Cost-benefit Trade-off Research Articles

What environmental and social factors are most relevant to the efficiency and performance of companies? The case of a small bank-oriented country

PurposeThis work aims to analyze the factors that influence the performance and efficiency of Portuguese companies, namely the influence of social and environmental features.Design/methodology/approachTo achieve our aim, we have used the Portuguese benchmark index, the Portuguese stock index – PSI, during the period from 2016 to 2020. To test the hypothesis panel data methodology was used, specifically, the GMM system originally proposed by Arellano and Bond (1991) and the Value-Based DEA developed by Gouveia et al. (2008).FindingsThe results of the GMM model show that social performance has a negative relationship with the company’s performance, from the perspective of different stakeholders, reinforcing that the cost-benefit trade-off of social spending is not yet understood as advantageous for the company’s performance. On the other hand, environmental performance, for external stakeholders, positively influences the company’s performance, perhaps due to pressure from society and the tradition of disclosing environmental matters. The value-based DEA results reinforce that from the perspective of the external stakeholder, non-efficient companies must increase their environmental performance to become efficient, highlighting the role of environmental performance in explaining efficiency. It is unanimous that social performance is still not seen as a lever of efficiency.Originality/valueThis is the first work to use a hybrid methodology to understand the performance determinants of a small banking-oriented country, emphasizing environmental and social aspects.

Costs of induced defenses dissipate by maturity for diploid and triploid oysters

Oyster reef restoration efforts and on-bottom aquaculture are frequently plagued with high predation rates. Oysters are phenotypically plastic, and rearing juvenile oysters, Crassostrea virginica, with predator cues causes them to grow stronger shells that increases survivorship in the field. However, induced defenses (e.g., shell hardening in oysters) are often associated with cost-benefit trade-offs, and the extent the increased shell strength persists into adulthood and alters the growth of somatic and reproductive tissues remains unknown. We raised diploid oysters (used in reef restoration) and triploid oysters (used in aquaculture) with and without predator cues for one month before placing individuals on an oyster farm to grow to market size. Oyster shell characteristics, soft tissue mass, and reproductive investment were measured periodically over one year of culture and compared across treatments. Both diploid and triploid oysters had significantly stronger and smaller shells than controls at the end of their nursery period. However, while diploid shells became 15 % stronger and 17 % smaller than controls, triploid shells became 28 % stronger and 23 % smaller. Additionally, triploid oysters exposed to predator cues returned to the size of controls faster and maintained their shell strength differences longer than diploids. Differences in soft tissue mass between treatments mirrored the patterns exhibited in shell size and weight with greater initial physiological costs and faster recovery for triploid individuals. There was no significant difference in somatic or reproductive tissue mass between induced and control oysters of the same ploidy after seven months in the field. Triploid oysters were 15–110 % larger than diploids depending on the characteristic measured at maturity. Additionally, there was a significant interaction between treatment and ploidy because induced triploids had marginally greater growth than their control counterparts while induced diploids had marginally less growth than controls. These findings demonstrate that physiological costs of oysters reacting to predators in early life stages are minimal by the time individuals reach maturity. Early exposure to predator cues is a promising tool for improving oyster survivorship in restoration and aquaculture operations, especially in regions with high predation pressure.

Evaluating the cost of classifier discrimination choices for IoT sensor attack detection

The intrusion detection of IoT devices through the classification of malicious traffic packets have become more complex and resource intensive as algorithm design and the scope of the problems have changed. In this research, we compare the cost of a traditional supervised pattern recognition algorithm (k-Nearest Neighbor (KNN)), with the cost of a current deep learning (DL) unsupervised algorithm (Convolutional Neural Network (CNN)) in their simplest forms. The classifier costs are calculated based on the attributes of design, computation, scope, training, use, and retirement. We find that the DL algorithm is applicable to a wider range of problem-solving tasks, but it costs more to implement and operate than a traditional classifier. This research proposes an economic classifier model for deploying suitable AI-based intrusion detection classifiers in IoT environments. The model was empirically validated on the IoT-23 dataset using KNN and CNN. This study closes a gap in prior research that mostly concentrated on technical elements by incorporating economic factors into the evaluation of AI algorithms for IoT intrusion detection. This research thus evaluated the economic implications of deploying AI-based intrusion detection systems in IoT environments, considering performance metrics, implementation costs, and the cost of classifier discrimination choices. Researchers and practitioners should focus on the cost–benefit trade-offs of any artificial intelligence application for intrusion detection, recommending an economic evaluation and task fit assessment before adopting automated solutions or classifiers for IoT intrusion detection, particularly in large-scale industrial settings that involve active attacks.

