Two prominent objectives in social choice are utilitarian - maximizing the sum of agents' utilities, and leximin - maximizing the smallest agent's utility, then the second-smallest, etc. Utilitarianism is typically computationally easier to attain but is generally viewed as less fair. This paper presents a general reduction scheme that, given a utilitarian solver, produces a distribution over states (deterministic outcomes) that is leximin in expectation. Importantly, the scheme is robust in the sense that, given an approximate utilitarian solver, it produces a lottery that is approximately-leximin (in expectation) - with the same approximation factor. We apply our scheme to several social choice problems: stochastic allocations of indivisible goods, giveaway lotteries, and fair lotteries for participatory budgeting.

Multi-Robot Coverage problems have been extensively studied in robotics, planning and multi-agent systems. In this work, we consider the coverage problem when there are constraints on the proximity (e.g., maximum distance between the agents, or a blue agent must be adjacent to a red agent) and the movement (e.g., terrain traversability and material load capacity) of the robots. Such constraints naturally arise in many real-world applications, e.g. in search-and-rescue and maintenance operations. Given such a setting, the goal is to compute a covering tour of the graph with a minimum number of steps, and that adheres to the proximity and movement constraints. For this problem, our contributions are four: (i) a formal formulation of the problem, (ii) an exact algorithm that is FPT in parameters ||F||, d and ω - the set of robot formations that encode the proximity constraints, the maximum nodes degree, and the tree-width of the graph, respectively, (iii) for the case that the graph is a tree: a PTAS approximation scheme, that given an ε produces a tour that is within a 1+ ε⋅error(||F||, d)) of the optimal one, and the computation runs in time poly(n) ⋅ h(1/ε, ||F||). (iv) for the case that the graph is a tree, with k=3 robots, and the constraint is that all agents are connected: a PTAS scheme with multiplicative approximation error of 1 + O(ε), independent of d.

Issue salience is a major determinant in voters' decisions. Candidates and political parties campaign to shift salience to their advantage - a process termed priming. We study the dynamics, strategies and equilibria of campaign spending for voter priming in multi-issue multi-party settings. We consider both parliamentary elections, where parties aim to maximize their share of votes, and various settings for presidential elections, where the winner takes all. For parliamentary elections, we show that pure equilibrium spending always exists and can be computed in time linear in the number of voters. For two parties and all settings, a spending equilibrium exists such that each party invests only in a single issue, and an equilibrium can be computed in time that is polynomial in the number of issues and linear in the number of voters. We also show that in most presidential settings no equilibrium exists. Additional properties of optimal campaign strategies are also studied.

We consider a setting where goods are allocated to agents by way of an allocation platform (e.g., a matching platform). An “allocation facilitator” aims to increase the overall utility/social-good of the allocation by encouraging (some of the) agents to relax (some of) their restrictions. At the same time, the advice must not hurt agents who would otherwise be better off. Additionally, the facilitator may be constrained by a “bound” (a.k.a. ‘budget’), limiting the number and/or type of restrictions it may seek to relax. We consider the facilitator’s optimization problem of choosing an optimal set of restrictions to request to relax under the aforementioned constraints. Our contributions are three-fold: (i) We provide a formal definition of the problem, including the participation guarantees to which the facilitator should adhere. We define a hierarchy of participation guarantees and also consider several social-good functions. (ii) We provide polynomial algorithms for solving various versions of the associated optimization problems, including one-to-one and many-to-one allocation settings. (iii) We demonstrate the benefits of such facilitation and relaxation, and the implications of the different participation guarantees, using extensive experimentation on three real-world datasets.

Abstract In this work we explore the opportunities presented by partitioning contestants in contest into disjoint groups, each competing in an independent contest, with its own prize. This, as opposed to most literature on contest design, which focuses on the setting of a single “grand” (possibly multi-stage) contest, wherein all potential contestants ultimately compete for the same prize(s), with few exceptions that do consider contest partitioning, yet with conflicting preference results concerning the optimal structure to be used. Focusing on binary contests, wherein the quality of contestants’ submissions are endogenously determined, we show that contest partitioning is indeed beneficial under some condition, e.g., whenever the number of contestants, or the prize amount, are “sufficiently large”, where the exact size requirements are a function of the partitioning cost. When partitioning does not entail any cost, we show that it is either a dominating or weakly dominating strategy, depending on the way the organizer’s expected benefit is determined. The analysis is further extended to consider partitioning where some of the sub-contests used contain a single contestant (a singleton). We conclude that contest partitioning is an avenue that contest designers can and should consider, when aiming to maximize their profit.

