Human reasoning in hypothesis-testing tasks like Wason's (1966, 1968) selection task has been depicted as prone to systematic biases. However, performance on this task has been assessed against a now outmoded falsificationist philosophy of science. Therefore, the experimental data is reassessed in the light of a Bayesian model of optimal data selection in inductive hypothesis testing. The model provides a rational analysis (Anderson, 1990) of the selection task that fits well with people's performance on both abstract and thematic versions of the task. The model suggests that reasoning in these tasks may be rational rather than subject to systematic bias. Over the past 30 years, results in the psychology of reasoning have raised doubts about human rationality. The assumption of human rationality has a long history. Aristotle took the capacity for rational thought to be the defining characteristic of human beings, the capacity that separated us from the animals. Descartes regarded the ability to use language and to reason as the hallmarks of the mental that separated it from the merely physical. Many contemporary philosophers of mind also appeal to a basic principle of rationality in accounting for everyday, folk psychological explanation whereby we explain each other's behavior in terms of our beliefs and desires (Cherniak, 1986; Cohen, 1981; Davidson, 1984; Dennett, 1987; but see Stich, 1990). These philosophers, both ancient and modern, share a common view of rationality: To be rational is to reason according to rules (Brown, 1989). Logic and mathematics provide the normative rules that tell us how we should reason. Rationality therefore seems to demand that the human cognitive system embodies the rules of logic and mathematics. However, results in the psychology of reasoning appear to show that people do not reason according to these rules. In both deductive (Evans, 1982, 1989;

The aim of this research essay is to examine the structural nature of theory in information systems. Despite the importance of theory, questions relating to its form and structure are neglected in comparison with questions relating to epistemology. The essay addresses issues of causality, explanation, prediction and generalization that underlie an understanding of theory. A taxonomy is proposed that classifies information systems theories with respect to the manner in which four central goals are addressed: analysis, explanation, prediction and prescription. Five interrelated types of theory are distinguished: (i) theory for analysing; (ii) theory for explaining, (iii) theory for predicting; (iv) theory for explaining and predicting; and (v) theory for design and action. Examples illustrate the nature of each theory type. The applicability of the taxonomy is demonstrated by classifying a sample of journal articles. The paper contributes by showing that multiple views of theory exist and by exposing the assumptions underlying different viewpoints. In addition, it is suggested that the type of theory under development can influence the choice of an epistemological approach. Support is given for the legitimacy and value of each theory type. The building of integrated bodies of theory that encompass all theory types is advocated.

Design work and design knowledge in Information Systems (IS) is important for both research and practice. Yet there has been comparatively little critical attention paid to the problem of specifying design theory so that it can be communicated, justified, and developed cumulatively. In this essay we focus on the structural components or anatomy of design theories in IS as a special class of theory. In doing so, we aim to extend the work of Walls, Widemeyer and El Sawy (1992) on the specification of information systems design theories (ISDT), drawing on other streams of thought on design research and theory to provide a basis for a more systematic and useable formulation of these theories. We identify eight separate components of design theories: (1) purpose and scope, (2) constructs, (3) principles of form and function, (4) artifact mutability, (5) testable propositions, (6) justificatory knowledge (kernel theories), (7) principles of implementation, and (8) an expository instantiation. This specification includes components missing in the Walls et al. adaptation of Dubin (1978) and Simon (1969) and also addresses explicitly problems associated with the role of instantiations and the specification of design theories for methodologies and interventions as well as for products and applications. The essay is significant as the unambiguous establishment of design knowledge as theory gives a sounder base for arguments for the rigor and legitimacy of IS as an applied discipline and for its continuing progress. A craft can proceed with the copying of one example of a design artifact by one artisan after another. A discipline cannot.

