IA Scholar Query: On the Complexity of Efficiency and Envy-Freeness in Fair Division of Indivisible Goods with Additive Preferences.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgSat, 24 Sep 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Computing Welfare-Maximizing Fair Allocations of Indivisible Goods
https://scholar.archive.org/work/qbe4yvuxb5cofhtlsmcfcqqtpq
We analyze the run-time complexity of computing allocations that are both fair and maximize the utilitarian social welfare, defined as the sum of agents' utilities. We focus on two tractable fairness concepts: envy-freeness up to one item (EF1) and proportionality up to one item (PROP1). We consider two computational problems: (1) Among the utilitarian-maximal allocations, decide whether there exists one that is also fair; (2) among the fair allocations, compute one that maximizes the utilitarian welfare. We show that both problems are strongly NP-hard when the number of agents is variable, and remain NP-hard for a fixed number of agents greater than two. For the special case of two agents, we find that problem (1) is polynomial-time solvable, while problem (2) remains NP-hard. Finally, with a fixed number of agents, we design pseudopolynomial-time algorithms for both problems. We extend our results to the stronger fairness notions envy-freeness up to any item (EFx) and proportionality up to any item (PROPx).Haris Aziz, Xin Huang, Nicholas Mattei, Erel Segal-Haleviwork_qbe4yvuxb5cofhtlsmcfcqqtpqSat, 24 Sep 2022 00:00:00 GMTPricing Optimal Outcomes in Coupled and Non-Convex Markets: Theory and Applications to Electricity Markets
https://scholar.archive.org/work/y37tdv44v5endph2cuuam3t67m
Classical results in general equilibrium theory assume divisible goods and convex preferences of market participants. In many real-world markets, participants have non-convex preferences and the allocation problem needs to consider complex constraints. Electricity markets are a prime example. In such markets, Walrasian prices are impossible, and heuristic pricing rules based on the dual of the relaxed allocation problem are used in practice. However, these rules have been criticized for high side-payments and inadequate congestion signals. We show that existing pricing heuristics optimize specific design goals that can be conflicting. The trade-offs can be substantial, and we establish that the design of pricing rules is fundamentally a multi-objective optimization problem addressing different incentives. In addition to traditional multi-objective optimization techniques using weighing of individual objectives, we introduce a novel parameter-free pricing rule that minimizes incentives for market participants to deviate locally. Our findings show how the new pricing rule capitalizes on the upsides of existing pricing rules under scrutiny today. It leads to prices that incur low make-whole payments while providing adequate congestion signals and low lost opportunity costs. Our suggested pricing rule does not require weighing of objectives, it is computationally scalable, and balances trade-offs in a principled manner, addressing an important policy issue in electricity markets.Mete Şeref Ahunbay and Martin Bichler and Johannes Knörrwork_y37tdv44v5endph2cuuam3t67mThu, 15 Sep 2022 00:00:00 GMTFairly Allocating (Contiguous) Dynamic Indivisible Items with Few Adjustments
https://scholar.archive.org/work/bby6uamza5aoldf3a4kozvwfqm
We study the problem of dynamically allocating indivisible items to a group of agents in a fair manner. We assume that the items are goods and the valuation functions are additive without specification. Due to the negative results to achieve fairness, we allow adjustments to make fairness attainable with the objective to minimize the number of adjustments. We obtain positive results to achieve EF1 for (1) two agents with mixed manna, (2) restricted additive or general identical valuations, and (3) the default setting. We further impose the contiguity constraint on the items and require that each agent obtains a consecutive block of items. We obtain both positive and negative results to achieve either EF1 or proportionality with an additive approximate factor. In particular, we establish matching lower and upper bounds to achieve approximate proportionality for identical valuations. Our results exhibit the large discrepancy between the identical model and nonidentical model in both contiguous and noncontiguous settings. All our positive results are computationally efficient.Mingwei Yangwork_bby6uamza5aoldf3a4kozvwfqmWed, 14 Sep 2022 00:00:00 GMTBest of Both Worlds: Agents with Entitlements
https://scholar.archive.org/work/k6jq4xwilbewvdcieswsaws2gm
