arXiv subjects

Frank Schweitzer

Publications and source records attributed to Frank Schweitzer.

At least 19 recordsLinked to original sources

arXiv2026

Resilience: Understand Breakdown, Foster Recovery, and Choose the Right Perspective

Resilience denotes the capacity of a system to withstand shocks and to recover from them. We distinguish between two different types of dynamics. The first allows for a separation between phases of normalcy and phases of rapid breakdown followed by slow recovery. The second applies to volatile organizations in which such phases are intertwined. Breakdown is often self-inflicted. Situation awareness is impaired by psychological mechanisms that lead to incorrect expectations regarding societal dynamics. Through positive feedback, the failure of a few elements is amplified into a failure cascade. However, positive feedback can also be harnessed to enable recovery. In volatile systems, resilience must be understood as an emergent property arising from the interaction of agents. This necessitates a data-driven approach to inform agent-based models, drawing on repositories, knowledge graphs, or tools from artificial intelligence. Such models help demonstrate that resilience represents a compromise between robustness and adaptivity. Maximizing performance frequently comes at the expense of resilience, and second-order solutions aimed at transforming the system prove more promising than attempts to reconstruct past conditions.

cs.SI↗

arXiv2026

Making Sense of Symbols: Yin and Yang in Zurich

The widely known Yin-Yang symbol (Taijitu) is based on nested circles of different radii whose areas are colored black and white such that the interface traces an $\mathcal{S}$-shaped curve. We address the question of how this symbol can be related to physical phenomena such as daytime and nighttime duration and the annual seasons. Using a simple dynamic model of daytime duration, we introduce the excess daytime fraction and reconstruct the symbol using the latitude of Zurich. In particular, we explain how the black and white areas are linked to the stability of Yin or Yang predominance. We further demonstrate that the Golden and Silver Ratios found in the geometry of the symbol carry meaning with respect to the Gregorian calendar. Finally, we construct an alternative Yin-Yang symbol using logarithmic spirals with the Golden Ratio as the growth parameter. The didactical quantitative derivation of the Yin-Yang symbol and its grounding in real-world observations can be regarded as a novel perspective on this iconic pattern.

physics.soc-ph↗

arXiv2026

How malicious AI swarms can threaten democracy: The fusion of agentic AI and LLMs marks a new frontier in information warfare

Advances in AI offer the prospect of manipulating beliefs and behaviors on a population-wide level. Large language models and autonomous agents now let influence campaigns reach unprecedented scale and precision. Generative tools can expand propaganda output without sacrificing credibility and inexpensively create falsehoods that are rated as more human-like than those written by humans. Techniques meant to refine AI reasoning, such as chain-of-thought prompting, can just as effectively be used to generate more convincing falsehoods. Enabled by these capabilities, a disruptive threat is emerging: swarms of collaborative, malicious AI agents. Fusing LLM reasoning with multi-agent architectures, these systems are capable of coordinating autonomously, infiltrating communities, and fabricating consensus efficiently. By adaptively mimicking human social dynamics, they threaten democracy. Because the resulting harms stem from design, commercial incentives, and governance, we prioritize interventions at multiple leverage points, focusing on pragmatic mechanisms over voluntary compliance.

cs.CY↗

arXiv2025

Active matter synchronization and synergetics

We study the collective behavior in a stochastic agent-based model of active matter. Provided a critical take-up of energy, agents produce two types of goods $x$, $y$ that follow a generalized Lotka-Volterra dynamics. For isolated agents, production would either reach a fixed point or diverge. Coupling agents' production via a mean field of $x$, however, can lead to synchronized oscillations if agents cooperate in the production of $x$. The production of $y$ supports the emergence of the synchronized dynamics by suppressing fluctuations and mitigating competition between agents, this way stabilizing the production of $x$. We find that in the synchronized state different groups of agents coexist, each following their own limit cycle. The Kuramoto order parameter is large within groups, and small across groups. The collective state is stable against shocks from agents temporarily switching between cooperation and competition. The model dynamics illustrates the principles of synergetics, i.e., the spontaneous emergence of order given a critical energy supply and cooperative interactions.

nlin.AO↗

arXiv2024

The nonlinear economy (I): How resource constrains lead to business cycles

We explore the nonlinear dynamics of a macroeconomic model with resource constraints. The dynamics is derived from a production function that considers capital and a generalized form of energy as inputs. Energy, the new variable, is depleted during the production process and has to be renewed, whereas capital grows with production and decreases from depreciation. Dependent on time scales and energy related control parameters, we obtain steady states of high or low production, but also sustained oscillations that show properties of business cycles. We also find conditions for the coexistence of stable fixed points and limit cycles. Our model allows to specify investment and saving functions for Kaldor's model of business cycles. We provide evidence for an endogenous origin of business cycles if depleting resources are taken into account.

