Optimal Incentive Scheme Research Articles

Developing incentive mechanisms for long-term and short-term technological innovation in major engineering projects using principal-agent theory

PurposePromoting technological innovation is important to address the complexity of major engineering challenges. Technological innovations include short-term innovations at the project level and long-term innovations that can enhance competitive advantages. The purpose of this study is to develop an incentive mechanism for the public sector that considers short-term and long-term efforts from the private sector, aiming to promote technological innovation in major engineering projects.Design/methodology/approachThis study constructs an incentive model considering the differences in short-term and long-term innovation efforts from the private sector. This model emphasizes the spillover effect of long-term efforts on current projects and the cost synergy effect between short-term and long-term efforts. It also explores the factors influencing the optimal incentive strategies for the public sector and innovation strategies for the private sector.FindingsThe results indicate that increasing the output coefficient of short-term and long-term efforts and reducing the cost coefficient not only enhance the innovation efforts of the private sector but also prompt the public sector to increase the incentive coefficient. The spillover effect of long-term innovation efforts and the synergy effect of the two efforts are positively related to the incentive coefficient for the public sector.Originality/valueThis research addresses the existing gap in understanding how the public sector should devise incentive mechanisms for technological innovation when contractors acting as the private sector are responsible for construction within a public-private partnership (PPP) model. In constructing the incentive mechanism model, this study incorporates the private sector's short-term efforts at the project level and their long-term efforts for sustained corporate development, thus adding considerable practical significance.

Optimal incentive schemes to achieve a given market share target for new energy vehicles under China's dual credit policy

The Chinese national government and local governments have introduced multiple incentive measures to increase the market share of new energy vehicles (NEVs), such as dual credit policy, financial subsidies, and building new charging infrastructures. However, the government's budget to support the development of NEVs is limited. In this paper, we consider a duopolistic market consisting of a gasoline vehicle (GV) firm and an electric vehicle (EV) firm and develop a multi-level game-theoretic model based on the fact that the Chinese government seeks to achieve a given market share target with the minimum expenditure. A comparison of the equilibrium results in three incentive schemes differentiated by the financial subsidy is conducted to investigate the effectiveness of various incentive schemes. Furthermore, we consider a real situation in China that the government attempts to achieve a target for the total number of charging infrastructures through a reasonable policy design. The results in this study reveal that, with the EV market share target in mind, raising the requirements of dual credit policy has no effect on the EV firm's profit under EV purchase subsidy, is beneficial to the EV firm's profit under construction cost sharing subsidy, and is detrimental to the EV firm's profit under per-unit construction subsidy. It is worthwhile for the government to subsidize for infrastructure construction effort rather than consumers' purchase cost. Given a target for the total number of charging infrastructures, construction cost sharing subsidy can provide more motivation for the EV firm to build charging infrastructures than per-unit construction subsidy.

Optimal incentive strategy in blockchain-based mobile crowdsensing using game theory

Mobile crowdsensing (MCS) is a cutting-edge technology that leverages the computing power of mobile devices to gather environmental data, efficiently utilizing underutilized resources to complete sensing tasks. However, the lack of initiative and motivation among participants in existing MCS systems is a crucial factor limiting its development. As a result, examining the incentive mechanism in MCS becomes essential for attracting a greater number of participants to join. In addition, existing MCS systems typically employ a centralized platform, but this structure poses security issues, as the platform can manipulate MCS operations to gain inappropriate profits and is prone to single point of failures. In response to the above-mentioned issues, this paper proposes a novel blockchain-based MCS system, referred to as CrowdBS. Firstly, the system adopts a decentralized structure and distributed consensus methods to enhance the reliability and security of the system. Additionally, the use of smart contracts allows the system to automatically execute predefined operations without the need for human intervention. In MCS, this means that it can automatically trigger operations such as data collection, reward distribution, task assignment, etc., thereby improving efficiency and reducing the risk of human intervention. Secondly, the system introduces a two-stage Stackelberg game model, attracting more sensing users to participate in tasks through reasonable incentive mechanisms and ensuring fairness in reward distribution, thereby promoting efficient, reliable, and sustainable execution of crowdsensing tasks. Finally, the correctness of the model is verified through simulation experiments, and compared with the existing MCS system. The comparison results show that the system and its incentive mechanism have more comprehensive advantages in terms of reliability, security and performance.

