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University of Mannheim
Why we think this paper is great for you:
This paper directly explores the pursuit of democratic principles within novel organizational structures, offering insights into evolving forms of governance. It delves into how decision-making can be redistributed, which is highly relevant to your interests.
This paper directly explores the pursuit of democratic principles within novel organizational structures, offering insights into evolving forms of governance. It delves into how decision-making can be redistributed, which is highly relevant to your interests.
Abstract
This chapter explores how Decentralized Autonomous Organizations (DAOs), a novel institutional form based on blockchain technology, challenge traditional centralized governance structures. DAOs govern projects ranging from finance to science and digital communities. They aim to redistribute decision-making power through programmable, transparent, and participatory mechanisms. This chapter outlines both the opportunities DAOs present, such as incentive alignment, rapid coordination, and censorship resistance, and the challenges they face, including token concentration, low participation, and the risk of de facto centralization. It further discusses the emerging intersection of DAOs and artificial intelligence, highlighting the potential for increased automation alongside the dangers of diminished human oversight and algorithmic opacity. Ultimately, we discuss under what circumstances DAOs can fulfill their democratic promise or risk replicating the very power asymmetries they seek to overcome.
This chapter explores how Decentralized Autonomous Organizations (DAOs), a novel institutional form based on blockchain technology, challenge traditional centralized governance structures. DAOs govern projects ranging from finance to science and digital communities. They aim to redistribute decision-making power through programmable, transparent, and participatory mechanisms. This chapter outlines both the opportunities DAOs present, such as incentive alignment, rapid coordination, and censorship resistance, and the challenges they face, including token concentration, low participation, and the risk of de facto centralization. It further discusses the emerging intersection of DAOs and artificial intelligence, highlighting the potential for increased automation alongside the dangers of diminished human oversight and algorithmic opacity. Ultimately, we discuss under what circumstances DAOs can fulfill their democratic promise or risk replicating the very power asymmetries they seek to overcome.
AI Summary
- Anonymity in DAOs, while a core feature, facilitates the formation of hidden coalitions and conflicts of interest, undermining accountability and trust, as seen in incidents like the Beanstalk DAO attack and Curve's gauge wars. [3]
- DAOs, despite their decentralized ethos, frequently suffer from de facto centralization due to highly concentrated token ownership and low voter participation, often replicating the power asymmetries they aim to overcome. [2]
- The integration of AI into DAOs presents a dual challenge: while offering potential for enhanced automation and efficiency, it risks exacerbating algorithmic opacity, diminishing human oversight, and reinforcing centralization if not designed with participatory and transparent standards. [2]
- Governance token trading introduces significant vulnerabilities, enabling strategic market manipulation, vote buying/selling, and governance attacks, as exemplified by the Compound 'Humpy' incident. [2]
- The 'one token, one vote' principle, combined with extreme wealth inequality in cryptoassets (Gini coefficient near 1), fundamentally undermines the democratic promise of DAOs, often reducing them to 'autonomous organizations' rather than truly decentralized ones. [2]
- DAOs offer significant opportunities for incentive alignment, global coordination, auditable governance, broad participation, evolutionary governance, and resilience in adversarial contexts, provided their inherent challenges are actively mitigated. [2]
- Decentralized Autonomous Organizations (DAOs): Novel institutional forms based on blockchain technology that redistribute decision-making power through programmable, transparent, and participatory mechanisms, removing intermediaries. [2]
- Distributed Ledger Technologies (DLTs): A relatively recent innovation integrating game theory, cryptography, and distributed systems to solve the double-spending problem, with blockchain being the most widely adopted type. [2]
- Smart Contracts: Software programs encapsulating functions and algorithms running on top of DLTs (like Ethereum) that offer financial services or encode governance rules without the need for an intermediary. [2]
- Technical solutions like the Agent-to-Agent (A2A) protocol and ERC-8004 are crucial for integrating AI into DAOs transparently, by establishing verifiable frameworks for AI agent communication, identity, reputation, and action validation. [1]
Why we think this paper is great for you:
You will find this paper highly relevant as it focuses on securing the integrity and privacy of electoral processes. It directly addresses key challenges in modern voting systems, which is crucial for robust democratic practices.
You will find this paper highly relevant as it focuses on securing the integrity and privacy of electoral processes. It directly addresses key challenges in modern voting systems, which is crucial for robust democratic practices.
