Incentive Compatible Mechanism Research Articles

Crowding in School Choice

We consider the market design problem of matching students to schools in the presence of crowding effects. These effects are salient in parents’ decision-making and the empirical literature; however, they cause difficulties in the design of satisfactory mechanisms and, as such, are not currently considered. We propose a new framework and an equilibrium notion that accommodates crowding, no-envy, and respect for priorities. The equilibrium has a student-optimal element that induces an incentive-compatible mechanism and is implementable via a novel algorithm. Moreover, analogs of fundamental structural results of the matching literature (the rural hospitals theorem, welfare lattice, etc.) survive. (JEL D47, H75, I21, I28)

Environmental and social disclosure, managerial entrenchment, and investment efficiency

This study investigates the effect of environmental and social (E&S) disclosure and managerial entrenchment on investment efficiency. E&S disclosure increases not only capital accessibility but also external monitoring of entrenched managers’ actions. We develop a theoretical model that demonstrates how these benefits and costs of firms’ voluntary E&S disclosure affect investment efficiency. Using a large sample of U.S.-listed firms over the period 2016–2022, we test the model’s predictions and provide empirical evidence suggesting that E&S disclosure is positively associated with investment efficiency and that this effect is stronger for firms with lower managerial entrenchment or those disclosing more financially material E&S information. Our study contributes to the investment efficiency literature by demonstrating the relevance of an incentive-compatible mechanism reflecting managers’ choice of E&S voluntary disclosure to the efficient capital allocation.

Incentive-compatible mechanism for manufacturing carbon emission supervision under carbon control policies in China.

Enhance performance in manufacturing carbon emission (MCE) reduction has become a widespread consensus and a necessary part, which cannot be achieved without the joint participation of manufacturing enterprises and supervisory departments. Accordingly, how to coordinate the interests of both sides and design a reasonable incentive-compatible mechanism becomes an urgent task at present. Considering the two subsidy funding channels of peer funds and government finance, this study applies the evolutionary game model to analyze feasible schemes for designing incentive-compatible mechanism of MCE supervision, discusses and simulates the realistic scenarios and influencing factors of incentive-compatible mechanism under the non-subsidized and subsidized schemes. The results show that MCE supervision is in an incentive-incompatible state under the non-subsidized incentive scheme, while in a constrained incentive-compatible state under the subsidized incentive scheme. With the increase of peer funds and penalty coefficient or the decrease of subsidy coefficient, the period of MCE supervision to reach an incentive-compatible state becomes shorter. However, a lower peer fund and penalty coefficient or a higher subsidy coefficient will contribute to a state of incentive-incompatible or a periodic cycle state of "incentive-compatible → incentive-incompatible →incentive-compatible→…" in the MCE supervision.

A second-pricing based incentive-compatible mechanism for matching and pricing in ride-sharing

As a novel and economical transportation way, ride-sharing has attracted more and more passengers and drivers to participate. How to match passengers with drivers efficiently has become a key issue. Specifically, drivers are usually heterogeneous with different costs, and they may behave strategically (e.g. reveal their private cost information untruthfully) in order to make more profits. Drivers’ strategic behavior may lead to inefficient matching, which results in the loss of social welfare of ride-sharing platforms and drivers. In this paper, an incentive-compatible and efficient mechanism is proposed to solve this issue, which can match passengers with drivers and determine the payments to drivers in order to maximize social welfare while ensuring drivers reveal their cost information truthfully. Specifically, an order matching algorithm with branch and bound based route planning is designed to accelerate the matching process. Meanwhile, the payments to drivers are computed based on the second-pricing algorithm. In so doing, a second-pricing based ride-sharing mechanism (SPRM) is proposed, which satisfies incentive compatibility, individual rationality, budget balance and computational efficiency. Based on the real Manhattan taxi order data and vehicle fuel consumption data, extensive experiments are conducted to evaluate the proposed mechanism. The experimental results show that SPRM can guarantee drivers’ profits and improve the ratio of drivers’ participation and the ratio of served orders, and eventually achieve greater social welfare than four typical benchmark approaches, GPri, ND, mT-share and mdp. The research of this paper can provide useful insights for designing efficient ride-sharing system to maximize the social welfare of all participants in the real life.

