Instructor: Luis M. Rocha, Center for Complex Networks and Systems, School of Informatics, Computing, and Engineering and Cognitive Science Program, Indiana University

Associate Instructor: Thomas Parmer

Class Location and Time:Wednesdays, 11:15AM - 1:45PM, Room: Informatics East, 901 E. 10th St., Room 122


Course Description

Description:The course deals with the foundations of Informatics as an interdisciplinary field. We will study concepts such as Information, Technology, Knowledge, Modeling, as well as their impact on science and society. The course will also attempt to define and understand what computational and systems thinking can bring to science and society. The course is required for the PhD in Informatics as well as the NSF-NRT Interdisciplinary Training Program in Complex Networks and Systems at Indiana University.

Aims: The course is designed to present and discuss the history, methodology and impact of informatics; students are introduced to various approaches to informatics via the appropriate literature. Finally, students are expected to develop a understanding of what constitutes research in the field, via a familiarization with relevant funding opportunities.

Syllabus

Lecture Outline

Course Evaluation

Assignments and Proposal

Office Hours

Luis Rocha: Wednesdays 9-11AM, 919 E. 10th St, Room #301

Course Materials and Readings

Readings available in the OnCourse Class Folder)

Mandatory articles

week papers
week 1

Borges, Jorge Luis. [1941]. The Library of Babel.

Borges, Jorge Luis. [1941]. The Garden of Forking Paths .

week 2

Heims, S.G. [1991]. The Cybernetics Group. MIT Press. Chapters: 1,2

Weaver, W. [1948]. "Science and Complexity". American Scientist, 36(4): 536-44.

Simon, H.A. [1962]. "The Architecture of Complexity". Proceedings of the American Philosophical Society, 106: pp. 467-482.

week 3

Freeth, Tony. 2014. “Eclipse Prediction on the Ancient Greek Astronomical Calculating Machine Known as the Antikythera Mechanism.” PloS One 9 (7): e103275.

Gleick, J. [2011]. The Information: A History, a Theory, a Flood. Random House. Chapter 8.

Wasserman, M., X.H.T. Zeng, and L.A.N. Amaral [2015]. “Cross-evaluation of metrics to estimate the significance of creative works”. PNAS 112 (5) 1281-1286

week 4

Barabasi and Albert (1999) Emergence of Scaling in Random Networks, Science 296 (5439)

Hofman, Jake M., Amit Sharma, and Duncan J. Watts. "Prediction and explanation in social systems." Science 355.6324 (2017): 486-488.

Prokopenko, Mikhail, Fabio Boschetti, and Alex J. Ryan. [2009]. "An information-theoretic primer on complexity, self-organization, and emergence. ". Complexity 15(1): 11-28

week 5

Young, T. (2017). Cease and desist. Nature, 541(7637), 430–430. https://doi.org/10.1038/541430a

Lazebnik, Y [2002]. "Can a biologist fix a radio?--Or, what I learned while studying apoptosis". Cancer Cell, 2(3):179-182.

Jonas, E., and Kording, K. P., (2017). Could a Neuroscientist Understand a Microprocessor? PLOS Computational Biology, 13(1), e1005268.

Adamic, Lada A., Thomas M. Lento, Eytan Adar, and Pauline C. Ng. "Information evolution in social networks." In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 473-482. ACM, 2016.

Jeremy A. Frimer, Karl Aquino, Jochen E. Gebauer, Luke (Lei) Zhu, and Harrison Oakes [2015] "A decline in prosocial language helps explain public disapproval of the US Congress" PNAS 2015 112 (21) 6591-6594; doi:10.1073/pnas.1500355112

week 6

G.L. Ciampaglia, P. Shiralkar, L.M. Rocha, J. Bollen, F. Menczer, A. Flammini [2015]. “Computational fact checking from knowledge networks.” PLoS One. 10(6): e0128193. doi:10.1371/journal.pone.0128193.

week 7

Gauvrit, N., Zenil, H., Soler-Toscano, F., Delahaye, J.-P., Brugger, P., Schuknecht, B., … Brugger, P. (2017). Human behavioral complexity peaks at age 25. PLOS Computational Biology, 13(4), e1005408.

Klir, G.J. [2001]. Facets of systems Science. Springer. Chapters: 1,2,3

Piantadosi, S. T.,et al (2011). Word lengths are optimized for efficient communication. PNAS, 108(9), 3526–3529.

Schmälzle, R., Brook O’Donnell, M., Garcia, J. O., Cascio, C. N., Bayer, J., Bassett, D. S., … Falk, E. B. (2017). Brain connectivity dynamics during social interaction reflect social network structure. Proceedings of the National Academy of Sciences, 114(20), 5153–5158.

week 8

Loreto, Vittorio, et al. "Dynamics on expanding spaces: modeling the emergence of novelties." Creativity and Universality in Language. Springer International Publishing, 2016. 59-83.

