Info introduction to informaticsINDIANA UNIVERSITY I501 introduction to informatics Informatics...

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Informatics luis rocha 2017

I501introduction

to informatics

introduction to informaticslecture 18


INDIANAUNIVERSITY

I501introduction

to informatics


introduction to informatics

Participation: 15%. class discussion, especially about readings engagement in class

Paper presentation and handout: 15% Covering key paper points, handling

discussion Think-Pair-Share

Black Box assignments: 40% 2 assignments during the semester.

15% , Assignment I: 20% , Assignment II: Due November 15

GRFP Research proposal: 30% Elevator pitch and proposal

due December 8, 2014

evaluation


INDIANAUNIVERSITY

I501introduction

to informatics


Assignment I

Due October 11th

Focus on uncovering quadrants using data

collection and induction.

Propose a formal model or algorithm of what each quadrant is doing. Analyze, using

deduction, the behavior of this algorithm.

Q1 Q2

Q3 Q4

The Black Box: Due October 11th, 2017


INDIANAUNIVERSITY

I501introduction

to informatics


Assignment I

Much observation And thinking And videos

overall observations

Q1 Q2

Q3 Q4

Clara, Andrew, Nicholas & StephenGroup 1


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I501introduction

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Assignment I


Define language and terminology of observation Iterations/steps, cells, quadrants,

states/colors, etc.


Q1 Q2

Q3 Q4


Sirag, Swapna, Vincent, LoganGroup 3


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I501introduction

to informatics


Assignment I



states/colors, etc.


Q1 Q2

Q3 Q4



Jayati, Lucas, Stephen & KaichengGroup 2


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I501introduction

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Assignment I



states/colors, etc. At start iteration

Different each time States (numbers/colors) uniformly

distributed All states in similar proportions

Chi-square or Kolmogorov–Smirnov test goodness of fit ???


Q1 Q2

Q3 Q4




Cassie, Gianpaolo, Rosemary & VincentGroup 5


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I501introduction

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Assignment I



states/colors, etc. At start iteration

Different each time States (numbers/colors) uniformly

distributed All states in similar proportions

Chi-square or Kolmogorov–Smirnov test goodness of fit ???

With iterations Up to 1 change per quadrant

4 in total Randomness/stochasticity

“because moving back and forth a single time step can result in different pixel transitions.”


Q1 Q2

Q3 Q4


Zackary, Patrick, & FilipGroup 4





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I501introduction

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Assignment I

Results are published Anyone can now use shared

knowledge Citing sources

Data should be shared Collected up to now only Cited

Impressive data collection Beware of data overkill

Deduction from micro- and macro-level organization Direct visualization techniques

and observations powerful

Data collection


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I501introduction

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Assignment I

Final state Does not depend

of n step

Dynamics observations



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I501introduction

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Assignment I


of n step state distributions in

time Aggregate

measure of activity per quadrant Quadrants very

distinguishable





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I501introduction

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Assignment I



time Aggregate


distinguishable Converges

when? Better

statistics will help decide model alternatives

Varying observations about final behavior Attractors? Distributions of

cell states





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I501introduction

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Assignment I



time Aggregate


distinguishable Converges

when? Better

statistics will help decide model alternatives


cell states Entropy!





INDIANAUNIVERSITY

I501introduction

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Assignment I



time Aggregate


distinguishable Converges when?

Better statistics will help decide model alternatives


cell states Entropy! “Inter-run Cell

Entropy”! Surprise as to

what other state cells change to





INDIANAUNIVERSITY

I501introduction

to informatics


Assignment I



time Aggregate


distinguishable Converges when?

Better statistics will help decide model alternatives


cell states Entropy! “Inter-run Cell

Entropy”! Surprise as to

what other state cells change to

Average in time demonstrates initial random state





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I501introduction

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Assignment I

Observations Does not die off Favors lower states (0, 1 , 2…)

Quadrant 3

Q3



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I501introduction

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Assignment I


but state 9 more prevalent than others (7th most prevalent)

Quadrant 3

Q3




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I501introduction

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Assignment I



All cells doing same thing?

Quadrant 3

Q3





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I501introduction

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Assignment I



All cells doing same thing? A proposed model

Quadrant 3

Q3






INDIANAUNIVERSITY

I501introduction

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Assignment I



All cells doing same thing? A proposed model

What does it do? Statistics from data What can cause this type of random behavior? Does it fit known processes?

Serial correlation and goodness of fit tests

Quadrant 3

Q3






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I501introduction

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Assignment I

Observations Random probability of cell being picked for transition?

Not yet validated No transitions from 0

P(0 -> j)=0 Best to report the support for this assertion

Ratio of transitions to 0 to transitions to other states changes day to day !!!???

Statistically significant? Test further?

states behave differently Transition to 0 most frequent

Quadrant dies off When? Compute statistics to differentiate models

Even numbers can only transition to other even numbers Odd numbers transition to any other numbers

Except 5 only transitions to 0 And to itself?

Conclusion must be justified from observation How does it do this?

