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Signed Relations and Structural Balance in Complex Systems: From Data to Models

English title Signed Relations and Structural Balance in Complex Systems: From Data to Models
Applicant Schweitzer Frank
Number 192746
Funding scheme Project funding (Div. I-III)
Research institution Departement Management, Technologie und Ökonomie D-MTEC ETH Zürich
Institution of higher education ETH Zurich - ETHZ
Main discipline Communication sciences
Start/End 01.10.2020 - 30.09.2023
Approved amount 355'660.00
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All Disciplines (2)

Discipline
Communication sciences
Other disciplines of Physics

Keywords (5)

Structural Balance; Data-driven modeling; Network Science; Dynamic Social Networks; Complex systems

Lay Summary (German)

Lead
Wir betrachten Systeme aus einer Vielzahl von Agenten ("complex system"), zum Beispiel ein soziales System aus Individuenzwischen denen Verbindungen bestehen ("complex network"). Das Vorzeichen einer Verbindung ("signed relation") ist Ausdruck der sozialen Beziehung zwischen diesen Individuen, zum Beispiel Freundschaft oder Feindschaft. Die strukturelle Balance bezeichnet einen Zustand, in dem Beziehungskonflikte zwischen Agenten minimiert werden ("der Freund meines Freundes sollte nicht mein Feind sein"). Das gelingt nicht immer widerspruchsfrei, so dass solche Systeme keinen Gleichgewichtszustand erreichen können. Das macht sie für die Forschung interessant, denn die Versuche, Konflikte zu minimieren, treiben die Evolution solcher Systeme voran.
Lay summary

Das Ziel des Projektes ist es, Agenten-basierte Modelle so zu entwickeln, dass sie mit Hilfe von Daten kalibriert und hinsichtlich ihrer Systemdynamik validiert werden können. Die schwierigste Aufgabe besteht darin, aus vorhandenen Daten über die Interaktion von Individuen Informationen über deren Beziehungen  zu gewinnen. Dazu benötigen wir ein Netzwerk-Modell, welches uns Hypothesen über diese Beziehungen generiert. Wenn die "signed relations" aus den Daten extrahiert wurden, können Masse für die strukturelle Balance des Systems entwickelt und deren Zeitverlauf studiert werden.

Das Projekt liefert einen Beitrag, um die Minimierung von Konflikten, aber auch Polarisation in sozialen Systemen besser zu verstehen. Strukturelle Ungleichgewichte beeinträchtigen die Funktionsweise komplexer Systeme.
Wir stellen Methoden bereit, um Informationen über diese Ungleichheiten aus Daten zu gewinnen, und Modelle, um ihre Entwicklung zu verstehen.

Direct link to Lay Summary Last update: 04.11.2020

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Abstract

According to the theory of structural balance, interacting systems balance the (positive or negative) relations between different system elements such that local conflicts are minimized. Hence, structural imbalances induce a dynamics to resolve such conflicts. This dynamics plays a vital role in evolutionary processes because a multitude of possible solutions exists. At the same time, if these solutions cannot be reached, this can hamper the functionality of systems. This general problem also occurs in social systems, where instead of a more balanced state, for instance, the polarization of opinions emerges. Are we able to address this problem from a formal perspective? Do we have data available to study it in real systems? Can we develop models that help us to understand when structural balance fails, and how it can be mitigated?Data about interactions between system elements (agents) is ubiquitous. This applies particularly to interactions between individuals, thanks to new communication technologies, sensor recordings, and online interactions. To analyze, to model and to interpret these data, however, is one of the biggest challenges for data science. Instead of applying existing method- ologies to just another data set, we need to implement system specific concepts. Such problems do not just concern data analysis, they pertain even more to the modeling of such systems.This proposal makes an important step forward, by addressing these methodological challenges. To understand structural balance, the differences between many-particle systems and socio-economic multi-agent systems need to be addressed. While in physical systems with given spin-spin interactions, spins try to align such that the local frustration is minimized, in social systems agents have the opportunity to also change the sign of their relations, to obtain a better balanced state. But how do we know about their signed relations, for example their friend-or-foe relationship? The data in almost all cases records only the observed interactions, but to model and to understand the problem of structural balance, we need their relations. Therefore, in this project, we first solve the methodological problem of inferring signed relations from interactions by developing a novel statistical approach to analyze such data.Structural balance theory provides hypotheses of how unbalanced relations are settled that have been hardly tested for real-world systems. To investigate how structural balance evolves over time, we have to solve the problem that agents’ attributes, relations and interactions change on different time scales and therefore need to be identified and separated. We will further develop a new agent-based model (ABM) that considers the co-evolution of attributes and signed relations of agents, to understand how these constrain their interactions. Through a novel calibration-validation procedure developed in this project, the formal ABM will then be turned into a data-driven model. It takes data from real-world systems as input to reproduce their dynamics of structural balance. This way, we produce new data sets about the evolution of signed relations in social systems and at the same time, also provide a calibrated and validated interaction model to understand how structural balance evolves.These insights will help to understand the problem of structural balance in a comprehensive and novel manner. This concerns the theoretical perspective, where we provide methodologies to link structural balance to the inference problem of signed relations and, subsequently, to observed interactions. It also regards the applied perspective, where we provide an interaction model calibrated and validated against real-world data. This opens new avenues towards research questions that were difficult to address before, like the co-evolution of agents and their relations or the impact of their attributes, i.e., their internal degrees of freedom, on the observed systemic properties. Eventually, the results of this project will allow to develop new research questions that build on an informed understanding of structural balance, to investigate its relation to the functionality and performance of systems.
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