Energy-Efficient Computation Offloading for UAV-Assisted MEC: A Two-Stage Optimization Scheme

In addition to the stationary mobile edge computing (MEC) servers, a few MEC surrogates that possess a certain mobility and computation capacity, e.g., flying unmanned aerial vehicles (UAVs) and private vehicles, have risen as powerful counterparts for service provision. In this article, we design a two-stage online scheduling scheme, targeting computation offloading in a UAV-assisted MEC system. On our stage-one formulation, an online scheduling framework is proposed for dynamic adjustment of mobile users' CPU frequency and their transmission power, aiming at producing a socially beneficial solution to users. But the major impediment during our investigation lies in that users might not unconditionally follow the scheduling decision released by servers as a result of their individual rationality. In this regard, we formulate each step of online scheduling on stage one into a non-cooperative game with potential competition over the limited radio resource. As a solution, a centralized online scheduling algorithm, called ONCCO, is proposed, which significantly promotes social benefit on the basis of the users' individual rationality. On our stage-two formulation, we are working towards the optimization of UAV computation resource provision, aiming at minimizing the energy consumption of UAVs during such a process, and correspondingly, another algorithm, called WS-UAV, is given as a solution. Finally, extensive experiments via numerical simulation are conducted for an evaluation purpose, by which we show that our proposed algorithms achieve satisfying performance enhancement in terms of energy conservation and sustainable service provision.

Hart–Mas-Colell consistency and the core in convex games

This paper formally introduces Hart–Mas-Colell consistency for general (possibly multi-valued) solutions for cooperative games with transferable utility. This notion is used to axiomatically characterize the core on the domain of convex games. Moreover, we characterize all nonempty solutions satisfying individual rationality, anonymity, scale covariance, superadditivity, weak Hart–Mas-Colell consistency, and converse Hart–Mas-Colell consistency. This family consists of (a) the Shapley value, (b) all homothetic images of the core with the Shapley value as center of homothety and with positive ratios of homothety not larger than one, and (c) their relative interiors.

Budget-feasible Mechanisms for Representing Groups of Agents Proportionally

In this paper, we consider the problem of designing budget-feasible mechanisms for selecting agents with private costs from various groups to ensure proportional representation, where the minimum proportion of the selected agents from each group is maximized. Depending on agents' membership in the groups, we consider two main models: single group setting where each agent belongs to only one group, and multiple group setting where each agent may belong to multiple groups. We propose novel budget-feasible proportion-representative mechanisms for these models, which can select representative agents from different groups. The proposed mechanisms guarantee theoretical properties of individual rationality, budget-feasibility, truthfulness, and approximation performance on proportional representation.

Fair Pairwise Exchange among Groups

We study the pairwise organ exchange problem among groups motivated by real-world applications and consider two types of group formulations. Each group represents either a certain type of patient-donor pairs who are compatible with the same set of organs, or a set of patient-donor pairs who reside in the same region. We address a natural research question, which asks how to match a maximum number of pairwise compatible patient-donor pairs in a fair and individually rational way. We first propose a natural fairness concept that is applicable to both types of group formulations and design a polynomial-time algorithm that checks whether a matching exists that satisfies optimality, individual rationality, and fairness. We also present several running time upper bounds for computing such matchings for different graph structures.

Optimal Monopoly Mechanisms with Demand Uncertainty

This paper analyzes a monopoly firm’s profit-maximizing mechanism in the following context. There is a continuum of consumers with a unit demand for a good. The distribution of the consumers’ valuations is given by one of two possible demand distributions/states. The consumers are uncertain about the demand state, and they update their beliefs after observing their own valuation for the good. The firm is uncertain about the demand state but infers it from the consumers’ reported valuations. The firm’s problem is to maximize profits by choosing an optimal mechanism among the class of anonymous, deterministic, direct revelation mechanisms that satisfy interim incentive compatibility and ex post individual rationality. We show that, under certain sufficient conditions, the firm’s optimal mechanism is to set the monopoly price in each demand state. Under these conditions, Segal’s optimal ex post mechanism is robust to relaxing ex post incentive compatibility to interim incentive compatibility.

Systemic optimal risk transfer equilibrium

We propose a novel concept of a Systemic Optimal Risk Transfer Equilibrium (SORTE), which is inspired by the Bühlmann’s classical notion of an Equilibrium Risk Exchange. We provide sufficient general assumptions that guarantee existence, uniqueness, and Pareto optimality of such a SORTE. In both the Bühlmann and the SORTE definition, each agent is behaving rationally by maximizing his/her expected utility given a budget constraint. The two approaches differ by the budget constraints. In Bühlmann’s definition the vector that assigns the budget constraint is given a priori. On the contrary, in the SORTE approach, the vector that assigns the budget constraint is endogenously determined by solving a systemic utility maximization. SORTE gives priority to the systemic aspects of the problem, in order to optimize the overall systemic performance, rather than to individual rationality.

Making Marketplaces Safe: Dominant Individual Rationality and Applications to Market Design

Often market designers cannot force agents to join a marketplace rather than using pre-existing institutions. We propose a new desideratum for marketplace design that guarantees the safety of participation: dominant individual rationality (DIR). A marketplace is DIR if every pre-existing strategy is weakly dominated by some strategy within the marketplace. We study applications to the design of labor markets and the sharing economy. We also provide a general construction to achieve approximate DIR across a wide range of marketplace designs. This paper was accepted by Yan Chen, decision analysis.

Dilemma of collective and individual rationality

The article discusses the dichotomies of concepts encountered in socio-economic research when constructing a unified theory of human activity. These include the dichotomy of "action and structure" when it comes to embedding a person in a social order; "Egoism and altruism" in the study of rational human behavior in society, as well as the dichotomy of various forms of action such as instrumental and communication action. In connection with this problem, the question arises of the possibility of their study in the framework of incommensurable forms of rationality - "individual and collective".

Platform-Based Collaborative Routing using Dynamic Prices as Incentives

Over the last decade, platforms have emerged in numerous industries and often transformed them, posing new challenges for transportation research. Platform providers such as Uber, Uber Freight, Blackbuck, or Lyft mostly do not have immediate control over the physical resources needed to move people or goods. They often operate in a multi-sided market setting, where it is crucial to design clear incentives to motivate a third party to engage in collaboration. As a consequence, collaboration incentives become an integral part of decision support models for platform providers and they need to be developed at the operational level and applied dynamically. Naturally, this involves a trade-off between the interests of platform providers, shippers, and carriers. In this work, we investigate the real-world case of a platform provider operating as an intermediary between shippers and carriers in a less-than-truckload (LTL) business. We propose a new mixed-integer programming (MIP) formulation for the underlying collaborative pickup and delivery problem with time windows (PDPTW) that minimizes the price the platform pays to the carriers and enforces collaboration incentives for carriers through individual rationality constraints. This is facilitated by a dynamic pricing approach which ensures that carriers are better off collaborating than working on their own. The pricing is bounded by the costs and market conditions to keep the price range reasonable. We explore possible policies to be implemented by the platform and find that their business remains profitable when individual rationality is enforced and the platform could even guarantee increased profit margins to the carriers as incentives.