This book contains contributions presented during the international conference on Model-Based Reasoning (MBR'012), held on June 21-23 in Sestri Levante, Italy. Interdisciplinary researchers discuss in this volume how scientific cognition and other kinds of cognition make use of models, abduction, and explanatory reasoning in order to produce important or creative changes in theories and concepts. Some of the contributions analyzed the problem of model-based reasoning in technology and stressed the issues of scientific and (...) technological innovation. The book is divided in three main parts: models, mental models, representations; abduction, problem solving and practical reasoning; historical, epistemological and technological issues.. (shrink)

The book Model-Based Reasoning in Scientific Discovery, aims to explain how specific modeling practices employed by scientists are productive methods of ...

There are several key ingredients common to the various forms of model-based reasoning considered in this book. The term ‘model’ comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations and are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. The book’s contributors are researchers active in the area of creative reasoning in science and technology.

This paper examines the nature of model-based reasoning in the interplay between theory and experiment in the context of biomedical engineering research laboratories, where problem solving involves using physical models. These "model systems" are sites of experimentation where in vitro models are used to screen, control, and simulate specific aspects of in vivo phenomena. As with all models, simulation devices are idealized representations, but they are also systems themselves, possessing engineering constraints. Drawing on research in contemporary (...) cognitive science that construes cognition as occurring in a complex distributed system comprising people and artifacts, I argue that reasoning with model systems is a constraint satisfaction process involving co-construction, manipulation, and revision of mental and physical models. (shrink)

This volume is based on the papers presented at the international conference Model-Based Reasoning in Science and Technology (MBR09_BRAZIL), held at the University of Campinas (UNICAMP), Campinas, Brazil, December 2009. The presentations given at the conference explored how scientific cognition, but several other kinds as well, use models, abduction, and explanatory reasoning to produce important or creative changes in theories and concepts. Some speakers addressed the problem of model-based reasoning in technology, and (...) stressed the issue of science and technological innovation. The various contributions of the book are written by interdisciplinary researchers who are active in the area of creative reasoning in logic, science, and technology: the most recent results and achievements about the topics above are illustrated in detail in the papers. The book is divided in three parts, which cover the following main areas: part I, abduction, problem solving, and practical reasoning; part II: formal and computational aspects of model based reasoning; part III, models, mental models, representations. (shrink)

The study of creative, diagnostic, visual, spatial, analogical, and temporal reasoning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help only of traditional notions of reasoning such as classical logic. Understanding the contribution of modeling practices to discovery and conceptual change in science requires expanding scientific reasoning to include complex forms of creative reasoning that are not always successful and can lead to incorrect solutions. (...) The study of these heuristic ways of reasoning is situated at the crossroads of philosophy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitive science. There are several key ingredients common to the various forms of model-based reasoning. The term "model" comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. Moreover, in the modeling process, various forms of abstraction are used. Evaluation and adaptation take place in light of structural, causal, and/or functional constraints. Model simulation can be used to produce new states and enable evaluation of behaviors and other factors. Several of the papers in this volume aim at increasing epistemological knowledge about the role of model-based reasoning in various scientific tasks, other papers address fundamental cognitive issues related to model-based reasoning and illustrate novel analyses of cognitive "logical" models of model-based reasoning and of the interplay abduction/model-based reasoning/creative inferences. The volume is based on the papers that were presented at the International Conference Model-Based Reasoning in Science and Engineering: Abduction, Visualization, Simulation, held at the Collegio Ghislieri, University of Pavia, Pavia, Italy, in December 2004. (shrink)

This book discusses how scientific and other types of cognition make use of models, abduction, and explanatory reasoning in order to produce important or creative changes in theories and concepts. It includes revised contributions presented during the international conference on Model-Based Reasoning (MBR’015), held on June 25-27 in Sestri Levante, Italy. The book is divided into three main parts, the first of which focuses on models, reasoning and representation. It highlights key theoretical concepts from an (...) applied perspective, addressing issues concerning information visualization, experimental methods and design. The second part goes a step further, examining abduction, problem solving and reasoning. The respective contributions analyze different types of reasoning, discussing various concepts of inference and creativity and their relationship with experimental data. In turn, the third part reports on a number of historical, epistemological and technological issues. By analyzing possible contradictions in modern research and describing representative case studies in experimental research, this part aims at fostering new discussions and stimulating new ideas. All in all, the book provides researchers and graduate students in the field of applied philosophy, epistemology, cognitive science and artificial intelligence alike with an authoritative snapshot of current theories and applications of model-based reasoning. (shrink)