Abstract
A simple and practical method for achieving everlasting privacy in e-voting systems, without relying on advanced cryptographic techniques, is to use anonymous voter credentials. The simplicity of this approach may, however, create some challenges, when combined with other security features, such as cast-as-intended verifiability with second device and second-factor authentication. This paper considers a simple augmentation to the anonymous credential mechanism, using perfectly hiding commitments to link such credentials to the voter identities. This solution strengthens the binding between voters and their credentials while preserving everlasting privacy. It ensures that published ballots remain unlinkable to voter identities, yet enables necessary consistency checks during ballot casting and ballot auditing
A simple and practical method for achieving everlasting privacy in e-voting systems, without relying on advanced cryptographic techniques, is to use anonymous voter credentials. The simplicity of this approach may, however, create some challenges, when combined with other security features, such as cast-as-intended verifiability with second device and second-factor authentication. This paper considers a simple augmentation to the anonymous credential mechanism, using perfectly hiding commitments to link such credentials to the voter identities. This solution strengthens the binding between voters and their credentials while preserving everlasting privacy. It ensures that published ballots remain unlinkable to voter identities, yet enables necessary consistency checks during ballot casting and ballot auditing
Los Alamos Lab
Why we think this paper is great for you:
This research offers a practical application of advanced analytical methods to understand political communication and decision-making at the highest levels. It provides valuable insights into how political discourse can be systematically analyzed.
This research offers a practical application of advanced analytical methods to understand political communication and decision-making at the highest levels. It provides valuable insights into how political discourse can be systematically analyzed.
Abstract
Our research investigates how Natural Language Processing (NLP) can be used to extract main topics from a larger corpus of written data, as applied to the case of identifying signaling themes in Presidential Directives (PDs) from the Reagan through Clinton administrations. Analysts and NLP both identified relevant documents, demonstrating the potential utility of NLPs in research involving large written corpuses. However, we also identified discrepancies between NLP and human-labeled results that indicate a need for more research to assess the validity of NLP in this use case. The research was conducted in 2023, and the rapidly evolving landscape of AIML means existing tools have improved and new tools have been developed; this research displays the inherent capabilities of a potentially dated AI tool in emerging social science applications.
Our research investigates how Natural Language Processing (NLP) can be used to extract main topics from a larger corpus of written data, as applied to the case of identifying signaling themes in Presidential Directives (PDs) from the Reagan through Clinton administrations. Analysts and NLP both identified relevant documents, demonstrating the potential utility of NLPs in research involving large written corpuses. However, we also identified discrepancies between NLP and human-labeled results that indicate a need for more research to assess the validity of NLP in this use case. The research was conducted in 2023, and the rapidly evolving landscape of AIML means existing tools have improved and new tools have been developed; this research displays the inherent capabilities of a potentially dated AI tool in emerging social science applications.
Why we think this paper is great for you:
This paper presents a scalable method for analyzing vast amounts of political text, which is invaluable for understanding public discourse and political trends. It offers powerful tools for large-scale textual analysis in the social sciences.
This paper presents a scalable method for analyzing vast amounts of political text, which is invaluable for understanding public discourse and political trends. It offers powerful tools for large-scale textual analysis in the social sciences.
Abstract
This paper proposes a topic modeling method that scales linearly to billions of documents. We make three core contributions: i) we present a topic modeling method, Tensor Latent Dirichlet Allocation (TLDA), that has identifiable and recoverable parameter guarantees and sample complexity guarantees for large data; ii) we show that this method is computationally and memory efficient (achieving speeds over 3-4x those of prior parallelized Latent Dirichlet Allocation (LDA) methods), and that it scales linearly to text datasets with over a billion documents; iii) we provide an open-source, GPU-based implementation, of this method. This scaling enables previously prohibitive analyses, and we perform two real-world, large-scale new studies of interest to political scientists: we provide the first thorough analysis of the evolution of the #MeToo movement through the lens of over two years of Twitter conversation and a detailed study of social media conversations about election fraud in the 2020 presidential election. Thus this method provides social scientists with the ability to study very large corpora at scale and to answer important theoretically-relevant questions about salient issues in near real-time.
This paper proposes a topic modeling method that scales linearly to billions of documents. We make three core contributions: i) we present a topic modeling method, Tensor Latent Dirichlet Allocation (TLDA), that has identifiable and recoverable parameter guarantees and sample complexity guarantees for large data; ii) we show that this method is computationally and memory efficient (achieving speeds over 3-4x those of prior parallelized Latent Dirichlet Allocation (LDA) methods), and that it scales linearly to text datasets with over a billion documents; iii) we provide an open-source, GPU-based implementation, of this method. This scaling enables previously prohibitive analyses, and we perform two real-world, large-scale new studies of interest to political scientists: we provide the first thorough analysis of the evolution of the #MeToo movement through the lens of over two years of Twitter conversation and a detailed study of social media conversations about election fraud in the 2020 presidential election. Thus this method provides social scientists with the ability to study very large corpora at scale and to answer important theoretically-relevant questions about salient issues in near real-time.
Why we think this paper is great for you:
This paper directly addresses the critical intersection of individual rights and data privacy in a legal context. It explores a fundamental aspect of human rights within contemporary society.
