Cost Of Effort Research Articles

A generating absorbing boundary condition for simulating wave interaction with maritime structures in current or at forward speed

The lack of suitable boundary conditions in practical surface wave simulations with maritime structures in current or at forward speed may cause energy in the computational domain to accumulate due to spurious wave reflection. The common way to prevent wave reflection is to use passive wave absorbers, such as damping zones or relaxation zones, which requires larger domains at the cost of computational effort. Our goal is to derive a local generating absorbing boundary condition (GABC) for long-crested irregular waves on top of a mean flow, using the flow to model the forward speed of a structure such as a ship. Earlier work has demonstrated that a local GABC for free surface waves has a performance similar to passive wave absorbers, but at a reduced computational effort. New in the present work is that we extend, verify and validate the GABC in the presence of a nonzero mean flow. The GABC is designed to be accurate for a range of wave components in irregular sea states, with the resulting reflection coefficients for each component lower than a chosen value, say 5%. Having used potential flow theory for its derivation means that the boundary should not be placed at the exact location where wave breaking is expected, such as very close to the structure in the domain, or in the surf zone in coastal modeling. For the application with ships in this article that does not pose a limitation. The performance is demonstrated for a range of dimensionless wave number between 0 and 6. Such a boundary condition is obtained through a rational approximation of the linear dispersion relation with a mean flow, in combination with vertical derivatives of the solution variables along the boundary. Local linearization means that the GABC incorrectly considers bound, nonlinear wave components to be freely propagating wave components. Bound components, however, tend to have smaller amplitudes and do not appear to affect performance for the considered cases. Results of simulations with regular and irregular waves, on top of flows with different magnitudes and directions, are found to agree with the theory. The main source of differences is the implementation of the second derivate in the GABC near the free surface. Simulations of a Wigley hull at forward speed in irregular waves are compared to an experiment that was conducted specifically for validating the ABC. The data of the experiment are available as open data through doi: 10.4121/21320604. The comparison between simulation and experiment demonstrates that the GABC with a mean flow can be applied not only for theoretical simulations with propagating waves, but also for more practical applications with a structure in the domain.

Benefit distribution of guaranteed water saving management contract based on principal-agent theory

Aiming at benefit distribution of guaranteed water saving management contract (WSMC) projects, principal-agent theory is used to propose benefit distribution scheme. Firstly, principal-agent models are established in two cases: (1) the water user and WSCO are risk-neutral; (2) the water user is risk-neutral, while WSCO is risk-averse. Then the models are solved by the backward induction method under symmetric information and asymmetric information. The models' results are analyzed and the results show that (1) WSCO's benefit distribution proportion is negatively correlated with effort cost coefficient, risk aversion degree and project uncertainty; (2) the water user's expected utility is positively correlated with project investment scale and WSCO's water saving capacity; (3) WSCO's risk aversion will reduce the water user's expected utility; (4) incentives are only required when WSCO's effort is unobservable. Finally, numerical examples are used to show the above results' rationality and suggestions are given for the water user and WSCO.

Neuroscience: Therapy modulates decision-making in Parkinson’s disease

There is mounting evidence that decision-making can be affected by treatment in Parkinson's disease. A new study shows that dopamine and deep brain stimulation, two mainstay treatments of Parkinson's, differently affect how patients make decisions weighing rewards against effort costs.

What rationale would work? Unfolding the role of learners’ attitudes and motivation in predicting learning engagement and perceived learning outcomes in MOOCs

The aim of this study is to gain insight into the interplay between attitudes, motivation, learning engagement, and perceived learning outcomes in massive open online courses (MOOCs). An online survey was administered to 232 MOOC learners. This study provided comprehensive explanations for individual differences in learning engagement and perceived learning outcomes in MOOCs with a modified model of the expectancy-value theory of achievement motivation. The structural equation modeling revealed that attitudes served as a precursor of participation in MOOCs that significantly influenced self-efficacy, intrinsic value, and task effort cost; self-efficacy and intrinsic value were positively associated with both learning engagement and perceived learning outcomes, while attitudes toward MOOC learning was positively related to perceived learning outcomes only. Furthermore, the mediation analyses highlighted that intrinsic value was a powerful mediator, which positively influenced the effects of attitudes and self-efficacy on learning engagement and perceived learning outcomes. The moderation analyses discovered that task effort cost moderated the effects of attitudes on learning engagement and perceived learning outcomes. Curriculum designers and instructors could benefit from this study to understand what rationales drive individuals to be engaged in MOOC learning and to reach greater perceived learning outcomes in MOOCs.

