical use cases and applications (in science and in business) of some typi
cal textbook machine learning algorithms\, including regression\, clusteri
ng analysis\, association mining\, time series analysis\, and network anal
ysis.

\n

Dr. Borne advises and consults with numerous organizations\,
agencies\, and partners in the use of data and analytics for discovery\,
decision support\, and innovation. Previously\, he was Professor at George
Mason University (GMU) for 12 years in the CSI and CDS programs\, where h
e did research\, taught\, and advised students in data science. Prior to t
hat\, Dr. Borne spent nearly 20 years supporting data systems activities o
n NASA space science research programs\, including a role as NASA’s Data A
rchive Project Scientist for the Hubble Space Telescope.

Notice
and Invitation\nOral Defense o
f Doctoral Dissertation\nDoctor of Philosophy in Computational Scien
ces and Informatics\nDepartment of Computational and Data Sciences\nCollege of Science\nGeorge Mason University

The rapi
d growth of Light Detection And Ranging (Lidar) technologies that collect\
, process\, and disseminate 3D point clouds have allowed for increasingly
accurate spatial modeling and analysis of the real world. Lidar sensors ca
n generate massive 3D point clouds of a collection area that provide highl
y detailed spatial and radiometric information. Simultaneously\, the growt
h of other forms of geospatial data (e.g.\, crowdsourced Web 2.0 data) hav
e provided researchers with a wealth of freely available data sources that
cover a variety of geographic areas. However\, combining data from dispar
ate sources requires overcoming numerous technical challenges in order to
generate products that mitigate their respective disadvantages and combine
their advantages.

\n

Therefore\, this dissertation addresses the pro
blem of fusing two point clouds from potentially different sources by cons
idering two specific problems: scale matching and feature matching. To add
ress the problem of feature matching we develop a novel feature descriptor
referred to as “Self-Similar Spin Images” which combine the concept of lo
cal self-similarity with the descriptive power of Spin Images. To address
the problem of scale matching we develop a novel scale detection metric re
ferred to as “Self-Similar Keyscale” which analyzes the self-similarity of
two point clouds to identify a characteristic scale to match them. Finall
y\, we develop a novel change detection method as a sample use case of the
developed matching techniques.

Organizing Theories for Disaster Study in Computational Social Scien
ce

\n

Friday\, October 13\, 3:00
p.m.\nCenter for Social Complexity Suite\n3rd Floor\, Research
Hall

\n

Based on evolving understandings of the compl
ex\, interconnected characteristics of disasters and social adaptations we
propose to organize disaster theories in the interdisciplinary context of
complex adaptive systems (CAS). Within this context disasters can be unde
rstood to consist of three sets of interacting systems\, the socio-ecologi
cal system\, the system of collective\, social behavior\, and the individu
al actor’s cognitive system. Ongoing dynamic forces within each set period
ically build to disrupt events and cause failures in the overall system. I
n this talk\, we will explore the dominant theory and frameworks in disast
er studies from the perspective of these sets\, demonstrate how they expla
in disasters\, and discuss how Computational Social Science methodologies
can support theory-building. By identifying evolving understandings and re
search questions co-located in disaster theory and CAS we find that the CA
S features of heterogeneity\, flows\, interacting subsystems\, emergence f
rom self-organization and bottom-up processes\, and adaptation and learnin
g are integral to disaster studies.

