Notes  The following text explores, in a speculative manner, the evolutionary question: Did high-intensity affective states, specifically Pain, emerge early in evolutionary history, or did they develop gradually over time? Note: We are not neuroscientists; our work draws on our evolutionary biology background and our efforts to develop welfare metrics that accurately reflect reality and effectively reduce suffering. We hope these ideas may interest researchers in neuroscience, comparative cognition, and animal welfare science. This discussion is part of a broader manuscript in progress, focusing on interspecific comparisons of affective capacities—a critical question for advancing animal welfare science and estimating the Welfare Footprint of animal-sourced products.     Key points  Ultimate question: Do primitive sentient organisms experience extreme pain intensities, or fine-grained pain intensity discrimination, or both? Scientific framing: Pain functions as a biological signalling system that guides behavior by encoding motivational importance. The evolution of Pain signalling —its intensity range and resolution (i.e., the granularity with which differences in Pain intensity can be perceived)— can be viewed as an optimization problem, where neural architectures must balance computational efficiency, survival-driven signal prioritization, and adaptive flexibility. Mathematical clarification: Resolution is a fundamental requirement for encoding and processing information. Pain varies not only in overall intensity but also in granularity—how finely intensity levels can be distinguished.  Hypothetical Evolutionary Pathways: by analysing affective intensity (low, high) and resolution (low, high) as independent dimensions, we describe four illustrative evolutionary scenarios that provide a structured framework to examine whether primitive sentient organisms can experience Pain of high intensity, nuanced affective intensities, both, or neither.     Introdu

We’ve redesigned effectivealtruism.org to improve understanding and perception of effective altruism, and make it easier to take action.  View the new site → I led the redesign and will be writing in the first person here, but many others contributed research, feedback, writing, editing, and development. I’d love to hear what you think, here is a feedback form. Redesign goals This redesign is part of CEA’s broader efforts to improve how effective altruism is understood and perceived. I focused on goals aligned with CEA’s branding and growth strategy: 1. Improve understanding of what effective altruism is Make the core ideas easier to grasp by simplifying language, addressing common misconceptions, and showcasing more real-world examples of people and projects. 2. Improve the perception of effective altruism I worked from a set of brand associations defined by the group working on the EA brand project[1]. These are words we want people to associate with effective altruism more strongly—like compassionate, competent, and action-oriented. 3. Increase impactful actions Make it easier for visitors to take meaningful next steps, like signing up for the newsletter or intro course, exploring career opportunities, or donating. We focused especially on three key audiences: * To-be direct workers: young people and professionals who might explore impactful career paths * Opinion shapers and people in power: journalists, policymakers, and senior professionals in relevant fields * Donors: from large funders to smaller individual givers and peer foundations Before and after The changes across the site are aimed at making it clearer, more skimmable, and easier to navigate. Here are some side-by-side comparisons: Landing page Some of the changes: * Replaced the economic growth graph with a short video highlighting different cause areas and effective altruism in action * Updated tagline to "Find the best ways to help others" based on testing by Rethink

A while back (as I've just been reminded by a discussion on another thread), David Thorstad wrote a bunch of posts critiquing the idea that small reductions in extinction risk have very high value, because the expected number of people who will exist in the future is very high: https://reflectivealtruism.com/category/my-papers/mistakes-in-moral-mathematics/. The arguments are quite complicated, but the basic points are that the expected number of people in the future is much lower than longtermists estimate because: -Longtermists tend to neglect the fact that even if your intervention blocks one extinction risk, there are others it might fail to block; surviving for billions  (or more) of years likely  requires driving extinction risk very low for a long period of time, and if we are not likely to survive that long, even conditional on longtermist interventions against one extinction risk succeeding, the value of preventing extinction (conditional on more happy people being valuable) is much lower.  -Longtermists tend to assume that in the future population will be roughly as large as the available resources can support. But ever since the industrial revolution, as countries get richer, their fertility rate falls and falls until it is below replacement. So we can't just assume future population sizes will be near the limits of what the available resources will support. Thorstad goes on to argue that this weakens the case for longtermism generally, not just the value of extinction risk reductions, since the case for longtermism is that future expected population  is many times the current population, or at least could be given plausible levels of longtermist extinction risk reduction effort. He also notes that if he can find multiple common mistakes in longtermist estimates of expected future population, we should expect that those estimates might be off in other ways. (At this point I would note that they could also be missing factors that bias their estimates of