Milton Friedman's famous methodological essay contains, along with much else, some strands that look as though they were taken from the “empirical-scientific” fabric described by Karl Popper. Think, for example, of Friedman's conviction that the way to test a hypothesis is to compare its implications with experience. Or of his more or less explicit espousal of the view that while no amount of facts can ever prove a hypothesis true, a single “fact” may refute it. Or of his assertion that (...) hypotheses are to be accepted only as provisionally true, and then only after repeated efforts to refute them have failed. The appearance of these Popperian ideas is not surprising. (shrink)

Evidential reasoning is hard, and errors can lead to miscarriages of justice with serious consequences. Analytic methods for the correct handling of evidence come in different styles, typically focusing on one of three tools: arguments, scenarios or probabilities. Recent research used Bayesian networks for connecting arguments, scenarios, and probabilities. Well-known issues with Bayesian networks were encountered: More numbers are needed than are available, and there is a risk of misinterpretation of the graph underlying the Bayesian network, for instance as a (...) causal model. The formalism presented here models presumptive arguments about coherent hypotheses that are compared in terms of their strength. No choice is needed between qualitative or quantitative analytic styles, since the formalism can be interpreted with and without numbers. The formalism is applied to key concepts in argumentative, scenario and probabilistic analyses of evidential reasoning, and is illustrated with a fictional crime investigation example based on Alfred Hitchcock’s film ‘To Catch A Thief’. (shrink)

This paper is one in a series of rational analyses of the Dutch Simonshaven case, each using a different theoretical perspective. The theoretical perspectives discussed in the literature typically use arguments, scenarios, and probabilities, in various combinations. The theoretical perspective on evidential reasoning used in this paper has been designed to connect arguments, scenarios, and probabilities in a single formal modeling approach, in an attempt to investigate bridges between qualitative and quantitative analytic styles. The theoretical perspective uses the recently proposed (...) logical formalism of case models, where cases represent possible combinations of evidence and events, ordered by an ordering relation. In the context of evidential reasoning, the ordering relation can be represented by a probability function. As an ordering relation is qualitative in nature, the theoretical perspective is in a formally precise sense simultaneously with and without probabilities. (shrink)

The primary aim of this chapter is to explain the nature of evidential reasoning, the characteristic difficulties encountered, and the tools to address these difficulties. Our focus is on evidential reasoning in criminal cases. There is an extensive scholarly literature on these topics, and it is a secondary aim of the chapter to provide readers the means to find their way in historical and ongoing debates.

A variety of theoretical positions are emerging to explain the judicial process from such perspectives as hermeneutics, semiotics, critical theory and argumentation/rhetoric. They ask such questions as these: What is the source of judicial authority? How do judges arrive at their decisions? By what logic are decisions to be tested? In this essay I argue that a focus on decisions and their justifications alone masks the broader process in which judges, along with all the other relevant groups, engage in a (...) continuing and evolving dialogue to structure their normative universe through the complementary processes of dialectic and rhetoric. Contemporary concepts of argumentation can serve to analyze this process critically. (shrink)

This article examines the nature and structure of witness and defendant narrative accounts in the evidential portions of courtroom trials, using the O.J. Simpson, Oklahoma Bombers and Louise Woodward trials as a database. The article proposes a means of distinguishing narrative from non-narrative accounts, using Labov's definition of the `minimal narrative' as a starting point, and puts forward a modified model of narrative structure. A range of narrative structures are explored, and the model is used to analyse a series of (...) representative example narratives taken from the trial data. Different types of narrative fragmentation produced by multiple tellers of the same narrative are also examined, along with the frequent disjunction between `tellers' and `knowers'. Lastly, the article explores the relationship between narrative structures and narrative fragmentation and the discourse strategies used by lawyers in direct and cross-examination. (shrink)

