11287797
Collaborative Discovery in a Scientific Domain
This study compares Pairs of subjects with Single subjects in a task of discovering scientific laws with the aid of experiments. Subjects solved a molecular genetics task in a computer micro-world (Dunbar, 1993). Pairs were more successful in discovery than Singles and participated more actively in explanatory activities (i.e., entertaining hypotheses and considering alternative ideas and justifications). Explanatory activities were effective for discovery only when the subjects also conducted crucial experiments. Explanatory activities were facilitated when paired subjects made requests of each other for explanation and focused on them. The study extends from individual to collaborative discovery activities the importance to the discovery process of setting goals to find hypotheses and evidence (Dunbar, 1993) and to construct explanations of phenomena and processes encountered in examples (Chi, Bassok, Lewis, & Glaser, 1989). Discovery through collaboration is a common and growing practice in science whose processes have not yet been extensively studied. We do not yet know whether discovery processes are different when people work together, what role discussion plays in collaboration, how researchers entertain hypotheses and conduct experiments when working together or how they handle alternative hypotheses and justifications that arise in discussion. These questions may be approached through historical case studies, field observation, interviews with researchers involved in collaboration, and laboratory experiments. Each approach has its advantages and limitations. This paper follows an experimental approach. We are aware that, although experiments are highly useful for detailed analyses of discovery processes, we need to be cautious in interpreting these data as reflecting actual scientists’ collaborative discovery processes. Over the long run, combining various approaches
145761361
CaMeRa: A computational model of multiple representations
This research aims to clarify, by constructing and testing a computer simulation, the use of multiple representations in problem solving, focusing on their role in visual reasoning. The model is motivated by extensive experimental evidence in the literature for the features it incorporates, but this article focuses on the system's structure. We illustrate the model's behavior by simulating the cognitive and perceptual processes of an economics expert as he teaches some well-learned economics principles while drawing a graph on a blackboard. Data in the experimental literature and concurrent verbal protocols were used to guide construction of a linked production system and parallel network, CaMeRa (Computation with Multiple Representations), that employs a “Mind's Eye” representation for pictorial information, consisting of a bitmap and associated node-link structures. Propositional list structures are used to represent verbal information and reasoning. Small individual pieces from the different representations are linked on a sequential and temporary basis to form a reasoning and inferencing chain, using visually encoded information recalled to the Mind's Eye from long-term memory and from cues recognized on an external display. CaMeRa, like the expert, uses the diagrammatic and verbal representations to complement one another, thus exploiting the unique advantages of each.
17561186
Situated Action: A Symbolic Interpretation
The congeries of theoretical views collectively referred to as “situated action” (SA) claim that humans and their interactions with the world cannot be understood using symbol-system models and methodology, but only by observing them within real-world contexts or building nonsymbolic models of them. SA claims also that rapid, real-time interaction with a dynamically changing environment is not amenable to symbolic interpretation of the sort espoused by the cognitive science of recent decades. Planning and representation, central to symbolic theories, are claimed to be irrelevant in everyday human activity. 
 
We will contest these claims, as well as their proponents' characterizations of the symbol-system viewpoint. We will show that a number of existing symbolic systems perform well in temporally demanding tasks embedded in complex environments, whereas the systems usually regarded as exemplifying SA are thoroughly symbolic (and representational), and, to the extent that they are limited in these respects, have doubtful prospects for extension to complex tasks. As our title suggests, we propose that the goals set forth by the proponents of SA can be attained only within the framework of symbolic systems. The main body of empirical evidence supporting our view resides in the numerous symbol systems constructed in the past 35 years that have successfully simulated broad areas of human cognition.
