Search:
Match:
5 results

R&D Networks and Productivity Gaps

Published:Dec 29, 2025 09:45
1 min read
ArXiv

Analysis

This paper extends existing R&D network models by incorporating heterogeneous firm productivities. It challenges the conventional wisdom that complete R&D networks are always optimal. The key finding is that large productivity gaps can destabilize complete networks, favoring Positive Assortative (PA) networks where firms cluster by productivity. This has important implications for policy, suggesting that productivity-enhancing policies need to consider their impact on network formation and effort, as these endogenous responses can counteract intended welfare gains.
Reference

For sufficiently large productivity gaps, the complete network becomes unstable, whereas the Positive Assortative (PA) network -- where firms cluster by productivity levels -- emerges as stable.

Analysis

This paper challenges the conventional wisdom that exogenous product characteristics are necessary for identifying differentiated product demand. It proposes a method using 'recentered instruments' that combines price shocks and endogenous characteristics, offering a potentially more flexible approach. The core contribution lies in demonstrating identification under weaker assumptions and introducing the 'faithfulness' condition, which is argued to be a technical, rather than economic, restriction. This could have significant implications for empirical work in industrial organization, allowing researchers to identify demand functions in situations where exogenous characteristic data is unavailable or unreliable.
Reference

Price counterfactuals are nonparametrically identified by recentered instruments -- which combine exogenous shocks to prices with endogenous product characteristics -- under a weaker index restriction and a new condition we term faithfulness.

Analysis

This paper addresses the problem of achieving consensus in a dynamic network where agents update their states asynchronously. The key contribution is the introduction of selective neighborhood contraction, where an agent's neighborhood can shrink after an update, alongside independent changes in other agents' neighborhoods. This is a novel approach to consensus problems and extends existing theory by considering time-varying communication structures with endogenous contraction. The paper's significance lies in its potential applications to evolving social systems and its theoretical contribution to understanding agreement dynamics under complex network conditions.
Reference

The system reaches consensus almost surely under the condition that the evolving graph is connected infinitely often.

Research#Game Theory🔬 ResearchAnalyzed: Jan 10, 2026 12:59

Strategic Evolution: AI Games with Endogenous Players and Replicators

Published:Dec 5, 2025 21:58
1 min read
ArXiv

Analysis

This ArXiv article explores the dynamics of strategic evolution in game theory, focusing on how player populations and strategies change. Understanding these dynamics could significantly improve the design and analysis of AI agents in competitive scenarios.
Reference

The article likely investigates games with endogenous players and strategic replicators.

Research#AI and Biology📝 BlogAnalyzed: Jan 3, 2026 07:13

#102 - Prof. MICHAEL LEVIN, Prof. IRINA RISH - Emergence, Intelligence, Transhumanism

Published:Feb 11, 2023 01:45
1 min read
ML Street Talk Pod

Analysis

This article is a summary of a podcast episode. It introduces two professors, Michael Levin and Irina Rish, and their areas of expertise. Michael Levin's research focuses on the biophysical mechanisms of pattern regulation and the collective intelligence of cells, including synthetic organisms and AI. Irina Rish's research is in AI, specifically autonomous AI. The article provides basic biographical information and research interests, serving as a brief overview of the podcast's content.
Reference

Michael Levin's research focuses on understanding the biophysical mechanisms of pattern regulation and harnessing endogenous bioelectric dynamics for rational control of growth and form.