Managing ridesharing with incentives in a bottleneck model

Ridesharing is recognized as an environmentally friendly alternative to daily transportation that can potentially reduce vehicular carbon emissions. Transportation Network Companies (TNC) have been providing ridesharing services. However, generalized costs need to be balanced between rideshare drivers and passengers via financial instruments to reach a sustainable equilibrium. In this paper, we demonstrate the effectiveness of Incentive-Based Travel Demand Management (IBTDM) strategies in managing morning commutes with ridesharing. Separate time-varying incentive schemes for both rideshare drivers and passengers are proposed to maintain the user equilibrium. Since IBTDM strategies are usually non-mandatory, it is found that the optimal incentives vary with respect to market penetration. Subsequently, we took the difficulty of scheme implementation, system cost reduction, and net utility as the criteria, and obtained the optimal incentive schemes under different criteria. Finally, we verified the effectiveness of the above incentive scheme in promoting ridesharing and reducing system costs by means of experimental economics.

Blockchain-assisted computation offloading collaboration: A hierarchical game to fortify IoT security and resilience

With the rapid development of the Internet of Things (IoT), computation offloading collaboration has emerged as an important solution to the data processing challenges faced by resource-constrained devices. Nonetheless, the collaboration process introduces intricate, multi-layered security and privacy issues. This paper presents a blockchain-assisted computation offloading collaboration (BACOC) scheme that leverages blockchain technology to reinforce security and resilience during computation offloading. Concurrently, we adopt a hierarchical game model grounded in Stackelberg game and contract theory, which helps ascertain the optimal incentive strategies for the purpose of incentive compatibility. Experimental and simulation results demonstrate the effectiveness and feasibility of the BACOC scheme.

Collaborate or Compete? Time-Varying Incentives versus Tolling in Parallel Bottleneck

Incentive-based traffic demand management (IBTDM) schemes redistribute traffic demand across space and time. The relationship between IBTDM and traditional tolling programs impacts financial sustainability and decision making of both programs. This study aims to examine if an IBTDM and a tolling program can coexist and should collaborate or compete to establish a financially sustainable mobility ecosystem. We investigate a duopoly case with an IBTDM and a tolling program in a parallel bottleneck model with fixed demand. It is assumed that the IBTDM program has a user acquisition cost and is constrained by a total budget. Then, the collaboration and competition cases between two programs are investigated, respectively. We found that both programs can attract substantial amounts of commuters and thus can coexist with each other, while the IBTDM program could attract more commuters. Overall, the tolling agency will not cooperate with the IBTDM company if its goal is to raise revenue. Additionally, the IBTDM company held an advantage as it can determine an optimal incentive scheme with incomplete information when competing. This study fills the knowledge gap on the coexistence of incentive and tolling programs, which helps to optimize the profiles of incentive and tolling, as well as to make strategic decisions on competition and cooperation.

Monetary and social incentives in multi-tasking: The ranking substitution effect

Rankings are intended as incentive tools on labor markets. Yet, when agents perform multiple tasks, rankings might have unintended side-effects, especially if not all tasks can be ranked with respect to performance. We analyze the dynamics of multi-tasking and present an experiment with 286 finance professionals in which we identify hidden ranking costs when performance in one task is ranked while in another prosocial task it is not. We find that subjects lagging behind (leading) in the ranked task devote less (more) effort to the prosocial task. We discuss implications for optimal incentive schemes in organizations with multi-tasking.

