Boczkowski et al. (2018) considered the noisy PULL(h) model on the complete graph, where in each parallel round, every agent passively receives observations of the messages held by h randomly chosen agents, and where each message can be viewed as any other message in the alphabet ∑ with probability δ. The authors proved that in this model, the basic task of propagating a bit value from a single source to the whole population requires [EQUATION] rounds. The current work shows that the aforementioned lower bound is almost tight. We present two simple and efficient protocols that remain effective even in the presence of multiple conflicting sources, and quickly converge to their plurality opinion. Our first protocol operates with any alphabet of size at least two. Our second protocol, while slightly less efficient and requiring an alphabet of size four, is self-stabilizing. Overall, our results demonstrate how increasing the sample size can compensate for the lack of communication structure by linearly accelerating information spread.

We consider a search problem on trees aiming to find a treasure that an adversary places at one of the nodes. The algorithm can query nodes and extract directional information from them. That is, each node holds a pointer, termed advice , to one of its neighbors. Ideally, this advice points to the neighbor that is closer to the treasure, however, with probability \(q\) this advice points to a uniformly random neighbor. Crucially, the advice is permanent , hence querying the same node again yields the same answer. Let \(\Delta\) denote the maximal degree. Roughly speaking, we show that the expected number of queries incurs a phase transition when \(q\) is about \(1/\sqrt{\Delta}\) . In a recent paper, at TALG’21, we showed that if \(q\) is above the threshold then the expected number of queries is polynomial in \(n\) . Here, we prove that below the threshold, the expected number of queries is \(\mathcal{O}(\sqrt{\Delta}\log\Delta\cdot\log^{2}n)\) , which is tight up to an \(\mathcal{O}(\log n)\) factor when \(\Delta\) is small. We further show that this factor can be reduced to \(\mathcal{O}(\log\log n)\) in the case of regular trees and assuming that \(q for sufficiently small \(c>0\) . In addition, we study the case that the treasure must be found with some given probability. We show that for every fixed \(\varepsilon,\delta>0\) , if \(q<1/\Delta^{\varepsilon}\) then there exists a search strategy that with probability \(1-\delta\) finds the treasure using \((\delta^{-1}\log n)^{O(\frac{1}{\varepsilon})}\) queries, whereas \((\delta^{-1}\log n)^{\Omega(\frac{1}{\varepsilon})}\) queries are necessary.

Abstract How to efficiently and reliably spread information in a system is one of the most fundamental problems in distributed computing. Recently, inspired by biological scenarios, several works focused on identifying the minimal communication resources necessary to spread information under faulty conditions. Here we study the self-stabilizing bit-dissemination problem, introduced by Boczkowski, Korman, and Natale in [SODA 2017]. The problem considers a fully-connected network of nagents, with a binary world of opinions, one of which is called correct. At any given time, each agent holds an opinion bit as its public output. The population contains a source agent which knows which opinion is correct. This agent adopts the correct opinion and remains with it throughout the execution. We consider the basic $$\mathcal {PULL}$$ PULL model of communication, in which each agent observes relatively few randomly chosen agents in each round. The goal of the non-source agents is to quickly converge on the correct opinion, despite having an arbitrary initial configuration, i.e., in a self-stabilizing manner. Once the population converges on the correct opinion, it should remain with it forever. Motivated by biological scenarios in which animals observe and react to the behavior of others, we focus on the extremely constrained model of passive communication, which assumes that when observing another agent the only information that can be extracted is the opinion bit of that agent. We prove that this problem can be solved in a poly-logarithmic in n number of rounds with high probability, while sampling a logarithmic number of agents at each round. Previous works solved this problem faster and using fewer samples, but they did that by decoupling the messages sent by agents from their output opinion, and hence do not fit the framework of passive communication. Moreover, these works use complex recursive algorithms with refined clocks that are unlikely to be used by biological entities. In contrast, our proposed algorithm has a natural appeal as it is based on letting agents estimate the current tendency direction of the dynamics, and then adapt to the emerging trend.

We investigate opinion dynamics in a fully-connected system, consisting of n agents, where one of the opinions, called correct, represents a piece of information to disseminate. One source agent initially holds the correct opinion and remains with this opinion throughout the execution. The goal of the remaining agents is to quickly agree on this correct opinion. At each round, one agent chosen uniformly at random is activated: unless it is the source, the agent pulls the opinions of l random agents and then updates its opinion according to some rule. We consider a restricted setting, in which agents have no memory and they only revise their opinions on the basis of those of the agents they currently sample. This setting encompasses very popular opinion dynamics, such as the voter model and best-of-k majority rules. Qualitatively speaking, we show that lack of memory prevents efficient convergence. Specifically, we prove that any dynamics requires Omega(n^2) expected time, even under a strong version of the model in which activated agents have complete access to the current configuration of the entire system, i.e., the case l=n. Conversely, we prove that the simple voter model (in which l=1) correctly solves the problem, while almost matching the aforementioned lower bound. These results suggest that, in contrast to symmetric consensus problems (that do not involve a notion of correct opinion), fast convergence on the correct opinion using stochastic opinion dynamics may require the use of memory.

