Utility Maximization Framework Research Articles

Models for Individual Adoption of eCommerce, eBanking and eGovernment in Spain

This paper analyzes the adoption patterns of selected internet services such as eCommerce, eBanking and eGovernment in Spain. High quality official data from the Survey on Equipment and Use of Information and Communication Technologies in Households (ICT-H) of the National Institute of Statistics are used. The dataset is a cross section of 16,209 individuals for 2016. Theoretical demand models, grounded in a standard neoclassical utility maximization framework, are adapted to these services. Logistic regression techniques allow quantifying the impact of the socioeconomic characteristics of the individual on the adoption of each service. The resulting models are statistically significant and with a high predictive power. Age, education and levels of internet and computer skills are all significant in explaining the adoption of any of the three services; as are gender and income, but just for eCommerce and eBanking. Interestingly, the level of trust in internet is only significant to explain participation in eCommerce. Finally, policy recommendations are suggested, highlighting the desirability of using specific measures for the different socio-demographic groups and income strata.

Assortment Optimization Under the Paired Combinatorial Logit Model

When retailers decide which assortment of products to offer, they can make use of a choice model that describes how customers choose and substitute among the products. The key is to use a choice model that faithfully captures the choice process of customers, while making sure that the corresponding problem of finding the revenue-maximizing assortment remains tractable. In “Assortment Optimization Under the Paired Combinatorial Logit Model,” Zhang, Rusmevichientong, and Topaloglu consider the paired combinatorial logit model to capture the choice process of customers. This choice model uses a utility maximization framework to capture the customer choices, and the utilities of the products can have a rather general correlation structure. The authors demonstrate that one can construct algorithms with performance guarantees to solve the assortment optimization problem under this choice model.

Adaptive CSMA Under the SINR Model: Efficient Approximation Algorithms for Throughput and Utility Maximization

We consider a carrier sense multiple access (CSMA)-based scheduling algorithm for a single-hop wireless network under a realistic signal-to-interference-plus-noise ratio model for the interference. We propose two local optimization-based approximation algorithms to efficiently estimate certain attempt rate parameters of CSMA called fugacities. It is known that adaptive CSMA can achieve throughput optimality by sampling feasible schedules from a Gibbs distribution, with appropriate fugacities. Unfortunately, obtaining these optimal fugacities is an NP-hard problem. Furthermore, the existing adaptive CSMA algorithms use a stochastic gradient descent-based method, which usually entails an impractically slow (exponential in the size of the network) convergence to the optimal fugacities. To address this issue, we first propose an algorithm to estimate the fugacities, that can support a given set of desired service rates. The convergence rate and the complexity of this algorithm are independent of the network size, and depend only on the neighborhood size of a link. Furthermore, we show that the proposed algorithm corresponds exactly to performing the well-known Bethe approximation to the underlying Gibbs distribution. Then, we propose another local algorithm to estimate the optimal fugacities under a utility maximization framework, and characterize its accuracy. Numerical results indicate that the proposed methods have a good degree of accuracy, and achieve extremely fast convergence to near-optimal fugacities, and often outperform the convergence rate of the stochastic gradient descent by a few orders of magnitude.

Optimal dynamic asset-liability management with stochastic interest rates and inflation risks

This paper considers an optimal asset-liability management problem with stochastic interest rates and inflation risks under the expected utility maximization framework, where the stochastic interest rate follows the Hull-White interest rate model and the inflation risk is modelled by an additional stochastic process. The investor can invest in n+1 assets: cash, a default-free zero-coupon bond, an inflation-indexed bond and n−2 stocks. The liability process is given by a geometric Brownian motion rather than a Brownian motion to ensure a definite liability value. Applying the stochastic control theory and partial differential equation approach, we obtain the explicit solutions of optimal investment strategies for the power utility and exponential utility functions. We also provide numerical examples to show the effects of model parameters on the optimal investment strategies.

From Social Group Utility Maximization to Personalized Location Privacy in Mobile Networks

With increasing popularity of location-based services (LBSs), there have also been growing concerns for location privacy. To protect location privacy in an LBS, mobile users in physical proximity can work in concert to collectively change their pseudonyms, in order to hide spatial-temporal correlation in their location traces. In this paper, we leverage mobile users' social tie structure to motivate them to participate in pseudonym change. Drawing on a social group utility maximization framework, we cast users' decision making of whether to change pseudonyms as a socially aware pseudonym change game (SA-PCG). The SA-PCG further assumes a general anonymity model that allows a user to have its specific anonymity set for personalized location privacy. For the SA-PCG, we show that there exists a socially aware Nash equilibrium (SNE), and quantify the system efficiency of SNEs with respect to the optimal social welfare. Then, we develop a greedy algorithm that myopically determines users' strategies, based on the social group utility derived from only the users whose strategies have already been determined. We show that this algorithm efficiently finds an SNE that enjoys desirable properties: 1) it is socially aware coalition-proof, and thus is also Pareto-optimal; 2) it achieves higher social welfare than any SNE for the socially oblivious pseudonym change game. We further quantify the system efficiency of this SNE with respect to the optimal social welfare. We also show that this SNE can be achieved in a distributed manner. Numerical results using real data corroborate that social welfare can be significantly improved by exploiting social ties.

