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Pacific Rim International Conference on Artificial Intelligence

PRICAI 2019: PRICAI 2019: Trends in Artificial Intelligence pp 733–745Cite as

Modeling Convention Emergence by Observation with Memorization

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11670))

Abstract

Convention emergence studies how global convention arises from local interactions among agents. Traditionally, the studies on convention emergence are conducted by means of agent-based simulations, whereas very few studies are based on model-based approaches. In this paper, we employ model-based approach to study the convention emergence by observation with memorization in a large population under social learning. In particular, we derive the recurrence equations of the population dynamic, which is the evolution of action distribution over time, under the external majority (EM) strategy. The recurrence equations precisely predict the behaviour of the multi-agent system at any time point, which is verified with the agent-based simulations. Based on the recurrence equations, We prove the converge behavior under various situations and work out the optimal memory length under different number of actions. Finally, we show that the EM strategy outperforms other popular strategies such as Q-learning and Highest Cumulative Reward (HCR) in convergence speed under social learning, even in very large convention space.

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Notes

  1. 1.

    Consider the following example. Let the number of action be 3 and memory length be 5. Consider an agent with following observation sequence (\(a_1,a_1,a_1,a_2,a_2,a_3,a_3\)). We can see that the action \(a_1\) changes from majority to minority over time. However, the “stick to the current action” strategy will choose \(a_1\) even it is a minority action, since \(a_2,a_3\) are in the tie. However, it is not a reasonable choice. In general, when an action is changing from majority to minority, and other actions become majority and in a tie at the same time, then the “stick to the current action” strategy will choose a minority action. Therefore we make the modification in the case of tie to yield a reasonable outcome.

  2. 2.

    We measure the convergence rate based on agents’ policy, as the actual actions are affected by exploration.

  3. 3.

    In case of tie, the agent will randomly choose one action among the actions with highest cumulative reward. The changes is made for the same reason as in Sect. 3.

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Authors and Affiliations

  1. The Chinese University of Hong Kong, Hong Kong, China

    Chin-wing Leung, Shuyue Hu & Ho-fung Leung

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  1. Chin-wing Leung
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  2. Shuyue Hu
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  3. Ho-fung Leung
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Correspondence to Chin-wing Leung .

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Editors and Affiliations

  1. Department of Computing, Macquarie University, Sydney, NSW, Australia

    Abhaya C. Nayak

  2. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Alok Sharma

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Leung, Cw., Hu, S., Leung, Hf. (2019). Modeling Convention Emergence by Observation with Memorization. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11670. Springer, Cham. https://doi.org/10.1007/978-3-030-29908-8_57

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  • DOI: https://doi.org/10.1007/978-3-030-29908-8_57

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