The use of virtual agents (bots) has become essential for providing online assistance to customers. However, even though a lot of effort has been dedicated to the research, development, and deployment of such virtual agents, customers are frequently frustrated with the interaction with the virtual agent and require a human instead. We suggest that a holistic approach, combining virtual agents and human operators working together, is the path to providing satisfactory service. However, implementing such a holistic customer service system will not, and cannot, be achieved using any single AI technology or branch. Rather, such a system will inevitably require the integration of multiple and diverse AI technologies, including natural language processing, multi-agent systems, machine learning, reinforcement learning, and behavioral cloning; in addition to integration with other disciplines such as psychology, business, sociology, economics, operation research, informatics, computer-human interaction, and more. As such, we believe this customer service application offers a rich domain for experimentation and application of multidisciplinary AI. In this paper, we introduce the holistic customer service application and discuss the key AI technologies and disciplines required for a successful AI solution for this setting. For each of these AI technologies, we outline the key scientific questions and research avenues stemming from this setting. We demonstrate that integrating technologies from different fields can lead to a cost-effective successful customer service center. The challenge is that there is a need for several communities, each with its own language and modeling techniques, different problem-solving methods, and different evaluation methodologies, all of which need to work together. Real cooperation will require the formation of joint methodologies and techniques that could improve the service to customers, but, more importantly, open new directions in cooperation of diverse communities toward solving joint difficult tasks.

Leximin is a common approach to multi-objective optimization, frequently employed in fair division applications. In leximin optimization, one first aims to maximize the smallest objective value; subject to this, one maximizes the second-smallest objective; and so on. Often, even the single-objective problem of maximizing the smallest value cannot be solved accurately. What can we hope to accomplish for leximin optimization in this situation? Recently, Henzinger et al. (2022) defined a notion of approximate leximin optimality. Their definition, however, considers only an additive approximation. In this work, we first define the notion of approximate leximin optimality, allowing both multiplicative and additive errors. We then show how to compute, in polynomial time, such an approximate leximin solution, using an oracle that finds an approximation to a single-objective problem. The approximation factors of the algorithms are closely related: an (α,ϵ)-approximation for the single-objective problem (where α ∈ (0,1] and ϵ ≥ 0 are the multiplicative and additive factors respectively) translates into an (α2/(1 − α + α2), ϵ/(1 − α + α2))-approximation for the multi-objective leximin problem, regardless of the number of objectives. Finally, we apply our algorithm to obtain an approximate leximin solution for the problem of stochastic allocations of indivisible goods.

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.

Multi-defender Stackelberg Security Games (MSSG) have recently gained increasing attention in the literature. However, the solutions offered to date are highly sensitive, wherein even small perturbations in the attacker's utility or slight uncertainties thereof can dramatically change the defenders' resulting payoffs and alter the equilibrium. In this paper, we introduce a robust model for MSSGs, which admits solutions that are resistant to small perturbations or uncertainties in the game's parameters. First, we formally define the notion of robustness, as well as the robust MSSG model. Then, for the non-cooperative setting, we prove the existence of a robust approximate equilibrium in any such game, and provide an efficient construction thereof. For the cooperative setting, we show that any such game admits a robust approximate (alpha) core, and provide an efficient construction thereof. Lastly, we show that stronger types of the core may be empty. Interestingly, the robust solutions can substantially increase the defenders' utilities over those of the non-robust ones.

In many approval voting settings voters are a priori uncertain regarding their true preferences, yet can obtain this information if willing to incur some cost. This paper provides a comprehensive analysis of such model focusing in simultaneous and sequential voting. The analysis enables demonstrating that costly preference-related information acquisition changes some inherent model properties. In particular, the introduction of such cost may lead to all sorts of manipulations in the sequential case, resulting in an assortment of examples where the latter is dominated by simultaneous voting and vice versa. This, as opposed to the case where such information is freely available, where it can be proved that the two variants are truthful and equivalent. These findings suggest important implications to policy makers and the designers of voting systems.

