arXiv subjects

Sophia Knight

Publications and source records attributed to Sophia Knight.

14 recordsLinked to original sources

arXiv2026

An Epistemic Analysis of Random Coordinated Attack

The coordinated attack problem models the challenge of coordinating a joint action within a bounded time by communicating over unreliable links. It was the first distributed computing problem proven unsolvable. Its analysis also revealed the importance of common knowledge, a central concept in epistemic logic. However, the randomized version of coordinated attack, which is solvable, has not, to the best of our knowledge, been studied through the lens of probabilistic epistemic logic, where processes generate randomness by flipping coins. We present an epistemic logic framework for studying randomized algorithms that execute for a bounded number of rounds. The framework applies to coordinated attack, approximate agreement, and consensus, and supports dynamic graph models: synchronous systems in which reliable processes execute a bounded number of rounds while an adversary determines which messages are lost. Our approach combines techniques from the logical characterization of dynamic networks and task solvability with ideas from probabilistic dynamic epistemic logic. It is inspired by the operational model of Varghese and Lynch for randomized coordinated attack. More broadly, the resulting notion of probabilistic epistemic task solvability provides a foundation for the epistemic study of randomized distributed computation. Using this framework, we analyze the Varghese-Lynch algorithm from a knowledge-theoretic perspective, providing a formal treatment of the algorithm and its lower bound. As a byproduct, we strengthen the lower bound and show it is tight. The proof relies on indistinguishability arguments, demonstrating that reasoning about knowledge remains essential in the probabilistic setting. We also formalize the notion of information level introduced by Varghese and Lynch, showing that it corresponds to a specific epistemic formula.

cs.DC↗

arXiv2024

Opinion Update in a Subjective Logic Model for Social Networks

Subjective Logic (SL) is a logic incorporating uncertainty and opinions for agents in dynamic systems. In this work, we investigate the use of subjective logic to model opinions and belief change in social networks. In particular, we work toward the development of a subjective logic belief/opinion update function appropriate for modeling belief change as communication occurs in social networks. We found through experiments that an update function with belief fusion from SL does not have ideal properties to represent a rational update. Even without these properties, we found that an update function with cumulative belief fusion can describe behaviors not explored by the social network model defined by Alvim, Knight, and Valencia (2019).

cs.MA↗

arXiv2024

A Multi-Agent Model for Opinion Evolution under Cognitive Biases

We generalize the DeGroot model for opinion dynamics to better capture realistic social scenarios. We introduce a model where each agent has their own individual cognitive biases. Society is represented as a directed graph whose edges indicate how much agents influence one another. Biases are represented as the functions in the square region $[-1,1]^2$ and categorized into four sub-regions based on the potential reactions they may elicit in an agent during instances of opinion disagreement. Under the assumption that each bias of every agent is a continuous function within the region of receptive but resistant reactions ($\mathbf{R}$), we show that the society converges to a consensus if the graph is strongly connected. Under the same assumption, we also establish that the entire society converges to a unanimous opinion if and only if the source components of the graph-namely, strongly connected components with no external influence-converge to that opinion. We illustrate that convergence is not guaranteed for strongly connected graphs when biases are either discontinuous functions in $\mathbf{R}$ or not included in $\mathbf{R}$. We showcase our model through a series of examples and simulations, offering insights into how opinions form in social networks under cognitive biases.

cs.MA↗

arXiv2023

Truth Set Algebra: A New Way to Prove Undefinability

The article proposes a new technique for proving the undefinability of logical connectives through each other and illustrates the technique with several examples. Some of the obtained results are new proofs of the existing theorems, others are original to this work.

cs.AI↗

arXiv2023

A Formal Model for Polarization under Confirmation Bias in Social Networks

We describe a model for polarization in multi-agent systems based on Esteban and Ray's standard family of polarization measures from economics. Agents evolve by updating their beliefs (opinions) based on an underlying influence graph, as in the standard DeGroot model for social learning, but under a confirmation bias; i.e., a discounting of opinions of agents with dissimilar views. We show that even under this bias polarization eventually vanishes (converges to zero) if the influence graph is strongly-connected. If the influence graph is a regular symmetric circulation, we determine the unique belief value to which all agents converge. Our more insightful result establishes that, under some natural assumptions, if polarization does not eventually vanish then either there is a disconnected subgroup of agents, or some agent influences others more than she is influenced. We also prove that polarization does not necessarily vanish in weakly-connected graphs under confirmation bias. Furthermore, we show how our model relates to the classic DeGroot model for social learning. We illustrate our model with several simulations of a running example about polarization over vaccines and of other case studies. The theoretical results and simulations will provide insight into the phenomenon of polarization.

