The document discusses the role of narratives in legal contexts, proposing a model that emphasizes the importance of storytelling in conveying legal knowledge and its implications. It examines the complexity of narratives, addressing their partial and subjective nature, and explores methodologies for translating these stories into computational formats for deeper understanding. The analysis draws on the case of Pierson v. Post to illustrate narrative structures and the necessity for recognizing concurrent systems and social expectations in interpreting legal scenarios.

1. Legal Knowledge
Conveyed by Narratives
2 August 2014 - CMN presentation
Giovanni Sileno g.sileno@uva.nl
Alexander Boer
Tom van Engers
Leibniz Center for Law – University of Amsterdam
Towards a Representational Model

2. The social function of “stories”
Stories are “constituents of human memory,
knowledge, and social communication”
Schank and Abelson [1995], Knowledge and
memory: the real story

3. The social function of “stories”
“Many different root metaphors have been put
forth to represent the essential nature of human
beings: homo faber, homo economous, homo
politicus, [...], rational man. I now propose homo
narrans to be added to the list.
Fischer [1984], Narration as a Human
Communication Paradigm

4. What “stories” are:
Not only fictional narrations.. but also:
• personal experiences
• journalistic reports
• medical cases
• legal cases
• …

5. What is a legal case about?
witnesses’, experts’ speeches used to justify a
certain story reconstruction 
• events occurred in a social scenario
lawyers’, judges’ speeches used to justify a
certain legal interpretation of this story 
• legal implications

6. What is a legal case about?
witnesses’, experts’ speeches used to
justify a certain story reconstruction 
• events occurred in a social scenario
lawyers’, judges’ speeches used to justify a
certain legal interpretation of the story 
• legal implications

7. What is a legal case about?
witnesses’, experts’ speeches used to
justify a certain story reconstruction 
• events occurred in a social scenario
lawyers’, judges’ speeches used to justify a
certain legal interpretation of the story 
• legal implications
• nested narratives/speech acts
• originated from a failure of norm. expectations
• informative intent within the legal system
• brings part of background to the foreground

8. A relevant issue
Unfortunately, stories, by their own nature, are
partial (ill-defined) representations.
What is in a story depends on
• default assumptions
• common-sense knowledge
• expertise
• interest
• focus
• ...

9. A relevant issue
Unfortunately, stories, by their own nature, are
partial (ill-defined) representations.
What is told in a story depends on
• default assumptions
• common-sense knowledge
• expertise
• interest
• focus
• intent...
of the narrator !

10. A relevant issue
Unfortunately, stories, by their own nature, are
partial (ill-defined) representations.
What is read in a story depends on
• default assumptions
• common-sense knowledge
• expertise
• interest
• focus
• intent...
of the listener !

11. Three ontological domains
• discourse: the signal
• story: the meaning
• conversation: the context, i.e. the
knowledge and intent of narrator/listener
and those ascribed to the other party

12. Three ontological domains
• discourse: the signal
• story: the meaning
• conversation: the context, i.e. the
knowledge and intent of narrator/listener
and those ascribed to the other party
narrative interpretation:
discourse + conversation  story
narrative generation:
conversation + story  discourse

13. Pierson v PostPierson v Post (1805)

14. Pierson v Post
Post was hunting a fox with a horse and
hounds in a wild and uninhabited land, and
was about to catch it, but Pierson, although
conscious of Post's pursuit, intercepted the
fox, killed it and took the animal.
Tompkins: Possession of a fera naturae
occurs only if there is occupancy, i.e. taking
physical possession. Pierson took it, so he
owns it.

15. Pierson v Post
Post was hunting a fox with a horse and
hounds in a wild and uninhabited land, and
was about to catch it, but Pierson, although
conscious of Post's pursuit, intercepted the
fox, killed it and took the animal.
Livingston: If someone starts and hunts a fox
with hounds in a uninhabited land has a right
of taking the fox on any other person who saw
he was pursuing it.

16. “Shallow” story model
• sequence of events
Post was hunting a fox with a horse and
hounds in a wild and uninhabited land, and
was about to catch it, but Pierson, although
conscious of Post's pursuit, the
fox, it and the animal. [..]