This book discusses how scientific and other types of cognition make use of models, abduction, and explanatory reasoning in order to produce important and innovative changes in theories and concepts. Gathering revised contributions presented at the international conference on Model-Based Reasoning, held on October 24–26 2018 in Seville, Spain, the book is divided into three main parts. The first focuses on models, reasoning, and representation. It highlights key theoretical concepts from an applied perspective, and addresses (...) issues concerning information visualization, experimental methods, and design. The second part goes a step further, examining abduction, problem solving, and reasoning. The respective papers assess different types of reasoning, and discuss various concepts of inference and creativity and their relationship with experimental data. In turn, the third part reports on a number of epistemological and technological issues. By analyzing possible contradictions in modern research and describing representative case studies, this part is intended to foster new discussions and stimulate new ideas. All in all, the book provides researchers and graduate students in the fields of applied philosophy, epistemology, cognitive science, and artificial intelligence alike with an authoritative snapshot of the latest theories and applications of model-based reasoning. (shrink)

One approach to understanding model-based reasoning in science is to examine how it develops during infancy, childhood, and adolescence. The way in which thinking changes sometimes provides clues to its nature. This paper examines cognitive developmental aspects of modeling practices and discusses how a developmental perspective can enrich the study of model-based scientific reasoning in adults. The paper begins with issues concerning developmental change, followed by a model of model-based reasoning. (...) The rest of the paper describes how several key concepts from recent developmental work could contribute to current work on model-based reasoning. Specifically, developmental research shows that (a) social processes are involved in model-based reasoning and scientific discovery, (b) the development of a theory of mind contributes to the development of scientific reasoning, (c) changes in scientific reasoning are characterized by cognitive variability, and (d) microgenetic methods could clarify conceptual change during model-based reasoning. (shrink)

Johnson -Laird has argued that spatial reasoning is based on the construction and manipulation of mental models in memory. The present article addresses the question of whether reasoning about time relations is constrained by the same factors as reasoning about spatial relations. An experiment is reported that explored the similarities and the differences in the performance of subjects in comparable spatial and temporal reasoning tasks. The results indicated that, in both the temporal and the spatial (...) content domains, the data were in agreement with the view that subjects solved problems by constructing models in memory rather than with a logical rule conceptualisation of reasoning. An analysis of the premise-reading times on the basis of premise-linking order provided support for an on-line process of mental models construction, and offered an explanation for the finding that spatial problems that did require an inference of transitivity were easier than problems that did not. No essential differences in processing and performance were observed across the two content domains, although in the time domain the correctness data were in agreement with both the mental models theory and the logical rules view. The results are discussed with respect to the mental models theory and the structural characteristics of the problems. (shrink)

I begin with a typology of reasoning and cross it with types of processes. I demonstrate that the thrust of Plato’s Republic is theory-building. This involves the critical and dialectic processes which are paradigms of Platonic methodology. Book I displays abductive analogical reasoning joined by an induction that is embedded in a deduction; hence there is a deduction–induction–abduction chain. In Book VI, Plato constructs a visual model of the divided line, which also displays model-based and (...) abductive hypothesis generation that is essential to theory building. Book VII provides an abductive metaphor model of the allegory of the cave. Both models depict degrees of reality and the ascendency of knowledge. The multimodal model-based allegory has far reaching applications from criminal justice to information systems. I conclude by capturing the narrative of the Republic as a critical and dialectic process of theory building using deductive–inductive–abductive chains, an abductive visual model and an abductive metaphor model. Hence, the Republic is simultaneously a masterpiece of deductive reasoning and a marvel of complex model-based abduction, involving visual models, analogies and metaphors. (shrink)

Automated Software Testing (AST) using Model Checking is in this article epistemologically analysed in order to argue in favour of a model-based reasoning paradigm in computer science. Preliminarily, it is shown how both deductive and inductive reasoning are insufficient to determine whether a given piece of software is correct with respect to specified behavioural properties. Models algorithmically checked in Model Checking to select executions to be observed in Software Testing are acknowledged as analogical models (...) which establish isomorphic relations with the target system’s data set. Analogical models developed in AST are presented as abductive models providing hypothetical explanations to observed executions. The model assumption—algorithmic check—software testing process is understood as the abduction—deduction—induction process defining the selective abduction and turned to isolate a set of model-based hypotheses concerning the target system behaviours. A manipulative abduction process is finally recognized in the practice of adapting, abstracting and refining models that do not provide successful predictions. (shrink)

This is the preface of the special Issue: Formal Representations in Model-based Reasoning and Abduction, published at the Logic Jnl IGPL (2012) 20 (2): 367-369. doi: 10.1093/jigpal/jzq055 First published online: December 20, 2010.