We study temporal information design in contests, wherein the organizer may, possibly incrementally, disclose information about the participation and performance of some contestants to other (later) contestants. We show that such incremental disclosure can increase the organizer's profit. The expected profit, however, depends on the exact information disclosure structure, and the optimal structure depends on the parameters of the problem. We provide a game-theoretic analysis of such information disclosure schemes as they apply to two common models of contests: (a) simple contests, wherein contestants' decisions concern only their participation; and (b) Tullock contests, wherein contestants choose the effort levels to expend. For each of these we analyze and characterize the equilibrium strategy, and exhibit the potential benefits of information design.

In a seminal paper, McAfee (1992) presented a truthful mechanism for double auctions, attaining asymptotically-optimal gain-from-trade without any prior information on the valuations of the traders. McAfee's mechanism handles single-parametric agents, allowing each seller to sell a single unit and each buyer to buy a single unit. This paper presents a double-auction mechanism that handles multi-parametric agents and allows multiple units per trader, as long as the valuation functions of all traders have decreasing marginal returns. The mechanism is prior-free, ex-post individually-rational, dominant-strategy truthful and strongly-budget-balanced. Its gain-from-trade approaches the optimum when the market size is sufficiently large.

Much of the work on multi-agent contests is focused on determining the equilibrium behavior of contestants. This capability is essential for the principal for choosing the optimal parameters for the contest (e.g. prize amount). As it turns out, many contests exhibit not one, but many possible equilibria, hence precluding contest design optimization and contestants behavior prediction. In this paper we examine a variation of the classic contest that alleviates this problem by having contestants make the decisions sequentially rather than in parallel. We study this model in the setting of a simple contest, wherein contestants only choose whether or not to participate, while their performance level is exogenously set. We show that by switching to the revised mechanism the principal can not only force her most desired pure-strategies based equilibrium to emerge, but also, at times, end up with an equilibrium offering a greater expected profit. Further, we show that in the modified contest the optimal prize can be effectively computed. The theoretical analysis is complemented by comprehensive experiments with people over Amazon Mechanical Turk. Here, we find that the modified mechanism offers great benefit for the principal, both in terms of an increased over-participation in the contest (compared to theoretical expectations) and increased average profit.

Motivated by applications such as stock exchanges and spectrum auctions, there is a growing interest in mechanisms for arranging trade in two-sided markets. However, existing mechanisms are either not truthful, do not guarantee an asymptotically-optimal gain-from-trade, rely on a prior on the traders' valuations, or operate in limited settings such as a single type of good. We extend the random-sampling technique used in earlier works to multi-good markets where traders have gross-substitute valuations. We show a prior free, truthful and strongly-budget-balanced mechanism which guarantees near-optimal gain from trade when the market sizes of all goods grow to infinity at a similar rate.

We consider the classic problem of envy-free division of a heterogeneous good (“cake”) among several agents. It is known that, when the allotted pieces must be connected, the problem cannot be solved by a finite algorithm for three or more agents. The impossibility result, however, assumes that the entire cake must be allocated. In this article, we replace the entire-allocation requirement with a weaker partial-proportionality requirement: the piece given to each agent must be worth for it at least a certain positive fraction of the entire cake value. We prove that this version of the problem is solvable in bounded time even when the pieces must be connected. We present simple, bounded-time envy-free cake-cutting algorithms for (1) giving each of n agents a connected piece with a positive value; (2) giving each of three agents a connected piece worth at least 1/3; (3) giving each of four agents a connected piece worth at least 1/7; (4) giving each of four agents a disconnected piece worth at least 1/4; and (5) giving each of n agents a disconnected piece worth at least (1 − ϵ)/ n for any positive ϵ.

We consider the problem of fair division of a two dimensional heterogeneous good among several agents. Applications include division of land as well as ad space in print and electronic media. Classical cake cutting protocols either consider a one-dimensional resource, or allocate each agent several disconnected pieces. In practice, however, the two dimensional shape of the allotted piece is of crucial importance in many applications, e.g., squares or bounded aspect-ratio rectangles are most useful for building houses as well as advertisements. We thus introduce and study the problem of envy-free two-dimensional division wherein the utility of the agents depends on the geometric shape of the allocated pieces (as well as the location and size). In addition to envy-freeness, we require that the fraction allocated to each agent be at least a certain constant that depends only on the shape of the cake and the number of agents. We focus on the case where the allotted pieces must be square and the cakes are either squares or the unbounded plane. We provide algorithms for the problem for settings with two and three agents.