Incentive Mechanism and Subsidy Design for Continuous Monitoring of Energy Consumption in Public Buildings (CMECPB): An Overview Based on Evolutionary Game Theory

Rapid urbanization and the continued expansion of buildings have resulted in a consistent rise in the energy consumption of buildings. At the same time, the monitoring of building energy consumption has to achieve the goals of an “Emission peak” and “Carbon neutrality”. Numerous energy consumption monitoring systems have been established in several types of public buildings. However, there is a need to ensure that the data are continuously acquired and of superior quality. Scholars have noted that the in-depth research connected to the continuous monitoring of energy consumption in public buildings (CMECPB) is currently sparse. As a result, additional precise quantitative studies targeting the behavior of various stakeholders are also lacking. Hence, there is a need to explore the definition of value and the dynamic benefits of relevant subjects in continuous energy consumption monitoring based on evolutionary game theory and to propose incentive policies. This paper constructs an evolutionary game model for CMECPB between an energy service company (ESCO) and its owner to study the dynamic evolution path of a game system and the evolutionarily stable strategy under market-based mechanisms. Furthermore, by introducing government actions, the incentive policies and subsidy strategy for different subjects of interest are probed in detail by developing a principal-agent model to explore the incentive strength. The following conclusions can be reached: (1) it is inefficient and risky to rely only on the owner and the ESCO in achieving the optimal Pareto equilibrium; (2) the optimal incentives are “fixed incentives” in the case of information symmetry and a “fixed incentive + variable incentive” in the case of information asymmetry; (3) the choice of optimal incentive strategy is also influenced by the cost effort coefficient, risk aversion, external uncertainty, and integrated value transformation coefficient; (4) the incentive intensity and subsidy should be determined by comprehensive analysis with multiple indicators based on the conventional value of a project and the external value of a particular project. An in-depth understanding of each component of the CMECPB pathway yields insights into overcoming the challenges of building energy saving. Furthermore, the results may be useful in developing targeted, effective incentive policies for different disciplines and promoting the continued progress of monitoring building energy consumption and building energy efficiency.

Evaluation of Photovoltaic Consumption Potential of Residential Temperature-Control Load Based on ANP-Fuzzy and Research on Optimal Incentive Strategy

Temperature-control loads, such as residential air conditioners (ACs) and electric water heaters (EWHs), have become important demand response resources in the power system. However, due to the impact of various factors on users’ response behavior, it has been difficult for power grid operators to accurately evaluate the response potential under complex factor relationships to derive optimal incentive strategy. Therefore, it cannot achieve a win-win economic benefit between the grid and users. In this paper, a method combining Analytic Network Process (ANP) and Fuzzy logical inference is proposed to predict the user’s willingness firstly by taking residential AC load as an example. The weight of each factor affecting users’ willingness is analyzed, and main factors are selected as inputs of fuzzy logic inference to derive the willingness of the resident to actively regulate the AC. Then, this method is applied in evaluating the response potential of certain residential area in Beijing according to the survey. By further considering users’ house size and the sacrificed comfort temperature under the incentive strategy, the power potential curve of the AC load under different incentives is obtained by using the first-order equivalent thermal parameter (ETP) model and the regulation willingness. Finally, with the objective of maximizing the consumption of the photovoltaic (PV) power, the optimal operation is achieved through the coordinated regulation of residential ACs and EWHs based on the potential curve, and the corresponding optimal incentive strategy for the flexible temperature-control loads is obtained. Simulation results show that the optimal incentive strategy proposed not only increases the PV consumption ratio to 98.35% with an increase of 24.71%, but also maximizes the economic benefits of both sides of the power grid and users. This method of deriving incentive strategy can be used as a reference for grid companies to formulate the incentive strategy to realize optimal operation, such as the maximum new energy consumption.

Simulation of the role of emphasis on scheduling in the optimal incentive scheme for marine engineering employee's routine job and information security compliance

In the organizational context of marine engineering, employee individual often prefers to concentrate herself to the day-to-day routine job, but to shirk the responsibilities of the Information Security Policies (ISPs) compliance, after she has been delegated by the employer to perform the two different tasks in the same time period. This would lead to negative influences on the security of marine information systems and the employee's routine job performance. In view of the task structures of employee's routine job and marine ISPs compliance, the variables of emphasis on scheduling are incorporated into a multi-task principal-agent model to explore the optimal incentive scheme to motivate and control the employees to select appropriate effort levels for conducting the two highly structured tasks. The role of emphasis on scheduling on the incentive intensities for the two tasks have been clarified through modeling and simulation, and the corresponding incentive tactics are suggested. The new two-task incentive scheme is expected to provide useful insight for understanding and controlling marine engineering employee's routine job and ISPs compliance behavior.

