TU Clausthal
AI Insights
- Apportionment method: A mathematical method used to determine how many seats a party should receive based on the number of votes they received. (ML: 0.94)
- Divisor sequence methods and LRM (Least-Remainder Method) are two types of apportionment methods used to solve the bribery problem. (ML: 0.93)
- The algorithms work by simulating the election process and determining whether it is possible to bribe voters to achieve the desired outcome. (ML: 0.93)
- LRM (Least-Remainder Method): An apportionment method that assigns seats to parties based on their remainders after dividing the total number of votes by the number of seats available. (ML: 0.92)
- The problem of bribery in elections can be solved using algorithms that determine whether a party can win a certain number of seats by bribing voters. (ML: 0.91)
- Divisor sequence method: An apportionment method that uses a divisor to divide the total number of votes by the number of seats available. (ML: 0.91)
- The results show that both algorithms can solve the bribery problem in polynomial time, making them efficient tools for analyzing elections. (ML: 0.90)
- The bribery problem in elections can be solved using polynomial-time algorithms. (ML: 0.87)
- Bribery: The act of offering or giving something of value to a voter in exchange for their vote. (ML: 0.85)
- Algorithm 1 is a polynomial-time algorithm for solving the bribery problem in divisor sequence methods, while Algorithm 2 is a polynomial-time algorithm for solving the bribery problem in LRM. (ML: 0.82)
Abstract
In parliamentary elections, parties compete for a limited, typically fixed number of seats. Most parliaments are assembled using apportionment methods that distribute the seats based on the parties' vote counts. Common apportionment methods include divisor sequence methods (like D'Hondt or Sainte-LaguΓ«), the largest-remainder method, and first-past-the-post. In many countries, an electoral threshold is implemented to prevent very small parties from entering the parliament. Further, several countries have apportionment systems that incorporate multiple districts. We study how computationally hard it is to change the election outcome (i.e., to increase or limit the influence of a distinguished party) by convincing a limited number of voters to change their vote. We refer to these bribery-style attacks as \emph{strategic campaigns} and study the corresponding problems in terms of their computational (both classical and parameterized) complexity. We also run extensive experiments on real-world election data and study the effectiveness of optimal campaigns, in particular as opposed to using heuristic bribing strategies and with respect to the influence of the threshold and the influence of the number of districts. For apportionment elections with threshold, finally, we propose -- as an alternative to the standard top-choice mode -- the second-chance mode where voters of parties below the threshold receive a second chance to vote for another party, and we establish computational complexity results also in this setting.
In parliamentary elections, parties compete for a limited, typically fixed number of seats. Most parliaments are assembled using apportionment methods that distribute the seats based on the parties' vote counts. Common apportionment methods include divisor sequence methods (like D'Hondt or Sainte-LaguΓ«), the largest-remainder method, and first-past-the-post. In many countries, an electoral threshold is implemented to prevent very small parties from entering the parliament. Further, several countries have apportionment systems that incorporate multiple districts. We study how computationally hard it is to change the election outcome (i.e., to increase or limit the influence of a distinguished party) by convincing a limited number of voters to change their vote. We refer to these bribery-style attacks as \emph{strategic campaigns} and study the corresponding problems in terms of their computational (both classical and parameterized) complexity. We also run extensive experiments on real-world election data and study the effectiveness of optimal campaigns, in particular as opposed to using heuristic bribing strategies and with respect to the influence of the threshold and the influence of the number of districts. For apportionment elections with threshold, finally, we propose -- as an alternative to the standard top-choice mode -- the second-chance mode where voters of parties below the threshold receive a second chance to vote for another party, and we establish computational complexity results also in this setting.
Why we are recommending this paper?
Due to your Interest in Democratic Processes
This paper directly addresses the manipulation of electoral systems, a critical concern within democratic processes and political movements. Understanding apportionment methods is fundamental to analyzing how political power can be strategically altered within a parliamentary system.