We consider the problem of designing automated strategies for interactions with human subjects, where the humans must be rewarded for performing certain tasks of interest. We focus on settings where there is a single task that must be performed many times by different humans (e.g. answering a questionnaire), and the humans require a fee for performing the task. In such settings, our objective is to minimize the average cost for effectuating the completion of the task. We present two automated strategies for designing efficient agents for the problem, based on two different models of human behavior. The first, the Reservation Price Based Agent (RPBA), is based on the concept of a reservation price, and the second, the No Bargaining Agent (NBA), uses principles from behavioral science. The performance of the agents has been tested in extensive experiments with real human subjects, where NBA outperforms both RPBA and strategies developed by human experts.

We introduce a generalization of interval graphs, which we call Dotted Interval Graphs (DIG). A dotted interval graph is an intersection graph of arithmetic progressions (dotted intervals). Coloring of dotted interval graphs naturally arises in the context of high throughput genotyping. We study the properties of dotted interval graphs, with a focus on coloring. We show that any graph is a DIG, but that DIG d graphs, that is, DIGs in which the arithmetic progressions have a jump of at most d , form a strict hierarchy. We show that coloring DIG d graphs is NP-complete even for d = 2. For any fixed d , we provide a 5/6 d + o ( d ) approximation for the coloring of DIG d graphs. Finally, we show that finding the maximal clique in DIG d graphs is fixed parameter tractable in d .

In the k -matches problem, we are given a pattern and a text, and for each text location, the desired output consists of all aligned matching characters if there are k or fewer of them, and any k aligned matching characters if there are more than k of them. This problem is one of several string matching problems that seek not only to find where the pattern matches the text under different “match” definitions, but also to provide witnesses to the match. Other such problems include k -aligned ones, k -witnesses, and k -mismatches. In addition, the solutions to several other string matching problems rely on the efficient solutions of the witness finding problems. In this article we provide a general method for solving such witness finding problems efficiently. We do so by casting the problem as a generalization of group testing, which we then solve by a process we call peeling . Using this general framework we obtain improved results for all of the problems mentioned. We also show that our method also solves a couple of problems outside the pattern matching domain.

MOTIVATION: The resolution at which genomic alterations can be mapped by means of oligonucleotide aCGH (array-based comparative genomic hybridization) is limited by two factors: the availability of high-quality probes for the target genomic sequence and the array real-estate. Optimization of the probe selection process is required for arrays that are designed to probe specific genomic regions in very high resolution without compromising probe quality constraints. RESULTS: In this paper we describe a well-defined optimization problem associated with the problem of probe selection for high-resolution aCGH arrays. We propose the whenever possible in-cover as a formulation that faithfully captures the requirement of probe selection problem, and provide a fast randomized algorithm that solves the optimization problem in O(n logn) time, as well as a deterministic algorithm with the same asymptotic performance. We apply the method in a typical high-definition array design scenario and demonstrate its superiority with respect to alternative approaches. AVAILABILITY: Address requests to the authors.

We present problems in the following three application areas: identifying similar codes in which global register reallocation and spill code minimization were done (programming languages); protein threading (computational biology); and searching for color icons under different color maps (image processing). We introduce a new search model called function matching that enables us to solve the above problems. The function matching problem has as its input a text T of length n over alphabet $\Sigma_T$ and a pattern $P = P[1] P[2] \cdots P[m]$ of length m over alphabet $\Sigma_P$. We seek all text locations i, where the m-length substring that starts at i is equal to $f(P[1]) f(P[2]) \cdots f(P[m])$, for some function $f: \Sigma_P \rightarrow \Sigma_T$. We give a randomized algorithm that solves the function matching problem in time $O(n\log n)$ with probability ${1\over n}$ of declaring a false positive. We give a deterministic algorithm whose time is $O(n |\Sigma_P| \log m)$ and show that it is optimal in the convolutions model. We use function matching to efficiently solve the problem of two-dimensional parameterized matching.

