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infervote.org Democratizing democracy: a resource for political engagement
Robert Vogel
Why?
• We are constantly bombarded with political rhetoric that shape our political views.
• How are we ACTUALLY represented by our elected officials?
• How does and will our congress vote on topics we care about?
• Do senator voting records exhibit polarized behavior? • How can we find misbehaving and polarized senators? • What action can we take?
The Polarity Index
Senator i Votes
Republican Senator j
Votes
The Polarity Index
Senator i Votes
Republican Senator j
VotesVotes in common
The Polarity Index
Senator i Votes
Republican Senator j
VotesVotes in common
Jij =Votes in common
All votes
The Polarity Index
Senator i Votes
Republican Senator j
VotesVotes in common
Jij =Votes in common
All votes
Ji1 = ~1
The Polarity Index
Senator i Votes
Republican Senator j
VotesVotes in common
Jij =Votes in common
All votes
Ji1 = ~1
Ji2 =
+
~0
The Polarity Index
Senator i Votes
Republican Senator j
VotesVotes in common
Jij =Votes in common
All votes
+
Polarity=
Ji1 = ~1
Ji2 =
+
~0
The Polarity Index
Senator i Votes
Republican Senator j
VotesVotes in common
Jij =Votes in common
All votes
+
Polarity=
Ji1 = ~1
Ji2 =
+
~0
Clustering Senator Voting with Jaccard distance
Democrat RepublicanInd
Clustering Senator Voting with Jaccard distance
Democrat RepublicanInd
Clustering Senator Voting with Jaccard distance
dMitch McConnell (KY)John McCain (AZ)
Democrat RepublicanInd
Clustering Senator Voting with Jaccard distance
Elizabeth Warren (MA)
Dianne Feinstein (CA)
dMitch McConnell (KY)John McCain (AZ)
Democrat RepublicanInd
Clustering Senator Voting with Jaccard distance
Elizabeth Warren (MA)
Dianne Feinstein (CA)Bernie Sanders (VT)
dMitch McConnell (KY)John McCain (AZ)
Democrat RepublicanInd
Clustering Senator Voting with Jaccard distance
Elizabeth Warren (MA)
Dianne Feinstein (CA)Bernie Sanders (VT)
dMitch McConnell (KY)John McCain (AZ)
Rand Paul (KY) Marco Rubio (FL) Ted Cruz (TX)
Democrat RepublicanInd
Clustering Senator Voting with Jaccard distance
Elizabeth Warren (MA)
Dianne Feinstein (CA)Bernie Sanders (VT)
dMitch McConnell (KY)John McCain (AZ)
Rand Paul (KY) Marco Rubio (FL) Ted Cruz (TX)
Do votes align with bill sponsors?
Republican Sponsor
• The bill sponsor is the member of congress that introduces the document for consideration.
Democrat Sponsor
Infer directionality of biochemical reactions using Langevin dynamics
Robert Vogel
Developed new parameterization of therapeutic drugs using insight from nonlinear dynamical systems
Voting Distributions and the Simulated Senate• Sample 5000 experimental senates using parameters from data
• Data exhibit a more diverse distribution then simulation
• Potential next step, use the Ising model to model pairwise interactions
Republican SponsorDemocrat Sponsor
The Jaccard Index and Political Polarity
Jaccard Index for Measuring Polarity
• Jaccard Index measures the number identical votes between Senator i and Senator j normalized to total votes
• Polarity index is the average Jaccard index between Senator i and all Senators in party R.
Jij =|vi \ vj ||vi [ vj |
JiR =1
NR
X
j2R
Jij
Distribution of polarity index
• If party politics were not a factor, these distributions would overlap
Jaccard Distance for Senator Clustering
• Jij 1 the more similar Senator i votes to Senator j.
• Hierarchical clustering utilizes a dissimilarity measure. Standard solution 1 - Jij
dJ(i, j) = 1� |vi \ vj ||vi [ vj || {z }
Jij
Votes are strictly partisan
• Fraction of votes along party line, most votes are partisan
Topic Modeling
Topic modeling of legislative summariesW
ord
spac
e pe
r bill
Topi
c Sp
ace
Bills
Topi
cs
T S = S’
Y N0
Congress person Topic Probabilities
P
T’ = P’
New bill in topic space Probability of vote
P
Y
N0
Prediction
Clustering
Can we make predictions of senator votes from legislative documents?
Topic 1
Topic 2
Topic 3
Senator 1 Vote
Document 1
Document 2
Document NTopic M
Can we make predictions of senator votes from legislative documents?
Topic 1
Topic 2
Topic 3
Senator 1 Vote
Document 1
Document 2
Document NTopic M
VoteSenator 2
Can we make predictions of senator votes from legislative documents?
Topic 1
Topic 2
Topic 3
Senator 1 Vote
Document 1
Document 2
Document NTopic M Senator L Vote
VoteSenator 2
Legislative document reduction to topics
• 2559 legislative summaries
• Constructed 6403 word basis from text by:
• removing stop words (e.g and, that, this, a)
• removing non-english words
• stemming (e.g. rested equal to rest)
• TF-IDF
• Cosine similarity to group topics
Legislative document reduction to topics
• 2559 legislative summaries
• Constructed 6403 word basis from text by:
• removing stop words (e.g and, that, this, a)
• removing non-english words
• stemming (e.g. rested equal to rest)
• TF-IDF
• Cosine similarity to group topics
• Result: No structure in bill data, more data needed!
