Anchored Narratives in Reasoning about
Evidence
Floris Bex1, Henry Prakken1,2 and Bart Verheij3
Centre for Law & ICT, University of Groningen, the Netherlands
2
Department of Information and Computing Sciences, Utrecht University, theNetherlands
3
Department of Artificial Intelligence, University of Groningen, the Netherlands
1

Abstract. This paper concerns the reasoning with stories, evidence and generalisations in a legal context. We will make some notions from the existing AnchoredNarratives theory more clear by making use of two formal techniques from AI,
namely causal-abductive reasoning and default-style argumentation. We willpropose a combination of these two formalisms which solves some of the
problems of the causal-abductive approach.

1. Introduction
In matters of evidence, a judge, a juror and an investigating officer all have to reasonfrom the evidence in a case to a conclusion and, particularly in the investigative phase,
also vice versa. Often, the reasoner must not only apply the rules of law to the casefacts, but he must also use commonsense knowledge about the world. In the case of acriminal investigation, the fact finder must check whether the case facts comply withthe hypothesis he has in mind. In a complex case with much evidence, often different
viewpoints must be considered and it is not always clear whether or not the accused is
guilty (in a criminal case) or what direction a criminal investigation should take.
John Henry Wigmore was one of the first scholars who analysed reasoning withevidence and proof in a legal context [15]. Among other things, he developed agraphical notation for evidential arguments and their sources of doubt.. Based on
Wigmore’s work, Anderson, Twining, Tillers and Schum [1][10] have more recently
discussed such topic as the logic of inferences about facts, the use of formal
probabilistic methods in evaluating evidence and the visualisation of evidential
arguments. In AI & Law, Bex et al. [4] formalised Wigmore’s notation in a formal
argumentation framework, using an approach with argumentation schemes. Finally,
Walton [14] looked at reasoning with evidence from the standpoint of informal logic.
As an alternative to these argument-based approaches, psychologists haveproposed a more holistic approach to legal reasoning with evidence, based on thenotion of narrative, or story. For example, Bennet and Feldman [2] and Pennington andHastie [8], argue that reasoning about evidence is most persuasive if it takes the formof constructing stories about what happened. Crombag et al. [5] have further developed
this approach. According to their Anchored Narratives Theory (ANT)., the only viable
way in which factual judicial decision making can proceed is by constructing stories
and supporting, or “anchoring” them by common sense generalisations. This approach1has gained interest of others as well. For example, Anderson, Twining and Schumdevote a chapter to stories in their last edition of Analysis of Evidence ([1], pp. 280),
and Verheij [12] compares the anchored narratives approach with an argument based
approach in. Thagard [9] has applied his connectionist model of abductive inference tothe best explanation to legal cases, claiming that it provides a computational account of
the story approach to legal evidence. Likewise, Keppens and Schafer [6] have applied
model-based reasoning to automated support for the investigation of murder cases.
Despite this recent interest in the role of stories in evidential reasoning, an analytic
account of the elements of a story and how it relates to generalisations and evidence isstill lacking. Our aim in this paper is to clarify ANT in these two respects, making use
of formal techniques from AI (& Law). We will propose to model stories as a causalabductive theory and we will argue that these theories should be connected to theavailable evidence with argumentation. This gives rise to a distinction between two
kinds of story anchoring, viz. internal and external anchoring. We will then illustrate
our proposal in detail with an example also used by Crombag et al.

2. Anchored Narratives
In [5] and [13], Crombag, Van Koppen and Wagenaar both study empirically how
judge in Dutch criminal cases decide on the facts and propose their ANT as anormative model. According to ANT, legal decisions have to be based on stories,
which have to be anchored in common sense generalisations. In ANT, judges first
determine the quality of the stories of the parties presented to them by the prosecution
and the defence and then examine the anchoring of the stories using common sense
generalisations, some of which can be considered as strictly true, for example ’a person
cannot be in two places at the same moment’, while other ones can be doubted, forexample ’Witnesses under oath usually speak the truth’. The questions are whether the
implicit generalisations that the story itself invokes are safe to believe and whether thegeneralisations which allow us to reason from the evidence to the story are safe tobelieve. We now examine these two issues in more detail.
2.1. The quality of the story
When determining the quality of a story, Crombag et al. draw on work by Bennet andFeldman [2] and Pennington and Hastie [8]. The core of a good story about a criminalcase is an action, namely the criminal offence, and its consequences. A good story tells
how the circumstances brought the actor in such a psychological and/or physical statethat he had a motive and an opportunity to commit the criminal offence.
Pennington and Hastie [8] proposed the causal story structure in Figure 1 , as atemplate for stories about intentional actions and their consequences. Crombag et al.
adopted this structure since it captures the elements that have to be proven in a standard
crime: who is the offender (the actor in the story), what did he do (the actions) and did
he act intentionally or recklessly (the psychological states/goals). In ANT, a story’s
quality is measured in terms of how well it fits the story template.

