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In most U.S. jurisdictions, prosecutors are not required to clearly establish a reasonable basis for guilt prior to offering defendants plea deals. We apply Bayesian analyses, which are uniquely suited to illuminate the impact of prior probability of guilt on the informativeness of a particular outcome (i.e., a guilty plea), to demonstrate the risks of plea offers that precede evidence. Our primary prediction was that lower prior probabilities of guilt would coincide with a significantly higher risk for false guilty pleas. We incorporated data from Wilford, Sutherland into a Bayesian analysis allowing us to model the expected diagnosticity of plea acceptance across the full range of prior probability of guilt. Our analysis indicated that, as predicted, when plea offers are accepted at lower prior probabilities of guilt, the probability that a plea is actually false is significantly higher than when prior probabilities of guilt are higher. In other words, there is a trade-off between prior probability of guilt and information gain. For instance, in our analysis, when prior probability of guilt was 50%, posterior probability of guilt (after a plea) was 77.8%; when prior probability of guilt was 80%, posterior probability of guilt was 93.3%. Our results clearly indicate the importance of ensuring that there is a reasonable basis for guilt before a plea deal is extended. In the absence of shared discovery, no such reasonable basis can be established. Further, these results illustrate the additional insights gained from applying a Bayesian approach to plea-decision contexts. 

Individuals in positions of power are often required to make high-stakes decisions. The approach-inhibition theory of social power holds that elevated power activates approach-related tendencies, leading to decisiveness and action orientation. However, naturalistic decision-making research has often reported that increased power often has the opposite effect and causes more avoidant decision-making. To investigate the potential activation of avoidance-related tendencies in response to elevated power, this study employed an immersive scenario-based battery of least-worst decisions (the Least-Worst Uncertain Choice Inventory for Emergency Responses; LUCIFER) with members of the United States Armed Forces. In line with previous naturalistic decision-making research on the effect of power, this research found that in conditions of higher power, individuals found decisions more difficult and were more likely to make an avoidant choice. Furthermore, this effect was more pronounced in domain-specific decisions for which the individual had experience. These findings expand our understanding of when, and in what contexts, power leads to approach vs. avoidant tendencies, as well as demonstrate the benefits of bridging methodological divides that exist between “in the lab” and “in the field” when studying high-uncertainty decision-making. 

Consumer demand for augmented reality (AR) in mobile phone applications, such as the Apple ARKit. Such applications have potential to expand access to robot grasp planning systems such as Dex-Net. AR apps use structure from motion methods to compute a point cloud from a sequence of RGB images taken by the camera as it is moved around an object. However, the resulting point clouds are often noisy due to estimation errors. We present a distributed pipeline, DexNet AR, that allows point clouds to be uploaded to a server in our lab, cleaned, and evaluated by Dex-Net grasp planner to generate a grasp axis that is returned and displayed as an overlay on the object. We implement Dex-Net AR using the iPhone and ARKit and compare results with those generated with high-performance depth sensors. The success rates with AR on harder adversarial objects are higher than traditional depth images.