How information and communication technologies change trends in modelling political processes: towards an agent-based approach
https://doi.org/10.31249/poln/2021.01.01
Abstract
The development of information and communication technologies and computing power leads to the emergence of additional opportunities for modeling political processes. In the past decades, mathematical models have been developed mainly in a game-theoretic setting; today we witness an expanding stream of research applying agent-based (multi-agent) approach. This trend is quite natural. There have been changes in political participation and in the forms of collective interaction of individuals and groups, induced by digital technologies. Researchers have developed theoretical approaches to political participation, focusing on the network interaction and implementing the “bottom-up” logic that infers the macro-properties of the system from the characteristics and interactions of individual agents. Thus, the theoretical foundations for an agent-based modeling, most promising in its network version, have been developed. This approach, however, required a more complex description of the individual motivation and decision making in comparison to the dominant game-theoretic paradigm. One of the key points is that motivation is considered to be linked to the network position of agents, since the individual is guided by the actions of her neighbors. Thus, the course of the political process is determined not only by the properties and decisions of its participants, but also by the type of network architecture that connects them. Within this research framework, a computational experiment, assuming a controlled variation of parameters, plays a special role. Two main strategies of such an experiment are considered: the grid search and the Monte Carlo method. The prospects of agent-based modeling in its network form are related to the study of the dynamical political processes, taking into account the structures of trust and social capital, as well as the resources and mechanisms of collective action.
About the Authors
A. S. AkhremenkoRussian Federation
Moscow
A. P. Petrov
Russian Federation
Moscow
S. A. Zheglov
Russian Federation
Moscow
References
1. A common protocol for agent-based social simulation / M.G. Richiardi, R. Leombruni, N.J. Saam, M. Sonnessa // Journal of artificial societies and social simulation. 2006. Vol. 9. P. 16-31. DOI: 10.0000/papers.ssrn.com/931875
2. Akhremenko A., Yureskul E., Petrov A. Latent factors of protest participation: a basic computational model // Twelfth International Conference "Management of large-scale system development" (MLSD). IEEE, 2019. P. 1-4. DOI: 10.1109/MLSD.2019.8910999 EDN: WXWQTL
3. Are social bots a real threat? An agent-based model of the spiral of silence to analyse the impact of manipulative actors in social networks / B. Ross, L. Pilz, B. Cabrera, F. Brachten, G. Neubaum, S. Stieglitz // European Journal of Information Systems. 2019. Vol. 28,N 4. P. 394-412. DOI: 10.1080/0960085X.2018.1560920 EDN: AGHEHH
4. Ayanian A.H., Tausch N. How risk perception shapes collective action intentions in repressive contexts: a study of Egyptian activists during the 2013 post-coup uprising // British Journal of Social Psychology. 2016. Vol. 55, N 4. P. 700-721. DOI: 10.1111/bjso.12164
5. Bakshy E., Messing S., Adamic L. Exposure to ideologically diverse news and opinion on Facebook // Science. 2015. Vol. 348 (6239). P. 1130-1132. DOI: 10.1126/science.aaa1160
6. Barabási A.L., Albert R. Emergence of scaling in random networks // Science. 1999. Vol. 286 (5439). P. 509-512. DOI: 10.1126/science.286.5439.509 EDN: CWCBGE
7. Barabási A.-L., Albert R., Jeong H. Scale-free characteristics of random networks: the topology of the world-wide web // Physica A: Statistical Mechanics and its Applications. 2000. Vol. 281, N 1-4. P. 69-77. DOI: 10.1016/S0378-4371(00)00018-2 EDN: AFRCDH
8. Bennett L., Segerberg A. The logic of connective action // The Logic of Connective Action: Digital Media and the Personalization of Contentious Politics (Cambridge Studies in Contentious Politics). Cambridge: Cambridge University Press, 2013. P. 19-54. DOI: 10.1017/cbo9781139198752.002
9. Bennett L., Segerberg, A., Walker Sh. Organization in the crowd: peer production in large-scale networked protests // Information, Communication & Society. 2014. Vol. 17, N 2. P. 232-260. DOI: 10.1080/1369118x.2013.870379
10. Beskow D.M., Carley K.M. Agent based simulation of bot disinformation maneuvers in Twitter // Winter Simulation Conference (WSC). National Harbor, MD: IEEE, 2019. P. 750-761. DOI: 10.1109/WSC40007.2019.9004942
11. Bonabeau E. Agent-based modeling: Methods and techniques for simulating human systems // Proceedings of the National Academy of Sciences. 2002. Vol. 99 (s. 3). P. 7280-7287. DOI: 10.1073/pnas.082080899
12. Cederman L. Emergent actors in world politics: how states and nations develop and dissolve. Princeton, NJ: Princeton University Press, 1997. 260 p.