This paper describes a hybrid statistical and knowledge-based information extraction model, able to extract entities and relations at the sentence level. The model attempts to retain and improve the high accuracy levels of knowledge-based systems while drastically reducing the amount of manual labor by relying on statistics drawn from a training corpus. The implementation of the model, called TEG (Trainable Extraction Grammar), can be adapted to any IE domain by writing a suitable set of rules in a SCFG (Stochastic Context Free Grammar) based extraction language, and training them using an annotated corpus. The system does not contain any purely linguistic components, such as PoS tagger or parser. We demonstrate the performance of the system on several named entity extraction and relation extraction tasks. The experiments show that our hybrid approach outperforms both purely statistical and purely knowledge-based systems, while requiring orders of magnitude less manual rule writing and smaller amount of training data. The improvement in accuracy is slight for named entity extraction task and more pronounced for relation extraction.

Most information extraction systems focus on the textual content of the documents. They treat documents as sequences or of words, disregarding the physical and typographical layout of the information.. While this strategy helps in focusing the extraction process on the key semantic content of the document, much valuable information can also be derived form the document physical appearance. Often, fonts, physical positioning and other graphical characteristics are used to provide additional context to the information. This information is lost with pure-text analysis. In this paper we describe a general procedure for structural extraction, which allows for automatic extraction of entities from the document based on their visual characteristics and relative position in the document layout. Our structural extraction procedure is a learning algorithm, which knows how to automatically generalizes from examples. The procedure is a general one, applicable to any document format with visual and typographical information. We also then describe a specific implementation of the procedure to PDF documents, called PES (PDF Extraction System). PES works with PDF documents and is able to extract such fields such as Author(s), Title, Date, etc. with very high accuracy.

We address the problem of the-security of cryptographic protocols in face of future advances in computing technology and algorithmic research. The problem stems from the fact may be deemed that computations which at a given point in time may be deemed infeasible, can, in the course of years or decades, be made possible with improved hardware and/or breakthroughs in code-breaking algorithms. In such cases, the security of historical , but nonetheless highly confidential data may be in jeopardy. We present a scheme for efficient secure two-party communication with provable everlasting security. The security is guaranteed in face of any future technological advances, given the current state of of the art. Furthermore, the security of the messages is also guaranteed even if the secret encryption/decryption key is revealed in the future, The scheme is based on the bounded storage model and provides information-theoretic security in this model. The bounded storage model postulates an adversary who is computationally unbounded, and is only bounded in the amount of storage (not computation space) available to store the output of his computation. The bound on the storage can be arbitrarily large (e.g., 100 Tbytes), as long as it is fixed. Given this storage bound, our protocols guarantee that even a computationally all powerful adversary gains no information about a message (except with a probability that is exponentially small in the security parameter k). The bound on storage space need only hold at the time of the message transmission. Thereafter, no additional storage space or, computational power can help the adversary in deciphering the message. We present two protocols. The first protocol, which elaborates on the autoregressive (AR) protocol of Aumann and Rabin (see Advances in Cryptology-Crypto '99, p. 65-79, 1999), employs a short secret key whose size is independent of the length of the message, but uses many public random bits. The second protocol uses an optimal number of public random bits, but employs a longer secret key. Our proof of security utilizes a novel linear algebraic technique.

Text-Mining is a growing area of interest within the field of Data Mining and Knowledge Discovery. Given a collection of text documents, most approaches to Text Mining perform knowledgediscovery operations either on external tags associated with each document, or on the set of all words within each document. Both approaches suffer from limitations. This paper focuses on an intermediate approach, one that we call text mining via information extraction, in which knowledge discovery takes place on focused, relevant terms, phrases and facts, as extracted from the documents.

data-mining tool and as such have been well researched.

It is shown that the minimum cut ratio is within a factor of O(log k) of the maximum concurrent flow for k-commodity flow instances with arbitrary capacities and demands. This improves upon the previously best-known bound of O(log 2 k ) and is existentially tight, up to a constant factor. An algorithm for finding a cut with ratio within a factor of O(log k) of the maximum concurrent flow, and thus of the optimal min-cut ratio, is presented.