This paper directly addresses the critical intersection of individual rights and data privacy in a legal context. It explores a fundamental aspect of human rights within contemporary society.
Abstract
An attorney submitted a 'right to be forgotten' delisting request to Google, regarding a blog post about a criminal conviction of the attorney in another country. The Rotterdam District Court ruled that Google may no longer link to the blog post when people search for the attorney's name. The court granted the attorney's request because the blog post concerns a criminal conviction. Personal data regarding criminal convictions are, under Dutch law, special categories of data (sometimes called sensitive data). The reasoning of the court on special categories of data creates problems for freedom of expression. This paper, in Dutch, explores how these problems can be reduced. Google has appealed the decision; the judgment of the Court of Appeals is expected in March 2017.
An attorney submitted a 'right to be forgotten' delisting request to Google, regarding a blog post about a criminal conviction of the attorney in another country. The Rotterdam District Court ruled that Google may no longer link to the blog post when people search for the attorney's name. The court granted the attorney's request because the blog post concerns a criminal conviction. Personal data regarding criminal convictions are, under Dutch law, special categories of data (sometimes called sensitive data). The reasoning of the court on special categories of data creates problems for freedom of expression. This paper, in Dutch, explores how these problems can be reduced. Google has appealed the decision; the judgment of the Court of Appeals is expected in March 2017.
Why we think this paper is great for you:
This work delves into foundational concepts of freedom and individual choice, offering a new framework for evaluating capability sets. It provides a theoretical exploration highly pertinent to understanding societal structures and individual agency.
This work delves into foundational concepts of freedom and individual choice, offering a new framework for evaluating capability sets. It provides a theoretical exploration highly pertinent to understanding societal structures and individual agency.
Abstract
This paper proposes a new framework for evaluating capability sets by incorporating individual preferences over the diversity of accessible options. Building on the Capability Approach, we introduce a compromise method that balances between the notions of negative and positive freedom, effectively capturing the intrinsic and instrumental values of diverse choices within capability sets.
This paper proposes a new framework for evaluating capability sets by incorporating individual preferences over the diversity of accessible options. Building on the Capability Approach, we introduce a compromise method that balances between the notions of negative and positive freedom, effectively capturing the intrinsic and instrumental values of diverse choices within capability sets.
University of Passau
Why we think this paper is great for you:
This paper investigates fundamental aspects of fairness and power dynamics through behavioral experiments. It offers insights into decision-making and ethical considerations within simplified social interactions.
This paper investigates fundamental aspects of fairness and power dynamics through behavioral experiments. It offers insights into decision-making and ethical considerations within simplified social interactions.
Abstract
In behavioral sciences, experiments such as the ultimatum game are conducted to assess preferences for fairness or self-interest of study participants. In the dictator game, a simplified version of the ultimatum game where only one of two players makes a single decision, the dictator unilaterally decides how to split a fixed sum of money between themselves and the other player. Although recent studies have explored behavioral patterns of AI agents based on Large Language Models (LLMs) instructed to adopt different personas, we question the robustness of these results. In particular, many of these studies overlook the role of the system prompt - the underlying instructions that shape the model's behavior - and do not account for how sensitive results can be to slight changes in prompts. However, a robust baseline is essential when studying highly complex behavioral aspects of LLMs. To overcome previous limitations, we propose the LLM agent behavior study (LLM-ABS) framework to (i) explore how different system prompts influence model behavior, (ii) get more reliable insights into agent preferences by using neutral prompt variations, and (iii) analyze linguistic features in responses to open-ended instructions by LLM agents to better understand the reasoning behind their behavior. We found that agents often exhibit a strong preference for fairness, as well as a significant impact of the system prompt on their behavior. From a linguistic perspective, we identify that models express their responses differently. Although prompt sensitivity remains a persistent challenge, our proposed framework demonstrates a robust foundation for LLM agent behavior studies. Our code artifacts are available at https://github.com/andreaseinwiller/LLM-ABS.
In behavioral sciences, experiments such as the ultimatum game are conducted to assess preferences for fairness or self-interest of study participants. In the dictator game, a simplified version of the ultimatum game where only one of two players makes a single decision, the dictator unilaterally decides how to split a fixed sum of money between themselves and the other player. Although recent studies have explored behavioral patterns of AI agents based on Large Language Models (LLMs) instructed to adopt different personas, we question the robustness of these results. In particular, many of these studies overlook the role of the system prompt - the underlying instructions that shape the model's behavior - and do not account for how sensitive results can be to slight changes in prompts. However, a robust baseline is essential when studying highly complex behavioral aspects of LLMs. To overcome previous limitations, we propose the LLM agent behavior study (LLM-ABS) framework to (i) explore how different system prompts influence model behavior, (ii) get more reliable insights into agent preferences by using neutral prompt variations, and (iii) analyze linguistic features in responses to open-ended instructions by LLM agents to better understand the reasoning behind their behavior. We found that agents often exhibit a strong preference for fairness, as well as a significant impact of the system prompt on their behavior. From a linguistic perspective, we identify that models express their responses differently. Although prompt sensitivity remains a persistent challenge, our proposed framework demonstrates a robust foundation for LLM agent behavior studies. Our code artifacts are available at https://github.com/andreaseinwiller/LLM-ABS.
