Cognitive Computation

, Volume 6, Issue 1, pp 113–124 | Cite as

Toward a Formal, Visual Framework of Emergent Cognitive Development of Scholars

  • Amir Hussain
  • Muaz Niazi


Understanding the cognitive evolution of researchers as they progress in academia is an important but complex problem; one that belongs to a class of problems, which often require the development of models to gain further understanding of the intricacies of the domain. The research question that we address in this paper is: how to effectively model this temporal cognitive mental development of prolific researchers? Our proposed solution is based on noting that the academic progression and notability of a researcher are linked with a progressive increase in the citation count for the scholar’s refereed publications, quantified using indices such as the Hirsch index. We propose the use of an yearly increment of a scholar’s cognition quantifiable by means of a function of the scholar’s citation index, thereby considering the index as an indicator of the discrete approximation of the scholar’s cognitive development. Using validated agent-based modeling, a paradigm presented as part of our previous work aimed at the development of a cognitive agent-based computing framework, we present both formal as well as visual agent-based complex network representations for this cognitive evolution in the form of a temporal cognitive level network model. As proof of the effectiveness of this approach, we demonstrate the validation of the model using historic data of citations.


Agent-based modeling Cognitive development Cognitive agent-based computing Complex adaptive system Hirsch index Complex networks 



We would like to express thanks to Dr. Tamim Khan at Bahria University for taking time to verify the formal framework developed in the paper.


