Incentive Compatibility Research Articles

Online belief elicitation methods

How well do incentivized belief elicitation procedures work in online settings? We evaluate the quality of beliefs elicited from online respondents, comparing several characteristics of two widely used complex elicitation mechanisms (the Binarized Scoring Rule – BSR – and a stochastic variation of the Becker–deGroot–Marschak mechanism—BDM) against a flat fee baseline for a variety of beliefs (induced probabilities, first-order factual knowledge, second-order knowledge of others). We find that the flat-fee method requires the least amount of time, the BDM is the most difficult to understand, and that there are no differences in the average accuracy of induced beliefs across conditions. However, the methods are significantly different in terms of the frequency of first-order and second-order beliefs reported at exactly 50%: the flat-fee method leads to the most mass on this belief, followed by BDM and BSR. Regarding induced beliefs, we also find that less-educated participants’ accuracy is higher in the complex incentives treatments, and that attention, numeracy, and education are positively associated with the quality of these beliefs across methods. Our results suggest that the quality of beliefs elicited in online environments may depend less on the formal incentive compatibility properties of the elicitation procedure (whether the procedure prevents “dishonest” reporting) than on the difficulty of comprehending the task and how well incentives induce cognitive effort (thereby inducing subjects to quantify or construct their beliefs).

The influence of effort level on profit distribution strategies in IPD projects

PurposeConstruction projects have become increasingly long, complex and costly with waste and inefficiencies and often fail to achieve the desired results. Integrated project delivery (IPD) is believed to change these problems. A reasonable and fair profit distribution mechanism is a critical factor for ensuring the success of the IPD projects. This study aims to investigate the strategies of all participants in the profit distribution of an IPD project with respect to the factor of the effort level.Design/methodology/approachThis study describes the influence of owners and participants on profit distribution due to their respective efforts in the IPD project alliance. The influence of effort level on profit distribution is discussed based on the Holmstrom-Milgrom model of asymmetric information game theory and principal-agent theory, combined with incentive compatibility (IC) constraints and individual rationality (IR) constraints.FindingsThe results show that the optimal level of effort by each participant optimizes the profit distribution of an IPD project. At the same time, in the revenue incentive contract, the effort level of the participants is positively correlated with the profit distribution, proportional to their contribution coefficient and inversely proportional to the square of the cost of their creative activities in terms of effort. Each party of an IPD project can adopt a series of measures to improve their own effort level and choose the optimal level of effort based on the profit distribution, while satisfying their own utility maximization.Originality/valueThis study introduces the Holmstrom-Milgrom model in the principal-agent theory to explore the influence of the effort level on profit distribution in IPD projects. The quantitative model can contribute to establish a fair and efficient profit distribution scheme for the IPD projects.

Subsidies for green technology adoption under uncertain demand and incomplete information

In order to encourage manufacturers to replace traditional high-emission equipment with new ones powered by green technology, alternative regulations, notably equipment subsidy policy (ESP) and operation subsidy policy (OSP), have been proposed to align manufacturers’ decisions with governments’ objectives. This study investigates manufacturers’ timing decisions to adopt green technology under ESP and OSP. In our study the market demand is dynamically stochastic, and the certain demand promotion is observable only by the manufacturer and can be promoted by inserting costly effort. A principal-agent model is developed in which the regulation on the adoption timing is specified as a call option exercised by the government and constrained by the manufacturer's incentive compatibility (IC), participation constraint (PC), ex ante IC, and ex ante PC. We find that the optimal subsidy policies exhibit stepwise structures, which depend solely on the demand promotion under complete information, and on the demand promotion as well as the manufacturer's effort cost under incomplete information. The optimal ESP and OSP are equivalent under complete information. Under incomplete information, these two policies lead to the same adoption timing if the demand is high, although ESP may require a larger subsidy budget. Otherwise, ESP promotes earlier adoption than OSP.