Fair division of indivisible goods is a central challenge in artificial intelligence. For many prominent fairness criteria including envy-freeness (EF) or proportionality (PROP), no allocations satisfying these criteria might exist. Two popular remedies to this problem are randomization or relaxation of fairness concepts. A timely research direction is to combine the advantages of both, commonly referred to as Best of Both Worlds (BoBW). We consider fair division with entitlements, which allows to adjust notions of fairness to heterogeneous priorities among agents. This is an important generalization to standard fair division models and is not well-understood in terms of BoBW results. Our main result is a lottery for additive valuations and different entitlements that is ex-ante weighted envy-free (WEF), as well as ex-post weighted proportional up to one good (WPROP1) and weighted transfer envy-free up to one good (WEF(1,1)). It can be computed in strongly polynomial time. We show that this result is tight - ex-ante WEF is incompatible with any stronger ex-post WEF relaxation. In addition, we extend BoBW results on group fairness to entitlements and explore generalizations of our results to instances with more expressive valuation functions.Martin Hoefer, Marco Schmalhofer, Giovanna Varricchiowork_k6jq4xwilbewvdcieswsaws2gmThu, 08 Sep 2022 00:00:00 GMTMind the Gap: Cake Cutting With Separation
https://scholar.archive.org/work/r43ptzd7kjanzhbrzjsurabqqe
We study the problem of fairly allocating a divisible resource, also known as cake cutting, with an additional requirement that the shares that different agents receive should be sufficiently separated from one another. This captures, for example, constraints arising from social distancing guidelines. While it is sometimes impossible to allocate a proportional share to every agent under the separation requirement, we show that the well-known criterion of maximin share fairness can always be attained. We then provide algorithmic analysis of maximin share fairness in this setting -- for instance, the maximin share of an agent cannot be computed exactly by any finite algorithm, but can be approximated with an arbitrarily small error. In addition, we consider the division of a pie (i.e., a circular cake) and show that an ordinal relaxation of maximin share fairness can be achieved. We also prove that an envy-free or equitable allocation that allocates the maximum amount of resource exists under separation.Edith Elkind, Erel Segal-Halevi, Warut Suksompongwork_r43ptzd7kjanzhbrzjsurabqqeWed, 07 Sep 2022 00:00:00 GMTHow to cut a discrete cake fairly
https://scholar.archive.org/work/iwvb5otgpvauhcshdssiqc4zqe
Cake-cutting is a fundamental model of dividing a heterogeneous resource, such as land, broadcast time, and advertisement space. In this study, we consider the problem of dividing a discrete cake fairly in which the indivisible goods are aligned on a path and agents are interested in receiving a connected subset of items. We prove that a connected division of indivisible items satisfying a discrete counterpart of envy-freeness, called envy-freeness up to one good (EF1), always exists for any number of agents n with monotone valuations. Our result settles an open question raised by Bil\'o et al. (2019), who proved that an EF1 connected division always exists for the number of agents at most 4. Moreover, the proof can be extended to show the following secretive (1) and extra (2) versions: (1) for n agents with monotone valuations, the path can be divided into n connected bundles such that an EF1 assignment of the remaining bundles can be made to the other agents for any selection made by the secretive agent; (2) for n+1 agents with monotone valuations, the path can be divided into n connected bundles such that when any extra agent leaves, an EF1 assignment of the bundles can be made to the remaining agents.Ayumi Igarashiwork_iwvb5otgpvauhcshdssiqc4zqeSat, 03 Sep 2022 00:00:00 GMTPricing Problems with Buyer Preselection
https://scholar.archive.org/work/h4og7qm6fve3zct4phlbzkkt2y
We investigate the problem of preselecting a subset of buyers (also called agents) participating in a market so as to optimize the performance of stable outcomes. We consider four scenarios arising from the combination of two stability notions, namely market envy-freeness and agent envy-freeness, with the two state-of-the-art objective functions of social welfare and seller's revenue. When insisting on market envy-freeness, we prove that the problem cannot be approximated within n 1−ε (with n being the number of buyers) for any ε > 0, under both objective functions; we also provide approximation algorithms with an approximation ratio tight up to subpolynomial multiplicative factors for social welfare and the seller's revenue. The negative result, in particular, holds even for markets with single-minded buyers. We also prove that maximizing the seller's revenue is NP-hard even for a single buyer, thus