arXiv subjects

Paul Spirakis

Publications and source records attributed to Paul Spirakis.

At least 19 recordsLinked to original sources

arXiv2026

Game-Theoretic Foundations of Competition for Conscious Access

Conscious access in the human brain is often described as the outcome of a competition among candidate representations, but this competition is usually left at the level of mechanism or metaphor rather than analyzed as a strategic allocation problem. We introduce an access contest in which internal modules compete for a scarce broadcast slot by choosing a costly amplification effort. Access is allocated by a smooth probabilistic rule, allowing the model to interpolate between diffuse selection and winner-take-all competition. We establish pure-strategy equilibrium existence under standard convexity and bounded-benefit assumptions, and give sufficient conditions for uniqueness using diagonal strict concavity. We then analyze capture in the two-module case, and for quadratic costs, we derive a sharp threshold in the competition intensity above which capture occurs. For strongly convex costs, we prove an if-and-only-if capture criterion in terms of the cost-adjusted amplification advantage of the lower-value module. Under the same curvature-dominance condition that guarantees uniqueness, we show that the unique pure Nash equilibrium of the general $M$-module access contest can be approximated efficiently by projected pseudo-gradient dynamics, with logarithmic dependence on the desired accuracy. Finally, we prove an impossibility theorem for single-slot access mechanisms. Exact winner-take-all efficiency is incompatible with robustness to small score perturbations. Thus, smooth probabilistic access rules are not merely analytically convenient, but structurally motivated. These results provide a game-theoretic foundation for studying competition for conscious access, connecting equilibrium analysis, capture, computation, and mechanism-level limitations under a common formal model.

cs.GT↗

arXiv2026

Tractable Exclusion Zones for Instant-Runoff Voting on Trees and Beyond

Instant-runoff voting (IRV) is often used when voters rank candidates rather than choosing only one favourite. We study IRV under graph-induced metric preferences where each vertex of an unweighted undirected graph hosts one voter and is also a possible candidate location. Voters rank candidates by shortest-path distance with fixed deterministic tie-breaking. We focus on exclusion zones, i.e., sets S such that, whenever at least one candidate lies in S, the IRV winner must also lie in S. Such zones serve as robustness certificates, identifying regions whose participation prevents outside winners from emerging. For general graphs, exclusion-zone verification is co-NP-complete and minimum-zone computation is NP-hard. We show that both problems become polynomial-time solvable on trees. Our main tool is a membership test asking whether a candidate can be forced to lose using opponents from a restricted region. A round-1 reduction shows that any such loss has a witness in which the candidate is eliminated in the first IRV round, enabling a bottom-up dynamic program on trees. We also show that minimum-zone computation has a much smaller search space than its definition suggests. The pairwise-loss graph, obtained from all two-candidate elections, imposes closure constraints on every exclusion zone. With deterministic tie-breaking this graph is a tournament, implying that every nonempty exclusion zone on a tree is generated by the closure of one vertex. Thus, the minimum exclusion zone can be found by testing only linearly many candidate sets. On the opposite front, we refine the intractability range of computing minimum exclusion zones on general graphs, extending it to a much broader class of deterministic elimination rules, dubbed as Strong Forced Elimination.

cs.GT↗

arXiv2025

Towards Scalable Proteomics: Opportunistic SMC Samplers on HTCondor

Quantitative proteomics plays a central role in uncovering regulatory mechanisms, identifying disease biomarkers, and guiding the development of precision therapies. These insights are often obtained through complex Bayesian models, whose inference procedures are computationally intensive, especially when applied at scale to biological datasets. This limits the accessibility of advanced modelling techniques needed to fully exploit proteomics data. Although Sequential Monte Carlo (SMC) methods offer a parallelisable alternative to traditional Markov Chain Monte Carlo, their high-performance implementations often rely on specialised hardware, increasing both financial and energy costs. We address these challenges by introducing an opportunistic computing framework for SMC samplers, tailored to the demands of large-scale proteomics inference. Our approach leverages idle compute resources at the University of Liverpool via HTCondor, enabling scalable Bayesian inference without dedicated high-performance computing infrastructure. Central to this framework is a novel Coordinator-Manager-Follower architecture that reduces synchronisation overhead and supports robust operation in heterogeneous, unreliable environments. We evaluate the framework on a realistic proteomics model and show that opportunistic SMC delivers accurate inference with weak scaling, increasing samples generated under a fixed time budget as more resources join. To support adoption, we release CondorSMC, an open-source package for deploying SMC samplers in opportunistic computing environments.