Due to your Interest in Democratic Processes
This paper directly addresses the manipulation of electoral systems, a critical concern within democratic processes and political movements. Understanding apportionment methods is fundamental to analyzing how political power can be strategically altered within a parliamentary system.
Universidade Federal de Lavras UFLA
AI Insights
- The model assumes that individuals have a certain level of ideological extremism or moderation, which can be influenced by external factors such as propaganda or social interactions. (ML: 0.97)
- However, the authors also note that the model has limitations and simplifications, and that further research is needed to fully understand the complex interactions between individuals and their environments. (ML: 0.96)
- Social interactions: the ways in which individuals interact with each other and their environments. (ML: 0.95)
- Propaganda: the use of information or persuasion to influence people's attitudes or behaviors. (ML: 0.94)
- The results suggest that the model can capture some key features of real-world political dynamics, such as the emergence of moderate ideologies and the extinction of extreme ideologies. (ML: 0.91)
- Extremism: a state of being extremely extreme or radical in one's views or actions. (ML: 0.87)
- Stochastic differential equations (SDEs): a type of mathematical equation used to describe systems that are subject to random fluctuations or noise. (ML: 0.85)
- The paper discusses a model that uses stochastic differential equations to study the dynamics of ideological conflict and moderation. (ML: 0.85)
- Moment closure methods: a technique used to approximate the behavior of a system by closing the moment equations, which describe the average properties of the system. (ML: 0.79)
- The authors use a variety of techniques from physics and mathematics, including stochastic differential equations and moment closure methods, to analyze the behavior of the system. (ML: 0.66)
Abstract
Political moderation, a key attractor in democratic systems, proves highly fragile under realistic information conditions. We develop a stochastic model of opinion dynamics to analyze how noise and differential susceptibility reshape the political spectrum. Extending Marvel et al.'s deterministic framework, we incorporate stochastic media influence $ΞΆ(t)$ and neuropolitically-grounded sensitivity differences ($Ο_y > Ο_x$). Analysis reveals the moderate population -- stable in deterministic models -- undergoes catastrophic collapse under stochastic forcing. This occurs through an effective deradicalization asymmetry ($u_{B}^{\text{eff}} = u + Ο_y^2/2 > u_{A}^{\text{eff}}$) that drives conservatives to extinction, eliminating cross-cutting interactions that sustain moderates. The system exhibits a phase transition from multi-stable coexistence to liberal dominance, demonstrating how information flow architecture -- independent of content -- systematically dismantles the political center. Our findings reveal moderation as an emergent property highly vulnerable to stochastic perturbations in complex social systems.
Political moderation, a key attractor in democratic systems, proves highly fragile under realistic information conditions. We develop a stochastic model of opinion dynamics to analyze how noise and differential susceptibility reshape the political spectrum. Extending Marvel et al.'s deterministic framework, we incorporate stochastic media influence $ΞΆ(t)$ and neuropolitically-grounded sensitivity differences ($Ο_y > Ο_x$). Analysis reveals the moderate population -- stable in deterministic models -- undergoes catastrophic collapse under stochastic forcing. This occurs through an effective deradicalization asymmetry ($u_{B}^{\text{eff}} = u + Ο_y^2/2 > u_{A}^{\text{eff}}$) that drives conservatives to extinction, eliminating cross-cutting interactions that sustain moderates. The system exhibits a phase transition from multi-stable coexistence to liberal dominance, demonstrating how information flow architecture -- independent of content -- systematically dismantles the political center. Our findings reveal moderation as an emergent property highly vulnerable to stochastic perturbations in complex social systems.
Why we are recommending this paper?
Due to your Interest in Political Movements
This research delves into the mechanisms of polarization within democratic systems, aligning with your interest in political movements and the fragility of moderation. The stochastic model offers valuable insights into how information and stimulation can shape political attitudes.