closing a previous open question. Under agent envy-freeness and for both objective functions, instead, we design a polynomial time algorithm transforming any stable outcome for a market involving any subset of buyers into a stable outcome for the whole market without worsening its performance. This result creates an interesting middle-ground situation where, if on the one hand buyer preselection cannot improve the performance of agent envy-free outcomes, on the other one it can be used as a tool for simplifying the combinatorial structure of the buyers' valuation functions in a given market. Finally, we consider the restricted case of multi-unit markets, where all items are of the same type and are assigned the same price. For these markets, we show that preselection may improve the performance of stable outcomes in all of the four considered scenarios, and design corresponding approximation algorithms.Vittorio Bilò, Michele Flammini, Gianpiero Monaco, Luca Moscardelliwork_h4og7qm6fve3zct4phlbzkkt2ySun, 28 Aug 2022 00:00:00 GMTExplainability in Mechanism Design: Recent Advances and the Road Ahead
https://scholar.archive.org/work/ewfhrjz2rzhwnojowkm73zjgnu
Designing and implementing explainable systems is seen as the next step towards increasing user trust in, acceptance of and reliance on Artificial Intelligence (AI) systems. While explaining choices made by black-box algorithms such as machine learning and deep learning has occupied most of the limelight, systems that attempt to explain decisions (even simple ones) in the context of social choice are steadily catching up. In this paper, we provide a comprehensive survey of explainability in mechanism design, a domain characterized by economically motivated agents and often having no single choice that maximizes all individual utility functions. We discuss the main properties and goals of explainability in mechanism design, distinguishing them from those of Explainable AI in general. This discussion is followed by a thorough review of the challenges one may face when working on Explainable Mechanism Design and propose a few solution concepts to those.Sharadhi Alape Suryanarayana, David Sarne, Sarit Krauswork_ewfhrjz2rzhwnojowkm73zjgnuSun, 21 Aug 2022 00:00:00 GMTFair Division of Indivisible Goods: A Survey
https://scholar.archive.org/work/jhao62nlhjdqfl6vi5dvpejnhm
Allocating resources to individuals in a fair manner has been a topic of interest since ancient times, with most of the early mathematical work on the problem focusing on resources that are infinitely divisible. Over the last decade, there has been a surge of papers studying computational questions regarding the indivisible case, for which exact fairness notions such as envy-freeness and proportionality are hard to satisfy. One main theme in the recent research agenda is to investigate the extent to which their relaxations, like maximin share fairness (MMS) and envy-freeness up to any good (EFX), can be achieved. In this survey, we present a comprehensive review of the progress made in the related literature by highlighting different ways to relax fairness notions, common algorithm design techniques, and the most interesting questions for future research.Georgios Amanatidis, Haris Aziz, Georgios Birmpas, Aris Filos-Ratsikas, Bo Li, Hervé Moulin, Alexandros A. Voudouris, Xiaowei Wuwork_jhao62nlhjdqfl6vi5dvpejnhmThu, 18 Aug 2022 00:00:00 GMTFinding Fair Allocations under Budget Constraints
https://scholar.archive.org/work/l6viuhihpfdrraidocsofylqnu
We study the fair allocation of indivisible goods among agents with identical, additive valuations but individual budget constraints. Here, the indivisible goods–each with a specific size and value–need to be allocated such that the bundle assigned to each agent is of total size at most the agent's budget. Since envy-free allocations do not necessarily exist in the indivisible goods context, compelling relaxations–in particular, the notion of envy-freeness up to k goods (EFk)–have received significant attention in recent years. In an EFk allocation, each agent prefers its own bundle over that of any other agent, up to the removal of k goods, and the agents have similarly bounded envy against the charity (which corresponds to the set of all unallocated goods). Recently, Wu et al. (2021) showed that an allocation that satisfies the budget constraints and maximizes the Nash social welfare is 1/4-approximately EF1. However, the computation (or even existence) of exact EFk allocations remained an intriguing open problem. We make notable progress towards this by proposing a simple, greedy, polynomial-time algorithm that computes EF2 allocations under budget constraints. Our algorithmic result implies the universal existence of EF2 allocations in this fair division context. The analysis of the algorithm exploits intricate structural properties of envy-freeness. Interestingly, the same algorithm also provides EF1 guarantees for important special cases. Specifically, we settle the existence of EF1 allocations for instances in which: (i) the value of each good is proportional to its size, (ii) all goods have the same size, or (iii) all the goods have the same value. Our EF2 result extends to the setting wherein the goods' sizes are agent specific.Siddharth Barman, Arindam Khan, Sudarshan Shyam, K.V.N. Sreenivaswork_l6viuhihpfdrraidocsofylqnuWed, 17 Aug 2022 00:00:00 GMTA General Framework for Fair Allocation with Matroid Rank Valuations
