Avoiding costly mistakes in groups: The evolution of error management in collective decision making

Balancing the costs of alternative decisions is a fundamental challenge for decision makers. This is especially critical in social situations, where the choices individuals face are often associated with highly asymmetric error costs---such as pedestrian groups crossing the street, police squads holding a suspect at gunpoint, or animal groups evading predation. While a broad literature has explored how individuals acting alone adapt to asymmetric error costs, little is known about how individuals in groups cope with these costs. Here we investigate adaptive decision strategies of individuals in groups facing asymmetric error costs, modeling scenarios where individuals aim to maximize group-level payoff (‘‘cooperative groups’’) or individual-level payoff (‘‘competitive groups’’). We extended the drift--diffusion model to the social domain in which individuals first gather personal information independently; they can then either wait for additional social information or decide early, thereby potentially influencing others. We combined this social drift--diffusion model with an evolutionary algorithm to derive adaptive behavior. Under asymmetric costs, small cooperative groups evolved response biases to avoid the costly error. Large cooperative groups, however, did not evolve response biases, since the danger of response biases triggering false information cascades increases with group size. We show that individuals in competitive groups face a social dilemma: They evolve higher response biases and wait for more information, thereby undermining group performance. Our results have broad implications for understanding social dynamics in situations with asymmetric costs, such as crowd panics and predator detection.

A Proposal and Analysis of New Realistic Sets of Benchmark Instances for Vehicle Routing Problems with Asymmetric Costs

Despite their importance, relatively little attention has been paid to vehicle routing problems with asymmetric costs (ACVRPs), or their benchmark instances. Taking advantage of recent advances in map application programming interfaces (APIs) and shared spatial data, this paper proposes new realistic sets of ACVRP benchmark instances. The spatial data of urban distribution centers, postal hubs, large shopping malls, residential complexes, restaurant businesses and convenience stores are used. To create distance and time matrices, the T map API, one of the most frequently used real time path analysis and distance measurement tools in Korea, is used. This paper also analyzes some important issues prevailing in urban transportation environments. These include the challenges of accounting for the frequency and distance in which air travel differs from reality when measuring closeness, the differences in distance and time for outgoing and return trips, and the rough conversion ratios from air distance to road distance and to road time. This paper contributes to the research community by providing more realistic ACVRP benchmark instances that reflect urban transportation environments. In addition, the cost matrix analyses provide insights into the behaviors of urban road networks.

Two-stage stochastic integrated adjustment deviations and consensus models in an asymmetric costs context

Decision-makers usually have a variety of unsure situations in the environment of group decision-making. In this paper, we resolve this difficulty by constructing two-stage stochastic integrated adjustment deviations and consensus models (iADCMs). By introducing the minimum cost consensus models (MCCMs) with costs direction constraints and stochastic programming, we develop three types of iADCMs with an uncertainty of asymmetric costs and initial opinions. The factors of directional constraints, compromise limits and free adjustment thresholds previously thought to affect consensus separately are considered in the proposed models. Different from the previous consensus models, the resulting iADCMs are solved by designing an appropriate L-shaped algorithm. On the application in the negotiations on Grains to Green Programs (GTGP) in China, the proposed models are demonstrated to be more robust. The proposed iADCMs are compared to the MCCMs in an asymmetric costs context. The contrasting outcomes show that the two-stage stochastic iADCMs with no-cost threshold have the smallest total costs. Moreover, based on the case study, we give a sensitivity analysis of the uncertainty of asymmetric adjustment cost. Finally, conclusion and future research prospects are provided.

Using Random Forests with Asymmetric Costs to Predict Hospital Readmissions

Objective: Sufficiently accurate predictions of hospital readmissions are necessary for the allocation of scare clinical resources to reduce preventable readmissions. We describe the use of a data-driven approach that relies on machine learning algorithms to predict readmission at the time of discharge. Materials and Methods: We employ random forests to clinical and administrative electronic health record data available from a cohort of 103,688 patients discharged from the acute inpatient settings of the University of Pennsylvania Health System between June 25th, 2011 and June 30th, 2013. We predict both 30-day all-cause readmissions and 7-day unplanned readmissions using only predictors available by the time of discharge. Using oversampling and undersampling of the different outcome classes of readmission and no readmission, we incorporate into our models the asymmetric costs of a false negative relative to a false positive from the perspective of a hospital. We calculate variable importance scores for included predictors. Our approach was derived and validated using split-sample internal validation. Results: We developed a machine learning-based model using random forests with a 5:1 relative cost ratio for 30-day all-cause readmissions that achieves a sensitivity of 65% and specificity of 71% on validation data, as well as a random forests model with a 20:1 cost ratio for 7-day unplanned readmissions that achieves a sensitivity of 62% and specificity of 66% on validation data. Prior health system utilization, clinical discharging service, and vital sign information were most predictive of readmissions. Conclusion: By modeling the complex relationships between many predictor variables and readmission data for a large health system, we demonstrate successful predictive models that can be used upon discharge to flag patients at high risk of readmission.

Aggregate Dynamics in Lumpy Economies

How does an economy's capital respond to aggregate productivity shocks when firms make lumpy investments? We show that capital's transitional dynamics are structurally linked to two steady‐state moments: the dispersion of capital to productivity ratios—an indicator of capital misallocation—and the covariance of capital to productivity ratios with the time elapsed since their last adjustment—an indicator of asymmetric costs of upsizing and downsizing the capital stock. We compute these two sufficient statistics using data on the size and frequency of investment of Chilean plants. The empirical values indicate significant effects of aggregate productivity shocks and favor investment models with a strong downsizing rigidity and random opportunities for free adjustments.