cs.LO↗

arXiv2021

A Multi-Agent Model for Polarization under Confirmation Bias in Social Networks

We describe a model for polarization in multi-agent systems based on Esteban and Ray's standard measure of polarization from economics. Agents evolve by updating their beliefs (opinions) based on an underlying influence graph, as in the standard DeGroot model for social learning, but under a confirmation bias; i.e., a discounting of opinions of agents with dissimilar views. We show that even under this bias polarization eventually vanishes (converges to zero) if the influence graph is strongly-connected. If the influence graph is a regular symmetric circulation, we determine the unique belief value to which all agents converge. Our more insightful result establishes that, under some natural assumptions, if polarization does not eventually vanish then either there is a disconnected subgroup of agents, or some agent influences others more than she is influenced. We also show that polarization does not necessarily vanish in weakly-connected graphs under confirmation bias. We illustrate our model with a series of case studies and simulations, and show how it relates to the classic DeGroot model for social learning.

cs.LO↗

arXiv2021

Algebraic Structures from Concurrent Constraint Programming Calculi for Distributed Information in Multi-Agent Systems

Spatial constraint systems (scs) are semantic structures for reasoning about spatial and epistemic information in concurrent systems. We develop the theory of scs to reason about the distributed information of potentially infinite groups. We characterize the notion of distributed information of a group of agents as the infimum of the set of join-preserving functions that represent the spaces of the agents in the group. We provide an alternative characterization of this notion as the greatest family of join-preserving functions that satisfy certain basic properties. For completely distributive lattices, we establish that distributed information of a group is the greatest information below all possible combinations of information in the spaces of the agents in the group that derive a given piece of information. We show compositionality results for these characterizations and conditions under which information that can be obtained by an infinite group can also be obtained by a finite group. Finally, we provide an application on mathematical morphology where dilations, one of its fundamental operations, define an scs on a powerset lattice. We show that distributed information represents a particular dilation in such scs.

cs.MA↗

arXiv2020

Polarization and Belief Convergence of Agents in Strongly-Connected Influence Graphs

We describe a model for polarization in multi-agent systems based on Esteban and Ray's classic measure of polarization from economics. Agents evolve by updating their beliefs (opinions) based on the beliefs of others and an underlying influence graph. We show that polarization eventually disappears (converges to zero) if the influence graph is strongly-connected. If the influence graph is a circulation we determine the unique belief value all agents converge to. For clique influence graphs we determine the time after which agents will reach a given difference of opinion. Our results imply that if polarization does not disappear then either there is a disconnected subgroup of agents or some agent influences others more than she is influenced. Finally, we show that polarization does not necessarily vanish in weakly-connected graphs, and illustrate the model with a series of case studies and simulations giving some insights about polarization.

cs.MA↗

arXiv2019

Dealing with imperfect information in Strategy Logic

We propose an extension of Strategy Logic (SL), in which one can both reason about strategizing under imperfect information and about players' knowledge. One original aspect of our approach is that we do not force strategies to be uniform, i.e. consistent with the players' information, at the semantic level; instead, one can express in the logic itself that a strategy should be uniform. To do so, we first develop a "branching-time" version of SL with perfect information, that we call BSL, in which one can quantify over the different outcomes defined by a partial assignment of strategies to the players; this contrasts with SL, where temporal operators are allowed only when all strategies are fixed, leaving only one possible play. Next, we further extend BSL by adding distributed knowledge operators, the semantics of which rely on equivalence relations on partial plays. The logic we obtain subsumes most strategic logics with imperfect information, epistemic or not.

