No abstract

Decision making in uncertain scenarios is an ubiquitous challenge in real world systems. Tools to deal with this challenge include simulations to gather information and statistical emulation to quantify uncertainty. The machine learning community has developed a number of methods to facilitate decision making, but so far they are scattered in multiple different toolkits, and generally rely on a fixed backend. In this paper, we present Emukit, a highly adaptable Python toolkit for enriching decision making under uncertainty. Emukit allows users to: (i) use state of the art methods including Bayesian optimization, multi-fidelity emulation, experimental design, Bayesian quadrature and sensitivity analysis; (ii) easily prototype new decision making methods for new problems. Emukit is agnostic to the underlying modeling framework and enables users to use their own custom models. We show how Emukit can be used on three exemplary case studies.

Research examining citizen perception of the police have typically looked at expanded views of how the police interact in the community. By narrowing the focus to encompass two principles that associate citizen satisfaction with and perceptions of the police, this paper will include areas that influence satisfaction directly from citizens toward police officers themselves. Moreover, recent research stresses the importance of police/citizen relationships for positive outcomes to occur after interaction.  Research regarding race and ethnicity of the citizen is reviewed as well as youth and age to provide context for this article. Demographic variables have been shown to have an impact of citizen perception of the police thus affecting how they view the police and government agencies. Police literature is reviewed as to how these demographics contribute to neighborhood level changes in attitudes toward police officers. Community policing literature and the effects it has on neighborhood social cohesion and collective efficacy is also included. This will lead into a discussion of neighborhood social cohesion and collective efficacy and their impact on citizen perceptions of the police.