While microtask crowdsourcing provides a new way to solve large volumes of small tasks at a much lower price compared with traditional inhouse solutions, it suffers from quality problems due to the lack of incentives. On the other hand, providing incentives for microtask crowdsourcing is challenging since verifying the quality of submitted solutions is so expensive that it will negate the advantage of microtask crowdsourcing. We study cost-effective incentive mechanisms for microtask crowdsourcing in this paper. In particular, we consider a model with strategic workers, where the primary objective of a worker is to maximize his own utility. Based on this model, we first analyze two basic mechanisms and show their limitations in collecting high-quality solutions with low cost. Then, we propose a cost-effective mechanism that employs quality-aware worker training as a tool to stimulate workers to provide high-quality solutions. We prove theoretically that the proposed mechanism can be designed to obtain high-quality solutions from workers and ensure the budget constraint of the requester at the same time. Beyond its theoretical guarantees, we further demonstrate the effectiveness of our proposed mechanisms through a set of behavioral experiments.

In a dynamic system, how to perform learning and make decisions are becoming more and more important for users. Although there are some works in social learning-related literature regarding how to construct belief for an uncertain system state, few studies have been conducted on incorporating social learning with decision making. Moreover, users may have multiple concurrent options on different objects/resources and their decisions usually negatively influence each other's utility, which makes the problem even more challenging. In this paper, we propose an Indian Buffet Game to study how users in a dynamic system learn about the uncertain system state and make multiple concurrent decisions by not only considering the current myopic utility, but also the influence of subsequent users' decisions. We analyze the proposed Indian Buffet Game under two different scenarios: 1) on customers requesting multiple dishes without budget constraint and 2) with budget constraint. For both cases, we design recursive best response algorithms to find the subgame perfect Nash equilibrium (NE) for customers and characterize special properties of the NE profile under homogeneous setting. Moreover, we introduce a non-Bayesian social learning algorithm for customers to learn the system state, and theoretically prove its convergence. Finally, we conduct simulations to validate the effectiveness and efficiency of the proposed algorithms.

Many spectrum sensing and access algorithms have been proposed to improve secondary users' (SUs') opportunities of utilizing primary spectrums. However, most of them have separated the analysis of spectrum sensing and access. In this paper, we propose to integrate the design of spectrum sensing and access algorithms by taking into account the mutual influence of them. Due to selfish natures, SUs tend to access the primary channel without contribution to the spectrum sensing. Moreover, they may take out-of-equilibrium strategies because of the uncertainty of others' strategies. To model the complicated interactions among SUs, we formulate the joint spectrum sensing and access problem as an evolutionary game and derive the evolutionarily stable strategy (ESS) that no one will deviate from. Furthermore, we design a distributed learning algorithm for SUs to converge to the ESS. With the proposed algorithm, each SU senses and accesses the primary channel with the probabilities learned purely from its own past utility history, and finally achieves the desired ESS. Simulation results shows that our system can quickly converge to the ESS and such an ESS is robust to the sudden unfavorable deviations of selfish SUs.

With the foreseeable large scale deployment of electric vehicles (EVs) and the development of vehicle-to-grid (V2G) technologies, it is possible to provide ancillary services to the power grid in a cost efficient way, i.e., through the bidirectional power flow of EVs. A key issue in such kind of schemes is how to stimulate a large number of EVs to act coordinately to achieve the service request. This is challenging since EVs are self-interested and generally have different preferences toward charging and discharging based on their own constraints. In this paper, we propose a contract-based mechanism to tackle this challenge. Through the design of an optimal contract, the aggregator can provide incentives for EVs to participate in ancillary services to power grid, match the aggregated energy rate with the service request and maximize its own profits. We prove that under mild conditions, the optimal contract-based mechanism takes a very simple form, i.e., the aggregator only needs to publish an optimal unit price to EVs, which is determined based on the statistical distribution of EVs' preferences. We then consider a more practical scenario where the aggregator has no prior knowledge regarding the statistical distribution and study how should the aggregator learn the optimal unit price from its interactions with EVs. Simulation results are shown to verify the effectiveness of the proposed contract-based mechanism.

Many spectrum sensing methods and dynamic access algorithms have been proposed to improve the secondary users' opportunities of utilizing the primary users' spectrum resources. However, few of them have considered to integrate the design of spectrum sensing and access algorithms together by taking into account the mutual influence between them. In this paper, we propose to jointly analyze the spectrum sensing and access problem by studying two scenarios: synchronous scenario where the primary network is slotted and non-slotted asynchronous scenario. Due to selfish nature, secondary users tend to act selfishly to access the channel without contribution to the spectrum sensing. Moreover, they may take out-of-equilibrium strategies because of the uncertainty of others' strategies. To model the complicated interactions among secondary users, we formulate the joint spectrum sensing and access problem as an evolutionary game and derive the evolutionarily stable strategy (ESS) that no one will deviate from. Furthermore, we design a distributed learning algorithm for the secondary users to converge to the ESS. With the proposed algorithm, each secondary user senses and accesses the primary channel with the probabilities learned purely from its own past utility history, and finally achieves the desired ESS. Simulation results shows that our system can quickly converge to the ESS and such an ESS is robust to the sudden unfavorable deviations of the selfish secondary users.

