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A context-aware convention formation framework for large-scale networks

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Abstract

In this article, we present a decentralized convention formation framework for creating social conventions within large multiagent convention spaces. We study the role of the topological characteristics of the network in forming conventions with an emphasis on scale-free topologies. We hypothesize that contextual knowledge encapsulated in the topology can help improve both the quality of the emergent convention and the speed of forming such a convention. We also investigate the influence of network diversity. While recent research on diversity indicates that it improves organizational productivity, we observe that not all diversity is equally useful and identify the necessary conditions to maximize the benefit of diversity. We validate our convention formation framework using a language coordination problem in which agents in a multiagent system construct a common lexicon in a decentralized fashion. Agent interactions are modeled using a language game where every agent repeatedly plays with its neighbors. Each agent stochastically updates its lexicon based on the utility values of the lexicons received from its immediate neighbors. We introduce a novel context-aware utility computation mechanism and equip the agents with the ability to reorganize their neighborhood based on this utility estimate to expedite the convention formation process. A key idea behind our approach is the ability of socially influential high-utility-lexicon agents to bias their neighbors towards accepting their lexicons. Extensive experimentation results indicate that our proposed solution is both effective (able to converge into a large majority convention state with more than 90% agents sharing a high-quality lexicon) and efficient (faster) as compared to state-of-the-art approaches for social conventions in large convention spaces.

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Notes

  1. Henceforth these two approaches are referred as SRA and FGJ respectively.

  2. In SRA as well as FGJ, the time-period for investigating the emergence of a lexicon convention is comprised of 100,000 time-steps of the experiment. We use this duration as a definition of a reasonable amount of time for convergence to occur.

  3. Throughout the article, we use agent and node interchangeably.

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Acknowledgements

We thank Professor Sherief Abdallah for his contributions towards our initial investigations of scale-free networks. We also thank the two anonymous reviewers for their insightful comments.

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Authors and Affiliations

  1. University of Nebraska-Lincoln, Lincoln, NE, 68588, USA

    Mohammad Rashedul Hasan

  2. The Cooper Union, New York, NY, 10003, USA

    Anita Raja

  3. PPGC/UFRGS, CP 15064, Porto Alegre, RS, 91501-970, Brazil

    Ana Bazzan

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  1. Mohammad Rashedul Hasan
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Correspondence to Mohammad Rashedul Hasan.

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Hasan, M.R., Raja, A. & Bazzan, A. A context-aware convention formation framework for large-scale networks. Auton Agent Multi-Agent Syst 33, 1–34 (2019). https://doi.org/10.1007/s10458-018-9397-9

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