17. • sequence of events
• occurring at certain circumstances
Post a fox
, and
, but Pierson, although
, the
fox, it and the animal. [..]
“Shallow” story model

18. Post a fox
, and , but Pierson, although
, the fox, it and the animal.
Possession of a fera naturae occurs only if
there is occupancy, i.e. taking physical
possession. Pierson took it, so he owns it.
• sequence of events
• occurring at certain circumstances
+ explicit mechanisms relating
events/circumstances
“Shallow” story model

19. “Shallow” story model
• a sequence of events
• occurring at certain circumstances
+ explicit mechanisms relating events/
circumstances
• What about the implicit mechanisms?

20. Our objective
We look for a methodology to
• acquire in a computational form
• the systematic knowledge
• concerning a social scenario,
• presented through a narrative
(e.g. a legal case, a scenario given by an expert, etc.)
• allowing alternative interpretations

21. Requirements
• bypass natural language issues
• we are not targeting narrative
comprehension, but scenario acquisition
from different interpreters

22. • target non-IT experts (in principle)
we refer mostly to diagrams, or programming based
on high level and “intuitive” languages
 affinity with scenario-based modeling
Requirements

23. From “Shallow” to “Deep” story model
Issues:
• consecutiveness vs consequence
“the mainspring of the narrative activity is to be traced to that
very confusion between consecutiveness and consequence,
what-comes-after being read in a narrative as what-is-
caused-by”, Barthes [1968]
• story-relative vs discourse-relative timelines
ordering as events occur vs how they are told/observed

24. Three levels of constraints
• weak: discourse ordering
• medium: relative/absolute time indexing
• strong: dependencies (logic or causal)
From “Shallow” to “Deep” story model

25. Three levels of constraints
• weak: discourse ordering
• medium: relative/absolute time indexing
• strong: dependencies (logic or causal)
The last is by far the most important to our scope:
problem of Contingency vs Contextuality
From “Shallow” to “Deep” story model

26. Three levels of constraints
• weak: discourse ordering
• medium: relative/absolute time indexing
• strong: dependencies (logic or causal)
The last is by far the most important to our scope:
problem of Contingency vs Contextuality
Shallow
Deep
From “Shallow” to “Deep” story model

27. An important step is the recognition of sub-
systems operating concurrently (e.g. agents,
concurrent cognitive modules).
What is necessarily said in a sequential way, may
in fact be simultaneous.
From “Shallow” to “Deep” story model

28. An important step is the recognition of sub-
systems operating concurrently (e.g. agents,
concurrent cognitive modules).
What is necessarily said in a sequential way, may
in fact be simultaneous.
A specific story provides a synchronization
between concurrent systems (as agents).
From “Shallow” to “Deep” story model

29. Agent story scheme
Motive  Intent  Action  Act
Bex and Verheij (2011), Pennington and Hastie (1993)
• the scheme is used by investigators as
template to map plausible scenarios, and to
anchor evidence.
• intent is meaningful for legal purposes (both
in design that adjudication)
• the existence or absence of motive may
also influence the jury

30. Agent story scheme (Post)
Motive  Intent  Action  Act (Failure)
Motive
Post intends to hunt that fox
Intent
Post sees a fox

31. Agent story scheme (Post)
Motive  Intent  Action  Act (Failure)
Motive
Post intends to hunt that fox
Intent
Post sees a fox
1. Post sees a fox.

32. Agent story scheme (Post)
Motive  Intent  Action  Act (Failure)
1. Post sees a fox.
2. Post intends to
hunt that fox.
Is this enough?
Motive
Post intends to hunt that fox
Intent
Post sees a fox

33. Agent story scheme (Post)
Motive  Intent  Action  Act (Failure)
• Considering only these events the model is
not enough specified.
 We need circumstantial conditions

34. Motive  Intent  Action  Act (Failure)
Post is hunting foxes
Agent story scheme (Post)
Motivation
+ Motivation
Motive
Post intends to hunt that fox
Intent
Post sees a fox

35. Agent story scheme (Post)
Motive  Intent  Action  Act (Failure)
+ Motivation
Post is hunting foxes
Motivation
Motive
Post intends to hunt that fox
Intent
Post sees a fox

36. Agent story scheme (Post)
Motive  Intent  Action  Act (Failure)
+ Motivation
Post is hunting foxes
Motivation
Motive
Post intends to hunt that fox
Intent
Post sees a fox

37. Agent story scheme (Post)
Motive  Intent  Action  Act (Failure)
Intent
Post is hunting the fox
Action
+ Affordance
Post thinks he
has the power to
hunt that fox
Post intends to
hunt that fox
Affordance

38. Agent story scheme (Post)
Motive  Intent  Action  Act (Failure)
+ Affordance
Post is hunting the fox
Post thinks he
has the power to
hunt that fox
Post intends to
hunt that fox
Intent
Action
Affordance

39. Motive  Intent  Action  Act (Failure)
Action
Post has hunted the fox
Act
+ Disposition
Post has actually the
power to hunt that fox
Agent story scheme (Post)
Disposition
Post is
hunting..

