Incentives For Users Research Articles

Offline-Online Approximate Dynamic Programming for Stochastic Carsharing Systems with Relocation Incentives

The development of carsharing services is expected to achieve greater resource efficiency and provide a sustainable solution for future mobility systems. However, operators inevitably face the imbalance between demand and supply in one-way carsharing systems (CSSs). Also, it is challenging for them to make quick and efficient operational decisions when both travel time and trip requests are uncertain. This study leverages historical and online data and proposes a data-driven methodology to quickly make real-time decisions for CSSs, including vehicle assignment, relocation, and user incentive decisions. Compared to the literature in approximate dynamic programming (ADP), which mostly focuses on uncertainty in trip requests, we propose an offline-online ADP approach to consider the temporal and spatial uncertainty in both trip requests and travel time. To tackle our high-dimensional problem, we integrate an online look-ahead policy into the offline value function approximation (VFA) policy to produce a computational tractable high-quality dynamic fleet management policy. Furthermore, a user-based relocation strategy is investigated to rebalance the fleet distribution to meet the demand better. Specifically, we aim to solve the optimal incentives the operator could offer to users to relocate cars, while user preferences towards relocation incentives are generally unknown in practice. We further enhance our anticipatory policy by developing an online module via Bayesian learning that calibrates the preference model on the fly using users’ revealed preference data collected online. The numerical experiments in Singapore demonstrate our offline-online ADP approach improves the solution quality and computational efficiency significantly compared to offline VFA policy. The results also confirm the importance of incorporating uncertainty in travel time. The benefits of using online data to enhance anticipatory decisions and learn unknown user preferences are also illustrated. After a few online iterations, the preference parameters converge to the true values, which further reduces the relocation cost and increases the profit.

The cost-effectiveness of conservation auctions in the presence of asset specificity: An agent-based model

Payments for Environmental Services are a financial incentive for land users to conserve and restore ecosystems. One of the challenges in their implementation is to maximize their cost-effectiveness, or put in other words, to maximize the provision of environmental services for a given budget. This study focuses on two aspects that endanger the cost-effectiveness of such schemes: asymmetric information and asset specificity. If land users are better informed about their own provision costs, compared to the agency, they can increase their rents by demanding higher payments. The presence of asset specificity makes land users vulnerable to being harmed by opportunism. To compensate this risk, they could require higher payments or an exante compensation, likely to compromise compliance. Auctions are claimed to reduce informational rents by revealing land users’ true provision costs. However, their costeffectiveness has been shown to deteriorate if they are repeated over time because bidders can learn and adapt their strategies. Social interaction is particularly important in this context, as it allows land users to gather information on the bid cap; and it allows for trust building, which can substitute the costly formulation and enforcement of contracts, and thus reduce contracting costs. So far, there are only few studies analyzing the effect of asset specificity on the cost-effectiveness of auctions. Our study fills this gap using an agent-based model to analyze the cost-effectiveness of uniform and discriminatory one-shot and repeated auctions. In our model, land users are assumed to be embedded in a social network through which they can interact and learn. Our results suggest that repeated auctions can increase the cost-effectiveness of payments schemes in the presence of asset specificity despite of learning effects over time if land users face liquidity constraints and high time preferences.

Raceways, rebates, and retrofits: an exploration of several American cities’ policies to facilitate electric vehicle purchase and usage

ABSTRACT Plug-in Electric Vehicles (PEVs) form a critical part of the infrastructure needed for sustainable transportation. Local governments are expected to play a key role in ensuring widespread adoption of PEVs by their residents. This study specifically investigates how large American cities have used the policy process to prepare for broader PEV usage. Based on a questionnaire sent to policy leaders in major American cities, this study investigates issues of building codes, city-utility relations, tax incentives for PEV users, and social equity. The study identifies and analyzes common and influential policies perceived as being most effective at advancing PEV adoption. Findings suggest that cities are either not preparing for PEVs at all, or are preparing in a very substantive and tech-savvy manner. Policymakers also highlight key areas of needed focus, and lay out ways in which regulators and electricity providers can aid in PEV adoption.