Incentive-Compatible Forecasting Competitions

We initiate the study of incentive-compatible forecasting competitions in which multiple forecasters make predictions about one or more events and compete for a single prize. We have two objectives: (1) to incentivize forecasters to report truthfully and (2) to award the prize to the most accurate forecaster. Proper scoring rules incentivize truthful reporting if all forecasters are paid according to their scores. However, incentives become distorted if only the best-scoring forecaster wins a prize, since forecasters can often increase their probability of having the highest score by reporting more extreme beliefs. In this paper, we introduce two novel forecasting competition mechanisms. Our first mechanism is incentive compatible and guaranteed to select the most accurate forecaster with probability higher than any other forecaster. Moreover, we show that in the standard single-event, two-forecaster setting and under mild technical conditions, no other incentive-compatible mechanism selects the most accurate forecaster with higher probability. Our second mechanism is incentive compatible when forecasters’ beliefs are such that information about one event does not lead to belief updates on other events, and it selects the best forecaster with probability approaching one as the number of events grows. Our notion of incentive compatibility is more general than previous definitions of dominant strategy incentive compatibility in that it allows for reports to be correlated with the event outcomes. Moreover, our mechanisms are easy to implement and can be generalized to the related problems of outputting a ranking over forecasters and hiring a forecaster with high accuracy on future events.This paper was accepted by Yan Chen, behavioral economics and decision analysis.Funding: This work was supported by the European Research Council [Grant ERC StG 307036] and the National Science Foundation [Grant CCF-1445755].

Bundling Sequentially Released Durable Goods*

Suppose two durables are sequentially released and suppose that consumer valuations of these goods are positively correlated. By the time the second good is released, high‐valuation buyers are out of the market for the first good. Therefore, a bundle can be targeted at the low‐valuation consumers without violating the high‐valuation consumers' incentive compatibility constraint. We test the model's predictions on data from retail DVD sales in the 2000's. Consistent with theory, our estimates suggest that mixed bundling increases revenues, especially when the bundle components are similar (which in turn suggests positive correlation of valuations).

Robust optimal dynamic reinsurance policies under the mean-RVaR premium principle

This work investigates a robust optimal dynamic reinsurance problem for an ambiguity averse insurer (AAI) concerned about potential ambiguity of claim intensity. The study aims to determine a robust optimal reinsurance contract to minimize the discounted ruin probability imposed a penalization owing to model ambiguity, including discounting for the time of ruin. We suppose that the surplus process is modeled by a diffusion model. The AAI purchases reinsurance from the reinsurer to manage risk, subject to the incentive compatibility constraint, which rules out moral hazard. Moreover, the reinsurance premium is calculated based on the mean-RVaR premium principle, which generalizes the expected value premium principle and the mean-CVaR premium principle and reflects the different risk preferences of reinsurers. Based on the dynamic programming approach, we obtain the value function and optimal reinsurance policies (the dual excess-of-loss reinsurance). Finally, we show a numerical example to illustrate the effects of premium and ambiguity averse on the discounted ruin probability and the robust optimal contract.

S-shaped narrow framing, skewness and the demand for insurance

The existing literature in insurance economics has shown that narrow framing can explain why people buy too little insurance compared to what standard theory predicts. However, there is also ample evidence suggesting people sometimes buy too much insurance. In this paper, we assume S-shaped narrow framing, i.e., the local utility function for evaluating the net insurance payoff is convex in the loss domain but concave in the gain domain, and show that it can reconcile with both insurance puzzles simultaneously. Especially, we show the policyholder under S-shaped narrow framing is more likely to underinsure more negatively skewed risks of loss but to overinsure less negatively skewed risks of loss when only coinsurance is offered. We further characterize the optimal insurance scheme under S-shaped narrow framing while incentive compatibility is satisfied. It contains a straight deductible when the net insurance payoff is negative but partial insurance when the net insurance payoff is positive.

