Dynamic Incentive Research Articles

Credibility Dynamics and Inflation Expectations

We study the optimal design of a disinflation plan by a planner who lacks commitment and has imperfect control over inflation. The government’s reputation for being committed to the plan evolves as the public compares realized inflation to the announced targets. Reputation is valuable as it helps curb inflation expectations. At the same time, plans that are more tempting to break lead to larger expected reputational losses in the ensuing equilibrium. Taking these dynamics into consideration, the government announces a plan which balances promises of low inflation with dynamic incentives that make them credible. We find that, despite the absence of inflation inertia in the private economy, a gradual disinflation is preferred even in the zero-reputation limit.

Timeliness, Accuracy, and Relevance in Dynamic Incentive Contracts

Timeliness, Accuracy, and Relevance in Dynamic Incentive Contracts

Dynamic Incentive Schemes for Managing Dockless Bike-Sharing Systems

Dynamic Incentive Schemes for Managing Dockless Bike-Sharing Systems

Dynamic Digital Twin and Federated Learning With Incentives for Air-Ground Networks

The air-ground network provides users with seamless connections and real-time services, while its resource constraint triggers a paradigm shift from machine learning to federated learning. Federated learning enables clients to collaboratively train models without sharing data. Digital twins provide virtual representation of the air-ground networks to reflect the time-varying status, which in combination with federated learning reconcile the conflict between privacy protection and data training in air-ground networks. In this paper, we consider dynamic digital twin and federated learning for air-ground networks where a drone works as the aggregator and the ground clients collaboratively train the model based on the network dynamics captured by digital twins. We design incentives for federated learning based on Stackelberg game, in which the digital twin of the drone acts as the leader to set preferences for clients, and clients as follower choose the global training rounds. Furthermore, considering the varying digital twin deviations and network dynamics, we design a dynamic incentive scheme to adaptively adjust the selection of the optimal clients and their participation level. Numerical results show that the proposed schemes can significantly improve accuracy and energy efficiency.

Too good to fire: Non-assortative matching to play a dynamic game

We model stable, non-assortative labor-market matchings. We endogenize how transferable utility is: a matched firm and worker play an infinite-horizon game with one-sided moral hazard. Becker's (1973) result that complementarity yields positively assortative matching fails, because increasing match quality harms dynamic incentives: a firm cannot credibly threaten to fire a worker who is productive even with low effort. Thus, firms may prefer lower-type workers who will exert more effort. We offer a new interpretation of efficiency wages: committing to a high wage improves effort incentives indirectly by increasing the firm's willingness to walk away. We also show that the set of stable outcomes has a lattice structure.

Evolutionary Game Simulation on Government Incentive Strategies of Prefabricated Construction: A System Dynamics Approach

Prefabricated construction is a state-of-the-art construction technology of both socio-economic and environmental benefits, but sometimes, it is not welcome due to its high cost. Governments play an important role in deeply promoting prefabricated construction, but its effects are not clear. This paper developed a system dynamics model for investigating and simulating the impacts of government incentive strategies on prefabricated construction by considering the evolutionary game process between the government and contractors. Data of Shanghai, China, is collected for demonstration and validation of the developed simulation model. Results show that (1) the evolutionary stable strategy does not exist in static game process; (2) the rate of adopting prefabricated construction is affected by the level of penalties and subsidies; (3) dynamic incentive strategies can better improve the stability of the evolutionary game process; and (4) the rational range of incentive rate can be obtained. Findings of this study facilitate governments to formulate and improve the incentive strategies of prefabricated construction, thus boosting the development of construction industrialization.

A computational framework for analyzing dynamic auctions: The market impact of information sharing

AbstractThis article develops a computational framework to analyze dynamic auctions and uses it to investigate the impact of information sharing among bidders. We show that allowing for the dynamics implicit in many auction environments enables the emergence of equilibrium states that can only be reached when firms are responding to dynamic incentives. The impact of information sharing depends on the extent of dynamics and provides support for the claim that information sharing, even of strategically important data, need not be welfare reducing. Our methodological contribution is to show how to adapt the experience‐based equilibrium concept to a dynamic auction environment and to provide an implementable boundary‐consistency condition that mitigates the extent of multiple equilibria.