Let a text string T of n symbols and a pattern string P of m symbols from alphabet /spl Sigma/ be given. A swapped version T' of T is a length n string derived from T by a series of local swaps, (i.e. t/sup '//sub l//spl larr/t/sub l+1/ and t'/sub l+1//spl larr/t/sub l/) where each element can participate in no more than one swap. The Pattern Matching with Swaps problem is that of finding all locations i for which there exists a swapped version T' of T where there is an exact matching of P in location i of T'. It has been an open problem whether swapped matching can be done in less than O(mn) time. In this paper we show the first algorithm that solves the pattern matching with swaps problem in time O(mn). We present an algorithm whose time complexity is O(nm/sup 1/3/ log m log/sup 2/ /spl sigma/) for a general alphabet /spl Sigma/, where /spl sigma/=min(m, |/spl Sigma/|).

Abstract We consider the problem of asynchronous consensus with a weak dynamic adversary scheduler. We provide the first algorithm to obtain ~O(n) total work in the weak adversary model using only single-writer registers. For the multi-writer setting we give an O(log n) workper-processor algorithm, improving upon the previous O(log2 n) bound. The adversary model considered is the content oblivious adversary model, which assumes that the adversary does not know the content of a register until it is read by some processor [13].

The authors consider the tolerance of data structures to memory faults. They observe that many pointer-based data structures (e.g. linked lists, trees, etc.) are highly nonresilient to faults. A single fault in a linked list or tree may result in the loss of the entire set of data. They present a formal framework for studying the fault tolerance properties of pointer-based data structures, and provide fault tolerant versions of the stack, the linked list, and the dictionary tree.

We consider the problem of asynchronous execution of parallel programs. We assume that the original program is designed for a synchronous system, whereas the actual system may be asynchronous. We seek an automatic execution scheme, which allows the asynchronous system to execute the synchronous program. Previous execution schemes provide solutions only for the case where the original program is deterministic. Here, we provide the first solution for the more general case where the original program can be nondeterministic (e.g. randomized). Our scheme is based on a novel agreement protocol for the asynchronous parallel setting. Our protocol allows n asynchronous processors to agree on n word-sized values in O(n log n log log n) total work. Total work is defined to be the summation of the number of steps performed by all processors (including steps from busy waiting). 1 Introduction Motivation. Parallel programs are frequently designed assuming tightly-coupled processors, operating in ...

An n-thread parallel program p is large-grained if in every parallel step the computations on each of the threads are complex procedures requiring numerous processor instructions. This practically relevant style of programs differs from PRAM programs in its large granularity and the possibility that within a parallel step the computations on different threads may considerably vary in size. Let M be an n-processor asynchronous parallel system, with no restriction on the degree of asynchrony and without any specialized synchronization mechanisms. It is a challenging theoretical as well as practically important problem to ensure correct execution of P on such a parallel machine. Let P be a large-grained program requiring total work W for its execution on a synchronous a-processor parallel system. We present a transformation (compilation) of P into a program C(P) which correctly and efficiently effects the computation of P on the asynchronous machine M. Under moderate assumptions on the granularity of threads and the size of the program variables, execution of C(P) requires just O(Wlog* n) expected total work, and the memory space overhead is a small multiplicative constant.>

The authors discuss the question of simulating synchronous computations on asynchronous systems. They consider an asynchronous system with very weak, or altogether lacking any, atomicity assumptions. The first contribution of this paper is a novel clock for asynchronous systems. The clock is a basic tool for synchronization in the asynchronous environment. It is a very robust construction and can operate in a system with no atomicity assumptions, and in the presence of a dynamic scheduler. The behavior of the clock is obtained with overwhelming probability (1-2/sup - alpha n/, alpha >0). The authors show how to harness this clock to drive a PRAM simulation on an asynchronous system. The resulting simulation scheme is more efficient than existing ones, while actually relaxing the assumptions on the underlying asynchronous system.>

We present and O(1) slowdown emulation of a fault-free N x N two dimensional mesh with a slack of O(log N log log N) by a faulty mesh of the same size and slack. All components of the faulty mesh, including the memory modules, are assumed to be subject to failure. The faults may occur at any time during the emulation and the system readjusts dynamically.

A scheme for emulating the parallel random access machine (PRAM) on a faulty hypercube is presented. All components of the hypercube, including the memory modules, are assumed to be subject to failure. The faults may occur at any time during the emulation and the system readjusts dynamically. The scheme, which rests on L.G. Valiant's BSP model (1990), is the first to achieve optimal and work-preserving PRAM emulation on a dynamically faulty network.>