Markov, Igor L. 2014. “Limits on Fundamental Limits to Computation.” Nature 512 (7513) (August 13): 147–154. doi:10.1038/nature13570.

week 10

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

week 11

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

Popper, Karl (1963). Science: Conjecture and refutations.

week 12

Clark, A. [2003]. Natural-Born Cyborgs: Minds, technologies and the Future of Human Intelligence. Oxford University Press. Chapters 2 and 6

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 Biol11(4): e1004214. doi: 10.1371/journal.pcbi.1004214

Rubenstein, M., A. Cornejo, and R. Nagpal. 2014. “Programmable Self-Assembly in a Thousand-Robot Swarm.” Science 345 (6198) (August 14): 795–799.

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

week 13

Agar, J. E. "The curious history of curiosity-driven research." Notes and Records: The Royal Society Journal of the History of Science (2018).

Sandve, Geir Kjetil, Anton Nekrutenko, James Taylor, and Eivind Hovig. [2013]. "Ten Simple Rules for Reproducible Computational Research." PLoS Computational Biology 9 (10): 4.

Weinberger CJ, Evans JA, Allesina S (2015) Ten Simple (Empirical) Rules for Writing Science. PLoS Comput Biol 11(4): e1004205. doi: 10.1371/journal.pcbi.1004205

Zhang, W. [2014]. "Ten Simple Rules for Writing Research Papers." PLoS Computational Biology 10 (1): e1003453.

Optional

Heims, S.G. [1991]. The Cybernetics Group. MIT Press. Chapters: 11, and 12.
McCulloch, W. and W. Pitts [1943], "A Logical Calculus of Ideas Immanent in Nervous Activity". Bulletin of Mathematical Biophysics 5:115-133.
Aleksander, I. [2002]. Understanding Information Bit by Bitî. In: It must be beautiful : great equations of modern science. G. Farmelo (Ed.), Granta, London.
Klir, G.J. [2001]. Facets of systems Science. Springer. Chapters: 8 and 11
Klir, G.J. and D. Elias [2003]. Architecture of Systems Problem Solving. Springer. Chapters: 3 and 4

For Presentations (Choose One)

