This paper traces the origins of the styles project, originally presented as ‘styles of scientific reasoning’. ‘Styles of scientific thinking & doing’ is a better label; the styles can also be called genres, or, ways of finding out. A. C. Crombie’s template of six fundamentally distinct ones was turned into a philosophical tool, but with a tinge of Paul Feyerabend’s anarchism. Ways of finding out are not defined by necessary and sufficient conditions, but can be recognized as distinct within a (...) sweeping, anthropological, vision of the European sciences. The approach is unabashedly whiggish. The emergence of these styles is part of what Reviel Netz calls cognitive history, and is to be understood in an ecological way. How did a species like ours, on an Earth like this, develop a few quite general strategies for finding out about, and altering, its world? At a more analytical level, the project invokes Bernard Williams’ notion of truthfulness to explicate the idea that these styles are ‘self-authenticating’ and without foundations. The paper concludes with open questions. What role have these few fundamentally distinct genres of inquiry played in the formation of the anomalous Western idea of Nature apart from Man? (shrink)

One and many THE PLURALIST CHORUS There is a remarkable consensus on one point amongst recent thinkers and schools, even when they are otherwise radically ...

Every artificial-intelligence research project needs a working definition of "intelligence", on which the deepest goals and assumptions of the research are based. In the project described in the following chapters, "intelligence" is defined as the capacity to adapt under insufficient knowledge and resources. Concretely, an intelligent system should be finite and open, and should work in real time. ;If these criteria are used in the design of a reasoning system, the result is NARS, a non-axiomatic reasoning system. ;NARS uses a (...) term-oriented formal language, characterized by the use of subject-predicate sentences. The language has an experience-grounded semantics, according to which the truth value of a judgment is determined by previous experience, and the meaning of a term is determined by its relations with other terms. Several different types of uncertainty, such as randomness, fuzziness, and ignorance, can be represented in the language in a single way. ;The inference rules of NARS are based on three inheritance relations between terms. With different combinations of premises, revision, deduction, induction, abduction, exemplification, comparison, and analogy can all be carried out in a uniform format, the major difference between these types of inference being that different functions are used to calculate the truth value of the conclusion from the truth values of the premises. ;Since it has insufficient space-time resources, the system needs to distribute them among its tasks very carefully, and to dynamically adjust the distribution as the situation changes. This leads to a "controlled concurrency" control mechanism, and a "bag-based" memory organization. ;A recent implementation of the NARS model, with examples, is discussed. The system has many interesting properties that are shared by human cognition, but are absent from conventional computational models of reasoning. ;This research sheds light on several notions in artificial intelligence and cognitive science, including symbol-grounding, induction, categorization, logic, and computation. These are discussed to show the implications of the new theory of intelligence. ;Finally, the major results of the research are summarized, a preliminary evaluation of the working definition of intelligence is given, and the limitations and future extensions of the research are discussed. (shrink)

Explores the accomplishments of the golden age of "macrohistory," the sociologically informed analysis of long-term patterns of political, economic, and social change. The topics range from the Marxian-inspired theory of revolutions to the roots of the Holocaust.

In the popular misconception fostered by blockbuster action movies and best-selling thrillers--not to mention conventional explanations by social scientists--violence is easy under certain conditions, like poverty, racial or ideological hatreds, or family pathologies. Randall Collins challenges this view in Violence, arguing that violent confrontation goes against human physiological hardwiring. It is the exception, not the rule--regardless of the underlying conditions or motivations. -/- Collins gives a comprehensive explanation of violence and its dynamics, drawing upon video footage, cutting-edge forensics, and ethnography (...) to examine violent situations up close as they actually happen--and his conclusions will surprise you. Violence comes neither easily nor automatically. Antagonists are by nature tense and fearful, and their confrontational anxieties put up a powerful emotional barrier against violence. Collins guides readers into the very real and disturbing worlds of human discord--from domestic abuse and schoolyard bullying to muggings, violent sports, and armed conflicts. He reveals how the fog of war pervades all violent encounters, limiting people mostly to bluster and bluff, and making violence, when it does occur, largely incompetent, often injuring someone other than its intended target. Collins shows how violence can be triggered only when pathways around this emotional barrier are presented. He explains why violence typically comes in the form of atrocities against the weak, ritualized exhibitions before audiences, or clandestine acts of terrorism and murder--and why a small number of individuals are competent at violence. (shrink)

As this book reveals, his vision was much more complex and interesting. He hoped that machines would release people from relentless and repetitive drudgery in order to achieve more creative pursuits.

Sociology is split into two antagonistic or mutually oblivious wings: quantitative and nonquantitative. Statistics does not occupy a privileged methodological position vis-a-vis qualitative, verbal sociology. Probability is a theory like any other, and each statistical method contains its particular theoretical bias. Such biases should be brought into the open and tested. Statistics may continue to be useful, though, as a substantive theory of change processes in the social world. A reorientation in our views of statistics may bring mathematical and antimathematical (...) branches of sociology back into a common enterprise. (shrink)

Artificial intelligence can bring substantial benefits to society by helping to reduce costs, increase efficiency and enable new solutions to complex problems. Using Floridi’s notion of how to design the “infosphere” as a starting point, in this chapter I consider the question: what are the limits of design, i.e. what are the conceptual constraints on designing AI for social good? The main argument of this chapter is that while design is a useful conceptual tool to shape technologies and societies, collective (...) efforts towards designing future societies are constrained by both internal and external factors. Internal constraints on design are discussed by evoking Hardin’s thought experiment regarding “the Tragedy of the Commons”. Further, Hayek’s classical distinction between “cosmos” and “taxis” is used to demarcate external constraints on design. Finally, five design principles are presented which are aimed at helping policy makers manage the internal and external constraints on design. A successful approach to designing future societies needs to account for the emergent properties of complex systems by allowing space for serendipity and socio-technological coevolution. (shrink)