Preliminary evaluation of value—added bio-based building blocks

The global economic system is currently organised to exploit fossil fuel-based technologies. Resources are extracted from the earth and undergo a multitude of different processing steps to become a myriad of different and essential everyday products. Unfortunately, most of these products are disposed of soon after use and can cause huge waste disposal and environmental problems. In addition, the manufacturing process itself can cause further problems. This linear system has proven extremely effective at production; however, it is, ultimately, unsustainable. The dream future for both the environment and resource conservation is an economy that is capable to reusing used end-products – a circular economy. The path to a circular economy, however, is a winding and treacherous one. To make it work, it is necessary to consider the whole system and the wide variety of stakeholders therein. It is essential to work out the incentives for manufacturers, users and potential re-users, as well as those of governments and nations. Most importantly, the technology underpinning it has to be as good or better than those that are currently in use. Researchers at the Industrial Technology Research Institute (ITRI), Taiwan are working to develop one important section of the linear fossil fuel economy into a more circular form. The team, led by Dr Guang-Way Bill Jang, are working on producing polymers from biological sources. They are also working on developing new materials and chemicals from these sources.

Crypto access: Is it possible to use cryptocurrencies in scholarly periodicals?

Key points The integration of crypto assets into the traditional journal model offers potential for both user incentives and financial security, especially for new or small journals. The case study reveals low uptake by users, which may be attributed to a resistance to new technology and underestimation of the incentives required. Implementation of cryptocurrencies is likely to be more successful if it is implemented by many journals and in an open environment for publishers.

An equity-based incentive mechanism for persistent virtual world content service

Virtual world has the potential to become a future global electronic marketplace, integrating many isolated markets in many areas. To achieve this goal, future virtual world is required to be persistent, implying that a virtual world together with its accumulated content shall exist forever regardless of dynamic changes of its users and owners. Unfortunately, existing virtual worlds, owning by some entities, are not immune from death due to business entity failure. To provide a persistent virtual world, a decentralized architecture is explored, which is constructed on user contributed devices. However, there are many challenges to realize a decentralized virtual world. One important issue is user cooperation in reliable content storage. The devices contributed by users may not be reliable for maintaining all user contents, but users do not have the incentive to provide reliable devices for others. This paper addresses the issue by two steps. First, an indicator, called replica group reliability, is provided to users, which is based on the proposed replicability index. Based on the indicator, users can learn the reliability of their content storage. Then, a new user incentive mechanism, called equity-based node allocation strategy, is proposed to promote user cooperation to collectively maintain reliable content storage. A decentralized algorithm implementing the strategy is designed and the evaluation results show its effectiveness and efficiency.

Mobile parking incentives for vehicular networks: a deep reinforcement learning approach

In vehicular networks, parked vehicles can join vehicular communication as static nodes, but encouraging people to share their wireless devices during parking still suffer from user selfishness, unexpected parking locations, and delayed data transmission. In this study, we propose a centralized service provider (SP) system to distribute mobile parking incentives for vehicle users, as some parking tasks via a smartphone app. With the support of SP system, mobile users can accept parking tasks and park their vehicles on proposed locations for rewards, which are beneficial to vehicular communication. Meanwhile, a dynamic pricing algorithm based on reinforcement learning (RL) for SP system that aiming to maximize the connectivity of all road segments and minimize the total cost of the SP is considered. Since SP system has a slow learning speed in the presence of a large state space due to the curse of high dimensionality, we develop a deep Q-network (DQN) based payment strategy that exploits a deep neural network to compress the learning state space and estimate the Q-value for each payment value. Extensive simulations with realistic parking deployments demonstrate that the proposed scheme accelerates the learning speed to obtain an optimal payment policy and increases the completion rate of tasks, and the final parking results effectively enhance vehicular communication.

An Edge Computing Matching Framework With Guaranteed Quality of Service

Edge computing is a new computing paradigm, which aims at enhancing user experience by bringing computing resources closer to where data is produced by Internet of Things (IoT). Edge services are provided by small data centers located at the edge of the network, called cloudlets. However, IoT users often face strict Quality of Service (QoS) constraints for a proper remote execution of their applications on edge. Each user has specific resource requirements and budget limitations for her IoT application, while each cloudlet offers a limited number and types of resources, each with a specific cost. Therefore, a key challenge is how to efficiently match cloudlets to IoT applications and enable a convenient any-time access to edge computing services considering preferences and incentives of users and cloudlets. In this article, we address this problem by proposing a novel two-sided matching solution for edge services considering QoS requirements in terms of service response time. In addition, we determine dynamic pricing of edge services based on the preferences and incentives of cloudlets, IoT users, and the system. The proposed matching is incentive compatible, individually rational, weakly budget balanced, asymptotically allocative efficient, and computationally efficient. We perform a comprehensive assessment through extensive performance analysis experiments to evaluate our proposed matching and pricing solutions.