Abstract: Discerning the 3D context of individual cells in tissue is
fundamental for understanding tissue development\, homeostasis in healthy
adult organs and tumor emergence. Determining this context\, which requir
es 3D microscopic imaging to 100s of microns depth into tissues\, is hampe
red in multiple ways: (1) penetration of fluorescent dyes deep into tissue
\, (2) absorption and scattering of light\, (3) sufficiently high speed im
age acquisition and (4) automated image analysis\, because datasets are to
o large for human visual interpretation. Furthermore\, in the case of tum
ors\, standard practice in clinical pathology is to only image a thin slic
e about 5 microns thick\, which has several limitations: there is signific
ant inter-observer variability in the diagnosis\, the slice can miss the t
umor and determining the spatial relationships of cells to each other is i
ncomplete. To address these limitations\, we have evaluated tissue cleari
ng protocols\, which reduce scattering and spherical aberration\, and we h
ave achieved high spatial resolution imaging up 0.35 mm depth utilizing sp
inning disk confocal microscopy or lightsheet microscopy for high speed im
age acquisition. The image analysis tasks for 3D images are much more dem
anding than 2D images for the following reasons: (1) visualization of the
entirety of each object (cell or cell nucleus) is not possible in a single
display\, (2) purely interactive segmentation is impractical even for one
object\, (3) automatic algorithms are imperfect\, and (4) in the case of
basic research relatively few cells are imaged increasing the need to anal
yze each cell as accurately as possible. Consequently\, we are working on
tools that merge automatic segmentation algorithms\, computer vision and
human annotation for delineating objects in 3D images. We have developed
a graphcut-based algorithm for finding the globally-optimal surface deline
ating each manually seeded nucleus. The algorithm is restricted to point
convex objects\, which is generally satisfactory for nuclei\, but not for
entire cells that can have arbitrary shapes. For whole cell segmentation
in 3D\, our approach is slice-by-slice based. In the first 2D image slice
(approximately through the center of the cell)\, the user draws an approx
imate 2D border and the border is automatically optimized using active con
tour modeling. This optimized border then serves as the approximate borde
r for adjacent slices\, which are in turn automatically optimized. The me
thod is accurate except where the cell surface is in the plane of the slic
e\, so a next step is to facilitate segmenting each cell in different orie
ntations. We are starting to utilize this technology to understand the
complex interplay between cancer and normal cells\, particularly immune sy
stem cells and supporting mesenchyme leading to tumor growth or regression
in the case of treatment. The research was funded by NCI Contract No. HH
SN261200800001E and supported in part by the Intramural Research Program o
f the National Institutes of Health\, National Cancer Institute\, Center f
or Cancer Research.

\n

Stephen Lockett received the Ph.D. degree from
the Department of Medicine\, Birmingham University\, England. He has pub
lished over 120 research papers and has received several international awa
rds. His research interests include fluorescence microscopy and the devel
opment of analysis software for extracting quantitative information from i
mages.

ABSTRACT: I focus on the drivers of
diffusion and adoption of cultural traits\, such as values\, beliefs\, an
d behaviors. I adopt an evolutionary view of cultural dynamics. I use conc
epts from dual-inheritance theories of cultural evolution to develop and t
est an agent-based model capable of simulating the changing distributions
of cultural traits in a large population of actors over the course of prol
onged periods of time. Particularly\, I pay close attention to the mechani
sms of indirectly biased transmission of traits and guided variation\, whi
ch are both hypothesized to be significant aspects of cultural dynamics. I
ndirectly biased transmission consists of the adoption of specific trait v
ariants on the basis of possession of initially unrelated external markers
. Guided variation is then individual adaptation driven by self-exploratio
n.

\n

Furthermore\, I make use of
large publicly available datasets to validate my models. The first one of
these is the database of bill co-authorship in the U.S. House of Represent
atives from 1973 to 2008. The other is a comprehensive dataset of scientif
ic co-authorship in various disciplines stretching back for over a century
.

\n

The results show that cultura
l evolution models based on indirectly biased transmission and guided vari
ation are suitable to explaining the dynamics of various complex social ne
tworks.

Abstract: As de
vices continue to gain network connectivity and require less interactivity
from human operators\, the nature of network transmissions is shifting an
d data is being created faster and with more fidelity than ever before. On
e way to view this data is in the context of a cyber language\, analogous
but semantically/syntactically different than a natural language. After se
quences are constructed over a large dataset (PB)\, unsupervised machine l
earning and deep learning techniques are used to model communication\, ide
ntifying typical behavior and flagging unlikely events. This seminar prese
nts context for the foundations of this new approach to cyber anomaly dete
ction as well as the enabling analytic techniques.

\n

Dr. Richardson
has nearly a decade of experience in Data Science\, Cloud Computing\, Soft
ware Development\, and Machine Learning. He has served as both Department
Chair and Visiting Professor at two universities and has published over 10
articles. He is currently serving as a principal data scientist\, technic
al lead\, and principal investigator on multiple government sponsored proj
ects\, including one DARPA research program.