Political Theory
Imperial College London
Abstract
We develop a framework, in the style of Adler, for interpreting the notion of "witnessing" that has appeared (usually as a variant of Kim's Lemma) in different areas of neostability theory as a binary relation between abstract independence relations. This involves extending the relativisations of Kim-independence and Conant-independence due to Mutchnik to arbitrary independence relations. After developing this framework, we show that several results from simplicity, $\text{NTP}_2$, $\text{NSOP}_1$, and beyond follow as instances of general theorems for abstract independence relations. In particular, we prove the equivalence between witnessing and symmetry and the implications from this notion to chain local character and the weak independence theorem, and recover some partial converses. Finally, we use this framework to prove a dichotomy between $\text{NSOP}_1$ and Kruckman and Ramsey's $\text{BTP}$ that applies to most known $\text{NSOP}_4$ examples in the literature.
We develop a framework, in the style of Adler, for interpreting the notion of "witnessing" that has appeared (usually as a variant of Kim's Lemma) in different areas of neostability theory as a binary relation between abstract independence relations. This involves extending the relativisations of Kim-independence and Conant-independence due to Mutchnik to arbitrary independence relations. After developing this framework, we show that several results from simplicity, $\text{NTP}_2$, $\text{NSOP}_1$, and beyond follow as instances of general theorems for abstract independence relations. In particular, we prove the equivalence between witnessing and symmetry and the implications from this notion to chain local character and the weak independence theorem, and recover some partial converses. Finally, we use this framework to prove a dichotomy between $\text{NSOP}_1$ and Kruckman and Ramsey's $\text{BTP}$ that applies to most known $\text{NSOP}_4$ examples in the literature.
Imperial College
Abstract
Bridging the gap between individual agent behavior and macroscopic societal patterns is a central challenge in the social sciences. In this work, we propose a solution to this problem via a kinetic theory formulation. We demonstrate that complex, empirically-observed phenomena, such as the concentration of populations in cities and the emergence of power-law wealth distributions, can be derived directly from a microscopic model of agents governed by underdamped Langevin dynamics. Our multi-scale derivation yields the exact mesoscopic fluctuating (Dean-Kawasaki) dynamics and the macroscopic Vlasov-Fokker-Planck system of equations. The analytical solution of this system reveals how a heterogeneous resource landscape alone is sufficient to generate the coupled structures of spatial and economic inequality, thus providing a formal link between micro-level stochasticity and macro-level deterministic order.
Bridging the gap between individual agent behavior and macroscopic societal patterns is a central challenge in the social sciences. In this work, we propose a solution to this problem via a kinetic theory formulation. We demonstrate that complex, empirically-observed phenomena, such as the concentration of populations in cities and the emergence of power-law wealth distributions, can be derived directly from a microscopic model of agents governed by underdamped Langevin dynamics. Our multi-scale derivation yields the exact mesoscopic fluctuating (Dean-Kawasaki) dynamics and the macroscopic Vlasov-Fokker-Planck system of equations. The analytical solution of this system reveals how a heterogeneous resource landscape alone is sufficient to generate the coupled structures of spatial and economic inequality, thus providing a formal link between micro-level stochasticity and macro-level deterministic order.
Democratic Processes
ELTE University
Abstract
Neutron captures produce the vast majority of abundances of elements heavier than iron in the Universe. Beyond the classical slow (s) and rapid (r) processes, there is observational evidence for neutron-capture processes that operate at neutron densities in between, at different distances from the valley of $β$ stability. Here, we review the main properties of the s process within the general context of neutron-capture processes and the nuclear physics input required to investigate it. We describe massive stars and asymptotic giant branch stars as the s-process astrophysical sites and discuss the related physical uncertainties. We also present current observational evidence for the s process and beyond, which ranges from stellar spectroscopic observations to laboratory analysis of meteorites.
Neutron captures produce the vast majority of abundances of elements heavier than iron in the Universe. Beyond the classical slow (s) and rapid (r) processes, there is observational evidence for neutron-capture processes that operate at neutron densities in between, at different distances from the valley of $β$ stability. Here, we review the main properties of the s process within the general context of neutron-capture processes and the nuclear physics input required to investigate it. We describe massive stars and asymptotic giant branch stars as the s-process astrophysical sites and discuss the related physical uncertainties. We also present current observational evidence for the s process and beyond, which ranges from stellar spectroscopic observations to laboratory analysis of meteorites.
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