q-bio.QM↗

arXiv2025

Collision-Free Robot Scheduling

Robots are becoming an increasingly common part of scientific work within laboratory environments. In this paper, we investigate the problem of designing \emph{schedules} for completing a set of tasks at fixed locations with multiple robots in a laboratory. We represent the laboratory as a graph with tasks placed on fixed vertices and robots represented as agents, with the constraint that no two robots may occupy the same vertex at any given timestep. Each schedule is partitioned into a set of timesteps, corresponding to a walk through the graph (allowing for a robot to wait at a vertex to complete a task), with each timestep taking time equal to the time for a robot to move from one vertex to another and each task taking some given number of timesteps during the completion of which a robot must stay at the vertex containing the task. The goal is to determine a set of schedules, with one schedule for each robot, minimising the number of timesteps taken by the schedule taking the greatest number of timesteps within the set of schedules. We show that this problem is NP-complete for many simple classes of graphs, the problem of determining the fastest schedule, defined by the number of time steps required for a robot to visit every vertex in the schedule and complete every task assigned in its assigned schedule. Explicitly, we provide this result for complete graphs, bipartite graphs, star graphs, and planar graphs. Finally, we provide positive results for line graphs, showing that we can find an optimal set of schedules for $k$ robots completing $m$ tasks of equal length of a path of length $n$ in $O(kmn)$ time, and a $k$-approximation when the length of the tasks is unbounded.

cs.DS↗

arXiv2025

Anonymous Self-Stabilising Localisation via Spatial Population Protocols

In the distributed localization problem (DLP), $n$ anonymous robots (agents) $a_0, a_1, ..., a_{n-1}$ begin at arbitrary positions $p_0, ..., p_{n-1}$ in $S$, where $S$ is an Euclidean space. The primary goal in DLP is for agents to reach a consensus on a unified coordinate system that accurately reflects the relative positions of all points, $p_0, ..., p_{n-1}$. Extensive research on DLP has primarily focused on the feasibility and complexity of achieving consensus when agents have limited access to inter-agent distances, often due to missing or imprecise data. In this paper, however, we examine a minimalist, computationally efficient model of distributed computing in which agents have access to all pairwise distances, if needed. Specifically, we introduce a novel variant of population protocols, referred to as the spatial population protocols model. In this variant each agent can memorise one or a fixed number of coordinates, and when agents $a_i$ and $a_j$ interact, they can not only exchange their current knowledge but also either determine the distance $d(i,j)$ between them in $S$ (distance query model) or obtain the vector $v(i,j)$ spanning points $p_i$ and $p_j$ (vector query model). We propose several localisation protocols, including: (1) Two leader-based protocols with distance queries, stabilizing silently in $o(n)$ time using an efficient multi-contact epidemic, a generalization of the one-way epidemic in population protocols; (2) A distance-based protocol self-stabilizing silently in $O(n(\log n/n)^{1/(k+1)}\log n)$ time in $k$-dimensions, leveraging a leader election mechanism; (3) An optimally fast protocol with vector queries, self-stabilizing silently in $O(\log n)$ time.

cs.DC↗

arXiv2022

New Clocks, Optimal Line Formation and Self-Replication Population Protocols

In this paper we consider a variant of population protocols in which agents are allowed to be connected by edges, known as the constructors model. During an interaction between two agents the relevant connecting edge can be formed, maintained or eliminated by the transition function. The contributions of this paper are manifold. -- We propose and analyse a novel type of phase clocks allowing to count parallel time $Θ(n\log n)$ in the constructors model. This new type of clocks can be also implemented in the standard population protocol model assuming a unique leader is available. -- The new clock enables an optimal $O(n\log n)$ parallel time spanning line construction which improves dramatically on the best previously known $O(n^2)$ parallel time solution. -- We define a probabilistic version of bubble-sort in which random comparisons are allowed only between adjacent numbers in the sequence being sorted. We show that rather surprisingly this probabilistic bubble-sort requires $O(n^2)$ comparisons in expectation, i.e., on the same level as its deterministic counterpart. -- We propose the first self-replication protocol allowing to reproduce a strand (line-segment carrying information) of length $k$ in parallel time $O(n(k+\log n)).$ This result is based on the probabilistic bubble-sort argument. This protocol permits also simultaneous replication where $l$ copies of the strand can be obtained in time $O(n(k+\log n)\log l).$ All protocols in this paper operate with high probability.

