Stories About Evidence
Floris Bex and Bart Verheij

Introduction
Crime stories seem to come in an unbounded variation. This is evident from fictional crime
stories and real crimes alike. In fiction, crime stories have been popular since at least
Victorian times – with Sherlock Holmes and Jack the Ripper as prototypes of the rational
good and the emotional bad – and remain to be adapted to a contemporary perspective asin the bleak Scandinavian crime series filled with social criticism. Actual criminals also keep
surprising the public and the experts.
The variation of crime stories does not make the life of investigators easy. If only all criminals
would nicely fit a few prototypes, say a typical drug gang-related drive-by shooting or hotel
room crime passionel, it would be easy to determine what evidence can show what has
happened and solve a case. In practice, it is often hard to determine what exactly happened
in a crime, and – even if there is a strong hypothesis of who did it and how – it can be hardto uncover proof beyond a reasonable doubt.
A key issue is then how to rationally manage the evidence and hypotheses about what has
happened in a crime in such a way that it can be determined whether the belief in ahypothesis is justified or not. When should we believe the prosecutor’s story of what has
happened, and when has the defence raised a reasonable doubt?
To us it came as a surprise that stories play an important role in the rational analysis ofevidential reasoning. We came to this conviction through our meeting with Peter van Koppen– grand reciter of crime stories and expositor of whether to believe them or not. For one ofus (Bart), the exchange started in Maastricht when Peter was appointed as professor of legal
psychology in the Department of Metajuridica (from 2003 to 2014). But the importance ofstories only imbued our understanding of evidence when Peter joined Floris’ PhD research
project as a supervisor (with Henry Prakken and Bart; from 2005 to 2009). It was in thosedays that Peter explained to us that we did not understand evidence at all. Evidence is allabout stories, and only stories; such was his message. It took us a while to get a feeling forwhat he meant, and to appreciate the force of Peter’s message. Peters emphasis on open
discussion ‘with our legs on the table’ as a scientific method helped us make progress andenjoy the erratic journey that is research.
In fact, we have come to the conviction that stories play a role that is only addressed bystories: guarding the local and global coherence of the events in light of the evidence. Thisrole is neglected by the other two main rationality tools for evidential reasoning: arguments(focusing on managing conflicting information) and probabilities (analyzing evidential
strength).
In the following, we discuss three parts of our journey with Peter, his stories about evidence
and his perspective on artificial intelligence.

When to Believe a Story: Anchored Narratives
The first part of the journey concerns the theory of anchored narratives as developed byPeter, Hans Crombag and Willem Wagenaar. The theory is inspired by the psychologicalexperiments of Bennett and Feldman and Pennington and Hastie, who showed the dangerof story-telling in court: when a story is told well, for instance, when it is told in the actual
ordering of events, more test persons believe the story than when it is told in a different
order, for instance following the presentation of the evidence. These and similar experimentsemphasize that stories can actually pose a risk for the rational handling of evidence incourts.
The important proposal investigated by Peter, Hans and Willem in their anchored narratives
theory is that stories are not merely dangerous, they can actually also play a positive role incourt. They emphasized properties of stories and their relations to the evidence that could
show that a belief in a story was justified. And, importantly, they did not develop their theory
as arm-chair researchers in the ivory tower that a university so often is. On the contrary: they
prominently took on their role of what would now be called influencers, and applied theirresearch to real crime cases, serving as experts in the media and in court.
The starting point of the anchored narratives theory (ANT), as described in the books
Dubieuze zaken and Anchored narratives, is that decision makers have to decide on boththe quality of the story or stories presented by the parties and the anchoring of the stories.
When judging story quality, it is, for instance, considered whether the story is sufficiently
concrete, whether there are elements missing and whether it is consistent. Story anchoring
is about anchoring the story in our commonsense beliefs about the world around us.
Anchoring is a process in which stories are critically evaluated: questions are asked about
the story and the commonsense knowledge needed to believe the story, until thedecisionmaker is satisfied and the story can be anchored in the world knowledge. For
example, say we have a story about a shooter, let us call him Sacco1, travelling to the townof Braintree just outside Boston by train. In Braintree, Sacco shot a payroll guard and took
off with the payroll money. When Sacco is later apprehended, he is carrying a gun. Saccoargues he has this gun because he has a job as a night watchman. In order to believe this,we have to accept that the fact that ‘night watchmen usually carry guns’. We might say this is
not really the case, but that only night watchmen at specific organizations carry guns. We
can then ask for evidence showing that Sacco was a night watchman at such anorganization. Furthermore, we can also question why Sacco has the gun on him outside ofwork hours. Sacco might then respond with a new story that acts as a ‘sub-story’ to theoriginal story: that he was on his way home from work. This can be anchored in theknowledge that ‘night watchmen who carry guns take them home with them after work’.
Again, this may lead to new questions (‘is it normal that night watchmen take their gun homewith them?’, etc.), but at some point, we are satisfied and stop asking questions. Figure 1
illustrates how a story is anchored in commonsense knowledge. At the top, there is the main
story describing the course of events around a crime. It has substories which themselvesalso have substories, and the stories are finally grounded, ‘anchored’, in knowledge of the1
This name is obviously taken from the famous Sacco and Vanzetti robbery/murder case, see(Kadane & Schum, 1997).

