Incentive Compatibility Research Articles

Considering hydrogen policies with a focus on incentive compatibility towards electricity grids

On the Stability, Economic Efficiency, and Incentive Compatibility of Electricity Market Dynamics

This study develops an incentive mechanism model for outsourced personnel in product quality inspection, based on a principal-agent relationship. The core challenge lies in misaligned incentives, where agents often prioritize output volume over quality. By integrating Mechanism Design Theory (MDT) and Linear Programming (LP), our model aligns the principal's objective of minimizing defective products with the agent's utility maximization, subject to Incentive Compatibility and Individual Rationality constraints. Our analysis reveals that the optimal incentive structure combines a basic wage with a performance-based bonus. The optimal effort level of outsourced personnel increases with both rising losses due to defective products and enhanced detection effort effectiveness. The model also shows that optimal inspection allocation should be assigned to personnel with higher capabilities, especially for high-risk products. This research provides a theoretical contribution by integrating MDT and LP for incentive design and offers practical implications for improving product quality through a measurable incentive framework.

Data plays a crucial role in training contemporary AI models, but much of the available public data will be exhausted in a few years, directing the world's attention toward the massive decentralized private data. However, the privacy-sensitive nature of raw data and lack of incentive mechanism prevent these valuable data from being fully exploited. Here we propose inclusive and incentivized personalized federated learning (iPFL), which incentivizes data holders with diverse purposes to collaboratively train personalized models without revealing raw data. iPFL constructs a model-sharing market by solving a graph-based training optimization and incorporates an incentive mechanism based on game theory principles. Theoretical analysis shows that iPFL adheres to two key incentive properties: individual rationality and Incentive compatibility. Empirical studies on eleven AI tasks (e.g., large language models' instruction-following tasks) demonstrate that iPFL consistently achieves the highest economic utility, and better or comparable model performance compared to baseline methods.

Compared with government-oriented transfer payments, horizontal eco-compensation policy (HECP) incentivizes local governments to engage in environmental governance through market-based transfer payments, thereby achieving incentive compatibility. However, existing research has paid relatively little attention to the impact of HECP on enterprises’ environmental governance. Using data from Chinese industrial enterprises from 2008 to 2014, we employ a difference-in-differences (DID) approach to examine the impact of the horizontal eco-compensation agreement in the Xin’an River Basin on enterprise pollution emissions. We find that HECP significantly reduces enterprise pollution emissions. Heterogeneity analysis reveals that this impact is more pronounced in non-state-owned enterprises, older enterprises, and enterprises with lower financing constraints or closer regulatory proximity. Mechanism analysis suggests that HECP reduces pollution emissions primarily by limiting the entry of polluting enterprises and improving energy efficiency. Several robustness tests further confirm the reliability of our results. This paper provides a new policy perspective for promoting pollution reduction in enterprises and offers empirical evidence and policy guidance for both developing and developed countries in designing market-based environmental regulations to address cross-jurisdictional environmental challenges.

A recent line of work in mechanism design has focused on guaranteeing incentive compatibility for agents without contingent reasoning skills: obviously strategyproof mechanisms guarantee that it is “obvious” for these imperfectly rational agents to behave honestly, whereas non-obviously manipulable (NOM) mechanisms take a more optimistic view and assume that these agents will only misbehave when it is “obvious” for them to do so. Technically, obviousness requires comparing certain extrema (defined over the actions of the other agents) of an agent’s utilities for honest behaviour against dishonest behaviour. We present a technique for designing NOM mechanisms in settings with monetary transfers based on cycle monotonicity, which allows us to disentangle the specification of the mechanism’s allocation from its payments. By leveraging this framework, we completely characterise both allocation and payment functions of NOM mechanisms for single-parameter agents. We then look at the classical setting of bilateral trade and study how much subsidy, if any, is needed to guarantee NOM, efficiency, and individual rationality. We prove a stark dichotomy: no finite subsidy suffices if agents look only at best-case extremes, whereas no subsidy at all is required when agents focus on worst-case extremes.