Dynamic incentives in microfinance with commitment‐type borrower

AbstractShapiro (2015) points out an inherent fragility of dynamic incentives in microfinance without collateral or long‐term loans. He shows that the dynamic incentive mechanism unravels for all except a single value of initial beliefs. That is, under any parameter setup, only a single efficient equilibrium exists, in which loan terms improve over time, while an infinite number of inefficient equilibria exist, in which loan terms eventually deteriorate and even the most patient borrowers would default. I show that his concern vanishes if one introduces to the environment any positive proportion of “commitment‐type” borrowers, who never default by nature. Adding an infinitesimal proportion of commitment‐type borrowers eliminates all inefficient equilibria, while preserving the unique efficient equilibrium. Moreover, given any setup of parameters, a unique efficient equilibrium exists, in which the loan terms become more favourable over time, and the proportion of non‐defaulters converges to one.RésuméIncitations dynamiques en micro‐crédit avec emprunteurengagé. En matière de micro‐crédit, Shapiro (2015) montre qu’en l’absence de prêts à long terme ou sur nantissement, une fragilité inhérente aux incitations dynamiques demeure. Shapiro montre également qu’en matière de convictions premières, le mécanisme d’incitation dynamique échoue pour toutes les valeurs sauf une. Autrement dit, pour tous les paramètres de base, il n’existe qu’un seul équilibre efficace au sein duquel les conditions de prêt s’améliorent dans la durée, alors que parallèlement, il existe un nombre infini d’équilibres inefficaces au sein desquels les conditions de prêt finissent par se détériorer rendant le crédit impossible à rembourser même pour l’emprunteur le plus patient. Je montre que le problème soulevé par Shapiro disparaît dès lors que l’on introduit à cet environnement une proportion positive d’emprunteurs engagés qui, par nature, remboursent toujours leur dette. En introduisant une proportion infime d’emprunteurs engagés, tous les équilibres inefficaces disparaissent tout en préservant le seul qui s’avère efficace. En outre, quels que soient les paramètres de base, il n’existe qu’un seul équilibre efficace au sein duquel les conditions de prêt s’amélioreront avec le temps et où le taux de remboursement approchera un (1).

Incentives and contract design for knowledge sharing in construction joint ventures

Employing the differential game theory, this study proposes the dynamic incentive model for knowledge sharing in Design-Build joint ventures (DBJVs) under three types of contracts: no cost-sharing contract (NCSC), cost-sharing contract (CSC) and centralized decision-making contract (CDMC). The analytic model and numerical analysis approach are used to compare solutions in the three scenarios. The results indicate that the optimal amount of shared knowledge and the optimal payoff under the CDMC are both the highest. Besides, the leader will offer part of the knowledge sharing cost for the follower if the leader gets more than one-third payoff of the system. Compared with the NCSC, the system payoff under the CSC increases greatly, which is much larger than the payoff increment from the CSC to the CDMC. Furthermore, empirical evidence on major findings are provided. This research contributes to the literature on knowledge sharing in joint ventures.

Towards Demand-Driven Dynamic Incentive for Mobile Crowdsensing Systems

Incentive mechanisms have been commonly proposed to encourage people to participate in mobile crowdsensing (MCS). However, most of them set unchangeable rewards for sensing tasks, while the inherent inequality and on-demand feature of sensing tasks have been long ignored, especially for location-dependent sensing tasks (LDSTs). In this paper, we focus on location-dependent MCS systems and propose a demand-driven dynamic incentive mechanism that dynamically changes the rewards of sensing tasks at each sensing round in an on-demand way to balance their popularity. A demand indicator is introduced to characterize the demand of each sensing task by considering its deadline, completing progress, and number of potential participants. At each sensing round, we use the Analytic Hierarchy Process (AHP) to calculate the relative demands of all sensing tasks and then determine their rewards accordingly. Moreover, we consider two task selection problem with participatory users and opportunistic users, respectively, and prove that both of them are NP-hard. We propose an optimal dynamic programming based solution for participatory scenario and an optimal backtracking based solution for opportunistic scenario to help each user select tasks while maximizing its profit. Extensive experiments show that the demand-driven dynamic incentive mechanism outperforms existing incentive mechanisms.

Time is money: How landbanking constrains housing supply

Many housing policies aim to increase supply and reduce prices through rezoning, relying on the assumption that increasing allowable densities automatically accelerates the rate of housing supply. However, the existence of landbanking (land hoarding), where land able to be profitably developed for housing is withheld from the market in anticipation of future gains, undermines the logic of such policy changes.We expose major limits of the static housing supply model behind these policies by looking at the degree to which landbanking behaviour is consistent with model predictions. To do this, we assemble a new dataset of home sales and landbanks from the annual reports of Australia’s top eight publicly-listed residential developers from 2001 to 2018 and use complete state-level planning approvals and lot production data in Queensland, Australia.In contrast to the static model prediction that landbanks serve the function of inventories, and are hence minimised, we find that (1) over 200,000 housing lots, or 13 years of new supply, are held by the eight largest housing development companies, and eight years of these landbanks are held in housing subdivisions that are approved and already for sale, that (2) the amount of zoned supply in a region is unrelated to the rate of new housing supply, and that (3) housing developers routinely delay housing production to capitalise on market cycles. Dynamic incentives to maximise total returns, including capital gains in the option value of undeveloped land, could be related to this observed behaviour.

