Efficiency and Fairness in Resource Exchange

ABSTRACTCommunity collaboration is recognized as an effective means to address this obstacle by supporting sustainable disaster risk management. Despite the growing importance of collaboration in modern disaster management, literature has emphasized the significance of collaboration, without offering perspectives on which forms of collaboration may be more effective. Also, little attention has been paid to how collaborative activities, when aligned with the inherent characteristics of communities, can contribute to mitigating disaster impacts. This study aimed to fill those gaps related to community collaboration for disaster management. We explored the role of community collaborative networks, encompassing resource exchange and symbiotic collaborative networks, in disaster risk management. We employed a panel regression model to analyze the impact of community collaborative networks on disaster damage. We also examined the moderating effect of community collaboration on the relationships between community factors and disaster damage to assess how community collaboration enhances a community's strengths and mitigates its weaknesses. Our findings indicate that building active resource exchange networks and dense symbiotic collaborative networks with neighboring communities reduces the cost of disaster damage. Building symbiotic collaborative networks also enhances the communities' strength in mitigating disaster damages. However, population aging can be an obstacle to using symbiotic collaborative networks. Our findings highlight the importance of strategically applying more effective forms of collaboration, rather than engaging in collaboration for its own sake, for disaster risk management. It is also meaningful in that we reveal how collaboration can contribute to disaster damage mitigation by indirectly enhancing the inherent strengths of communities.

The study of emerged community structures is an important challenge in networks analysis. In fact, several methods have been proposed in the literature to statistically determine the signification of discovered structures. Nevertheless, most of existing analysis models consider only the structural aspect of emerged communities. In our study, we give interest to robustness study of emerged communities in resource exchange networks. More precisely, we consider the emerged communities in the induced graph by all the exchanges in these networks. Hence, rather than examining the robustness only on the structural properties of the graph, we give focus to the parameters that allow the emergence of community structures. In fact, perturbing these parameters might destroy most of the obtained properties at the emerged level. To the best of our knowledge, robustness of networks has never been considered from this angle before. In this paper, we study the impact of perturbing the content, the interest and the profile of nodes on the emerged social communities in resource exchange networks. We show how these alterations affect both structure and information supported by the emerged structures.

Efficient and fair allocation of multiple types of resources is a crucial objective in a cloud/distributed computing cluster. Users may have diverse resource needs. Furthermore, diversity in server properties/capabilities may mean that only a subset of servers may be usable by a given user. In platforms with such heterogeneity, we identify important limitations in existing multi-resource fair allocation mechanisms, notably Dominant Resource Fairness and its follow-up work. To overcome such limitations, we propose a new server-based approach ; each server allocates resources by maximizing a per-server utility function . We propose a specific class of utility functions which, when appropriately parameterized, adjusts the trade-off between efficiency and fairness, and captures a variety of fairness measures (such as our recently proposed Per-Server Dominant Share Fairness ). We establish conditions for the proposed mechanism to satisfy certain properties that are generally deemed desirable, e.g., envy-freeness, sharing incentive, bottleneck fairness, and Pareto optimality. To implement our resource allocation mechanism, we develop an iterative algorithm which is shown to be globally convergent. Subsequently, we show how the proposed mechanism could be implemented in a distributed fashion. Finally, we carry out extensive trace-driven simulations to show the enhanced performance of our proposed mechanism over the existing ones.

The fair allocation of indivisible resources is a fundamental problem. Existing research has developed variousallocation mechanisms to satisfy different fairness notions. For example, round robin (RR) was proposed tomeet the fairness criterion known as envy-freeness up to one good (EF1). Experts’ heuristics without mathematicalformulations are used in real world resource allocation problems to find preferable outcomes for users. Therefore,we aim to design mechanisms that strictly satisfy good properties with replicating expert knowledge. However, thisproblem is challenging because such heuristic allocation mechanisms are often difficult to formalize mathematically,complicating their integration into theoretical frameworks. Additionally, formal mechanisms struggle to find preferableoutcomes, and directly replicating these implicit allocation mechanisms can result in unfair allocations becausehuman decision-making can introduce biases. In this paper, we aim to learn implicit allocation mechanisms fromexamples while strictly satisfying fairness constraints, specifically focusing on learning EF1 allocation mechanismsthrough supervised learning on examples of reported valuations and corresponding allocation outcomes produced byimplicit mechanisms. To address this, we developed a neural RR (NRR), a novel neural network that parameterizesRR. NRR is built from a differentiable relaxation of RR and can be trained to learn the agent ordering used forRR. We conducted experiments to learn EF1 allocation mechanisms from examples, demonstrating that our methodoutperforms baselines in terms of the proximity to the example allocations and qualities of predicted allocations.

Burnout is a particular response to stress involving psychological withdrawal. It is a costly coping mechanism that can adversely affect the motivation, performance, and personal wellbeing of special service providers. The burnout response develops when more direct and active problem-solving efforts are perceived as useless. Research on the causes of burnout suggests that factors in the work environment are relatively more important than characteristics of individuals. Lack of clarity, control, support, and feedback, as well as work overload, are particularly significant sources of burnout in special education. Various strategies for preventing burnout have been developed, and some have been evaluated to a limited extent. They include individual strategies such as exercise, progressive relaxation, cognitive restructuring, and social problem-solving skills training. Group strategies such as social support groups and resource exchange networks, and organizational strategies such as participative decision-ma...