https://scholar.archive.org/work/syjs7zzy35bplied7mn5nk3jwe
We study the problem of fairly allocating a set of indivisible goods among agents with matroid rank valuations. We present a simple framework that efficiently computes any fairness objective that satisfies some mild assumptions. Along with maximizing a fairness objective, the framework is guaranteed to run in polynomial time, maximize utilitarian social welfare and ensure strategyproofness. We show how our framework can be used to achieve four different fairness objectives: (a) Prioritized Lorenz dominance, (b) Maxmin fairness, (c) Weighted leximin, and (d) Max weighted Nash welfare. In particular, our framework provides the first polynomial time algorithms to compute weighted leximin and max weighted Nash welfare allocations for matroid rank valuations.Vignesh Viswanathan, Yair Zickwork_syjs7zzy35bplied7mn5nk3jweMon, 15 Aug 2022 00:00:00 GMTAlmost Envy-Freeness for Groups: Improved Bounds via Discrepancy Theory
https://scholar.archive.org/work/wialzk3ijbhypemklvimbb7xn4
We study the allocation of indivisible goods among groups of agents using well-known fairness notions such as envy-freeness and proportionality. While these notions cannot always be satisfied, we provide several bounds on the optimal relaxations that can be guaranteed. For instance, our bounds imply that when the number of groups is constant and the n agents are divided into groups arbitrarily, there exists an allocation that is envy-free up to Θ(√(n)) goods, and this bound is tight. Moreover, we show that while such an allocation can be found efficiently, it is NP-hard to compute an allocation that is envy-free up to o(√(n)) goods even when a fully envy-free allocation exists. Our proofs make extensive use of tools from discrepancy theory.Pasin Manurangsi, Warut Suksompongwork_wialzk3ijbhypemklvimbb7xn4Thu, 04 Aug 2022 00:00:00 GMTCombinatorial Algorithms for Matching Markets via Nash Bargaining: One-Sided, Two-Sided and Non-Bipartite
https://scholar.archive.org/work/rtxu4dzf6zhvpexshayrdgugqy
In the area of matching-based market design, existing models using cardinal utilities suffer from two deficiencies, which restrict applicability: First, the Hylland-Zeckhauser (HZ) mechanism, which has remained a classic in economics for one-sided matching markets, is intractable; computation of even an approximate equilibrium is PPAD-complete [Vazirani, Yannakakis 2021], [Chen et al 2022]. Second, there is an extreme paucity of such models. This led [Hosseini and Vazirani 2021] to define a rich collection of Nash-bargaining-based models for one-sided and two-sided matching markets, in both Fisher and Arrow-Debreu settings, together with implementations using available solvers and very encouraging experimental results. [Hosseini and Vazirani 2021] raised the question of finding efficient combinatorial algorithms, with proven running times, for these models. In this paper, we address this question by giving algorithms based on the techniques of multiplicative weights update (MWU) and conditional gradient descent (CGD). Additionally, we make the following conceptual contributions to the proposal of [Hosseini and Vazirani 2021] in order to set it on a more firm foundation: 1) We establish a connection between HZ and Nash-bargaining-based models via the celebrated Eisenberg-Gale convex program, thereby providing a theoretical ratification. 2) Whereas HZ satisfies envy-freeness, due to the presence of demand constraints, the Nash-bargaining-based models do not. We rectify this to the extent possible by showing that these models satisfy approximate equal-share fairness notions. 3) We define, for the first time, a model for non-bipartite matching markets under cardinal utilities. It is also Nash-bargaining-based and we solve it using CGD.Ioannis Panageas, Thorben Tröbst, Vijay V. Vaziraniwork_rtxu4dzf6zhvpexshayrdgugqyWed, 03 Aug 2022 00:00:00 GMTFair Allocations for Smoothed Utilities
https://scholar.archive.org/work/mkqsk5cdgrhtbjgcqq3p744z7m