The proliferation of Internet of Things (IoT) devices has opened new roads for collaborative distributed applications, particularly in smart city environments, where a variety of resources, including sensing, actuation, computation, and storage, are essential for providing effective location-based services. This paper specifically focuses on the sharing of heterogeneous resources among IoT applications in smart cities. By leveraging game-theoretic principles, this study addresses resource allocation through a combinatorial double auction. The solution is rooted in the concept of Social IoT (SIoT), where Internet-connected objects create dynamic social networks based on rules set by their owners. Social relationships, such as ownership and co-location, are leveraged to form groups offering enhanced reliability resource bundles. The proposed solution offers several key economic properties, including incentive compatibility, individual rationality, and a balanced budget, while maintaining low computational complexity. Simulation results demonstrate that the proposed combinatorial double auction mechanism achieves over 70% successful resource allocation for up to 1000 requests, maintains computational efficiency with execution times under 30 s, and ensures economic properties such as incentive compatibility and individual rationality, making it a scalable and practical solution for large-scale smart city IoT applications.

Demand response (DR) has high regulation potential, which can reduce the power supply–demand imbalance caused by extreme disasters. However, its actual effectiveness still needs to be improved because of low user willingness and incomplete compensation mechanisms. To address this issue, a symmetrical incentive mechanism for DR is proposed. Building upon this mechanism, a bi-level load restoration optimization model under extreme events is proposed. The upper-level model minimizes grid-side costs during load restoration, determining load restoration ratios and incentive coefficients transmitted to the lower level. The lower-level model maximizes user profits while considering comfort-level losses from DR participation, solving for actual response quantities that are fed back to the upper level. To efficiently solve the proposed load restoration model, an iterative mixed-integer load restoration solver is proposed. Case studies demonstrate that the proposed symmetrical mechanism achieved an 89.6% participation rate, showing a 2.46% improvement over fixed incentive schemes. Grid payment costs were reduced by CNY 365,400, achieving incentive compatibility that facilitates rapid load restoration post extreme disasters.

In contemporary e-commerce platforms, search result pages display two types of items: ad items and organic items. Ad items are determined through an advertising auction system, while organic items are selected by a recommendation system. These systems have distinct optimization objectives, creating the challenge of effectively merging these two components. Recent research has explored merging mechanisms for e-commerce platforms, but none have simultaneously achieved all desirable properties: incentive compatibility, individual rationality, adaptability to multiple slots, integration of inseparable candidates, and avoidance of repeated exposure for ads and organic items. This paper addresses the design of a merging mechanism that satisfies all these properties. We first provide the necessary conditions for the optimal merging mechanisms. Next, we introduce two simple and effective mechanisms, termed the generalized fix mechanism and the generalized change mechanism. Finally, we theoretically prove that both mechanisms offer guaranteed approximation ratios compared to the optimal mechanism in both simplest and general settings.

Abstract The rise of the Internet of Things (IoT) has led to a huge amount of data beginning to emerge. Federated learning (FL) has received widespread attention and application as a new paradigm for data collection. However, data trading poses a threat to the privacy of data owners, and even participants in federated learning face the risk of data breaches. While many encryption methods have been proposed to mitigate these risks, the encrypted data negatively impacts the quality of the global model in federated learning. To this end, we propose an algorithm based on contract mechanisms to resolve the conflict between the privacy protection level of clients and the aggregation error on the federated learning server. Clients upload perturbed data according to their privacy protection levels, while mitigating the conflict between client data privacy protection and platform global model aggregation error. Through theoretical analysis and extensive experiments, our proposed trading method achieves desirable data utility while ensuring budget feasibility, individual rationality, and incentive compatibility.