How to efficiently and reliably spread information in a system is one of the most fundamental problems in distributed computing. Recently, inspired by biological scenarios, several works focused on identifying the minimal communication resources necessary to spread information under faulty conditions. Here we study the self-stabilizing bit-dissemination problem, introduced by Boczkowski, Korman, and Natale in [SODA 2017]. The problem considers a fully-connected network of n agents, with a binary world of opinions, one of which is called correct. At any given time, each agent holds an opinion bit as its public output. The population contains a source agent which knows which opinion is correct. This agent adopts the correct opinion and remains with it throughout the execution. We consider the basic PULL model of communication, in which each agent observes relatively few randomly chosen agents in each round. The goal of the non-source agents is to quickly converge on the correct opinion, despite having an arbitrary initial configuration, i.e., in a self-stabilizing manner. Once the population converges on the correct opinion, it should remain with it forever. Motivated by biological scenarios in which animals observe and react to the behavior of others, we focus on the extremely constrained model of passive communication, which assumes that when observing another agent the only information that can be extracted is the opinion bit of that agent. We prove that this problem can be solved in a poly-logarithmic in~n number of rounds with high probability, while sampling a logarithmic number of agents at each round. Previous works solved this problem faster and using fewer samples, but they did that by decoupling the messages sent by agents from their output opinion, and hence do not fit the framework of passive communication. Moreover, these works use complex recursive algorithms with refined clocks that are unlikely to be used by biological entities. In contrast, our proposed algorithm has a natural appeal as it is based on letting agents estimate the current tendency direction of the dynamics, and then adapt to the emerging trend.

We consider a search problem on trees in which an agent starts at the root of a tree and aims to locate an adversarially placed treasure, by moving along the edges, while relying on local, partial information. Specifically, each node in the tree holds a pointer to one of its neighbors, termedadvice. A node is faulty with probabilityq. The advice at a non-faulty node points to the neighbor that is closer to the treasure, and the advice at a faulty node points to a uniformly random neighbor. Crucially, the advice ispermanent, in the sense that querying the same node again would yield the same answer. Let Δ denote the maximum degree. For the expected number of moves (edge traversals) until finding the treasure, we show that a phase transition occurs when thenoise parameterqis roughly 1 √Δ. Below the threshold, there exists an algorithm with expected number of movesO(D√Δ), whereDis the depth of the treasure, whereas above the threshold, every search algorithm has an expected number of moves, which is both exponential inDand polynomial in the number of nodes n. In contrast, if we require to find the treasure with probability at least 1 − δ, then for every fixed ɛ > 0, ifq< 1/Δɛ, then there exists a search strategy that with probability 1 − δ finds the treasure using (Δ−1D)O(1/ε)moves. Moreover, we show that (Δ−1D)Ω(1/ε)moves are necessary.

Search patterns of randomly oriented steps of different lengths have been observed on all scales of the biological world, ranging from the microscopic to the ecological, including in protein motors, bacteria, T-cells, honeybees, marine predators, and more. Through different models, it has been demonstrated that adopting a variety in the magnitude of the step lengths can greatly improve the search efficiency. However, the precise connection between the search efficiency and the number of step lengths in the repertoire of the searcher has not been identified. Motivated by biological examples in one-dimensional terrains, a recent paper studied the best cover time on an n-node cycle that can be achieved by a random walk process that uses k step lengths. By tuning the lengths and corresponding probabilities the authors therein showed that the best cover time is roughly n 1+$Θ$(1/k). While this bound is useful for large values of k, it is hardly informative for small k values, which are of interest in biology. In this paper, we provide a tight bound for the cover time of such a walk, for every integer k > 1. Specifically, up to lower order polylogarithmic factors, the upper bound on the cover time is a polynomial in n of exponent 1+ 1/(2k--1). For k = 2, 3, 4 and 5 the exponent is thus 4/3 , 6/5 , 8/7 , and 10/9 , respectively. Informally, our result implies that, as long as the number of step lengths k is not too large, incorporating an additional step length to the repertoire of the process enables to improve the cover time by a polynomial factor, but the extent of the improvement gradually decreases with k.

Coordinating the actions of agents (e.g., volunteers analyzing radio signals in SETI@home) yields efficient search algorithms. However, such an efficiency is often at the cost of implementing complex coordination mechanisms which may be expensive in terms of communication and/or computation overheads. Instead, non-coordinating algorithms, in which each agent operates independently from the others, are typically very simple, and easy to implement. They are also inherently robust to slight misbehaviors, or even crashes of agents. In this article, we investigate the “price of non-coordinating,” in terms of search performance, and we show that this price is actually quite small. Specifically, we consider a parallel version of a classical Bayesian search problem, where set of k ≥1 searchers are looking for a treasure placed in one of the boxes indexed by positive integers, according to some distribution p . Each searcher can open a random box at each step, and the objective is to find the treasure in a minimum number of steps. We show that there is a very simple non-coordinating algorithm which has expected running time at most 4(1−1/ k +1) 2 OPT+10, where OPT is the expected running time of the best fully coordinated algorithm. Our algorithm does not even use the precise description of the distribution p , but only the relative likelihood of the boxes. We prove that, under this restriction, our algorithm has the best possible competitive ratio with respect to OPT. For the case where a complete description of the distribution p is given to the search algorithm, we describe an optimal non-coordinating algorithm for Bayesian search. This latter algorithm can be twice as fast as our former algorithm in practical scenarios such as uniform distributions. All these results provide a complete characterization of non-coordinating Bayesian search. The take-away message is that, for their simplicity and robustness, non-coordinating algorithms are viable alternatives to complex coordinating mechanisms subject to significant overheads. Most of these results apply as well to linear search, in which the indices of the boxes reflect their relative importance, and where important boxes must be visited first.

We consider a problem of searching for an unknown target vertex t in a (possibly edge-weighted) graph. Each vertex-query points to a vertex v and the response either admits that v is the target or provides any neighbor s of v that lies on a shortest path from v to t. This model has been introduced for trees by Onak and Parys [FOCS 2006] and for general graphs by Emamjomeh-Zadeh et al. [STOC 2016]. In the latter, the authors provide algorithms for the error-less case and for the independent noise model (where each query independently receives an erroneous answer with known probability p<1/2 and a correct one with probability 1-p). We study this problem both with adversarial errors and independent noise models. First, we show an algorithm that needs at most (log_2 n)/(1 - H(r)) queries in case of adversarial errors, where the adversary is bounded with its rate of errors by a known constant r<1/2. Our algorithm is in fact a simplification of previous work, and our refinement lies in invoking an amortization argument. We then show that our algorithm coupled with a Chernoff bound argument leads to a simpler algorithm for the independent noise model and has a query complexity that is both simpler and asymptotically better than the one of Emamjomeh-Zadeh et al. [STOC 2016]. Our approach has a wide range of applications. First, it improves and simplifies the Robust Interactive Learning framework proposed by Emamjomeh-Zadeh and Kempe [NIPS 2017]. Secondly, performing analogous analysis for edge-queries (where a query to an edge e returns its endpoint that is closer to the target) we actually recover (as a special case) a noisy binary search algorithm that is asymptotically optimal, matching the complexity of Feige et al. [SIAM J. Comput. 1994]. Thirdly, we improve and simplify upon an algorithm for searching of unbounded domains due to Aslam and Dhagat [STOC 1991].

