Cooperation in a Dynamic Fishing Game: A Framed Field Experiment

2015 ◽  
Vol 105 (5) ◽  
pp. 408-413 ◽  
Author(s):  
Charles N. Noussair ◽  
Daan van Soest ◽  
Jan Stoop
Keyword(s):  
Nash Equilibrium ◽  
Field Experiment ◽  
Strong Support ◽  
Social Dilemma ◽  
Renewable Resource ◽  
Resource Extraction ◽  
Recreational Fishing ◽  
Social Optimum ◽  
The Social ◽  
Maximum Extraction

We derive a dynamic theoretical model of renewable resource extraction. In the social optimum, maximum extraction occurs in the last period only, while in the unique subgame perfect Nash equilibrium, the resource is depleted immediately. The predictions are tested in a field experiment conducted at a recreational fishing pond. The subjects, experienced recreational fishermen, face a dynamic social dilemma, in which they risk depletion of the resource by overfishing. We find strong support for the Nash equilibrium. Fishermen exert as much effort in the last period as in preceding periods, and effort is independent of the stock of fish.

scholarly journals Give and Let Give: Alternative Mechanisms Based on Voluntary Contributions

Games ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 21 ◽  
Author(s):  
Philip D. Grech
Keyword(s):  
Nash Equilibrium ◽  
Social Preferences ◽  
Social Dilemma ◽  
Social Optimum ◽  
New Family ◽  
The Social ◽  
Collective Action Problems ◽  
Common Group ◽  
Vantage Points ◽  
Other Regarding

We propose a new family of mechanisms, whereby players may give more or less directly to one another. A cornerstone case is the regular linear public goods mechanism (LPGM), where all contribute into a single common group account, the total amount of which is then distributed equally among players. We show that with sufficiently (yet not necessarily fully) pro-social preferences, the social optimum can be reached in Nash equilibrium in all social dilemma situations described by our mechanisms (including the LPGM). In addition, for a given heterogeneity of pro-social preferences, we help to identify which specific mechanisms perform best in terms of incentivizing giving. Our results are therefore relevant from two vantage points. One, they provide proper rational choice benchmarks based on Nash equilibrium under the assumption of other-regarding preferences. Two, they provide arguments in favor of re-structuring many collective action problems currently implemented as LPGMs when it is feasible to gain some information concerning who has concern for whom.

scholarly journals Selfishness Level of Strategic Games

2014 ◽  
Vol 49 ◽  
pp. 207-240 ◽  
Author(s):  
K. R. Apt ◽  
G. Schaefer
Keyword(s):  
Nash Equilibrium ◽  
Social Welfare ◽  
Price Of Anarchy ◽  
Congestion Games ◽  
Price Of Stability ◽  
Social Optimum ◽  
Potential Game ◽  
Strategic Games ◽  
The Social ◽  
The Impact

We introduce a new measure of the discrepancy in strategic games between the social welfare in a Nash equilibrium and in a social optimum, that we call selfishness level. It is the smallest fraction of the social welfare that needs to be offered to each player to achieve that a social optimum is realized in a pure Nash equilibrium. The selfishness level is unrelated to the price of stability and the price of anarchy and is invariant under positive linear transformations of the payoff functions. Also, it naturally applies to other solution concepts and other forms of games. We study the selfishness level of several well-known strategic games. This allows us to quantify the implicit tension within a game between players' individual interests and the impact of their decisions on the society as a whole. Our analyses reveal that the selfishness level often provides a deeper understanding of the characteristics of the underlying game that influence the players' willingness to cooperate. In particular, the selfishness level of finite ordinal potential games is finite, while that of weakly acyclic games can be infinite. We derive explicit bounds on the selfishness level of fair cost sharing games and linear congestion games, which depend on specific parameters of the underlying game but are independent of the number of players. Further, we show that the selfishness level of the $n$-players Prisoner's Dilemma is c/(b(n-1)-c), where b and c are the benefit and cost for cooperation, respectively, that of the n-players public goods game is (1-c/n)/(c-1), where c is the public good multiplier, and that of the Traveler's Dilemma game is (b-1)/2, where b is the bonus. Finally, the selfishness level of Cournot competition (an example of an infinite ordinal potential game), Tragedy of the Commons, and Bertrand competition is infinite.

scholarly journals A game theoretic approach reveals that discretizing clinical information can reduce antibiotic misuse

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Maya Diamant ◽  
Shoham Baruch ◽  
Eias Kassem ◽  
Khitam Muhsen ◽  
Dov Samet ◽  
...  
Keyword(s):  
Antibiotic Prescription ◽  
Clinical Information ◽  
Social Optimum ◽  
Theoretic Approach ◽  
Theoretic Analysis ◽  
Game Theoretic Analysis ◽  
Optimal Output ◽  
The Social ◽  
Game Theoretic ◽  
Game Theoretic Approach

AbstractThe overuse of antibiotics is exacerbating the antibiotic resistance crisis. Since this problem is a classic common-goods dilemma, it naturally lends itself to a game-theoretic analysis. Hence, we designed a model wherein physicians weigh whether antibiotics should be prescribed, given that antibiotic usage depletes its future effectiveness. The physicians’ decisions rely on the probability of a bacterial infection before definitive laboratory results are available. We show that the physicians’ equilibrium decision rule of antibiotic prescription is not socially optimal. However, we prove that discretizing the information provided to physicians can mitigate the gap between their equilibrium decisions and the social optimum of antibiotic prescription. Despite this problem’s complexity, the effectiveness of the discretization solely depends on the type of information available to the physician to determine the nature of infection. This is demonstrated on theoretic distributions and a clinical dataset. Our results provide a game-theory based guide for optimal output of current and future decision support systems of antibiotic prescription.

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