Cost Of Effort Research Articles

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.

A Review on the Use of Modern Computational Methods in Alzheimer's Disease-Detection and Prediction.

Discoveries in the field of medical sciences are blooming rapidly at the cost of voluminous efforts. Presently, multidisciplinary research activities have been especially contributing to catering cutting-edge solutions to critical problems in the domain of medical sciences. The modern age computing resources have proved to be a boon in this context. Effortless solutions have become a reality, and thus, the real beneficiary patients are able to enjoy improved lives. One of the most emerging problems in this context is Alzheimer's disease, an incurable neurological disorder. For this, early diagnosis is made possible with benchmark computing tools and schemes. These benchmark schemes are the results of novel research contributions being made intermittently in the timeline. In this review, an attempt is made to explore all such contributions in the past few decades. A systematic review is made by categorizing these contributions into three folds, namely, First, Second, and Third Generations. However, priority is given to the latest ones as a handful of literature reviews are already available for the classical ones. Key contributions are discussed vividly. The objectives set for this review are to bring forth the latest discoveries in computing methodologies, especially those dedicated to the diagnosis of Alzheimer's disease. A detailed timeline of the contributions is also made available. Performance plots for certain key contributions are also presented for better graphical understanding.

Policy abstraction as a predictor of cognitive effort avoidance.

Consistent evidence has established that people avoid cognitively effortful tasks. However, the features that make a task cognitively effortful are still not well understood. Multiple hypotheses have been proposed regarding which task demands underlie cognitive effort costs, such as time-on-task, error likelihood, and the general engagement of cognitive control. In this study, we test the novel hypothesis that tasks requiring behavior according to higher degrees of policy abstraction are experienced as more effortful. Accordingly, policy abstraction, operationalized as the levels of contextual contingency required by task rules, drives task avoidance over and above the effects of task performance, such as time-on-task or error likelihood. To test this hypothesis, we combined two previously established cognitive control tasks that parametrically manipulated policy abstraction with the demand selection task procedure. The design of these tasks allowed us to test whether people avoided tasks with higher order policy abstraction while controlling for the contribution of factors such as time-on-task and expected error rate (ER). Consistent with our hypothesis, we observed that policy abstraction was the strongest predictor of cognitive effort choices, followed by ER. This was evident across both studies and in a within-subject cross-study analysis. These results establish at least one task feature independent of performance, which is predictive of task avoidance behavior. We interpret these results within an opportunity cost framework for understanding aversive experiences of cognitive effort while performing a task. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Sustainable strategies for green supply chain within the platform economy consider subsidies and marketing efforts.

As consumers' green awareness continues to grow, the level of a product's eco-friendliness and the quality of its marketing have become significant factors in shaping consumers' purchasing decisions. The power structures within the supply chain, as well as corresponding government subsidy policies, are also key elements influencing sustainable strategies for the green supply chain. In a green supply chain comprising one manufacturer and one e-commerce platform, two sales models exist within the online e-commerce platforms: reselling and agency selling. This paper establishes and analyzes three distinct Stackelberg game models, namely: manufacturer-led model without subsidy (bm), manufacturer-led model with subsidy (sm), and platform-led model with subsidy (sp). The results are shown as follows, with the rise of consumers' environmental awareness, more consumers opt for green products, inspiring the manufacturer to increase its optimal greenness and platform to enhance its optimal marketing efforts level. Notably, government subsidies provide a significant stimulus. An increase in the green technology cost coefficient leads to a decline in the manufacturer's profits across all three modes. Intriguingly, the manufacturer's profits are always highest in the sp mode. As the marketing efforts cost coefficient increases, the platform's profits decrease in the bm and sm modes. In contrast, in the sp mode, the platform's profits increase rather than a decrease. The choice of mode primarily depends on the platform's marketing efforts cost coefficient. When this coefficient exceeds a threshold, the platform chooses the sp mode. However, due to the relatively low marketing efficiency in this scenario, the manufacturer prefers the sm mode. For the government, the sp mode involves agency selling, serves as an effective mechanism to redistribute subsidies, thereby yielding the maximum social welfare benefits. Management insights are provided for the manufacturer and platform managers to make decisions about the degree of greenness and marketing efforts level, along with insights for governments to optimize subsidy policies.

Cost of effort in benchmarking: exploration and applications

The literature on incentives suggests that the cost of effort is a key determinant of production, as an agent’s utility is dependent on both consumption and the cost of effort. However, the benchmarking literature has neglected to consider the cost of effort in performance improvement, as it primarily focuses on selecting best-practice benchmarks for underperforming agents. This paper aims to bridge these two literatures by examining the cost of effort in benchmarking and its applications. Our approach is a direct extension of the rational inefficiency hypothesis. We use the information of slacks regarding technology to make inference about the cost of effort in benchmarking. Importantly, we show that inference about the cost of effort gives new sights into activity planning, incentive provision, and employee layoffs. Our analysis provides a new explanation for benchmarking failure in business practices. It also contributes to the rational inefficiency hypothesis by revealing that inefficiency can be beneficial in benchmarking since it can be regarded as a form of fringe reimbursement provided to stakeholders to offset the cost of effort.

Need for cognition moderates the relief of avoiding cognitive effort.

When making decisions, humans aim to maximize rewards while minimizing costs. The exertion of mental or physical effort has been proposed to be one those costs, translating into avoidance of behaviors carrying effort demands. This motivational framework also predicts that people should experience positive affect when anticipating demand that is subsequently avoided (i.e., a "relief effect"), but evidence for this prediction is scarce. Here, we follow up on a previous study [1] that provided some initial evidence that people more positively evaluated outcomes if it meant they could avoid performing an additional demanding task. However, the results from this study did not provide conclusive evidence that this effect was driven by effort avoidance. Here, we report two experiments that are able to do this. Participants performed a gambling task, and if they did not receive reward they would have to perform an orthogonal effort task. Prior to the gamble, a cue indicated whether this effort task would be easy or hard. We probed hedonic responses to the reward-related feedback, as well as after the subsequent effort task feedback. Participants reported lower hedonic responses for no-reward outcomes when high vs. low effort was anticipated (and later exerted). They also reported higher hedonic responses for reward outcomes when high vs. low effort was anticipated (and avoided). Importantly, this relief effect was smaller in participants with high need for cognition. These results suggest that avoidance of high effort tasks is rewarding, but that the size off this effect depends on the individual disposition to engage with and expend cognitive effort. They also raise the important question of whether this disposition alters the cost of effort per se, or rather offset this cost during cost-benefit analyses.