A simulation framework for measuring robustness of incentive mechanisms and its implementation in reputation systems

This paper presents a generic two-stage optimization model and a simulation framework for on-demand grocery delivery. The proposed simulation framework can be used to reproduce any historical day's behaviour or evaluating various what-if scenarios. The two-stage optimization model's objective is to minimize the Cost Per Delivery (CPD) and maximize Customer Experience (CX). In the first stage, Last-Mile (LM) delivery optimization is modelled as a Pickup and Delivery Problem with Time Windows (PDPTW). Both static and dynamic variants of the PDPTW model are tried out and compared to their performances. A Just-In-Time (JIT) heuristic is integrated with PDPTW to minimize waiting time. The second stage solves the First-Mile (FM) delivery optimization using a multi-objective assignment model for trading off between the CPD and CX. The proposed framework is simulated with actual order data and it was found that the the dynamic PDPTW model results in significant savings in CPD while maintaining a good CX.

In peer-to-peer (P2P) network, incentive mechanism is crucial to encourage cooperation among peers. Hence, how to construct a framework to analyze and evaluate the effectiveness of incentive mechanism is a very significant problem. Considering the peers' interactions are influenced by the network structure in real network, we propose a novel framework based on spatial evolutionary game theory. Different from most of other researches based on classical and evolutionary game theory, square lattice network is adopted as the network structure in this paper, without the assumption that peers are well-mixed in P2P network. The square lattice network structure can be easily extended to other realistic complex networks, such as small-world network and scale-free network. The reciprocative incentive mechanism is analyzed and evaluated under the framework with different service benefit. Through the simulation, the range of the parameter Q (cost/benefit) that makes the incentive mechanism work effectively under the framework is got, and the reason is analyzed. In addition, the influences of zero-cost identity and strategy mutation of peers on the incentive mechanism are evaluated. The framework is general to analyze and evaluate the effectiveness of other incentive mechanisms.

With the increasing popularization and application of the smart grid, the harm of the data silo issue in the smart grid is more and more prominent. Therefore, it is especially critical to promote data interoperability and sharing in the smart grid. Existing data-sharing schemes generally lack effective incentive mechanisms, and data holders are reluctant to share data due to privacy and security issues. Because of the above issues, a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory is proposed. Firstly, several basic assumptions about the evolutionary game model are given, and the evolutionary game payoff matrix is established. Then, we analyze the stabilization strategy of the evolutionary game based on the payoff matrix, and propose a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory according to the analysis results, aiming to encourage user participation in data sharing. We further write the above evolutionary game model into a smart contract that can be invoked by the two parties involved in data sharing. Finally, several factors affecting the sharing of data between two users are simulated, and the impact of different factors on the evolutionary stabilization strategy is discussed. The simulation results verify the positive or negative incentives of these parameters in the data-sharing game process, and several factors influencing the users’ data sharing are specifically analyzed. This dynamic incentive mechanism scheme for smart grid data sharing based on evolutionary game theory provides new insights into effective incentives for current smart grid data sharing.

The success of current trust and reputation systems is on the premise that truthful feedbacks are obtained. However, without appropriate mechanisms, silent and lying strategies usually yield higher payoffs for peers than truthful feedback strategies. Thus, to ensure trustworthiness, incentive mechanisms are critically needed for a reputation system to encourage rational peers to provide truthful feedbacks. In this paper, we model the feedback reporting process in reputation system as a reporting game. We propose a Wage-based Incentive Mechanism (WIM) for enforcing truthful report in self-interested P2P networks. We design, implement, and analyze incentive mechanisms and players' strategies. The extensive simulation results demonstrate that the proposed incentive mechanisms reinforce truthful feedbacks and achieve optimal welfare.

PurposeThe purpose of this paper is to model group advertising decisions, which are the collective decisions of every single advertiser within the set of advertisers who are competing in the same auction or vertical industry, and examine resulting market outcomes, via a proposed simulation framework named Experimental Platform for Search Engine Advertising (EXP-SEA) supporting experimental studies of collective behaviors in the context of search engine advertising.Design/methodology/approachThe authors implement the EXP-SEA to validate the proposed simulation framework, also conduct three experimental studies on the aggregate impact of electronic word-of-mouth (eWOM), the competition level and strategic bidding behaviors. EXP-SEA supports heterogeneous participants, various auction mechanisms and also ranking and pricing algorithms.FindingsFindings from the three experiments show that both the market profit and advertising indexes such as number of impressions and number of clicks are larger when the eWOM effect is present, meaning social media certainly has some effect on search engine advertising outcomes, the competition level has a monotonic increasing effect on the market performance, thus search engines have an incentive to encourage both the eWOM among search users and competition among advertisers, and given the market-level effect of the percentage of advertisers employing a dynamic greedy bidding strategy, there is a cut-off point for strategic bidding behaviors.Originality/valueThis is one of the first research works to explore collective group decisions and resulting phenomena in the complex context of search engine advertising via developing and validating a simulation framework that supports assessments of various advertising strategies and estimations of the impact of mechanisms on the search market.

Within the distribution system operator (DSO) framework, there could be alternative arrangements to enable market participation by all entities, including the privately owned distributed energy resources (DERs). In many cases, the retailers and similar aggregating agencies facilitate this. Even before finalizing the modalities of the market at the distribution level, it is essential to know about the possible market outcome and its impact on the operational decisions of DSO and vice-versa. This paper presents a simulation framework and associated case studies, where the market outcome and DSO’s operational activities are carried out in tandem to establish a realistic and feasible outcome. The market model is represented by a non-cooperative game where an efficient and fair solution is obtained using Nash equilibrium. For this, multi-player power transaction problem (MPTP) is solved using complementarity modeling. The proposed MPTP is further compounded with network reconfiguration and loss considerations which are the part of DSO’s operations. The inclusion of reconfiguration and loss allocation to entities makes the outcome realistic and feasible one. The proposed simulation framework is implemented on various test cases with a heterogeneous set of participants to establish its effectiveness.

