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1. Benevolent God Concepts and Past Kind Behaviors Induce Generosity Toward Outgroups

   https://doi.org/10.1521/soco.2023.41.4.321  

   Lee, Young-eun; Payir, Ayse; Heiphetz, Larisa (August 2023, Social Cognition)

   Humans behave more prosocially toward ingroup (vs. outgroup) members. This preregistered research examined the influence of God concepts and memories of past behavior on prosociality toward outgroups. In Study 1 (n = 573), participants recalled their past kind or mean behavior (between-subjects) directed toward an outgroup. Subsequently, they completed a questionnaire assessing their views of God. Our dependent measure was the number of lottery entries given to another outgroup member. Participants who recalled their kind (vs. mean) behavior perceived God as more benevolent, which in turn predicted more generous allocation to the outgroup (vs. ingroup). Study 2 (n = 281) examined the causal relation by manipulating God concepts (benevolent vs. punitive). We found that not only recalling kind behaviors but perceiving God as benevolent increased outgroup generosity. The current research extends work on morality, religion, and intergroup relations by showing that benevolent God concepts and memories of past kind behaviors jointly increase outgroup generosity. 

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2. Counterfactuals with Latent Information

   https://doi.org/10.1257/aer.20210496  

   Bergemann, Dirk; Brooks, Benjamin; Morris, Stephen (January 2022, American Economic Review)

   We describe a methodology for making counterfactual predictions in settings where the information held by strategic agents and the distribution of payoff-relevant states of the world are unknown. The analyst observes behavior assumed to be rationalized by a Bayesian model, in which agents maximize expected utility, given partial and differential information about the state. A counterfactual prediction is desired about behavior in another strategic setting, under the hypothesis that the distribution of the state and agents’ information about the state are held fixed. When the data and the desired counterfactual prediction pertain to environments with finitely many states, players, and actions, the counterfactual prediction is described by finitely many linear inequalities, even though the latent parameter, the information structure, is infinite dimensional. (JEL D44, D82, D83) 

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3. Will They Share? Predicting Location Sharing Behaviors of Smartphone Users through Self-Reflection on Past Privacy Behaviors

   Safi, M.I. (February 2019, The 2019 NDSS Workshop on Usable Security and Privacy)

   Location sharing is a particularly sensitive type of online information disclosure. To explain this behavior, we compared the effectiveness of using self-report measures drawn from the literature, behavioral data collected from mobile phones, and a new type of measure that represents a hybrid of self-report and behavioral data to contextualize users’ attitudes toward their past location sharing behaviors. This new measure was based on a reflective learning paradigm, where one reflects on past behavior to inform future behavior. Based on a study of Android smartphone users (N=114), we found that the construct ‘FYI About Myself’ and our new reflective measure of one’s comfort with sharing location with apps on the smartphone were the best predictors of location sharing behavior. Surprisingly, Behavioral Intention, a commonly used proxy for actual behavior, was not a significant predictor. These results have important implications for privacy research and designing systems to meet users’ location sharing privacy needs. 

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4. Will They Share? Predicting Location Sharing Behaviors of Smartphone Users through Self-Reflection on Past Privacy Behaviors

   Safi, M.I.; Jha, A.; Aly, M.E.; Page, X.; Patil, S.; Wisniewski, P. (February 2019, The 2019 NDSS Workshop on Usable Security and Privacy)

   Location sharing is a particularly sensitive type of online information disclosure. To explain this behavior, we compared the effectiveness of using self-report measures drawn from the literature, behavioral data collected from mobile phones, and a new type of measure that represents a hybrid of self-report and behavioral data to contextualize users’ attitudes toward their past location sharing behaviors. This new measure was based on a reflective learning paradigm, where one reflects on past behavior to inform future behavior. Based on a study of Android smartphone users (N=114), we found that the construct ‘FYI About Myself’ and our new reflective measure of one’s comfort with sharing location with apps on the smartphone were the best predictors of location sharing behavior. Surprisingly, Behavioral Intention, a commonly used proxy for actual behavior, was not a significant predictor. These results have important implications for privacy research and designing systems to meet users’ location sharing privacy needs. 

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5. Predicting smartphone location-sharing decisions through self-reflection on past privacy behavior

   https://doi.org/10.1093/cybsec/tyaa014  

   Wisniewski, Pamela; Safi, Muhammad Irtaza; Patil, Sameer; Page, Xinru (January 2020, Journal of Cybersecurity)

   null (Ed.)

   Abstract Smartphone location sharing is a particularly sensitive type of information disclosure that has implications for users’ digital privacy and security as well as their physical safety. To understand and predict location disclosure behavior, we developed an Android app that scraped metadata from users’ phones, asked them to grant the location-sharing permission to the app, and administered a survey. We compared the effectiveness of using self-report measures commonly used in the social sciences, behavioral data collected from users’ mobile phones, and a new type of measure that we developed, representing a hybrid of self-report and behavioral data to contextualize users’ attitudes toward their past location-sharing behaviors. This new type of measure is based on a reflective learning paradigm where individuals reflect on past behavior to inform future behavior. Based on data from 380 Android smartphone users, we found that the best predictors of whether participants granted the location-sharing permission to our app were: behavioral intention to share information with apps, the “FYI” communication style, and one of our new hybrid measures asking users whether they were comfortable sharing location with apps currently installed on their smartphones. Our novel, hybrid construct of self-reflection on past behavior significantly improves predictive power and shows the importance of combining social science and computational science approaches for improving the prediction of users’ privacy behaviors. Further, when assessing the construct validity of the Behavioral Intention construct drawn from previous location-sharing research, our data showed a clear distinction between two different types of Behavioral Intention: self-reported intention to use mobile apps versus the intention to share information with these apps. This finding suggests that users desire the ability to use mobile apps without being required to share sensitive information, such as their location. These results have important implications for cybersecurity research and system design to meet users’ location-sharing privacy needs. 

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