econ.TH↗

arXiv2024

Data2Neo - A Tool for Complex Neo4j Data Integration

This paper introduces Data2Neo, an open-source Python library for converting relational data into knowledge graphs stored in Neo4j databases. With extensive customization options and support for continuous online data integration from various data sources, Data2Neo is designed to be user-friendly, efficient, and scalable to large datasets. The tool significantly lowers the barrier to entry for creating and using knowledge graphs, making this increasingly popular form of data representation accessible to a wider audience. The code is available at https://github.com/jkminder/data2neo .

cs.DB↗

arXiv2023

Locating Community Smells in Software Development Processes Using Higher-Order Network Centralities

Community smells are negative patterns in software development teams' interactions that impede their ability to successfully create software. Examples are team members working in isolation, lack of communication and collaboration across departments or sub-teams, or areas of the codebase where only a few team members can work on. Current approaches aim to detect community smells by analysing static network representations of software teams' interaction structures. In doing so, they are insufficient to locate community smells within development processes. Extending beyond the capabilities of traditional social network analysis, we show that higher-order network models provide a robust means of revealing such hidden patterns and complex relationships. To this end, we develop a set of centrality measures based on the MOGen higher-order network model and show their effectiveness in predicting influential nodes using five empirical datasets. We then employ these measures for a comprehensive analysis of a product team at the German IT security company genua GmbH, showcasing our method's success in identifying and locating community smells. Specifically, we uncover critical community smells in two areas of the team's development process. Semi-structured interviews with five team members validate our findings: while the team was aware of one community smell and employed measures to address it, it was not aware of the second. This highlights the potential of our approach as a robust tool for identifying and addressing community smells in software development teams. More generally, our work contributes to the social network analysis field with a powerful set of higher-order network centralities that effectively capture community dynamics and indirect relationships.

cs.SE↗

arXiv2023

Adapting to Disruptions: Flexibility as a Pillar of Supply Chain Resilience

Supply chain disruptions cause shortages of raw material and products. To increase resilience, i.e., the ability to cope with shocks, substituting goods in established supply chains can become an effective alternative to creating new distribution links. We demonstrate its impact on supply deficits through a detailed analysis of the US opioid distribution system. Reconstructing 40 billion empirical distribution paths, our data-driven model allows a unique inspection of policies that increase the substitution flexibility. Our approach enables policymakers to quantify the trade-off between increasing flexibility, i.e., reduced supply deficits, and increasing complexity of the supply chain, which could make it more expensive to operate.

econ.GN↗

arXiv2023

Detecting and Optimising Team Interactions in Software Development

The functional interaction structure of a team captures the preferences with which members of different roles interact. This paper presents a data-driven approach to detect the functional interaction structure for software development teams from traces team members leave on development platforms during their daily work. Our approach considers differences in the activity levels of team members and uses a block-constrained configuration model to compute interaction preferences between members of different roles. We apply our approach in a case study to extract the functional interaction structure of a product team at the German IT security company genua GmbH. We subsequently validate the accuracy of the detected interaction structure in interviews with five team members. Finally, we show how our approach enables teams to compare their functional interaction structure against synthetically created benchmark scenarios. Specifically, we evaluate the level of knowledge diffusion in the team and identify areas where the team can further improve. Our approach is computationally efficient and can be applied in real time to manage a team's interaction structure.

cs.SE↗

arXiv2022

Modeling social resilience: Questions, answers, open problems

Resilience denotes the capacity of a system to withstand shocks and its ability to recover from them. We develop a framework to quantify the resilience of highly volatile, non-equilibrium social organizations, such as collectives or collaborating teams. It consists of four steps: (i) \emph{delimitation}, i.e., narrowing down the target systems, (ii) \emph{conceptualization}, .e., identifying how to approach social organizations, (iii) formal \emph{representation} using a combination of agent-based and network models, (iv) \emph{operationalization}, i.e. specifying measures and demonstrating how they enter the calculation of resilience. Our framework quantifies two dimensions of resilience, the \emph{robustness} of social organizations and their \emph{adaptivity}, and combines them in a novel resilience measure. It allows monitoring resilience instantaneously using longitudinal data instead of an ex-post evaluation.

cs.SI↗

arXiv2022

Struggling with change: The fragile resilience of collectives

Collectives form non-equilibrium social structures characterised by a volatile dynamics. Individuals join or leave. Social relations change quickly. Therefore, differently from engineered or ecological systems, a resilient reference state cannot be defined. We propose a novel resilience measure combining two dimensions: robustness and adaptivity. We demonstrate how they can be quantified using data from a software developer collective. Our analysis reveals a resilience life cycle, i.e., stages of increasing resilience are followed by stages of decreasing resilience. We explain the reasons for these observed dynamics and provide a formal model to reproduce them. The resilience life cycle allows distinguishing between short-term resilience, given by a sequence of resilient states, and long-term resilience, which requires collectives to survive through different cycles.