Q4

Quadrant 4





1. 0 02. { 5} {0, 5}3. {2, 4, 6, 8} {0, 2, 4, 6, 8}4. {1, 3, 7, 9}

{0 , 1, 2, 3, 4, 5, 6, 7, 8, 9}


INDIANAUNIVERSITY

I501introduction

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Assignment I




Q4

Quadrant 4



INDIANAUNIVERSITY

I501introduction

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Assignment I






Q4

Quadrant 4




INDIANAUNIVERSITY

I501introduction

to informatics


Assignment I








Q4

Quadrant 4




INDIANAUNIVERSITY

I501introduction

to informatics


Assignment I











Q4

Quadrant 4






INDIANAUNIVERSITY

I501introduction

to informatics


Assignment I











Q4

Quadrant 4





1. 0 02. { 5} {0, 5}3. {2, 4, 6, 8} {0, 2, 4, 6, 8}4. {1, 3, 7, 9}

{0 , 1, 2, 3, 4, 5, 6, 7, 8, 9}


INDIANAUNIVERSITY

I501introduction

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Assignment I

Observations Groups of black and

white cells Stable frequency

Quadrant 1

Q1Zackary, Patrick, & Filip

Group 4


INDIANAUNIVERSITY

I501introduction

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Assignment I

Observations Groups of black and

white cells Stable frequency Measure of cell “self-

information” How surprising is the state

a cell adopts What does it do and how?

How does clustering occur?

What processes are at play? From data

Investigate correlations , mutual information, etc. study self-transitions in first

iterations

Quadrant 1

Q1




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I501introduction

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Assignment I

Observations Behavior without borders more similar for every cell

Markov chain prediction more possible

Quadrant 2

Q2



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I501introduction

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Assignment I


Markov chain prediction more possible A proposed (descriptive) model

Quadrant 2


Q2



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I501introduction

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Assignment I


Markov chain prediction more possible A proposed (descriptive) model Transition constraints

How does it do this? Think of causal models! Specific rules that govern change Stochastic, deterministic, both? Serial correlation and goodness of fit tests

Quadrant 2


Q2


1. 9 {9,0}2. 0 {1,2,3,4}3. Most transitions

to {4,5,6,7,8,9}


INDIANAUNIVERSITY

I501introduction

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Assignment 1

Aggregate observations

Digging into the structure of quadrants



INDIANAUNIVERSITY

I501introduction

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Assignment 1


Mutual Information Reveals structure

of correlations between cells and neighborhoods

Repeating Patterns of correlations exist!





INDIANAUNIVERSITY

I501introduction

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Assignment 1


Mutual Information Reveals structure

of correlations between cells and neighborhoods


Reveals correlations (interactions?) between Q1 and Q2





INDIANAUNIVERSITY

I501introduction

to informatics


Assignment 1

Aggregate observations Mutual Information

Reveals structure of correlations between cells and neighborhoods


Reveals correlations (interactions?) between Q1 and Q2

Cells in Q2 most dependent on neighbors Contrast with Q3

Q3 and Q4 more uniform (less structures MI)





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I501introduction

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Black Box

Remember “published” facts Statistical behavior in Q3 Odd/Even behavior in Q4 Clustering and intricate dependencies, different processes in Q1 Rules of transitions in Q2

Are there quadrant dependencies? Focus on smaller grid subsets Think of neighborhoods and boundary conditions Move from descriptive to predictive models Induction and deduction

Data and reasoning Given a model, are things you have never seen possible?

Questions and suggestions

1. 0 02. { 5} {0, 5}3. {2, 4, 6, 8} {0, 2, 4, 6, 8}4. {1, 3, 7, 9} {0 , 1, 2, 3, 4, 5, 6, 7, 8, 9}

.....??, 1 ttjicellstate

1. 9 {9,0}2. 0 {1,2,3,4}3. Most transitions

to {4,5,6,7,8,9}


INDIANAUNIVERSITY

I501introduction

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Black Box

Data-driven analysis Klir’s GSPS

Mask analysis of smaller grids

E.g. predict behavior of a given cell in Q1

Correlations Information theory

Description model Statistical

Predictive model Causal

Validate Check distributions

observed against those predicted

Make predictions given models

Methods to employ

.....??, 1 ttjicellstate


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I501introduction

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Assignment II Due November

15th

Focus descriptive models of quadrant behavior using data

collection, induction, deduction and validation.

Propose a formal model or algorithm of what each quadrant is doing. Analyze, using

deduction, the behavior of this algorithm.

Q1 Q2

Q3 Q4


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I501introduction

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next class

Next classes Lecture

Klir, G.J. and D. Elias [2003]. Architecture of Systems Problem Solving. Springer. Chapters: 1,2, 3.1, 3.2, 3.10, 4.1, 4.2

Optional: Chapters 3, 4 Coutinho, A. [2003]. "On doing science: a speech by Professor

Antonio Coutinho". Economia, 4(1): 7-18, jan./jun. 2003. Knapp B, Bardenet R, Bernabeu MO, Bordas R, Bruna M, et

al. (2015) ”Ten Simple Rules for a Successful Cross-Disciplinary Collaboration”. PLoS Comput Biol 11(4): e1004214.

Schwartz, M.A. [2008]. "The importance of stupidity in scientific research". Journal of Cell Science, 121: 1771.

Presentations Thomas S. Kuhn (1970). Logic of discovery or Psychology of

Research. Misevic, Filip:

Karl Popper (1963). Science: Conjecture and refutations. Kempe-Cook, Lucas

Readings (available in OnCourse)

Info introduction to informaticsINDIANA UNIVERSITY I501 introduction to informatics Informatics...

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