The shortage of long-term data can be explained in practical terms. The field is quite young, clinical trials are expensive, and following up on patients for a long period of time often exceeds the budget. But what about the very idea of measuring human experiences? As a society, we have developed an obsession with measuring and quantitative reporting. In psychedelic science, we see an ever-growing number of questionnaires, designed to measure anything from psychological states to mystical experiences.
Until not long ago, complex calculations belonged to mathematicians, scientists, and businessmen, but those days are gone. For example, establishments providing public services, once considered unquantifiable, like government agencies, cultural and educational institutions, are required to write periodic reports proving their effectiveness in numbers. The UN plays a great role in this international trend through its Sustainable Development Goals (SDGs). The goals are for all countries to apply and concern 17 different issues, from eliminating poverty to supplying education and protecting nature. Looking at the site’s header proves my point: “The 17 Goals | 169 targets, 4075 events, 1371 publications, 8550 actions”. Conceptually and graphically, the measurements come before what is being measured.
Depending on the industry, psychedelics are presented as a new elixir that can solve your psychological issues, improve your performance, make your life longer, your chronic pain disappear, your addiction go away… and the list goes on. Yet the prevalence of stories about a single psychedelic experience is not only due to the before-and-after logic. It also reflects caution due to illegality and social stigma. Researchers, trial participants, journalists, and celebrities who are interviewed in countries where psychedelics are illegal often avoid discussing regular use. Instead, they talk about ‘that one time’ they took ayahuasca in South America or truffles in the Netherlands. But as Erik Davis notes, meaningful development happens over time, and the longevity of such processes is precisely what makes them so complicated to understand.
Cause-and-effect and measurement-based scientific methods are useful in explaining short and specific processes in a lab environment, but longer real-life processes are much more complex and unpredictable. Recently, it became clear that this is true even in the case of technology, as AI creators admitted that they do not understand how their own creations work. Indeed, Kumo CEO Vanja Josifovski argues that our expectation of simple explainability does not fit complex, intelligent systems like AI, where decisions “may be based on billions of micro-decisions encoded in massive matrices.” Now think about all the decisions, relationships, and systems entangled in human life and development.
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