Distributed Optimal Cooperation for Spectral and Energy Efficiency in Hyper-Dense Small Cell Networks

Hyper-dense small cell networks have been recognized as one of the most effective ways to enhance spectrum and energy efficiency. However, performance enhancement can be significantly degraded due to inter-cell interference and traffic loading imbalance. Optimal cooperation has been widely used to tackle these problems by exploring and exploiting cooperative gains. Various potential cooperation opportunities exist among heterogeneous small cells. The denser the small cells are, the more cooperation opportunities a network may have. In this article, we first survey different optimal cooperation techniques from the network-wide and user-centric perspectives. To explore and exploit these cooperative diversity gains, we present a utility maximization framework, where a newly-defined utility function is formulated to jointly consider spectrum and energy efficiency. As a use case of the presented framework, we further propose a distributed interference-aware traffic offloading scheme. Numerical results validate the improved spectrum and energy efficiency of the presented win-win scheme.

An Efficient Adaptive Real Coded Genetic Algorithm to Solve the Portfolio Choice Problem Under Cumulative Prospect Theory

Cumulative prospect theory (CPT) has become one of the most popular approaches for evaluating the behavior of decision makers under conditions of uncertainty. Substantial experimental evidence suggests that human behavior may significantly deviate from the traditional expected utility maximization framework when faced with uncertainty. The problem of portfolio selection should be revised when the investor’s preference is for CPT instead of expected utility theory. However, because of the complexity of the CPT function, little research has investigated the portfolio choice problem based on CPT. In this paper, we present an operational model for portfolio selection under CPT, and propose a real-coded genetic algorithm (RCGA) to solve the problem of portfolio choice. To overcome the limitations of RCGA and improve its performance, we introduce an adaptive method and propose a new selection operator. Computational results show that the proposed method is a rapid, effective, and stable genetic algorithm.

Risk aversion in crop insurance program purchase decisions

PurposeThe purpose of this paper is to contribute to the aforementioned literature on the linkage between economic activity and human preference by estimating the cross-sectional determinants of farmers’ participation in participation in crop insurance programs (CIPs) and identifying the impediments preventing the remaining farmers from participating.Design/methodology/approachUsing the unique data sets of risk preference experiments and maize producer surveys pertaining to the maize production areas of China, this paper explores the determinants of farmers’ CIPs and scrutinizes the role of risk aversion in farmers’ CIP purchase decisions under the expected utility maximization framework. And a “non-zero threshold probit model” is used for the analysis.FindingsThe results show that risk aversion plays an important role in CIP purchase decision-making, not only in the form of its direct effect but also with regard to the interaction term and expected loss. Furthermore, if the insured amount is high enough, then risk aversion will no longer affect insurance purchase. Additionally, purchase experience, CIP environment (village purchase ratio), and contract items (insured amounts) are significant determinates in these decisions. There is no significant evidence to suggest that serious adverse selection exists in the sampling areas.Originality/valueOne theoretical model is established which considered not only general variables like farmers’ production and household information, but also conditions and terms in the insurance policies. The unique experimental method is used to measure farmers’ risk aversion. Both the role of risk aversion and its’ interaction terms with others in CIP participation are scrutinized to identify complicated influences under the context of real society.

Femtocells in centralized systems: green operation and radio resource management techniques

In this paper, a system of femtocells controlled by a single controller is investigated. In such a scenario, femtocell access points (FAPs) are assumed connected via wired links to a central controller within a certain vicinity (e.g., building, compound, hotel, and campus.). Thus, radio resource management (RRM) and green network operation of LTE femtocell networks are investigated in an integrated wired/wireless system. Consequently, it becomes possible to perform RRM in a centralized and controlled way in order to enhance the quality of service (QoS) performance for the users in the network. Furthermore, energy-efficient operation consisting of switching off redundant FAPs can be implemented. A utility maximization framework is presented, and an RRM algorithm that can be used to maximize various utility functions is proposed. Another algorithm is presented for the scenario of FAP on/off switching to achieve green operation. It consists of selecting the best FAP to switch off, then moving the femto user equipments (FUEs) to other active FAPs without compromising their quality of service (QoS). Simulation results show that the proposed algorithms lead to significant performance gains.