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1992 Gordon Bell Prize Winners
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Editors' Introduction
Not since the publication of Rachel Carson's Silent Spring (1962) has a book about the environment provoked such controversy as Bjorn Lomborg's The Skeptical Environmen-talist (2001). In the four decades since Silent Spring a popular environmental awareness and suspicion of ''chemicals'' and other aspects of modern industrial life has increasingly challenged and, in some cases, displaced scientific and technological optimism as an orthodox view throughout most of the industrialized world. A proliferation of nongovern-mental organizations and scientific bodies repeatedly invoke science to warn us of the potential for irreversible and, possibly, catastrophic environmental decline, unless people on the planet mend their ways. Powerful interests resist this message, increasingly by attacking the science that informs it. In this context, it may seem unsurprising that Lomborg's challenge to contemporary catastrophic views of inexorable global environmental decline has itself attracted criticism from environmentalists. Perhaps what is surprising are the amount of attention, the degree of opprobrium, and the ad hominem nature of the criticism that has been launched at Lomborg for a book that builds upon a tradition of similar arguments. Indeed the central theme of TSE, that some combination of business-as-usual and incremental change will be sufficient for children born today to ''get more food, a better education, a higher standard of living, more leisure time and far more possibi-lities—without the global environment being destroyed'', seems a relatively modest assertion of the cornucopian world view by comparison with some of Lomborg's predecessors, such as deconstructions of environmental concerns over the preceding decade including Ron Bailey's Ecoscam (1993) and Aaron Wildavsky's But is it True? (1995) provoked few serious rebuttals from scientists or environmental activists. The controversy over TSE serves to remind us of the anthropological observation that science is the trump card that we play in disputes about values. In our contemporary society, science has become the medium through which we understand nature. However, science has also become the medium through which we wage political battles not just on the environment, but also over many other contested issues. Hence, the debate over TSE provides a rich case study through which to examine issues associated with the relationships among science, policy, and politics, which observers on the right (e.g., Gough, 2003) and left (e.g., UCS, 2004) of the US political spectrum agree is a critical area for governance. The five papers that comprise this special issue were originally solicited for a symposium titled …
6846387
Causality and Model Abstraction
Abstract Much of science and engineering is concerned with characterizing processes by equations describing the relations that hold among parameters of objects and govern their behavior over time. In formal descriptions of processes in terms of parameters and equations, the notion of causality is rarely made explicit. Formal treatments of the foundations of sciences have avoided discussions of causation and spoken only of functional relations among variables. Nevertheless, the notion of causality plays an important role in our understanding of phenomena. Even when we describe the behavior of a system formally in terms of acausal, mathematical relations, we often give an informal, intuitive explanation of why the system behaves the way it does in terms of cause-effect relations. In this paper, we will present an operational definition of causal ordering. The definition allows us to extract causal dependency relations among variables implicit in a model of a system, when a model is represented as a set of acausal, mathematical relations. Our approach is based on the theory of causal ordering first presented by Simon [22]. The paper shows how to use the theory and its extension in reasoning about physical systems. Further, the paper studies the relation of the theory to the problems of model aggregation.
16243913
Experiments with a Heuristic Compiler
This report describes some experiments in constructing a compiler that makes use of heuristic problem~solving techniques such as those incorporated in the General Problem Solver (GPS) [1]. The experiments were aimed at the dual objectives of throwing light on some of the problems of constructing more powerful programming languages and compilers, and of testing whether the task of writing a computer program can be regarded as a "problem" in the sense in which that term is used in GPS. The present paper is concerned primarily with the second objective--with analyzing some of the problem-solving processes that are involved in writing computer programs. At the present stage of their development, no claims will be made for the heuristic programming procedures described here as practical approaches to the construction of compilers. Their interest lies in what they teach us about the nature of the programming task.
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The Processes of Scientific Discovery: The Strategy of Experimentation
Abstract Hans Krebs' discovery, in 1932, of the urea cycle was a major event in biochemistry. This article describes a program, KEKADA, which models the heuristics Hans Krebs used in this discovery. KEKADA reacts to surprises, formulates explanations, and carries out experiments in the same manner as the evidence in the form of laboratory notebooks and interviews indicates Hans Krebs did. Furthermore, we answer a number of questions about the nature of the heuristics used by Krebs, in particular: How domain-specific are the heuristics? To what extent are they idiosyncratic to Krebs? To what extent do they represent general strategies of problem-solving search? The relative generality of KEKADA allows us to view the control structure of KEKADA and its domain-independent heuristics as a model of scientific experimentation that should apply over a broad domain.