2Figure 1: Pennington and Hastie's story structureThis criterion is also discussed by Pennington and Hastie. However, they also
discuss two other criteria not discussed in ANT, namely coverage and consistency. A
story’s coverage is the extent to which the story accounts for the evidence presented,
while a story is consistent to the extent that it does not contradict itself or any of theevidence. Pennington and Hastie also give a fourth principle, namely plausibility,
which says that a story is plausible to the extent that it corresponds to the decision
maker’s knowledge of the world. This last principle corresponds to what Crombag et
al. mean by the anchoring of the story so we will discuss it in section 2.2.
2.2. Anchoring the story
In addition to being good, stories must also be anchored in common sense
knowledge of the world around us. How this anchoring works is best explained throughan example, namely the King case ([5], pp. 460-463), a relatively simple case about analleged burglary. The prosecution presents the following story:
On the 18th of November, Andrew King climbs over the fence of thebackyard of the Zomerdijk family with the intention to look if there issomething interesting for him in the family’s house. Through this yard hewalks to the door that offers entry into the bedroom of the 5-year-old
son of the family. The door is not closed, so King opens it and enters
the bedroom to see if there is anything of interest in the house. Because
it is dark, King does not see the toy lying on the floor. King hits thetoy, causing it to make a sound which causes the dog to give tongue. King
hears the dog and runs outside, closing the door behind him. Mr.
Zomerdijk hears the toy and the dog. He goes to the bedroom and sees King
running away through the closed garden door. He shouts “there is aburglar, come and help me!” and runs into the garden after King. King,
who wants to pretend he is lost, does not run away. In spite of this,Zomerdijk jumps on King and, aided by his brother, who is visiting theZomerdijk family, molests King.

This is a fairly plausible story because it appeals to certain implicit generalisations
that seem obvious when made explicit; for example, King must have been the person
who hit the toy if the rest of the family and the visitors were in the living room, and he
was probably up to no good anyway. Why else did he enter the garden in the first place,and why did he run away when Mr. Zomerdijk appeared in the bedroom?
In order to evaluate the story, two types of generalisations are relevant. Firstly, thecausal links within the story must be justified by plausible causal generalisations, suchas “If a sound is heard in the bedroom and all the people you know are in the livingroom, there must be someone else in the bedroom” and “if someone runs away throughthe garden he was probably up to no good”. Secondly, the story must be linked to theavailable evidence by plausible evidential generalisations, such as “witnesses usually
speak the truth”. In the King case, the prosecution’s story is mainly based on thetestimonies given by Mr. and Mrs. Zomerdijk and their visitors. If the decision-makerbelieves the prosecutor’s story, he accepts the generalisation that “witnesses usually3

speak the truth”. Alternatively, the decision maker can think that the witnesses want toprotect each other. This is an exception to the generalisations that witnesses usually
speak the truth, and if the decision maker has a good reason to believe that thewitnesses want to protect each other, he will no longer believe that they speak the truth.
In ANT it is discussed in detail how generalisations can be analysed in this way
and decision makers are urged to make the common sense knowledge they use to cometo their decision explicit, so that the ultimate decision is better motivated and not basedon dubious generalisations. However, the details of what part the generalisations playin a story are left untreated. Also, the exact interplay between the evidence, a story andthe generalisations connecting these two is not elaborated upon.

