Exploring optimal institutional incentives for public cooperation

Wang, Shengxian; Chen, Xiaojie; Szolnoki, Attila

doi:10.1016/j.cnsns.2019.104914

Cited by 73 publications

(64 citation statements)

References 62 publications

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“…We start by noting that the payoff structure proposed in Eqs. (1) and (2) , is akin to the public goods and climate change dilemma games [99] , [100] , [101] , [102] where each agent payoff depends on the total number of agents in some other state. That is, the quarantine game is not a pairwise interaction game such as the prisoner dilemma [8] .…”

Section: Resultsmentioning

confidence: 99%

An epidemiological model with voluntary quarantine strategies governed by evolutionary game dynamics

Amaral

Oliveira

Javarone

2021

Chaos, Solitons & Fractals

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During pandemic events, strategies such as social distancing can be fundamental to reduce simultaneous infections and mitigate the disease spreading, which is very relevant to the risk of a healthcare system collapse. Although these strategies can be recommended, or even imposed, their actual implementation may depend on the population perception of the risks associated with a potential infection. The current COVID-19 crisis, for instance, is showing that some individuals are much more prone than others to remain isolated. To better understand these dynamics, we propose an epidemiological SIR model that uses evolutionary game theory for combining in a single process social strategies, individual risk perception, and viral spreading. In particular, we consider a disease spreading through a population, whose agents can choose between self-isolation and a lifestyle careless of any epidemic risk. The strategy adoption is individual and depends on the perceived disease risk compared to the quarantine cost. The game payoff governs the strategy adoption, while the epidemic process governs the agent’s health state. At the same time, the infection rate depends on the agent’s strategy while the perceived disease risk depends on the fraction of infected agents. Our results show recurrent infection waves, which are usually seen in previous historic epidemic scenarios with voluntary quarantine. In particular, such waves re-occur as the population reduces disease awareness. Notably, the risk perception is found to be fundamental for controlling the magnitude of the infection peak, while the final infection size is mainly dictated by the infection rates. Low awareness leads to a single and strong infection peak, while a greater disease risk leads to shorter, although more frequent, peaks. The proposed model spontaneously captures relevant aspects of a pandemic event, highlighting the fundamental role of social strategies.

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Section: Resultsmentioning

confidence: 99%

An epidemiological model with voluntary quarantine strategies governed by evolutionary game dynamics

Amaral

Oliveira

Javarone

2021

Chaos, Solitons & Fractals

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“…In short, our analysis has shown, within an idealised model of an AI race and using a game theoretical framework, that some simple forms of peer incentives, if used suitably (to avoid over-regulation, for example) can provide a way to escape the dilemma of acting safely even when speedy unsafe development is preferred. Future studies may look at more complex incentivising mechanisms [ 50 ] such as reputation and public image manipulation [ 64 , 65 ], emotional motives of guilt and apology-forgiveness [ 60 , 66 ], institutional and coordinated incentives [ 34 , 46 ], and the subtle combination of different forms of incentive (e.g., stick-and-carrot approach and incentives for agreement compliance) [ 37 , 39 , 67 – 69 ].…”

Section: Discussionmentioning

confidence: 99%

Mediating artificial intelligence developments through negative and positive incentives

2021

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The field of Artificial Intelligence (AI) is going through a period of great expectations, introducing a certain level of anxiety in research, business and also policy. This anxiety is further energised by an AI race narrative that makes people believe they might be missing out. Whether real or not, a belief in this narrative may be detrimental as some stake-holders will feel obliged to cut corners on safety precautions, or ignore societal consequences just to “win”. Starting from a baseline model that describes a broad class of technology races where winners draw a significant benefit compared to others (such as AI advances, patent race, pharmaceutical technologies), we investigate here how positive (rewards) and negative (punishments) incentives may beneficially influence the outcomes. We uncover conditions in which punishment is either capable of reducing the development speed of unsafe participants or has the capacity to reduce innovation through over-regulation. Alternatively, we show that, in several scenarios, rewarding those that follow safety measures may increase the development speed while ensuring safe choices. Moreover, in the latter regimes, rewards do not suffer from the issue of over-regulation as is the case for punishment. Overall, our findings provide valuable insights into the nature and kinds of regulatory actions most suitable to improve safety compliance in the contexts of both smooth and sudden technological shifts.

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“…As a paradigm, the public goods game has been commonly used to study such dilemmas characterized by the frustration between personal and collective interests ( Fehr and Gächter, 2002 ; Hauert et al., 2002 ; Szolnoki et al., 2011 ; Sasaki et al., 2012 ; Chen et al., 2015 ; Han et al, 2015 ; Wang et al., 2019 ; Ginsberg and Fu, 2019 ; Domingos et al., 2020 ; Santos et al., 2021 ). However, recent research emphasized that the traditional public goods game does not consider the risk of group failure; hence, the so-called collective-risk social dilemma game is a more competent tool to grab the essence of the conflict in several cases ( Milinski et al., 2008 ).…”

Section: Introductionmentioning

confidence: 99%

Combination of institutional incentives for cooperative governance of risky commons

et al. 2021

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Summary Finding appropriate incentives to enforce collaborative efforts for governing the commons in risky situations is a long-lasting challenge. Previous works have demonstrated that both punishing free-riders and rewarding cooperators could be potential tools to reach this goal. Despite weak theoretical foundations, policy makers frequently impose a punishment-reward combination. Here, we consider the emergence of positive and negative incentives and analyze their simultaneous impact on sustaining risky commons. Importantly, we consider institutions with fixed and flexible incentives. We find that a local sanctioning scheme with pure reward is the optimal incentive strategy. It can drive the entire population toward a highly cooperative state in a broad range of parameters, independently of the type of institutions. We show that our finding is also valid for flexible incentives in the global sanctioning scheme, although the local arrangement works more effectively.

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Exploring optimal institutional incentives for public cooperation

Cited by 73 publications

References 62 publications

An epidemiological model with voluntary quarantine strategies governed by evolutionary game dynamics

An epidemiological model with voluntary quarantine strategies governed by evolutionary game dynamics

Mediating artificial intelligence developments through negative and positive incentives

Combination of institutional incentives for cooperative governance of risky commons

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