Due to your Interest in Political Movements
This research delves into the mechanisms of polarization within democratic systems, aligning with your interest in political movements and the fragility of moderation. The stochastic model offers valuable insights into how information and stimulation can shape political attitudes.
Technical University of Munich
AI Insights
- The use of large language models for qualitative data analysis has the potential to significantly reduce the time and effort required for coding. (ML: 0.99)
- Inductive coding: A qualitative data analysis technique where codes are derived from the data itself, rather than being predetermined by the researcher. (ML: 0.99)
- The paper discusses the use of large language models for qualitative data analysis, specifically inductive coding. (ML: 0.99)
- They evaluate their approach on a dataset of parliamentary debates and compare it to human coders. (ML: 0.98)
- Limited evaluation on a larger scale (ML: 0.98)
- Label refinement: The process of refining and improving the accuracy of labels assigned to data points. (ML: 0.98)
- However, the authors note that their approach requires further refinement and evaluation on a larger scale. (ML: 0.97)
- The authors propose a method for automating inductive coding using ensemble LLMs and label refinement. (ML: 0.91)
- The proposed method shows promising results in automating inductive coding, with ensemble LLMs outperforming human coders in some cases. (ML: 0.87)
- Ensemble LLMs: A combination of multiple large language models that can be used for tasks such as text classification or generation. (ML: 0.84)
Abstract
Axial coding is a commonly used qualitative analysis method that enhances document understanding by organizing sentence-level open codes into broader categories. In this paper, we operationalize axial coding with large language models (LLMs). Extending an ensemble-based open coding approach with an LLM moderator, we add an axial coding step that groups open codes into higher-order categories, transforming raw debate transcripts into concise, hierarchical representations. We compare two strategies: (i) clustering embeddings of code-utterance pairs using density-based and partitioning algorithms followed by LLM labeling, and (ii) direct LLM-based grouping of codes and utterances into categories. We apply our method to Dutch parliamentary debates, converting lengthy transcripts into compact, hierarchically structured codes and categories. We evaluate our method using extrinsic metrics aligned with human-assigned topic labels (ROUGE-L, cosine, BERTScore), and intrinsic metrics describing code groups (coverage, brevity, coherence, novelty, JSD divergence). Our results reveal a trade-off: density-based clustering achieves high coverage and strong cluster alignment, while direct LLM grouping results in higher fine-grained alignment, but lower coverage 20%. Overall, clustering maximizes coverage and structural separation, whereas LLM grouping produces more concise, interpretable, and semantically aligned categories. To support future research, we publicly release the full dataset of utterances and codes, enabling reproducibility and comparative studies.
Axial coding is a commonly used qualitative analysis method that enhances document understanding by organizing sentence-level open codes into broader categories. In this paper, we operationalize axial coding with large language models (LLMs). Extending an ensemble-based open coding approach with an LLM moderator, we add an axial coding step that groups open codes into higher-order categories, transforming raw debate transcripts into concise, hierarchical representations. We compare two strategies: (i) clustering embeddings of code-utterance pairs using density-based and partitioning algorithms followed by LLM labeling, and (ii) direct LLM-based grouping of codes and utterances into categories. We apply our method to Dutch parliamentary debates, converting lengthy transcripts into compact, hierarchically structured codes and categories. We evaluate our method using extrinsic metrics aligned with human-assigned topic labels (ROUGE-L, cosine, BERTScore), and intrinsic metrics describing code groups (coverage, brevity, coherence, novelty, JSD divergence). Our results reveal a trade-off: density-based clustering achieves high coverage and strong cluster alignment, while direct LLM grouping results in higher fine-grained alignment, but lower coverage 20%. Overall, clustering maximizes coverage and structural separation, whereas LLM grouping produces more concise, interpretable, and semantically aligned categories. To support future research, we publicly release the full dataset of utterances and codes, enabling reproducibility and comparative studies.
Why we are recommending this paper?
