Maximize User Utility Research Articles

The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization

Online platforms have a wealth of data, run countless experiments, and use industrial-scale algorithms to optimize user experience. Despite this, many users seem to regret the time they spend on these platforms. One possible explanation is that incentives are misaligned: platforms are not optimizing for user happiness. We suggest the problem runs deeper, transcending the specific incentives of any particular platform, and instead stems from a mistaken foundational assumption. To understand what users want, platforms look at what users do. This is a kind of revealed-preference assumption that is ubiquitous in the way user models are built. Yet research has demonstrated, and personal experience affirms, that we often make choices in the moment that are inconsistent with what we actually want. The behavioral economics and psychology literatures suggest, for example, that we can choose mindlessly or that we can be too myopic in our choices, behaviors that feel entirely familiar on online platforms. In this work, we develop a model of media consumption where users have inconsistent preferences. We consider a platform which wants to maximize user utility, but only observes behavioral data in the form of the user’s engagement. We show how our model of users’ preference inconsistencies produces phenomena that are familiar from everyday experience but difficult to capture in traditional user interaction models. These phenomena include users who have long sessions on a platform but derive very little utility from it, and platform changes that steadily raise user engagement before abruptly causing users to go “cold turkey” and quit. A key ingredient in our model is a formulation for how platforms determine what to show users: they optimize over a large set of potential content (the content manifold) parametrized by underlying features of the content. Whether improving engagement improves user welfare depends on the direction of movement in the content manifold: For certain directions of change, increasing engagement makes users less happy, whereas in other directions on the same manifold, increasing engagement makes users happier. We provide a characterization of the structure of content manifolds for which increasing engagement fails to increase user utility. By linking these effects to abstractions of platform design choices, our model thus creates a theoretical framework and vocabulary in which to explore interactions between design, behavioral science, and social media. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This work was supported by the Vannevar Bush Faculty Fellowship and Multidisciplinary University Research Initiative [Grant W911NF-19-0217]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.03683 .

Preventive portfolio against data-selling ransomware—A game theory of encryption and deception

Ransomware has risen to be among the top cyber threats in recent years. There is an alarming trend of ransomware stealing data in addition to locking files. Compared to traditional ransomware, this new data-selling ransomware can be more harmful to the victims facing the data leakage threat. Traditional wisdom of defensive measures such as data backup is less effective in preventing the attacker from making money by selling data. We propose two preventive measures designed to defend against the data-selling ransomware, i.e., preventive data encryption and preventive data deception. Users may form a preventive portfolio made up of the two preventive measures. We contribute a novel game theoretical model of the data-selling ransomware to study the equilibrium strategies of the attacker and victims. The equilibrium solution of the portfolio and tradeoff analysis of both data encryption and deception are particularly useful for the users to optimize their system to defend against ransomware attacks. Simulation studies demonstrate the effectiveness of the preventive portfolio, which maximizes user utility while significantly reducing the profit of the attacker.

A Game-Based Scheme for Resource Purchasing and Pricing in MEC for Internet of Things

Mobile edge computing (MEC) is emerging as a promising paradigm to support the applications of Internet of Things (IoT). The edge servers bring computing resources to the edge of the network, so as to meet the delay requirements of the IoT devices’ service requests. At the same time, the edge servers can gain profit by leasing computing resources to IoT users and realize the allocation of computing resources. How to determine a reasonable resource leasing price for the edge servers and how to determine the number of resource purchased by users with different needs is a challenging problem. In order to solve the problem, this paper proposes a game-based scheme for resource purchasing and pricing aiming at maximizing user utility and server profit. The interaction between users and the edge servers is modeled based on Stackelberg game theory. The properties of incentive compatibility and envy freeness are theoretically proved, and the existence of Stackelberg equilibrium is also proved. A game-based user resource purchasing algorithm called GURP and a game-based server resource pricing algorithm called GSRP are proposed. It is theoretically proven that solutions of the proposed algorithms satisfy the individual rationality property. Finally, simulation experiments are carried out, and the experimental results show that the GURP algorithm and the GSRP algorithm can quickly converge to the optimal solutions. Comparison experiments with the benchmark algorithms are also carried out, and the experimental results show that the GURP algorithm and the GSRP algorithm can maximize user utility and server profit.