Public Administration in the Field of Using and Protecting Land Resources: International Experience and Ukrainian Realities

The generalized experience of management in the field of using and protecting land resources in such countries as Denmark, Canada, Latvia, Germany, Poland, the USA, Switzerland, France and others, as well as features of management realization of this sphere in Ukraine have been considered.
 The purpose of the article is to determine the possibility and feasibility of introducing some elements of positive experience of management of developed countries into Ukraine in the field of use and protection of land resources.
 The methodological basis of the article is a set of general scientific and special research methods. In particular, the method of analysis allowed to study the features of the management of developed countries in the field of use and protection of land resources and to identify inconsistencies in the provisions of national legislation regulating land relations; The comparative legal method was used to compare foreign and Ukrainian experience of management in the field of use and protection of land resources, as well as the possibility of using certain measures aimed at the rational use of land in Ukraine.
 Two types of measures used in other countries to ensure the rational use and protection of land resources have been identified and considered: administrative (which are the form of implementation of state land policy and part of government activities) and economic (which are positive or negative incentives for land owners and users).
 The main problems that exist in the field of land management in Ukraine have been revealed. It has been emphasized that the State Service of Ukraine for Geodesy, Cartography and Cadastre performs at the same time diametrically opposed powers, which are conducive to corruption.
 It has been concluded that in order to ensure the rational use and protection of land resources, especially in the conditions of opening the land market in Ukraine, it is necessary to adopt the experience of more developed countries and make appropriate amendments to national legislation.

Internalization of social cost in congestion games

Congestion models may be studied from either the users’ point of view or the social one. The first perspective examines the incentives of individual users, who are only interested in their own, personal payoff or cost and ignore the negative externalities that their choice of resources creates for the other users. The second perspective concerns social goals such as the minimization of the mean travel time in a transportation network. This paper studies a more general setting, in which individual users attach to the social cost some weight r that is not necessarily 0 or 1. It examines the comparative statics question of whether higher r necessarily means higher social welfare at equilibrium.

Towards Accessible Mental Healthcare through Augmented Reality and Self-Assessment Tools

Mental health presents a growing public health concern worldwide with mental illnesses affecting people’s quality of life and causing an economic impact on societies. The rapidly increasing demand for mental healthcare is calling for new ways of disseminating mental health knowledge and for supporting people with mental health illnesses. As an alternative to traditional mental health therapies and treatments, mental health self-assessment and self-management tools become widely available to the public. While such tools can potentially offer more timely personalised support, individuals seeking help are faced with the challenge of making an appropriate choice from an exhaustive number of online tools, mobile apps, and support programs.In this article, we present myGRaCE – a self-assessment and self-management mental health tool made accessible to users via Augmented Reality technologies. The advantage of the system is that it provides a direct pathway to relevant and reliable mental health resources and offers a positive incentive and interventions for at-risk users. The system offers service users the resources to gain a better understanding of their mental state and increase control of their mental health conditions via self-monitoring and self-help.

Joint Incentive and Resource Allocation Design for User Provided Network Under 5G Integrated Access and Backhaul Networks

User provided network (UPN) allows a user with high channel quality to share the network access for users with poor channel quality. As a result, both the quality of experience and efficiency of network resource can be improved by UPN. However, the success of UPN relies heavily on the willingness of users to participate in sharing, so the design of incentive mechanisms is critical for UPN. In this paper, the UPN formed by D2D links under 5G integrated access and backhaul (IAB) networks is considered. In IAB networks, both access and backhaul links use wireless transmissions and dynamically share all the spectrum resources. Thus, the resource allocation for all the links also has a great impact on the UPN efficiency. To this end, a novel joint incentive and resource allocation design is explored. More specifically, considering the fairness between users, a Nash bargaining problem as a cooperative game is formulated by considering the user utility, the sensitivity of battery energy, the incentive compensation, and the limitation of network resources. To achieve the optimal Nash bargaining solution, a centralized algorithm is first designed, in which all the user information is collected by the operator for conducting centralized optimization. Thus, the centralized algorithm leads to a privacy problem. To this end, a distributed algorithm is developed to decompose the primal problem into subproblems for the operator and each user. By passing intermediate parameters between users and iterative execution of subproblems, the solution of the distributed algorithm is proved to converge to the optimal solution of the centralized algorithm. Extensive numerical results have been conducted to show that our design can effectively improve both the user experience and network throughput, i.e., operator's revenue.

