Thank you very much for this thorough analysis and for the constructive comments. Cynthia will address the points related to the results of the study, while I’ll focus here on the methodological aspects.
One of the most important points you raise touches on the core of the Welfare Footprint Framework itself: we recognize that inferring the affective states of other beings is enormously challenging—both in scope and depth. This task can never be complete; it will always require revisions and corrections as new evidence becomes available. The Welfare Footprint Framework is, in essence, an attempt to structure this challenge into as many workable, auditable pieces as possible, so that the process of inference can be progressively improved and openly scrutinized.
You are absolutely right that several painful conditions in chickens were not included in this initial analysis. This was a conscious decision—not because those harms are unimportant, but because we had to start with a subset that we judged to be among the most influential and best documented. The framework is designed precisely so that others can build upon it by incorporating additional conditions, refining prevalence estimates, or reassessing intensities. In that sense, this work should be seen as a living model, not a closed dataset.
Regarding the concern about the lack of use of high-quality statistical techniques, our approach is pragmatic. Where robust statistical analyses are feasible—such as in estimating prevalence or duration—they are of course welcome and encouraged. But in areas where measurement is currently impossible—most notably the intensity of affective states—we deliberately avoid mathematical sophistication for its own sake. No amount of elegant equations can compensate for the fact that subjective experience is, for now, beyond direct measurement. What we can do is gather convergent evidence from different sources—e.g. behavior, physiology, neurology, evolutionary reasoning—and generalize that evidence into transparent, revisable estimates, and make every assumption explicit so that others can challenge and adjust them.
As for the legitimacy of this approach, we believe that, while imperfect and always improvable, quantifying affective experiences is vastly more informative than relying solely on indirect indicators such as mortality. Animals can live long, physically healthy lives that are nevertheless filled with frustration, chronic pain, fear, or monotony—forms of suffering invisible to metrics that focus only on death or disease. By directing efforts toward gathering as much evidence as possible to infer the intensity and duration of each stage spent in negative and positive affective states, we can begin to capture what actually matters to the animal.
The framework has also evolved since this analysis was first produced. At that time, we focused primarily on negative affective states, but we have now extended the methodology to include Cumulative Pleasure alongside Cumulative Pain. Positive affective states are now being systematically quantified using the same operational principles, creating a fuller picture of animal welfare.
Finally, we are developing an open, collaborative platform where Pain-Tracks and Pleasure-Tracks can be published, discussed, and iteratively improved by the broader scientific community. Each component of a track—for example, the probability assigned to a certain intensity within a phase of an affective experience—could be challenged and refined, potentially even through expert voting or consensus mechanisms. The aim is to make welfare quantification transparent, dynamic, and collective rather than proprietary.
Thanks again for putting our work under the microscope—this is exactly what it needs. The Framework is meant to evolve, and feedback like yours helps it grow in the right direction.
Thank you very much for this thorough analysis and for the constructive comments.
Cynthia will address the points related to the results of the study, while I’ll focus here on the methodological aspects.
One of the most important points you raise touches on the core of the Welfare Footprint Framework itself: we recognize that inferring the affective states of other beings is enormously challenging—both in scope and depth. This task can never be complete; it will always require revisions and corrections as new evidence becomes available. The Welfare Footprint Framework is, in essence, an attempt to structure this challenge into as many workable, auditable pieces as possible, so that the process of inference can be progressively improved and openly scrutinized.
You are absolutely right that several painful conditions in chickens were not included in this initial analysis. This was a conscious decision—not because those harms are unimportant, but because we had to start with a subset that we judged to be among the most influential and best documented. The framework is designed precisely so that others can build upon it by incorporating additional conditions, refining prevalence estimates, or reassessing intensities. In that sense, this work should be seen as a living model, not a closed dataset.
Regarding the concern about the lack of use of high-quality statistical techniques, our approach is pragmatic. Where robust statistical analyses are feasible—such as in estimating prevalence or duration—they are of course welcome and encouraged. But in areas where measurement is currently impossible—most notably the intensity of affective states—we deliberately avoid mathematical sophistication for its own sake. No amount of elegant equations can compensate for the fact that subjective experience is, for now, beyond direct measurement. What we can do is gather convergent evidence from different sources—e.g. behavior, physiology, neurology, evolutionary reasoning—and generalize that evidence into transparent, revisable estimates, and make every assumption explicit so that others can challenge and adjust them.
As for the legitimacy of this approach, we believe that, while imperfect and always improvable, quantifying affective experiences is vastly more informative than relying solely on indirect indicators such as mortality. Animals can live long, physically healthy lives that are nevertheless filled with frustration, chronic pain, fear, or monotony—forms of suffering invisible to metrics that focus only on death or disease. By directing efforts toward gathering as much evidence as possible to infer the intensity and duration of each stage spent in negative and positive affective states, we can begin to capture what actually matters to the animal.
The framework has also evolved since this analysis was first produced. At that time, we focused primarily on negative affective states, but we have now extended the methodology to include Cumulative Pleasure alongside Cumulative Pain. Positive affective states are now being systematically quantified using the same operational principles, creating a fuller picture of animal welfare.
Finally, we are developing an open, collaborative platform where Pain-Tracks and Pleasure-Tracks can be published, discussed, and iteratively improved by the broader scientific community. Each component of a track—for example, the probability assigned to a certain intensity within a phase of an affective experience—could be challenged and refined, potentially even through expert voting or consensus mechanisms. The aim is to make welfare quantification transparent, dynamic, and collective rather than proprietary.
Thanks again for putting our work under the microscope—this is exactly what it needs. The Framework is meant to evolve, and feedback like yours helps it grow in the right direction.