Asymmetric Information Scenario Research Articles

Effects of information asymmetry on green advertising for remanufacturing within a closed-loop supply chain

Manufacturers invest in Green Advertising for Remanufacturing (GAR) to attract consumers by promoting the eco-friendly benefits of their remanufacturing. Despite this, the production of remanufactured products is often influenced by Third-Party Collectors (3PCs) which handle the collection of used products. Unfortunately, market responsiveness to GAR levels is undisclosed due to proprietary information, which leaves 3PCs to unreasonably set pricing and collection quantities, potentially leading to profit loss. To explore how a two-part tariff (TPT) contract menu facilitates 3PCs in acquiring information regarding the manufacturer’s GAR effectiveness, a closed-loop supply chain comprising one manufacturer and one 3PC is taken into consideration. The optimal decisions are analyzed in three distinct decision-making scenarios: the centralized scenario, the complete information scenario, and the asymmetric information scenario. The research findings suggest that in the presence of information asymmetry, manufacturers with high GAR effectiveness (H-type manufacturers) are inclined to masquerade as manufacturers with low GAR effectiveness (L-type manufacturers). Consequently, H-type manufacturers gain additional profits due to information asymmetry. However, for the 3PC, information asymmetry leads to reduced expected profit when the manufacturer has high GAR effectiveness and increased expected profit when the manufacturer has low GAR effectiveness. Finally, our analysis demonstrates that information asymmetry does not consistently hurt consumer surplus.

Enabling intrinsic intelligence, ubiquitous learning and blockchain empowerment for trust and reliability in 6G network evolution

This study investigates the 6th Generation (6G) wireless communication network as a vital component of Ubiquitous Learning, which makes it possible to build trustworthy relationships and accurate intelligence in a variety of contexts. The important significance of blockchain technology is recognized, and its potential to improve the performance of the 6G network is investigated. Blockchain technology powers Ubiquitous Learning as a decentralized tool. But in Ubiquitous Learning contexts, problems arise when varied communication settings, information asymmetry, and resource limits confront wireless-connected devices functioning as blockchain consensus nodes. To solve these difficulties, which are critical for Ubiquitous Learning, a blockchain sharding and incentive system is presented. The purpose of this technique is to improve the learning process in the 6G network by cultivating reliable intelligence. A sharding architecture that integrates a contract theory-based incentive mechanism and a realistic Byzantine fault tolerance mechanism, particularly tailored for Ubiquitous Learning settings, is described. This model analyses the useful Byzantine consensus mechanism and block broadcast characteristics in wireless networks. This study finds the best contract combination for both completely symmetric and asymmetric information scenarios, optimizing block rewards for customers that seek blockchain services.

3C Resource Sharing for Personalized Content Delivery in B5G Networks: A Contract Approach

With the emergence of numerous new applications and the explosive growth of Internet-of-Things (IoT) devices in beyond 5G (B5G) networks, the massive yet delay-sensitive personalized content delivery has imposed a crucial challenge to mobile network operators (MNOs). Cooperation among MNOs for sharing the communication, caching, and computing (3C) three-dimensional resources in an economic yet real-time manner has provided a promising solution to address this challenge. In this paper, we investigate the 3C resource sharing among multiple MNOs to realize efficiently and economically personalized content delivery, where a third-party 3C resource provider (CRP) is introduced to manage the sharing 3C resource pool. By leveraging the multi-dimensional contract theory, we propose an optimal 3C resource contract scheme for the CRP in a realistic asymmetric information scenario, and the appointed 3C resources in one contract will be allocated to the MNO who signs it. For each MNO, we establish a partial transcoding model to achieve the optimal orchestration on the 3C resources, where the closed-form solution of caching placement and transcoding strategy is obtained. Then, MNOs can choose the most suitable contracts to sign based on the service requirements from end users. In particular, we analyze the global incentive compatibility and feasibility of the proposed multi-dimensional contract approach, which is theoretically proven to achieve the optimal solution. Extensive simulation results demonstrate the efficiency of the proposed 3C resource sharing mechanism compared with other benchmark schemes. Specifically, the proposed 3C resource sharing scheme can reduce 35% delivery delay compared with the non-sharing scheme.