Biological systems can share and collectively process information to yield emergent effects, despite inherent noise in communication. While man-made systems often employ intricate structural solutions to overcome noise, the structure of many biological systems is more amorphous. It is not well understood how communication noise may affect the computational repertoire of such groups. To approach this question we consider the basic collective task of rumor spreading, in which information from few knowledgeable sources must reliably flow into the rest of the population. In order to study the effect of communication noise on the ability of groups that lack stable structures to efficiently solve this task, we consider a noisy version of the uniform PULL model. We prove a lower bound which implies that, in the presence of even moderate levels of noise that affect all facets of the communication, no scheme can significantly outperform the trivial one in which agents have to wait until directly interacting with the sources. Our results thus show an exponential separation between the uniform PUSH and PULL communication models in the presence of noise. Such separation may be interpreted as suggesting that, in order to achieve efficient rumor spreading, a system must exhibit either some degree of structural stability or, alternatively, some facet of the communication which is immune to noise. We corroborate our theoretical findings with a new analysis of experimental data regarding recruitment in Cataglyphis Niger desert ants.

We consider a game-theoretic setting in which selfish individuals compete over resources of varying quality. The motivating example is a group of animals that disperse over patches of food of different abundances. In such scenarios, individuals are biased towards selecting the higher quality patches, while, at the same time, aiming to avoid costly collisions or overlaps. Our goal is to investigate the impact of collision costs on the parallel coverage of resources by the whole group. Consider M sites, where a site x has value f(x). We think of f(x) as the reward associated with site x, and assume that if a single individual visits x exclusively, it receives this exact reward. Typically, we assume that if l>1 individuals visit x then each receives at most f(x)/l. In particular, when competition costs are high, each individual might receive an amount strictly less than f(x)/l, which could even be negative. Conversely, modeling cooperation at a site, we also consider cases where each one gets more than f(x)/l. There are k identical players that compete over the rewards. They independently act in parallel, in a one-shot scenario, each specifying a single site to visit, without knowing which sites are explored by others. The group performance is evaluated by the expected coverage, defined as the sum of f(x) over all sites that are explored by at least one player. Since we assume that players cannot coordinate before choosing their site we focus on symmetric strategies. The main takeaway message of this paper is that the optimal symmetric coverage is expected to emerge when collision costs are relatively high, so that the following "Judgment of Solomon" type of rule holds: If a single player explores a site x then it gains its full reward f(x), but if several players explore it, then neither one receives any reward. Under this policy, it turns out that there exists a unique symmetric Nash Equilibrium strategy, which is, in fact, evolutionary stable. Moreover, this strategy yields the best possible coverage among all symmetric strategies. Viewing the coverage measure as the social welfare, this policy thus enjoys a (Symmetric) Price of Anarchy of precisely 1, whereas, in fact, any other congestion policy has a price strictly greater than 1. Our model falls within the scope of mechanism design, and more precisely in the area of incentivizing exploration. It finds relevance in evolutionary ecology, and further connects to studies on Bayesian parallel search algorithms.

Biological systems can share and collectively process information to yield emergent effects, despite inherent noise in communication. While man-made systems often employ intricate structural solutions to overcome noise, the structure of many biological systems is more amorphous. It is not well understood how communication noise may affect the computational repertoire of such groups. To approach this question we consider the basic collective task of rumor spreading, in which information from few knowledgeable sources must reliably flow into the rest of the population. We study the effect of communication noise on the ability of groups that lack stable structures to efficiently solve this task. We present an impossibility result which strongly restricts reliable rumor spreading in such groups. Namely, we prove that, in the presence of even moderate levels of noise that affect all facets of the communication, no scheme can significantly outperform the trivial one in which agents have to wait until directly interacting with the sources-a process which requires linear time in the population size. Our results imply that in order to achieve efficient rumor spread a system must exhibit either some degree of structural stability or, alternatively, some facet of the communication which is immune to noise. We then corroborate this claim by providing new analyses of experimental data regarding recruitment in Cataglyphis niger desert ants. Finally, in light of our theoretical results, we discuss strategies to overcome noise in other biological systems.

We introduce the dependent doors problem as an abstraction for situations in which one must perform a sequence of dependent decisions, without receiving feedback information on the effectiveness of previously made actions. Informally, the problem considers a set of d doors that are initially closed, and the aim is to open all of them as fast as possible. To open a door, the algorithm knocks on it, and it might open or not according to some probability distribution. This distribution may depend on which other doors are currently open, as well as on which other doors were open during each of the previous knocks on that door. The algorithm aims to minimize the expected time until all doors open. Crucially, it must act at any time without knowing whether or which other doors have already opened. In this work, we focus on scenarios where dependencies between doors are both positively correlated and acyclic. The fundamental distribution of a door describes the probability it opens in the best of conditions (with respect to other doors being open or closed). We show that if in two configurations of d doors corresponding doors share the same fundamental distribution, then these configurations have the same optimal running time up to a universal constant, no matter what the dependencies between doors and what the distributions. We also identify algorithms that are optimal up to a universal constant factor. For the case in which all doors share the same fundamental distribution, we additionally provide a simpler algorithm and a formula to calculate its running time. We furthermore analyse the price of lacking feedback for several configurations governed by standard fundamental distributions. In particular, we show that the price is logarithmic in d for memoryless doors but can potentially grow to be linear in d for other distributions. We then turn our attention to investigate precise bounds. Even for the case of two doors, identifying the optimal sequence is an intriguing combinatorial question. Here, we study the case of two cascading memoryless doors. That is, the first door opens on each knock independently with probability p 1 . The second door can only open if the first door is open, in which case it will open on each knock independently with probability p 2 . We solve this problem almost completely by identifying algorithms that are optimal up to an additive term of 1.

