Every little bit helps: The ga
https://hardfork.substack.com/p/every-little-bit-helps-the-game-of Al Pacino best speech - Any Given Sunday: https://www.youtube.com/watch?v=f1yWSePMqsk The point is that every little bit of effort and help matters. This is why I love startup life, because in an early stage startup every little dollar helps. Every little bit of energy and effort really matters. There is...
Impression management attenuates the effect of ability on trust in economic exchange
https://www.pnas.org/doi/10.1073/pnas.2118548119 Are competent actors still trusted when they promote themselves? The answer to this question could have far-reaching implications for understanding trust production in a variety of economic exchange settings in which ability and impression management play vital roles, from succeeding in one’s job to excelling in the sales of...
Algorithms for seeding social networks can enhance the adoption of a public health intervention in urban India
https://www.pnas.org/doi/10.1073/pnas.2120742119 Targeting structurally influential individuals within social networks can enhance adoption of health interventions within populations. We tested the effectiveness of two algorithms to improve social contagion that do not require knowledge of the whole network structure. We mapped the social interactions of 2,491 women in 50 residential buildings (chawls)...
Knowledge overconfidence is associated with anti-consensus views on controversial scientific issues
https://www.science.org/doi/full/10.1126/sciadv.abo0038 Public attitudes that are in opposition to scientific consensus can be disastrous and include rejection of vaccines and opposition to climate change mitigation policies. Five studies examine the interrelationships between opposition to expert consensus on controversial scientific issues, how much people actually know about these issues, and how much...
Examining the generalizability of research findings from archival data
https://www.pnas.org/doi/10.1073/pnas.2120377119 This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions...
Wolf attacks predict far-right voting
https://www.pnas.org/doi/10.1073/pnas.2202224119 Does the return of large carnivores affect voting behavior? We study this question through the lens of wolf attacks on livestock. Sustained environmental conservation has allowed the wolf (Canis lupus) to make an impressive and unforeseen comeback across Central Europe in recent years. While lauded by conservationists, local residents...
A recipe for success? Sustaining creativity among first-time creative producers
https://psycnet.apa.org/record/2022-61160-001?doi=1 Sustaining creativity is difficult. We identify the conditions that determine repeat production of novelty among first-time producers, and the psychological mechanism transmitting their effects. Our theoretical model highlights that the novelty of a first production can lower the probability of creating a second production, particularly when the first production...
Human-centred mechanism design with Democratic AI
https://www.nature.com/articles/s41562-022-01383-x Building artificial intelligence (AI) that aligns with human values is an unsolved problem. Here we developed a human-in-the-loop research pipeline called Democratic AI, in which reinforcement learning is used to design a social mechanism that humans prefer by majority. A large group of humans played an online investment game...
Why you should write weekly 15-5s
https://eugeneyan.com/writing/15-5/ A 15-5 is a weekly update that takes 15 minutes to write and 5 minutes to read. I learned about it from Gergely Orosz and Will Larson. (They call it the 5-15 but I’ve remembered it as 15-5 because the 15-min writing comes before the 5-min reading.) I’ve been...
How to build ML-first products
https://recsysml.substack.com/p/how-to-build-ml-first-products Problems #1 Sprinkling ML late in the product does not work well. “The problem is that ML takes time and iteration to do well and some product teams continue to think of ML as the last 20% in the 80/20 or the Run in Crawl/Walk/Run and this usually means...
About
a collection of excerpts from interesting research, blogs, events, etc...