Due to your Interest in Political Philosophy
Utilizing large language models to analyze political debates is a relevant approach to understanding the nuances of political theory and discourse. This work connects to your interest in political philosophy and the systematic analysis of political concepts.
Due to your Interest in Political Philosophy
Utilizing large language models to analyze political debates is a relevant approach to understanding the nuances of political theory and discourse. This work connects to your interest in political philosophy and the systematic analysis of political concepts.
University of Nottingham
AI Insights
- Temporary disruptions can have long-lasting effects on trade patterns and firm behavior. (ML: 0.93)
- Extensive margin: The number of buyers or sellers participating in trade. (ML: 0.88)
- High-degree importers experience a sharp increase in exit probability at the time of the shock, but the effect attenuates in later periods. (ML: 0.87)
- Temporary disruptions affect both the extensive and intensive margins of trade. (ML: 0.87)
- Portfolio reallocation: Firms shifting activity to other markets, updating beliefs and sourcing from suppliers in less-exposed regions. (ML: 0.87)
- Intensive margin: The quantity traded by each buyer or seller. (ML: 0.86)
- The intensive-margin response involves sending fewer but heavier units that can help preserve overall trade volumes during the crisis. (ML: 0.85)
- Firms appear to reduce the size of their portfolios without fully leaving the market, consistent with an allocation strategy. (ML: 0.79)
- Firm exit: Importers leaving the market due to portfolio contraction. (ML: 0.78)
- Importers with partially exposed portfolios delay adjustment by postponing shipments along affected relationships. (ML: 0.77)
Abstract
I study how firms adjust to temporary disruptions in international trade relationships organized through relational contracts. I exploit an extreme, plausibly exogenous weather shock during the 2010-11 La NiΓ±a season that restricted Colombian flower exporters' access to cargo terminals. Using transaction-level data from the Colombian-U.S. flower trade, I show that importers with less-exposed supplier portfolios are less likely to terminate disrupted relationships, instead tolerating shipment delays. In contrast, firms facing greater exposure experience higher partner turnover and are more likely to exit the market, with exit accounting for a substantial share of relationship separations. These findings demonstrate that idiosyncratic shocks to buyer-seller relationships can propagate into persistent changes in firms' trading portfolios.
I study how firms adjust to temporary disruptions in international trade relationships organized through relational contracts. I exploit an extreme, plausibly exogenous weather shock during the 2010-11 La NiΓ±a season that restricted Colombian flower exporters' access to cargo terminals. Using transaction-level data from the Colombian-U.S. flower trade, I show that importers with less-exposed supplier portfolios are less likely to terminate disrupted relationships, instead tolerating shipment delays. In contrast, firms facing greater exposure experience higher partner turnover and are more likely to exit the market, with exit accounting for a substantial share of relationship separations. These findings demonstrate that idiosyncratic shocks to buyer-seller relationships can propagate into persistent changes in firms' trading portfolios.
Why we are recommending this paper?
Due to your Interest in Political Economy
Examining the impact of crises on trade relationships offers a valuable perspective on the political economy and how disruptions can affect political systems and movements. This research provides a framework for understanding the economic dimensions of political change.
Due to your Interest in Political Economy
Examining the impact of crises on trade relationships offers a valuable perspective on the political economy and how disruptions can affect political systems and movements. This research provides a framework for understanding the economic dimensions of political change.