Cost-benefit analysis of 3D-printed vascular models in abdominal free flap breast reconstruction

3D-printed vascular models can enhance flap harvesting efficiency in abdominal free flap breast reconstruction, reducing the use of operating room time. However, no economic analyses with respect to model usein this context have been conducted to date. As such, this study examines model cost-benefit tradeoffs for use in abdominal free flap breast reconstruction.

Cost-benefit Tradeoff Mediates the Rule- to Memory-based Processing Transition during Practice.

Practice not only improves task performance, but also changes task execution from rule- to memory-based processing by incorporating experiences from practice. However, how and when this change occurs is unclear. We tested the hypothesis that strategy transition in task learning results from cost-benefit analysis. Participants learned two task sequences and were then queried about the task type at a cued sequence and position. Behavioral improvement with practice can be accounted for by a computational model implementing cost-benefit analysis. Model-predicted strategy transition points are related to behavioral slowing and changes in fMRI activation patterns in the dorsolateral prefrontal cortex. Strategy transition is also related to increased pattern separation in the ventromedial prefrontal cortex. The cost-benefit analysis model outperforms alternative models (e.g., both strategies racing for being expressed in behavior) in accounting for empirical data. These findings support cost-benefit analysis as a mechanism of practice-induced strategy shift.

Progressive Monitoring of Micro-Dumps Using Remote Sensing: An Applicative Framework for Illegal Waste Management

Illegal dumping is one of the major issues to be addressed by public managers in both developed and developing countries. The adequate tackling and enforcement of such unlawful activities require continuous territory monitoring, the lack of which is the principal cause of the failure of traditional systems based on patrolling, eventually triggered by sparse crowdsourced data. This work proposes the digitalization of the entire illegal waste management monitoring process through an innovative decision support system based on multiscale remote sensing data. These data enable the assessment of the risk level of specific areas, thus determining inspection priorities in line with the available patrolling resources. The proposed methodology provides a tool to assess the impact of diverse monitoring system parameters on overall the performance in light of the expected operating costs and to understand whether remote sensing can help a better management of illegal waste in a specific operational scenario, thus helping in the determination of the best cost–benefit trade-off. Simulation results over a demonstration scenario, based on realistic dumping/restoration dynamics and the typical performance of satellite detection systems, show that the adoption of remote sensing technologies in the monitoring process can increase the restoration capacity by about 10% compared to traditional patrolling.

Individual flexibility in group foraging behaviour of reef manta rays (Mobula alfredi)

Flexibility in animal foraging strategies can increase overall feeding efficiency for individuals. For example, group foraging can increase the efficiency of resource exploitation; conversely solo foraging can reduce intraspecific competition, particularly at low resource densities. The cost–benefit trade-off of such flexibility is likely to differ within and among individuals. Reef manta rays (Mobula alfredi) are large filter-feeding elasmobranchs that often aggregate to feed on ephemeral upwellings of zooplankton. Over three years in the Maldives, we free-dived to film 3106 foraging events involving 343 individually identifiable M. alfredi. Individuals fed either solo or in groups with a clear leader plus between one and eight followers. M. alfredi were significantly more likely to forage in groups than solo at high just prior to high tide and when aggregations were larger. Within aggregations, individuals foraged in larger groups when more food was available, and when the overall aggregations were relatively large suggesting that foraging in large groups was more beneficial when food is abundant, and the costs of intraspecific competition were outweighed by the efficiency resulting from group foraging strategies. Females, the larger sex, were more likely to lead foraging groups than males. The high within-individual variance (over 70%), suggested individuals were unpredictable across all foraging behaviours, thus individual M. alfredi cannot be classified into foraging types or specialists. Instead, each individual was capable of considerable behavioural flexibility, as predicted for a species reliant on spatially and temporally ephemeral resources.Significance statementReef manta rays (Mobula alfredi), listed as “Vulnerable” on the IUCN Red List, are at risk from targeted and by-catch fisheries due to their slow life history and aggregative behaviour. M. alfredi feed together in aggregations on short-lived glut of microscopic zooplankton. Over three years in the Maldives, we filmed 3106 foraging events involving 343 individually identifiable M. alfredi. Manta rays were more likely to forage in groups than solo just prior to high tide and when aggregations were larger, attracted by the influx of zooplankton. Foraging groups included more individuals when plankton was more abundant. However, individuals flipped between solo and group foraging and did not specialise. Foraging groups were most often led by females, the larger sex. Individuals were very flexible in how they foraged, which makes sense for a species that relies on a food source that varies enormously in when, where and for how long it is available. Understanding manta ray foraging behaviour will help conservation management efforts and predict their responses to climate change.