Credibility Dynamics and Disinflation Plans

We study the optimal design of a disinflation plan by a planner who lacks commitment. Having announced a plan, the Central banker faces a tradeoff between surprise inflation and building reputation, defined as the private sector's belief that the Central bank is committed to the plan. Some plans are harder to sustain: the planner recognizes that paving out future grounds with temptation leads the way for a negative drift of reputation in equilibrium. Plans that successfully create low inflationary expectations balance promises of lower inflation with dynamic incentives that make them more credible. When announcing the disinflation plan, the planner takes into account these anticipated interactions. We find that, even in the zero reputation limit, a gradual disinflation is preferred despite the absence of inflation inertia in the private economy.

A Sectoral Approach Allows an Artful Merger of Climate and Trade Policy

Climate and trade policy present serious contemporary challenges for all nations. Developed market economies are struggling with trade policy in the modern era of globalization, and the resulting realignments are straining the post-war international economic order. National emissions pledges under the Paris Agreement appear at present to fall far short of achieving the greenhouse gas (GHG) emissions cuts that science suggests are needed to remain in a <2°C world. Merging climate and trade policy could provide developed economies a strategy for limiting global emissions while protecting and promoting their economic competitiveness. Since the adoption of the Kyoto Protocol, border carbon adjustments (BCAs) that would help protect domestic energy-intensive industry and prevent leakage have been discussed as a mechanism to make unilateral climate mitigation more politically attractive. Especially if implemented non-cooperatively, BCAs open the backdoor to protectionism and retaliation, and potentially allow nations to retreat behind static barriers. Developments in international trade policy make this alternative to traditional climate diplomacy more viable today than previously, and also increase the chance of climate protectionism. We propose an alternative policy framework—a cooperative sectoral tariff reduction (CSTR)—that would help provide dynamic incentives to improve performance, reduce the chance of BCAs being coopted for protectionist purposes, and create the foundation of a carbon club.

Dynamic Incentive Mechanism for Delivery Slot Management in E-Commerce Attended Home Delivery

In attended home delivery, challenges arise in practice because of the short strict time windows, stochastic customer requests, and varying customers’ preferences for delivery slots. In this study, we focus on integrating dynamic time slot incentives and order delivery with the intention of reducing overall delivery cost and improving profitability. The proposed incentive mechanism is able to exploit the variability in the marginal fulfillment cost of an order and the customers’ preferences to influence the customers’ selection of delivery slots. We present an approximate dynamic programming approach to estimate the marginal fulfillment cost using the operational vehicle routing cost while accounting for future orders. We demonstrate that the proposed incentive mechanism can achieve a high level of savings (of up to 70%) with respect to the benchmark customer-free-choice scenario. It is also noted that the proposed mechanism effectively exploits higher order density and vehicle availability to achieve a higher level of savings.

Collaborative Innovation with Dynamic Incentive Contracts in a Supply Chain

The collection and sharing of consumers’ knowledge by retailers can help manufacturers improve the innovation level of products, thereby improving the performance of supply chain. However, due to the cost of collecting consumers’ knowledge, the wholesale price contract can no longer coordinate supply chain members effectively. It is necessary to study the problem how the retailers are encouraged to make more efforts for the cooperative innovation with manufacturers. This paper introduces two dynamic incentive contracts for improving collaborative innovation level in a two-player supply chain, and the impacts of these contracts on supply chain’s performance are investigated, by using a Stackelberg differential game model. The manufacturer, as a Stackelberg leader, determines the R&amp;D investment while the retailer is responsible for the retail price and the efforts in collection of the consumer’s information (or preference) to the products. The model incorporates a wholesale price contract and two incentive contracts to better understand how the manufacturer can facilitate the retailer’s efforts in the collection of consumer’s information and increase the profits of the members of supply chain. Our results suggest that the optimal profit of the supply chain, the retailer’s efforts in the collection of consumer’s knowledge, the retail price, and the innovation level under the reward incentive contract are higher than their counterparts in other contracts. In particular, the retailer’s optimal effort under the reward incentive contract is even higher than the one in the centralized decision scenario. However, if the manufacturer commits an effort target to the retailer, it shows that the retailer’s optimal effort is independent of the target. The manufacturer’s optimal R&amp;D investments are constants in the three contracts under the dynamic setting. Furthermore, numerical simulations show that the effort target has little impact on profits of the supply chain although it affects the decision making of supply chain members to some extent, whereas the retailer’s marginal reward offered by the manufacturer influences the innovation level of product and the supply chain’s profit significantly.