3. Logical tools for analysing the Anchored Narratives theory
From the discussion so far it has turned out that, while the ANT seems basically sound,no analytical account of how the interplay between stories, generalisations andevidence takes place has yet been provided. Our first aim therefore is to determine theexact role generalisations play in reasoning with stories and evidence. Our second aim
is to give evidence a more prominent place in the evaluation of stories by using thenotions of coverage and consistency.
In our analysis we will use some existing logical tools, and propose someadjustments to these tools. In section 3.1 we will model stories as a causal network thatcan be used in abductive inference to the best explanation. Section 3.2 introducesnotions from argumentation logics, which are used to connect stories to the pieces ofevidence in the case. Because of space limitations our notation will be largely
semiformal but they easily map onto well-known formalisms.
3.1. Stories as a causal networks
Before investigating how generalisations can be analysed, we will first have to saysomething about the formal structure of stories and the role they play in the anchoringprocess. An underlying principle in all above-discussed approaches to stories is thecausality between the states and events in a story. Our proposal is therefore to model
stories as causal networks. Figure 2 displays the King story as a causal network.

Figure 2: the King story as a causal network

Note that by modelling stories in this way, some information is lost. For example, thedifferent categories proposed in Pennington and Hastie’s template are no longer part of
the theory. However, we will argue that this way of modelling has its advantages andleave other aspects of stories for future research.
A causal network can be used as an abductive theory, in which observations are
explained by hypothesised events or states through abductive reasoning, or ‘inference4

to the best explanation’ (IBE). In AI many formal and computational accounts of IBE
are available. However, the following simplified account covers their main elements
and suffices for present purposes.
A causal theory T is a set of implications of the form c1 & … & cn ⇒c e, wherec1 … cn are literals, standing for causes that in conjunction produce an effect e, which
also is a literal. Causal rules can be chained, since effects can in turn be causes of othereffects. In addition there is a consistent set O of observations (a set of literals), which
are to be explained by T. More precisely, an observation o ∈ O is explained by T if forsome set H ⊆ T of hypotheses it holds that•H ∪ T ⊧ o; and•H ∪ T is consistent.

In AI usually only subset-minimal sets of hypotheses are considered. Also, in AI it isusually required that a hypothesis explains all observations in O but this condition is
unrealistic if the correctness and completeness of the causal theory is not guaranteed,(cf. [9]), as is often the case in legal evidential reasoning.
The IBE approach nicely captures the explanatory role of evidential stories.
However, it abstracts from their internal anchoring with causal generalisations; thetheory T contains causal rules, but there is no possibility to reason about these rules.
IBE also has a problem in dealing with witness testimonies as evidence. It requires thatwitness testimonies are regarded as caused by other things. For instance, the truth ofwhat is testified is regarded as causing the testimony. Of course, since witnesses do notalways speak the truth, other possible causes of a testimony must also be modelled,
such as a motive to lie, or circumstances that can cause flawed perception or memory.
So causal rules like the following ones should also be included:p ⇒c w said “p”
w has reason to lie about p ⇒c w said “p”
w hallucinated and thought he saw p ⇒c w said “p”

However, this creates the following problem. What we want to know is how the theory
explains the observation that w said “p”. In a causal theory with the above three rules,
the testimony can be explained in three ways: it can be explained in the ‘normal’ way
by supposing that the witness speaks the truth, i.e. by p, but it can also be explained by
supposing that w has reason to lie about p and by supposing that w hallucinated andthought he saw p. Moreover, these explanations fair equally well on coverage andconsistency so there is no obvious way to prefer the ‘normal’ explanation. In this way,
the abductive approach fails to capture that witness testimonies are usually true; w hasreason to lie about p and w hallucinated and thought he saw p should not be‘abducibles’: they are exceptions to a default rule and should therefore be assumed
false if there is no evidence to the contrary.
Our solution to this problem is to invert causal generalisations in which theantecedent is the usual cause of the consequent into evidential generalisations (thisterminology is due to Pearl (1998) [7]). These evidential generalisations are then used
to connect nodes in the story structure to evidence outside that structure; thisconnection is formalised as argumentation, in particular, default reasoning andreasoning with argumentation schemes.

53.2. Evidence and arguments
We assume a logic in which applications of deductive and/or defeasible inferences canbe chained into tree-structured arguments. Associated with a defeasible inference is a
generalisation that acts as the warrant (cf. Toulmin [11]) for the inference. Because wewant to be able to change generalisations, they are modelled as additional premises toan argument and are subject to a defeasible modus ponens inference rule which allows
for exceptions. We assume that the logic respects Pearl’s c-e rules [7], that is, it is notallowed to apply an evidential generalisation to a proposition that has been inferred byapplication of a causal rule.
A defeasible argument can be attacked in two ways. It can be rebutted with anargument for the opposite conclusion, while it can be undercut with an argument why ageneralisation does not apply in the given circumstances. Arguments can be either
justified, which means that they are not attacked by other justified arguments that arestronger, or overruled, which means that they are attacked by one or more otherstronger arguments that are justified, or defensible, which means that they are neither
justified nor overruled.
In this paper, causal networks, arguments and inferences are represented throughfigures (see Figure 3 for examples). This is a semi-formal way of representing them,
but a formal translation into well-known formalisms is straightforward.