Joint Task Offloading, Resource Sharing and Computation Incentive for Edge Computing Networks

Edge computing is a promising technology to enable edge servers (ESs) to share computing resources for task offloading. Due to the selfish characteristic of the ESs, how to design an efficient computation incentive mechanism with the appropriate task offloading and resource allocation strategies is an essential issue. In this letter, a contract-based framework is proposed to deal with the joint task offloading, resource sharing and computation incentive (TORSCI) issue under the asymmetric information scenario. A three-dimensional task-resource-reward contract is investigated to motivate the potential ESs to participate in resource sharing. Then, based on the individual rationality and incentive compatibility conditions, the joint TORSCI optimization problem is formulated to achieve the maximum utility of the cloud server. By deriving necessary and sufficient conditions, an optimal contract is designed to obtain the joint task offloading, resource sharing and computation incentive strategy with an efficient heuristic algorithm. Simulation results demonstrate the efficiency of our proposed contract-based incentive approach.

Procurement Strategies and Auction Mechanism for Heterogeneous Service Providers in a Service Supply Chain

This paper examines competition between two heterogeneous service providers (SPs) in a service procurement market. We investigate a service supply chain consisting of one service integrator (SI), and two SPs who compete the SI’s order with their own reservation profits. To select the favorable SP and centralize the total system, we propose an auction mechanism in symmetric and asymmetric information scenarios and find that channel coordination can be achieved by means of an efficient auction mechanism, hence, Pareto improvement conditions can be clearly illustrated. In addition, we characterize the effects of information asymmetry on optimal decisions and derive some important managerial insights into the value of information.

Towards Small AoI and Low Latency via Operator Content Platform: A Contract Theory-Based Pricing

Increasing demands of multimedia contents brings a great profit to the content providers, but also a challenge of how to efficiently delivery contents to make users have a good Quality of Experience (QoE). Take the advantage of owning wireless infrastructures, the telco operator is motivated to build a content platform for entering the market of content. In this paper, we consider a content platform belonging to the telco operator, which can provide periodically-updated contents with small Age of Information (AoI), namely, fresh contents. The content update consumes radio resource resulting in a trade-off between the AoI and the latency. We adopt the contract theory to monetize contents considering the above two factors in a realistic asymmetric information scenario. Necessary and sufficient conditions are derived to ensure the feasibility of the contract. We further propose the optimal update schemes and the corresponding fees, which maximizes the utility of the operator. Simulation reveals that the proposed contract enables the users, who attach importance to the freshness, to obtain frequently updating contents.

Contract Theory-Based Incentive Mechanism for Full Duplex Cooperative NOMA with SWIPT Communication Networks.

Cooperative Non-Orthogonal Multiple Access (NOMA) with Simultaneous Wireless Information and Power Transfer (SWIPT) communication can not only effectively improve the spectrum efficiency and energy efficiency of wireless networks but also extend their coverage. An important design issue is to incentivize a full duplex (FD) relaying center user to participate in the cooperative process and achieve a win–win situation for both the base station (BS) and the center user. Some private information of the center users are hidden from the BS in the network. A contract theory-based incentive mechanism under this asymmetric information scenario is applied to incentivize the center user to join the cooperative communication to maximize the BS’s benefit utility and to guarantee the center user’s expected payoff. In this work, we propose a matching theory-based Gale–Shapley algorithm to obtain the optimal strategy with low computation complexity in the multi-user pairing scenario. Simulation results indicate that the network performance of the proposed FD cooperative NOMA and SWIPT communication is much better than the conventional NOMA communication, and the benefit utility of the BS with the stable match strategy is nearly close to the multi-user pairing scenario with complete channel state information (CSI), while the center users get the satisfied expected payoffs.