We apply nine analytic methods employed currently in imaging neuroscience to simulated and actual BOLD fMRI signals and compare their performances under each signal type. Starting with baseline time series generated by a resting subject during a null hypothesis study, we compare method performance with embedded focal activity in these series of three different types whose magnitudes and time courses are simple, convolved with spatially varying hemodynamic responses, and highly spatially interactive. We then apply these same nine methods to BOLD fMRI time series from contralateral primary motor cortex and ipsilateral cerebellum collected during a sequential finger opposition study. Paired comparisons of results across methods include a voxel-specific concordance correlation

Abstract. Employing a logic program approach, this paper focuses on applying preferential reasoning to theory revision, both by means of preferences among existing theory rules, and by means of preferences on the possible abductive extensions to the theory. And, in particular, how to prefer among plausible abductive explanations justifying observations. 1

The focus of this research is on dynamical modeling in scientific reasoning. Dynamical modeling uses, transforms, and constructs models which capture the causal and structural dynamics of given phenomena. I describe a computational theory of dynamical modeling which utilizes representational and processing languages from artificial intelligence to represent and manipulate the dynamical models used in scientific reasoning practices. My theory uses transformational strategies such as model-based analogy (MBA) and Structure-based Model Transformation (SBMT) to allow the agent to draw on knowledge external to the model to solve problems. By doing so, the agent is able to revise and thus to evolve the model. To carry out this research I have developed a cognitive methodology which employs the following methods: cognitive analyses of historical case studies, expert problem solving protocols, and computational modeling. The thesis of this proposal is that a computational theory of dynamical m...

Some mathematical inconsistencies in the conventional form of Maxwell’s equations extended by Lorentz for a single charge system are discussed. To surmount these in framework of Maxwellian theory, a novel convection displacement current is considered as additional and complementary to the famous Maxwell displacement current. It is shown that this form of the Maxwell-Lorentz equations is similar to that proposed by Hertz for electrodynamics of bodies in motion. Original Maxwell’s equations can be considered as a valid approximation for a continuous and closed (or going to infinity) conduction current. It is also proved that our novel form of the Maxwell-Lorentz equations is relativistically invariant. In particular, a relativistically invariant gauge for quasistatic fields has been found to replace the non-invariant Coulomb gauge. The new gauge condition contains the famous relationship between electric and magnetic potentials for one uniformly moving charge that is usually attributed to the Lorentz transformations. Thus, for the first time, using the convection displacement current, a physical interpretation is given to the relationship between the components of the four-vector of quasistatic potentials. A rigorous application of the new gauge transformation with the Lorentz gauge transforms the basic field equations into an independent pair of differential equations responsible for longitudinal and transverse fields, respectively. The longitudinal components can be interpreted exclusively from the standpoint of the instantaneous ”action at a distance ” concept and leads to necessary conceptual revision of the conventional Faraday-Maxwell field. The concept of electrodynamic dualism is proposed for self-consistent classical electrodynamics. It implies simultaneous coexistence of instantaneous long-range (longitudinal) and Faraday-Maxwell short-range (transverse) interactions that resembles in this aspect the basic idea of Helmholtz’s electrodynamics.

The status of research methodology employed by studies on the application of AI techniques to solving problems in engineering design, analysis, and manufacturing is poor. There may be many reasons for this status including: unfortunate heritage from AI, poor educational system, and researchers' sloppiness. Understanding this status is a prerequisite for improvement. The study of research methodology can promote such understanding, but most importantly, it can assist in improving the situation. This paper introduces concepts from the philosophy of science and builds on them models of worldviews of science. These worldviews are combined with a research heuristics or research perspectives and criteria for evaluating research to create a layered model of research methodology. This layerd model can serve to organize and facilitate a better understanding of future studies of research methodologies. The paper discusses many of the issues involved in the study of AI and AIEDAM research methodo...

A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science. Clarity about these matters should benefit not just philosophy of science, but also statistical practice. At best, the inductivist view has encouraged researchers to fit and compare models without checking them; at worst, theorists have actively discouraged practitioners from performing model checking because it does not fit into their framework. 1 The usual story—which we don’t like In so far as I have a coherent philosophy of statistics, I hope it is “robust ” enough to cope in principle with the whole of statistics, and sufficiently undogmatic not to imply that all those who may think rather differently from me are necessarily stupid. If at times I do seem dogmatic, it is because it is convenient to give my own views as unequivocally as possible. (Bartlett, 1967, p. 458) Schools of statistical inference are sometimes linked to approaches to the philosophy of science. “Classical ” statistics—as exemplified by Fisher’s p-values, Neyman-Pearson hypothesis tests, and Neyman’s confidence intervals—is associated with the hypotheticodeductive and falsificationist view of science. Scientists devise hypotheses, deduce implications for observations from them, and test those implications. Scientific hypotheses can be rejected (that is, falsified), but never really established or accepted in the same way. Mayo (1996) presents the leading contemporary statement of this view. 1 In

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