Development of an open-source solution to facilitate the use of one-button wearables in experience sampling designs

The experience sampling method (ESM) allows for a high degree of ecological validity compared to laboratory research, at the cost of greater effort for participants. It would therefore benefit from implementations that reduce participant effort. In the present paper, we introduce a screenless wrist-worn one-button wearable as an unobtrusive measurement method that can be employed in ESM designs. We developed an open-source Android application to make this commercially available wearable easily configurable and usable. Over the course of six pilot studies, we explored the technical viability (e.g., battery life, reliability of inputs) of this wearable. We compared data quality between wearables and smartphones in a within-subjects design, exploring both the input options of using the number of button presses as a Likert scale, as well as using the angle of the device as a Physical Analogue Scale. Assessments of Extraversion made with either of these methods were highly correlated to comparable assessments made with comparable methods on a smartphone (i.e., Likert scale or a Visual Analogue Scale, respectively). Furthermore, in a preregistered ESM field experiment (N = 134, 4 weeks), we compared compliance to real-life event triggers between wearable devices and smartphones. We found higher numbers of logged events in the wearable group, indicating better adherence to the event-contingent scheduling. Overall, despite the device’s minimal capabilities and resulting limitations, one-button wearables can be beneficial for use in ESM designs.

The yardstick approach in a Tullock contest

AbstractA contest's organizational structure or mechanism is often determined by the organizer's goal. While most contest organizers are interested in maximizing the effort expended by the contestants, some are interested in maximizing social welfare. We examine the corresponding contest mechanisms using a Tullock contest (standard and winner‐pay) combined with the yardstick approach, which in some cases can achieve both goals simultaneously. Two cases are considered. The first is the yardstick approach that takes the form of a payoff scheme in a standard Tullock contest where the contestants (winners or losers) are reimbursed according to the average effort of the other contestants. In the second, the yardstick approach is introduced into a winner‐pay contest, in which the winner pays the average cost of the other contestants' efforts.

Research on the Optimal Leasing Strategy of Electric Vehicle Manufacturers

In the context of actively and steadily implementing the “dual carbon” strategy, two competing electric vehicle manufacturers (manufacturers m1 and m2) were selected as research objects to construct two different leasing strategy models for electric vehicle manufacturers, namely, m1 provided a unit rental electric vehicle strategy and m2 provided a fixed rental electric vehicle strategy. We studied the optimal car rental strategy and pricing of the two manufacturers under the situation of m2 providing and not providing rental service efforts, and the influence of relevant factors on the optimal decision are explored. It shows that the price of electric vehicles rented by consumers per unit increases with the combined effect of the coefficient of rental service effort and the marginal cost of the rental service effort, while the price of fixed rental electric vehicles decreases with the combined effect of both. When the unit rental preference coefficient is large, the unit rental of electric vehicles will give m1 maximum profit. When the rental service effort coefficient is high, m2 is the most profitable. The efforts to provide leasing services of m2 increase their own interests to a certain extent. The greater the effort coefficient of leasing services, the smaller the marginal cost of leasing services, and the optimal social welfare reaches the maximum. The conclusion of the article can provide relevant leasing insights for electric vehicle manufacturers and also provide certain theoretical guidance for promoting electric vehicle leasing service strategies.

Double dissociation of dopamine and subthalamic nucleus stimulation on effortful cost/benefit decision making

Deep brain stimulation (DBS) and dopaminergic therapy (DA) are common interventions for Parkinson's disease (PD). Both treatments typically improve patient outcomes, and both can have adverse side effects on decision making (e.g., impulsivity).1,2 Nevertheless, they are thought to act via different mechanisms within basal ganglia circuits.3 Here, we developed and formally evaluated their dissociable predictions within a single cost/benefit effort-based decision-making task. In the same patients, we manipulated DA medication status and subthalamic nucleus (STN) DBS status within and across sessions. Using a series of descriptive and computational modeling analyses of participant choices and their dynamics, we confirm a double dissociation: DA medication asymmetrically altered participants' sensitivities to benefits vs. effort costs of alternative choices (boosting the sensitivity to benefits while simultaneously lowering sensitivity to costs); whereas STN DBS lowered the decision threshold of such choices. To our knowledge, this is the first study to show, using a common modeling framework, a dissociation of DA and DBS within the same participants. As such, this work offers a comprehensive account for how different mechanisms impact decision making, and how impulsive behavior (present in DA-treated patients with PD and DBS patients) may emerge from separate physiological mechanisms.