world, typically in the form of generalizations (‘night watchmen usually carry guns, who takethem home with them after work’).

Figure 1. Graphical Illustration of the Anchored Narratives Theory (After Dubieuze zaken, p.
72)
ANT was used to establish ten ‘universal rules of evidence’’(Anchored narratives, p. 231 etseq.):
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.

The prosecution must present at least one well-shaped narrative.
The prosecution must present a limited set of well-shaped narratives.
Essential components of the narrative must be anchored.
Anchors for different components of the charge should be independent of each other.
The trier of fact should give reasons for the decision by specifying the narrative andthe accompanying anchoring.
A fact-finder's decision as to the level of analysis of the evidence should be explained
through an articulation of the general beliefs used as anchors.
There should be no competing story with equally good or better anchoring.
There should be no falsifications of the indictment's narrative and nested subnarratives.
There should be no anchoring onto obviously false beliefs.
The indictment and the verdict should contain the same narrative.

A first attempt at understanding ANT in light of AI & Law research was done at the occasionof the liber amicorum that Peter and Nico Roos prepared for Hans Crombag at the occasionof his valedictory lecture (Van Koppen & Roos, 2000). In that volume, it was considered towhat extent the ANT was connected to argumentation as studied in AI & Law (Verheij,
2000). It was concluded that the theories have similarities in the sense that both address the
problems of exceptions, conflicts and justification, and that both consider legal decisionmaking as a process of gradual theory construction, instead of as the straightforward
application of rules. Also, differences between the theories are noted. First, the two theories
use different primitives as starting point, namely stories and arguments, respectively.
Second, the anchored narratives theory is formally less explicit than the theory ofargumentation as studied in AI & Law. Third, the approaches have a different setting of use:

whereas anchored narratives theory was used from the start as a tool for evaluating real
crime cases, in order to possibly uncover miscarriages of justice, argumentation in AI & Law
was studied as a formal, computational model, intended for instance as the foundation ofsoftware for developing, diagramming and evaluating arguments.
A second attempt at understanding the anchored narratives theory used argumentation
schemes (Walton et al., 2008) as a modelling approach. Argumentation schemes can bethought of as semi-formal generalizations of the formal rules of logic, such as modus ponens
and classical syllogisms. An example is the following version of an argumentation schemeon expert testimony:
Argumentation scheme for expert testimony
Conclusion.
There is reason to believe that claim C is true.
Conditions.
Person P is an expert on topic T.
Person P claims that C is true.
Claim C belongs to topic T.
As can be seen from the example, argumentation schemes can cover reasoning patterns ina specific, concrete setting (here: expert testimony). Also, they do not guarantee theirconclusion when their conditions are satisfied. For instance, in the example the expert canmake a mistake or can be lying. Situations that block the use of a scheme are made explicitin the critical questions that come with argumentation schemes. For instance, for theexample scheme we could have the following example critical questions:
Critical questions.
Has the expert not made a mistake when assessing claim C?
Does the expert have a reason to lie about claim C?
In sources such as Walton et al., (2008), lists of argumentation schemes and their criticalquestions have been collected.
We have used argumentation schemes to make explicit how (in our interpretation) the
anchored narratives theory can be used to determine whether a story is true. Here are twoargumentation schemes we proposed (slightly rephrased from Verheij and Bex, 2009):
Argumentation scheme for accepting a story as true
Conclusion
Story S about topic T is true.
Conditions
Story S is good.
Story S is well-anchored.
Critical questions
Is there a story S’ about topic T (unequal to S) that is good?
Argumentation scheme for assessing story quality
Conclusion