Network Utility Maximization-Based Congestion Control Over Wireless Networks: A Survey and Potential Directives

Since the initial publication of Kelly et al.'s seminal paper on resource allocation in wired networks, a large number of studies based on the network utility maximization (NUM) framework have been conducted, for example, on resource allocations in wired networks as well as wireless networks. Congestion control, used to adapt the traffic rates injected into a network, can be controlled separately at the transport layer or mutually with other layers, using a cross-layer design. The focus of this paper is a review of the research following the NUM framework and addressing the problem of congestion control from several dimensions, specifically, elastic traffic or inelastic traffic, same-timescale or multi-timescale, and single-path or multipath transmissions. First, cross-layer designs for wireless networks, basics of optimization theory, and an example of cross-layer congestion control are summarized. Then, studies on NUM-based congestion control are sequentially presented from several points of view. In addition, we survey the recent literature on cross-layer resource allocation in next generation networks, software-defined networking, and cloud radio access networks, which can enhance the performances and address issues found in current networks. Finally, we outline what we consider to be the current challenges and open issues in the area of NUM-based congestion control for wireless networks.

Models of Count with Endogenous Choices

Abstract: In transportation and traffic analysis count data arises frequently, collectively emerging from individual traveler choices from a choice set of alternatives. Examples include network origin-destination (OD) flow rates and visitor counts at transit stations. From a modeling perspective, these data are aggregate counts at the top level, but comprised of individual discrete choices at the lower level. Models of count data are widely applied in the transportation and traffic fields. However, only a moderate level of applications jointly model count observations at the top level with discrete choice models at the bottom level under a random utility maximization (RUM) framework. This paper considers modeling count data with an underlying choice process as a joint model that merges an observed event count process with a discrete choice process, where the count level is Poisson distributed. This model captures both processes within a single random utility framework that preserves a direct mapping between the count intensity and the utility of the chosen alternative, including unobserved variables and latent factors. The decision-making context presented examines discretionary activity type choice for activities completed within a one-day period. The estimation results for this model are compared against (i) a mixed-logit model and (ii) a mixed-Poisson model, each with normally distributed parameters. The results indicate that a model of count with endogenous choices can account for the randomness associated with the utility of choice alternatives from lower level discrete choices, consequently leading to significantly different utility parameter estimates for the Poisson rate parameter in the upper level. Furthermore, while the linkage between the maximizing utility and rate parameter is preserved in this joint model, identifying the contribution of attributes between the two levels requires further parameterization.

Self-Organizing Algorithms for Interference Coordination in Small Cell Networks

This paper discusses novel joint (intracell and intercell) resource allocation algorithms for self-organized interference coordination in multicarrier multiple-input multiple-output (MIMO) small cell networks. The proposed algorithms enable interference coordination autonomously, over multiple degrees of freedom, such as base station transmit powers, transmit precoders, and user scheduling weights. A generic $\alpha$ -fair utility maximization framework is considered to analyze performance-fairness tradeoff and to quantify the gains achievable in interference-limited networks. The proposed scheme involves limited inter-base station signaling in the form of two step (power and precoder) pricing. Based on this decentralized coordination, autonomous power and precoder update decision rules are considered, leading to algorithms with different characteristics in terms of user data rates, signaling load, and convergence speed. Simulation results in a practical setting show that the proposed pricing-based self-organization can achieve up to $100\%$ improvement in cell-edge data rates when compared to baseline optimization strategies. Furthermore, the convergence of the proposed algorithms is also proved theoretically.

