\`x^2+y_1+z_12^34\`
Advanced Search
Article Contents
Article Contents

Game theoretical modelling of a dynamically evolving network Ⅱ: Target sequences of score 1

  • * Corresponding author: Mark Broom

    * Corresponding author: Mark Broom
Abstract Full Text(HTML) Figure(7) / Table(3) Related Papers Cited by
  • In previous work we considered a model of a population where individuals have an optimum level of social interaction, governed by a graph representing social connections between the individuals, who formed or broke those links to achieve their target number of contacts. In the original work an improvement in the number of links was carried out by breaking or joining to a randomly selected individual. In the most recent work, however, these actions were often not random, but chosen strategically, and this led to significant complications. One of these was that in any state, multiple individuals might wish to change their number of links. In this paper we consider a systematic analysis of the structure of the simplest class of non-trivial cases, where in general only a single individual has reason to make a change, and prove some general results. We then consider in detail an example game, and introduce a method of analysis for our chosen class based upon cycles on a graph. We see that whilst we can gain significant insight into the general structure of the state space, the analysis for specific games remains difficult.

    Mathematics Subject Classification: Primary: 05C57, 91A43; Secondary: 05C81.

    Citation:

    \begin{equation} \\ \end{equation}
  • 加载中
  • Figure 1.  The transition graph for the target $ 111 $ with six states. The vertex in deficit in each state is highlighted by a dot, and corresponding possible transitions are shown

    Figure 2.  The two possible configurations for members of the minimal set with target 11111. The uppermost vertex is the one not attaining its target. There are 15 different cases of the configuration on the left, and 30 of the configuration on the right

    Figure 3.  The Petersen triangle with associated sets. Vertices are joined if the set-intersection is empty

    Figure 4.  The transition graph for the graphs within the minimal set for target 11111. Each triangle is labelled internally with the edge fixed in its configurations. Note that each edge has a vertex at its mid-point. Each vertex is labelled $ a/ b $ with a unique state number $ a $, and the vertex $ b $ which is deficient in state $ a $. Three vertices 1, 2 and 32 are duplicated (shown as different shapes) to allow a planar representation

    Figure 5.  The choice graph for the graphs within the minimal set for target 11111. Each vertex has an arrow which is the choice that the deficient vertex would make in that state. There is a stable cycle of length 30, specifically (36; 37; 38; 31; 29; 25; 18; 19; 20; 14; 11; 15; 22; 27; 40; 41; 32; 33; 34; 24; 16; 12; 7; 3; 1; 4; 9; 5; 2; 43; 36), which is shown in bold

    Figure 6.  The choice graph for the minimal set for target 11111. Here we have a stable 24-cycle. (1; 4; 9; 5; 2; 6; 11; 14; 20; 21; 22; 27; 40; 41; 32; 23; 16; 17; 18; 13; 7; 3; 1)

    Figure 7.  Two intersecting 14-cycles with 9 common vertices

    Table 1.  Score 1 sequences generated from the graphic sequence 766543221. In each case we identify the set, one of $ S_{A} $, $ S_{B} $, $ S_{J} $ or $ S_{N} $ that each element is in, in the corresponding position to the target sequence

    766543221 $ f_{1}=-1, f_{2}=-2, f_{3}=-1, f_{4}=0, $
    $ \lambda=4, K=4, K'=4 $
    866543221 $ i=1, \lambda*=5 $ for $ m=5 $ up JJJJNBBBB
    666543221 $ i=1, \lambda*=5 $ for $ m=5 $ up AAAAAAAAA
    776543221 $ i=2, 3, \lambda*=5 $ for $ m=5 $ up JJJJNBBBB
    765543221 $ i=2, 3, \lambda*=5 $ for $ m=5 $ up AAAAAAAAA
    766643221 $ i=4, \lambda*=5 $ for $ m=5 $ up JJJJNBBBB
    766443221$ ^{a} $ $ i=4, \lambda*=5 $ never AAAAAAAAA
    766553221 $ i=5, \lambda*=5 $ for all but $ m=4, 5 $ down JJJJJBBBB
    766533221 $ i=5, \lambda*=5 $ never JJJJBBBBB
    766544221$ ^{b} $ $ i=6, \lambda*=5 $ for $ m=5 $ up AAAAAAAAA
    766542221 $ i=6, \lambda*=5 $ for $ m=5 $ up JJJJNBBBB
    766543321 $ i=7, 8, \lambda*=5 $ for $ m=5 $ up AAAAAAAAA
    766543211 $ i=7, 8, \lambda*=5 $ for $ m=5 $ up JJJJNBBBB
    766543222 $ i=9, \lambda*=5 $ for $ m=5 $ up AAAAAAAAA
    766543220 $ i=9, \lambda*=5 $ for $ m=5 $ up JJJJNBBBB
     | Show Table
    DownLoad: CSV