Story S is good.
Conditions
Story S has a central action to which all elements are related.
Story S explains how the central action was performed.
Story S explains why the central action was performed.
Story S is unambiguous.
Story S does not contain contradictions.
(For this scheme no critical questions were listed.)
The first scheme specifies the two mentioned evaluation criteria used in anchored narratives
theory: story quality and story anchoring. The second scheme shows how story quality isevaluated, in terms discussed in the original sources of the anchored narratives theory. In
Verheij & Bex (2009), this second scheme is later adapted to include the use of story
schemes, as inspired by discussions with Peter during Floris’ PhD research, in which story
schemes play a significant role, as discussed in the next section.
One of the more vocal critics of anchored narratives has been William Twining (1995, 2006).
Twining, who, as one of the New Evidence Theorists 2 re-popularized Wigmore’s charting
method for evidence (Wigmore 1913/1931), argues that in any case, the evidence itself isthe most important, and that this evidence should form the basis of the reasoning about what
happened in a case. Take as an example the Wigmore chart in Figure 2. Black circles
denote evidence brought forth by the prosecution, from which step-by-step inferences canbe made to reach the final conclusion ‘Sacco was conscious of having been involved in arobbery and shooting’ (white circle 155a). Similarly, black diamonds indicate defence
evidence, which can be used to infer other conclusions (white diamonds, e.g. ‘Sacco
believed he was being arrested because of his political beliefs’, 469), which can in turn be
used to counter the original prosecution's inferences.

2

Together with Terence Anderson, David Schum, Peter Tillers and others.

Figure 2. Wigmore Chart or Diagram (Kadane and Schum, 1996)
Twining’s main criticism of ANT was that ANT does not consider the concrete, case-specificevidence, but rather seems to consider only general commonsense knowledge to anchor
stories, which can contain stereotypes, biases, folk beliefs, and so on (Figure 1). And
indeed, in the anchoring process, the search for evidence definitely seems to play a part but
the role of the individual pieces of evidence is never really clarified in the description of theanchored narratives theory. At the same time, there does not seem to be deep disagreement
between Twining and Crombag, Van Koppen and Wagenaar, since they also argue that astory should be anchored in reality by means of the evidence.
All in all, the anchored narratives theory as developed by Peter and his colleagues provided
much inspiration for our thinking about reasoning with evidence, and also led to new ideas
for developing a hybrid theory that combined stories with arguments.
Arguments About Stories: A Hybrid Theory
The second part of our journey with Peter took off in early 2005. Henry Prakken, who like
Bart worked on logical theories of legal argument (see e.g. the influential ASPIC+ model,
Prakken 2010), had been awarded an NWO grant ‘Making sense of evidence: software
support for crime investigations’. He asked Bart and Peter to join as supervisors in aninterdisciplinary project performed by Floris for his PhD at both the Law Faculty and the AI
Department in Groningen.