cs.DC↗

arXiv2022

A spectral algorithm for finding maximum cliques in dense random intersection graphs

In a random intersection graph $G_{n,m,p}$, each of $n$ vertices selects a random subset of a set of $m$ labels by including each label independently with probability $p$ and edges are drawn between vertices that have at least one label in common. Among other applications, such graphs have been used to model social networks, in which individuals correspond to vertices and various features (e.g. ideas, interests) correspond to labels; individuals sharing at least one common feature are connected and this is abstracted by edges in random intersection graphs. In this paper, we consider the problem of finding maximum cliques when the input graph is $G_{n,m,p}$. Current algorithms for this problem are successful with high probability only for relatively sparse instances, leaving the dense case mostly unexplored. We present a spectral algorithm for finding large cliques that processes vertices according to respective values in the second largest eigenvector of the adjacency matrix of induced subgraphs of the input graph corresponding to common neighbors of small cliques. Leveraging on the Single Label Clique Theorem from [15], we were able to construct random instances, without the need to externally plant a large clique in the input graph. In particular, we used label choices to determine the maximum clique and then concealed label information by just giving the adjacency matrix of $G_{n, m, p}$ as input to the algorithm. Our experimental evaluation showed that our spectral algorithm clearly outperforms existing polynomial time algorithms, both with respect to the failure probability and the approximation guarantee metrics, especially in the dense regime, thus suggesting that spectral properties of random intersection graphs may be also used to construct efficient algorithms for other NP-hard graph theoretical problems as well.

cs.DM↗

arXiv2021

MAX CUT in Weighted Random Intersection Graphs and Discrepancy of Sparse Random Set Systems

Let $V$ be a set of $n$ vertices, ${\cal M}$ a set of $m$ labels, and let $\mathbf{R}$ be an $m \times n$ matrix of independent Bernoulli random variables with success probability $p$. A random instance $G(V,E,\mathbf{R}^T\mathbf{R})$ of the weighted random intersection graph model is constructed by drawing an edge with weight $[\mathbf{R}^T\mathbf{R}]_{v,u}$ between any two vertices $u,v$ for which this weight is larger than 0. In this paper we study the average case analysis of Weighted Max Cut, assuming the input is a weighted random intersection graph, i.e. given $G(V,E,\mathbf{R}^T\mathbf{R})$ we wish to find a partition of $V$ into two sets so that the total weight of the edges having one endpoint in each set is maximized. We initially prove concentration of the weight of a maximum cut of $G(V,E,\mathbf{R}^T\mathbf{R})$ around its expected value, and then show that, when the number of labels is much smaller than the number of vertices, a random partition of the vertices achieves asymptotically optimal cut weight with high probability (whp). Furthermore, in the case $n=m$ and constant average degree, we show that whp, a majority type algorithm outputs a cut with weight that is larger than the weight of a random cut by a multiplicative constant strictly larger than 1. Then, we highlight a connection between the computational problem of finding a weighted maximum cut in $G(V,E,\mathbf{R}^T\mathbf{R})$ and the problem of finding a 2-coloring with minimum discrepancy for a set system $Σ$ with incidence matrix $\mathbf{R}$. We exploit this connection by proposing a (weak) bipartization algorithm for the case $m=n, p=\frac{Θ(1)}{n}$ that, when it terminates, its output can be used to find a 2-coloring with minimum discrepancy in $Σ$. Finally, we prove that, whp this 2-coloring corresponds to a bipartition with maximum cut-weight in $G(V,E,\mathbf{R}^T\mathbf{R})$.