Application of edge computing and cloud service data system for doctors' psychological reconstruction after the epidemic

In response to the problems that the new crown pneumonia epidemic may bring to doctors in schools of Chinese medicine, such as information overload, traumatic stress, changes in mentality, crisis of trust, safety when going abroad, and employment choices, explain from the perspective of medical teachers in Chinese medicine schools. The understanding of the epidemic and the major events during the epidemic, combined with the experience of the psychological impact of the past epidemic on the doctors in the school, to highlight the outstanding contributions of medical staff in the fight against the epidemic, and to emphasize the strong leadership of the party and the unity of the people during the fight against the epidemic. The huge role played by Chinese medicine in the fight against the epidemic is the principle. In recent years, cloud computing technology has been continuously developed and improved. Traditional infrastructure as a service has been unable to meet users' needs for cloud computing delivery capabilities. They hope that more and more traditional IT software will be delivered in the form of cloud services. This paper mainly studies the utility optimization and stable matching strategy of UDRN based on EH and limited character input, divides the nodes into multiple clusters, and implements the utility optimization strategy within the clusters. Model users and relays as energy buyers and energy sellers. First, when users choose relays as their candidates, the golden section method is used to obtain the best relay power to maximize user utility. Secondly, based on the principle of maximizing the utility of matching, a mutual preference matrix between users and relays is established. Finally, based on the mutual preference matrix, an improved UDRN stable matching algorithm is proposed through the less complex GS algorithm. This algorithm will play a great role in the psychological reconstruction of doctors after the epidemic.

An Algorithm for Allocating Sponsored Recommendations and Content: Unifying Programmatic Advertising and Recommender Systems

Retailing and media platforms recommend two types of items to their users: sponsored items that generate ad revenue and nonsponsored ones that do not. The platform selects sponsored items to maximize ad revenue, often through programmatic auctions, and nonsponsored items to maximize user utility with a recommender system (RS). We develop a binary integer programming model to allocate sponsored recommendations considering dual objectives of maximizing ad revenue and user utility. We propose an algorithm to solve it in a computationally efficient way. Our method is a form of postfiltering to a traditional RS, making it widely applicable in two-sided markets. We apply the algorithm to data from an online grocery retailer and show that user utility for the recommended items can be improved while reducing ad revenue by a small amount. This multiobjective approach unifies programmatic advertising and RS and opens a new frontier for advertising and RS research. We provide an extended discussion of future research topics.

Utility Based Scheduling for Multi-UAV Search Systems in Disaster-Hit Areas

Using micro or small unmanned aerial vehicles (UAVs) is a promising solution for search and rescue of missing persons who have disappeared during emergencies, such as natural disasters. In actual situations, the processing time of image data should be considered due to the wide variety of computing resources provided by UAVs. In addition, network connectivity and transmission speed could be unstable since communication infrastructure may have been damaged in disaster-hit areas. Thus, both the processing time of the acquired data and the data transfer time are critical in search and rescue missions. Unlike the solutions proposed in the past, we propose a scheduling method of multi-UAV search systems that takes into account both the processing time of image data and the data transfer time. We present a utility-based problem formulation that ensures continuously updating information while obtaining as many pieces of information as possible for a certain period. The simulation results indicate that the proposed scheduling method maximizes user utility and performs better than a conventional scheduling method in terms of user-centric evaluation metrics.

A Truthful Auction Mechanism for Mobile Crowd Sensing With Budget Constraint

The selfishness and randomness of users in the mobile crowd sensing network could cause them unwilling to participate in sensing activities and lead to lower completion rates of sensing tasks. In order to deal with these problems, this paper proposes a novel incentive mechanism based on a new auction model for mobile crowd sensing, which consists of two consecutive stages. In the first stage, a novel Incentive Method based on Reverse Auction for Location-aware sensing (IMRAL) is proposed to maximize user utility. By introducing a task-centric method to determine the winning bids, it can provide higher user utility and higher task coverage ratio. To ensure the truthfulness of IMRAL, we design a unique payment determination algorithm based on critical payment for the incentive platform. In the second stage, we propose a user-interaction incentive model (UIBIM) to cover the situation that a user may drop out of the sensing activity. This new incentive model includes a dynamic double auction framework prompting users' interaction and a user matching algorithm based on a bipartite graph. The proposed new mechanism achieves the goal of improving task completion rates without increasing the cost of the incentive platform. The simulation results show that comparing with other solutions, such as a truthful auction for location-aware collaborative sensing in mobile crowdsourcing and incentive mechanism for crowdsourcing in the single-requester single-bid-model, IMRAL can achieve better performance in terms of average user utility and tasks coverage ratio, and the UIBIM can significantly improve task completion rates.