This article revisits the Pareto-optimal reinsurance problem under the Value at Risk (VaR) risk measure. To encapsulate the essence of reinsurance and mitigate moral hazard, we assume that the ceded loss function adheres to the Vajda condition and incentive compatibility condition. Given that the insurer and reinsurer hold diverse probability beliefs regarding potential risk losses, the article explores reinsurance design under arbitrary belief heterogeneity. Subsequently, we concentrate on belief heterogeneity that satisfies the monotone hazard rate (MHR) condition. Against this backdrop, we formulate a Pareto-optimal reinsurance model. Initially, we derive the explicit expression for optimal reinsurance without risk constraints by leveraging the relationship between the marginal indemnification function (MIF) and the ceded loss function. Subsequently, we derive the explicit expression for optimal reinsurance with risk constraints. Last, we present a numerical study to assess the impact of the weighting factors on Pareto-optimal reinsurance.

Currently, e-commerce platforms integrate ads and organic content into a mixed list for users. While platforms seek to maximize profit from advertisers, organic items enhance user experience. To ensure long-term development, platforms aim to design mechanisms that optimize both revenue and user satisfaction. Current methods rank ads and organic items separately before integrating them. Even if each part is locally optimal, the combined result may not be globally optimal. In this paper, we come up with the Joint Integrated Regret Network (JINTER Net). Unlike traditional methods, which pre-order ads and organic items separately, JINTER Net directly selects from the combined set of candidate ads and organic items to generate an optimal list. This approach aims to optimally balance platform revenue and user experience while satisfying approximate dominant strategy incentive compatibility and individual rationality. We validate the effectiveness of JINTER Net using both synthetic data and real dataset, and our experimental results show that it significantly outperforms baseline models across multiple metrics.

We investigate the problem of designing randomized obviously strategyproof (OSP) mechanisms in several canonical auction settings. Obvious strategyproofness, introduced by Li [American Economic Review 2017], strengthens the well-known concept of dominant-strategy incentive compatibility (DSIC). Loosely speaking, it ensures that even agents who struggle with contingent reasoning can identify that their dominant strategy is optimal. Thus, one would hope to design OSP mechanisms with good approximation guarantees. Unfortunately, Ron [SODA 2024] has showed that deterministic OSP mechanisms fail to achieve an approximation better than the minimum of the number of items and the number of bidders, even for the simple settings of additive and unit-demand bidders. We circumvent these impossibilities by showing that randomized mechanisms that are obviously strategy-proof in the universal sense obtain a constant factor approximation for these classes. We show that this phenomenon occurs also for the setting of a multi-unit auction with single-minded bidders. Thus, our results provide a more positive outlook on the design of OSP mechanisms and exhibit a stark separation between the power of randomized and deterministic OSP mechanisms. To complement the picture, we provide lower bounds on the performance of randomized OSP mechanisms in each setting. This further demonstrates that OSP mechanisms are significantly weaker than dominant-strategy mechanisms: it is well known that the deterministic VCG mechanism outputs an optimal allocation in dominant-strategies, whereas we show that even randomized OSP mechanisms cannot obtain more than 87.5% of the optimal welfare.

Abstract Explicit and implicit incentives and opportunities for mutually beneficial voluntary cooperation coexist in many economic relationships. In a series of eight laboratory gift-exchange experiments, we show that incentives can lead to crowding out of voluntary cooperation even after they have been abolished. This crowding-out also occurs in repeated relationships, which otherwise strongly increase effort compared to one-shot interactions. Using a unified econometric framework, we unpack these results as a function of positive and negative reciprocity, as well as the principals’ wage offer and the incentive compatibility of the contract. Crowding-out occurs mostly due to reduced wages and not a change in reciprocal wage–effort relationships. Our systematic analysis also replicates established results on gift exchange, incentives, and crowding out of voluntary cooperation while being exposed to incentives. Overall, our findings show that the behavioral consequences of explicit incentives strongly depend on the features of the situation in which they are embedded.