Preventing Bottlenecks in Organizations

Organizations may suffer from unforeseen negative shocks, potentially leading to severe bottlenecks. This paper studies an optimal incentive scheme robust to those shocks in the effort induction problem in team production. The optimal scheme under the no-shock assumption is typically vulnerable in that a single shock can lead to shirking of sizable non-shocked members. We show that an optimal scheme is associated with a uniform order of worker subsets; and identify relative complementarity as a novel source of the determinant of the optimal organization structure.

Optimal Incentive Strategy in Cloud-Edge Integrated Demand Response Framework for Residential Air Conditioning Loads

In the residential demand response area, currently the incentive-based method (e.g., direct load control, DLC) may impair users’ comfort and autonomy, while the price-based method can hardly guarantee users’ engagements. This paper proposes an edge-cloud integrated demand response framework to achieve an effect-predictable residential demand response without harming users’ benefits. First, we combine the cloud-computing resource (cloud) and the home-installed smart thermostats (edges) to formulate an efficient, cost-effective, and data-secured infrastructure to implement the demand response program. Then, we model the demand response problem between the load aggregator and its served residential users as a bi-level optimization problem, and the key is for the load aggregator to find the optimal incentive strategy. To solve this problem, we introduce an RL algorithm, i.e., Continuous Action Reinforcement Learning Automata, to quickly obtain the optimal incentive strategy under an incomplete information scenario. Simulation results based on 136 real-world residential users in Austin area demonstrate that the proposed CEI-DR framework can increase the social welfare by about $8.6/h compared to the traditional DLC method during a normal DR event.

Triggering User-Managed Smart Charging by Real-Time Pricing: Design of Optimal Incentive Scheme

Triggering User-Managed Smart Charging by Real-Time Pricing: Design of Optimal Incentive Scheme

The role of intrinsic incentives and corporate culture in motivating innovation

This paper examines the optimal incentive scheme in motivating people to innovate under ambiguity. When an innovation’s prospects are ambiguous, the use of extrinsic, high-powered incentives can lead the agent’s beliefs about the project’s outcome to deviate from that of the principal’s, which consequently deters innovation. The deterrent effect, however, is alleviated for firms in which agents have strong intrinsic incentives to adhere to firms’ goals and missions. In equilibrium, extrinsic and intrinsic incentives are complementary, and firms that face greater uncertainty invest more in fostering intrinsic incentives. Hence, firms that pursue more exploratory and radical innovation invest more in creating corporate identity and culture.

Research on the Knowledge-Sharing Incentive of the Cross-Boundary Alliance Symbiotic System

The rise of the cross-boundary alliance as a new organizational model profoundly affects innovation development. The incentive mode of knowledge-sharing among cross-boundary alliance members from the perspective of symbiosis is the key to improving the efficiency of knowledge-sharing and promoting the alliance’s sustainable development. Due to the interdisciplinary nature of knowledge and information asymmetry among alliance members, knowledge-sharing is prone to opportunistic behavior, which greatly impacts cross- boundary innovation cooperation. The symbiotic system of the cross-boundary alliance is constructed based on the analysis of symbiosis among alliance members to solve these problems. In this research study, the principal–agent theory is used to describe the expected benefits and the related constraints of knowledge-sharing partners. Moreover, an incentive model of knowledge-sharing in the cross-boundary alliance is established. By solving the model, the agent’s sharing intention and the principal’s reward incentive coefficient are obtained, which provide a theoretical basis for the formulation of the optimal incentive scheme of knowledge-sharing in the alliance. The results show that the knowledge potential difference, knowledge transmission efficiency, knowledge transformation, innovation effort, and the symbiotic environment will directly affect the knowledge transfer level of the alliance. In this research study, a theoretical research framework of the cross-boundary alliance symbiotic system linked by knowledge-sharing is constructed. The incentive model of knowledge-sharing is established especially on the basis of fully considering the internal and external factors of knowledge-sharing. This research study has provided some innovation in the theoretical system and method improvement, and has guaranteed the efficient integration, utilization, and innovation of knowledge resources in the cross-boundary alliance.