cs.DM↗

arXiv2021

An extension of the Moran process using type-specific connection graphs

The Moran process, as studied by [Lieberman, E., Hauert, C. and Nowak, M. Evolutionary dynamics on graphs. Nature 433, pp. 312-316 (2005)], is a stochastic process modeling the spread of genetic mutations in populations. In this process, agents of a two-type population (i.e. mutants and residents) are associated with the vertices of a graph. Initially, only one vertex chosen uniformly at random is a mutant, with fitness $r > 0$, while all other individuals are residents, with fitness $1$. In every step, an individual is chosen with probability proportional to its fitness, and its state (mutant or resident) is passed on to a neighbor which is chosen uniformly at random. In this paper, we introduce and study a generalization of the model of Lieberman et al. by assuming that different types of individuals perceive the population through different graphs, namely $G_R(V,E_R)$ for residents and $G_M(V,E_M)$ for mutants. In this model, we study the fixation probability, i.e. the probability that eventually only mutants remain in the population, for various pairs of graphs. First, we transfer known results from the original single-graph model of Lieberman et al. to our 2-graph model. Among them, we provide a generalization of the Isothermal Theorem of Lieberman et al., that gives sufficient conditions for a pair of graphs to have the same fixation probability as a pair of cliques. Next, we give a 2-player strategic game view of the process where player payoffs correspond to fixation and/or extinction probabilities. In this setting, we attempt to identify best responses for each player and give evidence that the clique is the most beneficial graph for both players. Finally, we examine the possibility of efficient approximation of the fixation probability and provide a FPRAS for the special case where the mutant graph is complete.

q-bio.PE↗

arXiv2021

On the undecidability of the Panopticon detection problem

The Panopticon (which means "watcher of everything") is a well-known structure of continuous surveillance and discipline proposed by Bentham in 1785. This device was, later, used by Foucault and other philosophers as a paradigm and metaphor for the study of constitutional power and knowledge as well as a model of individuals' deprivation of freedom. Nowadays, technological achievements have given rise to new, non-physical (unlike prisons), means of constant surveillance that transcend physical boundaries. This, combined with the confession of some governmental institutions that they actually collaborate with these Internet giants to collect or deduce information about people, creates a worrisome situation of several co-existing Panopticons that can act separately or in close collaboration. Thus, they can only be detected and identified through the expense of (perhaps considerable) effort. In this paper we provide a theoretical framework for studying the detectability status of Panopticons that fall under two theoretical, but not unrealistic, definitions. We show, using Oracle Turing Machines, that detecting modern day, ICT-based, Panopticons is an undecidable problem. Furthermore, we show that for each sufficiently expressive formal system, we can effectively construct a Turing Machine for which it is impossible to prove, within the formal system, either that it is a Panopticon or it is not a Panopticon.

cs.CR↗

arXiv2020

On the Distributed Construction of Stable Networks in Polylogarithmic Parallel Time

We study the class of networks which can be created in polylogarithmic parallel time by network constructors: groups of anonymous agents that interact randomly under a uniform random scheduler with the ability to form connections between each other. Starting from an empty network, the goal is to construct a stable network which belongs to a given family. We prove that the class of trees where each node has any k >= 2 children can be constructed in O(log n) parallel time with high probability. We show that constructing networks which are k-regular is Omega(n) time, but a minimal relaxation to (l, k)-regular networks, where l = k - 1 can be constructed in polylogarithmic parallel time for any fixed k, where k > 2. We further demonstrate that when the finite-state assumption is relaxed and k is allowed to grow with n, then k = log log n acts as a threshold above which network construction is again polynomial time. We use this to provide a partial characterisation of the class of polylogarithmic time network constructors.