The Privacy Paradox and Optimal Bias–Variance Trade-offs in Data Acquisition

Whereas users claim to be concerned about privacy, often they do little to protect their privacy in their online actions. One prominent explanation for this privacy paradox is that, when an individual shares data, it is not just the individual’s privacy that is compromised; the privacy of other individuals with correlated data is also compromised. This information leakage encourages oversharing of data and significantly impacts the incentives of individuals in online platforms. In this paper, we study the design of mechanisms for data acquisition in settings with information leakage and verifiable data. We design an incentive-compatible mechanism that optimizes the worst case trade-off between bias and variance of the estimation subject to a budget constraint, with which the worst case is over the unknown correlation between costs and data. Additionally, we characterize the structure of the optimal mechanism in closed form and study monotonicity and nonmonotonicity properties of the marketplace. Funding: This work is supported by the National Natural Science Foundation of China [Grants 62202512 and 62271434], Shenzhen Science and Technology Program [Grant JCYJ20210324120011032], Guangdong Basic and Applied Basic Research Foundation [Grant 2021B1515120008], Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network [Grant ZDSYS20220606100601002], and the Shenzhen Institute of Artificial Intelligence and Robotics for Society. This work is also supported by the National Science Foundation [Grants CNS-2146814, CPS-2136197, CNS-2106403, and NGSDI-2105648]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2023.0022 .

A High-Efficiency and Incentive-Compatible Peer-to-Peer Energy Trading Mechanism

Due to the rapid development of demand response management and distributed energy resources, prosumers are becoming more proactive, which also promotes the emergence of peer-to-peer (P2P) energy trading mechanisms. However, we find that it is quite difficult to simultaneously achieve high computational efficiency, decentralized operations and solution optimality in a P2P energy trading mechanism, which is called the “P2P energy trading trilemma”. In this paper, we first propose a novel negotiation mechanism for P2P energy trading that can maximize social welfare in a decentralized manner and respect physical network constraints. Then, the local optimization problem is transformed into a closed-form alternating update algorithm (AUA), so that the computational efficiency can be greatly improved. Therefore, our proposed mechanism solves the “P2P energy trading trilemma" to a certain extent. Another challenge is that the locational marginal price (LMP)-based market mechanism cannot satisfy the incentive-compatible property. To encourage prosumers to cooperate in a lack-of-trust environment, a novel incentive-compatible mechanism is proposed for P2P energy trading using consensus mechanism inspired by proof of solution (PoSo) and smart contract (SC). Finally, we simulate the functionality of the mechanism in terms of convergence performance, reliability, scalability, computational efficiency, and SC operations.

An Equivalence Between Fair Division and Wagering Mechanisms

We draw a surprising and direct mathematical equivalence between the class of fair division mechanisms, designed to allocate divisible goods without money, and the class of weakly budget-balanced wagering mechanisms, designed to elicit probabilities. Although this correspondence between fair division and wagering has applications in both settings, we focus on its implications for the design of incentive-compatible fair division mechanisms. In particular, we show that applying the correspondence to competitive scoring rules, a prominent class of wagering mechanisms based on proper scoring rules, yields the first incentive-compatible fair division mechanism that is both fair (proportional and envy-free) and responsive to agent preferences. Moreover, for two agents, we show that competitive scoring rules characterize the whole class of nonwasteful and incentive-compatible fair division mechanisms, subject to mild technical conditions. As one of several consequences, this allows us to resolve an open question about the best possible approximation to optimal utilitarian welfare that can be achieved by any incentive-compatible mechanism. Finally, because the equivalence greatly expands the set of known incentive-compatible fair division mechanisms, we conclude with an evaluation of this entire set, comparing the mechanisms’ axiomatic properties and examining their welfare performance in simulation. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Supplemental Material: The data and online appendix are available at https://doi.org/10.1287/mnsc.2022.02615 .

Examining the US amateur-radio community through a polycentricity lens

Amateur radio (AR) operators provide societal services in public safety, spectrum applications, and training future experts. However, benefits derived from these services are challenging to define formally or contractually, resulting in potential under-provisioning in traditional market economies. We propose that communities like AR that aim to promote such open-ended innovation may not benefit from exclusive-resource rights and trading. Instead of market mechanisms, non-exclusive rights regimes can be analyzed through a lens of polycentricity, but such regimes require consensus on adaptable non-market governance rules and incentive-compatible mechanisms for monitoring, sanctioning, and exclusion of nonmembers. Our AR case study exemplifies stakeholders replacing market governance with nonexclusive property-rights models to harmonize diverse autonomous entities in producing open-ended societal services.