Using economic experiments to assess the validity of stated preference contingent behavior responses

Contingent behavior (CB), a stated preference (SP) method, elicits individuals’ intentions about behavior in quantities or frequencies under hypothetical scenarios. CB has primarily been used to elicit preferences in recreation demand models or to assess market demand. Although CB shares the hypothetical nature of other SP methods, there has been limited assessment of CB validity and incentive compatibility. Focusing on hypothetical bias and framing effects, we design an incentive-compatible decision mechanism that examines the validity of CB in economic experiments. We find hypothetical bias associated with an overstatement of quantities in CB responses, but the overstatement does not appear to arise from strategic behavior. We also find that overstating quantities is not significantly affected by framing, but framing does affect the convergence of CB and revealed preference responses. These findings raise questions about the validity CB research and its demand revealing properties but provide some avenues to address these concerns.

Fuel regulation in a developing country: An interventional perspective

Given that the regulation system in most developing countries is immature, this paper investigates how to reduce firm's fossil fuel consumption for energy transition in a developing country. From an institutional perspective, we argue that the government can purposely intervene to regulate for firm-level fuel saving. With evidence from China, we construct the intensity of the intervention for fuel-saving regulation (FRI) by the interaction between two sources of variation: the jack-knifed average of government intervention and the ambient air quality at the city level. It confirms the constraint effect of the fuel-regulation intervention on a firm's fuel consumption with robustness to the endogenous bias. We further confirm that the constraint effect is larger for firms with government connections. Finally, we show that the fuel-regulation intervention improves firm's profitability through constraining its fuel consumption. Ultimately, we reveal that the fuel-saving regulation relies on government intervention with incentive compatibility. Considering that our analysis does not require any particular regulatory regime, approach or design, this paper offers general insights to show that a government in a developing country can intervene by its connections to stimulate fuel-saving incentive for energy transition.

Relational Contracts and Development

This article reviews an emerging body of evidence on relational contracts, defined as informal arrangements sustained by the value of future interactions. We focus on developing and international markets, which are often characterized as contexts with weak formal contract enforcement. We introduce relational contracting between firms as a governance form alternative to both firms and markets. We then review evidence on the prevalence of long-term relationships between firms and discuss why this governance form might be particularly common in developing countries. After introducing a simple framework, we discuss the measurement of relational contracting between firms. We review an approach that takes dynamic incentive compatibility constraints to the data to quantify the value of future interactions and illustrate how different types of shocks can be used to uncover the inner functioning of relational contracting. We also review structural models and conclude with policy implications and promising avenues for future research.

Obvious Strategyproofness, Bounded Rationality and Approximation

Obvious strategyproofness (OSP) has recently emerged as the solution concept of interest to study incentive compatibility in presence of agents with a specific form of bounded rationality, i.e., those who have no contingent reasoning skill whatsoever. We here want to study the relationship between the approximation guarantee of incentive-compatible mechanisms and the degree of rationality of the agents, intuitively measured in terms of the number of contingencies that they can handle in their reasoning. We weaken the definition of OSP to accommodate for cleverer agents and study the trade-off between approximation and agents’ rationality for two paradigmatic problems: machine scheduling and facility location. We prove that, for both problems, “good” approximations are possible if and only if the agents’ rationality allows for a significant number of contingencies to be considered, thus showing that OSP is not too restrictive a notion of bounded rationality from the point of view of approximation.

Random Serial Dictatorship for horizontal collaboration in logistics

In this paper, we propose the application of matching mechanisms to handle horizontal collaboration in logistics. Based on the requirements of a real-life setting, we introduce a new variation of matching under preferences. We provide a Random Serial Dictatorship (RSD) mechanism for finding a solution that incorporates Pareto optimality, incentive compatibility and fairness, which are desired properties for enabling collaboration between antagonistic participants. Further, we present an extensive experimental analysis that is twofold. We compare the proposed RSD mechanism to two well-known matching mechanisms, namely Maximising Cardinal Utilities (MAXCU) and Bundled Probabilistic Serial (BPS). We compare these mechanisms with respect to cardinal efficiency and envy, and highlight the complementarity between them for handling different settings. Most importantly, we show that RSD is the most appropriate choice for settings similar to the one motivating our study. Moreover, we examine the effect of randomisation of RSD to fairness. Finally, we discuss how our matching mechanism was applied in the real-life context of four 3rd Party Logistics providers. Results show that the proposed approach creates significant potential for synergies and may be used to support horizontal collaboration in logistics.