The emergence of new computational media is radically changing the practices of science, particularly in the way computational models are built and used to understand and engineer complex biological systems. These new practices present a novel variation of model-based reasoning, based on dynamic and opaque models. A new cognitive account of MBR is needed to understand the nature of this practice and its implications. To develop such an account, I first outline two cases where the building (...) and use of computational models led to discoveries. A theoretical model of the possible cognitive and neural mechanisms underlying such discoveries is then presented, based on the way the body schema is extended during tool use. This account suggests that the process of building the computational model gradually ‘incorporates’ the external model as a part of the internal imagination system, similar to the way tools are incorporated into the body schema through their active use. A central feature of this incorporation account is the critical role played by tacit and implicit reasoning. Based on this account, I examine how computational modeling would change model-based reasoning in science and science education. (shrink)

This book provides an argumentation model for means-end reasoning, a distinctive type of reasoning used for problem-solving gand decision-making. Means-end reasoning is modeled as goal-directed argumentation from an agent's goals and known circumstances, and from an action selected as a means, to a decision to carry out the action. Goal-based reasoning for argumentation provides an argumentation model for this kind of reasoning, showing how it is employed in settings of intelligent deliberation where (...) agents try to collectively arrive at a conclusion on what they should do to move forward in a set of circumstances. The book explains how this argumentation model can help build more realistic computational systems of deliberation and decision-making and shows how such systems can be applied to solve problems posed by goal-based reasoning in numerous fields, from social psychology and sociology, to law, political science, anthropology, cognitive science, artificial intelligence, multi-agent systems, and robotics." -- Back cover. (shrink)

These are the working notes of the workshop on Model-based and Qualitative Reasoning in Biomedicine, which was held during the European Conference on Artificial Intelligence in Medicine, AIME’03, on 19th October, 2003, in Protaras, Cyprus. The workshop brought together various researchers involved in the development and use of model-based and qualitative reasoning methods in tackling biomedical problems. Much of the biomedical knowledge is essentially model-based, as it is the understanding of the structure (...) and function of biomedical systems that researchers wish to achieve, and this is best done by developing models of these systems. In situations where it may not be appropriate or possible to use quantitative methods, researchers use qualitative approaches. Depending on the biomedical problem concerned, such descriptions may involve causal, temporal and spatial knowledge, possibly of an uncertain nature. Also in the medical management of disorders in patients, qualitative and model-based approaches are being used. For example, systems used for diagnosing disease rely on explicit models of normal or abnormal structure and behaviour (often referred to as ’first-principles models’) of the underlying disease process. Qualitative knowledge plays a role in the modelling of disease and treatment processes, including the handling of the uncertainty involved in these processes. Hence, there is little doubt that model-based and qualitative methods fit the biomedical domain really well. However, one of the problems with research in the biomedical field is that researchers applying model-based and qualitative-reasoning methods are often closely linked to their application field, such as, for example, cell biology or clinical medicine, and find it difficult to keep in contact with colleagues doing similar research, but working in a different biomedical application field. This is even more difficult if the techniques used are also different. For example, researchers involved in Bayesian network research and researchers using qualitative simulation methods hardly exchange views and ideas, despite the fact that their methods have in common that they emphasise representing qualitative biomedical knowledge. It was the aim of this workshop to bring together researchers along the entire spectrum of the biomedical field, from health-care research and clinical medicine to human biology, using a variety of methods and techniques, from (qualitative) Bayesian networks and symbolic machine learning, to qualitative simulation.. (shrink)

We apply three state‐of‐the‐art models for syllogistic reasoning to data from experiments where participants received feedback for their conclusions in order to demonstrate the use of model parameters to derive new hypotheses and present possible explanations for the feedback effect.