Scalable Privacy-Preserving Participant Selection for Mobile Crowdsensing Systems: Participant Grouping and Secure Group Bidding

Mobile crowdsensing (MCS) has been emerging as a new sensing paradigm where vast numbers of mobile devices are used for sensing and collecting data in various applications. Auction based participant selection has been widely used for current MCS systems to achieve user incentive and task assignment optimization. However, participant selection problems solved with auction-based approaches usually involve participants’ privacy concerns because a participant's bids may contain her private information (such as location visiting patterns), and disclosure of participants’ bids may disclose their private information as well. In this paper, we study how to protect such bid privacy in a temporally and spatially dynamic MCS system. We assume that both sensing tasks and mobile participants have dynamic characteristics over spatial and temporal domains. Following the classical VCG auction, we carefully design a scalable grouping based privacy-preserving participant selection scheme, where participants are grouped into multiple participant groups and then auctions are organized within groups via secure group bidding. By leveraging Lagrange polynomial interpolation to perturb participants’ bids within groups, participants’ bid privacy is preserved. In addition, the proposed solution does not affect the operation of current MCS platform since the groups act as regular users to the platform. Both theoretical analysis and real-life tracing data simulations verify the efficiency and security of the proposed solution.

Towards participatory sensing of regions of interest with adaptive sampling rate

Participatory Sensing (PS) is a known paradigm of collaborative networks which provides incentives for users to participate in sensing tasks of Regions of Interest (RoIs). A challenge in wireless networking, however, is to balance the amount of data collected by users without imposing excessive load to the network. In this direction, this paper proposes a centralized system to adapt the sampling rate assigned to each crowdsourcing participant sensor. The sampling rate is computed based on the standard deviation of samples collected from a given RoI. The results obtained via simulations show a tradeoff between the sampling rate and the number of crowdsourcing participants. The more crowdsourcing participants, the lower must be the individual sampling rate and the amount of data transferred. This strategy can increase the data delivery rate taking into account the available short contact times, even though it requires a larger number of sensors.

Optimal Control of Multiple Microgrids and Buildings by an Aggregator

The electrical grid has been changing in the last decade due to the presence of renewables, distributed generation, storage systems, microgrids, and electric vehicles. The introduction of new legislation and actors in the smart grid’s system opens new challenges for the activities of companies, and for the development of new energy management systems, models, and methods. A new optimization-based bi-level architecture is proposed for an aggregator of consumers in the balancing market, in which incentives for local users (i.e., microgrids, buildings) are considered, as well as flexibility and a fair assignment in reducing the overall load. At the lower level, consumers try to follow the aggregator’s reference values and perform demand response programs to contain their costs and satisfy demands. The approach is applied to a real case study.

Technical and Economic Analysis of Residential Photovoltaic Distributed Generation: Net Billing and Self-consumption in Peru

In Germany, Spain, Italy and other European countries, the promotion and development of distributed residential photovoltaic (PV) generation markets has been based mainly on the mechanism of regulated premiums by kWh generated - Feed in Tariff. The United States has employed the net metering mechanism. Both mechanisms have determined the price of the electricity injected to the grid energy be equal or higher than the grid electricity tariff. The accelerated cost reduction of PV technology has allowed, in diverse electricity markets, Grid parity. This circumstance has driven the revision and reformulation of the promotion mechanisms for distributed PV generation. This paper is to development to evaluate the net billing and self-consumption mechanism and the impact on the business sustainability of the electricity distribution company (DSO); as well as the economic incentive for the residential user to become prosumer. The mechanism has applied to the electricity retail market of Arequipa, located in southern Peru, with a specific solar potential higher than 2000 kWh/kWp. The results showed that: The subsidized electricity tariff for consumer less than 100 kWh/month, would not reach full grid parity before 2020. For consumer of higher than 100 kWh/month, the net billing and self-consumption mechanism allows the promotion of distributed PV generation. In addition, the under net billing, DOS would maintain their business sustainability. Finally, under the net billing mechanism the user will have the incentive not to oversize the grid-connected PV power

Optimal coordination of buildings and microgrids by an aggregator: a bi-level approach

The introduction of renewables, distributed generation, microgrids, electric vehicles, and new market actors, such as aggregators, have led to a remarkable change in the power network. To address the issues that such a profound modification implies on a modern energy system, here a new hierarchical architecture is presented. Specifically, the proposed approach considers the case of an aggregator of consumers in the balancing market, in which incentives for local users (i.e., microgrids, buildings) are considered as well as flexibility assessment for demand response, and CO2 emissions. The main innovation is related to the overall architecture and to the formalization of the upper level decision problem that aims at coordinating local users in a democratic way, while, at the lower level, consumers want to track the aggregator’s reference values performing demand response programs. The approach is applied to a real case study.