The project had as one of its goals to connect the Wigmorean approach, which renders
arguments and counterarguments based on evidence in a case as diagrams, with the storybased approach of ANT. Furthermore, the project aimed to incorporate ideas from Artificial
Intelligence and Data Visualization into rational theories on evidential reasoning. A clear link
already exists between evidential reasoning using arguments and the subfield of AI called
computational argumentation, the latter containing work on argument diagramming (Reed etal., 2007; Verheij, 2005), logics for legal and commonsense reasoning (Pollock, 1987;
Prakken, 1997, Bex et al., 2003), and the critical evaluation of arguments in a dialecticalprocess (Dung, 1995). Bex and his colleagues (2003) showed that there are standard
argument schemes for evidential reasoning, such as the scheme from expert opinion (seesection 2), the scheme from witness testimony, and so on. Using such schemes as inference
rules, we can derive conclusions from the evidence, thus building logical arguments, viz.
1.
2.
3.

Eyewitness 1 says ‘I saw Sacco in Boston’
Sacco was in Boston
Sacco was not in Braintree(Evidence)(1, Witness Testimony Scheme)(2)Here, the Witness Testimony Scheme is used to infer from the evidence that Sacco was in Boston.
This scheme is similar to the expert opinion scheme – we have reason to believe the claim is true
because an eyewitness says so. However, we can ask the critical question ‘Does the eyewitness
misremember?’. Say that answers this question affirmatively because we have evidence that thewitness had seen a photo of Sacco (e.g., in the newspaper) before being interrogated. We can thenbuild the following argument, which acts as a counterargument to the above.
1.
2.

Witness 1 had seen a photo of Sacco(Evidence)
Witness 2 misremembers seeing Sacco in Boston (1, Misremember Scheme,
counterargument to
Witness Testimony)

The Misremember Scheme mentioned in this argument is, like the other argument schemes, also a
generalization, something like ‘if people see photographs of suspects before they are interrogated,
they may mistakenly think they saw the suspect in person’. Note how the dialectical process ofasking and answering critical questions in argumentation is similar to the anchoring process as
described in the section entitled When to Believe a Story: Anchored Narratives.

Figure 3. Arguments in a Diagram.
Note Normal arrows denote a ‘support’ relation and the dashed arrow an ‘attack’ relation.
The arguments can also be rendered as a semi-formal diagram (Figure 3). Note thesimilarity between such a diagram and the Wigmore diagram in Figure 2. 3
Where the link between arguments and AI was thus easily made, the link between stories
and AI was less obvious. Looking for more formal work on stories in AI, we quickly foundscripts – general stories or explanation patterns that were a popular way of representing
episodic knowledge in AI (Schank & Abelson, 1977). Such a script is very similar to
Pennington and Hastie’s typical story structure, also mentioned in ANT: every (good) story
has a context, some motive and goals for the main character, a central action and clear
consequences of that action. While this is a quite generic ‘intentional action’ scheme, othermore specific story schemes are also possible. Take, for example, the ‘murder’ scheme (Bex& Verheij, 2012).

1.
2.
3.
4.
5.

Story scheme for murder
Anomaly that the scheme explains: person y is dead.
Central action of the scheme: person x kills person y.
Other relevant information: the motive m, the time of the killing t, the place of thekilling p, the weapon w.
Pattern of actions: person x has a motive m to kill person y – person x kills person y(at time t) (at place p) (with weapon w) – person y is dead.
More specific kinds of murder: assassination (e.g., liquidation), felony murder (e.g.,robbery murder), killing of one’s spouse.

Such crime story schemes are thus general scenarios detailing how crimes typically takeplace (Cornish, 1994). Note that, like argumentation schemes, we can ask critical questions
for a crime scheme.

3
The links between such diagramming methods and AI models of computational argumentation are
further explored in Bex et al., 2003; Reed et al., 2007; Verheij, 2005.

