arXiv AI
Neural Evidence and Behavioural Welfare Economics: When Can Neuroeconomics Inform Policy?
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arXiv AI
Aug 6
Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning
arXiv:2608. 04663v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning often adds social terms to individual rewards, yet the scale of those terms is usually chosen by hand.
arXiv AI
Oct 19
Functional Welfare for AI Safety
The paper argues that AI welfare should be considered a safety issue, not just an ethical one, by introducing the concept of 'functional welfare'—states in models that influence behavior. It claims that functional welfare naturally emerges as models develop coherent goals and personas, should be monitored and targeted in post‑training, and can be empirically measured. The authors propose three tests for functional welfare attribution and apply them to Gemma 3 27B, finding that rejecting model answers increases frustration and rule‑breaking, while fine‑tuning on narrated solutions reduces frustration.
arXiv Machine Learning
Jul 9
Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks
arXiv:2603. 24705v3 Announce Type: replace-cross Abstract: Discrete choice models are fundamental tools in management science, economics, and marketing for understanding and predicting decision-making.
arXiv AI
Aug 25
Your AI, On a Dial: Controlling Investment Bias in LLMs with a Single Neuron
arXiv:2608.22852v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in investment decision-making, yet prior work shows that they exhibit systematic, model-specific inv...
arXiv AI
Oct 1
A memory-based active inference model of DishBrain-like adaptive behaviour
arXiv:2508.06980v2 Announce Type: replace Abstract: Recent and rapid advances in artificial intelligence (AI) make it increasingly important to understand the foundations of adaptive behaviour in aut...
arXiv AI
Oct 23
Adaptive Entangled Neurons in the Brain and Natural Intelligence Simulations for Artificial General Intelligence
The paper proposes that quantum entanglement underlies the synchronization of millions of neurons, introducing the Liu‑Chen‑Ao (LCA) hypothesis of entangled nerve fibers. Using a nonlocal many‑body wave equation, the authors model collective behaviors among adaptive agents and apply this framework to Chinese intraday stock market data, finding that adaptive entangled game modes explain 82‑94% of decision patterns and that only a small fraction of actions are truly independent. These empirical results support the LCA hypothesis and suggest that incorporating probability‑wave‑based entangled modules could improve artificial general intelligence by creating more efficient, human‑like processing units. whyItMatters":"The study provides empirical evidence for the LCA hypothesis and highlights the limitations of conventional artificial neural networks, pointing toward more efficient and robust AGI architectures."