Hospital management and game theory

The COVID-19 pandemic has highlighted that we are stronger when joined around a shared vision. A challenging task in hospitals is to define the scenarios and face change in a manner that benefits the patients, clinical practices and themselves institutions. Game theory provides frames of study for healthcare decision-making at high levels as the government and professional societies. This allows us to study and incorporate this theory to define and approach solutions that can hold the different health systems feasible and wholesome. This chapter presents a conceptual framework that sheds light on medical tutoring in a hospital. Intensive care units are the focus of this study because they have a relevant role in this scenario. The new educational challenges in critical care services must face from a perspective that provides a proper response to changing actuality. This is done through enhanced practice to make decisions using game theory. The principles of this theory predict human behaviour, helping with decision-making and describing how determined results can appear that are not optimal for the entire group. The implementation of critical thinking between an intensive care unit and another service is studied. The results obtained agree with the expected behaviour. The study indicates that game theory provides a framework which manages educational collaboration between clinical units in the hospital. It can get suitable models for strategic interactions that frequently occur in education training and application in medicine. The chapter studies the environments wherein the theory has been applied and the upcoming challenges in this sector. © 2023 Nova Science Publishers, Inc. All rights reserved.

Individual costs and societal benefits of interventions during the COVID-19 pandemic.

Individual and societal reactions to an ongoing pandemic can lead to social dilemmas: In some cases, each individual is tempted to not follow an intervention, but for the whole society, it would be best if they did. Now that in most countries, the extent of regulations to reduce SARS-CoV-2 transmission is very small, interventions are driven by individual decision-making. Assuming that individuals act in their best own interest, we propose a framework in which this situation can be quantified, depending on the protection the intervention provides to a user and to others, the risk of getting infected, and the costs of the intervention. We discuss when a tension between individual and societal benefits arises and which parameter comparisons are important to distinguish between different regimes of intervention use.

Counterterrorism resource allocation during a pandemic: The effects of dynamic target valuations when facing a strategic terrorist.

The outbreak of pandemics such as COVID-19 can result in cascading effects for global systemic risk. To combat an ongoing pandemic, governmental resources are largely allocated toward supporting the health of the public and economy. This shift in attention can lead to security vulnerabilities which are exploited by terrorists. In view of this, counterterrorism during a pandemic is of critical interest to the safety and well-being of the global society. Most notably, the population flows among potential targets are likely to change in conjunction with the trend of the health crisis, which leads to fluctuations in target valuations. In this situation, a new challenge for the defender is to optimally allocate his/her resources among targets that have changing valuations, where his/her intention is to minimize the expected losses from potential terrorist attacks. In order to deal with this challenge, in this paper, we first develop a defender-attacker game in sequential form, where the target valuations can change as a result of the pandemic. Then we analyze the effects of a pandemic on counterterrorism resource allocation from the perspective of dynamic target valuations. Finally, we provide some examples to display the theoretical results, and present a case study to illustrate the usability of our proposed model during a pandemic.

The stochastic evolutionary game analysis of public prevention and control strategies in public health emergencies

PurposeThe COVID-19 epidemic is still spreading globally and will not be completely over in a short time. Wearing a mask is an effective means to combat the spread of COVID-19. However, whether the public wear a mask for epidemic prevention and control will be affected by stochastic factors such as vaccination, cultural differences and irrational emotions, which bring a high degree of uncertainty to the prevention and control of the epidemic. The purpose of this study is to explore and analyze the epidemic prevention and control strategies of the public in an uncertain environment.Design/methodology/approachBased on the stochastic evolutionary game model of the Moran process, the study discusses the epidemic prevention and control strategies of the public under the conditions of the dominance of stochastic factors, expected benefits and super-expected benefits.FindingsThe research shows that the strategic evolution of the public mainly depends on stochastic factors, cost-benefit and the number of the public. When the stochastic factors are dominant, the greater the perceived benefit, the lower the cost and the greater the penalty for not wearing masks, the public will choose to wear a mask. Under the dominance of expected benefits and super-expected benefits, when the number of the public is greater than a certain threshold, the mask-wearing strategy will become an evolutionary stable strategy. From the evolutionary process, the government's punishment measures will slow down the speed of the public choosing the strategy of not wearing masks. The speed of the public evolving to the stable strategy under the dominance of super-expected benefits is faster than that under the dominance of expected benefits.Practical implicationsThe study considers the impact of stochastic factors on public prevention and control strategies and provides decision-making support and theoretical guidance for the scientific prevention of the normalized public.Originality/valueTo the best of the authors' knowledge, no research has considered the impact of different stochastic interference intensities on public prevention and control strategies. Therefore, this paper can be seen as a valuable resource in this field.