ABSTRACT: The use of simulations in explori
ng theories and hypotheses by social scientists is well documented. As co
mputer systems have grown in capacity\, so have interests by social scient
ists in executing larger simulations. Social scientists often approach th
eir simulation design from the top down by selecting an Entity-Based Model
(EBM) framework from those that are readily available\, thus limiting mod
eling capability to the chosen framework. Ultimately\, the framework is de
pendent upon what is at the bottom\, the hardware that serves as the found
ation of the computing system. One underused hardware architecture suppor
ts the simultaneous execution of a problem split into multiple pieces. Th
us\, the problem is solved faster in parallel. In this seminar\, a selecti
on of parallel hardware architectures is examined with a goal of providing
support for EBMs. The hardware’s capability to support parallelization of
EBMs is described and contrasted. A simple EBM is tested to illustrate th
ese capabilities and implementation challenges specific to parallel hardwa
re are explored.

Robert Axtell\, PhD\nCo
mputational Social Science Program\, Department of Computational and Data
Sciences\,\nCollege of Science\nDepartment of Economics\, Coll
ege of Humanities and Social Sciences\nKrasnow Institute for Advance
d Study\nGeorge Mason University

\n

Co-Director\nComputational Public Policy Lab\nKrasnow Institut
e for Advanced Study and Schar School of Policy and Government

Ages and Lifetimes of U.S. Firms: Why Businesses Should NOT b
e Treated Like People

\n

Monday\, October 30\, 4:30-5:45\nExplorator
y Hall\, Room 3301

\n

ABSTRACT: Over the last 150 years American corporations have acquired many
rights associated with individual citizens\, such as free speech\, the ab
ility to make campaign contributions\, and so on. In this talk I will quan
tify the age-related demographic properties of U.S. business firms and arg
ue that the peculiar nature of firm aging suggests that businesses are ver
y much unlike individual people. Specifically\, using data on all 6 millio
n U.S. firms having employees\, I document that firm ages are discrete Wei
bull-distributed while firm lifetimes follow a closely-related distributio
n. Further\, the hazard rates associated with firm survival are monotone d
eclining according to a power law. From this the expected remaining lifeti
me can be computed and it will be demonstrated that this INCREASES as firm
s age. Specifically\, while a new firm in the U.S. can expect to live for
about 15 years\, a firm that has survived 50 years can expect to live for
30 more. Finally\, conditioning on firm size leads to even more extreme re
sults: increasing firm size by a decade cuts the hazard rate in half. In s
um these results suggest that firm aging is very different from biological
aging and makes analogies between firms and people both quantitatively in
accurate and qualitatively wrong-headed. Technically\, this talk will focu
s on the application of conventional demographic techniques to economic an
d financial data\, including failure/survival analysis with censored data.

\n

Rob Axtell earned an interdisciplinary Ph.D. degree at Carnegie M
ellon University\, where he studied computing\, social science\, and publi
c policy. His teaching and research involves computational and mathematica
l modeling of social and economic processes. Specifically\, he works at th
e intersection of multi-agent systems computer science and the social scie
nces\, building so-called agent-based models for a variety of market and n
on-market phenomena.

\n

His research has been published in the leadin
g scientific journals\, including Science and the Proceedings
of the National Academy of Sciences\, USA\, and reprised in
Nature\, and has appeared in top disciplinary journals (e.g.\, <
em>American Economic Review\, Computational and Mathematical Orga
nization Theory\, Economic Journal)\, in general interest jo
urnals (e.g.\, PLOS One) and in specialty journals (e.g.\, Jo
urnal of Regulatory Economics\, Technology Forecasting and Social
Change.) His research has been supported by American philanthropies
(e.g.\, John D. and Catherine T. MacArthur Foundation\, Institute for New
Economic Thinking) and government organizations (e.g.\, National Science F
oundation\, Department of Defense\, Small Business Administration\, Office
of Naval Research\, Environmental Protection Agency). Stories about his r
esearch have appeared in major magazines (e.g.\, Economist\,
Atlantic Monthly\, Scientific American\, New Yorker
\, Discover\, Wired\, New Scientist\, Techn
ology Review\, Forbes\, Harvard Business Review\, <
em>Science News\, Chronicle of Higher Education\, Byte
em>\, Le Temps Strategique) and newspapers (e.g.\, Wall St. J
ournal\, Washington Post\, Los Angeles Times\,
Boston Globe\, Detroit Free Press\, Financial Times
). He is co-author of Growing Artificial Societies: Social Science fro
m the Bottom Up (MIT Press) with J.M. Epstein\, widely cited as an ex
ample of how to apply modern computing to the analysis of social and econo
mic phenomena.

