Fig 6 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
As the most successful application of the sharing economy, ride-hailing service is popular worldwide and serves millions of users per day worldwide. Ride-hailing service providers usually collect users' personal data to improve their services via big data technologies. However, SPs may also use the collected user data to apply personalized prices t...
Contexts in source publication
Context 1
... assume linear utility function (U (x) = x) applied for both SPs and Users. We will demonstrate the possible optimal approaches by using the game bi-matrix (shown in Figure 6). The approaches allow the players to decide which strategy they will choose. ...
Context 2
... approaches by using the game bi-matrix (shown in Figure 6). The approaches allow the players to decide which strategy they will choose. A pure strategy Nash Equilibrium is a strategy profile in which no player would benefit from a change of pure strategy, given that the other participant's strategy remains unchanged. Given the payoff matrix in Fig. 6, in a singlestage game, there does not exist a pure strategy Nash Equilibrium. There is no stable combination of choice for both players. 2 A mixed strategy Nash equilibrium exists when at least one player uses a randomized strategy and the other player would not benefit from playing an alternate (randomized) strategy. in a long-run ...
Context 3
... a mixed strategy Nash Equilibrium, s plays the Fair Pricing and Personalized Pricing with probability (1-θ, θ) 2. The payoff matrix in Fig. 6 shows that, if the user chooses No Insurance, SP will choose Personalized Pricing. When SP chooses Personalized Pricing, the user will prefer With Insurance. When the user's choice is With Insurance, then the SP will choose Fair Pricing. If the SP chooses Fair Pricing, the user chooses No Insurance. Therefore, in this case, there does ...
Similar publications
The delegating computation has become an irreversible trend, together comes the pressing need for fairness and efficiency issues. To solve this problem, we leverage game theory to propose a smart contract-based solution. First, according to the behavioral preferences of the participants, we design an incentive contract to describe the motivation of...
Citations
... Lu et al. [36] study the hypothetical scenario where ride-hailing services use personal data about customers (e.g., purchase habits) to determine customized prices for each customer. The work introduces the concept of a rider buying "fair price insurance," which is a price auditing contract. ...
... The work introduces the concept of a rider buying "fair price insurance," which is a price auditing contract. The blockchain-based smart price auditing system (called 'Spas') determines when a customer was charged a price based on their profile (instead of the 'correct/expected' price calculated according to a pricing policy they have access to), and in such instances, issues a refund [36]. While this can help address a hypothetical source of discrimination, it does not explicitly address the disparate impact caused by current pricing techniques toward marginalized groups. ...
Ride-hailing services have skyrocketed in popularity due to their convenience. However, recent research has shown that their pricing strategies can have a disparate impact on some riders, such as those living in disadvantaged neighborhoods with a greater share of residents of color or residents below the poverty line. Analyzing real-world data, we additionally show that these communities tend to be more dependent on ride-hailing services (e.g., for work commutes) due to a lack of adequate public transportation infrastructure. To this end, we present the first thorough study on fair pricing for ride-hailing services by first devising applicable fairness measures to quantify this bias and then proposing novel fair pricing mechanisms to alleviate this bias. We present two pricing mechanisms to provide flexibility and account for different platform needs. By taking affordability into account and potentially providing discounts that may be government-subsidized, our approaches result in an increased number and more affordable rides for the disadvantaged community. Experiments on real-world Chicago ride-hailing data demonstrate worse scores for the proposed fairness metrics for rides corresponding to disadvantaged neighborhoods than those of a control group (random mix of neighborhoods). Subsequently, the results show that our fair pricing mechanisms eliminate this inequality gap. Our mechanisms provide a basis for the government and the ride-hailing platforms to implement fair ride-hailing policies.
... 1) Sharing Systems: Studies in sharing systems have sprung up in recent years. Those related studies on the economics aspects of sharing systems have focused on several key issues such as incentives (e.g., [21], [22]), pricing (e.g., [23], [24]), throughput (e.g., [25], [26]), subsidization (e.g., [27], [28]), popularity prediction (e.g., [29]), and matching (e.g., [30], [31]). Some of them also considered two-stage Stackelberg games under incomplete information but focused on different research problems from ours (e.g., [21], [22]). ...