Vaccine hesitancy promotes emergence of new SARS-CoV-2 variants.

The successive emergence of SARS-CoV-2 mutations has led to an unprecedented increase in COVID-19 incidence worldwide. Currently, vaccination is considered to be the best available solution to control the ongoing COVID-19 pandemic. However, public opposition to vaccination persists in many countries, which can lead to increased COVID-19 caseloads and hence greater opportunities for vaccine-evasive mutant strains to arise. To determine the extent that public opinion regarding vaccination can induce or hamper the emergence of new variants, we develop a model that couples a compartmental disease transmission framework featuring two strains of SARS-CoV-2 with game theoretical dynamics on whether or not to vaccinate. We combine semi-stochastic and deterministic simulations to explore the effect of mutation probability, perceived cost of receiving vaccines, and perceived risks of infection on the emergence and spread of mutant SARS-CoV-2 strains. We find that decreasing the perceived costs of being vaccinated and increasing the perceived risks of infection (that is, decreasing vaccine hesitation) will decrease the possibility of vaccine-resistant mutant strains becoming established by about fourfold for intermediate mutation rates. Conversely, we find increasing vaccine hesitation to cause both higher probability of mutant strains emerging and more wild-type cases after the mutant strain has appeared. We also find that once a new variant has emerged, perceived risk of being infected by the original variant plays a much larger role than perceptions of the new variant in determining future outbreak characteristics. Furthermore, we find that rapid vaccination under non-pharmaceutical interventions is a highly effective strategy for preventing new variant emergence, due to interaction effects between non-pharmaceutical interventions and public support for vaccination. Our findings indicate that policies that combine combating vaccine-related misinformation with non-pharmaceutical interventions (such as reducing social contact) will be the most effective for avoiding the establishment of harmful new variants.

The Game Analysis among Governments, the Public and Green Smart Supply Chain Enterprises in Necessity Purchase and Supply during COVID-19 Pandemic

During the COVID-19 pandemic, panic buying, price inflation, and the pollution of production processes led to economic and social unrest. In response to the current situation, the current research takes less account of the subjective perception of public panic buying and the lack of reference to the reality of effective governance. First, this paper uses prospect theory to portray the public's perceived value of goods in panic buying and non-panic buying situations. Then, drawing on the experience of effective governance in China, a tripartite evolutionary game model of local government, the public and green smart supply chain enterprises is constructed under the reward and punishment mechanism of the central government. Then, this paper analyzes the strategic choices of each game player and the stability of the system equilibrium. The structure of the study suggests the following. (1) Improving local government subsidies and penalties, the cost of positive response and the probability of response can lead to an evolutionary direction where the public chooses not to panic buy and green smart supply chain enterprises choose to ensure a balance between supply and demand and increase pollution control in the production process. (2) Our study yields three effective combinations of evolutionary strategies, of which an ideal combination of evolutionary strategies exists. Non-ideal evolutionary strategy combinations can occur due to improper incentives and penalties of local governments and misallocation of limited resources. However, we find four paths that can transform the non-ideal evolutionary strategy combination into an ideal evolutionary strategy combination. (3) The central government's reward and punishment mechanism is an important tool to stabilize the tripartite strategy, but the central government cannot achieve effective governance by replacing incentives with punishment.

Economics in game theory for hotel unemployment

This study uses the game theory to find a Nash equilibrium of price elasticities of hotel demand in the United States before and during the COVID-19 pandemic to interpret the decrease in hotel unemployment rate. The sample selected is the Oahu, Hawaii market due to its higher room rate and higher unemployment rate compared to those in the mainland US. Findings indicate that to increase hotel revenue and decrease unemployment rate, the price elasticity of hotel demand in the mainland US would be higher than the one in Oahu, Hawaii. While the government has built more value into hotels by financially supporting unemployed hotel employees, established hotel brands have maintained their excellent service for their guests during the pandemic. [ FROM AUTHOR] Copyright of Anatolia: An International Journal of Tourism & Hospitality Research is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Research on Social Governance of Network Public Opinion: An Evolutionary Game Model

Compared with the past, public opinion in the we-media era has become more difficult. How to incentivize social networking providers (SNPs) to participate in network public opinion governance and guide we-media practitioners (WPs) to standardize their dissemination are prominent problems that urgently need to be solved in response to network public opinion. This article supplements the perspective of network public opinion governance research and constructs a tripartite evolutionary game model including government, SNPs, and WPs. Then, after analyzing the influencing factors of the evolution of different agents' strategies through model solving and numerical simulation, this article finds that reasonable rewards and punishments can promote SNPs to participate in network public opinion governance. Finally, this article proposes that the government should build a social governance system for network public opinion, which will effectively reduce governance costs and improve governance efficiency.