When allocating indivisible items across agents, it is desirable for the allocation to be envy-free, which means that each agent prefers their own bundle over every other bundle. Even though envy-free allocations are not guaranteed to exist for worst-case utilities, they frequently exist in practice. To explain this phenomenon, prior work has shown that, if utilities are drawn from certain probability distributions, then envy-free allocations exist with high probability (as long as the number of items is sufficiently large relative to the number of agents). In this paper, we study a more general setting, a smoothed model of utilities, in which utility profiles are mainly worst-case, but are slightly perturbed at random to avoid brittle counter-examples. Specifically, we start from a worst-case profile of utilities and, with some small probability, increase an agent's value for an item by adding a random amount, where the probability of perturbation and the distribution of perturbations are parameters of the model. If the probability of such perturbations is sufficiently large relative to the number of agents and items, we show that envy-free allocations exist with high probability and can be found efficiently. This analysis is tight up to constant factors. We also give an efficient algorithm for finding allocations that are simultaneously proportional and Pareto-optimal, which succeeds with high probability in the smoothed model. CCS Concepts: • Theory of computation → Algorithmic game theory and mechanism design.Yushi Bai, Uriel Feige, Paul Gölz, Ariel D. Procacciawork_mkqsk5cdgrhtbjgcqq3p744z7mTue, 12 Jul 2022 00:00:00 GMTSequential Fair Allocation of Limited Resources under Stochastic Demands
https://scholar.archive.org/work/b6myi53xmvcbxd5i66ngql72fq
We consider the problem of dividing limited resources between a set of agents arriving sequentially with unknown (stochastic) utilities. Our goal is to find a fair allocation - one that is simultaneously Pareto-efficient and envy-free. When all utilities are known upfront, the above desiderata are simultaneously achievable (and efficiently computable) for a large class of utility functions. In a sequential setting, however, no policy can guarantee these desiderata simultaneously for all possible utility realizations. A natural online fair allocation objective is to minimize the deviation of each agent's final allocation from their fair allocation in hindsight. This translates into simultaneous guarantees for both Pareto-efficiency and envy-freeness. However, the resulting dynamic program has state-space which is exponential in the number of agents. We propose a simple policy, HopeOnline, that instead aims to 'match' the ex-post fair allocation vector using the current available resources and 'predicted' histogram of future utilities. We demonstrate the effectiveness of our policy compared to other heurstics on a dataset inspired by mobile food-bank allocations.Sean R. Sinclair, Gauri Jain, Siddhartha Banerjee, Christina Lee Yuwork_b6myi53xmvcbxd5i66ngql72fqSat, 09 Jul 2022 00:00:00 GMTYankee Swap: a Fast and Simple Fair Allocation Mechanism for Matroid Rank Valuations
https://scholar.archive.org/work/mycglk6nwbbdzi5hzdsvng4pqe
We study fair allocation of indivisible goods when agents have matroid rank valuations. Our main contribution is a simple algorithm based on the colloquial Yankee Swap procedure that computes provably fair and efficient Lorenz dominating allocations. While there exist polynomial time algorithms to compute such allocations, our proposed method improves on them in two ways. (a) Our approach is easy to understand and does not use complex matroid optimization algorithms as subroutines. (b) Our approach is scalable; it is provably faster than all known algorithms to compute Lorenz dominating allocations. These two properties are key to the adoption of algorithms in any real fair allocation setting; our contribution brings us one step closer to this goal.Vignesh Viswanathan, Yair Zickwork_mycglk6nwbbdzi5hzdsvng4pqeThu, 07 Jul 2022 00:00:00 GMTPlaying Divide-and-Choose Given Uncertain Preferences
https://scholar.archive.org/work/tguikj2r3zawnimgbvxsqc3cde
We study the classic divide-and-choose method for equitably allocating divisible goods between two players who are rational, self-interested Bayesian agents. The players have additive private values for the goods. The prior distributions on those values are independent and common knowledge. We characterize the structure of optimal divisions in the divide-and-choose game and show how to efficiently compute equilibria. We identify several striking differences between optimal strategies in the cases of known versus unknown preferences. Most notably, the divider has a compelling "diversification" incentive in creating the chooser's two options. This incentive, hereto unnoticed, leads to multiple goods being divided at equilibrium, quite contrary to the divider's optimal strategy when preferences are known. In many contexts, such as buy-and-sell provisions between partners, or in judging fairness, it is important to assess the relative expected utilities of the divider and chooser. Those utilities, we show, depend on the players' uncertainties about each other's values, the number of goods being divided, and whether the divider can offer multiple alternative divisions. We prove that, when values are independently and identically distributed across players and goods, the chooser is strictly better off for a small number of goods, while the divider is strictly better off for a large number of goods.Jamie Tucker-Foltz, Richard Zeckhauserwork_tguikj2r3zawnimgbvxsqc3cdeThu, 07 Jul 2022 00:00:00 GMTRepeatedly Matching Items to Agents Fairly and Efficiently
