Tsyganov Vladimir
Institute of Control Sciences, Russian Academy of Sciences, 65 Profsoyuznaya Street, Moscow, 117997 Russia.
AI Soc. 2021 Oct 16:1-10. doi: 10.1007/s00146-021-01293-y.
The article examines the problem of ensuring the political stability of a democratic social system with a shortage of a vital commodity (like vaccine against COVID-19). In such a system, members of society citizens assess the authorities. Thus, actions by the authorities to increase the supply of this commodity can contribute to citizens' approval and hence political stability. However, this supply is influenced by random factors, the actions of competitors, etc. Therefore, citizens do not have sufficient information about all the possibilities of supplying, and it is difficult for them to make the right decisions. Such citizen unawareness can be exploited by unscrupulous politicians to achieve personal targets. Therefore, it is necessary to organize public control to motivate politicians to use all available opportunities in supplying. The goal of the paper is to build such a digital mechanism of public control of the politicians by citizens, which would best assess and stimulate the activities of the authorities to improve the supply of a vital commodity. In the age of artificial intelligence, such digital public control in the face of uncertainty can be based on digital machine learning. In addition, it is necessary to take into account and model the activities of politicians associated with the presence of their own targets that do not coincide with public ones. Such politicians can use the learning of citizens for their own targets. The objective of the article is to build an optimal digital mechanism of public control in a two-level model of a democratic social system-a digital society. At its top level, there is the Citizen, who gives an assessment for the Politico located at the lower level. In turn, the Politico can influence the supplying of a vital commodity. Political stability is guaranteed if the Citizen regularly approves of the Politico's actions to increase this supply. However, the Politico may not use the opportunities available to him to offer a commodity to achieve a personal target. To avoid this, the Politico's control mechanism is proposed. It includes the procedure for digital learning of the Citizen, as well as a procedure for assessing the Politico activity. Sufficient conditions have been found for the synthesis of such the Politico's control mechanism, at which stochastic possibilities of increasing the supply of a vital commodity are used. The example of such the Politico's control mechanism is considered on the case of supply of the COVID-19 vaccine in England.
本文探讨了在一种重要商品（如新冠疫苗）短缺的情况下，确保民主社会制度政治稳定的问题。在这样的制度中，社会成员即公民会对当局进行评估。因此，当局增加此类商品供应的行动有助于获得公民的认可，进而有助于政治稳定。然而，这种供应受到随机因素、竞争对手的行动等影响。所以，公民没有足够的信息了解供应的所有可能性，他们很难做出正确的决策。这种公民的不知情可能会被无良政客利用以实现个人目标。因此，有必要组织公众监督，以促使政客利用供应中的所有可用机会。本文的目标是构建一种公民对政客进行公众监督的数字机制，该机制能最佳地评估和激励当局改善重要商品的供应。在人工智能时代，面对不确定性的这种数字公众监督可以基于数字机器学习。此外，有必要考虑并模拟与政客自身目标（这些目标与公众目标不一致）相关的活动。这样的政客可能会利用公民的认知来实现自己的目标。本文的目的是在民主社会制度的两级模型——数字社会中构建一种最优的数字公众监督机制。在其顶层是公民，公民会对位于下层的政客进行评估。反过来，政客可以影响重要商品的供应。如果公民定期认可政客增加这种供应的行动，政治稳定就能得到保障。然而，政客可能不会利用他所拥有的机会来提供商品以实现个人目标。为避免这种情况，提出了对政客的控制机制。它包括公民数字认知的程序以及评估政客活动的程序。已经找到了合成这种政客控制机制的充分条件，在这种条件下会利用增加重要商品供应的随机可能性。以英国新冠疫苗供应为例考虑了这种政客控制机制。