  1. 1.
    Aiello F, Fortino G, Gravina R, Guerrieri A. A java-based agent platform for programming wireless sensor networks. Comput J. 2011;54(3):439–54.CrossRefGoogle Scholar
  2. 2.
    Amsterdamska O, Leydesdorff L. Citations: indicators of significance? Scientometrics. 1989;15(5):449–71. doi: 10.1007/bf02017065.CrossRefGoogle Scholar
  3. 3.
    Batagelj V. Efficient algorithms for citation network analysis. Arxiv preprint cs/0309023. 2003.Google Scholar
  4. 4.
    Batagelj V, Mrvar A. Pajek datasets. 2006.
  5. 5.
    Batista PD, Campiteli MG, Kinouchi O, Martinez AS. Is it possible to compare researchers with different scientific interests? Scientometrics. 2006;68(1):179–89.CrossRefGoogle Scholar
  6. 6.
    Börner K, Maru J, Goldstone R. The simultaneous evolution of author and paper networks. Proc Natl Acad Sci USA. 2004;101(Suppl 1):5266.CrossRefPubMedCentralPubMedGoogle Scholar
  7. 7.
    Braun T, Glänzel W, Schubert A. A Hirsch-type index for journals. Scientometrics. 2006;69(1):169–73.CrossRefGoogle Scholar
  8. 8.
    Chen C. CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol. 2006;57(3):359–77.CrossRefGoogle Scholar
  9. 9.
    Chen P, Redner S. Community structure of the physical review citation network. J Informetr. 2010;4(3):278–90.Google Scholar
  10. 10.
    Clauset A, Newman M, Moore C. Finding community structure in very large networks. Phys Rev E. 2004;70(6):66111.CrossRefGoogle Scholar
  11. 11.
    Cronin B. The citation process. The role and significance of citations in scientific communication, vol. 1. London: Taylor Graham; 1984.Google Scholar
  12. 12.
    d’Inverno M, Luck M. Understanding agent systems. Berlin: Springer; 2004.CrossRefGoogle Scholar
  13. 13.
    Egghe L. Theory and practise of the g-index. Scientometrics. 2006;69(1):131–52.Google Scholar
  14. 14.
    Egghe L. Dynamic h index: the Hirsch index in function of time. J Am Soc Inf Sci Technol. 2007;58(3):452–4.CrossRefGoogle Scholar
  15. 15.
    Egghe L. The Hirsch-index and related impact measures. Ann Rev Inf Sci Technol. 2010;44:65–114.CrossRefGoogle Scholar
  16. 16.
    Epstein J. Why model? J Artif Soc Soc Simul. 2008;11(4):12.Google Scholar
  17. 17.
    Falagas M, Pitsouni E, Malietzis G, Pappas G. Comparison of PubMed, Scopus, web of science, and Google scholar: strengths and weaknesses. FASEB J. 2008;22(2):338.CrossRefPubMedGoogle Scholar
  18. 18.
    Fortino G, Rango F, Russo W. Statecharts-based JADE agents and tools for engineering multi-agent systems. In: Setchi R, Jordanov I, Howlett RJ, Jain LC, editors. Knowledge-based and intelligent information and engineering systems. Berlin: Springer, Heidelberg; 2010. p. 240–250.Google Scholar
  19. 19.
    Garfield E. Citation indexes for science: a new dimension in documentation through association of ideas. Science. 1955;122(3159):108.CrossRefPubMedGoogle Scholar
  20. 20.
    Garfield E. Citation analysis as a tool in journal evaluation. Science. 1972;178(4060):471–9.CrossRefPubMedGoogle Scholar
  21. 21.
    Garfield E. The history and meaning of the journal impact factor. JAMA. 2006;295(1):90.CrossRefGoogle Scholar
  22. 22.
    Harzing A, van der Wai R. Google Scholar as a new source for citation analysis. Ethics Sci Environ Polit (ESEP). 2008;8(1):61–73.CrossRefGoogle Scholar
  23. 23.
    Harzing A, van der Wal R. A Google Scholar h-index for journals: an alternative metric to measure journal impact in economics and business. J Am Soc Inf Sci Technol. 2009;60(1):41–6.CrossRefGoogle Scholar
  24. 24.
    Hayden S, Zermelo E, Fraenkel A, Kennison J. Zermelo-Fraenkel set theory. Columbus: CE Merrill; 1968.Google Scholar
  25. 25.
    Hirsch J. An index to quantify an individual’s scientific research output. Proc Natl Acad Sci. 2005;102(46):16569.CrossRefPubMedCentralPubMedGoogle Scholar
  26. 26.
    Hummon N, Dereian P. Connectivity in a citation network: the development of DNA theory. Soc Netw. 1989;11(1):39–63.CrossRefGoogle Scholar
  27. 27.
    Kajikawa Y, Takeda Y. Citation network analysis of organic LEDs. Technol Forecast Soc Chang. 2009;76(8):1115–23.CrossRefGoogle Scholar
  28. 28.
    McBurney DH, White T. Research methods. New York/Boston: Pearson/Wadsworth; 2006.Google Scholar
  29. 29.
    Moed H. Citation analysis in research evaluation. Dordrecht: Kluwer; 2005.Google Scholar
  30. 30.
    Newman M. Coauthorship networks and patterns of scientific collaboration. Proc Natl Acad Sci. 2004;101(Suppl 1):5200.CrossRefPubMedCentralPubMedGoogle Scholar
  31. 31.
    Newman MEJ. The structure and function of complex networks. SIAM Rev. 2003;45(2):167–256.CrossRefGoogle Scholar
  32. 32.
    Niazi M, Ahmed HF, Ali A. Introducing fault-tolerance and responsiveness in web applications using SREFTIA. In: Paper presented at the proceedings of the international multiconference on computer science and information technology, Wisla, Poland, Nov 6–10, 2006. 2006.Google Scholar
  33. 33.
