Search arXiv⌕ Search

arXiv · 0705.1761

Modeling and Controlling Interstate Conflict

Abstract

Bayesian neural networks were used to model the relationship between input parameters, Democracy, Allies, Contingency, Distance, Capability, Dependency and Major Power, and the output parameter which is either peace or conflict. The automatic relevance determination was used to rank the importance of input variables. Control theory approach was used to identify input variables that would give a peaceful outcome. It was found that using all four controllable variables Democracy, Allies, Capability and Dependency; or using only Dependency or only Capabilities avoids all the predicted conflicts.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Tshilidzi Marwala, Monica Lagazio. 2007-05-12. Modeling and Controlling Interstate Conflict. https://arxiv.org/abs/0705.1761

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Why They Disagree: Decoding Differences in Opinions about AI Risk

Identifying the reasons for disagreements between influential points of view on issues that affect the public can help produce informed policy responses, even if they do not bring disagreeing parties closer to agreement. We present a methodology for extracting reasoning chains - the sequences of premises that motivate or justify opinions - from natural discourse, and for characterizing the types of premises (facts, forecasts, definitions, causal beliefs, and evaluations) that make up these chains. We demonstrate the utility of this approach for two practical goals: diagnosing specific points of contention and aggregating arguments across speakers. We illustrate the methodology through an analysis of the debates on the nature of risks that AI poses to the public, using a corpus of interviews from the Lex Fridman podcast. We find that differences in perspectives among the podcast's guests on existential risk and employment risk from AI arise primarily from differences in causal premises and forecasts, whereas in the case of AI's effects on human social relationships, premises regarding what is valued and definitions about what counts as genuine human connection play a distinctively larger role. Our approach to analyzing reasoning chains at scale, using an ensemble of LLMs to parse textual data, can be applied to facilitate deliberation and aggregation of opinions on any topic.

cs.CY↗

Revisiting Sustainability by Design in AI Protocol Governance: An Empirical Review of Comparative DAO and Corporate-Led Standards for the SDGs

As artificial intelligence (AI) agents enter production infrastructure, interoperability protocols shape its governance and sustainability. This paper revisits our comparative study of two AI-agent interoperability standards, Ethereum Request for Comments 8004 (ERC-8004), governed by a decentralized autonomous organization (DAO), and Google's Agent2Agent (A2A), governed by a corporate consortium, through a Sustainability by Design (SbD) lens. Using an LLM-powered pipeline combining automated annotation, neural topic modeling, and multi-layer network analysis, we identify contrasting governance and innovation architectures. ERC-8004 relies on permissionless participation, rough consensus, and decoupled deployment, while A2A assigns binding authority to an eight-seat Technology Steering Committee. The DAO concentrates on constitutive questions of trust and security, including what to build and why, whereas the consortium distributes attention across executive engineering questions of how to implement, document, and deliver the protocol. Both show high participation inequality, while corporate contributors span roughly twice as many themes as DAO contributors. We ask how these architectures produce distinct SDG-relevant signatures and what design principles they suggest for sustainable AI governance. We interpret institutional, discursive, and network patterns through SDGs 8, 9, 10, 11, 12, 16, and 17, identifying capacities for transparency, participation, contestability, and cross-protocol coordination. We argue that sustainable AI infrastructure requires a corrective feedback loop between designed charters and governance in practice, advancing SDG 16 on strong institutions. By integrating computational evidence, organizational research, and sustainable development, this review derives actionable design principles for sustainable AI governance.

cs.CY↗

Initial results of the Digital Consciousness Model

Artificially intelligent systems have become remarkably sophisticated. They hold conversations, write essays, and seem to understand context in ways that surprise even their creators. This raises a crucial question: Are we creating systems that are conscious? The Digital Consciousness Model (DCM) is a first attempt to assess the evidence for consciousness in AI systems in a systematic, probabilistic way. It provides a shared framework for comparing different AIs and biological organisms, and for tracking how the evidence changes over time as AI develops. Instead of adopting a single theory of consciousness, it incorporates a range of leading theories and perspectives - acknowledging that experts disagree fundamentally about what consciousness is and what conditions are necessary for it. This report describes the structure and initial results of the Digital Consciousness Model. Overall, we find that the evidence is against 2024 LLMs being conscious, but the evidence against 2024 LLMs being conscious is not decisive. The evidence against LLM consciousness is much weaker than the evidence against consciousness in simpler AI systems.

cs.CY↗