When we tell people that Linear maintains a zero-bug policy a common response is disbelief. It may sound like a ridiculous approach, but we do it because no other way makes sense.
Lost in Bugspace
The temporality of implementation uncertainty in software
Putting nudges in perspective
Conventional economic policy focuses on ‘economic’ solutions (e.g. taxes, incentives, regulation) to problems caused by market-level factors such as externalities, misaligned incentives and information asymmetries. By contrast, ‘nudges’ provide behavioural solutions to problems that have generally been assumed to originate from limitations in human decision making, such as present bias. While policy-makers have good reason for exploiting the power of nudges, we argue that these extremes leave open a large space of policy options that have received less attention in the academic literature. First, there is no reason that solution and problem need have the same theoretical basis: there are promising behavioural solutions to problems that have causes that are well explained by traditional economics, and conventional economic solutions often offer the best line of attack on problems of behavioural origin. Second, there is a wide range of hybrid policy actions with both economic and behavioural components (e.g. framing a tax or incentive in a specific way), and there exist many societal problems – perhaps the majority – that arise from both economic and behavioural factors (e.g. firms’ exploitation of consumers’ behavioural biases). This paper aims to remind policy-makers that behavioural economics can influence policy in a variety of ways, of which nudges are the most prominent but not necessarily the most powerful.
Issue tracking is dead – Linear
The next era of product development is built on context and agency. Here's how Linear is evolving. And what we're launching today.
Mechanism Experiments and Policy Evaluations
Randomized controlled trials are increasingly used to evaluate policies. How can we make these experiments as useful as possible for policy purposes? We argue greater use should be made of experiments that identify the behavioral mechanisms that are central to clearly specified policy questions, what we call "mechanism experiments." These types of experiments can be of great policy value even if the intervention that is tested (or its setting) does not correspond exactly to any realistic policy option.
Rational Inattention: A Review
We review the recent literature on rational inattention, identify the main theoretical mechanisms, and explain how it helps us understand a variety of phenomena across fields of economics. The theory of rational inattention assumes that agents cannot process all available information, but they can choose which exact pieces of information to attend to. Several important results in economics have been built around imperfect information. Nowadays, many more forms of information than ever before are available due to new technologies, and yet we are able to digest little of it. Which form of imperfect information we possess and act upon is thus largely determined by which information we choose to pay attention to. These choices are driven by current economic conditions and imply behavior that features numerous empirically supported departures from standard models. Combining these insights about human limitations with the optimizing approach of neoclassical economics yields a new, generally applicable model.
The i-frame and the s-frame: How focusing on individual-level solutions has led behavioral public policy astray
An influential line of thinking in behavioral science, to which the two authors have long subscribed, is that many of society's most pressing problems can be addressed cheaply and effectively at the level of the individual, without modifying the system in which the individual operates. We now believe this was a mistake, along with, we suspect, many colleagues in both the academic and policy communities. Results from such interventions have been disappointingly modest. But more importantly, they have guided many (though by no means all) behavioral scientists to frame policy problems in individual, not systemic, terms: To adopt what we call the “i-frame,” rather than the “s-frame.” The difference may be more consequential than i-frame advocates have realized, by deflecting attention and support away from s-frame policies. Indeed, highlighting the i-frame is a long-established objective of corporate opponents of concerted systemic action such as regulation and taxation. We illustrate our argument briefly for six policy problems, and in depth with the examples of climate change, obesity, retirement savings, and pollution from plastic waste. We argue that the most important way in which behavioral scientists can contribute to public policy is by employing their skills to develop and implement value-creating system-level change.
Misperception of Exponential Growth: Are People Aware of Their Errors?
Previous research shows that individuals make systematic errors when judging exponential growth, which has harmful effects for their financial well-being. This study analyzes how far individuals are aware of their errors and how these errors are shaped by arithmetic and conceptual problems. Whereas arithmetic problems could be overcome using computational assistance like a pocket calculator, this is not the case for conceptual problems, a term we use to subsume other error drivers like a general misunderstanding of exponential growth or overwhelming task complexity. In an incentivized experiment, we find that participants strongly overestimate the accuracy of their intuitive judgment. At the same time, their willingness to pay for arithmetic assistance is too high on average, often much above the actual benefits a calculator provides. Using a multitier system of task complexity we can show that the willingness to pay for arithmetic assistance is hardly related to its benefits, indicating that participants do not really understand how the interplay of arithmetic and conceptual problems shape their errors in exponential growth tasks. Our findings are relevant for policymaking and financial advisory practice and can help to design effective approaches to mitigate the detrimental effects of misperceived exponential growth.
Ten FOSS Development Fallacies For User Facing Software
This is a list, inspired by the Five Geek Social Fallacies, of common patterns or behaviours that developers of free/open source software (FOSS) fall into when they try to develop software for users who are not technical, or for whom non-technical users would be the most obvious userbase. Hopefully writing these down (which frankly is cathartic more than anything) is helpful to some in recognising these thought patterns and avoiding them when developing software - I’ve got some of my own thoughts on this at the bottom.
Why Centralized AI Is Not Our Inevitable Future
This talk was given at the O’Reilly AI Codecon on September 9th. Here’s a recording of the talk as delivered. Here’s the official recording (which had a slight technical glitch day of). https://common.tools/talks/why-centralized-ai-is-not-our-inevitable-future points here —--- Hi, I'm Alex Komoro...
On technological optimism and technological pragmatism
"There are no guarantees that things are going to turn out very well for anyone.”
A protocol for structured robustness reproductions and replicability assessments
Abstract. Robustness reproductions and replicability discussions are on the rise in response to concerns about a potential credibility crisis in economics.
Microsoft Struggling With Hundreds of AI-Discovered Security Bugs — ProPublica
Anthropic’s Mythos has flagged bugs faster than Microsoft can fix them. Documents reviewed by ProPublica reveal the tech giant's “mad dash” behind the scenes to patch holes before hackers can find and exploit them.
How I stopped worrying and learned to love the easy fix
On the balance between perfect solutions and pragmatic fixes in software engineering