Critical questions
1. Is the motive a legitimate motive for the action?
2. Can the murder be induced by some other motive?
3. Can the action have the stated consequences?
The question was how to incorporate story schemes and stories into a more formalized, AIbased reasoning framework. The above story scheme already mentions an ‘anomaly to be
explained’, which leads us to diagnostic expert systems, 4 in which some evidence orobservations are explained by a hypothesis. This is very similar to explaining the evidence ina legal case by telling a hypothetical story. Take, for example, the fact that was mentioned
before, namely that Sacco reached for a gun when he was apprehended. This can be
explained by the following story:
Sacco travelled to the town of Braintree just outside Boston by train. In Braintree, Sacco shot
a payroll guard and took off with the payroll money. When Sacco was later apprehended, he
was still carrying the gun, which he wanted to use to prevent the police from arresting him.
However, it can also be explained by the following story:
Sacco was a night watchman who always carried a gun. When he was arrested, heinstinctively reached for his gun because he thought he was being discriminated against
because of his political beliefs.
As can be seen, the first story conforms to the ‘murder/robbery’ story scheme, whereas thesecond story recounts an unfortunate incident or mix-up. The question is then: how do wecompare these stories and choose the ‘best’ story? Bex proposes a hybrid theory of stories
and arguments, in which alternative hypothetical stories about ‘what happened’ in a case areconstructed and discussed in a dialectical process of argument and counterargument. The
hybrid theory allows for both alternative explanations and arguments to reason about these
explanations. For example, arguments can be used to support an explanation with evidenceor to reason about the plausibility of the explanations. Figure 4 shows a simplified exampleof how arguments can be used to support and attack a story using evidence, and how analternative explanation can be given. Here are two arguments that support the main story –that Sacco shot and robbed the guard –based on the evidence of eyewitness 2 and thepolice report. Note how the story is ‘anchored’ in evidence. Also note that, while thegeneralizations underlying the arguments and the stories have not been rendered in the
figure, they are represented by the arrows. Finally, note it is also possible to attack stories
and arguments: one argument based on witness testimony 12 attacks the story, but is itself
attacked by another argument.

4

Often used for medical or system diagnosis, see e.g. Buchannan & Shortliffe, 1984.

Figure 4. A Story Anchored in Evidence
Several criteria have been formulated that can be applied using the hybrid theory. Broadly
speaking, these criteria fall into two categories:
(1)

(2)

whether the explanation conforms to the specific evidence in the case at hand, thatis, how does the available evidence support and contradict the explanation, andwhich elements of the explanation are not supported by the evidence; andwhether the explanation conforms to general, commonsense knowledge of the world,that is, how plausible are the assumptions and generalizations the explanation
makes and how complete, detailed and consistent is the explanation.

These criteria can be defined in more detail using the machinery in the hybrid theory. We
can look at the number of pieces of evidence that support or contradict an explanation
through arguments. Note that only arguments which are themselves not attacked support orcontradict an explanation. Arguments can also be used to reason about the plausibility of an
explanation, as the plausibility of elements of an explanation or the explanation as a whole
can become the subject of an argumentation process. Arguments about the plausibility of
explanations are based on assumptions, as reasoning about plausibility is done using
commonsense knowledge about how the world generally works. For plausibility, we do notjust consider the individual elements of an explanation (events, causal relations), but also
the story as a whole by seeing if the story fits a plausible story scheme.