Profit allocation analysis among the distributed energy network participants based on Game-theory

The economic lot-sizing problem with perishable items and consumption order preference

Many carriers, such as airlines and ocean carriers, collaborate through the formation of alliances. The rules of alliances are important for both the stability of the alliance and the profitability of the alliance members. In this paper, we address the design of resource exchange alliances, a type of alliance agreement widely used in practice, especially in the ocean cargo industry. Resource exchange alliances often increase competition among alliance members since the members can sell substitutable products after the exchange. We propose a resource exchange model that takes into account the resulting competition and alliance profit allocation among alliance members. The problem of determining the optimal amounts of resources to exchange is formulated as a stochastic mathematical program with equilibrium constraints (MPECs). Although MPEC problems are, in general, very difficult to solve, we successfully demonstrate an efficient solution approach and provide valuable insights for the resource exchange design problem. In particular, we show that the resource exchange alliance we propose can induce greater profit for the alliance members (both total and individual profits) even though it leads to greater competition. This paper was accepted by Yinyu Ye, optimization.

Two food banks catering to populations of different sizes with different needs must divide among themselves a donation of food items. What constitutes a "fair" allocation of the items among them?

With the development of the Internet, the gradual rise of mass collaboration and showing more and more widely momentum, mass collaboration to subvert the traditional concept of resource ownership, use the rules to ensure that everyone involved can share resources, collaborate to jointly to an end, and the distribution of resources throughout the body linked to the exchange of ideas and information, exchange of resources, the formation of a large-scale collaboration of resources on the Internet network.

Auction based CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> allowance allocation is a fundamental element of all cap-and-trade programs that are being discussed around the world. Multi-unit auctions usually follow a uniform or discriminatory pricing mechanism. Each of the auction mechanisms result in different strategic behavior by bidders. In this paper, we develop a game-theoretic model to assess the impact of these auction mechanisms on cap-and-trade and electricity markets. The game-theoretic model is solved using a nested two-stage diagonalization algorithm. The methodology is demonstrated on power network data from northern Illinois electricity market and results are discussed.

We consider a simple supply chain in which a single supplier sells to several downstream retailers. The supplier has limited capacity, and retailers are privately informed of their optimal stocking levels. If retailer orders exceed available capacity, the supplier allocates capacity using a publicly known allocation mechanism, a mapping from retailer orders to capacity assignments. We show that a broad class of mechanisms are prone to manipulation: Retailers will order more than they need to gain a more favorable allocation. Another class of mechanisms induces the retailers to order exactly their needs, thereby revealing their private information. However, there does not exist a truth-inducing mechanism that maximizes total retailer profits. We also consider the supplier's capacity choice. We show that a manipulable mechanism may lead the supplier to choose a higher level of capacity than she would under a truth-inducing mechanism. Nevertheless, her choice will appear excessively restrictive relative to the prevailing distribution of orders. Furthermore, switching to a truth-inducing mechanism can lower profits for the supplier, the supply chain, and even her retailers. Hence, truth-telling is not a universally desirable goal.

**Read paper on the following link:** https://ifaamas.org/Proceedings/aamas2022/pdfs/p372.pdf **Abstract:** A major open question in fair allocation of indivisible items is whether there always exists an allocation of chores that is Pareto optimal (PO) and envy-free up to one item (EF1). We answer this question affirmatively for the natural class of bivalued utilities, where each agent partitions the chores into easy and difficult ones, and has cost p > 1 for chores that are difficult for her and cost 1 for chores that are easy for her. Such an allocation can be found in polynomial time using an algorithm based on the Fisher market. We also show that for a slightly broader class of utilities, where each agent i can have a potentially different integer p_i, an allocation that is maximin share fair (MMS) always exists and one that is both PO and MMS can be computed in polynomial time, provided that each p_i is an integer. Our MMS arguments also hold when allocating goods instead of chores, and extend to another natural class of utilities, namely weakly lexicographic utilities.

We study the decision problem of a Proposer who has a set of applications to submit for approval to an Authority and can choose an order of submission. The Proposer’s utility depends on the Authority’s rulings. The Authority has to be consistent with its past decisions, which we model using the nearest-neighbor criterion. If the Proposer’s utility increases with the set of approved applications, then any greedy strategy is optimal for her: She should submit any application that, given the current history, would be approved. However, if her utility increases with some approvals but decreases with others, the Proposer’s problem becomes significantly more complex. In the single-dimensional case, an optimal strategy can be computed in polynomial time. In the general case, however, finding an optimal strategy is NP-hard. Thus, even in the absence of uncertainty or strategic behavior on the part of the Authority, evaluating the impact of current submissions on future outcomes can be computationally intractable.

Auctions are believed to be effective methods to solve the problem of wireless spectrum allocation. Existing spectrum auction mechanisms are all centralized and suffer from several critical drawbacks of the centralized systems, which motivates the design of distributed spectrum auction mechanisms. However, extending a centralized spectrum auction to a distributed one broadens the strategy space of agents from one dimension (bid) to three dimensions (bid, communication, and computation), and thus cannot be solved by traditional approaches from mechanism design. In this paper, we propose two distributed spectrum auction mechanisms, namely distributed VCG and FAITH. Distributed VCG implements the celebrated Vickrey-Clarke-Groves mechanism in a distributed fashion to achieve optimal social welfare, at the cost of exponential communication overhead. In contrast, FAITH achieves sub-optimal social welfare with tractable computation and communication overhead. We prove that both of the two proposed mechanisms achieve faithfulness, i.e., the agents' individual utilities are maximized, if they follow the intended strategies. Besides, we extend FAITH to adapt to dynamic scenarios where agents can arrive or depart at any time, without violating the property of faithfulness. We implement distributed VCG and FAITH, and evaluate their performance in various setups. Evaluation results show that distributed VCG results in optimal allocation, while FAITH is more efficient in computation and communication.