40. Agent story scheme (Pierson)
Motive  Intent  Action  Act (Failure)
+ Disposition
Pierson has hunted the fox
Pierson has actually the
power to hunt that foxAction
Act
Pierson is
hunting..
Disposition

41. Agent story scheme (Pierson)
Motive  Intent  Action  Act (Failure)
+ Disposition
Pierson has hunted the fox
Pierson has actually the
power to hunt that foxAction
Act
Pierson is
hunting..
Disposition

42. Agent story scheme (Pierson)
Motive  Intent  Action  Act (Failure)
+ Disposition
Pierson has hunted the fox
Pierson has actually the
power to hunt that foxAction
Act
Pierson is
hunting..
Disposition

43. Failures and social failures
• The difference between expectations and
actual outcome allows to include in the model
the computation of failures (cf. Plot units)
 useful for validation!

44. Failures
Action failure
Post is hunting the foxPierson has
hunted the fox
= Post has not
hunted the fox
Post failed to hunt the fox
Action
The explicit/implicit expectations (intentions and
actions) allow to compute failures

45. Failures
Pierson has
hunted the fox
= Post has not
hunted the fox
Action failure
Post is hunting the fox
Post failed to hunt the fox
Action
The explicit/implicit expectations (intentions and
actions) allow to compute failures

46. Failures
Pierson has
hunted the fox
= Post has not
hunted the fox
Action failure
Post is hunting the fox
Post failed to hunt the fox
Action
The explicit/implicit expectations (intentions and
actions) allow to compute failures

47. Social failures
Social failure
Social expectation
Pierson is not permitted to
hunt the fox
Pierson has
hunted the fox
Normative expectations (obligations, permissions
and institutional powers) allow to determine social
failures

48. Social failures
Social failure
Social expectation
Pierson is not permitted to
hunt the fox
Pierson has
hunted the fox
Normative expectations (obligations, permissions
and institutional powers) allow to determine social
failures

49. Social failures
Social failure
Social expectation
Pierson is not permitted to
hunt the fox
Pierson has
hunted the fox
Normative expectations (obligations, permissions
and institutional powers) allow to determine social
failures

50. Reconstructing the puzzle…

51. Pierson v Post: Story Flow

52. Discussion
• This contribution is based on a weak definition
of validity for story models
• As long as the execution of the given
mechanisms with the right synchronization
produces the narrated events, the model is
valid.
 allow for alternative interpretations/fabulae

53. Discussion
• We do not consider the problem of increased
depth as the most difficult issue in our domain
1. The explicit modeling of the scheme of a
case is useful for clarification purposes

54. Discussion
• We do not consider the problem of increased
depth as the most difficult issue in our domain
2. Some story components may be easily
reused

55. Discussion
• We do not consider the problem of increased
depth as the most difficult issue in our domain
2. Some story components may be easily
reused
3. If they cannot be reused but seems
applicable, the system can ask the modeler
to provide circumstantial distinction
 constructivist acquisition model

56. Discussion
• We do not consider the problem of increased
depth as the most difficult issue in our domain
2. Some story components may be easily
reused
3. If they cannot be reused but seems
applicable, the system can ask the modeler
to provide circumstantial distinction
 constructivist acquisition model
The real issue? the HCI interface!

58. Critical realist framework

59. Discussion
• Petri Nets bring local causation and
concurrency. Perfect match with our story-
model.
• In addition, they provide good visualization,
and model execution for debugging purposes
(story animation).

60. Narrative generation

61. Narrative interpretation

62. Basic story scheme (events)
Motive  Intent  Action  Act (Failure)
Event characterization with different verbs:
Brutus murdered Caesar. intent
Brutus stabbed Caesar. action
Brutus killed Caesar. outcome