Figure 3: stories, arguments and generalisations

3.3. Combining causal and evidential reasoning
We now discuss how the abductive and argumentation formalisms can be combined.
The idea is that by providing stories and explanations for certain facts, the parties build
a causal network. By telling the main story, the prosecution provides us with the causal
network as seen in Figure 2. If King gives an alternative explanation for the fact that he
was in the yard, for example, because he was lost, he adds a node King is lost to the
network, which is linked to King climbs in into backyard by a causal link. King canalso tell a different story explaining, for example, why the toy made a sound. We will
explore this in further detail in section 4. In addition to giving (alternative)
explanations for facts, it is also possible to support or defeat facts in the story withevidence through evidential arguments. Important here is that only propositions
supported by evidence have to be explained; only if a proposition in the causal network
is supported by a justified argument, it becomes a member of the set O. It is also
possible that a proposition in the causal network is defeated by an evidential argument;in this case, the proposition should ideally not be explained by H ∪ T.
With such a causal network connected to evidence, generalisations can be criticallyexamined in two different ways. The first is by refining it, which adds a new condition,
which must also be proven (cf. [3]). The second way is to provide evidence that there
is an exception to the generalisation.. If the decision maker thinks that a certain
generalisation is implausible, he can change it into a refined version. For example, the6generalisation “if a person has the opportunity to enter a house (where he does notlive), then he will do so” can be refined into “if a person has the opportunity to enter ahouse (where he does not live) and he has bad intentions, then he will do so”. If thedecision maker instead believes a generalisation but thinks that in the case at hand an
exception to the default generalisation applies, then he can also attack thegeneralisation. For example, if one of the three witnesses (Mr. Zomerdijk, his wife andhis brother) explicitly stated that they wanted to protect each other, then the inference
the witnesses testify that they were in the living room ⇒e the threewitnesses were in the living room, which is based on the generalisation“witnesses usually speak the truth”, is undercut by an exception..

4. Anchoring the King story: a worked out example
In this section we illustrate the combination of causal and evidential reasoning given in
section 3 with an account of the King case. In addition to the story from section 2.2(see Figure 2), we also have a number of testimonies. King testified that he climbed
over a wall to get into the backyard. Mr. Zomerdijk, witness 1 (w1) from here on,
testified the following: “I was in the living room with my wife and my brother, andsuddenly I heard a toy making a sound in the bedroom. I went to the bedroom and,through the closed door, I saw a man, who I now know to be Andrew King, runningaway from the house in the backyard. I went outside and ran after the man. My brotheralso came to help me and we grabbed the man”. Mr. Zomerdijk’s brother (w2), testified
that he was in the living room and that suddenly, Mr. Zomerdijk stood up and went tothe bedroom. When the brother went after him a few seconds later, he saw a man
running in the garden and grabbed him. Mrs. Zomerdijk said she was in the living roomwith her husband and brother-in-law. This evidence can be used to support some of the
propositions in the story, viz.:

Figure 4: the main King story and its supporting evidence

According to section 3.3, the observations that have to be explained by the causal
theory are the propositions in the story that follow from undefeated evidential
arguments, in this case {King climbs into backyard, others in living room, w1 hears
toy, w1 goes to bedroom, w1 sees King, w1- w2 grab King, door is closed}. If we take ahypothesis H1 = {King has bad intentions, others in living room}, then all theobservations are explained by H1 ∪ T.
We next consider the evidential links between the evidence and the network. If we
accept the link between the three testimonies and others in the living room, we
implicitly accept the generalisation “if three witnesses testify that “p”, then usually p is7

true”. In section 3.3 we saw that one way to attack this generalisation is to say that “ifthree witnesses try to protect each other, then they will not speak the truth”, viz.:

