Abstract
Previous studies have shown that Instant-Runoff Voting (IRV) is highly resistant to coalitional manipulation (CM), though the theoretical reasons for this remain unclear. To address this gap, we analyze the susceptibility to CM of three major voting rules-Plurality, Two-Round System, and IRV-within the Perturbed Culture model. Our findings reveal that each rule undergoes a phase transition at a critical value theta\_c of the concentration of preferences: the probability of CM for large electorates converges exponentially fast to 1 below theta\_c and to 0 above theta\_c. We introduce the Super Condorcet Winner (SCW), showing that its presence is a key factor of IRV's resistance to coalitional manipulation, both theoretically and empirically. Notably, we use this notion to prove that for IRV, theta\_c = 0, making it resistant to CM with even minimal preference concentration.

Abstract
The dynamics of political opinion are a critical component of modern society with large-scale implications for the evolution of intra- and international political discourse and policy. Here we utilize recent high-resolution survey data to quantitatively capture leading-order psychological and information-environmental patterns. We then inform simulations of a theoretical dynamical framework with several different models for how populations' ideology evolves over time, including a model which reproduces current macro-scale ideological distributions given the empirical micro-scale data gathered. This effort represents an attempt to discover true underlying trends of political reasoning in general audiences, and to extrapolate the long-term implications of those trends as they interact with the political exposure landscape. Accurate modeling of this ecosystem has the potential to predict catastrophic outcomes such as hyperpolarization, and to inform effective intervention strategies aimed at preserving and rebuilding constructive political communication.

AI Insights

• Bounded‑confidence model limits opinion updates to within a tolerance threshold.
• Kinetic‑theory framework treats ideological shifts like particle collisions, predicting polarization rates.
• Simulations show opposing‑view exposure on social media can accelerate polarization, echoing Bail et al.
• Critique of voter model’s homogeneous mixing leads to a network‑aware variant reflecting real ties.
• Hybrid model blends media bias, confirmation bias, and bounded confidence to match U.S. ideological data.
• Read Sabin‑Miller et al.’s spectrograph and Mason’s Uncivil Agreement for deeper context.
• Future research must address model homogeneity to capture real‑world demographic diversity.

Abstract
We perform a validation analysis on the multipolar model of opinion dynamics. A general methodology for using the model on datasets of two correlated variables is proposed and tested using data on the relationship between COVID-19 vaccination rates and political participation in Sweden. The model is shown to successfully capture the opinion segregation demonstrated by the data and spatial correlation of biases is demonstrated as necessary for the result. A mixing of the biases on the other hand leads to a more homogeneous opinion distribution, and greater penetration of the majority opinion, which here corresponds to a decision to vote or vaccinate.

AI Insights

• Model accuracy rivals linear regression, with RMSE within the same order of magnitude on Swedish vaccination data.
• Randomizing biased agents across graph neighborhoods shows network topology critically shapes segregation outcomes.
• Framework supports arbitrary‑dimensional variables, enabling multi‑faceted social analysis beyond binary opinions.
• Mixing biases across communities yields a more homogeneous opinion distribution, boosting vaccine uptake.
• Future work will probe alternative topologies—small‑world, scale‑free—to test segregation robustness.
• Proskurnikov & Tempo’s tutorial on dynamic social networks supplies tools for multipolar model implementation.

Abstract
We investigate the effects of wariness (defined as individuals' concern for their minimum utility over time) on poverty traps and equilibrium multiplicity in an overlapping generations (OLG) model. We explore conditions under which (i) wariness amplifies or mitigates the likelihood of poverty traps in the economy and (ii) it gives rise to multiple intertemporal equilibria. Furthermore, we conduct comparative statics to characterize these effects and to examine how the interplay between wariness, productivity, and factor substitutability influences the dynamics of the economy.

Abstract
In this chapter we discuss the relation between privacy and freedom of expression in Europe. In principle, the two rights have equal weight in Europe - which right prevails depends on the circumstances of a case. We use the Google Spain judgment of the Court of Justice of the European Union, sometimes called the 'right to be forgotten' judgment, to illustrate the difficulties when balancing the two rights. The court decided in Google Spain that people have, under certain conditions, the right to have search results for their name delisted. We discuss how Google and Data Protection Authorities deal with such delisting requests in practice. Delisting requests illustrate that balancing privacy and freedom of expression interests will always remain difficult.