Are smart contracts too smart for Supply Chain 4.0? A blockchain framework to mitigate challenges: IMS

PurposeSmart contracts are self-executing computer programmes that have the potential to be used in several applications instead of traditional written contracts. With the recent rise of smart systems (e.g. Internet of things) and digital platforms (e.g. blockchain), smart contracts are gaining high interest in both business and academia. In this work, a framework for smart contracts was proposed with using reputation as the system currency, and conducts currency mining through fulfilling the physical commitments that are agreed upon.Design/methodology/approachA game theory model is developed to represent the proposed system, and then a system dynamics simulator is used to check the response of the blockchain with different sizes.FindingsThe numerical results showed that the proposed system could identify the takeover attacks and protect the blockchain from being controlled by an outsider. Another important finding is that careful setting of the maximum currency amount can improve the scalability of the blockchain and prevent the currency inflation.Research limitations/implicationsThis work is proposed as a conceptual framework for supply chain 4.0. Future work will be dedicated to implement and experiment the proposed framework for other characteristics that may be encountered in the context of supply chain 4.0, such as different suppliers' tiers, different customer typologies and smart logistics applications, which may reveal other challenges and provide additional interesting insights.Practical implicationsBy using the proposed framework, smart contracts and blockchains can be implemented to handle many issues in the context of operations and supply chain 4.0, especially in times of turbulence such as the COVID-19 global pandemic crisis.Originality/valueThis work emphasizes that smart contracts are not too smart to be applied in the context of supply chain 4.0. The proposed framework of smart contracts is expected to serve supply chain 4.0 by automating the knowledge work and enabling scenario planning through the game theory model. It will also improve online transparency and order processing in real-time through secured multitier connectivity. This can be applied in global supply chain functions backed with digitization, notably during the time of the pandemic, in which e-commerce and online shopping have changed the rules of the game.

NVAS: A non-interactive verifiable federated learning aggregation scheme for COVID-19 based on game theory.

The continued spread of COVID-19 seriously endangers the physical and mental health of people in all countries. It is an important method to establish inter agency COVID-19 detection and prevention system based on game theory through wireless communication and artificial intelligence. Federated learning (FL) as a privacy preserving machine learning framework has received extensive attention. From the perspective of game theory, FL can be regarded as a process in which multiple participants play games against each other to maximize their own interests. This requires that the user's data is not leaked during the training process. However, existing studies have proved that the privacy protection capability of FL is insufficient. In addition, the existing way of realizing privacy protection through multiple rounds of communication between participants increases the burden of wireless communication. To this end, this paper considers the security model of FL based on game theory, and proposes our scheme, NVAS, a non-interactive verifiable privacy-preserving FL aggregation scheme in wireless communication environments. The NVAS can protect user privacy during FL training without unnecessary interaction between participants, which can better motivate more participants to join and provide high-quality training data. Furthermore, we designed a concise and efficient verification algorithm to ensure the correctness of model aggregation. Finally, the security and feasibility of the scheme are analyzed.

A game theory-based COVID-19 close contact detecting method with edge computing collaboration.

People all throughout the world have suffered from the COVID-19 pandemic. People can be infected after brief contact, so how to assess the risk of infection for everyone effectively is a tricky challenge. In view of this challenge, the combination of wireless networks with edge computing provides new possibilities for solving the COVID-19 prevention problem. With this observation, this paper proposed a game theory-based COVID-19 close contact detecting method with edge computing collaboration, named GCDM. The GCDM method is an efficient method for detecting COVID-19 close contact infection with users' location information. With the help of edge computing's feature, the GCDM can deal with the detecting requirements of computing and storage and relieve the user privacy problem. Technically, as the game reaches equilibrium, the GCDM method can maximize close contact detection completion rate while minimizing the latency and cost of the evaluation process in a decentralized manner. The GCDM is described in detail and the performance of GCDM is analyzed theoretically. Extensive experiments were conducted and experimental results demonstrate the superior performance of GCDM over other three representative methods through comprehensive analysis.

