technocracy

Prediction markets turn collective knowledge into live probability estimates. Here’s how they work, how probabilities are represented in markets, and why prices aggregate information better than polls or pundits. The Room That Couldn’t Agree Imagine asking 50 intelligent, informed people the same question: “What’s the probability that the Federal Reserve cuts interest rates at its next meeting?” You’d get 50 different answers. Some would say 30%. Others would say 70%. A few would say they have no idea. All […]

Ver mais

Centralized value learning is often assumed to improve coordination and stability in multi-agent reinforcement learning, yet this assumption is rarely tested under controlled conditions. We directly evaluate it in a fully tabular predator-prey gridworld by comparing independent and centralized Q-learning under explicit embodiment constraints on agent speed and stamina. Across multiple kinematic regimes and asymmetric agent roles, centralized learning fails to provide a consistent advantage and is frequently outperformed by fully independent learning, even under full observability and […]

Ver mais

arXiv:2602.11250v1 Announce Type: new Abstract: Consider $n$ points generated uniformly at random in the unit square, and let $L_n$ be the length of their optimal traveling salesman tour. Beardwood, Halton, and Hammersley (1959) showed $L_n / sqrt n to beta$ almost surely as $nto infty$ for some constant $beta$. The exact value of $beta$ is unknown but estimated to be approximately $0.71$ (Applegate, Bixby, Chv’atal, Cook 2011). Beardwood et al. further showed that $0.625 leq beta leq 0.92116.$ […]

Ver mais

We study the problem of learning multi-index models (MIMs), where the label depends on the input $boldsymbol{x} in mathbb{R}^d$ only through an unknown $mathsf{s}$-dimensional projection $boldsymbol{W}_*^mathsf{T} boldsymbol{x} in mathbb{R}^mathsf{s}$. Exploiting the equivariance of this problem under the orthogonal group $mathcal{O}_d$, we obtain a sharp harmonic-analytic characterization of the learning complexity for MIMs with spherically symmetric inputs — which refines and generalizes previous Gaussian-specific analyses. Specifically, we derive statistical and computational complexity lower bounds within the Statistical Query (SQ) […]

Ver mais

While clients may join federated learning to improve performance on data they rarely observe locally, they often remain self-interested, expecting the global model to perform well on their own data. This motivates an objective that ensures all clients achieve a similar loss gap -the difference in performance between the global model and the best model they could train using only their local data-. To this end, we propose EAGLE, a novel federated learning algorithm that explicitly regularizes the […]

Ver mais

arXiv:2602.13320v1 Announce Type: new Abstract: As AI agents powered by large language models (LLMs) increasingly use external tools for high-stakes decisions, a critical reliability question arises: how do errors propagate across sequential tool calls? We introduce the first theoretical framework for analyzing error accumulation in Model Context Protocol (MCP) agents, proving that cumulative distortion exhibits linear growth and high-probability deviations bounded by $O(sqrt{T})$. This concentration property ensures predictable system behavior and rules out exponential failure modes. We develop […]

Ver mais

arXiv:2604.16963v1 Announce Type: new Abstract: The Deliberative Reason Index (DRI) is increasingly used to assess the coherence between considerations and preferences in deliberative settings, including applications to LLM-generated data. Under low-signal conditions, however, the standard DRI can produce inflated scores by treating near-zero correlations as evidence of consistency. Monte Carlo simulations across common study designs show that this bias increases with group size and yields positive values even under random response. A modified DRI is introduced that applies […]

Ver mais

Recent machine-learning approaches to weather forecasting often employ a monolithic architecture, where distinct physical mechanisms (advection, transport), diffusion-like mixing, thermodynamic processes, and forcing are represented implicitly within a single large network. This representation is particularly problematic for advection, where long-range transport must be treated with expensive global interaction mechanisms or through deep, stacked convolutional layers. To mitigate this, we present PARADIS, a physics-inspired global weather prediction model that imposes inductive biases on network behavior through a functional decomposition […]

Ver mais