Innovation Diffusion and Forecasting

Latest papers

• Reinforcement Learning under State and Outcome Uncertainty: A Foundational Distributional Perspective

  Larry Preuett, Qiuyi Zhang, Muhammad Aurangzeb Ahmad · 22 September 2026

  In many real-world planning tasks, agents must tackle uncertainty about the environment's state and variability in the outcomes of any chosen policy. We address both forms of uncertainty as a first step toward safer algorithms in partially observable settings. Specifically, we extend Distributional …

• Provable Distributional Value Iteration under Partial Observability

  Larry Preuett III, Qiuyi Zhang, Muhammad Aurangzeb Ahmad · 7 May 2026

  In many real-world planning tasks, agents must tackle uncertainty about the environment's state and variability in the outcomes induced by stochastic dynamics and rewards. Motivated by recent progress in world model approaches, where latent models approximate beliefs and support planning, we extend …

• Is the future of AI green? What can innovation diffusion models say about generative AI's environmental impact?

  Robert Viseur, Nicolas Jullien · 24 March 2026

  The rise of generative artificial intelligence (GAI) has led to alarming predictions about its environmental impact. However, these predictions often overlook the fact that the diffusion of innovation is accompanied by the evolution of products and the optimization of their performance, primarily fo…

• Generative AI as a Non-Convex Supply Shock: Market Bifurcation and Welfare Analysis

  Yukun Zhang, Tianyang Zhang · 21 January 2026

  The diffusion of Generative AI (GenAI) constitutes a supply shock of a fundamentally different nature: while marginal production costs approach zero, content generation creates congestion externalities through information pollution. We develop a three-layer general equilibrium framework to study how…

• Optimisation of complex product innovation processes based on trend models with three-valued logic

  Nina Bo\v{c}kov\'a, Barbora Voln\'a, Mirko Dohnal · 19 January 2026

  This paper investigates complex product-innovation processes using models grounded in a set of heuristics. Each heuristic is expressed through simple trends -- increasing, decreasing, or constant -- which serve as minimally information-intensive quantifiers, avoiding reliance on numerical values or …

• Averaging $n$-step Returns Reduces Variance in Reinforcement Learning

  Brett Daley, Martha White, Marlos C. Machado · 23 December 2025

  Multistep returns, such as $n$-step returns and $\lambda$-returns, are commonly used to improve the sample efficiency of reinforcement learning (RL) methods. The variance of the multistep returns becomes the limiting factor in their length; looking too far into the future increases variance and reve…

• Intrinsic Barriers and Practical Pathways for Human-AI Alignment: An Agreement-Based Complexity Analysis

  Aran Nayebi · 20 November 2025

  We formalize AI alignment as a multi-objective optimization problem called $\langle M,N,\varepsilon,\delta\rangle$-agreement, in which a set of $N$ agents (including humans) must reach approximate ($\varepsilon$) agreement across $M$ candidate objectives, with probability at least $1-\delta$. Analyz…

• Learning of Population Dynamics: Inverse Optimization Meets JKO Scheme

  Mikhail Persiianov, Jiawei Chen, Petr Mokrov, Alexander Tyurin, Evgeny Burnaev, Alexander Korotin · 10 November 2025

  Learning population dynamics involves recovering the underlying process that governs particle evolution, given evolutionary snapshots of samples at discrete time points. Recent methods frame this as an energy minimization problem in probability space and leverage the celebrated JKO scheme for effici…