A double moral hazard model for inter‐organizational knowledge sharing

AbstractIn inter‐organizational knowledge sharing, the firms share sensitive organizational knowledge to jointly solve a problem. However, the knowledge sender may conceal knowledge and the knowledge receiver may misappropriate the knowledge received from the sender. We formalize this situation using the double moral hazard framework and develop Stackelberg game models for symmetric and asymmetric information scenarios. First, we analyse the motivation for triggering double moral hazard. We find that by concealing knowledge, the sender cannot reduce the loss due to misappropriating knowledge. Second, we analyse the impact of improved punishment mechanism on the behaviours that trigger double moral hazard, and find that there exists a threshold policy with respect to monitoring effort to prevent double moral hazard. Third, we get the conditions under which the partner has the motivation to prevent the monitor from adopting a deception strategy.

Asymmetric solutions to asymmetric information problems

This paper studies markets plagued with asymmetric information on the quality of traded goods. In Akerlof's setting, sellers are better informed than buyers. In contrast, we examine cases where buyers are better informed than sellers. This creates an inverse adverse selection problem: the market tends to disappear from the bottom rather than from the top. In contrast to the traditional model, it is the high-value goods (gems) that are traded on the market, rather than the low-value goods (lemons). We refer to this asymmetric information scenario as the “market for gems.” We investigate the consequences of this undisclosed knowledge of hidden qualities — which we refer to as inverse adverse selection — and the reasons why legal theorists have given this form of asymmetric information substantially less consideration. Conventional legal and contractual solutions to the lemons problem are often ineffective in the gems case: the uninformed buyer in a traditional market for lemons experiences the quality of the good he purchased; in a market for gems, instead, the uninformed seller may never know the quality of the good that he sold. We study three alternative solutions to the gems problem — auctions, suppression of information, and inverse warranties — and identify the condition under which each of them is feasible. We then show how the theory sheds light on real-life gems problems arising in the multi-million dollar transactions involving soccer players, artworks, M&As, Hollywood movies, and diamonds.

Asymmetric Solutions to Asymmetric Information Problems

This paper studies markets plagued with asymmetric information on the quality of traded goods. In Akerlof’s setting, sellers are better in- formed than buyers. In contrast, we examine cases where buyers are better informed than sellers. This creates an inverse adverse selection problem: the market tends to disappear from the bottom rather than from the top. In contrast to the traditional model, it is the high-value goods (gems) that are traded on the market, rather than the low-value goods (lemons). We refer to this asymmetric information scenario as the “market for gems.” We investigate the consequences of this undisclosed knowledge of hidden qualities — which we refer to as inverse adverse selection — and the reasons why legal theorists have given this form of asymmetric information substantially less consideration. Conventional legal and contractual solutions to the lemons problem are often ineffective in the gems case: the uninformed buyer in a traditional market for lemons experiences the quality of the good he purchased; in a market for gems, instead, the uninformed seller may never know the quality of the good that he sold. We study three alternative solutions to the gems problem — auctions, suppression of information, and inverse warranties — and identify the condition under which each of them is feasible. We then show how the theory sheds light on real-life gems problems arising in the multi-million dollar transactions involving soccer players, artworks, M&As, Hollywood movies, and diamonds.

A Multi-Dimensional Contract Approach for Data Rewarding in Mobile Networks

Data rewarding is a novel business model leading a new economic trend in mobile networks, in which the operators stimulate mobile users to watch ads with data rewards and ask for corresponding payments from advertisers. Yet, due to the uncertain nature of users' preferences, it is always challenging for the advertiser to find the best choice of data rewards to attain an optimum balance between ad revenue and rewards spent. In this paper, we build a general contract-theoretic framework to address the problem of data rewards design in a realistic asymmetric information scenario, where each user is associated with multi-dimensional private information, i.e., data valuation, ad valuation, and ad sensitivity. In particular, we model the interplay between the advertiser and users by using a multi-dimensional contract approach, and theoretically analyze optimal data rewarding schemes. To ensure global incentive compatibility, we utilize the structural properties of our contract problem and convert the multi-dimensional contract into an equivalent one-dimensional contract. Necessary and sufficient conditions for an optimal and feasible contract are then derived to provide incentives for engagement of users in data rewarding scheme. Extensive numerical evaluations validate the efficiency of the designed multi-dimensional contract for data rewarding compared to other benchmark schemes.