This paper considers the basic PULL model of communication, in which in each round, each agent extracts information from few randomly chosen agents. We seek to identify the smallest amount of information revealed in each interaction (message size) that nevertheless allows for efficient and robust computations of fundamental information dissemination tasks. We focus on the Majority Bit Dissemination problem that considers a population of n agents, with a designated subset of source agents. Each source agent holds an input bit and each agent holds an output bit. The goal is to let all agents converge their output bits on the most frequent input bit of the sources (the majority bit). Note that the particular case of a single source agent corresponds to the classical problem of Broadcast (also termed Rumor Spreading). We concentrate on the severe fault-tolerant context of self-stabilization, in which a correct configuration must be reached eventually, despite all agents starting the execution with arbitrary initial states. In particular, the specification of who is a source and what is its initial input bit may be set by an adversary. We first design a general compiler which can essentially transform any self-stabilizing algorithm with a certain property (called “the bitwise-independence property”) that uses ℓ-bits messages to one that uses only log ¿-bits messages, while paying only a small penalty in the running time. By applying this compiler recursively we then obtain a self-stabilizing Clock Synchronization protocol, in which agents synchronize their clocks modulo some given integer T, within Õ(log n log T) rounds w.h.p., and using messages that contain 3 bits only. We then employ the new Clock Synchronization tool to obtain a self-stabilizing Majority Bit Dissemination protocol which converges in Õ(log n) time, w.h.p., on every initial configuration, provided that the ratio of sources supporting the minority opinion is bounded away from half. Moreover, this protocol also uses only 3 bits per interaction.

This paper is motivated by the aspiration to identify the weakest computational models that allow for efficient, robust distributed computation. We focus on one of the most fundamental building-blocks in distributed computing, namely, Broadcast. In this problem, a unique source agent $s$ needs to disseminate a bit $b$ to the rest of the population. To account for unpredictability issues that may result from uncoordinated executions, we consider a self-stabilizing setting, in which a correct configuration must be reached eventually, despite processors starting the execution with arbitrary initial states (that do not violate the requirement for the existence of a unique source). Similarly to many works on broadcast, we consider a synchronous communication model on a complete anonymous network, in which in each round, each agent can extract information from two other agents, chosen uniformly at random. Our focus is on identifying the smallest message size that is required in order to achieve fast self-stabilizing broadcast. We first observe that with an extra bit added to the message-size and a small additive penalty to the running time, the self-stabilizing broadcast problem can be reduced to a self-stabilizing clock-synchronization problem, where agents aim to synchronize their clocks modulo some integer T. Our main technical contribution lies in solving the latter problem in poly-logarithmic time using only 3 bits per interaction. This allows for a self-stabilizing broadcast protocol that uses only 4 bits per interaction and converges in O log n time.

We analyse parallel algorithms in the context of exhaustive search over totally ordered sets. Imagine an infinite list of “boxes”, with a “treasure” hidden in one of them, where the boxes’ order reflects the importance of finding the treasure in a given box. At each time step, a search protocol executed by a searcher has the ability to peek into one box, and see whether the treasure is present or not. Clearly, the best strategy of a single searcher would be to open the boxes one by one, in increasing order. Moreover, by equally dividing the workload between them, k searchers can trivially find the treasure k times faster than one searcher. However, this straightforward strategy is very sensitive to failures (e.g., crashes of processors), and overcoming this issue seems to require a large amount of communication. We therefore address the question of designing parallel search algorithms maximizing their speed-up and maintaining high levels of robustness, while minimizing the amount of resources for coordination. Based on the observation that algorithms that avoid communication are inherently robust, we focus our attention on identifying the best running time performance of non-coordinating algorithms. Specifically, we devise non-coordinating algorithms that achieve a speed-up of 9/8 for two searchers, a speed-up of 4/3 for three searchers, and in general, a speed-up of k/4(1+1/k)2 for any k≥ 1 searchers. Thus, asymptotically, the speed-up is only four times worse compared to the case of full coordination. Moreover, these bounds are tight in a strong sense as no non-coordinating search algorithm can achieve better speed-ups. Our algorithms are surprisingly simple and hence applicable. However they are memory intensive and so we suggest a practical, memory efficient version, with a speed-up of (k2 − 1)/4k. That is, it is only a factor of (k+1)/(k−1) slower than the optimal algorithm. Overall, we highlight that, in faulty contexts in which coordination between the searchers is technically difficult to implement, intrusive with respect to privacy, and/or costly in term of resources, it might well be worth giving up on coordination, and simply run our non-coordinating exhaustive search algorithms.