Designing an Efficient and Incentive-Compatible Mechanism of Self-Adjusting in the Provincial ‎Income-Expenditure System

Designing an Efficient and Incentive-Compatible Mechanism of Self-Adjusting in the Provincial ‎Income-Expenditure System

Withholding and damage in Bayesian trade mechanisms

We study the optimality of allowing the designer to withhold or damage resources in Bayesian incentive compatible mechanisms for bilateral trade with independent private values. The following results hold when the buyer and the seller have discrete value distributions. Burning money or withholding the good from both traders never enhances welfare. Similarly, damaging the good for the buyer cannot increase welfare. By contrast, damaging the good for the seller may improve welfare. However, such welfare improvements are possible only if the damage hurts some lower valuation type of seller more severely than the highest valuation type. Results extend to the case of continuous value distributions under certain hypotheses regarding virtual values. Methods also apply to optimal Bayesian implementation for allocation problems. In the absence of property rights, damaging goods for any agent has negative welfare consequences.

Incentive Mechanism Design for Responsible Data Governance: A Large-scale Field Experiment

A crucial building block of responsible artificial intelligence is responsible data governance, including data collection. Its importance is also underlined in the latest EU regulations. The data should be of high quality, foremost correct and representative, and individuals providing the data should have autonomy over what data is collected. In this article, we consider the setting of collecting personally measured fitness data (physical activity measurements), in which some individuals may not have an incentive to measure and report accurate data. This can significantly degrade the quality of the collected data. On the other hand, high-quality collective data of this nature could be used for reliable scientific insights or to build trustworthy artificial intelligence applications. We conduct a framed field experiment (N = 691) to examine the effect of offering fixed and quality-dependent monetary incentives on the quality of the collected data. We use a peer-based incentive-compatible mechanism for the quality-dependent incentives without spot-checking or surveilling individuals. We find that the incentive-compatible mechanism can elicit good-quality data while providing a good user experience and compensating fairly, although, in the specific study context, the data quality does not necessarily differ under the two incentive schemes. We contribute new design insights from the experiment and discuss directions that future field experiments and applications on explainable and transparent data collection may focus on.

Selecting a winner with external referees

We consider a problem of mechanism design without money, where a planner selects a winner among a set of agents with binary types and receives outside signals (like the report of external referees). We show that there is a gap between the optimal Dominant Strategy Incentive Compatible (DSIC) mechanism and the optimal Bayesian Incentive Compatible (BIC) mechanism. In the optimal BIC mechanism, the planner can leverage the outside signal to elicit information about agents' types. BIC mechanisms are lexicographic mechanisms, where the planner first shortlists agents who receive high reports from the referees and then uses agents' reports to break ties among agents in the shortlist. We compare the “self-evaluation” mechanism with a “peer evaluation” mechanism where agents evaluate other agents, and show that for the same signal precision, the self- evaluation mechanism outperforms the peer evaluation mechanism. We show that optimal Ex Post Incentive Compatible (EPIC) mechanisms give the planner an intermediate value between the optimal DSIC and BIC mechanisms

Incentive-compatible mechanisms for online resource allocation in Mobility-as-a-Service systems

In the context of Mobility-as-a-Service (MaaS), the transportation sector has been evolving towards user-centric business models, which put the user experience and tailored mobility solutions at the center of the offer. The emerging concept of MaaS emphasizes that users value experience-relevant factors, e.g., service time, inconvenience cost, and travel delay, over segmented travel modes choices. This study proposes an auction-based mechanism and tractable optimization models for the demand-side management of MaaS systems wherein users’ trip requests are represented as mode-agnostic mobility resources. Users’ requests arrive dynamically in the MaaS system and users compete for mobility resources by bidding for mobility services based on their willingness to pay and experience-relevant preferences. We take the perspective of a MaaS platform regulator who aims to maximize social welfare by optimally allocating mobility resources to users in real-time. The MaaS regulator first decides whether to offer each user a MaaS bundle and identifies the optimal allocation of mobility resources for the selected users. Users have the possibility to accept or reject offered MaaS bundles by comparing the associated utility obtained from MaaS with a reserve utility obtained from other travel options. We introduce mixed-integer programming formulations for this online mobility resource allocation problem. We show that the proposed MaaS mechanism is incentive-compatible, individually rational, budget balanced, and computationally efficient. We propose a polynomial-time online algorithm and derive its competitive ratio relative to an offline algorithm. We also explore rolling horizon configurations with varying look-ahead policies to implement the proposed mechanism. Extensive numerical simulations conducted on large-scale instances generated from realistic mobility data highlight the benefits of the proposed mechanism.

