Dynamics of Causal Dependencies in Multi-agent Settings

Maksim Gladyshev*, Natasha Alechina, Mehdi Dastani, Dragan Doder

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Article in proceedingAcademicpeer-review

Abstract

In this paper we discuss how causal models can be used for modeling multi-agent interaction in complex organizational settings, where agents’ decisions may depend on other agents’ decisions as well as the environment. We demonstrate how to reason about the dynamics of such models using concurrent game structures where agents can change the organisational setting and thereby their decision dependencies. In such concurrent game structure, agents can choose to modify their reactions on other agents’ decisions and on the environment by intervening on their part of a causal model. We propose a generalized notion of interventions in causal models that allow us to model and reason about the dynamics of agents’ dependencies in a multi-agent system. Finally, we discuss how to model uncertainty and reason about agents’ responsibility concerning their dependencies and thereby their choices.

Original languageEnglish
Title of host publicationEngineering Multi-Agent Systems - 11th International Workshop, EMAS 2023, Revised Selected Papers
EditorsAndrei Ciortea, Mehdi Dastani, Jieting Luo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages95-112
Number of pages18
ISBN (Print)9783031485381
DOIs
Publication statusPublished - 2023
Event11th International Workshop on Engineering Multi-Agent Systems, EMAS 2023 - London, United Kingdom
Duration: 29 May 202330 May 2023

Publication series

SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14378 LNAI
ISSN0302-9743

Conference

Conference11th International Workshop on Engineering Multi-Agent Systems, EMAS 2023
Abbreviated titleEMAS 2023
Country/TerritoryUnited Kingdom
CityLondon
Period29/05/2330/05/23

Keywords

  • Causal models
  • Interventions
  • Multi-agent systems

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