    Table 2.  Score 1 sequences generated from the graphic sequence 766444221. In each case we indentify the set, one of $ S_{A} $, $ S_{B} $, $ S_{J} $ or $ S_{N} $ that each element is in, in the corresponding position to the target sequence

    766444221 $ f_{1}=-1, f_{2}=-2, f_{3}=-1, f_{4}=-2, $
    $ \lambda=4, K=3, K'=0 $
    866444221 $ i=1, \lambda*=5 $ never JJJAAABBB
    666444221 $ i=1, \lambda*=5 $ never AAAAAAAAA
    776444221 $ i=2, 3, \lambda*=5 $ never JJJAAABBB
    765444221 $ i=2, 3, \lambda*=5 $ never AAAAAAAAA
    766544221$ ^{b} $ $ i=4, 5, 6, \lambda*=5 $ for $ m=5 $ up AAAAAAAAA
    766443221$ ^{a} $ $ i=4, 5, 6, \lambda*=5 $ never AAAAAAAAA
    766444321 $ i=7, 8, \lambda*=5 $ never AAAAAAAAA
    766444211 $ i=7, 8, \lambda*=5 $ never JJJAAABBB
    766444222 $ i=9, \lambda*=5 $ never AAAAAAAAA
    766444220 $ i=9, \lambda*=5 $ never JJJAAABBB
     | Show Table
    DownLoad: CSV

    Table 3.  Frequencies of stable cycles resulting from 2, 000 simulations starting from randomly generated initial choice graphs