Multi-Cap Optimization for Wireless Data Plans with Time Flexibility

An effective way for a Mobile network operator (MNO) to improve its revenue is price discrimination , i.e., providing different combinations of data caps and subscription fees. Rollover data plan (allowing the unused data in the current month to be used in the next month) is an innovative data mechanism with time flexibility . In this paper, we study the MNO's optimal multi-cap data plans with time flexibility in a realistic asymmetric information scenario. Specifically, users are associated with multi-dimensional private information, and the MNO designs a contract (with different data caps and subscription fees) to induce users to truthfully reveal their private information. This problem is quite challenging due to the multi-dimensional private information. We address the challenge in two aspects. First, we find that a feasible contract (satisfying incentive compatibility and individual rationality) should allocate the data caps according to users’ willingness-to-pay (captured by the slopes of users’ indifference curves). Second, for the non-convex data cap allocation problem, we propose a Dynamic Quota Allocation Algorithm, which has a low complexity and guarantees the global optimality. Numerical results show that the time-flexible data mechanisms increase both the MNO's profit (25 percent on average) and users’ payoffs (8.2 percent on average) under price discrimination.

Consortium Blockchain for Secure Resource Sharing in Vehicular Edge Computing: A Contract-Based Approach

Vehicular edge computing (VEC) extends edge computing to vehicular networks by exploiting computation resources of vehicles to offload tasks from other vehicles and pedestrians. However, VEC faces several critical challenges such as the potential security issues caused by untrusted and opaque environment and the lack of incentive mechanism under asymmetric information scenario. To solve the above challenges, we propose a consortium blockchain for secure resource sharing in VEC. We first design multi-step smart contracts to achieve secure resource sharing and defend against the malicious behaviours of service requesters and vehicles with selfish purposes. Then, a byzantine fault tolerance-based proof-of-stake (BFT-based PoS) consensus protocol is applied in consortium blockchain to reach consensus efficiently. Furthermore, we design a contract-based incentive mechanism to motivate vehicles to share their computation resources with service requesters. The optimal contracts are derived to maximize the service requesters' expected utility as well as social welfare. Finally, simulation results demonstrate that the proposed incentive mechanism is more effective and efficient than the traditional schemes.

Policies for resolving insurgencies – lessons from third-party intervention in India