physics.soc-ph↗

arXiv2022

Reconstructing signed relations from interaction data

Positive and negative relations play an essential role in human behavior and shape the communities we live in. Despite their importance, data about signed relations is rare and commonly gathered through surveys. Interaction data is more abundant, for instance, in the form of proximity or communication data. So far, though, it could not be utilized to detect signed relations. In this paper, we show how the underlying signed relations can be extracted with such data. Employing a statistical network approach, we construct networks of signed relations in four communities. We then show that these relations correspond to the ones reported in surveys. Additionally, the inferred relations allow us to study the homophily of individuals with respect to gender, religious beliefs, and financial backgrounds. We evaluate the importance of triads in the signed network to study group cohesion.

cs.SI↗

arXiv2022

Group relations, resilience and the I Ching

We evaluate the robustness and adaptivity of social groups with heterogeneous agents that are characterized by their binary state, their ability to change this state, their status and their preferred relations to other agents. To define group structures, we operationalize the hexagrams of the \emph{I Ching}. The relations and properties of agents are used to quantify their influence according to the social impact theory. From these influence values we derive a weighted stability measure for triads involving three agents, which is based on the weighted balance theory. It allows to quantify the robustness of groups and to propose a novel measure for group resilience which combines robustness and adaptivity. A stochastic approach determines the probabilities to find robust and adaptive groups. The discussion focuses on the generalization of our approach.

physics.soc-ph↗

arXiv2022

Disentangling Active and Passive Cosponsorship in the U.S. Congress

In the U.S. Congress, legislators can use active and passive cosponsorship to support bills. We show that these two types of cosponsorship are driven by two different motivations: the backing of political colleagues and the backing of the bill's content. To this end, we develop an Encoder+RGCN based model that learns legislator representations from bill texts and speech transcripts. These representations predict active and passive cosponsorship with an F1-score of 0.88. Applying our representations to predict voting decisions, we show that they are interpretable and generalize to unseen tasks.

cs.LG↗

arXiv2022

Network embeddedness indicates the innovation potential of firms

Firms' innovation potential depends on their position in the R&D network. But details on this relation remain unclear because measures to quantify network embeddedness have been controversially discussed. We propose and validate a new measure, coreness, obtained from the weighted k-core decomposition of the R&D network. Using data on R&D alliances, we analyse the change of coreness for 14,000 firms over 25 years and patenting activity. A regression analysis demonstrates that coreness explains firms' R&D output by predicting future patenting.

econ.GN↗

arXiv2022

Social nucleation: Group formation as a phase transition

The spontaneous formation and subsequent growth, dissolution, merger and competition of social groups bears similarities to physical phase transitions in metastable finite systems. We examine three different scenarios, percolation, spinodal decomposition and nucleation, to describe the formation of social groups of varying size and density. In our agent-based model, we use a feedback between the opinions of agents and their ability to establish links. Groups can restrict further link formation, but agents can also leave if costs exceed the group benefits. We identify the critical parameters for costs/benefits and social influence to obtain either one large group or the stable coexistence of several groups with different opinions. Analytic investigations allow to derive different critical densities that control the formation and coexistence of groups. Our novel approach sheds new light on the early stage of network growth and the emergence of large connected components.

physics.soc-ph↗

arXiv2022

The role of network embeddedness on the selection of collaboration partners: An agent-based model with empirical validation

We use a data-driven agent-based model to study the core-periphery structure of two collaboration networks, R&D alliances between firms and co-authorship relations between scientists. To characterize the network embeddedness of agents, we introduce a coreness value, obtained from a weighted $k$-core decomposition. We study the change of these coreness values when collaborations with newcomers or established agents are formed. Our agent-based model is able to reproduce the empirical coreness differences of collaboration partners and to explain why we observe a change in partner selection for agents with high network embeddedness.

physics.soc-ph↗

arXiv2022

Consensus from group interactions: An adaptive voter model on hypergraphs

We study the effect of group interactions on the emergence of consensus in a spin system. Agents with discrete opinions $\{0,1\}$ form groups. They can change their opinion based on their group's influence (voter dynamics), but groups can also split and merge (adaptation). In a hypergraph, these groups are represented by hyperedges of different sizes. The heterogeneity of group sizes is controlled by a parameter $β$. To study the impact of $β$ on reaching consensus, we provide extensive computer simulations and compare them with an analytic approach for the dynamics of the average magnetization. We find that group interactions amplify small initial opinion biases, accelerate the formation of consensus and lead to a drift of the average magnetization. The conservation of the initial magnetization, known for basic voter models, is no longer obtained.

physics.soc-ph↗