Cooperation or Competition: Does Game Theory Have Relevance for Public Health?

Westhoff, Wayne W.; Cohen, Cynthia F.; Cooper, Elizabeth; Corvin, Jaime; McDermott, Robert J.

doi:10.1080/19325037.2012.10872089

Cited by 7 publications

(8 citation statements)

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“…This effect may be potentially reinforced when combined with communication, institutions, or costly commitments ( Han et al., 2017 ; Smead et al., 2014 ; Tavoni et al., 2011 ; Vasconcelos et al., 2013 , 2015 ). The implications of these observations within the framing of real-world CRDs in health ( Brewer et al., 2007 ; Ferguson, 2007 ; Perisic and Bauch, 2009 ; Van Segbroeck et al, 2010 ; Westhoff et al., 2012 ) and socio-political processes ( Abou Chakra and Traulsen, 2012 ; Barfuss et al., 2020 ; Barrett, 2016 ; Barrett and Dannenberg, 2012 ; Santos et al., 2012 ; Vasconcelos et al., 2013 , 2015 ), as mentioned above, may vary depending on the specific problem. We may nonetheless highlight that, in light of our results, uncertainty on, e.g., the urgency of reducing CO 2 emissions or the risk of a pandemic, may trigger reciprocal behaviors and further reinforce polarization, with potential detrimental impacts that can be hardly overemphasized.…”

Section: Discussionmentioning

confidence: 92%

“…Rational selfish behavior stipulates that it is best to not contribute; yet such decision would be detrimental, as a group is better off when all contribute. These games not only serve as a good model for social public benefits (e.g., social security, retirement funds) but are also recurrent in many other collective endeavors, from group hunting ( Hill, 2002 ; Kaplan et al., 2000 ) to public health ( Brewer et al., 2007 ; Ferguson, 2007 ; Perisic and Bauch, 2009 ; Van Segbroeck et al, 2010 ; Westhoff et al., 2012 ) and socio-political processes like climate change ( Abou Chakra and Traulsen, 2012 ; Barfuss et al., 2020 ; Barrett, 2016 ; Barrett and Dannenberg, 2012 ; Santos et al., 2012 ; Vasconcelos et al., 2013 , 2015 ).…”

Section: Introductionmentioning

confidence: 99%

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Timing Uncertainty in Collective Risk Dilemmas Encourages Group Reciprocation and Polarization

et al. 2020

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Social dilemmas are often shaped by actions involving uncertain returns only achievable in the future, such as climate action or voluntary vaccination. In this context, uncertainty may produce non-trivial effects. Here, we assess experimentally-through a collective risk dilemma-the effect of timing uncertainty, i.e. how uncertainty about when a target needs to be reached affects the participants' behaviors. We show that timing uncertainty prompts not only early generosity but also polarized outcomes, where participants' total contributions are distributed unevenly. Furthermore, analyzing participants' behavior under timing uncertainty reveals an increase in reciprocal strategies. A data-driven game-theoretical model captures the self-organizing dynamics underpinning these behavioral patterns. Timing uncertainty thus casts a shadow on the future that leads participants to respond early, whereas reciprocal strategies appear to be important for group success. Yet, the same uncertainty also leads to inequity and polarization, requiring the inclusion of new incentives handling these societal issues. INTRODUCTION Public good games (PGGs) provide abstractions of many real-world problems wherein personal and shortterm interests of multiple players are in conflict with societal, long-term interests (Perc et al, 2013, 2017; Ramamoorthy, 2013). Participants in such games can contribute voluntarily to a common good, which can, once established, be accessed without restrictions by all, thus even those that did not contribute. Rational selfish behavior stipulates that it is best to not contribute; yet such decision would be detrimental, as a group is better off when all contribute. These games not only serve as a good model for social public benefits (e.g., social security, retirement funds) but are also recurrent in many other collective endeavors,

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Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Timing Uncertainty in Collective Risk Dilemmas Encourages Group Reciprocation and Polarization

et al. 2020

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“…Weingarten ( 11 ) applies a generalized game theory model to study the price-setting strategy of competitive medical institutions. Westhoff et al ( 5 ) and Agee et al ( 8 ) apply game theory to study cooperation and competition in disease treatment payment and compensation. Robinson ( 16 ) adopt a Stackelberg game theory model in discussing the relationship between cost transfer and payment methods for medical institutions.…”

Section: Related Workmentioning

confidence: 99%

“…Many scholars apply the game theory to the pricing of medical services, which solves the conflict of interests of the stakeholders in the pricing of medical services (5)(6)(7)(8)(9)(10). For example, Weingarten (11) applies a generalized game theory model to study the price-setting strategy of competitive medical institutions.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Weingarten ( 11 ) applies a generalized game theory model to study the price-setting strategy of competitive medical institutions. Westhoff et al ( 5 ) and Agee et al ( 8 ) apply game theory to study cooperation and competition in disease treatment payment and compensation. Nagurney ( 12 ) focus on the supply chain of blood products in an asymmetric information environment.…”

Section: Introductionmentioning

confidence: 99%

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A Game Model for Medical Service Pricing Based on the Diagnosis Related Groups

Duan

Zhi-bin

Feng

2021

Front. Public Health

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Background: Currently there are various issues that exist in the medical institutions in China as a result of the price-setting in DRGs, which include the fact that medical institutions tend to choose patients and that the payment standard for complex cases cannot reasonably compensate the cost.Objective: The main objective is to prevent adverse selection problems in the operations of a diagnosis-related groups (DRGs) system with the game pricing model for scientific and reasonable pricing.Methods: The study proposes an improved bargaining game model over three stages, with the government and patients forming an alliance. The first stage assumes the alliance is the price maker in the Stackelberg game to maximize social welfare. Medical institutions are a price taker and decide the level of quality of medical service to maximize their revenue. A Stackelberg equilibrium solution is obtained. The second stage assumes medical institutions dominate the Stackelberg game and set an optimal service quality for maximizing their revenues. The alliance as the price taker decides the price to maximize the social welfare. Another Stackelberg equilibrium solution is achieved. The final stage establishes a Rubinstein bargaining game model to combine the Stackelberg equilibrium solutions in the first and second stage. A new equilibrium between the alliance and medical institutions is established.Results: The results show that if the price elasticity of demand increases, the ratio of cost compensation on medical institutions will increase, and the equilibrium price will increase. The equilibrium price is associated with the coefficient of patients' quality preference. The absolute risk aversion coefficient of patients affects government compensation and total social welfare.Conclusion: In a DRGs system, considering the demand elasticity and the quality preference of patients, medical service pricing can prevent an adverse selection problem. In the future, we plan to generalize these models to DRGs pricing systems with the effects of competition of medical institutions. In addition, we suggest considering the differential compensation for general hospitals and community hospitals in a DRGs system, in order to promote the goal of hierarchical diagnosis and treatment.

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