In this article, we solve the ancestry -labeling scheme problem, which aims at assigning the shortest possible labels (bit strings) to nodes of rooted trees, so ancestry queries between any two nodes can be answered by inspecting their assigned labels only. This problem was introduced more than 20 years ago by Kannan et al. [1988] and is among the most well-studied problems in the field of informative labeling schemes. We construct an ancestry-labeling scheme for n -node trees with label size log 2 n + O (log log n ) bits, thus matching the log 2 n + Ω(log log n ) bits lower bound given by Alstrup et al. [2003]. Our scheme is based on a simplified ancestry scheme that operates extremely well on a restricted set of trees. In particular, for the set of n -node trees with a depth of at most d , the simplified ancestry scheme enjoys label size of log 2 n + 2log 2 d + O (1) bits. Since the depth of most XML trees is at most some small constant, such an ancestry scheme may be of practical use. In addition, we also obtain an adjacency -labeling scheme that labels n -node trees of depth d with labels of size log 2 n + 3log 2 d + O (1) bits. All our schemes assign the labels in linear time, and guarantee that any query can be answered in constant time. Finally, our ancestry scheme finds applications to the construction of small universal partially ordered sets (posets). Specifically, for any fixed integer k , it enables the construction of a universal poset of size Õ ( n k ) for the family of n -element posets with a tree dimension of at most k . Up to lower-order terms, this bound is tight thanks to a lower bound of n k − o (1) by to Alon and Scheinerman [1988].

Distributed computing models typically assume reliable communication between processors. While such assumptions often hold for engineered networks, e.g., due to underlying error correction protocols, their relevance to biological systems, wherein messages are often distorted before reaching their destination, is quite limited. In this study we aim at bridging this gap by rigorously analyzing a model of communication in large anonymous populations composed of simple agents which interact through short and highly unreliable messages. We focus on the rumor-spreading problem and the majority-consensus problem, two fundamental tasks in distributed computing, and initiate their study under communication noise. Our model for communication is extremely weak and follows the push gossip communication paradigm: In each synchronous round each agent that wishes to send information delivers a message to a random anonymous agent. This communication is further restricted to contain only one bit (essentially representing an opinion). Lastly, the system is assumed to be so noisy that the bit in each message sent is flipped independently with probability 1/2-ε, for some small Aε >0.

Social animals may share information to obtain a more complete and accurate picture of their surroundings. However, physical constraints on communication limit the flow of information between interacting individuals in a way that can cause an accumulation of errors and deteriorated collective behaviors. Here, we theoretically study a general model of information sharing within animal groups. We take an algorithmic perspective to identify efficient communication schemes that are, nevertheless, economic in terms of communication, memory and individual internal computation. We present a simple and natural algorithm in which each agent compresses all information it has gathered into a single parameter that represents its confidence in its behavior. Confidence is communicated between agents by means of active signaling. We motivate this model by novel and existing empirical evidences for confidence sharing in animal groups. We rigorously show that this algorithm competes extremely well with the best possible algorithm that operates without any computational constraints. We also show that this algorithm is minimal, in the sense that further reduction in communication may significantly reduce performances. Our proofs rely on the Cramér-Rao bound and on our definition of a Fisher Channel Capacity. We use these concepts to quantify information flows within the group which are then used to obtain lower bounds on collective performance. The abstract nature of our model makes it rigorously solvable and its conclusions highly general. Indeed, our results suggest confidence sharing as a central notion in the context of animal communication.

Do unique node identifiers help in deciding whether a network G has a prescribed property P? We study this question in the context of distributed local decision, where the objective is to decide whether G has property P by having each node run a constant-time distributed decision algorithm. In a yes-instance all nodes should output yes, while in a no-instance at least one node should output no.

A central theme in distributed network algorithms concerns understanding and coping with the issue of locality . Yet despite considerable progress, research efforts in this direction have not yet resulted in a solid basis in the form of a fundamental computational complexity theory for locality. Inspired by sequential complexity theory, we focus on a complexity theory for distributed decision problems . In the context of locality, solving a decision problem requires the processors to independently inspect their local neighborhoods and then collectively decide whether a given global input instance belongs to some specified language. We consider the standard LOCAL model of computation and define LD( t ) (for local decision ) as the class of decision problems that can be solved in t communication rounds. We first study the intriguing question of whether randomization helps in local distributed computing, and to what extent. Specifically, we define the corresponding randomized class BPLD( t , p , q ), containing all languages for which there exists a randomized algorithm that runs in t rounds, accepts correct instances with probability at least p , and rejects incorrect ones with probability at least q . We show that p 2 + q = 1 is a threshold for the containment of LD( t ) in BPLD( t , p , q ). More precisely, we show that there exists a language that does not belong to LD( t ) for any t = o ( n ) but does belong to BPLD( 0 , p , q ) for any p , q ∈ (0,1) such that p 2 + q ≤ 1. On the other hand, we show that, restricted to hereditary languages, BPLD( t , p , q )=LD( O ( t )), for any function t , and any p , q ∈ (0,1) such that p 2 + q > 1. In addition, we investigate the impact of nondeterminism on local decision, and establish several structural results inspired by classical computational complexity theory. Specifically, we show that nondeterminism does help, but that this help is limited, as there exist languages that cannot be decided locally nondeterministically. Perhaps surprisingly, it turns out that it is the combination of randomization with nondeterminism that enables to decide all languages in constant time . Finally, we introduce the notion of local reduction, and establish a couple of completeness results.

We use distributed computing tools to provide a new perspective on the behavior of cooperative biological ensembles. We introduce the Ants Nearby Treasure Search (ANTS) problem, a generalization of the classical cow-path problem [10, 20, 41, 42], which is relevant for collective foraging in animal groups. In the ANTS problem, k identical (probabilistic) agents, initially placed at some central location, collectively search for a treasure in the two-dimensional plane. The treasure is placed at a target location by an adversary and the goal is to find it as fast as possible as a function of both k and D, where D is the distance between the central location and the target. This is biologically motivated by cooperative, central place foraging, such as performed by ants around their nest. In this type of search there is a strong preference to locate nearby food sources before those that are further away. We focus on trying to find what can be achieved if communication is limited or altogether absent. Indeed, to avoid overlaps agents must be highly dispersed making communication difficult. Furthermore, if the agents do not commence the search in synchrony, then even initial communication is problematic. This holds, in particular, with respect to the question of whether the agents can communicate and conclude their total number, k. It turns out that the knowledge of k by the individual agents is crucial for performance. Indeed, it is a straightforward observation that the time required for finding the treasure is Ω(D + D2/k), and we show in this paper that this bound can be matched if the agents have knowledge of k up to some constant approximation.