Figure 5: a generalisation is attacked

However, in this case the decision maker does not have a direct reason to believe thatthe witnesses tried to protect each other, so the generalisation is not defeated and theargument for other in living room is justified.
The causal generalisations within the story can also be analysed. A dubious causal
link in the story is the one between King climbs into backyard, others in living room
and King opens door. This link stands for the (more abstract) generalisation “if aperson has the opportunity to enter a house (where he does not live), he will do so”.
This generalisation arguably is too general to be acceptable as an anchor; noteverybody who has the opportunity enters another person’s house. One possible
remedy is to refine the generalisation, for example, into “if a person has theopportunity to enter a house (where he does not live) and he has bad intentions, he will
do so”. The resulting generalisation is more acceptable as an anchor.. Figure 6 shows azoomed-in part of the causal network with the new generalisations and links added.
if a person has the opportunity to entera house (where he does not live) andhe has bad intentions, he will do soKing has bad
intentions

King
opens
door

King climbs
into backyard

King
testimony...

Others inliving room

w1
testimonyw2
testimonyw3
testimony

Figure 6: part of the King network with added links and generalisations

King has his own explanation for the fact that the toy made a sound; he claims that thewind blew open the door, hit the toy (which caused it to make a sound) and then blew
the door shut again. This explanation extends the causal network of Figure 4 with thefollowing rules:
wind opens door ⇒c wind hits toy; wind hits toy ⇒c toy makes sound;8wind opens door ⇒c wind closes door; wind closes door ⇒c door closed

This extension of the theory adds a new explanation for the propositions toy makes
sound and door is closed. However, assume next that none of the witnesses heard abang just after the sound of the toy and assume also that normally, when the windblows a door shut, there is a loud bang. With this information, two things can be done.
First, the causal theory can be further expanded with the rule wind closes door ⇒cloud bang. Next, an argument can be built that defeats loud bang by first inferring
none of the witnesses heard a loud bang from the testimonies, and then inferring there
was no loud bang from this. Figure 7 shows the updated causal network together withthe extra argument. Because of space limitations, only part of the updated causal theory
is shown in Figure 7.

Figure 7: the causal theory after King's explanation is added

Hypothesis H1 still explains all the observations; King’s hypothesis, H2, only
contains the proposition wind opens door. H2 also explains the facts that the toy made asound and that the door was closed; however, it does not explain that King was in the
backyard. If King wants to explain this fact, he either has to accept that he had badintentions or must give an alternative explanation.
Another problem with H2 is that it explains loud bang, while there is an undefeatedevidential argument for the opposite. So applying Pennington and Hastie’s coveringand consistency conditions, it can be argued that the prosecution’s explanation covers
more (more observations are explained by H1 than by H2, and H1 is more consistent(because it does not explain propositions that are defeated).

5. Conclusions and future research
In conclusion, we have made the following contributions. First we have argued for
a combination of a causal-abductive and a default-style argumentation modelling ofevidential reasoning, thus avoiding a problem with reasoning about witness testimonies
in a purely causal-abductive approach. In result, we have seen that there are twodifferent kinds of links that have to be supported by safe generalisations: the causal
links that are part of the story itself and the evidential links that are part of thearguments that connect the evidence to the story. Also, we have shown that there are
two ways of analysing generalisations: if the generalisation itself is dubious, then it canbe refined while if the generalisation is safe but in the case at hand a possible exception
applies, then it can be attacked. Thus we have refined the ANT notion of anchoring in
two important ways.

9However, our approach still leaves some questions unanswered. Firstly, the exact
details of how a hypothesis explains a set of observations and how hypotheses should
be compared have not been discussed. For example, in this paper we have assumed that
all observations are equally important but there may be different categories ofobservations, namely some that have to be explained by the hypothesis and some that
add to the explaining hypothesis’ believability but do not necessarily have to be
explained. Another important question is: when can one hypothesis be consideredbetter than another? For example, is coverage more important than consistency?
Secondly, the ideas proposed in this paper are just a start in the precise analysis of
the role of stories in legal reasoning. For example, the exact role of templates like theone proposed by Pennington and Hastie for assessing the quality of a story as a wholeis still unclear.

Acknowledgements
This research was supported by the Netherlands Organisation for Scientific Research(NWO) under project numbers 634.000.017 and 634.000.429. Henry Prakken was also
partially supported by the EU under IST-FP6-002307 (ASPIC).

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