cs.DC↗

arXiv2018

Cover Time in Edge-Uniform Stochastically-Evolving Graphs

We define a general model of stochastically-evolving graphs, namely the \emph{Edge-Uniform Stochastically-Evolving Graphs}. In this model, each possible edge of an underlying general static graph evolves independently being either alive or dead at each discrete time step of evolution following a (Markovian) stochastic rule. The stochastic rule is identical for each possible edge and may depend on the past $k \ge 0$ observations of the edge's state. We examine two kinds of random walks for a single agent taking place in such a dynamic graph: (i) The \emph{Random Walk with a Delay} (\emph{RWD}), where at each step the agent chooses (uniformly at random) an incident possible edge, i.e., an incident edge in the underlying static graph, and then it waits till the edge becomes alive to traverse it. (ii) The more natural \emph{Random Walk on what is Available} (\emph{RWA}) where the agent only looks at alive incident edges at each time step and traverses one of them uniformly at random. Our study is on bounding the \emph{cover time}, i.e., the expected time until each node is visited at least once by the agent. For \emph{RWD}, we provide a first upper bound for the cases $k = 0, 1$ by correlating \emph{RWD} with a simple random walk on a static graph. Moreover, we present a modified electrical network theory capturing the $k = 0$ case. For \emph{RWA}, we derive some first bounds for the case $k = 0$, by reducing \emph{RWA} to an \emph{RWD}-equivalent walk with a modified delay. Further, we also provide a framework, which is shown to compute the exact value of the cover time for a general family of stochastically-evolving graphs in exponential time. Finally, we conduct experiments on the cover time of \emph{RWA} in Edge-Uniform graphs and compare the experimental findings with our theoretical bounds.

cs.DC↗

arXiv2017

Learning Powers of Poisson Binomial Distributions

We introduce the problem of simultaneously learning all powers of a Poisson Binomial Distribution (PBD). A PBD of order $n$ is the distribution of a sum of $n$ mutually independent Bernoulli random variables $X_i$, where $\mathbb{E}[X_i] = p_i$. The $k$'th power of this distribution, for $k$ in a range $[m]$, is the distribution of $P_k = \sum_{i=1}^n X_i^{(k)}$, where each Bernoulli random variable $X_i^{(k)}$ has $\mathbb{E}[X_i^{(k)}] = (p_i)^k$. The learning algorithm can query any power $P_k$ several times and succeeds in learning all powers in the range, if with probability at least $1- δ$: given any $k \in [m]$, it returns a probability distribution $Q_k$ with total variation distance from $P_k$ at most $ε$. We provide almost matching lower and upper bounds on query complexity for this problem. We first show a lower bound on the query complexity on PBD powers instances with many distinct parameters $p_i$ which are separated, and we almost match this lower bound by examining the query complexity of simultaneously learning all the powers of a special class of PBD's resembling the PBD's of our lower bound. We study the fundamental setting of a Binomial distribution, and provide an optimal algorithm which uses $O(1/ε^2)$ samples. Diakonikolas, Kane and Stewart [COLT'16] showed a lower bound of $Ω(2^{1/ε})$ samples to learn the $p_i$'s within error $ε$. The question whether sampling from powers of PBDs can reduce this sampling complexity, has a negative answer since we show that the exponential number of samples is inevitable. Having sampling access to the powers of a PBD we then give a nearly optimal algorithm that learns its $p_i$'s. To prove our two last lower bounds we extend the classical minimax risk definition from statistics to estimating functions of sequences of distributions.

cs.DS↗

arXiv2017

A 3-player protocol preventing persistence in strategic contention with limited feedback

In this paper, we study contention resolution protocols from a game-theoretic perspective. In a recent work, we considered acknowledgment-based protocols, where a user gets feedback from the channel only when she attempts transmission. In this case she will learn whether her transmission was successful or not. One of the main results of ESA2016 was that no acknowledgment-based protocol can be in equilibrium. In fact, it seems that many natural acknowledgment-based protocols fail to prevent users from unilaterally switching to persistent protocols that always transmit with probability 1. It is therefore natural to ask how powerful a protocol must be so that it can beat persistent deviators. In this paper we consider age-based protocols, which can be described by a sequence of probabilities of transmitting in each time step. Those probabilities are given beforehand and do not change based on the transmission history. We present a 3-player age-based protocol that can prevent users from unilaterally deviating to a persistent protocol in order to decrease their expected transmission time. It is worth noting that the answer to this question does not follow from the results and proof ideas of ESA2016. Our protocol is non-trivial, in the sense that, when all players use it, finite expected transmission time is guaranteed. In fact, we show that this protocol is preferable to any deadline protocol in which, after some fixed time, attempt transmission with probability 1 in every subsequent step. An advantage of our protocol is that it is very simple to describe, and users only need a counter to keep track of time. Whether there exist $n$-player age-based protocols that do not use counters and can prevent persistence is left as an open problem for future research.