Indian Institute of Science
AI Insights
- The authors also investigate the effects of different parameter regimes on the model's behavior. (ML: 0.88)
- The model is based on a set of stochastic differential equations (SDEs) that describe the time evolution of the state variables representing the fraction of individuals in different states. (ML: 0.88)
- Order parameters (m and v): Quantities that describe the state of the system, such as the fraction of individuals in different states or the speed of movement. (ML: 0.87)
- The results show that variable speed has a significant impact on collective behavior, leading to changes in movement patterns such as increased speed, altered directionality, and reduced cohesion. (ML: 0.85)
- Microscopic model: A mathematical representation of the interactions between individual particles or agents, which can be used to study collective behavior. (ML: 0.83)
- Stochastic differential equations (SDEs): A mathematical tool used to describe the time evolution of stochastic processes, such as the movement of particles or individuals in a population. (ML: 0.83)
- ItΓ΄ SDEs: A specific type of SDE that describes the dynamics of a system using ItΓ΄ calculus, which is a mathematical framework for describing stochastic processes. (ML: 0.83)
- The paper provides insights into the complex dynamics of animal groups and highlights the importance of considering variable speed when modeling collective behavior. (ML: 0.78)
- The paper presents a mathematical model for collective behavior in animal groups, focusing on the effects of variable speed on movement patterns. (ML: 0.75)
- They then transform the SDEs into ItΓ΄ SDEs form and obtain the ODE approximation valid for large group sizes. (ML: 0.72)
- The paper demonstrates that the SDE approximation closely matches the true dynamics of the microscopic model across parameter ranges and group sizes, using joint distributions of the model's order parameters (m and v) obtained from both microscopic simulations and SDE models. (ML: 0.70)
- The authors use Gillespie's Chemical Langevin method to derive SDE approximations for the dynamics of the microscopic interaction model, which involves 14 reactions through which the state variables can change. (ML: 0.62)
- Gillespie's Chemical Langevin method: A numerical method used to derive SDE approximations for the dynamics of microscopic interaction models, such as chemical reactions or population dynamics. (ML: 0.61)
Abstract
Collective movement is observed widely in nature, where individuals interact locally to produce globally ordered, coherent motion. In typical models of collective motion, each individual takes the average direction of multiple neighbors, resulting in ordered movement. In small flocks, noise induced order can also emerge with individuals copying only a randomly chosen single neighbor at a time. We propose a new model of collective movement, inspired by how real animals move, where individuals can move in two directions or remain stationary. We demonstrate that when individuals interact with a single neighbor through a novel form of halting interaction -- where an individual may stop upon encountering an oppositely moving neighbor rather than instantly aligning -- persistent collective order can emerge even in large populations. This represents a fundamentally different mechanism from conventional averaging-based or noise-induced ordering. Using deterministic and stochastic mean-field approximations, we characterize the conditions under which such ``flocking by stopping'' behavior can occur, and confirm the mean-field predictions using individual-based simulations. Our results highlight how incorporating a stopped state and halting interactions can generate new routes to order in collective movement.
Collective movement is observed widely in nature, where individuals interact locally to produce globally ordered, coherent motion. In typical models of collective motion, each individual takes the average direction of multiple neighbors, resulting in ordered movement. In small flocks, noise induced order can also emerge with individuals copying only a randomly chosen single neighbor at a time. We propose a new model of collective movement, inspired by how real animals move, where individuals can move in two directions or remain stationary. We demonstrate that when individuals interact with a single neighbor through a novel form of halting interaction -- where an individual may stop upon encountering an oppositely moving neighbor rather than instantly aligning -- persistent collective order can emerge even in large populations. This represents a fundamentally different mechanism from conventional averaging-based or noise-induced ordering. Using deterministic and stochastic mean-field approximations, we characterize the conditions under which such ``flocking by stopping'' behavior can occur, and confirm the mean-field predictions using individual-based simulations. Our results highlight how incorporating a stopped state and halting interactions can generate new routes to order in collective movement.
Why we are recommending this paper?
Due to your Interest in Social Movements
The study of collective movement and emergent order provides a fascinating lens through which to examine social movements and the dynamics of group behavior. Understanding how individuals interact to create coordinated patterns is relevant to analyzing social and political organization.
Due to your Interest in Social Movements
The study of collective movement and emergent order provides a fascinating lens through which to examine social movements and the dynamics of group behavior. Understanding how individuals interact to create coordinated patterns is relevant to analyzing social and political organization.
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