PurposeIn this paper, the author develops a game theoretical model to understand why Union Government of India, as a third party, has used different schemes at different times in history to assist the State Governments in fighting the Naxalite insurgency. Comparing across schemes, it was found that though Matching Security Grants scheme was preferred in general, during asymmetric information scenario it led to an emergency situation wherein the Union Government had to provide the less preferred Bulk Security Grants. Later, it became difficult to withdraw these grants as the State Governments free rode by reducing own security contribution. The author finds that instead, in this scenario, Matching Development Grants are more suitable, as they incentivize the State Governments to reveal private information and help the Union Government exit its third-party role. For a practitioner involved in conflict resolution, these conclusions imply that as the desirability of policies can change diametrically overtime, Union Government must spend resources only on those heads of expenditure that provide both security and development benefits provided they aid in preventing flow of resources to Naxalites. Further, to end its assistance, the Union Government’s expenditures should also complement the capabilities of the State Government rather than substituting them. These results can also guide policy in other protracted civil wars with substantial third-party intervention, which are common these days.Design/methodology/approachThe paper is an historical analysis of strategies used by Union and State Governments and Naxalites. The analysis is based on game theoretic tools supported with examples.FindingsThe Union Government must provide matching grants instead of bulk grants such as Central Armed Police Forces, and the grants should be aimed at building complementarities with the state governments’ security contributions. Under asymmetric information scenario, the Union and State Governments reduce their expenses incurred to fight the Naxalites. A Matching Development Grants scheme would have done better. Union Government must spend resources on heads of expenditure that provides both Development and Security benefits, to curb flow of resources to Naxalites, besides complementing the Security Contributions of the State Government.Research limitations/implicationsThe research is limited by disaggregated data to test the hypotheses. It is also limited by the data on hidden variables like the contribution of the Naxalites to fighting. The research is also limited to the extent that individual groups in the war like police commanders, politicians and Naxalite commanders are not incorporated. Multiple asymmetric parties are also not considered; that may generalize the model to other theaters of insurgency.Practical implicationsCertain heads of expenditure such as roads, mobile communication, improving quality of investigation, preventing human rights violations by the security forces, etc. are both security and development enhancing. The Union Government's expenditures must be directed toward this end. Therefore, from a practitioner's perspective, the debate between greed and grievances exists not as a limitation but as a guide. The relevant articles of Constitution of India must be redrafted on these principles. Third-party interventions in other insurgencies may be revisited under these conclusions.Social implicationsSecurity and Development policies are tools for controlling Naxalite insurgency, which can also be used to prevent flow of resources to Naxalites. Security and development policies to resolving insurgencies are useful at different information scenarios. Therefore, information neutral policies should be preferred.Originality/valueThis paper has contributed theoretically in modeling continuing conflicts like Naxalite insurgency, explicitly. The author also shows that though the field of civil wars may have evolved along the Greed vs Grievance debate (Collier and Hoeffler, 2004), for a practitioner, the lines blur when it comes to solutions, as many heads of expenditures have features of both security and development. This paper also shows that when the Union Government faced asymmetric information scenario, the policy of matching development grants would be beneficial in long run though of limited value in short run. This is an important conclusion as the most intense period of violence was preceded by the asymmetric information scenario. Besides, it has relevance for the other civil wars with third-party intervention, such as NATO in Afghanistan.

Option contract design for supply chains under asymmetric cost information

Purpose A lack of visibility into the manufacturer’s production cost information impedes a retailer’s ability to maximize her own profits, especially when market demand is uncertain. The purpose of this paper is to investigate the use of an option contract within a one-period two-echelon supply chain in the presence of asymmetric cost information. Design/methodology/approach Based on the principal-agent model, the retailer, acting as a Stackelberg leader, offers a menu of option contracts to mitigate the risk of uncertain demand and reveal asymmetric production cost information. The optimal contract in asymmetric and symmetric information scenarios is derived. Finally, the impact of production costs on the optimal contracts and the actors’ profits is explored by numerical experiments. Findings By comparing the optimal equilibrium solutions in two scenarios, the authors show that asymmetric cost information has a large impact on the optimal option contract and profits. In addition, information rent is affected by the type differential. The results prove that the level of information asymmetry plays a vital role in option contracts and profits. Originality/value Different from the existing literature on private demand information, this paper considers a supply chain with asymmetric cost information in the context of option contracts. Interestingly, not only the production cost but also the probability of a low production cost can affect the option strike price. In addition, from the perspective of the manufacturer, a high cost does not always bring a high information rent. These findings can provide some guidance to decision-makers.

Secure computation protocols under asymmetric scenarios in enterprise information system

ABSTRACT It has become acentral question to guarantee the security of sensitive data in enterprise information systems. Actually, different enterprises may grasp asymmetric information due to their scales. How to guarantee the computation security under such scenario is acrucial problem for enterprise information system. In this paper, we propose asecure computation protocol under asymmetric information scenario in the presence of rational parties. We also work towards the achievement of security properties in enterprise information systems. Our new definition of utilities is proved to be coincident (and compatible) with the former definitions under asymmetric information scenario.