Optimization Based Hybrid Congestion Alleviation for 6LoWPAN Networks

The IPv6 over low-power wireless personal area network (6LoWPAN) protocol stack is a key part of the Internet of Things (IoT) where the 6LoWPAN motes will account for the majority of the IoT “things.” In 6LoWPAN networks, heavy network traffic causes congestion which significantly effects the network performance and the quality of service metrics. Generally, two main strategies are used to control and alleviate congestion in 6LoWPAN networks: 1) resource control and 2) traffic control. All the existing work of congestion control in 6LoWPAN networks use one of these. In this paper, we propose a novel congestion control algorithm called optimization-based hybrid congestion alleviation (OHCA) which combines both strategies into a hybrid solution. OHCA utilizes the positive aspects of each strategy and efficiently uses the network resources. The proposed algorithm uses a multiattribute optimization methodology called gray relational analysis for resource control by combining three routing metrics (buffer occupancy, expected transmission count, and queuing delay) and forwarding packets through noncongested parents. Also, OHCA uses optimization theory and network utility maximization framework to achieve traffic control when the noncongested parent is not available where the optimal nodes’ sending rate are computed by using Lagrange multipliers and Karush–Kuhn–Tucker conditions. The proposed algorithm is aware of node priorities and application priorities to support the IoT application requirements where the applications’ sending rate allocation is modeled as a constrained optimization problem. OHCA has been tested and evaluated through simulation by using Contiki OS and compared with comparative algorithms. Simulation results show that OHCA improves performance in the presence of congestion by an overall average of 28.36%, 28.02%, 48.07%, 31.97%, and 90.35% in terms of throughput, weighted fairness index, end-to-end delay, energy consumption, and buffer dropped packets as compared to duty cycle-aware congestion control for 6LoWPAN networks and queue utilization-based-IPv6 routing protocol for low-power and lossy network.

Linguistic Distance, Networks and Migrants' Regional Location Choice

This paper analyzes the interaction between migrant networks and linguistic distance in the location choice of migrants to the EU at the regional level. We test the hypothesis that networks and the ability to communicate in the host country language, proxied by linguistic distance, are substitutes in the location decision. Based on individual level data from a special evaluation of the European Labour Force Survey (EU-LFS) and a random utility maximization framework, we find that networks have a positive effect on the location decisions while the effect of linguistic distance is negative. We also find a strong positive interaction effect between the two factors: networks are more important the larger the linguistic distance between the home country and the host region, and the negative effect of linguistic distance is smaller the larger the network size. In several extensions and robustness checks, we show that this substitutable relationship is extremely robust.

Exploiting Social Tie Structure for Cooperative Wireless Networking: A Social Group Utility Maximization Framework

We develop a social group utility maximization (SGUM) framework for cooperative wireless networking that takes into account both social relationships and physical coupling among users. Specifically, instead of maximizing its individual utility or the overall network utility, each user aims to maximize its social group utility that hinges heavily on its social tie structure with other users. We show that this framework provides rich modeling flexibility and spans the continuum between non-cooperative game and network utility maximization (NUM)—two traditionally disjoint paradigms for network optimization. Based on this framework, we study three important applications of SGUM, in database assisted spectrum access, power control, and random access control, respectively. For the case of database assisted spectrum access, we show that the SGUM game is a potential game and always admits a socially-aware Nash equilibrium (SNE). We also develop a distributed spectrum access algorithm that can converge to the SNE and also quantify the trade-off between the performance and convergence time of the algorithm. For the cases of power control and random access control, we show that there exists a unique SNE and the network performance improves as the strength of social ties increase. Numerical results corroborate that the SGUM solutions can achieve superior performance using real social data trace. Furthermore, we show that the SGUM framework can be generalized to take into account both positive and negative social ties among users, which can be a useful tool for studying network security problems.

MicroCast: Cooperative Video Streaming Using Cellular and Local Connections

We consider a group of mobile users, within proximity of each other, who are interested in watching the same online video. The common practice today is that each user downloads the video independently on her mobile device using her own cellular connection, which wastes access bandwidth and may also lead to poor video quality. We propose a novel cooperative system where each mobile device uses simultaneously two network interfaces: (i) cellular to connect to the video server and download parts of the video and (ii) WiFi to connect locally to all other devices in the group to exchange those parts. Devices cooperate to efficiently utilize all network resources and to adapt to varying wireless network conditions. In the local WiFi network, we exploit overhearing, which we further combine with network coding. The end result is savings in cellular bandwidth and improved user experience. We follow a complete approach, from theory to practice. First, we formulate the problem using a network utility maximization (NUM) framework, decompose the problem, and provide a distributed solution. Then, based on the structure of the NUM solution, we design a system called MicroCast, and we implement a prototype as an Android application. We provide both simulation results of the NUM solution and experimental evaluation. We demonstrate that the proposed approach brings significant performance benefits (namely, faster download on the order of the group size) without battery penalty.