After Floris’ PhD (Bex, 2011), he and Bart kept working together on story schemes in AI (Bex& Verheij, 2010a), the link between legal reasoning and evidential reasoning in AI (Bex &Verheij, 2011, 2013) and also an informal, less schematic version of the hybrid theory forpractitioners (Bex & Verheij, 2010b). In this article, of which an extended English version
was published later (Bex & Verheij, 2012), we formulated critical questions that point totypical sources of doubt in a hybrid case. Some of these questions concern the arguments inthe case – these are the questions associated with the argumentation schemes. Other
critical questions in the hybrid theory concern the causal coherence of the explanations. For
example, we can ask what kind of general scheme the explanation adheres to – for example,
if an explanation speaks of a restaurant visit which lasted only two minutes this is not clear.
Furthermore, we can ask whether the explanation is complete – for example, why does arestaurant story not mention any of the actors eating food? Finally, there are generalquestions that concern the case and comparison of explanations on a higher level. For
example, we can ask whether the search for alternative explanations was thorough enough,how decisively some explanation surpasses the alternatives, or how good an explanation is
independently of the alternatives. The latter two concern the quality of explanations, and we
can use the criteria for explanations given above, taking into account the various standardsof proof given by the law.
Floris’ PhD project was very successful, being awarded a cum laude. At the graduation
party, Peter gave a speech, emphasizing how he loved the meetings with the team, justchatting and arguing about evidential reasoning, arguments and stories. The project also
included Peter in a publication in an AI journal (Bex et al., 2010). Peter would later mention
Floris’ work in his 2011 book. He lauded the critical thinking involved with ‘scheme-building’,
the use of argumentation and story schemes, but questioned whether the formal schemes
and diagrams such as the ones by Wigmore and Bex et al. (Figures 2-4) could contain all thenecessary knowledge needed in a complex case. Such diagrams and schemes, he argues,
necessarily abstract from the complexities of the world around us, which can only be
captured by stories. In later work (Bex & Verheij, 2012; Bex, 2019), the focus of the hybrid
theory would be less on the diagrams and more on stories, natural language arguments andcritical questions.
On Strong and Weak Stories (and the Role of Numbers)
A third part of the journey took the third primitive for the rational handling of evidential
reasoning on board: probabilities. In contrast with the qualitative primitives of arguments andscenarios that formed the core of ANT and the hybrid theory, probabilities add a quantitative,
numeric perspective. And indeed, both from theory and from practice, there was, and still is,a strong incentive to combine qualitative and quantitative elements. Theoretically, forinstance, because the combination of logic (analyzing the logical combinations ofpropositions) and probability theory (studying probabilistic variables and their relations) has along and intricate history, connecting to deep debates in epistemology and the philosophy ofscience (see e.g. Dewey et al., 2019). Practically, in particular, because of the rise offorensic DNA evidence, that revolution in crime investigation with a strong scientific andstatistical underpinning.
Consider, for instance, the issue of whether the suspect is the source of a tissue sample
found on the crime scene or someone else. When the found sample has a rare DNA profile,as measured by a database with reference DNA, a good match between the sample's andthe suspect's DNA has a high evidential value. The match then comes with a high likelihood
ratio distinguishing between the suspect or someone else being the source of the DNA.
Using Bayesian calculations, the prior odds that the suspect is the source instead ofsomeone else (before considering the DNA evidence) can be ‘updated' to find the posterior
odds (after the evidence) by multiplying the prior odds with the likelihood ratio. With a highlikelihood ratio, even a low prior odds can then lead to a high posterior odds.
The success and value of the statistics about DNA sample matching was sometimes used tosupport that all evidential analysis should be cast in statistical terms, suggesting likelihood
ratio as a measure of the strength of evidential support. In practice, it is often notstraightforward how to perform a numerical, statistical analysis of the evidence, especiallywhen modelling all the evidence and all the relevant interpretations. For instance, it is not
obvious how to combine stories with their emphasis on temporal and causal links between
events, with probabilities and the Bayesian calculus.
One attempt to combine scenarios with the Bayesian calculus was performed by Charlotte
Vlek in her PhD research (Vlek, 2016). 5 Charlotte’s project was part of the project ‘Designingand Understanding Forensic Bayesian Networks with Arguments and Scenarios’ in the NWO
Forensic Science research programme. 6 In her PhD research, supervised by Bart, Henry
Prakken and Silja Renooij, Charlotte used the AI tool of Bayesian networks as a means to
combine qualitative and quantitative elements. Bayesian networks combine a graphicalstructure (formally a directed acyclic graph) with conditional probability tables. Each node in
the graph represents a probabilistic variable and has an associated table of the probabilitiesof the node’s values conditioned on the values of the node’s parents in the graph. Charlotte
developed a method to model the evidence and possible scenarios about a criminal case ina Bayesian network. In the method (Vlek et al., 2014), scenarios are modelled as clusters ofpossible events, and then connected to the evidence. Conditional probability tables makeexplicit how the events and the evidence are probabilistically related. Charlotte also
developed a method to extract an explanatory text from a Bayesian network designed using
her method. In the text, the scenarios in the network are made explicit, scenario quality is
analyzed, and for each scenario it is shown how it is connected to the evidence (Vlek et al.,2016). Charlotte tested her theory with case studies, such as the Anjum case (Crombag &Isräels, 2008). The method was successful in the sense that it combined scenarios withprobabilistic degrees of uncertainty. In other words, it provided a way to systematicallyanalyze weakly and strongly supported stories in terms of probabilities. Also, it showed a
way to model the coherence of scenarios probabilistically. At the same time, it proved hard
to sensibly provide all probabilistic information needed for a Bayesian network (a standard
issue for Bayesian networks since they represent full probability distributions).
A second attempt to connect scenarios to probabilities uses the method of case models thatprovide a formal analysis of argument validity (Verheij, 2014, 2017). Case models consist ofcases that (in the setting of evidential reasoning) model the conjunctive combination of
hypothetical events and the evidence. The cases come with a preference ordering, that can
5 Other recent attempts include Di Bello (2013) and Urbaniak (2018). See also Di Bello & Verheij(2018).
6
Further information about the project is available at https://www.ai.rug.nl/~verheij/nwofs/.

