Providing decision makers with more information is often expected to result in more informed and superior decisions. This is especially true when leveraging artificial intelligence (AI) to explore and find complex patterns in vast amounts of data. Although AI can enable an “information advantage,” truly intelligent systems should buffer scarce human cognitive resources from information overload and be well adapted to the environment in which they are deployed. Paradoxically, some practitioners have conflated AI's information processing superiority with a contradictory decision-support goal: to provide human decision makers withmore, higher quality, or more novel courses of action, regardless of context, than they could generate without AI. In this article, I review the evidence examining the costs and benefits of providing decision makers with more or less choice and identify the factors that moderate the relationship between the amount of choice and decision effectiveness. Although providing more information and choice increases confidence and certainty in one's decision, it can make decision making more difficult, decrease satisfaction, and result in poorer decision outcomes. The research indicates that such negative effects are influenced by the level of entropy and variety provided and can be reduced with increased familiarity but are further compounded when decisions are increasingly effortful, difficult, or complex. The review concludes with guidance on how designers might leverage knowledge of choice overload and associated moderator effects to create more adaptive and effective decision support systems.

The energy benefits of building recommissioning are well documented, but their efficacy decreases with staff turnover and as systems drift or malfunction over time. Most models recommend recommissioning approximately every five years. Despite this cyclical reinvestment, recommissioning remains one of, if not the most, effective methods for organizations looking to reduce energy consumption and utility expenditures and has become a priority for the facilities management group at the University of Oregon. Given the large variety of buildings on campus and connections with local building research organizations, the recommissioning team has developed and tested a general model for a continuous commissioning process. This process leverages new visualization and analytics software that interfaces directly with building automation systems to drastically increase the visibility of building operational and performance characteristics. This visibility has created both monetary and energy savings via faster and more efficient maintenance response, has guided recommissioning efforts, facilitated continued system optimization, and reduced the investment required for ongoing efforts. Initial results are promising. Significant reductions in energy consumption have been observed in the first three months since the process was deployed in the pilot building and occupants are reporting improved comfort characteristics. Given the unique attributes at a university setting, the team also uncovered non-energy benefits associated with this new program, including increased research and teaching productivity.

Vehicles are currently being developed and sold with increasing levels of connectivity and automation. As with all networked computing devices, increased connectivity often results in a heightened risk of a cyber security attack. Furthermore, increased automation exacerbates any risk by increasing the opportunities for the adversary to implement a successful attack. In this paper, a large volume of publicly accessible literature is reviewed and compartmentalized based on the vulnerabilities identified and mitigation techniques developed. This review highlighted that the majority of studies are reactive and vulnerabilities are often discovered by friendly adversaries (white-hat hackers). Many gaps in the knowledge base were identified. Priority should be given to address these knowledge gaps to minimize future cyber security risks in the connected and autonomous vehicle sector.

Performance assessment and enhancement strategies have often focused upon the user or the intelligent technology rather than the interaction between the two. Where system interaction has occurred, we have often settled for co-operation rather than collaboration or symbiosis. This special issue is devoted to the topic of the symbiotic relationship that people have with intelligent systems, and nontraditional computer support for design and user evaluation of this relationship. An objective in coordinating this issue was to encourage a range of papers that addressed the nature of the relationship between human and intelligent agents, the ways in which performance could be augmented, measured and improved, and the need for additional and nontraditional measures of performance for evaluating the anticipated progress as people interact with intelligent computer systems. The potential benefit of such measurement tools and techniques has been recognized in numerous domains including education, information visualization, digital libraries, and computer supported cooperative work. The summaries from several conferences and workshops on human-centered systems contain many references to this subject. However, many questions still remain on how and what to measure for interactive symbiotic systems. Currently, if systems are tested, designers rely on users to test their systems. While this constitutes an acceptable metric it also raises many issues including comparison of skills of users, standardization of test situations, amount of training given, help given, and time allotted for the evaluation. In addition, the influence of the interactions on the users’ performance is rarely studied.