Abstract
We consider settings of group decision-making where agents’ preferences/choices are influenced (and thus changed) by each other. As the influence of reality faced by an agent usually comes from more than one agent, previous work discussed at length multiple influences but in an individual way, which assumed that all influencing agents exert their own influences independently from each other and that the resulting preference/choice of the influenced agent could be a simple linear weighted aggregation of all influencing agents’ preferences/choices. Some works discussed the influence of coalitions of multiple agents. As some agents hold the same beliefs, opinions or choices (such as in an “opinion alliance”), an extra influencing effect in addition to that of the separate individual influences should be considered. However, the structural influence has been ignored. The structure here mainly refers to the influencing relations among agents (which can be represented as links in social networks). Actually, previous work considers the structure (links) among agents just as the paths or channels of influence but ignores the fact that the structure itself can also exert an extra influencing effect. Moreover, it is not easy to address the influence of structures on an agent: as the influencing subject and the influenced object are disparate; the former are inter-relationships between agents, while the latter is the preference/choice of an individual agent. In this paper, we proposed a elementary framework to address the three levels of influence (individual, coalitional and structural influence) and their mixed effects.
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Notes
- 1.
The influence from oneself cannot be ignored. In reality, your current preference or choice over an issue is remarkably affected by your previous preferences or choices over the same or similar issue, which explains well why under identical influences from other people, some people can insist on their own preferences or choices while others change [17].
- 2.
A similar concept is the peer pressure.
- 3.
This is just a general expression of how to address the coalitional influence, one specific coalitional influence function can be found in the Sect. 4.
- 4.
This is just a general expression of how to address the structural influence, one specific structural influence function will be discussed in the Section 4.
References
Capuano, N., Chiclana, F., Fujita, H., Herrera-Viedma, E., Loia, V.: Fuzzy group decision making with incomplete information guided by social influence. IEEE Trans. Fuzzy Syst. 26(3), 1704–1718 (2018)
Degroot, M.H.: Reaching a consensus. J. Am. Stat. Assoc. 69(345), 118–121 (1974)
Demarzo, P.M., Vayanos, D., Zwiebel, J.: Persuasion bias, social influence, and unidimensional opinions. Quart. J. Econ. 118(3), 909–968 (2003)
Friedkin, N.E., Johnsen, E.C.: Social influence and opinions. J. Math. Soc. 15(3–4), 193–206 (1990)
Friedkin, N.E., Johnsen, E.C.: Social positions in influence networks. Soc. Networks 19(3), 209–222 (1997)
Golub, B., Jackson, M.O.: Naive learning in social networks and the wisdom of crowds. Am. Econ. J. Microecon. 2(1), 112–149 (2010)
Grabisch, M., Rusinowska, A.: A model of influence in a social network. Theory Decis. 69(1), 69–96 (2010)
Grabisch, M., Rusinowska, A.: A model of influence with an ordered set of possible actions. Theory Decis. 69(4), 635–656 (2010)
Grabisch, M., Rusinowska, A.: Iterating influence between players in a social network. In: 16th Coalition Theory Network Workshop (2011)
Grabisch, M., Rusinowska, A.: Measuring influence in command games. Soc. Choice Welfare 33(2), 177–209 (2009)
Grabisch, M., Rusinowska, A.: Influence functions, followers and command games. Games Econ. Behav. 72(1), 123–138 (2011)
Grabisch, M., Rusinowska, A.: A model of influence based on aggregation function. Math. Soc. Sci. 66(3), 316–330 (2013)
Grandi, U., Lorini, E., Perrussel, L.: Propositional opinion diffusion. In: International Conference on Autonomous Agents and Multiagent Systems, pp. 989–997 (2015)
Hoede, C., Bakker, R.: A theory of decisional power. J. Math. Soc. 8, 309–322 (1982)
Jackson, M.O.: Social and Economic Networks. Princeton University Press, Princeton (2008)
Luo, H.: Agent-based modeling of the UN Security Council decision-making: with signed and weighted mutual-influence. In: 14th Social Simulation Conference (2018)
Luo, H.: How to address multiple sources of influences in group decision-making: from a non-ordering to an ordering approach. In: 19th International Conference on Group Decision and Negotiation (2019)
Luo, H., Meng, Q.: Multi-agent simulation of SC reform and national game. World Econ. Polit. 6, 136–155 (2013). in Chinese
Maran, A., Maudet, N., Pini, M.S., Rossi, F., Venable, K.B.: A framework for aggregating influenced CP-nets and its resistance to bribery. In: Twenty-Seventh AAAI Conference on Artificial Intelligence (2013)
Maudet, N., Pini, M.S., Venable, K.B., Rossi, F.: Influence and aggregation of preferences over combinatorial domains. In: International Conference on Autonomous Agents and Multiagent Systems (2012)
Prez, L.G., Mata, F., Chiclana, F., Gang, K., Herrera-Viedma, E.: Modelling influence in group decision making. Soft. Comput. 20(4), 1653–1665 (2016)
Salehi-Abari, A., Boutilier, C.: Empathetic social choice on social networks. In: International Conference on Autonomous Agents and Multi-Agent Systems, pp. 693–700 (2014)
Acknowledgment
This study is supported by a National Natural Science Foundation of China Grant (71804006) and a National National Natural Science Foundation of China and European Research Council Cooperation and Exchange Grant (7161101045).
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Luo, H. (2019). Individual, Coalitional and Structural Influence in Group Decision-Making. In: Torra, V., Narukawa, Y., Pasi, G., Viviani, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2019. Lecture Notes in Computer Science(), vol 11676. Springer, Cham. https://doi.org/10.1007/978-3-030-26773-5_7
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