13. Chan C., Fu K. The "mutual ignoring" mechanism of cyberbalkanization: triangulating observational data analysis and agent-based modelling // Journal of Information Technology & Politics. 2018. Vol. 15, N 4. P. 378-387. DOI: 10.1080/19331681.2018.1519480
14. Combining social network analysis and agent-based modelling to explore dynamics of human interaction: A review / M. Will, J. Groeneveld, K. Frank, B. Müller // Socio-Environmental Systems Modelling. 2020. Vol. 2, 16325. 18 p. DOI: 10.18174/sesmo.2020a16325
15. Dacrema E., Benati S. The mechanics of contentious politics: an agent-based modeling approach // The Journal of Mathematical Sociology. 2020. Vol. 44, N 3. P. 163-198. DOI: 10.1080/0022250X.2020.1753187
16. Epstein J.M. Agent_Zero: toward neurocognitive foundations for generative social science. Princeton: Princeton University Press, 2014. 182 p.
17. Epstein J.M. Modeling civil violence: An agent-based computational approach // Proceedings of the National Academy of Sciences. 2002. Vol. 99 (3). P. 7243-7250. DOI: 10.1073/pnas.092080199
18. Erdös P., Rényi A. On random graphs // Publicationes Mathematicae. 1959. Vol. 6. P. 290-297.
19. Filippov I., Yureskul E., Petrov A. Online protest mobilization: building a computational model // Thirteenth International Conference "Management of large-scale system development" (MLSD). IEEE, 2020. (In print). EDN: WZJIVE
20. Ideological and temporal components of network polarization in online political participatory media / D. Garcia, A. Abisheva, S. Schweighofer, U. Serdült, F. Schweitzer // Policy &.
21. Internet. 2015. Vol. 7, N 1. P. 46-79. DOI: 10.1002/poi3.82
22. Laver M. Agent-based models of social life: fundamentals. Cambridge: Cambridge University Press, 2020. 132 p.
23. Laver M., Sergenti E. Party competition: an agent-based model. Princeton, NJ: Princeton University Press, 2011. 278 p.
24. Lemos C.M. Agent-based modeling of social conflict from mechanisms to complex behavior. Cham: Springer International Publishing, 2018. 119 p. DOI: 10.1007/978-3-319-67050-8
25. Makowsky M.D., Rubin J. An agent-based model of centralized institutions, social network technology, and revolution // PLoS ONE. 2013. Vol. 8(11). P. e80380. DOI: 10.1371/journal.pone.0080380
26. Mastroeni L., Vellucci P., Naldi M. Agent-based models for opinion formation: a bibliographic survey // IEEE Access. 2019. Vol. 7. P. 58836-58848. DOI: 10.1109/ACCESS.2019.2913787 EDN: INUTNR
27. Milgram S. The small world problem // Psychology Today. 1967. Vol. 1 (1). P. 61-67.
28. Miller H., Page E. Complex adaptive systems: an introduction to computational models of social life. Princeton: Princeton University Press, 2009. 288 p.