We study the verification problem in distributed networks, stated as follows. Let $H$ be a subgraph of a network $G$ where each vertex of $G$ knows which edges incident on it are in $H$. We would like to verify whether $H$ has some properties, e.g., if it is a tree or if it is connected (every node knows at the end of the process whether $H$ has the specified property or not). We would like to perform this verification in a decentralized fashion via a distributed algorithm. The time complexity of verification is measured as the number of rounds of distributed communication. In this paper we initiate a systematic study of distributed verification and give almost tight lower bounds on the running time of distributed verification algorithms for many fundamental problems such as connectivity, spanning connected subgraph, and $s$-$t$ cut verification. We then show applications of these results in deriving strong unconditional time lower bounds on the hardness of distributed approximation for many classical optimization problems including minimum spanning tree (MST), shortest paths, and minimum cut. Many of these results are the first nontrivial lower bounds for both exact and approximate distributed computation, and they resolve previous open questions. Moreover, our unconditional lower bound of approximating MST subsumes and improves upon the previous hardness of approximation bound of Elkin [M. Elkin, SIAM J. Comput., 36 (2006), pp. 433--456] as well as the lower bound for (exact) MST computation of Peleg and Rubinovich [D. Peleg and V. Rubinovich, SIAM J. Comput., 30 (2000), pp. 1427--1442]. Our result implies that there can be no distributed approximation algorithm for MST that is significantly faster than the current exact algorithm for any approximation factor. Our lower bound proofs show an interesting connection between communication complexity and distributed computing which turns out to be useful in establishing the time complexity of exact and approximate distributed computation of many problems.

A central theme in distributed network algorithms concerns understanding and coping with the issue of locality. Despite considerable progress, research efforts in this direction have not yet resulted in a solid basis in the form of a fundamental computational complexity theory for locality. Inspired by sequential complexity theory, we focus on a complexity theory for distributed decision problems. In the context of locality, solving a decision problem requires the processors to independently inspect their local neighborhoods and then collectively decide whether a given global input instance belongs to some specified language. We consider the standard LOCAL model of computation and define LD(t) (for local decision) as the class of decision problems that can be solved in t communication rounds. We first study the intriguing question of whether randomization helps in local distributed computing, and to what extent. Specifically, we define the corresponding randomized class BPLD(t,p,q), containing all languages for which there exists a randomized algorithm that runs in t rounds, accepts correct instances with probability at least p and rejects incorrect ones with probability at least q. We show that p2+q = 1 is a threshold for the containment of LD(t) in BPLD(t,p,q). More precisely, we show that there exists a language that does not belong to LD(t) for any t=o(n) but does belong to BPLD(0,p,q) for any p,q ∈ (0,1] such that p2+q≤1. On the other hand, we show that, restricted to hereditary languages, BPLD(t,p,q) = LD(O(t)), for any function t and any p,q ∈ (0,1] such that p2+q>;1. In addition, we investigate the impact of non-determinism on local decision, and establish some structural results inspired by classical computational complexity theory. Specifically, we show that non-determinism does help, but that this help is limited, as there exist languages that cannot be decided non-deterministically. Perhaps surprisingly, it turns out that it is the combination of randomization with non-determinism that enables to decide all languages in constant time. Finally, we introduce the notion of local reduction, and establish some completeness results.

This paper demonstrates the usefulness of distributed local verification of proofs, as a tool for the design of algorithms. In particular, it introduces a somewhat generalized notion of distributed local proofs, and utilizes it for improving the memory size complexity, while obtaining time efficiency too.

Numerous sophisticated local algorithm were suggested in the literature for various fundamental problems. Notable examples are the MIS and (Δ+1)-coloring algorithms by Barenboim and Elkin [6], by Kuhn [22], and by Panconesi and Srinivasan [33], as well as the OΔ2-coloring algorithm by Linial [27]. Unfortunately, most known local algorithms (including, in particular, the aforementioned algorithms) are non-uniform, that is, they assume that all nodes know good estimations of one or more global parameters of the network, e.g., the maximum degree Δ or the number of nodes n.

Consider the setting of randomly weighted graphs, namely, graphs whose edge weights are chosen independently according to probability distributions with finite support over the non-negative reals. Under this setting, weighted graph properties such as the diameter, the radius (with respect to a designated vertex), and the weight of a minimum spanning tree become random variables and we are interested in computing their expectation. Unfortunately, this turns out to be #P-hard. In this paper, we define a family of weighted graph properties (that includes the above three) and show that for each property in this family, the problem of computing the kth moment (and in particular, the expectation) of the corresponding random variable admits a fully polynomial-time randomized approximation scheme (FPRAS) for every fixed k.

This paper establishes tight bounds for the Minimum-weight Spanning Tree (MST) verification problem in the distributed setting. Specifically, we provide an MST verification algorithm that achieves simultaneously tilde ~O(|E|) messages and $tilde O(sqrt{n} + D) time, where |E| is the number of edges in the given graph G and D is G's diameter. On the negative side, we show that any MST verification algorithm must send Omega(|E|) messages and incur ~Omega(sqrt{n} + D) time in worst case. Our upper bound result appears to indicate that the verification of an MST may be easier than its construction, since for MST construction, both lower bounds of Omega(|E|) messages and Omega(sqrt{n} + D) time hold, but at the moment there is no known distributed algorithm that constructs an MST and achieves simultaneously tilde O(|E|) messages and ´~O(sqrt{n} + D) time. Specifically, the best known time-optimal algorithm (using ~O(sqrt{n} + D) time) requires O(|E|+n^{3/2}) messages, and the best known message-optimal algorithm (using ~O(|E|) messages) requires O(n) time. On the other hand, our lower bound results indicate that the verification of an MST is not significantly easier than its construction.