represent the plausibility of a case. Since a preference ordering is qualitative in nature, but
can be given a numeric representation, the approach can be said to be simultaneously ‘withand without numbers’. Figure 5 shows an example of the use of a case model to analyze acrime case involving the murder of a student in her apartment (Van Leeuwen & Verheij,2019). The main suspect was the landlord’s son (another tenant of the apartment, P in the
figure), who was indeed convicted. The figure illustrates graphically how a case model isgradually constructed by considering the evidence one piece at a time. The construction
starts at the top when the victim’s body is found. The case is immediately considered amurder case because of the signs of violence. A friend reported that she had had a phone
conversation with the victim that ended abruptly after she heard someone say ‘goodmorning’, and then screaming and loud noise. All tenants other than the landlord’s son were
found to be at work at the time, so two possibilities are considered: P is guilty, or he is not.
Gradually, the guilty scenario becomes more concrete after P has fled in the victim’s car andher bloodstains are found in the car. The suspect has a motive, as follows from witness
testimony about a conflict, and when the suspect provides a contradictory testimony about amedical examination and confesses to his parents (in a phone call), only one hypotheticalscenario is considered possible given the totality of the evidence: the suspect killed thevictim with the gun found because of a conflict about a washing machine.
In a 2019 honour’s project at the University of Groningen, Ludi van Leeuwen compared theBayesian network and case model methods, and concluded that both can model scenarios
as clusters of events, and connect them to the evidence. Bayesian networks allow for a morefine-grained numerical representation of the weakness or strength of a scenario’s support,
but require more numbers than available. Case models allow for a more crude, qualitativemodelling of the level of support of a scenario, but have the advantage that no commitment
to explicit probability estimates is required. Both allow for an analysis of the effect ofevidence as it is gradually added. Further details can be found in Van Leeuwen & Verheij,
2019.

Figure 5. Constructing a Case Model (Van Leeuwen and Verheij, 2019)Conclusion
We have described our journey with Peter and his stories about evidence in three parts. In
the first part, we described the development of our understanding of the anchored narratives
approach to the rational handling of the evidence in crime investigation as developed byPeter and his colleagues Hans Crombag and Willem Wagenaar. In the second part, we
discussed the combined use of stories and arguments for evidential analysis in the hybrid
theory developed by Floris in his PhD research, supervised by Peter, Bart and Henry
Prakken. In the third part, we addressed the role of numbers for evidential reasoning, and inparticular of probabilities, by summarizing Charlotte Vlek's approach to the embedding ofstories in Bayesian networks and Bart's case models ‘with and without numbers'.
The journey led us to the belief that stories indeed have an important role to play in therational handling of the evidence in crime cases: guarding the local and global coherence ofthe events in light of the evidence. This role is characteristic for the use of stories, and notequally well or straightforwardly addressed by the other two important rationality tools ofarguments and stories (see Di Bello & Verheij, 2018 for a systematic discussion of the rolesof stories, arguments and probabilities). 7
Peter’s emphasis on case studies has played a significant role in the journey, as again
recently in a series of symposia on evidential reasoning and AI. 8 During these symposia, the
7 And the story continues. See the special issue on interdisciplinary approaches to evidence &decision making in the law in the Artificial Intelligence and Law journal (Di Bello and Verheij, 2020).
8
In 2015 a three-day workshop at the Zentrum für Interdisziplinäre Forschung (Centre forInterdisciplinary Research) in Bielefeld, Germany was held, with legal scholars and practitioners,

