About Suffering in the World (homepage)
Developing the science about suffering
This document proposes to create a systematic discipline, called algoscience,* in which verifiable and cumulative knowledge concerning the whole variety of matters specifically related to the phenomenon of suffering is sought in accordance with recognized scientific or academic methods. The algoscientific methodology brings forth a new paradigm that allows the phenomenon of suffering to be considered as a specific, primary, empirical object, and worthy of the most objective and comprehensive treatment possible. The question of terminology is addressed. Preparatory notes are presented concerning algometry, the quantitative study of suffering. Collection and classification (of facts, ideas, documents) are to be done regarding kinds of suffering, people or animals that suffer, causes of suffering, solutions or strategies for suffering, and other topics. A bibliography started to be developed. Lastly, a supplements section is included.
* The word algonomy has also been proposed. It was used in older texts, but is now relinquished because it had different meanings depending on the context: it could refer to the discipline, or the sphere of interest, or the general approach that were suggested about suffering, which are now more clearly referred to as the algoscience, the algo-sphere, and the algospheric approach.
METHODOLOGY IN ALGOSCIENCE
Methodology is necessary to algoscience in order to develop formally its conceptual basis and its methods. The word methodology here refers to the rationale and the philosophical assumptions that underlie a particular discipline, and that determine how methods (specific principles, practices, procedures) are deployed and interpreted. There can be no detailed guide on how to create a new discipline, but algoscientists could probably draw many lessons from studies on how modern knowledge is pursued, or on how new fields are being developed (e.g. pain research, scientific study of consciousness, positive psychology, sociology of happiness...). For now, the main ideas that are proposed in algoscience methodology can be summed up as follows.
The future characteristics of algoscience are a matter for people to explore, to invent, and to agree upon. This discipline is originally conceived as a comprehensive body of theoretical and practical knowledge. It appears to be a very large discipline, given its specific object, the phenomenon of suffering, and its field, the set of all things that may concern directly or indirectly that formal object. Every modern science, it should be noted, seems to be exceedingly large, or indefinitely expansible. At this time, embryonic algoscience can be handled by "general algoscientists", but eventually the discipline, like others, will probably have to be divided into a number of specialized parts.
Recognition from the academic community will come to algoscience inasmuch as its "paradigm" helps to produce new theoretical and technical knowledge about suffering and its management. But prior to any demonstrative results, the following considerations may invite confidence in the new paradigm.
Algoscience considers the phenomenon of suffering as the "specific object" of a "comprehensive" discipline. For the first time, suffering is dealt with as a whole and intrinsic concern. Until now, this concern has generally been subordinated to other preoccupations in politics, economy, society, religion, morals, philosophy, medicine, psychology, neurology, etc., and advances about suffering have mostly followed from our interest in health, knowledge, love, welfare, security, etc. In algoscience, there is a reversal of perspective: suffering is not only specifically and extensively considered, but it is also the chief concern to which other preoccupations are subordinated. Suffering, in its own specificity, is the matter of algoscience: it is not as such the matter of neuroscience, psychotherapy, social work, or medicine because such disciplines are primarily concerned with aspects of suffering that are specific not to suffering itself, but to neuron and brain, or mind and behavior, or social problems, or health and illness. Hopefully, a general discipline about suffering will allow, in knowledge and action, new progress that other fields cannot make possible.
From a scientific point of view, algoscience considers suffering as a conceptually defined phenomenon: events or things in the real world are particular and unique, and it is the role of science to turn them into conceptually defined phenomena or facts that are general and comparable to one another. As a conceptually defined phenomenon, suffering is a kind of abstraction comprising temporal, spatial, subjective or other types of attributes, but devoid of particularities such as a date, a place, a specific individual's presence or any other contingent condition of manifestation. This abstractive process makes scientific knowledge possible, because it makes it "verifiable". It may be reminded that there is no truth in science, but only theories that at all time can be proved or disproved. In the same line of thought, it may be noted that all matters that may concern suffering can be treated in algoscience, but only inasmuch as they are amenable to scientific verification. Religious or philosophical viewpoints on suffering, for example, may be approached from a scientific point of view by algoscience or other sciences, although they belong in their specificity to religion or philosophy.
Algoscience considers suffering as an empirical concept, because it is a psychological process that can be observed through the behavior or the functioning of groups, individuals, bodies, brains, neurons… Suffering can be measured and modified, augmented or diminished, started or stopped. Objective correlations can be established, and empirical knowledge can be developed.