cs.LO↗

arXiv2018

Proceedings 11th Interaction and Concurrency Experience

This volume contains the proceedings of ICE'18, the 11th Interaction and Concurrency Experience, which was held in Madrid, Spain on the 20th and 21st of June 2018 as a satellite event of DisCoTec'18. The ICE workshop series features a distinguishing review and selection procedure, allowing PC members to interact anonymously with authors. As in the past ten editions, this interaction considerably improved the accuracy of the feedback from the reviewers and the quality of accepted papers, and offered the basis for lively discussion during the workshop. For the second time, the 2018 edition of ICE included double blind reviewing of original research papers, in order to increase fairness and avoid bias in reviewing. Each paper was reviewed by three PC members, and altogether six papers were accepted for publication (the workshop also featured four oral presentations which are not part of this volume). We were proud to host three invited talks, by Elvira Albert, Silvia Crafa, and Alexey Gotsman. The abstracts of these talks are included in this volume together with the regular papers. Final versions of the contributions, taking into account the discussion at the workshop, are included.

cs.LO↗

arXiv2017

Proceedings 10th Interaction and Concurrency Experience

This volume contains the proceedings of ICE 2017, the 10th Interaction and Concurrency Experience, which was held in Neuchâtel, Switzerland on the 21st and 22nd of June 2017 as a satellite event of DisCoTec'17. The ICE procedure for paper selection allows PC members to interact, anonymously, with authors. During the review phase, each submitted paper is published on a discussion forum whose access is restricted to the authors and to all the PC members not declaring a conflict of interest. The PC members post comments and questions that the authors reply to. For the first time, the 2017 edition of ICE included a double blind reviewing of original research papers, in order to increase fairness and avoid bias in reviewing. The gender balance of accepted papers was also more even than in past years. Each paper was reviewed by three PC members, and altogether five papers were accepted for publication (the workshop also featured five brief announcements which are not part of this volume). We were proud to host three invited talks, by Christian Cachin, Marieke Huismann, and Paweł Sobocinski. The abstracts of these talks are included in this volume together with the regular papers.

cs.LO↗

arXiv2016

Proceedings 9th Interaction and Concurrency Experience

This volume contains the proceedings of ICE 2016, the 9th Interaction and Concurrency Experience, which was held in Heraklion, Greece on the 8th and 9th of June 2016 as a satellite event of DisCoTec 2016. The ICE procedure for paper selection allows PC members to interact, anonymously, with authors. During the review phase, each submitted paper is published on a discussion forum whose access is restricted to the authors and to all the PC members not declaring a conflict of interest. The PC members post comments and questions that the authors reply to. For the first time, the 2016 edition of ICE included a feature targeting review transparency: reviews of accepted papers were made public on the workshop website and workshop participants in particular were able to access them during the workshop. Each paper was reviewed by three PC members, and altogether nine papers were accepted for publication (the workshop also featured three brief announcements which are not part of this volume). We were proud to host two invited talks, by Alexandra Silva and Uwe Nestmann. The abstracts of these two talks are included in this volume together with the regular papers.

cs.PL↗

arXiv2016

Announcement as effort on topological spaces

We propose a multi-agent logic of knowledge, public and arbitrary announcements, that is interpreted on topological spaces in the style of subset space semantics. The arbitrary announcement modality functions similarly to the effort modality in subset space logics, however, it comes with intuitive and semantic differences. We provide axiomatizations for three logics based on this setting, and demonstrate their completeness.

cs.LO↗

arXiv2015

Proceedings 8th Interaction and Concurrency Experience

This volume contains the proceedings of ICE 2015, the 8th Interaction and Concurrency Experience, which was held in Grenoble, France on the 4th and 5th of June 2015 as a satellite event of DisCoTec 2015. The ICE procedure for paper selection allows PC members to interact, anonymously, with authors. During the review phase, each submitted paper is published on a discussion forum with access restricted to the authors and to all the PC members not declaring a conflict of interest. The PC members post comments and questions to which the authors reply. Each paper was reviewed by three PC members, and altogether 9 papers, including 1 short paper, were accepted for publication (the workshop also featured 4 brief announcements which are not part of this volume). We were proud to host three invited talks, by Leslie Lamport (shared with the FRIDA workshop), Joseph Sifakis and Steve Ross-Talbot. The abstracts of the last two talks are included in this volume together with the regular papers.

cs.PL↗