A bartering double auction resource allocation model in cloud environments

AbstractResource provisioning and allocation are two essential operations of cloud service providers (CSPs). Due to limited resources, it may sometimes not be cost‐effective for CSPs to obtain them from the market. On the other hand, releasing some less profitable resources may violate the service level agreement (SLA). This article presents an auction‐inspired model for providing/demanding resources without exchanging money among CSPs. The core idea is that CSPs do not need to pay monetary values. Instead, they exchange free resources through an auction mechanism and are encouraged to cooperate. This model, which we call bartering double auction resource allocation, helps CSPs receive the resources they need in the form of virtual machine instances with different types of functionalities. Under this mechanism, if one party violates the SLA, the other takes back its resources to avoid possible loss. The experimental results using the CloudSim simulator and the statistical analyses show that the proposed mechanism can increase CSPs' social welfare and profitability while significantly reducing SLA violations. Also, the proposed model provides four essential economic properties, that is, individual rationality, incentive compatibility, budget balancing, and economic efficiency.

Novel double auctions for spatially distributed parking slot assignment with externalities

This article considers a parking slot assignment problem in a sharing economy where parking slots are spatially heterogeneous. When buyers (i.e., the slot users) park and take their cars in the reserved parking slots, environment externalities are created. We incorporate the externality costs in our winner determination model and construct a padding-based Vickrey-Clarke-Groves (PV) double auction where each buyer submits an XOR bid on his/her desired parking slots at the same price. In the PV double auction, the padding intuition is adopted on either the supply or demand side. We then propose a padding-based shadow price (PS) double auction by integrating the padding method with the shadow price method. Due to the rises in buying prices and declines in selling prices, the PS double auction is likely to realize higher auctioneer’s payoff. Both PV and PS double auctions achieve incentive compatibility, individual rationality, budget balance and asymptotical efficiency. Our numerical experiments demonstrate that the proposed double auctions can realize high efficiency.

On the Reliability of the BDM Mechanism

The Becker-DeGroot-Marschak (BDM) mechanism is frequently used in experimental research to measure willingness-to-pay (WTP). The mechanism is very appealing given its incentive compatibility property. However, a growing body of experimental evidence finds that WTP is sensitive to the underlying price distribution of the BDM mechanism. The literature suggests that these findings can be rationalized only if subjects have context-dependent preferences. Under context-dependent preferences, the use of the BDM mechanism momentarily changes subjects’ preferences; subjects do not have incentives to report their intrinsic valuation, which is what the BDM attempts to measure, but instead they report the amount they are willing to pay after their preferences have momentarily changed. This lack of incentive compatibility would raise serious concerns about the validity of the BDM as an elicitation procedure; indeed, virtually all studies that use the BDM mechanism choose that elicitation procedure precisely for its incentive compatibility property. In this paper, we propose that an assumption of context dependence is not necessary to rationalize the aforementioned experimental findings. We provide an alternative plausible theory and experimental evidence to support it. Our theory relies on the premise that subjects need to exert effort to learn their preferences. We find that the price distribution of the BDM mechanism influences the subject’s incentives to exert effort. The results presented here are particularly appealing to the experimental research: our theory is consistent with the experimental findings that WTP is sensitive to the price distribution of the BDM mechanism while preserving the incentive compatibility property. This paper was accepted by Duncan Simester, marketing. Funding: F. Sguera acknowledges the support from the Portuguese Foundation of Science and Technology [Project UIDB/00407/2020 (CUBE)]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.4409 .

The incentive contract of subject librarians in university library under the non-linear task importance

Abstract Under the information asymmetry, we consider the impact of task importance on the income of university managers, introducing the non-linear income function of university managers. Based on the principal-agent theory, we construct a non-linear incentive optimisation model for subject librarians. We discussed how university managers allocate incentive intensity and how the subject librarians allocate the effort level to achieve the best net expected income for university administrators and subject librarians while satisfying the constraints of participation and incentive compatibility. The results show that the optimal incentive contract for subject librarians is related to task importance, ability level, risk aversion characteristics and the randomness of the external environment in university libraries. The relative incentive intensity increases with the increase of task importance, risk aversion, ability level and randomness of information retrieval tasks, and decreases with the increase of randomness of subject service tasks.