Towards a throughput-optimal routing algorithm for data collection on satellite networks

To perform Earth observation, more and more satellites are equipped with high-resolution sensors like hyperspectral imagers, which generate data at tens of Gbps. Researchers aim to maximize the volume of data available for use on the ground in near-real time. To achieve this goal, many solutions have recently been proposed to perform data collection through satellite networks. Most of these solutions have so far been focusing on either source rate control and load balancing, or heuristic routing. However, optimizing routing together with resources allocation is critical for improving delivery performance. In this paper, we challenge the fact that transmission paths have to be built under high link variability with limited resources, and develop a throughput-optimal solution based on the utility maximization framework. For delay performance, we embed a distance factor into the objective of the framework and derive a geographic-location-aware backpressure algorithm. Further, we exploit transmission opportunities missed by backpressure-type algorithms to accelerate data transfer. The simulation results show that our algorithms are able to deliver large volumes of data in time, and scale well in all scenarios tested.

Rationality of Choices in Subsidized Crop Insurance Markets

AbstractThe U.S. crop insurance market has several features that set it apart from other insurance markets. These include explicit government subsidies with an average premium subsidy rate of about 60% in recent years, and the legislative requirement that premium rates be set at actuarially fair levels, where the federal government sets rates and pays all costs related to insurance policy sales and services. Bearing these features in mind, we examine the extent to which farmers’ crop insurance choices conform to economic theory. A standard expected utility maximization framework is constructed to analyze trade‐offs between higher risk protection and larger subsidy payments. We decompose the effect of coverage level on expected utility into insurance, premium loading, and subsidy transfer effects where the loading effect vanishes if rates are actuarially fair. Given an actuarially fair premium, we infer that a rational farmer should choose either the coverage level with the highest premium subsidy or a higher coverage level. Evidence from a large insurance unit‐level dataset contradicts this theoretical inference, and so suggests anomalous insurance decisions. In a novel application of the mixed logit framework, we show that the probability an insurance product is chosen declines as out‐of‐pocket premium expenditures increase, even though higher values of these expenditures should reflect improved grower welfare. Premium expenditures appear to be more salient than the uncertain future benefits they support. We apply our regression to the recent trend yield adjustment innovation in crop insurance.

A Rate Allocation Framework for Multi-Class Services in Software-Defined Networks

Software defined networking (SDN) is a network architecture with a programmable control plane (e.g., controllers) and simple data plane (e.g., forwarders). One of the popular SDN protocols/standards is OpenFlow, for which researchers have recently proposed some quality-of-service (QoS) supports. However, the proposals for rate allocation have some limitations in network scalability and multi-class services' supports. In the literature, rate allocation formulations are commonly based on the framework of network utility maximization (NUM). Nevertheless, multi-class services are rarely considered in that framework since they make the formulated NUM become nonconvex and prevent its subgradient-based algorithm from converging. In this paper, we propose a scalable QoS rate allocation framework for OpenFlow in which multi-class services are considered. The convergence issue in the algorithm of our NUM-based framework is resolved by an admission control scheme. The network scalability is improved by our decentralized algorithms that can run on multiple parallel controllers. Extensive simulation and emulation results are provided to evaluate the performance of our method.

The Optimal Noise-Adding Mechanism in Differential Privacy

Differential privacy is a framework to quantify to what extent individual privacy in a statistical database is preserved while releasing useful aggregate information about the database. In this paper, within the classes of mechanisms oblivious of the database and the queries beyond the global sensitivity, we characterize the fundamental tradeoff between privacy and utility in differential privacy, and derive the optimal $\epsilon $ -differentially private mechanism for a single real-valued query function under a very general utility-maximization (or cost-minimization) framework. The class of noise probability distributions in the optimal mechanism has staircase-shaped probability density functions which are symmetric (around the origin), monotonically decreasing and geometrically decaying. The staircase mechanism can be viewed as a geometric mixture of uniform probability distributions, providing a simple algorithmic description for the mechanism. Furthermore, the staircase mechanism naturally generalizes to discrete query output settings as well as more abstract settings. We explicitly derive the parameter of the optimal staircase mechanism for $\ell _{1}$ and $\ell _{2}$ cost functions. Comparing the optimal performances with those of the usual Laplacian mechanism, we show that in the high privacy regime ( $\epsilon $ is small), the Laplacian mechanism is asymptotically optimal as $\epsilon \to 0$ ; in the low privacy regime ( $\epsilon $ is large), the minimum magnitude and second moment of noise are $\Theta (\Delta e^{(-{\epsilon }/{2})})$ and $\Theta (\Delta ^{2} e^{(-{2\epsilon }/{3})})$ as $\epsilon \to +\infty $ , respectively, while the corresponding figures when using the Laplacian mechanism are ${\Delta }/{\epsilon }$ and ${2\Delta ^{2}}/{\epsilon ^{2}}$ , where $\Delta $ is the sensitivity of the query function. We conclude that the gains of the staircase mechanism are more pronounced in the moderate-low privacy regime.