AI Insights

• Google limits delisting to EU domains, debate on extraterritorial reach.
• CNIL fined Google €100k for refusing to delist google.com results, exposing enforcement gaps across borders.
• Article 29 Working Party says EU‑only delisting fails to meet data‑subject rights, urging a broader scope.
• Case‑by‑case analysis must balance privacy against the public’s right to information.
• The European Court of Human Rights offers a nuanced framework, listing criteria to weigh privacy versus expression.
• Open‑data initiatives and newspaper archives raise similar tensions, hinting at a wider “right to be forgotten” ecosystem.
• For extraterritorial insights, read Van Alsenoy & Koekkoek’s “Internet and jurisdiction after Google Spain.”

Abstract
We introduce NAEL (Non-Anthropocentric Ethical Logic), a novel ethical framework for artificial agents grounded in active inference and symbolic reasoning. Departing from conventional, human-centred approaches to AI ethics, NAEL formalizes ethical behaviour as an emergent property of intelligent systems minimizing global expected free energy in dynamic, multi-agent environments. We propose a neuro-symbolic architecture to allow agents to evaluate the ethical consequences of their actions in uncertain settings. The proposed system addresses the limitations of existing ethical models by allowing agents to develop context-sensitive, adaptive, and relational ethical behaviour without presupposing anthropomorphic moral intuitions. A case study involving ethical resource distribution illustrates NAEL's dynamic balancing of self-preservation, epistemic learning, and collective welfare.

Abstract
Do industrial "superstars" help others up or crowd them out? We examine the relationship between the spillovers of superstar firms (those with the top market share in their industry) and the productivity dynamics in Indonesia. Employing data on Indonesian manufacturing firms from 2001 to 2015, we find that superstar exposures in the market raise both the productivity level and the growth of non-superstar firms through horizontal (within a sector-province) and vertical (across sectors) channels. When we distinguish by ownership, foreign superstars consistently encourage productivity except through the horizontal channel. In contrast, domestic superstars generate positive spillovers through both horizontal and vertical linkages, indicating that foreign firms do not solely drive positive externalities. Furthermore, despite overall productivity growth being positive in 2001-2015, the source of negative growth is mainly driven by within-group reallocation, evidence of misallocation among surviving firms, notably by domestic superstars. Although Indonesian superstar firms are more efficient in their operations, their relatively modest growth rates suggest a potential stagnation, which can be plausibly attributed to limited innovation activity or a slow pace of adopting new technologies.

AI Insights

• Dynamic OP decomposition reveals plant upgrades drive most TFP gains among surviving firms.
• Reallocation within surviving firms dampens growth, while entry–exit reallocation boosts it.
• Foreign‑owned superstars lift TFP, yet domestic superstars depress it through misallocation.
• Exporting firms exhibit lower TFP than non‑exporters, while larger firms outperform smaller ones.
• The study builds on Amiti et al. (2024) and Krugman (1995) to link trade frictions with productivity spillovers.
• “Superstar” is defined as a firm with exceptionally high productivity, not merely market share.
• “TFP” stands for total factor productivity, the efficiency measure central to the analysis.

Abstract
Large Language Models (LLMs) have become increasingly incorporated into everyday life for many internet users, taking on significant roles as advice givers in the domains of medicine, personal relationships, and even legal matters. The importance of these roles raise questions about how and what responses LLMs make in difficult political and moral domains, especially questions about possible biases. To quantify the nature of potential biases in LLMs, various works have applied Moral Foundations Theory (MFT), a framework that categorizes human moral reasoning into five dimensions: Harm, Fairness, Ingroup Loyalty, Authority, and Purity. Previous research has used the MFT to measure differences in human participants along political, national, and cultural lines. While there has been some analysis of the responses of LLM with respect to political stance in role-playing scenarios, no work so far has directly assessed the moral leanings in the LLM responses, nor have they connected LLM outputs with robust human data. In this paper we analyze the distinctions between LLM MFT responses and existing human research directly, investigating whether commonly available LLM responses demonstrate ideological leanings: either through their inherent responses, straightforward representations of political ideologies, or when responding from the perspectives of constructed human personas. We assess whether LLMs inherently generate responses that align more closely with one political ideology over another, and additionally examine how accurately LLMs can represent ideological perspectives through both explicit prompting and demographic-based role-playing. By systematically analyzing LLM behavior across these conditions and experiments, our study provides insight into the extent of political and demographic dependency in AI-generated responses.