Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2608.24251

Transition Systems from Causal Reversible Bundle Event Structures

Nataliya Gribovskaya·Irina Virbitskaite
View original source ↗Read source document ↗Export metadata ↓

Abstract

Reversible computing is a novel paradigm that has recently emerged and extends traditional forwards-only computation with the capability to execute in the reverse direction, making it possible for computation to run backwards as well as forwards. Event structures are a foundational model in concurrency theory, providing a way to understand computational processes by describing the events that occur and the relationships between them. In the literature, two structurally different approaches to associating transition system semantics with event structure models have been distinguished. One approach is based on configurations, which are sets of already executed events. The other approach is based on model residuals, which are not yet executed fragments of the model. Configuration-based transition systems appear to be primarily used for semantic representations. Residual-based transition systems are actively applied to demonstrate the consistency between operational and denotational semantics of concurrent process calculi, as well as to visualize the dynamics of models. The present paper focuses on bundle event structures with causal reversibility, which are used in the study of reversible extensions of CCS- and π-like systems. Mappings from the reversible event structure model to the two transition system semantics are developed, which made it possible to prove the isomorphism of the semantics. A category-theoretic characterization of the mappings is provided.

Explore related subjects

cs.GT

Keep this discovery

BibTeXRIS

Nataliya Gribovskaya, Irina Virbitskaite. 2026-08-25. Transition Systems from Causal Reversible Bundle Event Structures. https://doi.org/10.4204/eptcs.451.12

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

MMS Allocation for Chores with Online Agent Arrivals

We study the fair allocation of $m$ indivisible chores to $n$ agents with subadditive cost functions arriving online in an arbitrary order. Upon an agent's arrival, we are informed of her cost function and must irrevocably assign her a set of chores. We focus on the Maximin Share (MMS) fairness notion and aim to compute an allocation in which all items are assigned, and no agent incurs a cost more than $\alpha$ times her MMS. Without any prior information about the instance (other than $n$ and $m$), we design an algorithm with a competitive ratio of $O(\min\{n, k\log^{1+\epsilon}k, \log m\})$ for any constant $\epsilon > 0$, where $k$ denotes the number of cost function types. Our bound matches the best known offline approximation guarantees for MMS under subadditive costs and is nearly optimal with respect to all three parameters: we show that even for binary additive cost functions, no online algorithm can achieve a competitive ratio of $o(\min\{n, k\log k, \log m\})$. We then consider the setting in which the $k$ cost function types are known in advance (though the realized types of arriving agents are not). For additive cost functions, we provide an algorithm with a competitive ratio of $O(\min\{\log k, \log(kn)/\log\log(kn)\})$, and show that constant-competitive algorithms do not exist for general $k$, even for the binary additive setting. For binary additive functions when $k \le n$, we propose a $3$-competitive algorithm and establish a lower bound of $2$.

cs.GT↗
arXiv2026

Truncated Noisy Best-Response Algorithms: Toward Game Theoretic Learning with Safety Guarantees

We consider a game theoretic approach to solve multi-agent coordination problems with submodular maximization objectives. It is known for such problems that the Nash equilibria for the corresponding game are always within 50% of the optimal, but that the equilibria which achieve this worst-case bound are not stable. To exploit this instability, we propose a family of algorithms which we call Truncated Noisy Best-Response (TNBR) Algorithms. These algorithms are flexibly characterized by agents asynchronously and stochastically selecting actions from a neighbourhood of their best response payoffs. We compute bounds on the recurrent classes of TNBR algorithms' associated Markov chains. Our bounds fall into two categories: first, "Performance" bounds ensure that TNBR algorithms always have a high-value recurrent state; second, "Safety" bounds ensure that TNBR algorithms never have arbitrarily-bad recurrent states. Furthermore, these two types of bounds are linked by a waterbed-like effect: every game with a poor Safety guarantee necessarily has a favorable Performance guarantee.

cs.GT↗
arXiv2026

Existence of the Core in Approval-Based Committee Elections

We settle the main open question in the theory of approval-based multi-winner elections: we show that there always exists a committee in the core. The core is a stability and group fairness concept. The proof introduces a new voting rule that optimizes an entropy-like objective function over committees and payment systems. All local optima of this objective function lie in the core, which implies that a core committee can be found in polynomial time.

cs.GT↗