Normative Practical Reasoning via Argumentation and Dialogue

Postprin

Civil Procedure as a Critical Discussion

This Article develops a model for analyzing legal dispute resolution systems as systems for argumentation. Our model meshes two theories of argument conceived centuries apart: contemporary argumentation theory and classical stasis theory. In this Article, we apply the model to the Federal Rules of Civil Procedure as a proof of concept. Specifically, the model analyzes how the Federal Rules of Civil Procedure function as a staged argumentative critical discussion designed to permit judge and jury to rationally resolve litigants’ differences in a reasonable manner. At a high level, this critical discussion has three phases: a confrontation, an (extended) opening, and a concluding phase. Those phases are the umbrella under which discrete argumentation phases occur at points we call stases. Whenever litigants seek a ruling or judgment, they reach a stasis—a stopping or standing point for arguing procedural points of disagreement. During these stases, the parties make arguments that fall into predictable “commonplace” argument types. Taken together, these stock argument types form a taxonomy of arguments for all civil cases. Our claim that the Federal Rules of Civil Procedure function as a system for argumentation is novel, as is our claim that civil cases breed a taxonomy of argument types. These claims also mark the beginning of a broader project. Starting here with the Federal Rules of Civil Procedure, we embark on a journey that we expect to follow for several years (and which we hope other scholars will join), exploring our model’s application across dispute resolution systems and using it to make normative claims about those systems. From a birds-eye view, this Article also represents a short modern trek in a much longer journey begun by advocates in city states in and near Greece nearly 2500 years ago

Explaining Explanations in AI

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly

The false promise of the better argument

Effective argumentation in international politics is widely conceived as a matter of persuasion. In particular, the ‘logic of arguing’ ascribes explanatory power to the ‘better argument’ and promises to illuminate the conditions of legitimate normative change. This article exposes the self-defeating implications of the Habermasian symbiosis between the normative and the empirical force of arguments. Since genuine persuasion is neither observable nor knowable, its analysis critically depends on what scholars consider to be the better argument. Seemingly, objective criteria such as universality only camouflage such moral reification. The paradoxical consequence of an explanatory concept of arguing is that moral discourse is no longer conceptualized as an open-ended process of contestation and normative change, but has recently been recast as a governance mechanism ensuring the compliance of international actors with pre-defined norms. This dilemma can be avoided through a positivist reification of valid norms, as in socialization research, or by adopting a critical and emancipatory focus on the obstacles to true persuasion. Still, both solutions remain dependent on the ‘persuasion vs. coercion’ problem that forestalls an insight into successful justificatory practices other than rational communication. The conclusion therefore pleas for a pragmatic abstention from better arguments and points to the insights to be gained from pragmatist norms research in sociology

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

Effective online interaction: mapping course design to bridge from research to practice

Quantitative and qualitative research of a case study course confirmed that the course achieved a highly interactive learning experience, associated with more effective student support and high student retention. Computer conferencing achieved high participation from the beginning and evidence of dialogue and argumentation within online tutor groups. This was achieved not by active tutor moderation but by a sequence of structured tasks. Compendium mind mapping software has been used to represent the design of this sequence of tasks and this has refined interpretation of the research findings. The positive outcomes identified relate not purely to computer conferencing but to an integration of individual and group tasks feeding forward into a well-designed assignment. The usability of case study data relates to the ability of practitioners to compare their own context with that of the case. The visual representation of the design of the task sequence is providing a better bridge from the research to the practice context than the use of general description of findings alone. This is particularly important in an area which has generated a range of sometimes conflicting findings, with weak links to the challenges of course design