29. Moro A. Understanding the dynamics of violent political revolutions in an agent-based framework // PLoS ONE. 2016. Vol. 11 (4). P. e0154175. DOI: 10.1371/journal.pone.0154175
30. Moss S. Alternative approaches to the empirical validation of agent-based models // Journal of Artificial Societies and social simulation. 2008. Vol. 11, N 1. P. 5. Mode of access: http://jasss.soc.surrey.ac.uk/11/1/5.html (accessed: 22.09.2010).
31. On the fate of protests: dynamics of social activation and topic selection online and in the streets / A. Asgharpourmasouleh, M. Fattahzadeh, D. Mayerhoffer, J. Lorenz // Computational Conflict Research. Computational Social Sciences / E. Deutschmann, J. Lorenz, L. Nardin, D. Natalini, A. Wilhelm (eds). Cham: Springer, 2020. P. 141-164. DOI: 10.1007/978-3-030-29333-8
32. Schelling T. Micromotives and macrobehavior. N.Y.: Norton, 1978. 252 p.
33. Siegel D. Analyzing computational models // American Journal of Political Science. 2018. Vol. 62, N 3. P. 745-759. DOI: 10.1111/ajps.12364
34. Siegel D. When does repression work? Collective action in social networks // The Journal of Politics. 2011. Vol. 73, N 4. P. 993-1010. DOI: 10.1017/S0022381611000727
35. Social media, political polarization, and political disinformation: a review of the scientific literature / J. Tucker, A. Guess, P. Barbera, C. Vaccari, A. Siegel, S. Sanovich, D. Stukal, B. Nyhan. Loughborough University Report, 2018. 95 p. 10.2139/ssrn. 3144139. DOI: 10.2139/ssrn.3144139
36. Stocker R., Green D.G., Newth D. Consensus and cohesion in simulated social networks // Journal of Artificial Societies and Social Simulation. 2001. Vol. 4, N 4. Mode of access: http://jasss.soc.surrey.ac.uk/4/4/5.html (accessed: 22.09.2010).
37. The contagion effects of repeated activation in Social Networks / P. Piedrahita, J. Borge-Holthoefer, Y. Moreno, S. González-Bailón // Social Networks. 2018. Vol. 54. P. 326-335. DOI: 10.1016/j.socnet.2017.11.001
38. The dynamics of protest recruitment through an online network / S. González-Bailón, J. Borge-Holthoefer, A. Rivero, Y. Moreno // Scientific reports. 2011. Vol. 1. P. 1-7. DOI: 10.1038/srep00197 EDN: YCUZNZ
39. Tweeting from left to right: is online political communication more than an echo chamber? / P. Barberá, J.T. Jost, J. Nagler, J.A. Tucker, R. Bonneau // Psychological Science. 2015. Vol. 26 (10). P. 1531-1542. DOI: 10.1177/0956797615594620
40. Van Stekelenburg J., Klandermans B. the social psychology of protest // Current.
41. Sociology. 2013. Vol. 61, N 5-6. P. 886-905. DOI: 10.1177/0011392113479314
42. Watts D.J., Strogatz S.H. Collective dynamics of small-world networks // Nature. 1998. Vol. 393, N 6684. P. 440-442. DOI: 10.1038/30918
43. Which models are used in social simulation to generate social networks? A review of 17 years of publications in JASSS / F. Amblard, A. Bouadjio-Boulic, C. Sureda Gutiérrez, B. Gaudou // Winter Simulation Conference (WSC). IEEE, 2015. P. 4021-4032. DOI: 10.1109/WSC.2015.7408556 EDN: YDDOYY
44. Wilensky U., Rand W. An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo. Cambridge, Massachusetts; London, England: The MIT Press, 2015. 481 p.
45. Wilson R.C., Collins A.G.E. Ten simple rules for the computational modeling of behavioral data // Elife. 2019. Vol. 8. P. e49547. DOI: 10.7554/eLife.49547
46. Wunder M., Suri S., Watts D. Empirical agent based models of cooperation in public goods games // Proceedings of the fourteenth ACM conference on Electronic commerce. 2013. P. 891-908. DOI: 10.1145/2482540.2482586