This article discusses the evolution of futures studies. The article starts with an evaluation of the different rival taxonomies and definitions for futures studies, and proceeds to discuss the very concept of paradigm. Are there paradigms in this discipline? If we think there are, what kind of arguments can we use to define those? I argue that there have been two paradigms in the evolution of futures studies so far, and there are signs of emergence of a new one. Both of the existing paradigms have had many rival macro-level methodological approaches, ontological and epistemological branches, and phases of evolution. The first paradigm is the age-old prediction tradition that combines thinking about the future into mystic explanations. This line of thinking bases its argument on the deterministic future and effects of the world of spirits. The second paradigmwas basically started in the U.S. military after World War II. This modern line of thinking bases its argument on indeterministic futures, probabilities, aim to control and plan,modelling and systems thinking, and the effects of external trends. The new emerging paradigm may base its line of thinking on disconnecting from the western control based technical thinking, and accepting internal dynamic fluctuations, paradoxes and dialectic thinking.
“In this paper I describe the early development of the so-called mathematical biophysics, as conceived by Nicolas Rashevsky back in the 1920 ́s, as well as his latter idealization of a “relational biology”. I also underline that the creation of the journal “The Bulletin of Mathematical Biophysics” was instrumental in legitimating the efforts of Rashevsky and his students, and I finally argue that his pioneering efforts, while still largely unacknowledged, were vital for the development of important scientific contributions, most notably the McCulloch-Pitts model of neural networks.”
“The perspective of time and the evidence of increasing interest from the scientific community in understanding anticipatory processes speak in favor of describing the premises for the initial definition of anticipation.”
This is the preface to the second edition of Rosen’s Anticipatory Systems. Read it here: AnticipatSystRosen
A new century/millennium provides an opportune time to reflect on how the science of ecology evolved during the 19th and 20th centuries, and to predict how it is likely to change during the 21st century (at least to reflect on how it might evolve in order to best serve societies during the decades ahead). This viewpoint article will attempt to: (a) provide an overview regarding the emergence of ecology from a subdiscipline of biology to a discipline of its own during the past century (Odum 1977); (b) discuss the academic fragmentation of ecology into numerous subdisciplines of study; and (c) argue that a new field of transdisciplinary science is urgently needed that will not only integrate these emerging fields of the ecological sciences, but will interface with the humanities and the social sciences as well (i.e., similar to C. P. Snow’s “third culture,” Snow 1963). Earlier we termed this 21st century field of study “integrative science” (Barrett & Odum 1998; Barrett & Kress 2001).
Anticipation, ascertaining an alternative perspective, suggests a new frontier in science. The realisation of the integrated nature of knowledge about anticipation will eventually supersede the current fragmentation of research in this new inquiry domain. The subject’s inter- and cross-disciplinarity justifies the effort to document the breadth and depth of the anticipation research, even when the word anticipation is not spelled out. The identifier is clear: what happens before a possible outcome is even triggered? The aim is to assist those who are still not fully aware of the encompassing nature of anticipation, but interested in the subject, to formulate and test their own hypotheses. In some areas (such as computer-based applications), the expectation of reproducible results (characteristic of the nomothetic) is justified; in others, pertinent to the living (characteristic of the idiographic), anticipation proves rather difficult to define and probably impossible to emulate.
Ecosystem, which lays the basis for defining ecology, has always been viewed as an integrated unit of plants, animals and microbes interacting reciprocally with the biotic, abiotic and climatic factors composing their environment so that there is flow of energy, recycling of nutrients and display of regulatory functions. This kind of interpretation, however, is not adequate enough to understand the total systems dynamics. Considering the importance of integration of social, economic and cultural perspectives of human life with the conventional concept of ecosystem there was the milestone setting inception of the concept of ‘noosystem’ that paved the pathway to the genesis of such disciplines as environmental science, conservation biology, restoration ecology and deep ecology. The present work reviews all such perspectives so as to consolidate our concern with noosystem in general and deep ecology in particular.
Empirical evidence indicates that anticipatory representations grounded in the sensorimotor neural apparatus are crucially involved in several low and high level cognitive functions, including attention, motor control, planning, and goal-oriented behavior. A unitary theoretical framework is emerging that emphasizes how simulative capabilities enable social abilities, too, including joint attention, imitation, perspective taking and communication. We argue that anticipation will be a key element for bootstrapping high level cognitive functions in cognitive robotics, too. We thus propose the challenge of understanding how anticipatory representations, that serve for coordinating with the future and not only with the present, develop in situated agents.
An article by James Kobielus, overly optimistic. Please add your own critical thinking.
“AI is essentially a predictive technology. No matter what its algorithmic underpinnings, its core function is to make sophisticated inferences about what’s likely to happen based on myriad variables that have been distilled both from historical and real-time data. When it’s embedded in every device and refined continuously with fresh data, AI becomes a ubiquitous resource helping us all to anticipate what’s coming and do what’s necessary to keep our lives running smoothly.”