The Ultimatum Game: Optimal Strategies without Fairness

Burnell, Stephen; Evans, Lewis; Yao, Shuntian

doi:10.1006/game.1998.0669

Cited by 14 publications

(4 citation statements)

References 9 publications

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“…For instance, Burnell et. al., have researched the optimal strategies without fairness when the Repeated Ultimatum Game is played [23] and Oosterbeek found common behavioural patterns regardless of cultural differences in a meta analysis into the Repeated Ultimatum Game [24]. Besides, individual di↵erences related with reputation [25], attractiveness [26], and the strategies displayed during the Ultimatum Game (UG) have been studied in depth in the economics field.…”

Section: Alternated Repeated Ultimatum Gamementioning

confidence: 99%

Robot Likeability and Reciprocity in Human Robot Interaction: Using Ultimatum Game to determinate Reciprocal Likeable Robot Strategies

Sandoval

Brandstatter

Yalcin

et al. 2020

Int J of Soc Robotics

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Among of the factors that a↵ect likeability, reciprocal response towards the other party is one of the multiple variables involved in social interaction. However, in HRI, likeability is constrained to robot behavior, since mass-produced robots will have identical physical embodiment. A reciprocal robot response is desirable in order to design robots as likeable agents for humans. In this paper, we discuss how perceived likeability in robots is a crucial multi-factorial phenomenon that has a strong influence on interactions based on reciprocal robot decisions. Our general research question is: What type of reciprocal robot behavior is perceived as likeable for humans when the robot's decisions a↵ect them? We designed a between/within 2x2x2 experiment in which the participant plays our novel Alternated Repeated Ultimatum Game (ARU G) for 20 rounds. The robot used in the experiment is an NAO robot using four di↵erent reciprocal strategies. Our results suggest that participants tend to reciprocate more towards the robot who starts the game and using the pure reciprocal strategy compared with other strategies. These results confirm that the Norm of the Reciprocity applies in HRI when participants play ARUG with social robots. However, the human reciprocal response also depends on the profits gained in the game and who starts the interaction. Similarly, the likeability score is a↵ected by robot

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Section: Alternated Repeated Ultimatum Gamementioning

confidence: 99%

Robot Likeability and Reciprocity in Human Robot Interaction: Using Ultimatum Game to determinate Reciprocal Likeable Robot Strategies

Sandoval

Brandstatter

Yalcin

et al. 2020

Int J of Soc Robotics

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show abstract

“…In an attempt to explain ultimatum outcomes without "fairness," Burnell, Evans, and Yao (1999) use these preferences with β = 0.…”

Section: Outcome-based Preferencesmentioning

confidence: 99%

Minimally Acceptable Altruism and the Ultimatum Game

Rotemberg

2006

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. I suppose that people react with anger when others show themselves not to be minimally altruistic. With heterogeneous agents, this can account for the experimental results of ultimatum and dictator games. Moreover, it can account for the surprisingly large fraction of individuals who offer an even split, with parameter values that are more plausible than those required to explain outcomes in these experiments with the models Terms of use: Documents in EconStor may

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“…Page et al surveyed that "some 60∼80% of proponents offer fractions between 0.4 and 0.5, and only 3% offer less than 0.2. They are well advised to do this-indeed, some 50% of respondents reject any split offering them less than one-third of the sum" [7,[13][14][15]. It seems discrepancy between game theory and experimental data results from the notion of fairness and the absence of common knowledge of rationality [16][17][18].…”

Section: Sequential Bargaining Gamementioning

confidence: 99%

Coevolution of Artificial Agents Using Evolutionary Computation in Bargaining Game

Lee

2015

Advances in Multimedia

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Analysis of bargaining game using evolutionary computation is essential issue in the field of game theory. This paper investigates the interaction and coevolutionary process among heterogeneous artificial agents using evolutionary computation (EC) in the bargaining game. In particular, the game performance with regard to payoff through the interaction and coevolution of agents is studied. We present three kinds of EC based agents (EC-agent) participating in the bargaining game: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). The agents’ performance with regard to changing condition is compared. From the simulation results it is found that the PSO-agent is superior to the other agents.

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The Ultimatum Game: Optimal Strategies without Fairness

Abstract: Abstract:The Ultimatum game is simple and this facilitates its use in the study of how it is generated by expectations.

Cited by 14 publications

References 9 publications

Robot Likeability and Reciprocity in Human Robot Interaction: Using Ultimatum Game to determinate Reciprocal Likeable Robot Strategies

Robot Likeability and Reciprocity in Human Robot Interaction: Using Ultimatum Game to determinate Reciprocal Likeable Robot Strategies

Minimally Acceptable Altruism and the Ultimatum Game

Coevolution of Artificial Agents Using Evolutionary Computation in Bargaining Game

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