Submodularity and supermodularity in contest games

AbstractThis paper presents various examples of two‐player submodular or supermodular contest games. Emphasizing the three main elements of a contest model, our examples revolve around situations where (i) contest success function allows for a draw, (ii) winning prize is not exogenously given but rather jointly produced, or (iii) individual effort cost also depends on the rival's effort. We then illustrate how submodularity and supermodularity can be used to study the existence of equilibrium, the order structure of the equilibrium set, and monotone comparative statics in such contest game examples.

Insight into differing decision-making strategies that underlie cognitively effort-based decision making using computational modeling in rats.

The rat cognitive effort task (rCET), a rodent model of cognitive rather than physical effort, requires animals to choose between an easy or hard visuospatial discrimination, with a correct hard choice more highly rewarded. Like in humans, there is stable individual variation in choice behavior. In previous reports, animals were divided into two groups-workers and slackers-based on their mean preference for the harder option. Although these groups differed in their response to pharmacological challenges, the rationale for using this criterion for grouping was not robust. We collated experimental data from multiple cohorts of male and female rats performing the rCET and used a model-based framework combining drift diffusion modeling with cluster analysis to identify the decision-making processes underlying variation in choice behavior. We verified that workers and slackers are statistically different groups but also found distinct intra-group profiles. These subgroups exhibited dissociable performance during the attentional phase, linked to distinct decision-making profiles during choice. Reanalysis of previous pharmacology data using this model-based framework showed that serotonergic drug effects were explained by changes in decision boundaries and non-decision times, while scopolamine's effects were driven by changes in decision starting points and rates of evidence accumulation. Modeling revealed the decision-making processes that are associated with cognitive effort costs, and how these differ across individuals. Reanalysis of drug data provided insight into the mechanisms through which different neurotransmitter systems impact cognitively effortful attention and decision-making processes, with relevance to multiple psychiatric disorders.

Effect of Corruption on Human Capital in Franc Zone Countries: An Opportunity Cost Analysis

The objective of this paper is to analyze the impact of corruption on human capital in African Franc zone countries. Assuming that the representative agent is rational, we develop a theoretical model in which effort, the real component of the human capital accumulation process, encompasses learning time, the learner’s personal resources, and the educational public good. The learner makes a trade-off between effort and corruption according to the opportunity cost of each option. Thus, in an environment where corruption (large and small) is endemic and where there are very few sanction mechanisms, the learner considers it rational to bribe at the expense of effort. Empirical analysis shows that a drop in the corruption perception index (reflecting an increase in corruption) of one-point leads to a drop in the human capital index of 0.175 percentage points. On the other hand, the impact is positive for public spending on education, official development assistance, real GDP per capita, and the management ratio. These variables reduce the opportunity cost of the effort. A number of recommendations are put forward with a view to reducing the cost of effort and increasing the cost of corruption.

Truck-drone joint path planning for post-disaster emergency material deployment considering fairness

As the expert gear of the emergency rescue system, drones are frequently utilized to distribute supplies following a calamity. The cost and effectiveness of rescue efforts as well as equitable distribution should be taken into account when allocating emergency supplies to disaster-affected areas. This work explores the emergency material allocation problem for truck-drone joint transportation with dynamic energy restrictions based on taking the fairness of emergency material allocation into consideration. In order to guarantee the equitable distribution of materials, the psychological stress experienced by the victims at each catastrophe site is measured using the relative deprivation cost. An adaptive large-scale neighborhood search method serves as the foundation for the creation of a two-stage heuristic algorithm, which reduces the overall cost of the system. The integer programming model MIP is built for this purpose. The research findings can serve as a useful guide for developing a just and effective emergency drone rescue system, and the testing results demonstrate the viability and effectiveness of the two-stage heuristic algorithm.