We study the verification problem in distributed networks, stated as follows. Let H be a subgraph of a network G where each vertex of G knows which edges incident on it are in H. We would like to verify whether H has some properties, e.g., if it is a tree or if it is connected (every node knows in the end of the process whether H has the specified property or not). We would like to perform this verification in a decentralized fashion via a distributed algorithm. The time complexity of verification is measured as the number of rounds of distributed communication.

Consider a distributed network in which events occur at arbitrary nodes and at unpredicted times. An event occurring at node u is sensed only by u which in turn may invoke a communication protocol that allows nodes to exchange messages with their neighbors. We are interested in the following threshold detection (TD) problem inherent to distributed computing: Given some threshold k, the goal of a TD protocol is to broadcast a termination signal when at least k events have occurred (throughout the network).

In this paper, we solve the ancestry problem, which was introduced more than twenty years ago by Kannan et al. [STOC '88], and is among the most well-studied problems in the field of informative labeling schemes. Specifically, we construct an ancestry labeling scheme for n-node trees with label size log2 n + O(log log n) bits, thus matching the log2 n + Ω(log log n) bits lower bound given by Alstrup et al. [SODA '03]. Besides its optimal label size, our scheme assigns the labels in linear time, and guarantees that any ancestry query can be answered in constant time. In addition to its potential impact in terms of improving the performances of XML search engines, our ancestry scheme is also useful in the context of partially ordered sets. Specifically, for any fixed integer k, our scheme enables the construction of a universal poset of size O(nk log4k n) for the family of n-element posets with tree-dimension at most k. This bound is almost tight thanks to a lower bound of nk-o(1) due to Alon and Scheinerman [Order '88].

This article studies labeling schemes for the vertex connectivity function on general graphs. We consider the problem of assigning short labels to the nodes of any n -node graph is such a way that given the labels of any two nodes u and v , one can decide whether u and v are k -vertex connected in G , that is, whether there exist k vertex disjoint paths connecting u and v . This article establishes an upper bound of k 2 log n on the number of bits used in a label. The best previous upper bound for the label size of such a labeling scheme is 2 k log n .

The (M, W)-controller, originally studied by Afek, Awerbuch, Plotkin, and Saks, is a basic distributed tool that provides an abstraction for managing the consumption of a global resource in a distributed dynamic network. We establish new bounds on the message complexity of this tool based on a surprising connection between the controller problem and the monotonic labeling problem.

Informative labeling schemes consist in labeling the nodes of graphs so that queries regarding any two nodes (e.g., are the two nodes adjacent?) can be answered by inspecting merely the labels of the corresponding nodes. Typically, the main goal of such schemes is to minimize the label size, that is, the maximum number of bits stored in a label. This concept was introduced by Kannan et al. [STOC'88] and was illustrated by giving very simple and elegant labeling schemes, for supporting adjacency and ancestry queries in n-node trees; both these schemes have label size 2log n. Motivated by relations between such schemes and other important notions such as universal graphs, extensive research has been made by the community to further reduce the label sizes of such schemes as much as possible. The current state of the art adjacency labeling scheme for trees has label size log n+O(log*n) by Alstrup and Rauhe [FOCS'02], and the best known ancestry scheme for (rooted) trees has label size log n+O(√log n) by Abiteboul et al., [SICOMP 2006].

A classical routing problem consists of assigning a label and distinct port numbers to each node of a graph, such that for every node v, given its own label and the label of any destination vertex u, node v can find which of its incident port numbers leads to the next vertex on a shortest path connecting v and u. In the static (fixed topology) setting, such a routing scheme is evaluated by the label size, i.e., the maximal number of bits stored in a label. Naturally, special attention is given to compact schemes, which are schemes enjoying asymptotically optimal labels. Many routing schemes were proposed for the static setting. However, the more realistic and complex dynamic setting, in which topology changes may occur at arbitrary nodes, has received much less attention. In the dynamic setting, the occurrence of topology changes may force the scheme to occasionally update the (hopefully short) labels, by delivering information from place to place. This raises a natural tradeoff between the size of the labels and the number of messages required for maintaining them. The above dynamic routing problem was proposed by Afek, Gafni, and Ricklin (1989), who also presented an elegant and rather efficient dynamic routing scheme for trees, supporting one type of topology change, namely, the addition of a leaf. Various attempts for improving the tradeoff between the label size and the message complexity as well as for supporting more types of topology changes on trees, were subsequently proposed. Still, the best known compact routing scheme for dynamic trees has very high message complexity, namely, O(nε) amortized messages per topological change. Moreover, previous routing schemes for dynamic trees support at most two kinds of topology changes, namely, the addition and the removal of a leaf node. In this paper, we present two compact routing schemes for dynamic trees that incur extremely low message complexity and can support more types of topology changes than previous schemes. We first present a dynamic compact routing scheme that supports the additions of both leaves and internal nodes and incurs only O(log n) amortized message complexity per node. We then extend that scheme obtaining a dynamic compact routing scheme that supports additions of both leaves and internal nodes as well as deletions of nodes of degree at most 2. The extended scheme incurs O(log2 n) amortized message complexity per topological change.

A finite automaton, simply referred to as a robot , has to explore a graph, that is, visit all the nodes of the graph. The robot has no a priori knowledge of the topology of the graph, nor of its size. It is known that for any k -state robot, there exists a graph of maximum degree 3 that the robot cannot explore. This article considers the effects of allowing the system designer to add short labels to the graph nodes in a preprocessing stage, for helping the exploration by the robot. We describe an exploration algorithm that, given appropriate 2-bit labels (in fact, only 3-valued labels), allows a robot to explore all graphs. Furthermore, we describe a suitable labeling algorithm for generating the required labels in linear time. We also show how to modify our labeling scheme so that a robot can explore all graphs of bounded degree, given appropriate 1-bit labels. In other words, although there is no robot able to explore all graphs of maximum degree 3, there is a robot R, and a way to color in black or white the nodes of any bounded-degree graph G , so that R can explore the colored graph G . Finally, we give impossibility results regarding graph exploration by a robot with no internal memory (i.e., a single-state automaton).