    Cycle-length 6 10 12 14 16 18 20 22 24 26 28 30
    Frequency 358 436 735 319 96 29 17 7 3 0 0 0
     | Show Table
    DownLoad: CSV
  • [1] B. Allen and M. A. Nowak, Games on graphs, EMS Surveys in Mathematical Sciences, 1 (2014), 113-151.  doi: 10.4171/EMSS/3.
    [2] V. Bala and S. Goyal, A model of non-cooperative network formation, Econometrica, 68 (2000), 1181-1230.  doi: 10.1111/1468-0262.00155.
    [3] M. Broom and C. Cannings, A dynamic network population model with strategic link formation governed by individual preferences, J. Theor. Biol., 335 (2013), 160-168.  doi: 10.1016/j.jtbi.2013.06.024.
    [4] M. Broom and C. Cannings, Graphic deviation, Discrete Mathematics, 338 (2015), 701-711.  doi: 10.1016/j.disc.2014.12.011.
    [5] M. Broom and C. Cannings, Game theoretical modelling of a dynamically evolving network Ⅰ: General target sequences, Journal of Dynamic Games, 4 (2017), 285-318.  doi: 10.3934/jdg.2017016.
    [6] C. Capitanio, Sociability and response to video playback in adult male rhesus monkeys (macac mulatta), Primates, 43 (2002), 169-177. 
    [7] R. C. ConnorM. R. Helthaus and L. M. Barre, Superalliances of bottlenose dolphins, Nature, 397 (1999), 571-572.  doi: 10.1038/17501.
    [8] P. I. M. Dunbar, Neocortex size as a constraint on group size in primates, J. Human Evoluion, 22 (1992), 469-493.  doi: 10.1016/0047-2484(92)90081-J.
    [9] B. Dutta and M. O. Jackson, The stability and effeciency of directed communication networks, Rev. Econ. Design, 5 (2015), 251-272. 
    [10] C. S. Elton, Animal Ecology, Sidgwick & Jackson, London, 1927.
    [11] P. Erdos and P. T. Gallai, Grafok eloirt fokszamu pontokkal, Matematikai Lapo, 11 (1960), 264-274. 
    [12] S. L. Hakimi, On the realizability of a set of integers as degrees of the vertices of a graph. Ⅰ, J. Soc. Indust. Appl. Math., 10 (1962), 496-506.  doi: 10.1137/0110037.
    [13] W. Hässelbarth, Die Verzweightheit von Graphen, Comm. in Math. and Computer Chem. (MATCH), 16 (1984), 3-17. 
    [14] V. Havel, A remark on the existence of finite graphs, Časopis Pěst. Mat., 81 (1956), 477–480.
    [15] M. O. Jackson, The stability and efficiency of economic and social networks, Networks and Groups, Springer, (2003).
    [16] M. O. Jackson, Social and Economic Networks, Princeton University Press, Princeton, NJ, 2008.
    [17] M. O. Jackson, An overview of social networks and economic applications, Handbook of Social Economics, Elsevier, (2011), 512–585
    [18] M. O. Jackson and A. Wolinsky, A strategic model of social and economic networks, J. Econ. Theory, 71 (1996), 44-74.  doi: 10.1006/jeth.1996.0108.
    [19] R. Merris and T. Roby, The lattice of threshold graphs, J. Inequal. Pure and Appl. Math., 6 (2005), Art. 2, 21 pp.
    [20] R. Noë, Biological markets: Partner choice as the driving force behind the evolution of cooperation, Economics in Nature. Social Dilemmas, Mate Choice and Biological Markets, (2001), 93–118.
    [21] R. Noë and P. Hammerstein, Biological markets: Supply and demand determine the effect of partner choice in cooperation, mutualism and mating, Behav. Ecol. Sociobio., 35 (1994), 1-11. 
    [22] J. M. PachecoA. Traulsen and M. A. Nowak, Active linking in evolutionary games, J. Theor. Biol., 243 (2006), 437-443.  doi: 10.1016/j.jtbi.2006.06.027.
    [23] J. M. Pacheco, A. Traulsen and M. A. Nowak, Coevolution of strategy and structure in complex networks with dynamical linking, Phys. Rev. Lett., 97 (2006), 258103. doi: 10.1103/PhysRevLett.97.258103.
    [24] J. PepperJ. Mitani and D. Watts, General gregariousness and specific social preferences among wild chimpanzees, Int. J. Primatol., 20 (1999), 613-632. 
    [25] M. Perc and A. Szolnoki, Coevolutionarygames - A mini review, BioSystems, 99 (2010), 109-125. 
    [26] E. Ruch and I. Gutman, The branching extent of graphs, J. Combin. Inform. Systems Sci., 4 (1979), 285-295. 
    [27] L. S. Shapley, Stochastic games, Proc. Nat. Acad. Sci. U.S.A., 39 (1979), 1095-1100.  doi: 10.1073/pnas.39.10.1953.
    [28] A. M. Sibbald and R. J. Hooper, Sociability and willingness of individual sheep to move away from their companions in order to graze, Applied Animal Behaviour, 86 (2004), 51-62.  doi: 10.1016/j.applanim.2003.11.010.
    [29] R. Southwell and C. Cannings, Some models of reproducing graphs: Ⅰ pure reproduction, Applied Mathematics, 1 (2010), 137-145.  doi: 10.4236/am.2010.13018.
    [30] R. Southwell and C. Cannings, Some models of reproducing graphs. Ⅱ. Age capped reproduction, Applied Mathematics, 1 (2010), 251-259. 
    [31] R. Southwell and C. Cannings, Some models of reproducing graphs. Ⅲ. Game based reproduction, Applied Mathematics, 1 (2010), 335-343.  doi: 10.4236/am.2010.15044.
    [32] B. Voelkl and C. Kasper, Social structure of primate interaction networks facilitates the emergence of cooperation, Biology Letters, 5 (2009), 462-464.  doi: 10.1098/rsbl.2009.0204.
    [33] B. Voelkl and R. Noë, The influence of social structure on the propagation of social information in artificial primate groups: A graph-based simulation approach, J. Theor. Biol., 252 (2008), 77-86.  doi: 10.1016/j.jtbi.2008.02.002.
    [34] J. WiszniewskiC. Brown and L. M. Möller, Complex patterns of male alliance formation in dolphin social networks, Journal of Mammalogy, 93 (2012), 239-250.  doi: 10.1644/10-MAMM-A-366.1.
  • 加载中

Figures(7)

Tables(3)

SHARE

Article Metrics

HTML views(1687) PDF downloads(310) Cited by(0)

Access History

Other Articles By Authors

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return