Dynamic Contract Incentive Mechanism for Cooperative Wireless Networks

Cooperative communication is a promising technique to mitigate channel impairment and improve spectral efficiency. Due to the mobility of relay nodes (RNs) and the unfavourable effects of wireless channels, channel conditions may change as time passes. Therefore, how to design proper long-term incentive mechanisms for RNs in such dynamic communication environments is an essential issue. This paper investigates the contract incentive method under asymmetric information scenario, where the relay channel condition and cost are the RNs’ private information. To capture the dynamic characteristic of the RNs’ cooperative information during the long-term cooperation, two dynamic contract mechanisms are introduced into long-term relay incentive with different RNs’ relay information structures. The optimal contract is derived to maximize the source's expected utility for both independent asymmetric information and correlated asymmetric information . Numerical results are presented to evaluate the effectiveness of the proposed dynamic contract-based incentive mechanism.

Incentive Mechanism Design for Wireless Energy Harvesting-Based Internet of Things

Radio frequency energy harvesting (RFEH) is a promising technology to charge unattended Internet of Things (IoT) low-power devices remotely. To enable this, in future IoT system, besides the traditional data access points (DAPs) for collecting data, energy access points (EAPs) should be deployed to charge IoT devices to maintain their sustainable operations. Practically, the DAPs and EAPs may be operated by different operators, and the DAPs thus need to provide effective incentives to motivate the surrounding EAPs to charge their associated IoT devices. Different from existing incentive schemes, we consider a practical scenario with asymmetric information, where the DAP is not aware of the channel conditions and energy costs of the EAPs. We first extend the existing Stackelberg game-based approach with complete information to the asymmetric information scenario, where the expected utility of the DAP is defined and maximized. To deal with asymmetric information more efficiently, we then develop a contract theory-based framework, where the optimal contract is derived to maximize the DAP's expected utility as well as the social welfare. Simulations show that information asymmetry leads to severe performance degradation for the Stackelberg game-based framework, while the proposed contract theory-based approach using asymmetric information outperforms the Stackelberg game-based method with complete information. This reveals that the performance of the considered system depends largely on the market structure (i.e., whether the EAPs are allowed to optimize their received power at the IoT devices with full freedom or not) than on the information availability (i.e., the complete or asymmetric information).

Contract-Based Incentive Mechanism for Mobile Crowdsourcing Networks

Mobile crowdsourcing networks (MCNs) are a promising method of data collecting and processing by leveraging the mobile devices’ sensing and computing capabilities. However, because of the selfish characteristics of the service provider (SP) and mobile users (MUs), crowdsourcing participants only aim to maximize their own benefits. This paper investigates the incentive mechanism between the above two parties to create mutual benefits. By modeling MCNs as a labor market, a contract-based crowdsourcing model with moral hazard is proposed under the asymmetric information scenario. In order to incentivize the potential MUs to participate in crowdsourcing tasks, the optimization problem is formulated to maximize the SP’s utility by jointly examining the crowdsourcing participants’ risk preferences. The impact of crowdsourcing participants’ attitudes of risks on the incentive mechanism has been studied analytically and experimentally. Numerical simulation results demonstrate the effectiveness of the proposed contract design scheme for the crowdsourcing incentive.

Monitoring strategy for relay incentive mechanism in cooperative communication networks

Cooperative communication is a promising technique to improve wireless network’s capacity and resource management’s efficiency. However, due to the limited wireless network resource, the potential selfish relay nodes may not be willing to cooperate without any extra incentives. In this paper, a contract-based relay incentive mechanism is investigated under the dual asymmetric network information scenario. The optimal contract design is proposed to achieve the twin objectives of ability-discrimination and effort-incentive. Considering the feature of asymmetric information, the various monitoring strategies are designed by introducing a monitoring node. The optimal contract designs with the information-monitoring and the action-monitoring strategies are presented. Simulation results show that the optimal contract design effectively incentivizes relay nodes’ participation, and the performance of the action-monitoring strategy yields a significant enhancement compared with the non-monitoring and the information-monitoring strategies.