Data Science and Cybersecu
rity at the Department of Homeland Security

\n

Monday\, TBA\, 4:30-5:45\nExploratory Hall\, Room 3301

\n

ABSTRACT: Among its many responsibilities\, the Department
of Homeland Security works to improve the security of the computer network
s of the federal government and our nation’s critical infrastructure. Thi
s will be a discussion of some of the ways in which that is done\, and som
e of the ways in which data science can contribute to that goal.

A Simple Direct Estima
te of Rule-of-Thumb Consumption using the Method of Simulated Quantiles an
d Cross Validation

\n

Friday\, No
vember 3\, 3:00 p.m.\nCenter for Social Complexity Suite\n3rd
Floor\, Research Hall

\n

\n

Campbe
ll and Mankiw (1989\, 1990) famously demonstrated that aggregate data supp
orted a model of household consumption in which roughly 50% of agents foll
owed an optimizing strategy while the other 50% followed a “rule of thumb”
strategy\, consuming their current income. This paper revisits that hypot
hesis using structural\, micro-level\, semi-parametric estimation and form
ally selecting between different models of agent behavior. I find strong e
vidence supporting a generalization of Campbell and Mankiw (1989\, 1990)’s
original conclusion: roughly 50% of the population behaves in a way simil
ar to “rule of thumb” consumers\, even when the data is allowed to dictate
how severe that rule of thumb behavior is. In addition\, this paper demon
strates the usefulness and flexibility of both the Method of Simulated Qua
ntiles and K-fold cross validation for selecting between of agent behavior
. This type of model selection is crucial for creating agents to populate
robust\, richly-featured agent-based models of macroprudential and macro-f
inancial systems.

ABSTRACT: The onslaug
ht of digital detectors has created the ability to capture massive amounts
of image data. Analysis techniques have been maturing for decades\, but t
his new flood of image data will tax the foundations of information dissem
ination. Published descriptions of the image processes often consume much
more real estate than does the scripts required to execute the processes.
Furthermore\, many published descriptions are imprecise. This talk will pr
eview a new mathematical language solely dedicated to the fields of image
processing and analysis. This language is coincident with implementations
in Python and Matlab\, thus there is a one-to-one correspondence between m
athematical description and computer execution. This talk will present sev
eral examples and culminate with an interactive analysis of image processi
ng protocols.

Abstract: Cluster analysis represents a broad class of unsupervised
algorithms that are applicable to a variety of data science problems. An o
verview of some clustering models is provided and example use cases for cl
ustering are discussed. Multivariate Gaussian mixture models are then disc
ussed in detail and estimation techniques are outlined. K-selection is als
o discussed in the context of Gaussian mixture models. The talk concludes
with a short discussion about how clustering techniques might be used in t
he context of cybersecurity.

\n

Dr. Radford is a Principal Data Scien
tist at Sotera Defense Solutions where he works on data-driven cybersecuri
ty research programs for the Department of Defense. He received his Ph.D.
in political science from Duke University in 2016. His research interests
include political methodology\, security and political conflict\, the poli
tical implications of cyberspace\, and automated event data coding. Dr. Ra
dford’s dissertation demonstrated the semi-automated population of diction
aries for event-coding in novel domains. He is also an avid guitarist.

Unive
rsity Dissertation and Thesis Services: \n Here to
help you submit your thesis or Dissertation CORRECTLY and ON TIME

\n

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University Dissertation and Thesis Services understands
that there are many steps in the process toward graduation and it is thei
r goal is to make the process as clear\, easy\, and stress-free as possibl
e. After Ms. Evans’ presentation\, you will have an opportunity to ask que
stions

ABSTRACT: We use image-based computati
onal fluid dynamics to model the blood flow in human cerebral arteries and
aneurysms with three specific goals: 1) identify hemodynamic conditions t
hat predispose aneurysms for instability and rupture and thus help with mo
re precise selection of patients at high risk\, 2) advance the understandi
ng of the disease mechanisms and enable drug based therapies targeting spe
cific pathways of wall degeneration and weakening\, and 3) evaluation of d
evices and procedures to improve treatment planning and long term outcomes
. In this talk I will summarize some our recent progress and results along
these three lines of research\, and discuss future directions.

