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Journal of Mechanical Design | Volume 136 | Issue 6 | research-article
Research Papers

Estimating Local Decision-Making Behavior in Complex Evolutionary Systems

[+] Author and Article Information
Zhenghui Sha

Graduate Research Assistant
School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: zsha@purdue.edu

Jitesh H. Panchal

Assistant Professor
School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: panchal@purdue.edu

In network terminology, a network is composed of nodes and links. A network can be mathematically represented as a graph, where nodes are represented as vertices and the links are represented as edges. Within complex networked systems, a node can refer to individual decision makers or other entities such as organizations that make decisions.

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received July 7, 2013; final manuscript received February 4, 2014; published online April 11, 2014. Assoc. Editor: Bernard Yannou.

J. Mech. Des 136(6), 061003 (Apr 11, 2014) (11 pages) Paper No: MD-13-1289; doi: 10.1115/1.4026823 History: Received July 07, 2013; Revised February 04, 2014
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Citing Articles

Research in systems engineering and design is increasingly focused on complex sociotechnical systems whose structures are not directly controlled by the designers, but evolve endogenously as a result of decisions and behaviors of self-directed entities. Examples of such systems include smart electric grids, Internet, smart transportation networks, and open source product development communities. To influence the structure and performance of such systems, it is crucial to understand the local decisions that result in observed system structures. This paper presents three approaches to estimate the local behaviors and preferences in complex evolutionary systems, modeled as networks, from its structure at different time steps. The first approach is based on the generalized preferential attachment model of network evolution. In the second approach, statistical regression-based models are used to estimate the local decision-making behaviors from consecutive snapshots of the system structure. In the third approach, the entities are modeled as rational decision-making agents who make linking decisions based on the maximization of their payoffs. Within the decision-centric framework, the multinomial logit choice model is adopted to estimate the preferences of decision-making nodes. The approaches are illustrated and compared using an example of the autonomous system (AS) level Internet. The approaches are generally applicable to a variety of complex systems that can be modeled as networks. The insights gained are expected to direct researchers in choosing the most applicable estimation approach to get the node-level behaviors in the context of different scenarios.
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References

1
Pahl, G., and Beitz, W., 1996, Engineering Design: A Systematic Approach, 2nd ed., Springer, London.
2
Buede, D. M., 2000, The Engineering Design of Systems: Models and Methods, John Wiley and Sons, Inc., New York.
3
NASA, 2007, NASA Systems Engineering Handbook (NASA/SP-2007-6105 Rev1), National Aeronautics and Space Administration, Washington, DC.
4
Hawthorne, B. D., and Panchal, J. H., 2012, “Policy Design for Sustainable Energy Systems Considering Multiple Objectives and Incomplete Preferences,” 2012 ASME International Design Engineering and Technical Conferences (Design Automation Conference), Chicago, IL, Paper No. DETC2012-70426.
5
Sha, Z., and Panchal, J., 2013, “Towards the Design of Complex Evolving Networks with High Robustness and Resilience,” Procedia Computer Science, Proceedings of the 2013 Conference on Systems Engineering Research (CSER), Vol. 16, pp. 522–531.
6
Barabasi, A. L., and Albert, R., 1999, “Emergence of Scaling in Random Networks,” Science, 286(5439), pp. 509–512. [CrossRef] [PubMed]
7
Albert, R., and Barabasi, A. L., 2002, “Statistical Mechanics of Complex Networks,” Rev. Mod. Phys., 74(1), pp. 47–97. [CrossRef]
8
Dorogovtsev, S. N., and Mendes, J. F. F., 2002, “Evolution of Networks,” Adv. Phys., 51(4), pp. 1079–1187. [CrossRef]
9
Faloutsos, M., Faloutsos, P., and Faloutsos, C., 1999, “On Power-Law Relationships of the Internet Topology,” Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, SIGCOMM ’99, pp. 251–262.
10
Barabasi, A. L., Albert, R., and Jeong, H., 2000, “Scale-Free Characteristics of Random Networks: The Topology of the World-Wide Web,” Phys. A: Stat. Mech. Appl., 281(1–4), pp. 69–77. [CrossRef]
11
Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., and Barabási, A. L., 2000, “The Large-Scale Organization of Metabolic Networks,” Nature, 407(6804), pp. 651–653. [CrossRef] [PubMed]
12
Tangmunarunkit, H., Govindan, R., Jamin, S., Shenker, S., and Willinger, W., 2002, “Network Topology Generators: Degree-Based vs. Structural,” Proceedings of the 2002 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, SIGCOMM ’02, pp. 147–159.
13
Hogg, R. V., Craig, A., and W., M. J., 2004, Introduction to Mathematical Statistics, 6th ed., Prentice Hall, Upper Saddle River, NJ.
14
Clauset, A., Shalizi, C. R., and Newman, M. E. J., 2009, “Power-Law Distributions in Empirical Data,” SIAM Rev., 51(4), pp. 661–703. [CrossRef]
15
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P., 2007, Numerical Recipes: The Art of Scientific Computation, 3rd ed., Cambridge University Press, New York.
16
Clauset, A., Newman, M. E. J., and Moore, C., 2007. “Finding Community Structure in Very Large Networks,” Phys. Rev. E, 70(6), p. 066111. [CrossRef]
17
Ben-Akiva, M., and Lerman, S. R., 1985, Discrete Choice Analysis: Theory and Application to Travel Demand, MIT Press, Cambridge, MA.
18
Williams, H. C. W. L., 1977, “On the Formation of Travel Demand Models and Economic Evaluation Measures of User Benefit,” Environ. Plann., 9(3), pp. 285–344. [CrossRef]
19
Chen, W., Hoyle, C., and Wassenaar, H. J., 2013, Decision-Based Design: Integrating Consumer Preferences into Engineering Design, Springer, London.
20
Train, K., 2009, Discrete Choice Methods with Simulation, 2nd ed., Cambridge University Press, New York.
21
Viton, P. A., 2012, “Discrete-Choice Logit Models With R,” http://facweb.knowlton.ohio-state.edu/pviton/courses2/crp5700/5700-mlogit.pdf
22
Croissant, Y., 2012, “Estimation of Multinomial Logit Models in R: The Mlogit Packages,” http://cran.r-project.org/web/packages/mlogit/vignettes/mlogit.pdf
23
R Development Core Team, 2008, “R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.” Available at: http://www.r-project.org/
24
Watts, D. J., and Strogatz, S., 1998, “Collective Dynamics of “Small-World” Networks,” Nature, 393, pp. 440–442. [CrossRef] [PubMed]
25
Freeman, L., 1977, “A Set of Measures of Centrality Based on Betweenness,” Sociometry, 40(1), pp. 35–41. [CrossRef]
26
Leguay, J., 2004, “An Analysis on the Internet Topology,” PhD thesis, Linkoping University, Linkoping, Sweden.
27
Hawkinson, J., and Bates, T., 1996, “Guidelines for Creation, Selection, and Registration of an Autonomous System (AS), Network Working Group.” Available at: http://tools.ietf.org/html/rfc1930
28
Chen, Q., Chang, H., Govindan, R., Jamin, S., Shenker, S. J., and Willinger, W., 2002, “The Origin of Power Laws in Internet Topologies Revisited,” Proceedings of Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2002, Vol. 2, pp. 608–617.
29
CAIDA, 2013, “The Cooperative Association for Internet Data Analysis.” Available at: http://www.caida.org/home/
30
RouterView, 2013, “The RouterView Project.” Available at: http://www.routeviews.org/
31
Newman, M. E., 2003, “The Structure and Function of Complex Networks,” SIAM Rev., 45(2), pp. 167–256. [CrossRef]
32
Kullback, S., and Leibler, R., 1951, “On Information and Sufficiency,” Ann. Math. Stat., 22(1), pp. 79–86. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Five levels and the associated mappings in complex endogenously evolving networks

