The Impact of Transparency and Decision Risk on Human–Automation Teaming Outcomes

Objective Examine the effects of decision risk and automation transparency on the accuracy and timeliness of operator decisions, automation verification rates, and subjective workload. Background Decision aids typically benefit performance, but can provide incorrect advice due to contextual factors, creating the potential for automation disuse or misuse. Decision aids can reduce an operator’s manual problem evaluation, and it can also be strategic for operators to minimize verifying automated advice in order to manage workload. Method Participants assigned the optimal unmanned vehicle to complete missions. A decision aid provided advice but was not always reliable. Two levels of decision aid transparency were manipulated between participants. The risk associated with each decision was manipulated using a financial incentive scheme. Participants could use a calculator to verify automated advice; however, this resulted in a financial penalty. Results For high- compared with low-risk decisions, participants were more likely to reject incorrect automated advice and were more likely to verify automation and reported higher workload. Increased transparency did not lead to more accurate decisions and did not impact workload but decreased automation verification and eliminated the increased decision time associated with high decision risk. Conclusion Increased automation transparency was beneficial in that it decreased automation verification and decreased decision time. The increased workload and automation verification for high-risk missions is not necessarily problematic given the improved automation correct rejection rate. Application The findings have potential application to the design of interfaces to improve human–automation teaming, and for anticipating the impact of decision risk on operator behavior.

Grand Corruption in the Contracting Out of Public Services: Lessons from a Pilot Study in Colombia

Do targeted transparency interventions reduce corrupt behavior when corrupt actors are few and politically influential; their behavior imposes small costs on numerous individuals; and corrupt behavior is difficult to observe? Results from a study of informal audits and text messages to parents, meant to curb corruption in the School Meals Program of Colombia, suggests that they can. Theory is pessimistic that transparency interventions can change the behavior of actors who exert significant influence over supervisory authorities. Moreover, inherent methodological obstacles impede the identification of treatment effects. Results substantiate the presence of these obstacles, especially considerable spillovers from treated to control groups. Despite spillovers, we find that parental and operator behavior are significantly different between treatment and control groups. Additional evidence explains why operator behavior changed: out of concern that systematic evidence of corrupt behavior would trigger enforcement actions by high-level enforcement agencies outside of the political jurisdictions where they are most influential.

Credit Systems for Bycatch and Biodiversity Conservation

Credit systems for mitigation of bycatch and habitat impact, incentive-based approaches, incentivize changes in fishery operator behavior and decision-making and allow flexibility in a least-cost method. Three types of credit systems, originally developed to address environmental pollution, are presented and evaluated as currently underutilized incentive-based approaches. The first, a cap-and-trade approach, evolved out of direct regulation through restricted limits with flexibility through the creation of tradeable unused portion of the limit, called credits. The second, a penalty-reward system, incentivizes bycatch- and habit-impact- reducing vessel behavior through rewards for positive behavior, and penalties for negative behavior. The third is a hybrid of the first two. All three systems can be used in the context of both absolute (fixed) and relative (rate-based or proportional) credits. Transferable habitat impact credit systems are developed for area management. The cap-and-trade credit system is directly compared to a comparable property rights system in terms of characteristics, strengths, weakness, and applicability. The Scottish Conservation Scheme and halibut bycatch reduction in the Alaskan multispecies groundfish fishery provide real-world examples of success with credit systems. The strengths, weaknesses, and applicability of credit systems are summarized, along with a set of recommendations. Cap-and-trade credit systems provide an important alternative to property rights, such as when rights are not feasible, and for this reason should prove useful for international fisheries. Penalty-reward and hybrid credit systems can substitute for cap-and-trade credit systems or property rights or complement them by addressing a related but otherwise unaddressed issue.

Potential of Private Autonomous Vehicles for Parcel Delivery

Using the same vehicles for both passenger and freight transport, to increase vehicle occupancy and decrease their number, is an idea that drives transport planners and is also being addressed by manufacturers. This paper proposes a methodology to simulate the behavior of such vehicles within an urban traffic system and evaluate their performance. The aim is to investigate the impacts of resignation from fleet ownership by a transport service company (TSC) operating on a city-wide scale. In the simulation, the service provider hires private autonomous cars for tour performance. Based on assumptions concerning the operation of such vehicles and TSCs, the software Multi-Agent Transport Simulation (MATSim) is extended to model vehicle and operator behavior. The proposed framework is applied to a case study of a parcel delivery service in Berlin serving a synthetic parcel demand. Results suggest that the vehicle miles traveled for freight purposes increase because of additional access and egress trips. Moreover, the number of vehicles en route is higher throughout the day. The lowering of driver costs can reduce the costs of the operator by approximately 74.5%. If the service provider additionally considers the resignation from fleet ownership, it might lower the operation cost by another 10%, not taking into account the costs of system transfer or risks like vehicle non-availability. From an economic perspective, the reduction of the overall number of vehicles in the system seems to be beneficial.

Press Start to Play: Classifying Multi-Robot Operators and Predicting Their Strategies through a Videogame

One of the active challenges in multi-robot missions is related to managing operator workload and situational awareness. Currently, the operators are trained to use interfaces, but in the near future this can be turned inside out: the interfaces will adapt to operators so as to facilitate their tasks. To this end, the interfaces should manage models of operators and adapt the information to their states and preferences. This work proposes a videogame-based approach to classify operator behavior and predict their actions in order to improve teleoperated multi-robot missions. First, groups of operators are generated according to their strategies by means of clustering algorithms. Second, the operators’ strategies are predicted, taking into account their models. Multiple information sources and modeling methods are used to determine the approach that maximizes the mission goal. The results demonstrate that predictions based on previous data from single operators increase the probability of success in teleoperated multi-robot missions by 19%, whereas predictions based on operator clusters increase this probability of success by 28%.