Consensus and sharing based distributed coordination of home energy management systems with demand response enabled baseboard heaters

The repercussions from excessive penetration of uncoordinated Home Energy Management Systems (HEMSs) have proven to be pernicious in the distribution system regarding contingencies, instabilities, and rebound peaks. This paper aims to design a distributed coordination technique with the required topology to coordinate transactive HEMSs with demand response enabled electric baseboard heater thermostats to avoid the detrimental effects of uncoordinated HEMSs in a residential group. Specifically, the proposed technique establishes a consensus to fulfill individual as well as shared objectives by modifying consumers’ consumption patterns. The shared objective is to flatten the aggregated profile and decrease the total cost in the grid. In addition, an incentive policy has been designed to pay a total reward to the team for encouraging consumers to participate in the coordination. The presented coordination technique comprises a Shapley game-based reward-sharing mechanism and an incentive-compatible mechanism, where the team’s gain is distributed among the players based on their contribution. Besides, the coordination leads to agents’ complementary decision-making and mitigates the grid challenges. The functionality and effectiveness of the proposed coordinated HEMSs algorithm are tested for a set of different case studies based on user preferences and coordination levels. The simulation results indicate that the proposed coordination improves aggregated profile’s load factor up to 0.85 and reduces the electricity bill by 21.4%.

Interactive influences of ecosystem services and socioeconomic factors on watershed eco-compensation standard “popularization” based on natural based solutions

Watershed eco-compensation is a policy tool to realize watershed environment improvement and regional economic development. It is important to eliminate the influence of economic differences between upstream & downstream regions and realize the fairness of regional social development based on Natural based Solutions (NbS). At present, lack of clarity in coupling and coordination analysis of ecosystem services & socioeconomic based on NbS could hamper watershed eco-compensation standards “popularization” and reduce the ability to successfully ecological governance. To meet the needs of economic development and ecological service value realization, dynamic equilibrium game research based on multidimensional relationship coordination and a multi-objective optimization solution of economic benefit distribution was carried out. To achieve the bargaining Bayesian/Nash equilibrium of the watershed eco-compensation standard in the game, the existence conditions of the equilibrium solution of the eco-compensation standard based on the mixed equilibrium game implementation process were studied. To carry out the complete information dynamic game, the equilibrium solution of the watershed eco-compensation standard based on the dynamic transfer payment was solved, and the rational analysis of the dynamic Bayesian equilibrium game of bargaining based on the incentive compatibility mechanism was also discussed. Water quantity and quality eco-compensation can ensure balanced development between ecological protection and the social economy in the Mihe River Basin. Combined with the variation law of socioeconomic water intake-utilization standards and the water use value, the city of Shouguang City & Qingzhou City should pay Linqu County 4.78 million US$ and 1.29 million US$ as watershed eco-compensation standards per year based on NbS, respectively. To verify the rationality of the results derived from the economically optimal model, two modes of “bargaining” & “perfect competition”, were used to study the characteristics of the protocols generated by the equilibrium game, and the applicable conditions of the nonzero-sum game solution upstream and downstream of the watershed were also explored. Based on the nonzero-sum processing of the survey results, the current relationship between the input value of eco-compensation and the willingness to pay satisfies v≥c+1/4. Based on the dynamic game & Bayesian equilibrium solution of bargaining, the watershed eco-compensation quota of water quantity & quality is 6.07 million US$, the willingness to pay is 65.63 US$/month. These findings contribute to the quantifying process of bargaining & dynamic equilibrium by transforming “ambiguous” information to achieve sustainable ecosystem service management and develop socioeconomic strategies associated with different compensation features based on NbS, thus helping to inform watershed management.