This study aims to investigate a retailer’s optimal decisions under “Value Increasing” promotion, where speculative consumers will deliberately purchase add-on items to qualify for discounts if the purchase amount is less than the “Value Increasing” promotional threshold and then return the add-on items after successful payment. The models without and with the “Value Increasing” promotion are established to investigate the effects of speculative consumers’ add-on items refund behavior on the optimal pricing strategies and the optimal profits. The results show that participating in the “Value Increasing” promotional campaigns does not always benefit retailers. When the promotional discounts degree meets the incentive compatibility conditions, a low probability of the product being added by speculative consumers or a small proportion of speculative consumers makes retailers benefit more from participating in the “Value Increasing” promotional campaigns. However, when these conditions are not met, not participating in the “Value Increasing” promotion is better for retailers. Moreover, compared to without “Value Increasing” promotional campaigns, retailers will set a higher regular price to offset the losses associated with speculative returns under the “Value Increasing” promotional campaigns, which may result in consumers’ final price after the discount not necessarily be lower than the price they would pay under non-promotional campaigns.

ABSTRACTThe efficient recycling of electric and electronic waste products (e‐waste) not only provides environmental and societal benefits but also is recognized as being profitable from economic and business perspectives. However, how to design incentive mechanisms for efficient e‐waste recycling with dual responsibilities, namely, corporate social responsibility (CSR) and government rural vitalization, remains unclear. In this paper, we consider an e‐waste recycling problem in which a company collects a certain amount of e‐waste from developed and poor regions. With the goal of CSR, the company reserves a portion of demands for the poor region while realizing as much social welfare as possible. In pursuit of the rural revitalization strategy, the government offers subsidies to agents from the poor region. We introduce the concept of the price of CSR, which is defined as the loss of social welfare. We find the price of CSR is nondecreasing when the demand in the poor region increases. We propose a family of novel Vickrey‐Clarke‐Groves (VCG) auctions with CSR and government subsidies, which are all incentive‐compatible (IC) and individual‐rational (IR). Two strategies are considered for allocating government subsidies, namely, agent subsidy policy and company subsidy policy. Interestingly, if the bid‐ask spread is large enough in the developed region, then the company with CSR obtains higher profits. The outcome of e‐waste recycling is used as a signal for the precise allocation of the budget for government subsidies. The agent subsidy policy can ensure that a “poorer” agent or village receives a higher subsidy. When the company exhibits a low level of CSR, the company subsidy policy can bring more benefits for agents in the poor region at a relatively lower overall subsidy than the agent subsidy policy. This paper enriches the existing auction theory and provides insights into the impacts of firm‐level action (CSR), government policy (subsidies), and individual behavior on rural vitalization.

In this study, we propose a polyhedral clinching auction for indivisible goods, which has so far been studied for divisible goods. As in the divisible setting by Goel et al. (2015), our mechanism enjoys incentive compatibility, individual rationality, and Pareto optimality, and works with polymatroidal environments. A notable feature of this mechanism for the indivisible setting is that the entire procedure can be conducted in time polynomial of the number of buyers and goods. Moreover, we show additional efficiency guarantees, recently established by Sato for the divisible setting: the liquid welfare (LW) of our mechanism achieves more than half of the optimal LW, and the social welfare is more than the optimal LW.

Abstract Despite wave after wave of reform, the existing international business tax system remains poor. This article evaluates the system against five standard evaluative criteria: economic efficiency, robustness to profit shifting, fairness, ease of administration, and incentive compatibility, and concludes that it performs poorly on all five. The article argues that the root cause of this poor performance is the underlying fundamental structure of the system, in particular its origin basis and the separate entity approach.

Incentive-compatible double auction for Peer-to-Peer energy trading considering heterogeneous power losses and transaction costs

We study the joint design of dynamic incentives and performance feedback for an environment with a coarse (all‐or‐nothing) measure of performance, and show that hiding information from the agent can be an optimal way to motivate effort. Using a novel approach to incentive compatibility, we derive a two‐phase solution that begins with a “silent phase” where the agent is given no feedback and is asked to work non‐stop, and ends with a “full‐transparency phase” where the agent stops working as soon as a performance threshold is met. Hiding information leads to greater effort, but an ignorant agent is also more expensive to motivate. The two‐phase solution—where the agent's ignorance is fully frontloaded—stems from a “backward compounding effect” that raises the cost of hiding information as time passes.