    Niazi M, Hussain A. A novel agent-based simulation framework for sensing in complex adaptive environments. IEEE Sens J. 2010;11(2):404–12.Google Scholar
  34. 34.
    Niazi M, Hussain A. Agent-based computing from multi-agent systems to agent-based models: a visual survey. Scientometrics. 2011; 1–21. doi: 10.1007/s11192-011-0468-9.
  35. 35.
    Niazi M, Hussain A, Baig AR, Bhatti S. Simulation of the research process. In: Paper presented at the 40th conference on winter simulation, Miami, FL. 2008.Google Scholar
  36. 36.
    Niazi M, Siddique Q, Hussain A, Kolberg M. Verification and validation of an agent-based forest fire simulation model. In: Paper presented at the SCS spring simulation conference, Orlando, FL, USA, April 2010. 2010.Google Scholar
  37. 37.
    Niazi MA. Self-organized customized content delivery architecture for ambient assisted environments. Paper presented at the Proceedings of the third international workshop on Use of P2P, grid and agents for the development of content networks, Boston, MA, USA. 2008.Google Scholar
  38. 38.
    Niazi MA. Complex adaptive systems modeling: a multidisciplinary roadmap. Complex Adapt Syst Model. 2013;1(1):1.CrossRefGoogle Scholar
  39. 39.
    Niazi MA, Hussain A. Cognitive agent-based computing-I: a unified framework for modeling complex adaptive systems using agent-based and complex network-based methods. Springer Briefs in Cognitive Computation. Springer: Dordrecht; 2012. doi: 10.1007/978-94-007-3852-2.
  40. 40.
    Niazi MA, Hussain A, Kolberg M. Verification and validation of agent based simulations using the VOMAS (virtual overlay multi-agent system) approach. Paper presented at the MAS&S 09 at Multi-Agent Logics, Languages, and Organisations Federated Workshops, Torino, Italy, 7–10 September 2009. 2009.Google Scholar
  41. 41.
    Niazi MA, Laghari S. An intelligent self-organizing power-saving architecture: an agent-based approach. In: Computational intelligence, modelling and simulation (CIMSiM). IEEE fourth international conference on 2012. 2012. p 70–75.Google Scholar
  42. 42.
    Niazi MA, Siddique Q, Hussain A, Fortino G. SimConnector: An approach to testing disaster-alerting systems using agent-based simulation models. Paper presented at the Federated conference on computer science and information systems, Szczecin, Poland. 2011.Google Scholar
  43. 43.
    Niazi MAK. Towards a novel unified framework for developing formal, network and validated agent-based simulation models of complex adaptive systems. Stirling: University of Stirling; 2011.Google Scholar
  44. 44.
    Schreiber M. A modification of the h-index: the hm-index accounts for multi-authored manuscripts. J Informetr. 2008;2(3):211–6. doi: 10.1016/j.joi.2008.05.001.CrossRefGoogle Scholar
  45. 45.
    Siddiqa A, Niazi MA, Mustafa F, Bokhari H, Hussain A, Akram N, Shaheen S, Ahmed F, Iqbal S. A new hybrid agent-based modeling and simulation decision support system for breast cancer data analysis. In: Information and Communication Technologies, 2009. ICICT ‘09 international conference on 15–16 Aug 2009. 2009. p 134–39.Google Scholar
  46. 46.
    Spivey JM. Understanding Z: a specification language and its formal semantics. Cambridge: Cambridge Univ Press; 1988.Google Scholar
  47. 47.
    Team N. Network workbench tool. USA: Indiana University/Northeastern University/University of Michigan; 2006.Google Scholar
  48. 48.
    Watts C, Gilbert N. Does cumulative advantage affect collective learning in science? An agent-based simulation. Scientometrics. 2011;89(1):437–63. doi: 10.1007/s11192-011-0432-8.CrossRefGoogle Scholar
  49. 49.
    Weingart P. Impact of bibliometrics upon the science system: inadvertent consequences? Scientometrics. 2005;62(1):117–31.CrossRefGoogle Scholar
  50. 50.
    Weng J, McClelland J, Pentland A, Sporns O, Stockman I, Sur M, Thelen E. Autonomous mental development by robots and animals. Science. 2001;291(5504):599–600. doi: 10.1126/science.291.5504.599.CrossRefPubMedGoogle Scholar
  51. 51.
    Clapham PJ. Publish or Perish. Bioscience. 2005;55(5):390–91.Google Scholar
  52. 52.
    Wilensky U. NetLogo. Evanston, IL: Center for Connected Learning Comp.-Based Modeling, Northwestern University; 1999.Google Scholar
  53. 53.
    Cambria E, Hussain A. Sentic computing: techniques, tools, and applications. Dordrecht, Netherlands: Springer; 2012. ISBN: 978-94-007-5069-2.Google Scholar
  54. 54.
    Cambria E, Song Y, Wang H, Howard N. Semantic multi-dimensional scaling for open-domain sentiment analysis. IEEE Intell Syst. 2013. doi: 10.1109/MIS.2012.118.
  55. 55.
    Cambria E, Mazzocco T, Hussain A. Application of multi-dimensional scaling and artificial neural networks for biologically inspired opinion mining. Biol Inspir Cogn Arch. 2013;4:41–53.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.University of StirlingScotlandUK
  2. 2.Bahria UniversityIslamabadPakistan

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