Algoscience considers suffering with a radical, typically scientific stance of objectivity. It does not value suffering negatively nor positively. Consequently, parts of algoscience that are evaluative (e.g. critical studies of theories), or prescriptive (e.g. developmental studies of antalgic factors), or even factual (e.g. inventorial collections), are scientific only inasmuch as "statements of existence of value" are used rather than "intrinsic value judgments". Criteria must be made explicit, in particular, when suffering is said to be good or bad, useful or useless, acceptable or unacceptable, avoidable or unavoidable, light or severe, etc. Authors of documents in algoscience should mandatorily identify in a formal fashion what, how, and especially "whose" values or interests are taken as parameters in their work. Neutral objectivity in science has often been a heuristic device, and hopefully it will have the same serendipity with suffering. Besides, there is a place for ethics in algoscience. The discipline itself cannot and should not have an ethical position, but students of suffering should have one! In short, algoscience as a discipline has only one purpose: universal knowledge about suffering. By itself, it has no other goal, value, strategy, or program of action.
new discipline has to develop its
own technical vocabulary. In
algoscience, the word 'suffering' itself is highly
problematic, because its definition remains an elusive
matter. For a start, let us simply talk about suffering as
the phenomenon of unpleasant feeling. It is
proposed to use the ancient Greek word algos,
which means suffering, as a convenient root for
forming neologisms, alleviating language
repetition, and facilitating a new more technical
The Wikipedia article about suffering has a section about words that are often used ambiguously when dealing with this topic. Here is an excerpt, as of 2022-02-02:
The word suffering is sometimes used in the narrow sense of physical pain, but more often it refers to mental or emotional pain, or more often yet to pain in the broad sense, i.e. to any unpleasant feeling, emotion or sensation. The word pain usually refers to physical pain, but it is also a common synonym of suffering. The words pain and suffering are often used both together in different ways. For instance, they may be used as interchangeable synonyms. Or they may be used in 'contradistinction' to one another, as in "pain is inevitable, suffering is optional", or "pain is physical, suffering is mental". Or they may be used to define each other, as in "pain is physical suffering", or "suffering is severe physical or mental pain".
Qualifiers, such as mental, emotional, psychological, and spiritual, are often used for referring to certain types of pain or suffering. In particular, mental pain (or suffering) may be used in relationship with physical pain (or suffering) for distinguishing between two wide categories of pain or suffering. A first caveat concerning such a distinction is that it uses physical pain in a sense that normally includes not only the 'typical sensory experience of physical pain' but also other unpleasant bodily experiences including air hunger, hunger, vestibular suffering, nausea, sleep deprivation, and itching. A second caveat is that the terms physical or mental should not be taken too literally: physical pain or suffering, as a matter of fact, happens through conscious minds and involves emotional aspects, while mental pain or suffering happens through physical brains and, being an emotion, involves important physiological aspects.
The word unpleasantness, which some people use as a synonym of suffering or pain in the broad sense, may refer to the basic affective dimension of pain (its suffering aspect), usually in contrast with the sensory dimension, as for instance in this sentence: “Pain-unpleasantness is often, though not always, closely linked to both the intensity and unique qualities of the painful sensation.” Other current words that have a definition with some similarity to suffering include distress, unhappiness, misery, affliction, woe, ill, discomfort, displeasure, disagreeableness.
in preparation concerning the usage and study of
terms and expressions used in algoscience can be
seen here: Terminology in
PREPARATORY NOTES FOR QUANTITATIVE STUDY OF SUFFERING
and estimation are of prime importance for most
rational activities dealing with suffering, and
quantitative studies concerning suffering should be
developed as an independent subdiscipline, algometry.
See a document in the making, Preparatory Notes for the
Measurement of Suffering, which begins as
follows (as of 2022-02-07):
(... continued at Preparatory Notes for the Measurement of Suffering...)
COLLECTING AND CLASSIFYING
Collecting and classifying are usually among the first activities that are done within a new discipline. It is necessary to collect facts, ideas, documents, and to classify them methodically for convenient retrieval and handling. In algoscience, lists as exhaustive as possible should be set up concerning people or animals who suffer, kinds of suffering, causes of suffering, people and organizations who cause suffering, solutions or strategies relative to suffering, people and organizations who contribute to stop, diminish or prevent excessive suffering, documents having to do with suffering, and many other topics. See a page in preparation: Collecting and Classifying in Algoscience.
is important in algoscience to
develop a bibliographic subspecialty
dealing with documents that can be
found on paper, or on the Internet,
or on other media, and that are
relevant to knowledge and action
about suffering. See a page in
for the Study of Suffering.
See in particular this personal
Last modification: 2024/02/09