Dynamic performance incentive model with supervision mechanism for PPP projects

PurposeThis study is aimed to explore the dynamic performance incentive model for a flexible PPP contract to handle uncertainties based on supervision during the long-time concession period, so as to ensure operation performance and benefits of the public sector while protecting the economic benefit of the private sector, thus avoiding unnecessary renegotiation.Design/methodology/approachThe microeconomic and principal–agent theories and relevant studies on the basic incentive model and flexible contract are fully utilized. The procedure for developing the dynamic incentive model and the assumptions about the quantitative relationships among fundamental variables or factors are first proposed. The static incentive model without incentive parameter adjustment and then the dynamic incentive model allowing incentive parameter adjustment are successively developed. Finally, the propositions regarding the valid adjustment ranges of the incentive parameter with respect to the economic, social and hybrid benefits of the public sector and the economic benefit of the private sector are suggested.FindingsThe dynamic incentive model enables to achieve a flexible contract to handle uncertainties on the PPP project to ensure the benefits of the public sector while protecting the benefit of the private sector. The economic, social and hybrid benefits of the public sector and the economic benefit of the private sectors can be respectively realized through adjusting the reward–punishment coefficient under different adjustment ranges and different importance. The incentive model is able to ensure the benefits of the public sector while protecting the benefit of the private sector by controlling the private sector's effort level unknown to the public sector.Originality/valueThe dynamic incentive model helps implement a flexible PPP contract to handle uncertainties during the operation period, thus controlling the effort level of the private sector and ensuring the benefits of the public sector while protecting the economic benefit of the sector. It enables to clarify the quantitative relationships between the operation performance, the benefits of the stakeholders, the effort level of the private sector and the reward–punishment coefficient. This study contributes to the domain knowledge of the incomplete contract theory for designing a flexible PPP contract with dynamic incentive and supervision mechanism by applying the microeconomic and principal–agent theories.

An incentive mechanism design for mobile crowdsensing with demand uncertainties

Mobile crowdsensing (MCS) has shown great potential in addressing large-scale data sensing problem by allocating sensing tasks to pervasive mobile users (MU). The MUs will participate in the MCS if they can receive sufficient compensation. Existing work has designed lots of incentive mechanisms for MCS, but ignores the MUs’ resource demand uncertainties that is critical for resource-constrained mobile devices. In this paper, we propose to design an incentive mechanism for MCS by taking the MUs’ own resource demand into the economic model. As different MUs will have different behavior, they will participate in the MCS with different levels. Based on this idea, we formulate the incentive mechanism by using the Stackelberg game theory. Furthermore, a dynamic incentive mechanism (DIM) based on deep reinforcement learning (DRL) approach is investigated without knowing the private information of the MUs. It enables the SP to learn the optimal pricing strategy directly from game experience. Finally, numerical simulations are implemented to evaluate the performance and theoretical properties of the proposed mechanism and approach.

Cost-Sharing or Rebate: The Impact of Health Insurance Design on Reducing Inefficient Care

This paper investigates how the design of health insurance plans impacts individuals' treatment decisions. We vary the design between cost-sharing and rebates. We derive several hypotheses how the design of the health insurance plan affects behavior. In a laboratory experiment, we find that rebates are more effective in containing cost as individuals request significantly less inefficient low-value care under rebates than under cost-sharing. We link individual decisions to a direct measure of loss aversion but find no evidence that loss aversion influences utilization rates. Our results suggest that relative thinking and the difficulty to understand the dynamic incentive structure drive the design effect.

Effects of minimum wage on workers’ on-the-job effort and labor market outcomes

Can higher minimum wages motivate workers to work harder? If so, what are the effects of workers’ on-the-job effort responses on the labor market outcomes? To answer these questions, we apply a model with directed on-the-job search and dynamic incentive contracts in a frictional labor market. The steady-state comparison of the calibrated model shows that a higher minimum wage increases workers’ on-the-job effort. It also reduces the average hiring and layoff rates. Since the reduction in the hiring rate is higher than the reduction in the layoff rate, the un-employment rate increases, and hence lowers the aggregate output. Moreover, we find that the higher minimum wage has a spillover effect on higher-income workers. It suggests that agents’ incentive decisions can provide a new explanation of the spillover effect of the minimum wage. Lastly, shutting down the effort channel leads to greater labor market impacts. These results suggest that workers’ on-the-job effort responses have moderate offsetting effects on the cost of the higher minimum wage.

An incentive mechanism for data sharing based on blockchain with smart contracts

Data sharing techniques have progressively drawn increasing attention as a means of significantly reducing repetitive work. However, in the process of data sharing, the challenges regarding formation of mutual-trust relationships and increasing the level of user participation are yet to be solved. The existing solution is to use a third party as a trust organization for data sharing, but there is no dynamic incentive mechanism for data sharing with a large number of users. Blockchain 2.0 with smart contract has the natural advantage of being able to enable trust and automated transactions between a large number of users. This paper proposes a data sharing incentive model based on evolutionary game theory using blockchain with smart contract. The smart contract mechanism can dynamically control the excitation parameters and continuously encourages users to participate in data sharing.