  • Ackermann, K., Angus, S. D., and Raschky, P. A. (2017). The Internet as Quantitative Social Science Platform: Insights from a Trillion Observations. arXiv:1701.05632
  • Adamic, Lada A., Thomas M. Lento, Eytan Adar, and Pauline C. Ng. "Information evolution in social networks." In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 473-482. ACM, 2016.
  • Barabasi and Albert (1999) Emergence of Scaling in Random Networks, Science 296 (5439)
  • Eytan Bakshy, Solomon Messing, and Lada A. Adamic [2015] "Exposure to ideologically diverse news and opinion on Facebook." Science 5 June 2015: 348 (6239), 1130-1132.
  • Luís M. A. Bettencourt at al (2007) - Growth, innovation, scaling, and the pace of life in cities, PNAS, vol. 104 no. 17 7301-7306 + Shalizi, C.R. [2011]. "Scaling and Hierarchy in Urban Economies.", arxiv.org.
  • Bollen J, Crandall D, Junk D, Ding Y, Borner K. [2014] “From funding agencies to scientific agency: Collective allocation of science funding as an alternative to peer review”. EMBO Rep. DOI: 10.1002/embr.201338068
  • C. Cattuto, V. Loreto, L. Pietronero [2007] "Semiotic dynamics and collaborative tagging", PNAS 104(17), 1461
  • Clark, A. [2003]. Natural-Born Cyborgs: Minds, technologies and the Future of Human Intelligence. Oxford University Press. Chapters 2 and 6
  • R.B. Correia, L. Li, L.M. Rocha [2016]. "Monitoring potential drug interactions and reactions via network analysis of Instagram user timeliness". Pacific Symposium on Biocomputing. 21:492-503.
  • Eiben. A. and Jim Smith [2015]. "From evolutionary computation to the evolution of things." Nature 521, 476–482. doi:10.1038/nature14544
  • Erez, Zohar, et al. "Communication between viruses guides lysis–lysogeny decisions." Nature 541.7638 (2017): 488-493.
  • Dunn, M. C., Bourne, P. E., AAlberg, I., Appleton, G., Axton, M., and Baak, A. (2017). Building the biomedical data science workforce. PLOS Biology, 15(7), e2003082.
  • Freeth, Tony. 2014. “Eclipse Prediction on the Ancient Greek Astronomical Calculating Machine Known as the Antikythera Mechanism.” PloS One 9 (7) (January): e103275. doi:10.1371/journal.pone.0103275.
  • Jeremy A. Frimer, Karl Aquino, Jochen E. Gebauer, Luke (Lei) Zhu, and Harrison Oakes [2015] "A decline in prosocial language helps explain public disapproval of the US Congress" PNAS 2015 112 (21) 6591-6594; doi:10.1073/pnas.1500355112
  • A. Gates and L.M. Rocha. [2016] "Control of complex networks requires both structure and dynamics." Scientific Reports 6, 24456. doi: 10.1038/srep24456.
  • Gauvrit, N., Zenil, H., Soler-Toscano, F., Delahaye, J.-P., Brugger, P., Schuknecht, B., … Brugger, P. (2017). Human behavioral complexity peaks at age 25. PLOS Computational Biology, 13(4), e1005408.
  • Helbing, Dirk, Dirk Brockmann, Thomas Chadefaux, Karsten Donnay, Ulf Blanke, Olivia Woolley-Meza, Mehdi Moussaid et al. "Saving human lives: what complexity science and information systems can contribute." Journal of statistical physics 158, no. 3 (2015): 735-781.
  • Heylighen, Francis. 2015. “Stigmergy as a Universal Coordination Mechanism: Components, Varieties and Applications.” In Human Stigmergy: Theoretical Developments and New Applications (Studies in Applied Philosophy, Epistemology and Rational Ethics), edited by Ted; Lewis and Leslie Marsh, In Press.
  • Hofman, Jake M., Amit Sharma, and Duncan J. Watts. "Prediction and explanation in social systems." Science 355.6324 (2017): 486-488.
  • Hughes (2009) Quantification of artistic style through sparse coding analysis in the drawings of Pieter Bruegel the Elder. PNAS 107(4):1279–1283
  • Jonas, E., and Kording, K. P., (2017). Could a Neuroscientist Understand a Microprocessor? PLOS Computational Biology, 13(1), e1005268.
  • Thomas S. Kuhn (1970). Logic of discovery or Psychology of Research.
  • Kull, Deacon, Pattee, Kauffman[2012] . Approaches to biosemiotics. In Gatherings in Biosemiotics. Eds. S Rattasepp and T. Bennett. University of Tartu Press.
  • Yann LeCun, Yoshua Bengio and Geoffrey Hinton [2015]. "Deep learning" Nature 521, 436–444 (28 May 2015) doi:10.1038/nature14539
  • Danielle Li and Leila Agha [2015]. "Big names or big ideas: Do peer-review panels select the best science proposals?" Science 24 April 2015: 348 (6233), 434-438.
  • Loreto, Vittorio, et al. "Dynamics on expanding spaces: modeling the emergence of novelties." Creativity and Universality in Language. Springer International Publishing, 2016. 59-83.
  • Mann, R. P., and Helbing, D. (2017). Optimal incentives for collective intelligence. Proceedings of the National Academy of Sciences of the United States of America, 201618722.
  • Markov, Igor L. 2014. “Limits on Fundamental Limits to Computation.” Nature 512 (7513) (August 13): 147–154. doi:10.1038/nature13570.
  • Piantadosi, S. T.,et al (2011). Word lengths are optimized for efficient communication. PNAS, 108(9), 3526–3529.
  • Karl Popper (1963). Science: Conjecture and refutations.
  • Radicchi, F. (2008) Universality of citation distributions: Toward an objective measure of scientific impact. PNAS 105(45): 17268–17272.
  • Rubenstein, M., A. Cornejo, and R. Nagpal. 2014. “Programmable Self-Assembly in a Thousand-Robot Swarm.” Science 345 (6198) (August 14): 795–799.
  • Suderman, R., Bachman, J. A., Smith, A., Sorger, P. K., Deeds, E. J. (2017). Fundamental trade-offs between information flow in single cells and cellular populations. Proceedings of the National Academy of Sciences of the United States of America, 201615660.
  • Suzuki, R. et al (2006) Information entropy of humpback whale songs, J. Acoust. Soc. Am, 199(3), March
  • Scheffer (2009) Early-warning signals for critical transitions. Nature 461, 53-59 - doi:10.1038/nature08227
  • Schmälzle, R., Brook O’Donnell, M., Garcia, J. O., Cascio, C. N., Bayer, J., Bassett, D. S., … Falk, E. B. (2017). Brain connectivity dynamics during social interaction reflect social network structure. Proceedings of the National Academy of Sciences, 114(20), 5153–5158.
  • Wasserman, M., X.H.T. Zeng, and L.A.N. Amaral [2015]. “Cross-evaluation of metrics to estimate the significance of creative works”. PNAS 112 (5) 1281-1286, doi:10.1073/pnas.1412198112
  • I.B Wood, P.L. Varela, J. Bollen, L.M. Rocha, J. Gonçalves-Sá [2017]. Human Sexual Cycles are Driven by Culture and Match Collective Moods. arXiv:1707.03959.

Last Modified: November 15, 2017