Fermatean Fuzzy IWP-TOPSIS-GRA Multi-Criteria Group Analysis and Its Application to Healthcare Waste Treatment Technology Evaluation

The growth of healthcare waste (HCW) was driven by the spread of COVID-19. Effective HCW eradication has become a pressing global issue that requires immediate attention. Selecting an effective healthcare waste treatment technology (HCWTT) can aid in preventing waste buildup. HCWTT selection can be seen as a complex multi-criteria group evaluation problem as the process involves multiple types of criteria and decision-makers (DMs) facing uncertain and vague information. The key objective of this study is to create a useful tool for the evaluation of HCWTT that is appropriate for the organization's needs. A novel index system for assessing the HCWTT during the decision-making evaluation process is first presented. Then a new approach based on entropy measure, decision-making trial and evaluation laboratory (DEMATEL), and game theory for the integrated weighting procedure (IWP) is presented under a Fermatean fuzzy environment. A multi-criteria group analysis based on IWP, a technique for order of preference by similarity to ideal solution (TOPSIS) and grey relational analysis (GRA), named IWP-TOPSIS-GRA framework suited to Fermatean fuzzy evaluation information, is developed. In a real-world case of HCWTT selection, through comparative analysis and sensitivity analysis, it is verified that the presented method is feasible and robust. © 2023 by the authors.

Understanding the intent behind sharing misinformation on social media

Several studies have been conducted in annotating and collecting the misinformation spread on various social media sites. The misinformation spread during COVID-19 pandemic increased many folds. Understanding the reasons and intent of the misinformation during COVID-19 is a crucial task. Existing approaches have not focused on understanding the intent behind sharing misinformation in the first place. To understand the intent, we introduce a new dataset MisMemoir that apart from annotating misinformation, also collects the social context and site history of the user sharing misinformation. Utilising the established benefits of game theory in social media behaviour analysis, we deploy two-person cooperative games to understand how prominent positive feedback cues like likes and retweets are in motivating an individual to share misinformation on the platform Twitter. Experimental results demonstrate that the spread of misinformation's primary intent is the intentional/unintentional manoeuvre to increased reach and possibly a false sense of accomplishment. Empirically, we show that in a competitive environment like social media, feedback cues like retweets and comments assume the role of 'attention' payoff that significantly affects the strategy of a user on Twitter to share misinformation intentionally. [ FROM AUTHOR] Copyright of Journal of Experimental & Theoretical Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Non-Cooperative Game for Edge Server Placement in Wireless Body Area Network

The outbreak of the coronavirus disease 2019 (COVID-19) has become the worst public health event in the whole world, threatening the physical and mental health of hundreds of millions of people. However, because of the high survivability of the virus, it is impossible for humans to eliminate viruses completely. For this reason, it is particularly important to strengthen the prevention of the transmission of viruses and monitor the physical status of the crowd. Wireless sensors are a key player in the fight against the current global outbreak of the Covid-19 pandemic, where they are playing an important role in monitoring human health. The Wireless Body Area Network (WBAN) composed of these wireless sensor devices can monitor human health data without interference for a long time, and update the data in almost real time through the Internet of Things (IoT). However, because the data monitored by the devices is relatively large and the transmission distance is long, only transmitting the data to medical centers through the personal devices (PB) cannot get feedback in time. We propose a non-cooperative game-based server placement method, which is named ESP-19 to improve the efficiency of transmission data of wireless sensors. In this paper, experimental tests are conducted based on the distribution of Shanghai Telecom's base stations, and then the performance of ESP-19 is evaluated. The results show that the proposed method in this paper outperforms the comparison method in terms of service delay. © 2022 IEEE.

Resilience Regulation Strategy for Container Port Supply Chain under Disruptive Events

There are many inevitable disruptive events, such as the COVID-19 pandemic, natural disasters and geopolitical conflicts, during the operation of the container port supply chain (CPSC). These events bring ship delays, port congestion and turnover inefficiency. In order to enhance the resilience of the CPSC, a modified two-stage CPSC system containing a container pretreatment system (CPS) and a container handling system (CHS) is built. A two-dimensional resilience index is designed to measure its affordability and recovery. An adaptive fuzzy double-feedback adjustment (AFDA) strategy is proposed to mitigate the disruptive effects and regulate its dynamicity. The AFDA strategy consists of the first-level fuzzy logic control system and the second-level adaptive fuzzy adjustment system. Simulations show the AFDA strategy outperforms the original system, PID, and two pipelines for improved dynamic response and augmented resilience. This study effectively supports the operations manager in determining the proper control policies and resilience management with respect to indeterminate container waiting delay and allocation delay due to disruptive effects.

