A.I., Scientific Discovery, and Realism

none1Epistemologists have debated at length whether scientific discovery is a rational and logical process. If it is, according to the Artificial Intelligence hypothesis, it should be possible to write computer programs able to discover laws or theories; and if such programs were written, this would definitely prove the existence of a logic of discovery. This far, however, all attempts in this direction have been unsuccessful: the programs written by Herbert Simon’s group, indeed, infer famous laws of physics and chemistry; but having found no new law, they cannot properly be considered discovery machines. The programs written in the “Turing tradition”, instead, produced new and useful empirical generalization, but no theoretical discovery, thus failing to prove the logical character of the most significant kind of discoveries. A new cognitivist and connectionist approach by Holland, Holyoak, Nisbett and Thagard, looks more promising. They picture scientific discovery as the construction of mental models of natural systems through analogical and abductive inferences, activated and constrained by an undetermined number of inputs and feedbacks from the environment. The connectionist architecture of mind accounts for the open-ended and intrinsically complex character which makes scientific discovery non programmable and unpredictable. At the same time, the assumption that by analogy and induction we can achieve faithful representations of nature explains the rationality and success of theorization. Reflection on this meta-research program, therefore, shows that a scientific-realist interpretation of scientific practice is required to account for both the rationality of discovery processes and the failure of past attempts to mechanize them. In fact, it might be argued that the Baconian and Millian belief in a logic of discovery was abandoned by logical positivists precisely because they lacked on the one hand a fully realist and cognitivist approach, and on the other hand a sufficiently wide conception of “logic”: they couldn’t foresee procedures which are rule-governed but complex and holistic because influenced but numberless factors escaping human control.anche al sito: http://www.kluweronline.com/issn/0924-6495/contentsopenAlai, MarioAlai, Mari

An Introduction to Mechanized Reasoning

Mechanized reasoning uses computers to verify proofs and to help discover new theorems. Computer scientists have applied mechanized reasoning to economic problems but -- to date -- this work has not yet been properly presented in economics journals. We introduce mechanized reasoning to economists in three ways. First, we introduce mechanized reasoning in general, describing both the techniques and their successful applications. Second, we explain how mechanized reasoning has been applied to economic problems, concentrating on the two domains that have attracted the most attention: social choice theory and auction theory. Finally, we present a detailed example of mechanized reasoning in practice by means of a proof of Vickrey's familiar theorem on second-price auctions

Verification conditions for source-level imperative programs

This paper is a systematic study of verification conditions and their use in the context of program verification. We take Hoare logic as a starting point and study in detail how a verification conditions generator can be obtained from it. The notion of program annotation is essential in this process. Weakest preconditions and the use of updates are also studied as alternative approaches to verification conditions. Our study is carried on in the context of a While language. Important extensions to this language are considered toward the end of the paper. We also briefly survey modern program verification tools and their approaches to the generation of verification conditions.Fundação para a Ciência e a Tecnologia (FCT

The Metaphysics of Information: the Power and the Glory of Machinehood

Res-Publica : Revista Lusófona de Ciência Política e Relações InternacionaisNão há disciplina em qualquer ramo da ciência, seja esta natural, social, humana, descritiva, experimental ou teórica, qualitativa ou quantitativa, que não tenha sido afectada a vários níveis da instrumentalidade, conceptualização, construção de modelos, escolha de metáforas heurísticas ou ontológicas, e sentidO da investigação, em alguns casos muito profunda e decisivamente, pela influência crescente da constelação informacional computacional. A investigação baseada em simulações por computador é uma “terceira espécie de ciência”, que se soma aos tipos teórico e físico-experimental de trabalho científico. A ciber-ciência é um lugar natural para simular ciência, ou meta-ciberciência, mas todo o conhecimento científico cai no domínio da meta-ciberciência ou da filosofia da ciência computacional. A meta-ciência simula a ciência(o estudo computacional da produção do conhecimento científico); a ciber-ciência é por definição simulatória; a ciber-ciência simula a Natureza; a Natureza, segundo alguns físicos, é ela mesma uma simulação. Receber a categoria da informação nas ciências da vida e nas ciências humanas e sociais, da maneira específica como tem vindo a ocorrer, traz um considerável lastro metafísico: os humanos como máquinas, ultrapassáveis por máquinas inteligentes ou “espirituais”. A informação emerge como a alavanca de Arquimedes para as nossas intervenções n o domínio da vida e do espírito, de máquinas informacionais naturais, com evidentes implicações para a ciência política.There is no discipline in any branch of science, natural science, social science, human science, descriptive, experimental or theoretical, qualitative or quantitative, that has not been affected at various levels of instrumentality, conceptualization, model-building, in the choice of heuristic or ontological metaphors, and the direction of research, in some cases quite profoundly and decisively, by the ascent of the informational computational constellation. Computer simulation research is a “third kind of science”, in addition to theoretical and physical-experimental types of scientific work . Cyber-science is a natural topic for simulating science, or meta-cyberscience, but all scientific knowledge falls within the domain of meta-cyberscience or the computational philosophy of science. Metascience simulates science (the computational study of scientific knowledge production); cyber-science is by definition simulational; cyberscience simulates nature; nature, according to some physicists, is itself a simulation. To receive the category of information in the life-sciences, the human and social sciences, in the specific way that has been taking place, carries quite a metaphysical baggage: humans as machines, surpassable by intelligent or “spiritual” machines. Information emerges as the Archimedean lever for our interventions in the realm of life and mind, of natural information machines, with evident implications for political science

Computability and Evolutionary Complexity: Markets As Complex Adaptive Systems (CAS)

The purpose of this Feature is to critically examine and to contribute to the burgeoning multi disciplinary literature on markets as complex adaptive systems (CAS). Three economists, Robert Axtell, Steven Durlauf and Arthur Robson who have distinguished themselves as pioneers in different aspects of how the thesis of evolutionary complexity pertains to market environments have contributed to this special issue. Axtell is concerned about the procedural aspects of attaining market equilibria in a decentralized setting and argues that principles on the complexity of feasible computation should rule in or out widely held models such as the Walrasian one. Robson puts forward the hypothesis called the Red Queen principle, well known from evolutionary biology, as a possible explanation for the evolution of complexity itself. Durlauf examines some of the claims that have been made in the name of complex systems theory to see whether these present testable hypothesis for economic models. My overview aims to use the wider literature on complex systems to provide a conceptual framework within which to discuss the issues raised for Economics in the above contributions and elsewhere. In particular, some assessment will be made on the extent to which modern complex systems theory and its application to markets as CAS constitutes a paradigm shift from more mainstream economic analysis