Multi-objective optimisation framework for Blue-Green Infrastructure placement using detailed flood model

Designing city-scale Blue-Green Infrastructure (BGI) for flood risk management requires detailed and robust methods. This is due to the complex interaction of flow pathways and the need to assess cost-benefit trade-offs for various BGI options. This study aims to find a cost-effective BGI placement scheme by developing an improved approach called the Cost OptimisatioN Framework for Implementing blue-Green infrastructURE (CONFIGURE). The optimisation framework integrates a detailed hydrodynamic flood simulation model with a multi-objective optimisation algorithm (Non-dominated Sorting Genetic Algorithm II). The use of a high-resolution flood simulation model ensures the explicit representation of BGI and other land use features to simulate flow pathways and surface flood risk accurately, while the optimisation algorithm guarantees achieving the best cost-benefit trade-offs for given BGI options. The current study uses the advanced CityCAT hydrodynamic flood model to evaluate the efficiency of the optimisation framework and the impact of location and size of permeable interventions on the optimisation process and subsequent cost-benefit trade-offs. This is achieved by dividing permeable surface areas into intervention zones of varying size and quantity. Furthermore, rainstorm events with 100-year and 30-year return periods are analysed to identify any common optimal solutions for different rainfall intensities. Depending on the number of intervention locations, the automated framework reliably achieves optimal BGI implementation solutions in a fraction of the time required to find the best solutions by trialling all possible options. Designing and optimising interventions with smaller sizes but many permeable zones save a good fraction of investment. However, such a design scheme requires more computational time to find optimal options. Furthermore, the optimal spatial configuration of BGI varies with different rainstorm severities, suggesting a need for careful selection of the rainstorm return period. Based on the results, CONFIGURE shows promise in devising sustainable urban flood risk management designs.

What Forms of Redistribution Do Americans Want? Understanding Preferences for Policy Benefit-Cost Tradeoffs

What Forms of Redistribution Do Americans Want? Understanding Preferences for Policy Benefit-Cost Tradeoffs

Labor Costs of Implementing New Accounting Standards

SYNOPSIS Although much research focuses on informational benefits of new accounting standards, the costs of implementing them remain largely unexamined. We consider one such cost in the adoption of two recent standards: lease accounting and revenue recognition. We find an increase in the number of job postings demanding skills related to accounting for those standards around their issuance. Firms most affected by new standards, measured by accounting complexity and early adoption behavior, post more accounting jobs. Using job postings as a proxy for hiring, we estimate incremental labor costs at about 30 percent of median audit fees for each standard for the most affected firms. Our tests indicate greater regulatory compliance burden for smaller firms. We provide large-sample evidence on the lower bound for the costs of implementing new accounting standards. Our findings should interest standard setters as they evaluate the cost-benefit tradeoffs of issuing new standards. Data Availability: Data are commercially available from the sources cited in the text. JEL Classifications: J23; M41; M51.

What is the most profitable targeting strategy from mass marketing to personalization? Results from a simulation in tourism

ABSTRACT Scholars know that setting the right target, from the aggregated market to personalization, raises a cost-benefit trade-off, but we found little research on this topic. Personalization is very popular, but it may be costly. Using broader market segments can be affordable but less accurate. We did a simulation analysis in the tourism industry based on a case-study. We compared the profits of four different targets, aggregated market, large segments, micro-segments, individuals, in different conditions by varying consumers’ attitudes, price, and scale/scope economies. Our findings show that micro-segmentation is the most profitable strategy on average, but the comparison strongly depends on the business conditions.

Mandatory versus voluntary non-financial reporting: reporting practices and economic consequences

ABSTRACT Non-financial reporting mandates are considered in many jurisdictions, with the European Union (EU) already having a mandate in place: Directive 2014/95/EU (“the Directive,” henceforth). Previous studies indicate that the Directive has achieved its goal of enhancing non-financial transparency, resulting in declines in firm value. We find that the transparency enhancements are concentrated in firms that voluntarily start non-financial reporting in anticipation of the Directive's entry-into-force date, without these firms facing net costs associated with reporting. In contrast, firms that only report when mandated are more likely to provide boilerplate disclosures, consistent with facing a negative cost–benefit trade-off of reporting, leading them to experience increased information asymmetries. Our results suggest that the flexibility embedded in and lack of enforcement of the Directive renders it ineffective in promoting non-financial transparency among firms with weak incentives to disclose meaningful information.

Aberrant effort-based reward dynamics in anhedonia.