Keith Waters\, Ph.D. Cand
idate\nSchar School of Public Policy and Government\nGeorge Ma
son University

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Endogenous Regio
n Formation

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I apply spatial attr
ibutes to the Endogenous Firm Formation model by Axtell (2016) to grow reg
ions. Migration occurs as workers switch between firms located in differen
t regions. Overall\, regional growth and decline depend on the performance
of the firms located in them.

ABS
TRACT: Have you ever had one of the following thoughts while working on y
our research?

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I can’t remember where I put that data fi
le.

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I knew what these variables meant when I wrote them
last year.

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Did I accidentally delete that email with t
he final version of our research paper attached?

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Why do
es my collaborator’s program delete the last row and column of this array
before entering the main loop?

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If so\, then you’re no
t alone\, because “most researchers are never taught the equivalent of bas
ic lab skills for research computing” [1]. This situation persi
sts even as the average scientific researcher devotes as much as 30% of th
eir time developing and 40% of their time using scientific software [
2]. Underdeveloped skills in programming\, project organization\, an
d documentation can lead to general frustration\, productivity losses\, an
increase in the risk that a researcher won’t be able to reproduce his or
her work\, and can even result in serious computational errors that invali
date a study’s general conclusions [3]. At the same time\, the
number of scientific research groups that are integrating data science top
ics and methods into their programs is increasing at a rapid pace1 \, furt
her increasing the overall need to address this disparity. In response\, a
growing movement of researchers has emerged that are interested in tackli
ng this problem\, leading to the creation of organizations like the Softwa
re Carpentry Foundation [4]\, guidelines for reproducible resea
rch [5]\, and suggestions of “best practices” for scientific computing [1\, 6\, 7]. However\, although there is more awareness about thes
e potential solutions than in past years\, these ideas are still not commo
n knowledge. In this seminar\, I will review the general background behind
these ideas and what computational researchers can learn from other field
s such as the software industry. Drawing on my own experience with impleme
nting these ideas\, I will provide examples of how you can integrate repro
ducible research ideas into your work using open source tools. Using the “
best practices” suggestions as a guide\, I will also show ways in which yo
u can better organize your projects and some ways to make your code more r
eadable\, and then explain how this can help streamline scientific collabo
ration. Finally\, I will close by reflecting on the role that automation c
an play in achieving these principles and goals.

Fr
iday\, December 8\, 3:00 p.m.\nCenter for Social Complexity Suite\n3rd Floor\, Research Hall

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ABSTRACT: Computational social scie
nce can enable understanding – and design – of a wide variety of phenomena
at an enormous range of levels. This lecture will address processes\, org
anizations\, ecosystems\, and society for phenomena associated with diseas
e and medicine\, health and well being\, and technology adoption\, particu
larly in automobiles. The process level is addressed in terms of scaling a
nd optimization of medical innovations. The level of organizations is cons
idered in the context of health provider corporations’ responses to the Af
fordable Care Act. The ecosystem level is discussed in addressing populati
on health – integrated delivery of health\, education\, and social service
s – in the highly fragmented US ecosystem. The society level is considered
in terms of the expected disruptive impacts of driverless cars on automot
ive\, insurance\, and finance industries. Approaches to and challenges of
modeling at these differing levels are discussed.

Friday\, December 15\, 3:00 p.m.\nCenter for Social Complexity
Suite\n3rd Floor\, Research Hall

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Abstract: Cities are ever changing and growing phenomenon with many underlying com
plexities. Through its life cycle\, a city experiences various forms of dy
namics. Models allow for a better understanding of such complexities and d
ynamics. The model presented in this talk simulates the dynamics of certai
n processes such as: an urban market\, agent interactions in that market\,
urban growth\, sprawl and shrinkage and gentrification. The purpose of th
is model is to understand the behavioral pattern of the agents and demonst
rate the life cycle of a city based on individual agents’ actions. This mo
del is significant in its integration of various subsystems creating a lar
ger system while observing developers’ behavior. Specifically\, the model
explores some well-known issues\, including the Smith’s rent-gap theory\,
Burgess’s concentric zones model of urban growth\, and Alonso’s bid rent t
heory. The main results from the model show that the agents move to and re
side in properties within their income range\, with similar neighbors. Thi
s is one of the first models that provides a new lens to explore urban dev
elopment.