Combination of institutional incentives for cooperative governance of risky commons

SummaryFinding appropriate incentives to enforce collaborative efforts for governing the commons in risky situations is a long-lasting challenge. Previous works have demonstrated that both punishing free-riders and rewarding cooperators could be potential tools to reach this goal. Despite weak theoretical foundations, policy makers frequently impose a punishment-reward combination. Here, we consider the emergence of positive and negative incentives and analyze their simultaneous impact on sustaining risky commons. Importantly, we consider institutions with fixed and flexible incentives. We find that a local sanctioning scheme with pure reward is the optimal incentive strategy. It can drive the entire population toward a highly cooperative state in a broad range of parameters, independently of the type of institutions. We show that our finding is also valid for flexible incentives in the global sanctioning scheme, although the local arrangement works more effectively.

Give Me a Challenge or Give Me a Raise

I study the effect of task difficulty on workers’ effort. I find that task difficulty has an inverse-U effect on effort and that this effect is quantitatively large, especially when compared to the effect of conditional monetary rewards. Difficulty acts as a mediator of monetary rewards: conditional rewards are most effective at the intermediate or high levels of difficulty. The inverse-U pattern of effort response to difficulty is inconsistent with many popular models in the literature, including the Expected Utility models with the additively separable cost of effort. I propose an alternative mechanism for the observed behavior based on non-linear probability weighting. I structurally estimate the proposed model and find that it successfully captures the behavioral patterns observed in the data. I discuss the implications of my findings for the design of optimal incentive schemes for workers and for the models of effort provision.

Give me a challenge or give me a raise

I study the effect of task difficulty on workers’ effort. I find that task difficulty has an inverse-U effect on effort and that this effect is quantitatively large, especially when compared to the effect of conditional monetary rewards. Difficulty acts as a mediator of monetary rewards: conditional rewards are most effective at the intermediate or high levels of difficulty. The inverse-U pattern of effort response to difficulty is inconsistent with many popular models in the literature, including the Expected Utility models with the additively separable cost of effort. I propose an alternative mechanism for the observed behavior based on non-linear probability weighting. I structurally estimate the proposed model and find that it successfully captures the behavioral patterns observed in the data. I discuss the implications of my findings for the design of optimal incentive schemes for workers and for the models of effort provision.

Optimal Incentives Schemes under Homo Moralis Preferences

This study focuses on the optimal incentive schemes in a multi-agent moral hazard model, where each agent has other-regarding preferences and an individual measure of output, with both being observable by the principal. In particular, the two agents display homo moralis preferences. I find that, contrary to the case with purely selfish preferences, tournaments can never be optimal when agents are risk averse, and as the degree of morality increases, positive payments are made in a larger number of output realizations. Furthermore, I extend the analysis to a dynamic setting, in which a contract is initially offered to the agents, who then repeatedly choose which level of effort to provide in each period. I show that the optimal incentive schemes in this case are similar to the ones obtained in the static setting, but for the role of intertemporal discounting.

Rank Uncertainty in Organizations

A principal incentivizes a team of agents to work by privately offering them bonuses contingent on team success. We study the principal’s optimal incentive scheme that implements work as a unique equilibrium. This scheme leverages rank uncertainty to address strategic uncertainty. Each agent is informed only of a ranking distribution and his own bonus, the latter making work dominant provided that higher-rank agents work. If agents are symmetric, their bonuses are identical. Thus, discrimination is strictly suboptimal, in sharp contrast with the case of public contracts (Winter 2004). We characterize how agents’ ranking and compensation vary with asymmetric effort costs. (JEL D23, D62, D81, D82, D86)