https://scholar.archive.org/work/6xtzjan7uvhi3by5stk7spukfe
We consider a novel setting where a set of items are matched to the same set of agents repeatedly over multiple rounds. Each agent gets exactly one item per round, which brings interesting challenges to finding efficient and/or fair repeated matchings. A particular feature of our model is that the value of an agent for an item in some round depends on the number of rounds in which the item has been used by the agent in the past. We present a set of positive and negative results about the efficiency and fairness of repeated matchings. For example, when items are goods, a variation of the well-studied fairness notion of envy-freeness up to one good (EF1) can be satisfied under certain conditions. Furthermore, it is intractable to achieve fairness and (approximate) efficiency simultaneously, even though they are achievable separately. For mixed items, which can be goods for some agents and chores for others, we propose and study a new notion of fairness that we call swap envy-freeness (swapEF).Ioannis Caragiannis, Shivika Narangwork_6xtzjan7uvhi3by5stk7spukfeMon, 04 Jul 2022 00:00:00 GMTFair and Truthful Giveaway Lotteries
https://scholar.archive.org/work/hey6cepk4fardcqu3eqviylgnq
We consider a setting where a large number of agents are all interested in attending some public resource of limited capacity. Attendance is thus allotted by lottery. If agents arrive individually, then randomly choosing the agents – one by one - is a natural, fair and efficient solution. We consider the case where agents are organized in groups (e.g. families, friends), the members of each of which must all be admitted together. We study the question of how best to design such lotteries. We first establish the desired properties of such lotteries, in terms of fairness and efficiency, and define the appropriate notions of strategy proofness (providing that agents cannot gain by misrepresenting the true groups, e.g. joining or splitting groups). We establish inter-relationships between the different properties, proving properties that cannot be fulfilled simultaneously (e.g. leximin optimality and strong group stratagy proofness). Our main contribution is a polynomial mechanism for the problem, which guarantees many of the desired properties, including: leximin optimality, Pareto-optimality, anonymity, group strategy proofness, and adjunctive strategy proofness (which provides that no benefit can be obtained by registering additional - uninterested or bogus - individuals). The mechanism approximates the utilitarian optimum to within a factor of 2, which, we prove, is optimal for any mechanism that guarantees any one of the following properties: egalitarian welfare optimality, leximin optimality, envyfreeness, and adjunctive strategy proofness.Tal Arbiv, Yonatan Aumannwork_hey6cepk4fardcqu3eqviylgnqTue, 28 Jun 2022 00:00:00 GMTA Little Charity Guarantees Fair Connected Graph Partitioning
https://scholar.archive.org/work/qy7vxrgarzcopc5ezmlvgnwhkq
Motivated by fair division applications, we study a fair connected graph partitioning problem, in which an undirected graph with m nodes must be divided between n agents such that each agent receives a connected subgraph and the partition is fair. We study approximate versions of two fairness criteria: \alpha-proportionality requires that each agent receive a subgraph with at least (1/\alpha)*m/n nodes, and \alpha-balancedness requires that the ratio between the sizes of the largest and smallest subgraphs be at most \alpha. Unfortunately, there exist simple examples in which no partition is reasonably proportional or balanced. To circumvent this, we introduce the idea of charity. We show that by "donating" just n-1 nodes, we can guarantee the existence of 2-proportional and almost 2-balanced partitions (and find them in polynomial time), and that this result is almost tight. More generally, we chart the tradeoff between the size of charity and the approximation of proportionality or balancedness we can guarantee.Ioannis Caragiannis, Evi Micha, Nisarg Shahwork_qy7vxrgarzcopc5ezmlvgnwhkqTue, 28 Jun 2022 00:00:00 GMT