Anhedonia is a transdiagnostic symptom and associated with a spectrum of reward deficits among which the motivational dysfunction is poorly understood. Previous studies have established the abnormal cost-benefit trade-off as a contributor to motivational deficits in anhedonia and its relevant psychiatric diseases. However, it remains elusive how the anhedonic neural dynamics underlying reward processing are modulated by effort expenditure. Using an effort-based monetary incentive delay task, the current event-related potential study examined the neural dynamics underlying the effort-reward interplay in anhedonia using a nonclinical sample who scored high or low on an anhedonia questionnaire. We found that effort prospectively decreased reward effect on the contingent variation negativity and the target-P3 but retrospectively enhanced outcome effect on the feedback-P3 following effort expenditure. Compared to the low-anhedonia group, the high-anhedonia group displayed a diminished effort effect on the target-P3 during effort expenditure and an increased effort-enhancement effect for neutral trials during the feedback-P3 period following effort expenditure. Our findings suggest that anhedonia is associated with an inefficient control and motivation allocation along the efforted-based reward dynamics from effort preparation to effort production.

Brain stimulation over the left DLPFC enhances motivation for effortful rewards in patients with major depressive disorder

BackgroundAmotivation is a typical feature in major depressive disorder (MDD), which produces reduced willingness to exert effort. The dorsolateral prefrontal cortex (DLPFC) is a crucial structure in goal-directed actions and therefore is a potential target in modulating effortful motivation. However, it remains unclear whether the intervention is effective for patients with MDD. MethodsWe employed transcranial magnetic stimulation (TMS), computational modelling and event-related potentials (ERPs) to reveal the causal relationship between the left DLPFC and motivation for effortful rewards in MDD. Fifty patients underwent both active and sham TMS sessions, each followed by performing an Effort-Expenditure for Rewards Task, during which participants chose and implemented between low-effort/low-reward and high-effort/high-reward options. ResultsThe patients showed increased willingness to exert effort for rewards during the DLPFC facilitated session, compared with the sham session. They also had a trend in larger P3 amplitude for motivated attention toward chosen options, larger CNV during preparing for effort exertion, and larger SPN during anticipating a high reward. Besides, while behavior indexes for effortful choices were negatively related to depression severity in the sham session, this correlation was weakened in the active stimulation session. ConclusionsThese findings provide behavioral, computational, and neural evidence for the left DLPFC on effortful motivation for rewards. Facilitated DLPFC improves motor preparation and value anticipation after making decisions especially for highly effortful rewards in MDD. Facilitated DLPFC also has a potential function in enhancing motivated attention during cost-benefit trade-off. This neuromodulation effect provides a potential treatment for improving motivation in clinics.

The value of admission avoidance: cost-consequence analysis of one-year activity in a consolidated service.

Many advantages of hospital at home (HaH), as a modality of acute care, have been highlighted, but controversies exist regarding the cost-benefit trade-offs. The objective is to assess health outcomes and analytical costs of hospital avoidance (HaH-HA) in a consolidated service with over ten years of delivery of HaH in Barcelona (Spain). A retrospective cost-consequence analysis of all first episodes of HaH-HA, directly admitted from the emergency room (ER) in 2017-2018, was carried out with a health system perspective. HaH-HA was compared with a propensity-score-matched group of contemporary patients admitted to conventional hospitalization (Controls). Mortality, re-admissions, ER visits, and direct healthcare costs were evaluated. HaH-HA and Controls (n = 441 each) were comparable in terms of age (73 [SD16] vs. 74 [SD16]), gender (male, 57% vs. 59%), multimorbidity, healthcare expenditure during the previous year, case mix index of the acute episode, and main diagnosis at discharge. HaH-HA presented lower mortality during the episode (0 vs. 19 (4.3%); p < 0.001). At 30 days post-discharge, HaH-HA and Controls showed similar re-admission rates; however, ER visits were lower in HaH-HA than in Controls (28 (6.3%) vs. 34 (8.1%); p = 0.044). Average costs per patient during the episode were lower in the HaH-HA group (€ 1,078) than in Controls (€ 2,171). Likewise, healthcare costs within the 30 days post-discharge were also lower in HaH-Ha than in Controls (p < 0.001). The study showed higher performance and cost reductions of HaH-HA in a real-world setting. The identification of sources of savings facilitates scaling of hospital avoidance. ClinicalTrials.gov (26/04/2017; NCT03130283).

Digital twins in mechanical and aerospace engineering.

Digital twins bring value to mechanical and aerospace systems by speeding up development, reducing risk, predicting issues and reducing sustainment costs. Realizing these benefits at scale requires a structured and intentional approach to digital twin conception, design, development, operation and sustainment. To bring maximal value, a digital twin does not need to be an exquisite virtual replica but instead must be envisioned to be fit for purpose, where the determination of fitness depends on the capability needs and the cost-benefit trade-offs.