This article presents a fundamental advance in the theory of mental models as an explanation of reasoning about facts, possibilities, and probabilities. It postulates that the meanings of compound assertions, such as conditionals (if) and disjunctions (or), unlike those in logic, refer to conjunctions of epistemic possibilities that hold in default of information to the contrary. Various factors such as general knowledge can modulate these interpretations. New information can always override sentential inferences; that is, reasoning in daily life (...) is defeasible (or nonmonotonic). The theory is a dual process one: It distinguishes between intuitive inferences (based on system 1) and deliberative inferences (based on system 2). The article describes a computer implementation of the theory, including its two systems of reasoning, and it shows how the program simulates crucial predictions that evidence corroborates. It concludes with a discussion of how the theory contrasts with those based on logic or on probabilities. (shrink)

Taxonomy Based modeling was applied to describe drivers' mental models of variable message signs (VMS's) displayed on expressways. Progress in road telematics has made it possible to introduce variable message signs (VMS's). Sensors embedded in the carriageway every 500m record certain variables (speed, flow rate, etc.) that are transformed in real time into 'driving times' to a given destination if road conditions do not change. VMS systems are auto-regulative Man-Machine (AMMI) systems which incorporate a model of the user: (...) if the traffic flow is too high, then drivers should choose alternative routes. In so doing, the traffic flow should decrease. The model of the user is based on suppositions such as: people do not like to waste time, they fully understand the displayed messages, they trust the displayed values, they know of alternative routes. However, people also have a model of the way the system functions. And if they do not believe the contents of the message, they will not act as expected. We collected data through interviews with drivers using the critical incidents technique (Flanagan, 1985). Results show that the mental models that drivers have of the way the VMS system works are various but not numerous and that most of them differ from the 'ideal expert' mental model. It is clear that users don't have an adequate model of how the VMS system works and that VMS planners have a model of user behaviour that does not correspond to the behaviour of the drivers we interviewed. Finally, Taxonomy Based Modeling is discussed as a tool for mental model remediation. (shrink)

Taxonomy Based modeling was applied to describe drivers’ mental models of variable message signs (VMS’s) displayed on expressways. Progress in road telematics has made it possible to introduce variable message signs (VMS’s). Sensors embedded in the carriageway every 500m record certain variables (speed, flow rate, etc.) that are transformed in real time into “driving times” to a given destination if road conditions do not change.VMS systems are auto-regulative Man-Machine (AMMI) systems which incorporate a model of the user: if (...) the traffic flow is too high, then drivers should choose alternative routes. In so doing, the traffic flow should decrease. The model of the user is based on suppositions such as: people do not like to waste time, they fully understand the displayed messages, they trust the displayed values, they know of alternative routes. However, people also have a model of the way the system functions. And if they do not believe the contents of the message, they will not act as expected.We collected data through interviews with drivers using the critical incidents technique (Flanagan, 1985). Results show that the mental models that drivers have of the way the VMS system works are various but not numerous and that most of them differ from the“ideal expert” mental model. It is clear that users don’t have an adequate model of how the VMS system works and that VMS planners have a model of user behaviour that does not correspond to the behaviour of the drivers we interviewed. Finally, Taxonomy Based Modeling is discussed as a tool for mental model remediation. (shrink)

This paper argues that the linguistic approach to analyzing induction, according to which induction is a type of inference or argument composed of statements or propositions, is unsuitable to account for scientific reasoning. Consequently, a novel approach to induction in model-based science is suggested. First, in order to show their adherence to the linguistic treatment of induction, two strategies are reviewed: (i) Carnap and Reichenbach’s attempts to justify induction and (ii) Norton’s recent material theory of induction. Second, (...) three reasons are provided to support the claim that the linguistic treatment of induction is insufficient in accounting for model-based reasoning in science. Finally, a framework focused on models—rather than statements or propositions—is suggested to address induction in science. William Whewell’s theory of induction is briefly outlined as an example of a non-propositional treatment of induction that is consistent with model-based scientific practice. (shrink)