An online hybrid mechanism for dynamic first-mile ridesharing service

This paper leverages the “Mechanism Design” theory to design the ridesharing-based transit feeder service with mixed scheduled and on-demand passenger requests. An online hybrid mechanism is proposed with four incentive objectives: promoting passengers to participate by satisfying their mobility preferences, inducing passengers to truthfully reveal their mobility preferences, incentivizing the service provider to be financially sustainable, and incentivizing more regular commuters to early schedule the service. We propose and prove four properties, “preference-based individual rationality”, “preference-based incentive compatibility”, “financial sustainability”, and “scheduling preferability” to achieve the four incentive objectives, respectively. This online hybrid mechanism is comprised of a dynamic re-optimization methodology for re-matching and re-routing and a hybrid real-time pricing mechanism discriminative against different passenger types. In order to obtain the large-scale solutions for the online hybrid mechanism, this paper improves the solution pooling approach (SPA), which was originally proposed in our previous work for a static offline mechanism, to adapt for the online hybrid mechanism. The improved SPA successfully sustains the “preference-based individual rationality”, “financial sustainability”, and “scheduling preferability” properties. The simulation results demonstrate the superiority of the proposed online hybrid mechanism over the static offline mechanism and the outperformance of the improved SPA over the original SPA.

Ordinal Bayesian incentive compatibility in random assignment model

We explore the consequences of weakening the notion of incentive compatibility from strategy-proofness to ordinal Bayesian incentive compatibility (OBIC) in the random assignment model. If the common prior of the agents is the uniform prior, then a large class of random mechanisms are OBIC with respect to this prior—this includes the probabilistic serial mechanism. We then introduce a robust version of OBIC: a mechanism is locally robust OBIC if it is OBIC with respect all independent and identical priors in some neighborhood of a given independent and identical prior. We show that every locally robust OBIC mechanism satisfying a mild property called elementary monotonicity is strategy-proof. This leads to a strengthening of the impossibility result in Bogomolnaia and Moulin (J Econ Theory 100:295–328, 2001): if there are at least four agents, there is no locally robust OBIC and ordinally efficient mechanism satisfying equal treatment of equals.

Scheduling EV charging with uncertain departure times

In an EV charging facility, with multiple vehicles requesting charge simultaneously, scheduling becomes crucial to provide adequate service under vehicle sojourn time constraints. However, these departure times may not be known accurately, and typical policies such as Earliest-Deadline- First or Least-Laxity-First are affected by this uncertainty in information. In this paper, we analyze the performance of these policies under uncertain deadlines, using a meanfield approach. We characterize the deviation in individual attained service as a function of the uncertainty. Since incentives appear to under-report deadlines in order to be prioritized, we analyze a simple modification of the policies to enforce incentive compatibility. Simulation experiments are carried out with a practical data set.

“Choose your opponent”: A new knockout design for hybrid tournaments†

We present a new, simple knockout format for sports tournaments, that we call “Choose Your Opponent”, where the teams that have performed best during a preliminary group stage can choose their opponents during the subsequent knockout stage. The main benefit of this format is that it essentially solves a recently identified incentive compatibility problem when more than one teams from a group advance to the knockout stage, by effectively canceling the risk of tanking. This new design also makes the group stage more exciting, by giving teams a strong incentive to perform at their best level, and more fair, by limiting the risk of collusion and making sure that the best group winners are fairly rewarded in the knockout round. The choosing procedure would add a new, exciting strategic component to the competition. Advancing teams would choose their opponent during new, much anticipated TV shows which would attract a lot of media attention. We illustrate how this new format would work for the round of 16 of the UEFA Champions League, the most popular soccer club competition in the world.