Optimal contests with incomplete information and convex effort costs

I investigate the design of effort‐maximizing mechanisms when agents have both private information and convex effort costs, and the designer has a fixed prize budget. I first demonstrate that it is always optimal for the designer to utilize a contest with as many participants as possible. Further, I identify a necessary and sufficient condition for the winner‐takes‐all prize structure to be optimal. When this condition fails, the designer may prefer to award multiple prizes of descending sizes. I also provide a characterization of the optimal prize allocation rule for this case. Finally, I illustrate how the optimal prize distribution evolves as the contest size grows.

Novel Darknet traffic data synthesis using Generative Adversarial Networks enhanced with oscillatory Growing Cosine Unit activated convolution layers

The Darknet is an anonymous, encrypted collection of websites, with a passive listening nature - accepting incoming packets, while not supporting outgoing packets. Thus, it can potentially host criminal or malicious activity and software, becoming a cyber security threat. Network Detection Systems are effective in identifying dark net traffic and mitigating its ill effects. However, capturing and extracting data from raw network traffic for training these systems can be time-intensive and costly. Using the CIC-Darknet 2020 dataset, this paper proposes using a Novel Generative Adversarial Networks (GAN) Architecture generating the required training and testing data for these systems. This uses a combination of Growing Cosine Unit (GCU) activated convolution layers and Dense layers for the Generator. Feature selection with statistical correlation methods is used to select the most relevant features. An independently trained Evaluator network is used to evaluate the generated data. The proposed system is compared to other established Tabular data GANs like Conditional Tabular GAN (CTGAN) and Copula GAN with similar parameters and on the same data. The Proposed GAN architecture outperforms CTGAN and CopulaGAN by 20 % and 10 % in ters of accuracy while also taking 90 % and 30 % less time to train respectively. Results from measuring similarity of data using the Inverted Kolmogorov - Smirnov D statistic also show significantly better results for the Proposed GAN Architecture. This shows significant promise in using Generative models to reduce the time and effort costs associated with collecting and formatting data to use in research and for training detection systems.

Incentive contract design considering quotas production: A principal-agent perspective

The quality of primary products in a supply chain is determined by the agent and the principal. Simultaneously, there are quota production constraints on the principals. Our discourse centers on the design of incentive contracts for agents within these supply chains. We derived the parameters describing the contract, level of effort, profits for both sides, and the minimum requirements of the principal for the proportion of high-quality primary products. This study compares the decision-making paradigms of agents and principals in various contexts. The results show that decision-making mechanisms are strongly influenced by individual effort costs, various internal and external organizational variables, and the interplay of efforts on both sides. Using numerical experiments, we investigate the effects of different situations between clients and contractors on contracting and effort strategies. The results show that when the agent exerts unilateral effort, reasonable incentive contracts can spur the agent to increase effort and thus increase the proportion of high-quality primary products. In the case of bilateral efforts, a well-calibrated contract design benefits the agent (bearing less risk). For the principal, the expected profit increases in most cases. When considering the quota production, it is necessary to set an appropriate limit on the proportion of high-quality primary products.

Do conservation contests work? An analysis of a large-scale energy competitive rebate program

Contests are widely used as mechanisms to incentivize efforts in various contexts, from research innovation to athletic and employee performance. This paper builds an analytical model and provides empirical evidence to assess the effectiveness of a large-scale, real-world residential energy conservation contest in Vietnam. The model suggests that contests offering multiple prizes and requiring a minimum amount of effort can effectively promote conservation. It also identifies factors that influence the conservation effort, such as the cost of effort, size of prizes, and types of contestants. To empirically estimate the effect of the energy conservation contest, this paper uses a unique confidential dataset of monthly residential electricity usage with over 45 million observations. The results indicate that the contest significantly reduced electricity consumption among contestants, and the effect of the contest could persist for months after its conclusion, leading to an average abatement cost of $27 per ton of CO2 emissions.

Is telemedicine worth the effort? A study on the impact of effort cost on healthcare platform with heterogeneous preferences

Telemedicine involves remote delivery of healthcare and virtual meetings between patients and doctors. However, its implementation in a traditional hospital can incur extra costs stemming from additional staffing, training, and IT infrastructure requirements. The effort needed by a doctor to treat a patient can be different depending on the mode of treatment. A principal-agent model is developed to find the optimal policy for both the agent (service provider, such as doctors) and the principal (platform, such as a hospital). We assume there are two types of service options, and the doctor prefers the service type with the lower effort cost. If the doctor’s policy is to accept both types of service options, her revenue may increase or decrease with an increase in the arrival rate of the less preferred service option. Offering only the service option with the higher average revenue is preferred if the service model is under the full control of the hospital. More importantly, the doctor and the hospital may not choose the same optimal strategies for certain values of effort costs. We show that this incentive misalignment issue can be solved by offering extra compensation for the less preferred service type of the doctor, which in turn affects the doctor’s strategy as well as the hospital’s rewards. Our study contributes to the literature on service queues with heterogeneous demands, as well as research on implementing telemedicine applications in healthcare systems.

