Emotional Expressions of Real Humanoid Robots and Their Influence on Human Decision-Making in a Finite Iterated Prisoner’s Dilemma Game

2021 ◽  
Author(s):  
Yasutake Takahashi ◽  
Yuki Kayukawa ◽  
Kazunori Terada ◽  
Hiroyuki Inoue
Keyword(s):  
Decision Making ◽  
Prisoner's Dilemma ◽  
Prisoner’S Dilemma ◽  
Humanoid Robots ◽  
Emotional Expressions ◽  
Prisoner’S Dilemma Game ◽  
Iterated Prisoner's Dilemma ◽  
Human Decision ◽  
Prisoner's Dilemma Game ◽  
Iterated Prisoner’S Dilemma

scholarly journals Does imperfect information always disturb the evolution of reciprocity?

2016 ◽  
Vol 7 (1) ◽  
pp. 14-16 ◽  
Author(s):  
Shun Kurokawa
Keyword(s):  
Imperfect Information ◽  
Prisoner's Dilemma ◽  
Prisoner’S Dilemma ◽  
Perfect Information ◽  
Prisoner’S Dilemma Game ◽  
Problematic Case ◽  
Iterated Prisoner's Dilemma ◽  
Prisoner's Dilemma Game ◽  
Explanatory Mechanism ◽  
Iterated Prisoner’S Dilemma

Reciprocity has long been regarded as a potential explanatory mechanism for the maintenance of cooperation. However, a possible problematic case relevant to the theory of reciprocity evolution arises when the information about an opponent’s behavior is imperfect. Although it has been confirmed that imperfect information disturbs the evolution of reciprocity, this argument is based on the assumption that those who attempt to cooperate always succeed in doing so. In reality, mistakes can occur, and previous studies have demonstrated that this can sway the evolution of reciprocity. In this study, removing the assumption that mistakes do not occur, we examine whether imperfect information disturbs the evolution of reciprocity in the iterated prisoner’s dilemma game with errors in behavior. It might be expected that when mistakes occur, reciprocity can evolve more in the case of imperfect information than in the case of perfect information. This is because in the former case, reciprocators can miss defections incurred by other reciprocators’ mistakes owing to imperfect information, which allows cooperation to persist. Contrary to this expectation, however, our analysis reveals that imperfect information still disturbs the evolution of reciprocity when mistakes occur. Additionally, we have determined that the condition under which reciprocity evolves remains unaffected, whatever reciprocators subsequently do when the opponent's last behavior was missed.

scholarly journals Predicting human decision making in psychological tasks with recurrent neural networks

2021 ◽  
Author(s):  
Baihan Lin ◽  
Djallel Bouneffouf ◽  
Guillermo Cecchi
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Prisoner's Dilemma ◽  
Iowa Gambling Task ◽  
Human Subjects ◽  
Prisoner’S Dilemma ◽  
Gambling Task ◽  
Iterated Prisoner's Dilemma ◽  
Human Decision ◽  
Iterated Prisoner’S Dilemma

Unlike traditional time series, the action sequences of human decision making usually involve many cognitive processes such as beliefs, desires, intentions and theory of mind, i.e. what others are thinking. This makes predicting human decision making challenging to be treated agnostically to the underlying psychological mechanisms. We propose to use a recurrent neural network architecture based on long short-term memory networks (LSTM) to predict the time series of the actions taken by the human subjects at each step of their decision making, the first application of such methods in this research domain. In this study, we collate the human data from 8 published literature of the Iterated Prisoner's Dilemma comprising 168,386 individual decisions and postprocess them into 8,257 behavioral trajectories of 9 actions each for both players. Similarly, we collate 617 trajectories of 95 actions from 10 different published studies of Iowa Gambling Task experiments with healthy human subjects. We train our prediction networks on the behavioral data from these published psychological experiments of human decision making, and demonstrate a clear advantage over the state-of-the-art methods in predicting human decision making trajectories in both single-agent scenarios such as the Iowa Gambling Task and multi-agent scenarios such as the Iterated Prisoner's Dilemma. In the prediction, we observe that the weights of the top performers tends to have a wider distribution, and a bigger bias in the LSTM networks, which suggests possible interpretations for the distribution of strategies adopted by each group.

scholarly journals The interplay of emotion expressions and strategy in promoting cooperation in the iterated prisoner’s dilemma

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Celso M. de Melo ◽  
Kazunori Terada
Keyword(s):  
Decision Making ◽  
Prisoner's Dilemma ◽  
Prisoner’S Dilemma ◽  
Emotional Expressions ◽  
Behavioral Strategies ◽  
Competitive Strategies ◽  
Iterated Prisoner's Dilemma ◽  
Insight Into ◽  
Iterated Prisoner’S Dilemma

Abstract The iterated prisoner’s dilemma has been used to study human cooperation for decades. The recent discovery of extortion and generous strategies renewed interest on the role of strategy in shaping behavior in this dilemma. But what if players could perceive each other’s emotional expressions? Despite increasing evidence that emotion signals influence decision making, the effects of emotion in this dilemma have been mostly neglected. Here we show that emotion expressions moderate the effect of generous strategies, increasing or reducing cooperation according to the intention communicated by the signal; in contrast, expressions by extortionists had no effect on participants’ behavior, revealing a limitation of highly competitive strategies. We provide evidence that these effects are mediated mostly by inferences about other’s intentions made from strategy and emotion. These findings provide insight into the value, as well as the limits, of behavioral strategies and emotion signals for cooperation.

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