Precision agriculture with AI-based responsive monitoring algorithm

Precision Agriculture (PA) is a relatively new farming approach, applying science and technology to enhance cost-effectiveness and improve food security by optimizing agricultural practices through the treatment of each crop individually. To support the new practice, an AI-based, responsive monitoring algorithm, called the Dynamic-Adaptive Search algorithm, has been developed to minimize operation costs with the benefit of acquiring new and timely information. Three modules of the algorithm are 1) Module for image processing based on AI, 2) Module for error-responsive search expansion, and 3) Module for estimating stress propagation. Computational experiments have demonstrated that the newly developed algorithm outperforms other alternatives, yielding significantly higher system performance and system gain, compared to other algorithms. The sensitivity analysis confirms the algorithm's ability to deliver within ± 10% of the theoretical optimal value, resulting in economic benefits under varying conditions. The algorithm's applications can be extended to other decision-making situations involving cost-benefit tradeoffs of acquiring more data.

Welfare fragmented information effects: The cost-benefit analysis and Trade-offs

&lt;p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"&gt;&lt;span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;"&gt;We offer an extensive analysis of the significance of information within the realm of Gaussian quadratic economies. We build upon the seminal papers of Morris and Shin (2002, 2007) and consider a signal game of incomplete information. Particularly, we question the suitability of partial transparency portrayed by fragmented information in addition to the private signal in terms of welfare effects. We can summarize our findings in two main points. First, fragmented information, in conjunction with a private signal, can reduce the reliance on public signals. Second, a conflicting effect arises between increasing full disclosure and increasing the precision of fragmented (semi-public) information when examining different complex scenarios, involving for example endogenous private information or imperfect correlated signals. For a critical threshold, an optimal communication strategy designed by fragmented information should be implemented whenever that kind of information is acquired at a high precision.&lt;/span&gt;&lt;/p&gt;

Onscreen presence of instructors in video lectures affects learners’ neural synchrony and visual attention during multimedia learning

COVID-19 forced students to rely on online learning using multimedia tools, and multimedia learning continues to impact education beyond the pandemic. In this study, we combined behavioral, eye-tracking, and neuroimaging paradigms to identify multimedia learning processes and outcomes. College students viewed four video lectures including slides with either an onscreen human instructor, an animated instructor, or no onscreen instructor. Brain activity was recorded via fMRI, visual attention was recorded via eye-tracking, and learning outcome was assessed via post-tests. Onscreen presence of instructor, compared with no instructor presence, resulted in superior post-test performance, less visual attention on the slide, more synchronized eye movements during learning, and higher neural synchronization in cortical networks associated with socio-emotional processing and working memory. Individual variation in cognitive and socio-emotional abilities and intersubject neural synchronization revealed different levels of cognitive and socio-emotional processing in different learning conditions. The instructor-present condition evoked increased synchronization, likely reflecting extra processing demands in attentional control, working memory engagement, and socio-emotional processing. Although human instructors and animated instructors led to comparable learning outcomes, the effects were due to the dynamic interplay of information processing vs. attentional distraction. These findings reflect a benefit-cost trade-off where multimedia learning outcome is enhanced only when the cognitive benefits motivated by the social presence of onscreen instructor outweigh the cognitive costs brought about by concurrent attentional distraction unrelated to learning.

The Flow Termini Coverage Model for Locating Bike-Sharing Stations

As of 2022, more than 1,900 cities around the world had implemented bike-sharing systems (BSSs) with docking stations. Because there is a limit on how far most people are willing to walk to and from BSS stations, maximal coverage models have been a popular approach to optimizing dock station locations. Existing maximal coverage models, however, mainly evaluate demand coverage based on proximity to individual stations without considering the fact that BSS users must have dock stations convenient to both their pickup and drop-off sites. Given this, traditional single-node maximal coverage models might not locate stations to maximize the number of complete-able trips and will also tend to overestimate the coverage of demand. This study proposes a flow termini coverage model (FTCM) to locate BSS stations by considering the flow-based BSS usage demands and network connectivity. The model is tested on the Youbike system in Taipei, one of the most heavily used dock-station BSSs worldwide. This study compares the FTCM with both the traditional single-station maximal coverage model and Taipei’s official planning strategy. Results suggest that the FTCM consistently covers more origin–destination trips, especially in areas with unbalanced BSS usage. We also provide discussion on the cost–benefit trade-offs for transportation planners to choose the most appropriate modeling approaches. The FTCM could also be adapted to optimizing micromobility hubs for parking dockless shared bikes and e-scooters.