This article studies approximate distributed routing schemes on dynamic communication networks. The work focuses on dynamic weighted general graphs where the vertices of the graph are fixed, but the weights of the edges may change. Our main contribution concerns bounding the cost of adapting to dynamic changes. The update efficiency of a routing scheme is measured by the time needed in order to update the routing scheme following a weight change. A naive dynamic routing scheme, which updates all vertices following a weight change, requires Ω( Diam ) time in order to perform the updates after every weight change, where Diam is the diameter of the underlying graph. In contrast, this article presents approximate dynamic routing schemes with average time complexity Θ˜( D ) per topological change, where D is the local density parameter of the underlying graph. Following a weight change, our scheme never incurs more than Diam time; thus, our scheme is particularly efficient on graphs which have low local density and large diameter. The article also establishes upper and lower bounds on the size of the databases required by the scheme at each site.

Afek, Awerbuch, Plotkin, and Saks identified an important fundamental problem inherent to distributed networks, which they called the Resource Controller problem. Consider, first, the problem in which one node (called the "root") is required to estimate the number of events that occurred all over the network. This counting problem can be viewed as a useful variant of the heavily studied and used task of topology update (that deals with collecting all remote information). The Resource Controller problem generalizes the counting problem: such remote events are considered as requests, and the counting node, i.e., the "root", also issues permits for the requests. That way, the number of request granted can be controlled (bounded).

We use the recently introduced advising scheme framework for measuring the difficulty of locally distributively computing a Minimum Spanning Tree (MST). An (m,t)-advising scheme for a distributed problem P is a way, for every possible input I of P, to provide an "advice" (i.e., a bit string) about I to each node so that: (1) the maximum size of the advices is at most m bits, and (2) the problem P can be solved distributively in at most t rounds using the advices as inputs. In case of MST, the output returned by each node of a weighted graph G is the edge leading to its parent in some rooted MST T of G. Clearly, there is a trivial (log n,0)-advising scheme for MST (each node is given the local port number of the edge leading to the root of some MST T), and it is known that any (0,t)-advising scheme satisfies t ≥ Ω (√n). Our main result is the construction of an (O(1),O(log n))-advising scheme for MST. That is, by only giving a constant number of bits of advice to each node, one can decrease exponentially the distributed computation time of MST in arbitrary graph, compared to algorithms dealing with the problem in absence of any a priori information. We also consider the average size of the advices. On the one hand, we show that any (m,0)-advising scheme for MST gives advices of average size Ω(log n). On the other hand we design an (m,1)-advising scheme for MST with advices of constant average size, that is one round is enough to decrease the average size of the advices from log(n) to constant.

The problem of verifying a Minimum Spanning Tree (MST) was introduced by Tarjan in a sequential setting. Given a graph and a tree that spans it, the algorithm is required to check whether this tree is an MST. This paper investigates the problem in the distributed setting, where the input is given in a distributed manner, i.e., every node "knows" which of its own emanating edges belong to the tree. Informally, the distributed MST verification problem is the following. Label the vertices of the graph in such a way that for every node, given its own label and the labels of its neighbors only, the node can detect whether these edges are indeed its MST edges. In this paper we present such a verification scheme with a maximum label size of O(log n log W), where n is the number of nodes and W is the largest weight of an edge. We also give a matching lower bound of Ω(log n log W) (except when W ≤ log n). Both our bounds improve previously known bounds for the problem.For the related problem of tree sensitivity also presented by Tarjan, our method yields rather efficient schemes for both the distributed and the sequential settings. Our techniques (both for the lower bound and for the upper bound) may indicate a strong relation between the fields of proof labeling schemes and implicit labeling schemes.

This paper addresses the problem of locally verifying global properties. Several natural questions are studied, such as "how expensive is local verification?" and more specifically "how expensive is local verification compared to computation?" A suitable model is introduced in which these questions are studied in terms of the number of bits a node needs to communicate. In particular, it is shown that the cost of verification is sometimes rather high, even higher than the number of bits needed for a computation. On the other hand, approaches are presented for the efficient construction of schemes, and upper and lower bounds are established on the cost of schemes for multiple basic problems. The paper also studies the role and cost of unique identities in terms of impossibility and complexity.Previous studies on related questions deal with distributed algorithms that simultaneously compute a configuration and verify that this configuration has a certain desired property. It turns out that this combined approach enables verification to be less costly, since the configuration is typically generated so as to be easily verifiable. In contrast, our approach separates the configuration design from the verification. That is, it first generates the desired configuration without bothering with the need to verify, and then handles the task of constructing a suitable verification scheme. Our approach thus allows for a more modular design of algorithms, and has the potential to aid in verifying properties even when the original design of the structures for maintaining them was done without verification in mind.

This paper studies labeling schemes for flow and connectivity functions. A flow labeling scheme using $O(\log n\cdot\log {\hat{\omega}}+\log^2n)$-bit labels is presented for general n-vertex graphs with maximum (integral) capacity ${\hat{\omega}}$. This is shown to be asymptotically optimal. For edge-connectivity, this yields a tight bound of $\Theta(\log^2 n)$ bits. A k-vertex connectivity labeling scheme is then given for general n-vertex graphs using at most 3 log n bits for k = 2, 5 log n bits for k = 3, and 2 k log n bits for k > 3. Finally, a lower bound of $\Omega (k\log n)$ is established for k -vertex connectivity on n-vertex graphs, where k is polylogarithmic in n.