43 citations · 52 across the 12 of their papers we have counts for
13 papers
Methodology for Modelling Token Economies and Performing Event Impact Analysis with DeTEcT
Rem Sadykhov, Geoffrey Goodell, Philip Treleaven
The objective of this paper is to provide a methodology for applying the DeTEcT framework to modelling token economies, to formalise the configuration of the simulation environment…
Impacts of Economic Policies on Wealth Distribution in Token Economies
Rem Sadykhov, Geoff Goodell, Philip Treleaven
In this paper, we analyse the impacts of exogenous and endogenous factors on wealth distribution in the Bitcoin token economy, where wealth distribution refers to the distribution…
Knowledge Collapse in LLMs: When Fluency Survives but Facts Fail under Recursive Synthetic Training
Figarri Keisha, Zekun Wu, Ze Wang +2
Large language models increasingly rely on synthetic data due to human-written content scarcity, yet recursive training on model-generated outputs leads to model collapse, a degene…
Personality as a Probe for LLM Evaluation: Method Trade-offs and Downstream Effects
Gunmay Handa, Zekun Wu, Adriano Koshiyama +1
Personality manipulation in large language models (LLMs) is increasingly applied in customer service and agentic scenarios, yet its mechanisms and trade-offs remain unclear. We pre…
Economic Policy Taxonomy
Rem Sadykhov, Geoff Goodell, Philip Treleaven
This paper proposes a framework for categorizing economic policies in a form of a tree taxonomy. The purpose of this approach is to construct an exhaustive and standardized list of…
HEARTS: A Holistic Framework for Explainable, Sustainable and Robust Text Stereotype Detection
Theo King, Zekun Wu, Adriano Koshiyama +2
Stereotypes are generalised assumptions about societal groups, and even state-of-the-art LLMs using in-context learning struggle to identify them accurately. Due to the subjective…
THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models
Mengfei Liang, Archish Arun, Zekun Wu +5
Hallucination, the generation of factually incorrect content, is a growing challenge in Large Language Models (LLMs). Existing detection and mitigation methods are often isolated a…
From Text to Emoji: How PEFT-Driven Personality Manipulation Unleashes the Emoji Potential in LLMs
Navya Jain, Zekun Wu, Cristian Munoz +5
The manipulation of the personality traits of large language models (LLMs) has emerged as a key area of research. Methods like prompt-based In-Context Knowledge Editing (IKE) and g…
HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications
Rishi Kalra, Zekun Wu, Ayesha Gulley +4
Large Language Models (LLMs) face limitations in AI legal and policy applications due to outdated knowledge, hallucinations, and poor reasoning in complex contexts. Retrieval-Augme…
DeTEcT: Dynamic and Probabilistic Parameters Extension
Rem Sadykhov, Geoffrey Goodell, Philip Treleaven
This paper presents a theoretical extension of the DeTEcT framework proposed by Sadykhov et al., DeTEcT, where a formal analysis framework was introduced for modelling wealth distr…
Decentralized Token Economy Theory (DeTEcT)
Rem Sadykhov, Geoffrey Goodell, Denis de Montigny +2
This paper presents a pioneering approach for simulation of economic activity, policy implementation, and pricing of goods in token economies. The paper proposes a formal analysis…
Multiscale Governance
David Pastor-Escuredo, Philip Treleaven
Future societal systems will be characterized by heterogeneous human behaviors and also collective action. The interaction between local systems and global systems will be complex.…
Towards an Ethical Framework in the Complex Digital Era
David Pastor-Escuredo, Ricardo Vinuesa
The digital revolution has brought ethical crossroads of technology, behavior and truth. However, the need of a comprehensive and constructive ethical framework is emerging as digi…