利用基于代理的模拟和强化学习实现人工智能设计的创新扩散政策:农业中采用数字工具的案例

IF 1.3 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
M. Vinyals, R. Sabbadin, S. Couture, L. Sadou, R. Thomopoulos, Kevin Chapuis, Baptiste Lesquoy, P. Taillandier
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引用次数: 0

摘要

在本文中,我们从一个制度的角度来解决创新扩散问题,该制度旨在鼓励采用一种新产品(即创新),主要是社会而不是个人利益。设计这样的创新采用政策是一项非常具有挑战性的任务,因为难以量化和预测其对非采用者行为的影响,以及可能的政策空间的指数大小。为了解决这些问题,我们提出了一种方法,使用基于代理的建模以可信的方式模拟可能的采用者的行为,并(深度)强化学习来有效地探索策略搜索空间。将我们的方法应用于农业中使用数字技术的问题。案例研究的实证结果验证了我们的方案,并显示了我们的方法在学习有效的创新扩散政策方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Toward AI-designed innovation diffusion policies using agent-based simulations and reinforcement learning: The case of digital tool adoption in agriculture
In this paper, we tackle innovation diffusion from the perspective of an institution which aims to encourage the adoption of a new product (i.e., an innovation) with mostly social rather than individual benefits. Designing such innovation adoption policies is a very challenging task because of the difficulty to quantify and predict its effect on the behaviors of non-adopters and the exponential size of the space of possible policies. To solve these issues, we propose an approach that uses agent-based modeling to simulate in a credible way the behaviors of possible adopters and (deep) reinforcement learning to efficiently explore the policy search space. An application of our approach is presented for the question of the use of digital technologies in agriculture. Empirical results on this case study validate our scheme and show the potential of our approach to learn effective innovation diffusion policies.
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来源期刊
Frontiers in Applied Mathematics and Statistics
Frontiers in Applied Mathematics and Statistics Mathematics-Statistics and Probability
CiteScore
1.90
自引率
7.10%
发文量
117
审稿时长
14 weeks
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