Welcome to our Research Section. And thanks for joining the “scholarly conversation” on the topics of argumentation, logical reasoning, critical thinking, analytical thinking, and argument mapping. That conversation is a “discursive practice in which ideas are formulated, debated, and weighed against one another over an extended period of time…a process [which] builds upon and negotiates meaning by communicating, contesting, and adding new interpretations, perspectives, and results in response to existing research.” https://libguides.ashland.edu/instruction/acrl . As you see in the definition, the debating, weighing against one another, and contesting of ideas and perspectives are critical processes in scholarly research. Our work follows in that critical collegial process to improve the “work” so that society benefits. In our scholarship, we challenge ideas and approaches that we conclude are outdated and inadequate based on evidence for the important need of raising the level of critical and analytical thinking in the world.
Right now research indicates that the academia is largely failing in that goal. (e.g., “Moreover, standard university education provides at best modest improvements in students’ analytical-reasoning abilities”.Cullen, S., Fan, J., van der Brugge, E. et al. Improving analytical reasoning and argument understanding: a quasi-experimental field study of argument visualization. npj Science Learn 3, 21 (2018). https://doi.org/10.1038/s41539-018-0038-5)
The solution we propose is found in the Defeasible Class-Inclusion Transitivity (DCIT), the Logic-BridgeTM, mode of inference, argument structure, logical reasoning templates, and teaching approach. Argument mapping has been around from at least 1826 by English Logician Richard Whately. John Henry Wigmore, in the “Science of Proof” (1937), was another proponent. And the traditional old-style underlying argument structures and form in that argument mapping is still taught today. That approach, among other critical weaknesses, (1) lacks the rigor to ensure that the line of reasoning is logically flawless and (2) fails to accommodate ancillary evidence that is a critical component of real-world arguments.
So much of Joseph’s and Vanessa’s research will provide comparisons in action between the traditional old-style approaches and the Defeasible Class Inclusion Transitivity (DCIT) approach. And we welcome scholarly critiques of the DCIT approach so that the field of Argumentation can advance. Let us know what you think (email@example.com).
1. The scholarly theoretical support for the LogicǁGuaranteed logical reasoning model and method (DCIT) is found in two peer-reviewed Oxford Journal articles:
Law, Probability & Risk (Oxford Journal) 2012
Evaluating Universal Sufficiency of a Single Logical Form for Inference in Court
The Journal of Logic & Computation (Oxford Journal) 2009
A Generalizable Argument Structure for Evaluating Evidentiary Probative Relevancy in Litigation
2. The LogicǁGuaranteed logical reasoning model was also presented at the 13th International Conference on Artificial Intelligence and the Law (ICAIL 2011) (Pittsburgh, PA) 2011 (AI & Evidential Inference Workshop Paper).
3. Our training manuals (see link below) take the argument theory discussed above and translate it into easily understandable text and visuals for trainees age 12 to PhD level. The following file consists of sample pages from our LogicǁGuaranteed Basic Resource Manual, Level 1 of 3. The Level 1 manual consists of 231 pages in its entirety. The concepts in this manual are taught with our Logic-BridgeTM board game, which provides active-learning and supports deep engagement.
4. Demonstrating probative relevancy of an item of evidence is normally one of the most challenging tasks in analytical thinking, which is a part of critical thinking. The entire argument structure supporting the ultimate conclusion (e.g., Bob ran the red light.) must be shown. The logic of the reasoning in that argument must be flawless and clear to the audience. And the impact on the degree of certainty of the ultimate conclusion by the item of evidence must be demonstrated. This task arises in court when the admissibility of an item of evidence is challenged for relevancy and in other real-world context where there is a critical analysis of the reasoning. The following presentation shows how the use of the Logic-BridgeTM makes this task easy and straightforward.
5. An important feature of any argument structure, like the Logic-BridgeTM, is robustness. For example, the ability to handle supporting assumptions (e.g., ancillary evidence) is often crucial. Supporting assumptions are not co-premises and are not inferentially linked to the logical line of reasoning. Any argument structure that lacks this feature is ineffective and misleading in many circumstances. A supporting assumption is an item of evidence or fact that provides support to an inferentially linked premise in the line of reasoning. The following paper illustrates with the Logic-BridgeTM logical reasoning template the importance of considering supporting assumptions as ancillary evidence in constructing a logical argument. The issue in this example is whether fleeing from the police indicates a guilty mind.
6. The Graduate Records Examinations (GRE) “Analyze an Argument” task test provides an excellent opportunity to illustrate how the Logic-BridgeTM logical reasoning template and method can easily and simply deconstruct any logical argument. See the following demonstration:
7. An argument structure needs to easily capture the changes in inferential reasoning that occur in a debate-like format. The Logic-Bridge™ has the capability to easily reflect the ongoing changes in logical reasoning during such a critical questioning process. The following example uses the basic The Logic-Bridge™ logical reasoning template to reflect such changes.
