Tootfinder

One of the things I've come to realise is most important to teach students in a subject trying to improve their social media critical literacy concerns the nature of financialization: students have a 'lay' perception that a business idea needs to be somewhat viable, whereas investors make money out of stupid ideas by riding the hype cycle and getting out before the trough of disappointment. Most of the platforms we use are epiphenomenal to investors making money by attracting oth…

"Cash-strapped London council starts crowdfunding drive to pay for green upgrades"
#UK #UnitedKingdom

Machine learning?: Definitely useful. AI?: Unclear. https://hbr.org/2023/06/the-ai-hype-cycle-is-distracting-companies

The AI Hype Cycle Is Distracting Companies
Machine learning has an “AI” problem. With new breathtaking capabilities from generative AI released every several months — and AI hype escalating at an even higher rate — it’s high time we differentiate most of today’s practical ML projects from those research advances. This begins by correctly naming such projects: Call them “ML,” not “AI.” Including all ML initiatives under the “AI” umbrella oversells and misleads, contributing to a high failure rate for ML business d…

Penetration Testing and Legacy Systems
Sandra Smyth
https://arxiv.org/abs/2402.10217 https://arxiv.org/pdf/2402.10217

Penetration Testing and Legacy Systems
As per Adusumilli (2015),'70% of corporate business systems today are legacy applications. Recent statistics prove that over 60% of IT budget is spent on maintaining these Legacy systems, showing the rigidity and the fragile nature of these systems.' Usually, testing is included during the software development cycle, using testing techniques such as unit testing, integration testing, and system testing before releasing the product. After the software product is released to production, no additi…

Testing Business Cycle Theories: Evidence from the Great Recession
Bo Li
https://arxiv.org/abs/2403.04104 https://arxiv.org/pdf/2403.…

Testing Business Cycle Theories: Evidence from the Great Recession
Empirical business cycle studies using cross-country data usually cannot achieve causal relationships while within-country studies mostly focus on the bust period. We provide the first causal investigation into the boom period of the 1999-2010 U.S. cross-metropolitan business cycle. Using a novel research design, we show that credit expansion in private-label mortgages causes a differentially stronger boom (2000-2006) and bust (2007-2010) cycle in the house-related industries in the high net-ex…

Testing Business Cycle Theories: Evidence from the Great Recession
Bo Li
https://arxiv.org/abs/2403.04104 https://arxiv.org/pdf/2403.…

Testing Business Cycle Theories: Evidence from the Great Recession
Empirical business cycle studies using cross-country data usually cannot achieve causal relationships while within-country studies mostly focus on the bust period. We provide the first causal investigation into the boom period of the 1999-2010 U.S. cross-metropolitan business cycle. Using a novel research design, we show that credit expansion in private-label mortgages causes a differentially stronger boom (2000-2006) and bust (2007-2010) cycle in the house-related industries in the high net-ex…

Estimating HANK with Micro Data
Man Chon Iao, Yatheesan J. Selvakumar
https://arxiv.org/abs/2402.11379 https://arxiv.org/pdf/2402.113…

Estimating HANK with Micro Data
We propose an indirect inference strategy for estimating heterogeneous-agent business cycle models with micro data. At its heart is a first-order vector autoregression that is grounded in linear filtering theory as the cross-section grows large. The result is a fast, simple and robust algorithm for computing an approximate likelihood that can be easily paired with standard classical or Bayesian methods. Importantly, our method is compatible with the popular sequence-space solution method, unlik…