Game-Theoretic Approach to Modeling Collective Labor Incentives for Regional Civil Servants in Russia

Currently, a number of external factors (in particular, sanctions pressure from the US and EU countries, the pandemic of a new coronavirus infection) have a strong negative impact on the socio-economic development of the constituent entities of the Russian Federation. Therefore, the head of almost any Russian region is forced to perform the functions of an anti-crisis manager. The modern system of stimulating the work of civil regional employees can help improve the efficiency of public administration, and as a result, ensure the socio-economic development of a constituent entity of the Russian Federation. The author's approach to this issue is based on the simultaneous application of a number of modern scientific methods, namely index, neural network technologies and mathematical game theory. The use of neural network technologies makes it possible to objectively assess the achieved level of socio-economic development of a constituent entity of the Russian Federation. Taking into account the positive foreign (Singapore and South Korean) experience in the field of civil service reform, it is proposed to make the amount of collective incentive payments (bonus fund) of regional civil servants directly dependent on the achieved level of socio-economic development of the subject of the Russian Federation. In turn, this implies the development of a hierarchical system of collective-individual stimulation of the work of regional civil servants. During the construction of the game-theoretic model, the correct establishment of the relationship between the level of collective incentive payments to regional civil servants and the socio-economic development of a constituent entity of the Russian Federation is ensured. After experimental refinement of the parameters of the game-theoretic model, there is a possibility of its practical application to stimulate the work of regional civil servants in Russia. The introduction of the author's approach into the practice of regional management, firstly, will allow coordinating the activities of ministries and departments, and secondly, will strengthen control over the targeted spending of budgetary funds on bonus payments to civil civil servants.

Individual Altruism Cannot Overcome Congestion Effects in a Global Pandemic Game

A key challenge in responding to public health crises such as COVID-19 is the difficulty of predicting the results of feedback interconnections between the disease and society. As a step towards understanding these interconnections, we pose a simple game-theoretic model of a global pandemic in which individuals can choose where to live, and we investigate the global behavior that may emerge as a result of individuals reacting locally to the competing costs of isolation and infection. We study the game-theoretic equilibria that emerge from this setup when the population is composed of either selfish or altruistic individuals. First, we demonstrate that as is typical in these types of games, selfish equilibria are in general not optimal, but that all stable selfish equilibria are within a constant factor of optimal. Second, there exist infinitely-many stable altruistic equilibria;all but finitely-many of these are worse than the worst selfish equilibrium, and the social cost of altruistic equilibria is unbounded. Our work is in sharp contrast to recent work in network congestion games in which all altruistic equilibria are socially optimal. This suggests that a population without central coordination may react very poorly to a pandemic, and that individual altruism could even exacerbate the problem. © 2022 IEEE.

Understanding the Interplay Between Herd Behaviors and Epidemic Spreading Using Federated Evolutionary Games

The recent COVID-19 pandemic has led to an increasing interest in the modeling and analysis of infectious diseases. Our social behaviors in the daily lives have been significantly affected by the pandemic. In this paper, we propose a federated evolutionary game-theoretic framework to study the coupling of herd behaviors changes and epidemics spreading. Our framework extends the classical degree-based mean-field epidemic model over complex networks by integrating it with the evolutionary game dynamics. The statistically equivalent individuals in a population choose their social activity intensities based on the fitness or the payoffs that depend on the state of the epidemics. Meanwhile, the spread of infectious diseases over the complex network is reciprocally influenced by the players' social activities. We address the challenge of federated dynamics by breaking the analysis into the studies of the stationary properties of the epidemic for given herd behavior and the structural properties of the game for a given epidemic process. We use numerical experiments to show that our framework enables the prediction of the historical COVID-19 statistics. © 2022 American Automatic Control Council.

Mask or No Mask during the COVID-19 Pandemic: A Game-Theoretic Approach

Wearing a mask as one of the crucial non-pharmaceutical interventions has demonstrated to be effective in the battle against the COVID-19 pandemic. The implementation of face coverings for the public remains controversial and still faces some challenges. Whether or not to wear the mask could be a complex decision-making processing, involving the trade-offs between self-interest and collective interest among multiple stakeholders. In the literature, there is a lack of quantitative analysis for strategic mask-wearing decisions during the pandemic. This paper fills the gap by studying a game-theoretic model on wearing the mask considering conflicting interests. Using a susceptible-infected-susceptible (SIS) model, we consider the players as either susceptible or infectious, characterized by homogeneous preferences within the group but heterogeneous preferences between groups. Then we propose a game-theoretic framework to model how both susceptible and infectious players make their decisions. We implement one-way sensitivity analyses to examine how the equilibrium solutions are sensitive to changes in the model parameters. The proposed game model shows that susceptible player is more likely to wear face masks compared to infectious player, when the likelihood or the cost of infection is large. Decreasing the cost of wearing masks or increasing the mask efficacy could help mitigate the reluctance of mask wearing. This paper provides insights on population mask-wearing behaviors, which can support policy makers to design regulations and incentives. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

Control of COVID-19 transmission dynamics, a game theoretical approach

We analyze a mathematical model of COVID-19 transmission control, which includes the interactions among different groups of the population: vaccinated, susceptible, exposed, infectious, super-spreaders, hospitalized and fatality, based on a system of ordinary differential equations, which describes compartment model of a disease and its treatment. The aim of the model is to predict the development disease under different types of treatment during some fixed time period. We develop a game theoretic approach and a dual dynamic programming method to formulate optimal conditions of the treatment for an administration of a vaccine. Next, we calculate numerically an optimal treatment. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.

Tze Leung Lai and Anna Choi's contribution to the discussion of 'Testing by betting: A strategy for statistical and scientific communication' by Glenn Shafer

Comments on an article by Glenn Shafer (see record 2021-44219-001). It is exciting to follow Glenn Shafer's investigations into forecasting, betting, reasoning with uncertainty and foundational issues in probability, beginning with his 1973 PhD thesis at Princeton and culminating in Shafer on the Dempster-Shafer theory of belief functions, and its evolution during the past five decades to the present paper on betting scores and game-theoretic probability. Betting scores are particularly relevant in this momentous year of intensive global search for COVID19 vaccines and treatments, and upcoming presidential and congressional elections in the United States, about which pundits keep giving time-varying forecasts of the outcomes while betting markets on presidential election odds have been particularly active, similar to online sports betting markets. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Towards Privacy-Preserving Spatial Distribution Crowdsensing: A Game Theoretic Approach

Acquiring the spatial distribution of users in mobile crowdsensing (MCS) brings many benefits to users (e.g., avoiding crowded areas during the COVID-19 pandemic). Although the leakage of users’location privacy has received a lot of research attention, existing works still ignore the rationality of users, resulting that users may not obtain satisfactory spatial distribution even if they provide true location information. To solve the problem, we employ game theory with incomplete information to model the interactions among users and seek an equilibrium state through learning approaches of the game. Specifically, we first model the service as a game in the satisfaction form and define the equilibrium for this service. Then, we design a LEFS algorithm for the privacy strategy learning of users when their satisfaction expectations are fixed, and further design LSRE that allows users to have dynamic satisfaction expectations. We theoretically analyze the convergence conditions and characteristics of the proposed algorithms, along with the privacy protection level obtained by our solution. We conduct extensive experiments to show the superiority and various performances of our proposal, which illustrates that our proposal can get more than 85% advantage in terms of the sensing distribution availability compared to the well-known differential privacy based solutions. IEEE