This paper examines the concept of coherence and its role in legal reasoning. First, it identifies some problem areas confronting coherence theories of legal reasoning about both disputed questions of fact and disputed questions of law. Second, with a view to solving these problems, it proposes a coherence model of legal reasoning. The main tenet of this coherence model is that a belief about the law and the facts under dispute is justified if it is “optimally coherent,” that is, if (...) it is such that an epistemically responsible legal decision-maker would have accepted it as justified by virtue of its coherence in like circumstances. Last, looking beyond the coherence theory, the paper explores the implications of the version of legal coherentism proposed for a general theory of legal reasoning and rationality. (shrink)

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...) psychology. Despite the ubiquity of causal reasoning, textbooks of cognitive psychology have neglected this growing field. The goal of The Oxford Handbook of Causal Reasoning is to fill this gap. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields. 1. Causal Reasoning: An Introduction Michael R. Waldmann Part I: Theories of Causal Cognition 2. Associative Accounts of Causal Cognition Mike E. Le Pelley, Oren Griffiths, and Tom Beesley 3. Rules of Causal Judgment: Mapping Statistical Information Onto Causal Beliefs José C. Perales, Andrés Catena, Antonio Cándido, and Antonio Maldonado 4. The Inferential Reasoning Theory of Causal Learning: Toward a Multi- Process Propositional Account Yannick Boddez, Jan De Houwer, and Tom Beckers 5. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World: Generalizing the Invariance of Causal Power Patricia W. Cheng and Hongjing Lu 6. The Acquisition and Use of Causal Structure Knowledge Benjamin Margolin Rottman 7. Formalizing Prior Knowledge in Causal Induction Thomas L. Griffiths 8. Causal Mechanisms Samuel G. B. Johnson and Woo-kyoung Ahn 9. Force Dynamics and Causation Phillip Wolff and Robert Thorstad 10. Mental Models and Causation P. N. Johnson- Laird and Sangeet S. Khemlani 11. Pseudocontingencies Klaus Fiedler and Florian Kutzner 12. Singular Causation David Danks 13. Cognitive Neuroscience of Causal Reasoning Joachim T. Operskalski and Aron K. Barbey Part II: Basic Cognitive Functions 14. Visual Impressions of Causality Peter White 15. Goal-Directed Actions Bernhard Hommel 16. Planning and Control Magda Osman 17. Reinforcement Learning and Causal Models Samuel J. Gershman 18. Causation and the Probability of Causal Conditionals David E. Over 19. Causal Models and Conditional Reasoning Mike Oaksford and Nick Chater 20. Concepts as Causal Models: Categorization Bob Rehder 21. Concepts as Causal Models: Induction Bob Rehder 22. Causal Explanation Tania Lombrozo and Nadya Vasilyeva 23. Diagnostic Reasoning Björn Meder and Ralf Mayrhofer 24. Inferring Causal Relations by Analogy Keith J. Holyoak and Hee-Seung Lee 25. Causal Argument Ulrike Hahn, Roland Bluhm, and Frank Zenker 26. Causality in Decision- Making York Hagmayer and Philip M. Fernbach Part III: Domains of Causal Reasoning 27. Intuitive Theories Tobias Gerstenberg and Joshua B. Tenenbaum 28. Space, Time, and Causality Marc J. Buehner 29. Causation in Legal and Moral Reasoning David A. Lagnado and Tobias Gerstenberg 30. The Role of Causal Knowledge in Reasoning About Mental Disorders Woo-kyoung Ahn, Nancy S. Kim, and Matthew S. Lebowitz 31. Causality and Causal Reasoning in Natural Language Torgrim Solstad and Oliver Bott 32. Social Attribution and Explanation Denis Hilton Part IV: Development, Phylogeny, and Culture 33. The Development of Causal Reasoning Paul Muentener and Elizabeth Bonawitz 34. Causal Reasoning in Non-Human Animals Christian Schloegl and Julia Fischer 35. Causal Cognition and Culture Andrea Bender, Sieghard Beller, and Douglas L. Medin. (shrink)

To resolve a conflict of opinion regarding the past it is inevitable to present a reconstruction of that past, explicitly or implicitly. This we call the mimetic element. On an abstract level, a complete argumentation in the genus iudiciale requires a start that is mimetic and a follow-up that is diegetic. The question to be discussed is whether mimetic elements need to be formatted as sets of propositions and if so by whom.