A Hybrid Approach for Radio Access Technology Selection in Heterogeneous Wireless Networks

In heterogeneous wireless networks, different radio access technologies are integrated and may be jointly managed. To optimize network performance and capacity, efficient common radio resource management (CRRM) mechanisms need to be defined. This paper tackles the radio access technology (RAT) selection, a key CRRM functionality, and proposes a hybrid decision framework that dynamically integrates operator objectives and user preferences. Mobile users are assisted in their decisions by the network that broadcasts cost and QoS information. Our hybrid approach involves two inter-dependent decision-making processes. The first one, on the network side, consists in deriving appropriate network information so as to guide user decisions in a way to meet operator objectives. The second one, where individual users combine their needs and preferences with the signaled network information, consists in selecting the RAT to be associated with in a way to maximize user utility. We first focus on the user side and present a satisfaction-based multi-criteria decision-making method. By avoiding inadequate decisions, our algorithm outperforms existing solutions and maximizes user utility. Further, we introduce two heuristic methods, namely the staircase and the slope tuning policies, to dynamically derive network information in a way to enhance resource utilization. The performance of each decision-making process, on the network and user sides, is evaluated separately through extensive simulations. A comparison of our hybrid approach with six different RAT selection schemes is also presented.

A privacy mechanism for mobile-based urban traffic monitoring

In mobile-based traffic monitoring applications, each user provides real-time updates on their location and speed while driving. This data is collected by a centralized server and aggregated to provide participants with current traffic conditions. Successful participation in traffic monitoring applications utilizing participatory sensing depends on two factors: the information utility of the estimated traffic condition, and the amount of private information (position and speed) each participant reveals to the server. We assume each user prefers to reveal as little private information as possible, but if everyone withholds information, the quality of traffic estimation will deteriorate. In this paper, we model these opposing requirements by considering each user to have a utility function that combines the benefit of high quality traffic estimates and the cost of privacy loss. Using a novel Markovian model, we mathematically derive a policy that takes into account the mean, variance and correlation of traffic on a given stretch of road and yields the optimal granularity of information revelation to maximize user utility. We validate the effectiveness of this policy through real-world empirical traces collected during the Mobile Century experiment in Northern California. The validation shows that the derived policy yields utilities that are very close to what could be obtained by an oracle scheme with full knowledge of the ground truth.

A Dual Framework and Algorithms for Targeted Online Data Delivery

A variety of emerging online data delivery applications challenge existing techniques for data delivery to human users, applications, or middleware that are accessing data from multiple autonomous servers. In this paper, we develop a framework for formalizing and comparing pull-based solutions and present dual optimization approaches. The first approach, most commonly used nowadays, maximizes user utility under the strict setting of meeting a priori constraints on the usage of system resources. We present an alternative and more flexible approach that maximizes user utility by satisfying all users. It does this while minimizing the usage of system resources. We discuss the benefits of this latter approach and develop an adaptive monitoring solution Satisfy User Profiles (SUPs). Through formal analysis, we identify sufficient optimality conditions for SUP. Using real (RSS feeds) and synthetic traces, we empirically analyze the behavior of SUP under varying conditions. Our experiments show that we can achieve a high degree of satisfaction of user utility when the estimations of SUP closely estimate the real event stream, and has the potential to save a significant amount of system resources. We further show that SUP can exploit feedback to improve user utility with only a moderate increase in resource utilization.

Research Note—User Design of Customized Products

User design offers tantalizing potential benefits to manufacturers and consumers, including a closer match of products to user preferences, which should result in a higher willingness to pay for goods and services. There are two fundamental approaches that can be taken to user design: parameter-based systems and needs-based systems. With parameter-based systems, users directly specify the values of design parameters of the product. With needs-based systems, users specify the relative importance of their needs, and an optimization algorithm recommends the combination of design parameters that is likely to maximize user utility. Through an experiment in the domain of consumer laptop computers, we show that for parameter-based systems, outcomes, including measures for comfort and fit, increase with the expertise of the user. We also show that for novices, the needs-based interface results in better outcomes than the parameter-based interface.

Maximizing user utility in video streaming applications

We study some of the design tradeoffs of video streaming systems in networks with QoS guarantees. We approach this problem by using a utility function to quantify the benefit a user derives from the quality of the received video sequence. We also consider the cost to the network user for streaming the video sequence. We have formulated this utility maximization problem as a joint constrained optimization problem where we maximize the difference between the utility and the network cost, subject to the constraint that the decoder buffer does not underflow. In this manner, we can find the optimal tradeoff between video quality and network cost. We present a deterministic dynamic programming approach for both the constant bit rate and renegotiated constant bit rate service classes. Experimental results demonstrate the benefits and the performance of the proposed approach.