8. The first use of argument mapping was probably by English Logician Richard Whately in 1826. John Henry Wigmore, in the “Science of Proof” (1937), was another proponent. Professor Tim van Gelder greatly advanced argument mapping with software applications. Using that software with its traditional old-style argument structure and visual grammar for each student, Joseph taught a course in Advanced Argumentation at Lewis & Clark Law School. And ThinkerAnalytix is a current provider, among others, of argument mapping instruction using their “How We Argue” model which also relies upon the traditional old-style argument structure. And while there are definite benefits to argument mapping with the traditional old-style argument structure as compared to no argument mapping at all as shown by research, all the models, such as “How We Argue,” rely upon an underlying argument structure that fails to ensure, unlike the Logic-BridgeTM, that the lines of reasoning are logically flawless and sufficiently complete. This is a fundamental weakness in important settings, such as court or other real-world contexts, where rigor and robustness is required. While there are other weaknesses to the traditional old-style argument structure (see Joseph’s “Law, Probability, and Risk” Oxford Journal article listed above), the following unpublished paper examines the failure of such old-style models to ensure that the lines of reasoning are logically flawless and sufficiently complete.
9. The following video was one of three logical reasoning training videos that Joseph prepared specifically for Professor Peter Tillers to assist him in teaching real-world logical reasoning using Defeasible Class-Inclusion Transitivity (DCIT) for his two-semester Cardozo School of Law “Fact Investigation” course. This first training video for his law students discusses six common mistakes in argument mapping (i.e., charting) that were developed by Professor Andrew Palmer by using DCIT to illustrate those mistakes. The fact situation is a real murder case with items of evidence. Here, you can see an actual tutorial helping students apply the DCIT logic template to a complex factual situation where the question is whether the defendant committed the murder based on the factual evidence. The video demonstrates how DCIT enables one to easily construct logically flawless complex arguments. With such complex arguments, the traditional old-style argument structure is inadequate because it lacks sufficient rigor and robustness. (see posts #8-10) Unlike DCIT, the old-style argument structure does not (1) ensure logical flawless arguments or (2) effectively account for ancillary evidence, which is different from premises and co-premises. (In actuality, the name co-premise is misleading, but that explanation is for another post.) Ancillary evidence or premises are not inferentially linked in the line of reasoning, but still provide support. The inadequacies do not rest with the use of box and line (nodes and edges) argument mapping. DCIT easily fits within boxes and lines. It is a common way we express DCIT. But the argument structure and visual grammar is completely different in DCIT. For example, the tree-like form is avoided since it has a number of serious problems. ((see Joseph’s “Law, Probability, and Risk” Oxford Journal article listed above and post #10) And with DCIT there is a specific logical form to the sentence structure within the boxes to ensure a logically flawless inferential line of reasoning.
10. (WARNING, MATURE CONTENT – The video below analyzes evidence in an online court published legal case that was also used in a law school textbook and law review article that deals with the question of paternity–who was the father of the child.)
The following video was another one of the three DCIT logical reasoning training videos that Joseph prepared specifically for Professor Peter Tillers to assist him in teaching real-world logical reasoning using Defeasible Class-Inclusion Transitivity (DCIT) for his two semester Cardozo School of Law “Fact Investigation” course. This second training video for his law students discusses other inadequacies in the traditional old-style argument structures for argument mapping as promulgated by ThinkerAnalytix in their “How We Argue” instruction model, by other similar argument mapping instruction, and in the instructions for the standard argument mapping software programs. Other than the lack of ensuring a logically flawless line of reasoning, among other problems, these traditional old-style argument structures rely upon a tree-like form which has several inherent problems for actual use when lines of logical reasoning are being critically assessed in the real-world. This law school training video illustrates some of the inherent problems with this tree-like form and demonstrates how the Logic-BridgeTM (Defeasible Class-Inclusion Transitivity (DCIT)) avoids all of these inherent weaknesses. The DCIT training video provides a good demonstration of how to untangle lines of inference that naturally occur with the use of the traditional old-style argument structure and tree-like form.
11. Are you ready to learn all you need to know about probative weight of evidence or a fact? This DCIT training video uses animation to illustrate probative weight flowing through the inferential line of reasoning. With this understanding, you will better be able to assess the impact of a premise on the level of certainty or acceptability of the ultimate conclusion. When standards of proof are at issue, such as “beyond a reasonable doubt” in a criminal case or “preponderance of evidence” in a typical civil lawsuit making a determination of the strength of evidence is critical. This DCIT training video also demonstrates again the importance of accounting for the support provided by ancillary evidence that is not inferentially linked in the line of reasoning. The traditional old-style argument structure used in conventional argument mapping fails to account for this important component of an argument.
12. LogicǁGuaranteed has also produced many other unpublished research papers and training videos.
One area of current research interest that we are pursuing is how the use of the Logic-BridgeTM (Defeasible Class-Inclusion Transitivity) can help train AI (Artificial Intelligence) through Deep Learning data sets to dramatically increase its natural language reasoning abilities as part of the development of AGI (Artificial General Intelligence).
We are also exploring how the fundamental “generalizations” that we use in our logical reasoning can be shaped by our biases, unfounded beliefs, and unique individual experiences. This exploration continues the excellent work of Professor Terrence J. Anderson and others: https://repository.law.miami.edu/cgi/viewcontent.cgi?article=1126&context=fac_articles ;