+Five levels and the associated mappings in complex endogenously evolving networks
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Five levels and the associated mappings in complex endogenously evolving networks
Grahic Jump Location
Fig. 2

Complementary cumulative degree distribution of networks generated by generalized BA model with different A values

+Complementary cumulative degree distribution of networks generated by generalized BA model with different A values
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Complementary cumulative degree distribution of networks generated by generalized BA model with different A values
Grahic Jump Location
Fig. 7

Node-level behavior for three network evolution steps

+Node-level behavior for three network evolution steps
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Node-level behavior for three network evolution steps
Grahic Jump Location
Fig. 3

Complementary cumulative degree distribution of AS-level Internet on Jan. 5th, 2004

+Complementary cumulative degree distribution of AS-level Internet on Jan. 5th, 2004
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Complementary cumulative degree distribution of AS-level Internet on Jan. 5th, 2004
Grahic Jump Location
Fig. 4

Exponent (γ) in the degree distribution versus network size

+Exponent (γ) in the degree distribution versus network size
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Exponent (γ) in the degree distribution versus network size
Grahic Jump Location
Fig. 5

Number of edges (E) versus network size (J)

+Number of edges (E) versus network size (J)
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Number of edges (E) versus network size (J)
Grahic Jump Location
Fig. 6

The exponent (γ) of 122 AS-level Internet networks obtained with the maximum likelihood fitting with goodness-of-fit tests based on KS statistic and likelihood ratio

+The exponent (γ) of 122 AS-level Internet networks obtained with the maximum likelihood fitting with goodness-of-fit tests based on KS statistic and likelihood ratio
View Large  |  Save Figure  |  Download Slide (.ppt)
The exponent (γ) of 122 AS-level Internet networks obtained with the maximum likelihood fitting with goodness-of-fit tests based on KS statistic and likelihood ratio
Grahic Jump Location
Fig. 8

Network size versus α in the node's decision model

+Network size versus α in the node's decision model
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Network size versus α in the node's decision model
Grahic Jump Location
Fig. 9

Network size versus β in the node's decision model

+Network size versus β in the node's decision model
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Network size versus β in the node's decision model
Grahic Jump Location
Fig. 10

Network size versus parameter β1 in the node's decision model

+Network size versus parameter β1 in the node's decision model
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Network size versus parameter β1 in the node's decision model
Grahic Jump Location
Fig. 11

Network size versus parameter β2 in the node's decision model

+Network size versus parameter β2 in the node's decision model
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Network size versus parameter β2 in the node's decision model
Grahic Jump Location
Fig. 12

Comparison of the node-level behavior of AS-level Internet on Jan. 5th, 2004 deduced by three approaches

+Comparison of the node-level behavior of AS-level Internet on Jan. 5th, 2004 deduced by three approaches
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Comparison of the node-level behavior of AS-level Internet on Jan. 5th, 2004 deduced by three approaches
Grahic Jump Location
Fig. 13

Comparison of complementary cumulative degree distribution between the real network and simulated network with three approaches

+Comparison of complementary cumulative degree distribution between the real network and simulated network with three approaches
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Comparison of complementary cumulative degree distribution between the real network and simulated network with three approaches
Grahic Jump Location
Fig. 14

KL divergence on the degree distribution of simulated networks

+KL divergence on the degree distribution of simulated networks
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KL divergence on the degree distribution of simulated networks

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