Adapt
This book outlines a counterintuitive approach to changing the world by assessing its failures, drawing on myriad disciplines to argue that complex challenges must be met through adaptive trial-and-error practices that do not depend on expert opinions or ready-made solutions. The author shows us a new and inspiring approach to solving the most pressing problems in our lives. When faced with complex situations, we have all become accustomed to looking to our leaders to set out a plan of action and blaze a path to success. He argues that today's challenges simply cannot be tackled with ready-made solutions and expert opinion; the world has become far too unpredictable and profoundly complex. Instead, we must adapt. Weaving together psychology, evolutionary biology, anthropology, physics, and economics, along with the compelling story of hard-won lessons learned in the field, he makes a case for the importance of adaptive trial and error in tackling issues such as climate change, poverty, and financial crises, as well as in fostering innovation and creativity in our business and personal lives. Taking us from corporate boardrooms to the deserts of Iraq, this book explains the necessary ingredients for turning failure into success. It is a handbook for surviving and prospering in our complex and ever-shifting world.
Stats for nerds
3recommend
5mentions
5people
2012–2023years
60% recommend40% neutral
Mentions per year
What Hacker News says
- Tim Harford’s “Adapt: Why Success Always Starts with Failure”¹, an enthralling collection of tales from really diverse projects. I’ll freely admit to only picking it up because I recognised the name from the More or Less² podcast, but it was well worth the read.
1. http://timharford.com/books/adapt/ 2. https://www.bbc.co.uk/programmes/b006qshd
- Regardless of the applicability to pg, I think this is a smart conversation. When evaluating hypotheses, we have a cognitive bias in favor of past success, when past failure might be the more informative signal. Planet Money did an interesting podcast with the author of this book:
http://www.amazon.com/Adapt-Success-Always-Starts-Failure/dp...
discussing just such a hypothesis:
http://www.npr.org/blogs/money/2012/09/04/160555540/episode-...
- "Adapt: Why Success Always Starts with Failure" by Tim Harford
- According to Tim Harford's book "Adapt" it's wrong to expect a company like shell to be good at renewables. Shell are good at handling liquid fuels and negotiating with governments. These are not useful skills for photovoltaics.
- Why not consider this an experiment in funding efficiency? This should be supported by scientists across the board, especially as it's such a tiny fraction of the NIH budget. It's a bit much to claim to fully know the outcome of such an experiment before doing it.
This being said, my priors are that it's a better funding model for engineering than for science. Engineering is more goal-oriented, whereas science is more exploratory. In science, it's not always a good idea to have a strongly desired outcome, because of problems associated with confirmation bias, or from spending too much time in the wrong rabbit hole. Apart from concerns about gaming the system (which are valid), the wrong phrasing of the goal could make it impossible to reach, or otherwise direct people in the wrong way. And it's not always clear that the understanding of some process or phenomenon will ever be done to some satisfaction; sometimes you get there and it's much more convoluted than anyone had previously assumed.
The economist Tim Harford discusses the merits of this funding model quite a bit in Adapt, and is quite supportive of it, as are other economists such as Alex Tabarrok. But it's really not clear that they understand the differences between engineering and science, either. Most of the DARPA/X-prize research, which are the typical examples of this funding model working effectively, are engineering research rather than science qua science, or the exploration of natural phenomena.
But given the NIH's aims (which are more practical than NSF's) and the recognition that blue-sky research needs to be funded differently, it might just be a great complement to the existing funding model. I would love to have a bit more of it for those of my own projects that are geared more towards tool/methods development than immediate phenomena understanding; I work on them in my spare time or in conjunction with scientific studies, and it'd be great if I could occasionally pay myself back for that time upon successful completion.