idea arose to model a real legal case using the various approaches, in order to compare
their strong and weak points. Peter proposed the Simonshaven case, which concerns thedeath of a woman who was violently killed near the village of Simonshaven, close toRotterdam, while walking there with her husband, Ed Lourens. A recent special issue on
Models of Rational Proof in Criminal Law (Prakken, Bex & Mackor, 2020) collects thedifferent analyses, including an analysis of the case in the hybrid theory, an analysis using
the ‘scenario-approach’ (an amended version of ANT) and numerous Bayesian orprobabilistic approaches. Interestingly, the hybrid analysis as well as the scenario-based
analysis, which Peter developed together with Anne Ruth Mackor (Mackor & Van Koppen,2019), are presented as tables of stories, sub-stories, arguments and evidence, showing thatconceptually, the hybrid theory and ANT have relevant connections.
Peter has always been simultaneously interested and skeptical about AI approaches toevidential reasoning. He sees a role for ‘scheme building’ (whether Wigmore or in anotherway) but has consistently emphasized that human dossier analysis cannot quickly be
replaced by computers (see, for instance, recently his comments on computer-supported
cold case research 9, a ‘crazy plan’; see also Schraagen et al., 2018, which proposed an
automated intake system for the police based on the hybrid theory).
Indeed, the journey has not ended. New parts will follow and many stories on evidence andits rational use in crime investigation (with or without computer support) will follow. We hopeto often find Peter as a companion on this journey and look forward to the stories that he willcontribute.
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Prof. dr. Floris Bex is in 2009 gepromoveerd aan de Rijksuniversiteit Groningen met een
proefschrift getiteld "Arguments, stories and criminal evidence: A formal hybrid theory". Petervan Koppen was, naast Henry Prakken, één van zijn promotores. Sindsdien heeft Floris op
meerdere manieren samengewerkt met Peter aan theorieën van verhaal, argument en
bewijs, waar Floris de wiskundige aanpak uit de Artificiële Intelligentie (AI) probeert te rijmen
met de meer natuurlijke verhaalsaanpak van Peter. Thans is Floris hoogleraar bij het Tilburg
Institute for Law, Society & Technology (TILT) van de University of Tilburg, wetenschappelijk
directeur van het Nationaal Politielab AI bij het Innovation Centre for AI (ICAI) en universitair
docent AI bij het departement Informatica, Universiteit Utrecht. Floris' onderzoek
concentreert zich op de verantwoorde inzet van AI voor de politie en de rechtspraak.
Prof. dr. Bart Verheij bekleedt de leerstoel kunstmatige intelligentie en argumentatie aan de
Rijksuniversiteit Groningen, waar hij hoofd is van de afdeling kunstmatige intelligentie van
het Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence. Bart enPeter ontmoetten elkaar toen ze beiden werkten bij de capaciteitsgroep Metajuridica van derechtenfaculteit van de Universiteit Maastricht (2003-2004). Het gesprek over verstandig
omgaan met bewijs werd voortgezet bij de gezamenlijke begeleiding van Floris Bex'
proefschrift (met Henry Prakken en Peter als promotores en Bart als copromotor; 20052009). Peters lucide skepsis en nadruk op het belang van realistische case studies bleveninvloedrijk toen Bart een project leidde in het NWO Forensic Science programma (met Henryen Floris) over de combinatie van argumentatie, verhalen en kanstheorie (2012-2017). Bart
werkt aan de verbanden tussen kennis, data en redeneren, als bijdrage aan goede
kunstmatige intelligentie (zoals uitgelegd in zijn oratie `Arguments for Good Artificial
Intelligence'). Hij is co-coordinator van de onderzoekslijn `Responsible Hybrid Intelligence'
van het NWO Zwaartekrachtproject Hybrid Intelligence (net gestart, 10-jarig).