Evolutionary Game Analysis of SME Social Responsibility Performance under Public Health Emergencies

Performing corporate social responsibility is the only way to adapt to sustainable economic and social development and is also the inevitable choice to enhance the core competitiveness of enterprises. At the beginning of 2020, the rapid spread of the COVID-19 epidemic made SMEs face a survival crisis. Therefore, SMEs need to continue to shoulder their social responsibilities in this special period. In view of this, this paper, with the COVID-19 outbreak as the background, constructed the evolution of the government regulatory agency, SME, and consumer evolutionary game model. This paper studies the strategy choice of three subjects in the process of fulfilling social responsibility before and after public health emergencies and analyzes the influence of dynamic incentive and punishment measures, cash, and inventory on the performance of SMEs' social responsibility using MATLAB. The results show that the government regulatory agencies play a guiding role in the enterprise responsibility process and need to provide appropriate liquidity for SMEs;SMEs should actively participate in social responsibility activities, optimize internal governance, and prepare enough cash for a crisis;consumers need to develop responsible consumer market, expand the responsible consumption scale, and help SMEs share the difficulties.

Advertising strategy and channel structure selection on an online retail platform

PurposeThis paper considers a supply chain with a manufacturer (she) selling through an online retail platform (he) and studies the channel structure choices of two firms when investing in advertising.Design/methodology/approachThe authors assume that the platform provides the manufacturer with an agency and/or reselling channel;thus, there are three possible channel structures: agency channel, reselling channel and dual channel. By developing a game-theoretic model, the authors investigate the channel structure choices of two firms when advertising separately, simultaneously and cooperatively and analyze the optimal combination strategy of channel structure and advertising scheme for both firms.FindingsWhen the advertising efforts of the two firms are independent of each other, the equilibrium results show that different advertising schemes lead to different channel choices. For the manufacturer, it is optimal to choose the dual channel structure and adopt the advertising scheme that both subsidizes platform advertising and advertises on her own. For the platform, this combination is also optimal at a high commission rate;otherwise, the advertising scheme in which both firms advertise simultaneously is optimal and he is better off switching from the dual channel structure to the reselling channel structure as interchannel substitution intensity increases. The above results still hold for complementary advertising efforts and asymmetric marginal advertising costs, while in the case of substitutable advertising efforts, one firm may ride on another firm's advertising efforts, leading to different strategic combinations.Originality/valueThis paper not only provides useful guidance for manufacturers and platforms in channel selection and advertising strategy, but also theoretically enriches the literature on manufacturer encroachment.

Games in the Time of COVID-19: Promoting Mechanism Design for Pandemic Response

Most governments employ a set of quasi-standard measures to fight COVID-19, including wearing masks, social distancing, virus testing, contact tracing, and vaccination. However, combining these measures into an efficient holistic pandemic response instrument is even more involved than anticipated. We argue that some non-trivial factors behind the varying effectiveness of these measures are selfish decision making and the differing national implementations of the response mechanism. In this article, through simple games, we show the effect of individual incentives on the decisions made with respect to mask wearing, social distancing, and vaccination, and how these may result in sub-optimal outcomes. We also demonstrate the responsibility of national authorities in designing these games properly regarding data transparency, the chosen policies, and their influence on the preferred outcome. We promote a mechanism design approach: It is in the best interest of every government to carefully balance social good and response costs when implementing their respective pandemic response mechanism;moreover, there is no one-size-fits-all solution when designing an effective solution. © 2022 held by the owner/author(s). Publication rights licensed to ACM.

To obey or not to obey? Can game theory explain human behavior in the context of coronavirus disease?

The objective of the current study is to explain noncompliance to social distancing rules in Western societies in the absence of a stringent law enforcement mechanism and vaccines. In the first part of the analysis, an evolutionary game theory mechanism of two players is developed. The theoretical model assumes the existence of the prisoner's dilemma due to personal inconveniences associated with mask wearing, hand washing, and lockdowns. The model demonstrates that in the absence of sufficient law enforcement mechanism, and regardless of the initial strategy undertaken, one of the three potential equilibria solutions is the convergence of the system to defection of both players. In the second part of the analysis, based on the freedom-house measures, we provide empirical evidence supporting the notion that law enforcement efficiency is higher in autocratic countries. We show the perseverance of higher projected infection rates per 100,000 persons in democratic countries even 8 months after the outbreak of the COVID-19 pandemic. Given the well-known inclination to cooperate more often than expected by game theory, this real-life outcome of noncompliance is remarkable. Moreover, the recent protests against lockdowns in China might reflect a shift from one equilibrium point (cooperation) to another (noncompliance). © 2023 John Wiley & Sons Ltd.