Life scientists increasingly rely upon abstraction-based modeling and reasoning strategies for understanding biological phenomena. We introduce the notion of constraint-based reasoning as a fruitful tool for conceptualizing some of these developments. One important role of mathematical abstractions is to impose formal constraints on a search space for possible hypotheses and thereby guide the search for plausible causal models. Formal constraints are, however, not only tools for biological explanations but can be explanatory by virtue of clarifying general (...) dependency-relations and patterning between functions and structures. We describe such situations as constraint-based explanations and argue that these differ from mechanistic strategies in important respects. While mechanistic explanations emphasize change-relating causal features, constraint-based explanations emphasize formal dependencies and generic organizational features that are relatively independent of lower-level changes in causal details. Our distinction between mechanistic and constraint-based explanations is pragmatically motivated by the wish to understand scientific practice. We contend that delineating the affordances and assumptions of different explanatory questions and strategies helps to clarify tensions between diverging scientific practices and the innovative potentials in their combination. Moreover, we show how constraint-based explanation integrates several features shared by otherwise different philosophical accounts of abstract explanatory strategies in biology. (shrink)

Recent decades have witnessed a sea change in thinking about emotion, which has gone from being seen as a disruptive force in human thought and action to being seen as an important source of situation- and goal-relevant information and evaluation, continuous with perception and cognition. Here I argue on philosophical and empirical grounds that the role of emotion in contributing to our ability to respond to reasons for action runs deeper still: The affective system is at the core of the (...) process of evaluatively modeling situations, actions, and outcomes, which is the foundation upon which rational deliberation and action can be built. Taking up this perspective affords new approaches to long-standing problems in the theory of reason-based action. (shrink)

What I call theoretical abduction (sentential and model-based)certainly illustrates much of what is important in abductive reasoning, especially the objective of selecting and creating a set of hypotheses that are able to dispense good (preferred) explanations of data, but fails to account for many cases of explanation occurring in science or in everyday reasoning when the exploitation of the environment is crucial. The concept of manipulative abduction is devoted to capture the role of action in many (...) interesting situations: action provides otherwise unavailable information that enables the agent to solve problems by starting and performing a suitable abductive process of generation or selection of hypotheses. Many external things, usually inert from the epistemological point of view, can be transformed into what I call epistemic mediators, which are illustrated in the last part of the paper, together with an analysis of the related notions of ``perceptual and inceptual rehearsal'' and of ``external representation''. (shrink)

Reasoners compare problems to prior cases to draw conclusions about a problem and guide decision making. All Case-Based Reasoning (CBR) employs some methods for generalizing from cases to support indexing and relevance assessment and evidences two basic inference methods: constraining search by tracing a solution from a past case or evaluating a case by comparing it to past cases. Across domains and tasks, however, humans reason with cases in subtly different ways evidencing different mixes of and mechanisms for (...) these components.In recent CBR research in Artificial Intelligence (AI), five paradigmatic approaches have emerged: statistically-oriented, model-based, planning/design-oriented, exemplar-based, and adversarial or precedent-based. The paradigms differ in the assumptions they make about domain models, the extent to which they support symbolic case comparison, and the kinds of inferences for which they employ cases. (shrink)

Modelers are tackling ever more complex systems with the aid of computation. Model-based inferences can play a key role in their ability to handle complexity and produce reliable or informative models. We study here the role of model-based inference in the modern field of computational systems biology. We illustrate how these inferences operate and analyze the material and theoretical bases or conditions underlying their effectiveness. Our investigation reiterates the significance and centrality of model-based (...) class='Hi'>reasoning in day-to-day problem-solving practices, and the role that debugging processes of partial or incomplete models can play in scientific inference and scientific discovery, particularly with respect to complex systems. We present several deeper implications such an analysis has for philosophy of science regarding the role of models in scientific practice. (shrink)

Taxonomy Based modeling was applied to describe drivers’ mental models of variable message signs (VMS’s) displayed on expressways. Progress in road telematics has made it possible to introduce variable message signs (VMS’s). Sensors embedded in the carriageway every 500m record certain variables (speed, flow rate, etc.) that are transformed in real time into “driving times” to a given destination if road conditions do not change. VMS systems are auto-regulative Man-Machine (AMMI) systems which incorporate a model of the user: (...) if the traffic flow is too high, then drivers should choose alternative routes. In so doing, the traffic flow should decrease. The model of the user is based on suppositions such as: people do not like to waste time, they fully understand the displayed messages, they trust the displayed values, they know of alternative routes. However, people also have a model of the way the system functions. And if they do not believe the contents of the message, they will not act as expected. (shrink)