Effort cost of harvest affects decisions and movement vigor of marmosets during foraging

Our decisions are guided by how we perceive the value of an option, but this evaluation also affects how we move to acquire that option. Why should economic variables such as reward and effort alter the vigor of our movements? In theory, both the option that we choose and the vigor with which we move contribute to a measure of fitness in which the objective is to maximize rewards minus efforts, divided by time. To explore this idea, we engaged marmosets in a foraging task in which on each trial they decided whether to work by making saccades to visual targets, thus accumulating food, or to harvest by licking what they had earned. We varied the effort cost of harvest by moving the food tube with respect to the mouth. Theory predicted that the subjects should respond to the increased effort costs by choosing to work longer, stockpiling food before commencing harvest, but reduce their movement vigor to conserve energy. Indeed, in response to an increased effort cost of harvest, marmosets extended their work duration, but slowed their movements. These changes in decisions and movements coincided with changes in pupil size. As the effort cost of harvest declined, work duration decreased, the pupils dilated, and the vigor of licks and saccades increased. Thus, when acquisition of reward became effortful, the pupils constricted, the decisions exhibited delayed gratification, and the movements displayed reduced vigor.

Effort cost of harvest affects decisions and movement vigor of marmosets during foraging.

Our decisions are guided by how we perceive the value of an option, but this evaluation also affects how we move to acquire that option. Why should economic variables such as reward and effort alter the vigor of our movements? In theory, both the option that we choose and the vigor with which we move contribute to a measure of fitness in which the objective is to maximize rewards minus efforts, divided by time. To explore this idea, we engaged marmosets in a foraging task in which on each trial they decided whether to work by making saccades to visual targets, thus accumulating food, or to harvest by licking what they had earned. We varied the effort cost of harvest by moving the food tube with respect to the mouth. Theory predicted that the subjects should respond to the increased effort costs by choosing to work longer, stockpiling food before commencing harvest, but reduce their movement vigor to conserve energy. Indeed, in response to an increased effort cost of harvest, marmosets extended their work duration, but slowed their movements. These changes in decisions and movements coincided with changes in pupil size. As the effort cost of harvest declined, work duration decreased, the pupils dilated, and the vigor of licks and saccades increased. Thus, when acquisition of reward became effortful, the pupils constricted, the decisions exhibited delayed gratification, and the movements displayed reduced vigor.

Effort Foraging Task reveals positive correlation between individual differences in the cost of cognitive and physical effort in humans

Effort-based decisions, in which people weigh potential future rewards against effort costs required to achieve those rewards involve both cognitive and physical effort, though the mechanistic relationship between them is not yet understood. Here, we use an individual differences approach to isolate and measure the computational processes underlying effort-based decisions and test the association between cognitive and physical domains. Patch foraging is an ecologically valid reward rate maximization problem with well-developed theoretical tools. We developed the Effort Foraging Task, which embedded cognitive or physical effort into patch foraging, to quantify the cost of both cognitive and physical effort indirectly, by their effects on foraging choices. Participants chose between harvesting a depleting patch, or traveling to a new patch that was costly in time and effort. Participants’ exit thresholds (reflecting the reward they expected to receive by harvesting when they chose to travel to a new patch) were sensitive to cognitive and physical effort demands, allowing us to quantify the perceived effort cost in monetary terms. The indirect sequential choice style revealed effort-seeking behavior in a minority of participants (preferring high over low effort) that has apparently been missed by many previous approaches. Individual differences in cognitive and physical effort costs were positively correlated, suggesting that these are perceived and processed in common. We used canonical correlation analysis to probe the relationship of task measures to self-reported affect and motivation, and found correlations of cognitive effort with anxiety, cognitive function, behavioral activation, and self-efficacy, but no similar correlations with physical effort.