Reasoning, Modelling and Data Science

6 EC

Semester 1, period 2

5274RMDS6Y

Owner Master Forensic Science
Coordinator dr. Radboud Winkels
Part of Master Forensic Science, year 1

Course manual 2018/2019

Course content

There are many things that can go wrong in reasoning: we can have flawed formal arguments, informal arguments that refer to false facts, fallacious arguments. In order to avoid the pitfalls of reasoning, it is important for a forensic scientist to learn what can go wrong and how it can go wrong. In this course we will also discuss what tools and methods we can use to counter human shortcomings.

The following topics are covered during the course:

  1. Introduction to evidential reasoning and formal methods
  2. Syllogisms, Propositional logic, truth tables
  3. Syntax, Semantics, Pragmatics of languages
  4. Quantifiers and predicate logic
  5. Problems with logic and formal modeling
  6. Hypotheses, data mining and digital evidence
  7. Argumentation Theory and Critical Questions
  8. Common Sense knowledge, generalizations
  9. Tools for supporting argumentation
  10. Psychological Theory of Evidential Reasoning

Study materials

Literature

  • Johan van Benthem, Hans van Ditmarsch, Jan van Eijck, Jan Jaspars (2016). Logic in Action. Chapters 1-5

  • Mike Groen & Charles Berger (2017). Crime Scene Investigation, Archeology and Taphonomy: Reconstructing Activities at Crime Scenes

  • Douglas Walton (2005). Informal Logic Methods for Law. From: Argumentation Methods for Artificial Intelligence in Law, pp. 1-16 en 30-34

  • Eveline Feteris (2010). Toulmin’s Argumentation Model. From: Fundamentals of Legal Argumentation, pp. 40-47

  • W.A. Wagenaar, P.J. van Koppen & H.F.M. Crombag (1994). The theory of anchored narratives. From: Anchored Narratives, pp. 20-43

  • Daniel Kahneman (2011). Introduction. From: Thinking Fast and Slow, pp. 3-18

  • Floris Bex & Bart Verheij (2012). Arguments, stories and evidence: critical questions for fact-finding

  • Bart Verheij (2014). To catch a thief with and without numbers: arguments, scenarios and probabilities in evidential reasoning. Law, Probability and Risk

Objectives

At the end of this course the student will be able to:

  1. Distinguish classical reasoning faults and detect them in presented and actual cases;
  2. Explain and implement formal and informal arguments and deconstruct arguments given these models;
  3. Solve simple logical and forensic data science problems;
  4. Apply (semi-)formal methods to concrete case descriptions in natural language;
  5. Sketch plausible scenarios for a given fact set;
  6. Criticize given lines of reasoning and make implicit assumptions explicit;
  7. Judge which approach to argument analysis is best given a specific case.

Teaching methods

  • Lecture
  • Computer lab session/practical training

Learning activities

Activity

Hours

Computerpracticum

9

Excursie

16

Hoorcollege

24

Tentamen

3

Tutoraat

4

Werkcollege

8

Self study

104

Total

168

(6 EC x 28 uur)

Attendance

The programme does not have requirements concerning attendance (OER-B).

Additional requirements for this course:

It is presupposed that all students will be present in practical classes. More than 25% absence will result in failing that particular part of the course.

Assessment

Item and weight Details

Final grade

60%

Tentamen

40%

Practicumopdrachten

Assignments

Practical classes will i.a. consist of practical assignments. These will have to be made on an individual basis and handed in in time via Canvas.

All components will be graded on a scale of 1-10, with a maximum of one decimal after the point. These grades are used to calculate the final grade. In order to pass the course, the student has to have attended at least 75% of practical classes and the written exam and the final grade have to be sufficient, i.e. at least a five and a half. When a student has not fulfilled this requirement, the examiner will register the mark ‘did not fulfil all requirements’ (NAV) whether or not the averaged grade is sufficient.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Weeknummer Onderwerpen Studiestof
1

Introduction, critical thinking, System I and II, human reasoning, types of reasoning.

Wason tasks, formal languages, propositional logic.

D. Kahnemann (2011). Thinking fast and slow.

J. van Benthem e.a. (2016). Logic in Action. Chapter 1 & 3.
2 Propositional logic, truth tables, proofs, quantifiers J. van Benthem e.a. (2016). Logic in Action. Chapter 2.
3

Predicate logic;

Toulmin's Argumentation Model

 J. van Benthem e.a. (2016). Logic in Action. Chapter 4.
4 Data Science, data mining  
5 Argumentation; Reasoning with evidence using arguments, scenarios and probabilities

Douglas A. Walton (2005). Argumentation Methods for Artificial Intelligence in Law.

Floris Bex & Bart Verheij (2012). Arguments, stories and evidence: critical questions for fact-finding.
Bart Verheij (2014). To catch a thief with and without numbers: arguments, scenarios and probabilities in evidential reasoning.
6 Anchored narratives W.A. Wagenaar,P.J. van Koppen, H.F.M. Crombag (1993). Anchored Narratives: The Psychology of Criminal Evidence.
7 Recap and scenario game  
8    

Timetable

The schedule for this course is published on DataNose.

Last year's course evaluation

In order to provide students some insight how we use the feedback of student evaluations to enhance the quality of education, we decided to include the table below in all course guides.

Course Name (#EC) N 24
Strengths
  • The slides used during the lectures were very clear
  • Students liked the scenario game
  • Practicals really helped to understand the subject matter
 Notes for improvement
  • Course design wasn’t very clear: Students didn’t really understand how archaeology (& digital forensics) fitted into the reasoning course. 
  • There was not enough time for feedback, or it was given too late
Response lecturer:

  • Teachers make sure all the feedback on assignments is given before the exam, however it is very difficult to give every student feedback individually in between every practical, because giving individual feedback takes a lot of time. Students can get feedback in two ways. First, they receive the correct answers and with their own answers in mind, can ask questions during tutorials or lectures. Second, students can ask questions via blackboard (now Canvas) and all students can read all the feedback from the teachers. This is more efficient in case several students have the same question, while at the same time students also learn about feedback on questions they did not pose themselves.

  • The digital week will be reviewed and better integrated in the course.

Contact information

Coordinator

  • dr. Radboud Winkels