From Local to Global Norm Emergence: Dissolving Self-reinforcing Substructures with Incremental Social Instruments

Yiwei Liu, Jiamou Liu, Kaibin Wan, Zhan Qin, Zijian Zhang, Bakhadyr Khoussainov, Liehuang Zhu
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:6871-6881, 2021.

Abstract

Norm emergence is a process where agents in a multi-agent system establish self-enforcing conformity through repeated interactions. When such interactions are confined to a social topology, several self-reinforcing substructures (SRS) may emerge within the population. This prevents a formation of a global norm. We propose incremental social instruments (ISI) to dissolve these SRSs by creating ties between agents. Establishing ties requires some effort and cost. Hence, it is worth to design methods that build a small number of ties yet dissolve the SRSs. By using the notion of information entropy, we propose an indicator called the BA-ratio that measures the current SRSs. We find that by building ties with minimal BA-ratio, our ISI is effective in facilitating the global norm emergence. We explain this through our experiments and theoretical results. Furthermore, we propose the small-degree principle in minimising the BA-ratio that helps us to design efficient ISI algorithms for finding the optimal ties. Experiments on both synthetic and real-world network topologies demonstrate that our adaptive ISI is efficient at dissolving SRS.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-liu21n, title = {From Local to Global Norm Emergence: Dissolving Self-reinforcing Substructures with Incremental Social Instruments}, author = {Liu, Yiwei and Liu, Jiamou and Wan, Kaibin and Qin, Zhan and Zhang, Zijian and Khoussainov, Bakhadyr and Zhu, Liehuang}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {6871--6881}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/liu21n/liu21n.pdf}, url = {https://proceedings.mlr.press/v139/liu21n.html}, abstract = {Norm emergence is a process where agents in a multi-agent system establish self-enforcing conformity through repeated interactions. When such interactions are confined to a social topology, several self-reinforcing substructures (SRS) may emerge within the population. This prevents a formation of a global norm. We propose incremental social instruments (ISI) to dissolve these SRSs by creating ties between agents. Establishing ties requires some effort and cost. Hence, it is worth to design methods that build a small number of ties yet dissolve the SRSs. By using the notion of information entropy, we propose an indicator called the BA-ratio that measures the current SRSs. We find that by building ties with minimal BA-ratio, our ISI is effective in facilitating the global norm emergence. We explain this through our experiments and theoretical results. Furthermore, we propose the small-degree principle in minimising the BA-ratio that helps us to design efficient ISI algorithms for finding the optimal ties. Experiments on both synthetic and real-world network topologies demonstrate that our adaptive ISI is efficient at dissolving SRS.} }
Endnote
%0 Conference Paper %T From Local to Global Norm Emergence: Dissolving Self-reinforcing Substructures with Incremental Social Instruments %A Yiwei Liu %A Jiamou Liu %A Kaibin Wan %A Zhan Qin %A Zijian Zhang %A Bakhadyr Khoussainov %A Liehuang Zhu %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-liu21n %I PMLR %P 6871--6881 %U https://proceedings.mlr.press/v139/liu21n.html %V 139 %X Norm emergence is a process where agents in a multi-agent system establish self-enforcing conformity through repeated interactions. When such interactions are confined to a social topology, several self-reinforcing substructures (SRS) may emerge within the population. This prevents a formation of a global norm. We propose incremental social instruments (ISI) to dissolve these SRSs by creating ties between agents. Establishing ties requires some effort and cost. Hence, it is worth to design methods that build a small number of ties yet dissolve the SRSs. By using the notion of information entropy, we propose an indicator called the BA-ratio that measures the current SRSs. We find that by building ties with minimal BA-ratio, our ISI is effective in facilitating the global norm emergence. We explain this through our experiments and theoretical results. Furthermore, we propose the small-degree principle in minimising the BA-ratio that helps us to design efficient ISI algorithms for finding the optimal ties. Experiments on both synthetic and real-world network topologies demonstrate that our adaptive ISI is efficient at dissolving SRS.
APA
Liu, Y., Liu, J., Wan, K., Qin, Z., Zhang, Z., Khoussainov, B. & Zhu, L.. (2021). From Local to Global Norm Emergence: Dissolving Self-reinforcing Substructures with Incremental Social Instruments. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:6871-6881 Available from https://proceedings.mlr.press/v139/liu21n.html.

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