Cooperative peer-to-peer (P2P) streaming model, which enables cooperation among peers with large intra-group bandwidths, has been shown as a promising approach for video streaming applications. However, due to the inherent conflict between the individual peer's utility and the social optimality, designing an effective cooperative P2P streaming system becomes challenging. In this work, we first formulate the interactions among group peers as a cooperative P2P streaming game, which takes into account the unique characteristics of video. Then, a cheat-proof strategy is proposed to enforce rational group peers to achieve the optimal social welfare co-operatively. In particular, we prove theoretically that the proposed strategy can enforce truth-telling as an equilibrium, satisfy individual rationality and guarantee the budget balance of the system in an average sense. Finally, simulation results are shown to verify the effectiveness of the proposed scheme.

This paper presents a game-theoretical approach to provide a framework for optimal template selection in image prediction. Image prediction is an effective tool for coding still images and intra pictures in videos. Template matching algorithms which use neighboring blocks of the prediction target as templates have been widely used for image prediction. The assumption of these approaches is that the template has similar textural structures as the prediction target. Up to now these approaches all use pre-fixed templates for all prediction targets. However, in real images, these fixed templates are very likely to contain textures that are not or are not significant in the prediction targets and these insignificant textures introduce larger prediction residues. In this paper, we propose a coalitional game in which every pixel is treated as a player and tries to seek partners to form a coalition to capture the textural structure. By forming a coalition, every player in the coalition can obtain a gain of improving the ability of capturing the textural structure of coalition while incurring a cost of introducing textural variance within the coalition. Experimental results show that the proposed game-theoretical approach outperforms the conventional pre-fixed template matching prediction up to 2dB coding gain.

The viability of cooperative communications largely depends on the willingness of users to help. However, in future wireless networks where users are rational and pursue different objectives, they will not help relay information for others unless this can improve their own utilities. Therefore, it is very important to study the incentive issues when designing cooperative communication systems. In this paper, we propose a cooperation stimulation scheme for multiuser cooperative communications using indirect reciprocity game. By introducing the notion of reputation and social norm, rational users who care about their future utilities get the incentive to cooperate with others. Different from existing works on reputation based schemes that mainly rely on experimental verifications, we prove theoretically that cooperating with users having good reputation can be sustained as an equilibrium. Moreover, by modeling the action spreading as an evolutionary game, we show through simulations that the equilibria we found are evolutionarily stable and can be reached with proper initial conditions. Finally, simulation results are shown to verify the efficiency and effectiveness of the proposed scheme.

The viability of cooperative communications largely depends on the willingness of users to help. However, in future wireless networks where users are rational and pursue different objectives, they will not help relay information for others unless this can improve their own utilities. Therefore, it is very important to study the incentive issues when designing cooperative communication systems. In this paper, we propose a cooperation stimulation scheme for multiuser cooperative communications using indirect reciprocity game. By introducing the notion of reputation and social norm, rational users who care about their future utilities get the incentive to cooperate with others. Different from existing works on reputation based schemes that mainly rely on experimental verifications, we theoretically demonstrate the effectiveness of the proposed scheme in two steps. First, we conduct steady state analysis of the game and show that cooperating with users having good reputation can be sustained as an equilibrium when the cost-to-gain ratio is below a certain threshold. Then, by modeling the action spreading at transient states as an evolutionary game, we show that the equilibria we found in the steady state analysis are stable and can be reached with proper initial conditions. Moreover, we introduce energy detection to handle possible cheating behaviors of users and study its impact to the proposed indirect reciprocity game. Finally, simulation results are shown to verify the effectiveness of the proposed scheme.

In this paper, we study the image interpolation from the game theoretic perspective and formulate the image interpolation problem as an evolutionary game. In this evolutionary game, the players are the unknown high resolution pixels and the pure strategies of the players are the corresponding low resolution neighbors. By regarding the non-negative weights of the low resolution pixels as the probabilities of selecting the pure strategies, the problem of estimating the high resolution pixels becomes finding the evolutionarily stable strategies for the evolutionary game. Experimental results show that the proposed game theoretical approach can achieve better performance than the state-of-the-art image interpolation methods in terms of both PSNR and visual quality.