Sharing systems have facilitated the redistribution of underused resources by providing convenient online marketplaces for individual sellers and buyers. However, sellers in these systems may not fully disclose the information of their shared commodities, due to strategic behaviors or privacy concerns. Sellers' strategic information disclosure significantly affects buyers' user experiences and systems' reputation. This paper presents the first analytical study on information disclosure and pricing of competing sellers in sharing systems. In particular, we propose a two-stage game framework to capture sellers' strategic behaviors and buyers' decisions. Although the optimization problem is challenging due to sellers' non-convex and non-monotonic objectives, we completely characterize the complex market equilibria by decomposing it into several tractable subproblems. We demonstrate that full disclosure by all sellers or non-disclosure by all sellers will both lead to intense price competition. The former all-disclosure case is never an equilibrium even when all sellers have good commodity qualities and low privacy costs, while the latter non-disclosure case can be an equilibrium under which all sellers get zero profit. We also reveal several critical factors that affect sellers' information disclosure. Interestingly, sellers' sharing capacity limitation and buyers' estimation biases encourage information disclosure as they mitigate sellers' competition.
... During the data transmitting process, attacks such as sniffer attacks and jamming attacks could disable the connections by congesting the network. Further, the data in the edge networks are divided into many parts and stored in different storage domains, which may cause data reliability issues [1], [4]. ...
As a promising approach to extending cloud resources and services, blockchain-enabled internet of things (IoT)-based smart grid edge computing has attracted much attention. However, the edge node’s resource-constraint nature makes it difficult to store the entire chain as the sensing IoT data volume increases. To address this issue, we propose an FS scheme, a fast and secure multi-threshold trapdoor chameleon hash scheme which serves as the basis for block substitution at the edge nodes to solve the storage limitation problem. FS scheme is used to achieve a consensus-based block substitution, which allows t-out-of-n edge nodes to compute a hash collision collaboratively to reliably substitute a historical block without leaking the randomness R. Also, inspired by the rationale of fast polynomial interpolation, we optimize the FS scheme to FS-I to reduce the time complexity from O(nt) to O(tlog2t). In addition, we further optimize FS-I to FS-II by using a fast Fourier transform (FFT) to dramatically improve the computational efficiency of Lagrange interpolation, which leads to a significant improvement in terms of block substitution performance. Finally, We provide security analysis and evaluate the performance through comprehensive experiments and the results show that FS can achieve up to several magnitudes better than DTTCH. The results also demonstrate that the FS scheme can provide high service quality for large-scale IoT-based smart grid systems.
... With the emergence of IoT and the data markets [11], new service models such as the SaaS are being developed enabling new possibilities such as resource trading [12], [13], opportunistic IoT [14], task offloading and outsourcing [15], and performance oriented resource provisioning [16], [17]. Therefore, there is a need for developing effective contracts [18] and pricing schemes [19], [20] that incentivize the interactions between users and service providers of data in the IoT ecosystem. The data markets and contract solutions can be implemented using blockchain infrastructure over IoT networks [21]- [23]. ...
... Note that (20) and (21) are boundary conditions. Specifically, the initial state of x 1 is fixed, and we only have freedom in specifying boundary condition at the terminal time. ...
... In addition, (18) and (20) indicate that ...
The massive deployment of Internet of Things (IoT) devices, including sensors and actuators, is ushering in smart and connected communities of the future. The massive deployment of Internet of Things (IoT) devices, including sensors and actuators, is ushering in smart and connected communities of the future. The availability of real-time and high-quality sensor data is crucial for various IoT applications, particularly in healthcare, energy, transportation, etc. However, data collection may have to be outsourced to external service providers (SPs) due to cost considerations or lack of specialized equipment. Hence, the data market plays a critical role in such scenarios where SPs have different quality levels of available data, and IoT users have different application-specific data needs. The pairing between data available to the SP and users in the data market requires an effective mechanism design that considers the SPs' profitability and the quality-of-service (QoS) needs of the users. We develop a generic framework to analyze and enable such interactions efficiently, leveraging tools from contract theory and mechanism design theory. It can enable and empower emerging data sharing paradigms such as Sensing-as-a-Service (SaaS). The contract design creates a pricing structure for on-demand sensing data for IoT users. By considering a continuum of user types, we capture a diverse range of application requirements and propose optimal pricing and allocation rules that ensure QoS provisioning and maximum profitability for the SP. Furthermore, we provide analytical solutions for fixed distributions of user types to analyze the developed approach. For comparison, we consider the benchmark case assuming complete information of the user types and obtain optimal contract solutions. Finally, a case study is presented to demonstrate the efficacy of the proposed contract design framework.
