Fifty years of effort in artificial intelligence (AI) and the formalization of legal reasoning have produced both successes and failures. Considerable success in organizing and displaying evidence and its interrelationships has been accompanied by failure to achieve the original ambition of AI as applied to law: fully automated legal decision-making. The obstacles to formalizing legal reasoning have proved to be the same ones that make the formalization of commonsense reasoning so difficult, and are most evident where legal reasoning has to (...) meld with the vast web of ordinary human knowledge of the world. Underlying many of the problems is the mismatch between the discreteness of symbol manipulation and the continuous nature of imprecise natural language, of degrees of similarity and analogy, and of probabilities. (shrink)

Both the traditional Aristotelian and modern symbolic approaches to logic have seen logic in terms of discrete symbol processing. Yet there are several kinds of argument whose validity depends on some topological notion of continuous variation, which is not well captured by discrete symbols. Examples include extrapolation and slippery slope arguments, sorites, fuzzy logic, and those involving closeness of possible worlds. It is argued that the natural first attempts to analyze these notions and explain their relation to reasoning fail, so (...) that ignorance of their nature is profound. (shrink)

Despite its importance, the linkage between genetic and cultural evolution has until now been little explored. An understanding of this linkage is needed to extend evolutionary theory so that it can deal for the first time with the phenomena of mind and human social history. We characterize the process of gene-culture coevolution, in which culture is shaped by biological imperatives while biological traits are simultaneously altered by genetic evolution in response to cultural history. A case is made from both theory (...) and evidence that genetic and cultural evolution are inseverable, and that the human mind has tended to evolve so as to bias individuals toward certain patterns of cognition and choice rather than others. With the aid of mathematical models we trace the coevolutionary circuit: The genes prescribe structure in developmental pathways that lay down endocrine and neural systems, imposing regularities in the development of cognition and behavior; these regularities translate upward into holistic patterns of culture, which can be predicted in the form of probability density distributions ; natural selection acts within human history to favor certain epigenetic rules over others; and the selection alters the frequencies of the underlying genes. The effects of genetic and cultural changes reverberate throughout the circuit and are consequently tested with the passage of each life cycle. In addition to modeling gene-culture coevolution, we apply methods from island biogeography and information theory to examine the cultural capacity of the genes, the factors determining the magnitude of cultural diversity, and the possible reasons for the uniqueness of the human achievement. (shrink)

The goals of public education, as well as conceptions of human intelligence and learning, are undergoing a transformation through the application of military-sponsored information technologies and information processing models of human thought. Recent emphases in education on thinking skills, learning strategies, and computer-based technologies are the latest episodes in the postwar military agenda to engineer intelligent components, human and artificial, for the optimal performance of complex technological systems. Public education serves increasingly as a “human factors” laboratory and production site for (...) this military enterprise, whose high performance technologies and command and control paradigms have also played central roles in the emergence of the information economy. (shrink)

Cognitive architectures are unified theories of cognition that take the form of computational formalisms. They support computational models that collectively account for large numbers of empirical regularities using small numbers of computational mechanisms. Empirical coverage and parsimony are the most prominent criteria by which architectures are designed and evaluated, but they are not the only ones. This paper considers three additional criteria that have been comparatively undertheorized. (a) Successful architectures possess subjective and intersubjective meaning, making cognition comprehensible to individual cognitive (...) scientists and organizing groups of like-minded cognitive scientists into genuine communities. (b) Successful architectures provide idioms that structure the design and interpretation of computational models. (c) Successful architectures are strange: They make provocative, often disturbing, and ultimately compelling claims about human information processing that demand evaluation. (shrink)

Expert systems are knowledge-based information systems which are expected to have human attributes in order to replicate human capacity in ethical decision making. An expert system functions by virtue of its information, its inferential rules, and its decision criteria, each of which may be problematic. This paper addresses three basic reasons for ethical concern when using the currently available expert systems in a decisions-making capacity. These reasons are (1) expert systems' lack of human intelligence, (2) expert systems' lack of emotions (...) and values, and (3) expert systems' possible incorporation of intentional or accidental bias. For these reasons artificial ethics seems to be science fiction. Consequently, expert systems should be used only in an advising capacity and managers should not absolve themselves from legal and ethical responsibility when using expert systems in decision making. (shrink)

Despite its importance, the linkage between genetic and cultural evolution has until now been little explored. An understanding of this linkage is needed to extend evolutionary theory so that it can deal for the first time with the phenomena of mind and human social history. We characterize the process of gene-culture coevolution, in which culture is shaped by biological imperatives while biological traits are simultaneously altered by genetic evolution in response to cultural history. A case is made from both theory (...) and evidence that genetic and cultural evolution are inseverable, and that the human mind has tended to evolve so as to bias individuals toward certain patterns of cognition and choice rather than others. With the aid of mathematical models we trace the coevolutionary circuit: The genes prescribe structure in developmental pathways that lay down endocrine and neural systems, imposing regularities in the development of cognition and behavior; these regularities translate upward into holistic patterns of culture, which can be predicted in the form of probability density distributions ; natural selection acts within human history to favor certain epigenetic rules over others; and the selection alters the frequencies of the underlying genes. The effects of genetic and cultural changes reverberate throughout the circuit and are consequently tested with the passage of each life cycle. In addition to modeling gene-culture coevolution, we apply methods from island biogeography and information theory to examine the cultural capacity of the genes, the factors determining the magnitude of cultural diversity, and the possible reasons for the uniqueness of the human achievement. (shrink)

(2001). From expert systems to knowledge-based companies: How the AI industry negotiated a market for knowledge. Social Epistemology: Vol. 15, No. 3, pp. 231-245.

How are social relations appearing in computers? How are social relations realised in a different kind of medium, in the hardware and software of computers? How are the organising principles of computer building related to those of the life-worlds in a social system? Following a partly social constructivist and partly hermeneutic line a more general answer will be presented. The basic conclusion of this approach is simple: computers are constructed under the influence of the ideas of modernity and represent its (...) structure, interests and values, in contrast to computer networks, which embody the ideas of postmodernity. (shrink)