cs.GT↗

arXiv2017

Existence of Evolutionarily Stable Strategies Remains Hard to Decide for a Wide Range of Payoff Values

The concept of an evolutionarily stable strategy (ESS), introduced by Smith and Price, is a refinement of Nash equilibrium in 2-player symmetric games in order to explain counter-intuitive natural phenomena, whose existence is not guaranteed in every game. The problem of deciding whether a game possesses an ESS has been shown to be $Σ_{2}^{P}$-complete by Conitzer using the preceding important work by Etessami and Lochbihler. The latter, among other results, proved that deciding the existence of ESS is both NP-hard and coNP-hard. In this paper we introduce a "reduction robustness" notion and we show that deciding the existence of an ESS remains coNP-hard for a wide range of games even if we arbitrarily perturb within some intervals the payoff values of the game under consideration. In contrast, ESS exist almost surely for large games with random and independent payoffs chosen from the same distribution.

cs.CC↗

arXiv2016

Strategic Contention Resolution with Limited Feedback

In this paper, we study contention resolution protocols from a game-theoretic perspective. We focus on \emph{acknowledgment-based} protocols, where a user gets feedback from the channel only when she attempts transmission. In this case she will learn whether her transmission was successful or not. Users that do not transmit will not receive any feedback. We are interested in equilibrium protocols, where no player has an incentive to deviate. The limited feedback makes the design of equilibrium protocols a hard task as best response policies usually have to be modeled as Partially Observable Markov Decision Processes, which are hard to analyze. Nevertheless, we show how to circumvent this for the case of two players and present an equilibrium protocol. For many players, we give impossibility results for a large class of acknowledgment-based protocols, namely \emph{age-based} and \emph{backoff} protocols with finite expected finishing time. Finally, we provide an age-based equilibrium protocol, which has infinite expected finishing time, but every player finishes in linear time with high probability.

cs.GT↗

arXiv2016

Lipschitz Continuity and Approximate Equilibria

In this paper, we study games with continuous action spaces and non-linear payoff functions. Our key insight is that Lipschitz continuity of the payoff function allows us to provide algorithms for finding approximate equilibria in these games. We begin by studying Lipschitz games, which encompass, for example, all concave games with Lipschitz continuous payoff functions. We provide an efficient algorithm for computing approximate equilibria in these games. Then we turn our attention to penalty games, which encompass biased games and games in which players take risk into account. Here we show that if the penalty function is Lipschitz continuous, then we can provide a quasi-polynomial time approximation scheme. Finally, we study distance biased games, where we present simple strongly polynomial time algorithms for finding best responses in $L_1$, $L_2^2$, and $L_\infty$ biased games, and then use these algorithms to provide strongly polynomial algorithms that find $2/3$, $5/7$, and $2/3$ approximations for these norms, respectively.

cs.GT↗

arXiv2014

Computing Approximate Nash Equilibria in Polymatrix Games

In an $ε$-Nash equilibrium, a player can gain at most $ε$ by unilaterally changing his behaviour. For two-player (bimatrix) games with payoffs in $[0,1]$, the best-known$ε$ achievable in polynomial time is 0.3393. In general, for $n$-player games an $ε$-Nash equilibrium can be computed in polynomial time for an $ε$ that is an increasing function of $n$ but does not depend on the number of strategies of the players. For three-player and four-player games the corresponding values of $ε$ are 0.6022 and 0.7153, respectively. Polymatrix games are a restriction of general $n$-player games where a player's payoff is the sum of payoffs from a number of bimatrix games. There exists a very small but constant $ε$ such that computing an $ε$-Nash equilibrium of a polymatrix game is \PPAD-hard. Our main result is that a $(0.5+δ)$-Nash equilibrium of an $n$-player polymatrix game can be computed in time polynomial in the input size and $\frac{1}δ$. Inspired by the algorithm of Tsaknakis and Spirakis, our algorithm uses gradient descent on the maximum regret of the players. We also show that this algorithm can be applied to efficiently find a $(0.5+δ)$-Nash equilibrium in a two-player Bayesian game.

cs.GT↗