... Fair benefits for drivers on the ride-hailing platform has also been investigated, but fair prices for the riders are not considered [23]. In [13], the hypothetical scenario where ride-hailing services use personal data about customers to determine customized prices for each customer is studied. While this can help address a form of discrimination, it does not explicitly address the disparate impact caused by current pricing techniques. ...
Ride-hailing services have skyrocketed in popularity due to the convenience they offer, but recent research has shown that their pricing strategies can have a disparate impact on some riders, such as those living in disadvantaged neighborhoods with a greater share of residents of color or residents below the poverty line. Since these communities tend to be more dependent on ride-hailing services due to lack of adequate public transportation, it is imperative to address this inequity. To this end, this paper presents the first thorough study on fair pricing for ride-hailing services by devising applicable fairness measures and corresponding fair pricing mechanisms. By providing discounts that may be subsidized by the government, our approach results in an increased number and more affordable rides for the disadvantaged community. Experiments on real-world Chicago taxi data confirm our theoretical findings which provide a basis for the government to establish fair ride-hailing policies.
... In terms of blockchain-enabled auditing approaches, Lu et al. [14] introduced a decentralized auditor to address price discrimination in ride-hailing services. In addition, another work [15] used smart contract to detect disqualified drivers in ride-hailing services. ...
As the first legally binding global agreement on climate change, the Paris Agreement aims to limit long-term global warming to less than 2 • C, preferably below 1.5 • C compared to pre-industrial levels. Road transport, as one of the primary sources of energy consumption and carbon emissions, has great potential for energy conservation and emission reduction in achieving the climate goals. However, current approaches cannot efficiently reduce road travel, making it challenging to decarbonize in the transport sector. Therefore, in this paper, we propose STRICTs, a blockchain-enabled motor vehicle restrictive and trading system based on the carbon emissions cap. STRICTs allows automated carbon emission auditing and carbon emission violation punishment without relying on third-party escrow. Through a decentralized enforcer, STRICTs also enables carbon permit trading reliably and transparently. Finally, we implement a proof-of-concept prototype of the system based on Hyperledger Fabric and conduct experiments for comprehensive performance evaluation through CO2 Emission by Vehicles dataset. The experiment results show the practically affordable performance of STRICTs.
... Distributed ledger systems, such as blockchain, allow for secure and verifiable transactions without a trusted third party [1]. Blockchain technology is progressively turning into style, with applications in varied domains like finance, supply chains, healthcare, the Internet of Things (IoT), sharing economy, and vehicular edge computing [2][3][4][5][6][7][8]. A blockchain is a distributed ledger of transactions maintained by all of the blockchain network's participating nodes. ...
... In terms of throughput and latency, the results show that Fabric v1.0 outperforms Fabric v0. 6. To see how the Fabric network behaved, the authors altered block sizes, resource allocation, state database (DB), and endorsement policies in [19]. ...
Blockchain is a potential technology for migrating the central server's processing burden into a decentralized, secure, and transparent manner. This technology has had significant influence and revolution in various industries. However, the limited transaction processing power is a stumbling block, especially compared to proven alternatives like distributed database systems. This paper proposes a transaction traffic control approach based on fuzzy logic to enhance the blockchain network's transaction processing capacity. The proposed fuzzy controller is implemented in the smart contract to adjust the transaction traffic flow in real-time network conditions automatically. An experiment environment is built on Hyperledger Fabric to demonstrate the design approach's significance. According to the experiment results, the blockchain performance is significantly improved compared to the baseline. Furthermore, the proposed approach is integrated with an existing blockchain performance-enhancing tool, and the results indicate that the proposed approach is flexible enough to integrate with other existing approaches.
... (1) Access-to-trust system Access-to-trust systems require that any nodes get permission first before they are considered trustworthy and build trusted communication [15,16]. In general, the systems maintain a white list or black list to control this communication permission. ...