In the last decade, many efforts to solve traffic congestion and sustainable growth issues are going in the direction of research and investments in smart cities and consequently smart mobility.We use the proposed simulation framework is compatible with SUMO 1.1.0. We use it to study multi-modal commuting and parking optimization issues in a state-of-the-art large-scale mobility scenario, and we intend to demonstrate the ease of use and its capabilities.

Simulation of progressive failure in laminated composites under variable environmental conditions

This work address the channel capacity problem which is the key issue in the new generation mobile broadband network. The simulation framework for analysis of channel capacity solution based on usage of the heterogeneous networks in complex urban and indoor environment is introduced in this work. The impact of heterogeneous network on channel capacity is analyzed based on proposed simulation framework. The multi-parametric statistical channel model is used for simulation of the propagation conditions. The advantages and drawbacks for various deployment strategies of femtocells in presence of macro coverage are investigated. It is shown that shared frequency assignment with open access gives better overall channel capacity for majority of the mobile outdoor and indoor users.

The aim of this study was to evaluate the effect of integrated high-permittivity materials (HPMs) on excitation homogeneity and global specific absorption rate (SAR) for transmit arrays at 7T. A rapid electrodynamic simulation framework was used to calculate L-curves associated with excitation of a uniform 2D profile in a dielectric sphere. We used ultimate intrinsic SAR as an absolute performance reference to compare different transmit arrays in the presence and absence of a layer of HPM. We investigated the optimal permittivity for the HPM as a function of its thickness, the sample size, and the number of array elements. Adding a layer of HPM can improve the performance of a 24-element array to match that of a 48-element array without HPM, whereas a 48-element array with HPM can perform as well as a 64-element array without HPM. Optimal relative permittivity values changed based on sample and coil geometry, but were always within a range obtainable with readily available materials (εr = 100-200). Integration of HPMs could be a practical method to improve RF shimming performance, alternative to increasing the number of coils. The proposed simulation framework could be used to explore the design of novel transmit arrays for head imaging at ultra-high field strength. Magn Reson Med 80:391-399, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

The outbreak of COVID-19 resulted in severe pressure on the existing medical infrastructure in China. Several Chinese cities began to construct temporary hospitals for the centralized treatment of COVID-19 patients. The harmful exhaust air from the outlets of these hospitals may have a significant adverse impact on the fresh-air intakes and surrounding environment. Owing to the need to rapidly construct these hospitals within 6–10 days, just a few hours are allowed for the analysis of the impact of this exhaust air on the environment. To overcome this difficulty, a high-efficiency simulation framework is proposed in this study. Based on the open-source computational fluid dynamics software, FDS, the proposed framework is adaptive and incorporates building information with different levels of detail during various design phases of the hospital, and has been applied in the design of the Wuhan Huoshenshan Hospital, the first typical COVID-19 temporary hospital in China. According to the simulation results, neither the fresh-air intakes nor the surrounding buildings would be polluted by the harmful air discharged from the air outlets of the Huoshenshan hospital. The proposed simulation framework can provide a reference for the design and overall planning of similar hospitals in China and other affected countries.

Multi-level simulation studies on seismic performance evaluation of steel-sheathed cold-formed steel framing shear walls

This paper presents numerical analysis of domain wall propagation for dense embedded memory applications. Self-consistent simulation framework based on Four Component Spin Transport Model and Landau-Lifshitz-Gilbert equation is able to capture domain wall motion in terms of critical current density requirement, domain wall velocity, and power dissipation. Effect of patterned notches on memory stability, domain wall velocity and nanostrip resistance are also presented. Finally, the proposed simulation framework is used to investigate performance, scalability and organization of the domain wall motion based memory structure.

The optimization of the channel capacity in the new fourth generation (4G) wireless networks became a key issue during the last decade. In this work, the co-existence of macrocell Base Stations (MBSs) and Femtocell Access Points (FAPs) or home base stations (HBS) with shared and dedicated carrier frequency allocations is considered. The simulation framework for analysis of channel capacity improvement in complex urban environment is introduced. The analysis of heterogeneous network consisting of dedicated femtocells for home coverage in a presence of traditional macrocell pattern is presented. The advantages and drawbacks for different deployment strategies are investigated using the proposed simulation framework.

The public environment perception model regards people as a “data perceptron” and a human-centred participatory perception model. The enthusiasm and initiative of public participation will directly determine the effective operation of the model. This paper aims to understand how to stimulate public participation in data sensibility in public environment perception and establish an effective incentive mechanism. Based on the evolutionary game theory, a public environment perception evolutionary game model is established. The game selection between the data subject and the perceived participants is analysed. The group strategy selection and influencing factors of establishing effective incentive mechanism are studied. According to the replication dynamic equation, the behaviour evolution law and evolutionary stability strategy of public environmental group intelligence perception are obtained. The research results show that data users increase the proportion of investment in participants’ incentive strategies, which will motivate participants to share more data and thus help them to evolve to the desired results. Encouraging participants to actively share data is necessary to reduce participants’ participation costs, which requires data users to select target groups to participate in perceived tasks, and to select people who are more convenient to provide data; the data user’s incentives should be sufficient to offset the participant’s participation cost. The data user should ensure that both the quantity and quality of the public participation in the perceived task are optimal when the data user minimizes the payment.