Documents
Documents
Document dimensionality reduction not sufficient with PCA
95% of the variablity corresponds to > 1000 dimensions
A small topic space, represents a small portionof the variability
tSNE dimensionality reduction suggests no structure in bill data
• Each point is a document in the reduced space defined by tSNE
• t-distributed Stochastic Neighborhood Embedding maps points from a high to a low dimensional space by minimizing the Kullback-Leibler Divergence (minimize information loss).
The data
Why only choose Bills and Amendments?
• In general, these documents can become law
• Other votes are for approving nominations for office and resolutions.
• Resolutions can be very diverse as shown below.
Graduate Research: An overview
• Langevin Dynamics to:
• figure out direction in biochemical reactions, and
• testing isolation of a network motif.
• Bifurcation analysis to identify:
• nodes in a network sensitive to therapeutic inhibition
Biochemical Noise
• Flow cytometry measures the relative quantity of <= 12 biochemical species per cell at a rate of 20,000 cells per second.
• Fluorescent molecules are coupled to antibodies that specifically bind to a biochemical species.
• Quantity of molecules is proportional to fluorescent signal
−2 −1 0 1 2−4
−3
−2
−1
0
1
2
3
4
PMA 1
PMA 2
PMA 3
Log2 Normalized pMEK Log
2 N
orm
aliz
ed p
pER
K
PMA 1
PMA 2
PMA 3
PMA 1
PMA 2
PMA 3
−2 −1 0 1 2−4
−3
−2
−1
0
1
2
3
4
PMA : 1
Log2 pMEK
Log 2
ppE
RK
PMA : 1
−2 −1 0 1 2−4
−3
−2
−1
0
1
2
3
4
PMA : 2
Log2 pMEK
Log 2
ppE
RK
PMA : 2
−2 −1 0 1 2−4
−3
−2
−1
0
1
2
3
4
PMA : 3
Log2 pMEK
Log 2
ppE
RK
PMA : 3
Fluctuations break symmetry of average measurements
Variance of Y > XY
X𝜉x
𝜉y
O
O
• Fluctuations from source node propagates to target
X
Y𝜉y
𝜉x
O
OVariance of X > Y
True Model False Model
Fluctuations break symmetry of average measurements
Variance of Y > X
0.2 0.3 0.4 0.5 0.60.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Cov(pMEK, ppERK)
Varia
nce
True Model
pMEKppERK
0.2 0.3 0.4 0.5 0.60.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Cov(pMEK, ppERK)
Varia
nce
False Model
pMEKppERK
0.2 0.3 0.4 0.5 0.60.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Cov(pMEK, ppERK)
Varia
nce
True Model
pMEKppERK
0.2 0.3 0.4 0.5 0.60.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Cov(pMEK, ppERK)
Varia
nce
False Model
pMEKppERK
Y
X𝜉x
𝜉y
O
O
• Fluctuations from source node propagates to target
X
Y𝜉y
𝜉x
O
OVariance of X > Y
True Model False Model
Nonlinear dynamics of biochemical inhibition
Inhibition of biochemical signaling in cells, a new parameter 𝛼
L c SRC
pMEK MEKi
ppERK
In preparation for publicationChemical SpeciesChemical Complex Enzymatic reaction Enzymatic Inhibition
Inhibition of biochemical signaling in cells, a new parameter 𝛼
L c SRC
pMEK MEKi
ppERK
L c SRC
SRCi
pMEK
ppERK
In preparation for publicationChemical SpeciesChemical Complex Enzymatic reaction Enzymatic Inhibition
Inhibition of biochemical signaling in cells, a new parameter 𝛼
L c SRC
pMEK MEKi
ppERK
L c SRC
SRCi
pMEK
ppERK
In preparation for publicationChemical SpeciesChemical Complex Enzymatic reaction Enzymatic Inhibition
Inhibition of biochemical signaling in cells, a new parameter 𝛼
L c SRC
pMEK MEKi
ppERK
L c SRC
SRCi
pMEK
ppERK
In preparation for publicationChemical SpeciesChemical Complex Enzymatic reaction Enzymatic Inhibition
Nonlinear dynamics of biochemical inhibition in cells
Chemical Species
Chemical Complex
Enzymatic reaction
Enzymatic Inhibition
In preparation for publication
Nonlinear dynamics of biochemical inhibition in cells
Chemical Species
Chemical Complex
Enzymatic reaction
Enzymatic Inhibition
L c SRC
pMEK MEKi
ppERK
[MEKi] [MEKi]
In preparation for publication
Nonlinear dynamics of biochemical inhibition in cells
Chemical Species
Chemical Complex
Enzymatic reaction
Enzymatic InhibitionL c SRC
SRCi
pMEK
ppERK
[SRCi] [SRCi]
L c SRC
pMEK MEKi
ppERK
[MEKi] [MEKi]
In preparation for publication
Finding dysfunctional components in tumor samples
Single cell measurements find abnormalities in tumor patient profiles• Kullback-Leibler divergence measures the dissimilarity of the single cell
distribution of biochemical signaling features between patient and healthy donor samples.
Sjk =
X
i2HD
DKL (Pj(xk)||Pi(xk))
=
X
i2HD
Pj(xk) log
✓Pj(xk)
Pi(xk)
◆
• k = Biochemical species
• j = patient id
• i = Healthy donor