Vehicular reputation maintenance with distributed ledger is aimed at establishing trust among vehicles randomly meeting in a Vehicular Ad-hoc Network (VANET). It is, however, challenging in VANET, as congested areas in road networks, brought by traffic tides or accidents, challenge the ledger performance. Meanwhile, the reputation update is highly dependent on transaction consensus of the distributed ledger. To solve the problem, this paper proposes deploying directed acyclic graph- (DAG-) based distributed ledgers on vehicles, which use the vehicular distribution to adapt the unpredictable reputation update. Specifically, we first propose a partitioned DAG-based distributed ledger to manage vehicular reputation in partitioned VANET. Secondly, we introduce a novel reputation evaluation method to encourage vehicles to contribute to VANET interaction and ledger consensus maintenance, which can remedy the topology churn of the ledger network due to the mobility of VANET. Finally, we design a reputation update method based on the consistency of transactions in the partition to facilitate trust establishment. Experimental results on a real-world dataset show that the proposed ledger and reputation update method is effective and feasible in the large-scale dynamic VANET.
... Although some studies have carried out related discussion on the problem of big data killing [2,8], some solutions are proposed [9,10]. However, existing studies are mostly limited to the pricing between e-commerce companies and consumers [11,12], the strategic choice between e-commerce platform and consumer, and the supervision strategy choice between e-commerce platform and government [9,13]. ...
... Algorithmic price discrimination [16], artificial intelligence techniques, and digital system fingerprints [15] enable the e-commerce company to have the ability of price discrimination. Price discrimination is not only widespread in the field of commodity sales, and there are also discrimination and price difference by using customer information in the field of online car-hailing [8] and the field of advertising recommendation [7,17]. While consumers benefit from accurate recommendations, sellers may use this information to discriminate on price. ...
This paper focuses on the phenomenon of “big data killing” implied in e-commerce and discusses how to take the government as the lead to coordinately supervise the price discrimination behavior of e-commerce companies towards loyal customers. First, the four-party evolutionary game model of the government regulatory department, e-commerce platform, e-commerce company, and consumer is built. Second, the stability of the strategy choice of each game subject is analyzed. On this basis, the evolutionary stable strategy in the system based on First Law of Lyapunov is explored. Finally, the influences of key elements on system evolution are simulated and analyzed by MATLAB2021. Results demonstrate that (1) the government supervision mechanism can effectively supervise the price discrimination of e-commerce company based on big data to loyal customers; (2) when the government chooses the strict supervision strategy, reducing the information supervision cost of the e-commerce platform and the strict supervision cost of the government enable the government and the e-commerce platform to coordinate supervision and make the e-commerce company incline to choose the nondifferential pricing strategy; (3) when the government chooses the loose supervision strategy, reducing the information supervision cost of the e-commerce platform and increasing the probability of consumer discovering differential pricing and the penalties for differential pricing of e-commerce company enable the e-commerce platform and consumer to coordinate supervision, and make the e-commerce company incline to choose the nondifferential pricing strategy. The results of this study can provide theoretical guidance for the government and companies to make beneficial strategic decisions in the development of e-commerce.
... Steps 4a and 4b support explaining Why about the fairness. 5) Enable job planners to adjust planning parameters (e.g., commission fee [105], surge pricing rate [106], incentive threshold [107], working speed [108]) and run the allocation engine to simulate What If the parameters were different (5a), or request recommendations of fairer settings (5b). In general, IF-Alloc can be applied to applications involving resource or task allocation, planning parameters, and diverse stakeholders. ...
With the increasing pervasiveness of Artificial Intelligence (AI), many visual analytics tools have been proposed to examine fairness, but they mostly focus on data scientist users. Instead, tackling fairness must be inclusive and involve domain experts with specialized tools and workflows. Thus, domain-specific visualizations are needed for algorithmic fairness. Furthermore, while much work on AI fairness has focused on predictive decisions, less has been done for fair allocation and planning, which require human expertise and iterative design to integrate myriad constraints. We propose the Intelligible Fair Allocation (IF-Alloc) Framework that leverages explanations of causal attribution (Why), contrastive (Why Not) and counterfactual reasoning (What If, How To) to aid domain experts to assess and alleviate unfairness in allocation problems. We apply the framework to fair urban planning for designing cities that provide equal access to amenities and benefits for diverse resident types. Specifically, we propose an interactive visual tool, Intelligible Fair City Planner (IF-City), to help urban planners to perceive inequality across groups, identify and attribute sources of inequality, and mitigate inequality with automatic allocation simulations and constraint-satisfying recommendations. We demonstrate and evaluate the usage and usefulness of IF-City on a real neighborhood in New York City, US, with practicing urban planners from multiple countries, and discuss generalizing our findings, application, and framework to other use cases and applications of fair allocation.
