We describe a computational model of social norms based on identifying values that a certain culture finds desirable such as dignity, generosity and politeness. The model quantifies these values in the form of Culture-Sanctioned Social Metrics (CSSMs) and treats social norms as the requirement to maximize these metrics from the perspective of the self, peers and public. This model can be used to create realistic social simulations, to explain or predict human behavior in specific scenarios, or as a component of robots or agents that need to interact with humans in specific social-cultural settings. We validate the model by using it to represent a complex deception scenario and showing that it can yield non-trivial insights such as the explanation of apparently irrational human behavior.
The impact of emotions and empathy towards a fair or unfair interaction partner was studied by @cite_24 , by introducing the Inequality Game. The social behaviors studied included cooperative or competitive economic choices as well as nice or derogatory verbal behavior.
@cite_24
2298778277 617994985 2788842311 2077594926
In the prevention and resolution of conflicts in social contexts, an important step is to understand how different emotions and empathic traits are linked to punishment behaviors. Unfortunately, few paradigms exist to study these phenomena. Here, we developed the Inequality Game (IG) as an economic and verbal interaction paradigm in which participants are faced with an “unfair other” as opposed to a “fair other” and subsequently have the opportunity to engage in a range of social behaviors. These social behaviors include cooperative or competitive economic choices and nice or derogatory verbal behavior toward the unfair and fair other. Participants could thus engage in punishment or forgiveness behavior toward the unfair other as well as in cooperative or aggressive behavior toward the fair other. We validated the IG through multimodal measures comprising the assessment of personality traits, emotions (by means of facial expressions and self-reports), arousal (by means of skin conductance responses), physical effort (force exertion), and behavioral reactions. Second, we examined the influence of emotions and empathy-related traits on punishment behavior. With regard to emotions, we observed a positive relation between malicious joy and punishment behavior. This result highlights the role of reward-related mechanisms in favoring punishment behavior. In addition, different empathic traits had opposing effects on antisocial behavior. Whereas personal distress predicted aggressive verbal behavior, perspective taking and empathic concern predicted a reduction in punishment behavior. Empathic traits also modulated emotional experience and person evaluations, such that perspective taking was related to more positive affect (less frowning and more smiling) and a more favorable evaluation of the unfair other. The current data validate the IG, reveal that malicious joy is positively related to punishment behavior, and show that different types of empathic traits can have opposing effects on antisocial behavior as well as on related emotions and person evaluations. Overview. Part I: The Nature of Emotion. P. Shaver, J. Schwartz, D. Kirson, and C. O'Connor, Emotion Knowledge: Further Exploration of a Prototype Approach. N. Frijda, The Laws of Emotion. Part II: The Role of Cognition. S. Schachter and J. Singer, Cognitive, Social, and Physiological Determinants of Emotional State. C. Smith and R. Lazarus, Appraisal Components, Core Relational Themes, and the Emotions. Part III: Culture and Socialization. H. Markus and S. Kitayama, The Cultural Construction of Self and Emotion: Implications for Social Behavior. Part IV: Gender Differences: Evolution, Socialization, and Stereotyping. D. Buss, R. Larsen, D. Westen, and J. Semmelroth, Sex Differences in Jealousy: Questioning the "Fitness" of the Model. M. Robinson, J. Johnson, and S. Shields, the Gender Heuristic and the Database: Factors Affecting the Perception of Gender-related Differences in the Experience and Display of Emotions. Part V: The Social Functions of Emotions. D. Keltner and J. Haidt, Social Functions of Emotions at Four Levels of Analysis. P. Salovey, C. Hsee, and J. Mayer, Emotional Intelligence and the Self-Regulation of Affect. Part VI: Emotion and Social Cognition. J. Forgas and G. Bower, Mood Effects on Person-Perception Judgements. H. Bless, G. Bohner, N. Schwarz, and F. Strack, Mood and Persuasion: A Cognitive Response Analysis. Part VII: Emotion's Effects on Others. J. Coyne, Depression and the Response of Others. V. Christophe and B. Rime, Exposure to the Social Sharing of Emotion: Emotional Impact, Listener Responses and Secondary Social Sharing. Part VIII: Emotion and Facial Expressions. P. Ekman, W. Friesen, and S. Ancoli, Facial Signs of Emotional Experience. A. Fridlund, Sociality of Solitary Smiling: Potentiation by an Implicit Audience. Part IX: Shame, Guilt, Envy, and Jealousy. J. Tagney, P. Wagner, C. Fletcher, and R. Gramzow, Shamed into Anger? The Relation of Shame and Guilt to Anger and Self-reported Aggression. R. Baumeister, A. Stillwell, and T. Heatherton, Guilt: An Interpersonal Approach. W. Parrott, The Emotional Experiences of Envy and Jealousy. Part X: The Nature of Emotion, Revisited: The Case of Anger. L. Berkowitz, On the Formation and Regulation of Anger and Aggression: A Cognitive-neoassociationistic Approach. J. Averill, Studies on Anger and Aggression: Implications for Theories of Emotion. We consider the problem of online learning in the linear contextual bandits setting, but in which there are also strong individual fairness constraints governed by an unknown similarity metric. These constraints demand that we select similar actions or individuals with approximately equal probability (arXiv:1104.3913), which may be at odds with optimizing reward, thus modeling settings where profit and social policy are in tension. We assume we learn about an unknown Mahalanobis similarity metric from only weak feedback that identifies fairness violations, but does not quantify their extent. This is intended to represent the interventions of a regulator who "knows unfairness when he sees it" but nevertheless cannot enunciate a quantitative fairness metric over individuals. Our main result is an algorithm in the adversarial context setting that has a number of fairness violations that depends only logarithmically on @math , while obtaining an optimal @math regret bound to the best fair policy. We studied the behavioral and emotional dynamics displayed by two people trying to resolve a conflict. 59 groups of two people were asked to talk for 20 minutes to try to reach a consensus about a topic on which they disagreed. The topics were abortion, affirmative action, death penalty, and euthanasia. Behavior data were determined from audio recordings where each second of the conversation was assessed as proself, neutral, or prosocial. We determined the probability density function of the durations of time spent in each behavioral state. These durations were well fit by a stretched exponential distribution, with an exponent, , of approximately 0.3. This indicates that the switching between behavioral states is not a random Markov process, but one where the probability to switch behavioral states decreases with the time already spent in that behavioral state. The degree of this “memory” was stronger in those groups who did not reach a consensus and where the conflict grew more destructive than in those that did. Emotion data were measured by having each person listen to the audio recording and moving a computer mouse to recall their negative or positive emotional valence at each moment in the conversation. We used the Hurst rescaled range analysis and power spectrum to determine the correlations in the fluctuations of the emotional valence. The emotional valence was well described by a random walk whose increments were uncorrelated. Thus, the behavior data demonstrated a “memory” of the duration already spent in a behavioral state while the emotion data fluctuated as a random walk whose steps did not have a “memory” of previous steps. This work demonstrates that statistical analysis, more commonly used to analyze physical phenomena, can also shed interesting light on the dynamics of processes in social psychology and conflict management.