Dynamic Game Model for Ranking Bitcoin Transactions Under GSP Mechanism

Bitcoin is the first and most successful Blockchain system so far. In the Bitcoin system, miners use transaction attached fees as a driving force to mine a new block and package transactions, while users compete by bidding transaction fees for faster confirmation. Considering the particularity of Bitcoin trading system, we take time series into consideration to analyze the transaction rules of Bitcoin system from the perspective of multiple cycles and establish a dynamic game model related to time under Generalised Second Price(GSP) mechanism, and also confirm the model's superiority on saving users' fees, compared with the static game model. Also, we propose the quantification of the user experience quantified by calculating the price difference between the transactions uploaded by the same user within adjacent times, making the transaction process of the Bitcoin system no longer the final say of the transaction price. The dynamic game model shows that there is a perfect Bayesian game equilibrium solution in the payment decision, so there is no incentive for users to change the attached fee, and the whole system is maintained stably. In addition, we verify the dynamic game model from computational experiment. Firstly, it is proved that with the help of revenue discount, the cost saving of the dynamic model is generally higher than that of the static model. Then the user's revenue under the dynamic model is showing an upward trend, and the transactions order under the dynamic model is more stable than that under static model, which can be illustrated mathematically and computationally that the proposed dynamic game model in this paper will help all transactions be processed more efficiently in a uniform pipeline.

Qn: Quality and Quantity Based Unified Approach for Secure and Trustworthy Mobile Crowdsensing

A major challenge in mobile crowdsensing applications is the generation of false (or spam) contributions resulting from selfish and malicious behaviors of users, or wrong perception of an event. Such false contributions induce loss of revenue owing to undue incentivization, and also affect the operational reliability of the applications. To counter these problems, we propose an event-trust and user-reputation model, called QnQ, to segregate different user classes such as honest, selfish, or malicious. The resultant user reputation scores, are based on both `quality' (accuracy of contribution) and `quantity' (degree of participation) of their contributions. Specifically, QnQ exploits a rating feedback mechanism for evaluating an event-specific expected truthfulness, which is then transformed into a robust quality of information (QoI) metric to weaken various effects of selfish and malicious user behaviors. Eventually, the QoIs of various events in which a user has participated are aggregated to compute his reputation score, which in turn is used to judiciously disburse user incentives with a goal to reduce the incentive losses of the CS application provider. Subsequently, inspired by cumulative prospect theory (CPT), we propose a risk tolerance and reputation aware trustworthy decision making scheme to determine whether an event should be published or not, thus improving the operational reliability of the application. To evaluate QnQ experimentally, we consider a vehicular crowdsensing application as a proof-of-concept. We compare QoI performance achieved by our model with Jøsang's belief model, reputation scoring with Dempster-Shafer based reputation model, and operational (decision) accuracy with expected utility theory. Experimental results demonstrate that QnQ is able to better capture subtle differences in user behaviors based on both quality and quantity, reduces incentive losses, and significantly improves operational accuracy in presence of rogue contributions.

A Bike-sharing Optimization Framework Combining Dynamic Rebalancing and User Incentives

Bike-sharing systems have become an established reality in cities all across the world and are a key component of the Smart City paradigm. However, the unbalanced traffic patterns during rush hours can completely empty some stations, while filling others, and the service becomes unavailable for further users. The traditional approach to solve this problem is to use rebalancing trucks, which take bikes from full stations and deposit them at empty ones, reducing the likelihood of system outages. Another paradigm that is gaining steam is gamification, i.e., incentivizing users to fix the system by influencing their behavior with rewards and prizes. In this work, we combine the two efforts and show that a joint optimization considering both rebalancing and incentives results in a higher service quality for a lower cost than using simple rebalancing. We use simulations based on the New York CitiBike usage data to validate our model and analyze several schemes to optimize the bike-sharing system.