Mechanism design for decentralized peer-to-peer energy trading considering heterogeneous preferences

The bottom-up evolution of power systems demonstrates that the integrated decentralized grid is a compelling vision of the future distribution grid. Hence, the concept of fully decentralized peer-to-peer (P2P) energy markets allows the prosumers to directly trade electricity among each other and makes them one step closer to be independent from upstream generation. To this end, an appropriate market mechanism should be designed to not only the prosumers get encouraged to participate in the market but prosumers’ preferences get taken into consideration. In this paper, a fully decentralized market mechanism is designed in which the preferences of prosumers including willingness to trade green energy, trading partner’s reputation and location in the network are taken into account. Prosumers check other peers’ attributes and select the one who satisfies their preferences the most. The designed market mechanism satisfies market properties involving balanced budget, individual rationality, and Bayesian-Nash incentive compatibility. Prosumers with excess/shortage of power are motivated to individually participate in the market while preserving their privacy. Simulation results verify the effectiveness of the proposed mechanism and premiums in the matching pattern and achieving the desired outcome for prosumers over the course of a day. • A decentralized P2P energy trading considering heterogeneous preferences of prosumers. • Introducing premium coefficients to include prosumers preferences in energy trading. • Designing a budget balanced, individually rational and incentive compatible mechanism.

Application of Incentive-Compatible Mechanisms Design Theory (Self-adjusting, Effective/Efficient Mechanisms) in the Seventh National Development Planning

Application of Incentive-Compatible Mechanisms Design Theory (Self-adjusting, Effective/Efficient Mechanisms) in the Seventh National Development Planning

Near Optimal Learning-Driven Mechanisms for Stable NFV Markets in Multitier Cloud Networks

More and more 5G and AI applications demand flexible and low-cost processing of their traffic through diverse virtualized network functions (VNFs) to meet their security and privacy requirements. As such, the Network Function Virtualization (NFV) market has been emerged as a major service market that allows network service providers to trade their network services among customers. Since each service market usually involves complex interplays among players with different roles, efficient mechanisms that guarantee stable and efficient operations of the NFV market are urgently needed. One fundamental problem in the NFV market is how to maximize the social welfare of all players so that all players have incentives to participate in the activities of the market. In this paper, we first formulate a novel social welfare maximization problem in an NFV market of a multi-tier edge cloud network, with the aim to maximize the total revenue collected from all players, and we implement VNF services on Virtual Machines (VMs) leased by service providers to fulfill customers with service requests, where the edge cloud network consists of both cloudlets in edge networks and remote data centers in the core network. We then design an efficient incentive-compatible mechanism for the problem, and analyze the existence of a Nash equilibrium of the mechanism. Also, we consider an online social welfare maximization problem with uncertain values of customers and without the knowledge of future request arrivals, for which we devise an online learning algorithm by adopting the Multi-Armed Bandits (MAB) method with a bounded regret. We finally evaluate the performance of the proposed mechanisms through simulations and a testbed. Results show that the proposed mechanisms deliver up to 27% higher social welfare than those of existing studies

On the Foundations of Ex Post Incentive-Compatible Mechanisms

This paper considers an interdependent-value robust mechanism design problem, where the principal has little knowledge about the agent’s belief. Although ex post incentive-compatible (EPIC) mechanisms can implement allocations without any knowledge about the agent’s belief, we show that, under a certain condition (order-reversing interdependence), there exists a non-EPIC mechanism that achieves a strictly higher expected revenue than any EPIC mechanism given whatever (admissible) belief structure the agent may enjoy. Conversely, with sufficiently small interdependence, such a non-EPIC mechanism does not exist: for some (admissible) belief structure, an EPIC mechanism achieves the highest expected revenue. (JEL D82, D83)

Mechanism Design Under Approximate Incentive Compatibility

An assumption that is pervasive in revenue management and economics is that buyers are perfect optimizers. However, in practice, buyers may be limited by their computational capabilities or lack of information and may not be able to perfectly optimize their response to a selling mechanism. This has motivated the introduction of approximate incentive compatibility as a solution concept for practical mechanisms. In “Mechanism Design under Approximate Incentive Compatibility,” Balseiro, Besbes, and Castro study, for the first time, the problem of designing optimal selling mechanisms when buyers are imperfect optimizers. Their work characterizes structural properties of approximate incentive compatible mechanisms and establishes fundamental bounds on how much revenue can be garnered by moving from exact to approximate incentive constraints. Their work brings a new perspective to the theory of mechanism design by shedding light on a novel class of optimization problems, techniques, and challenges that emerge when relaxing incentive constraints.