Legal Knowledge Conveyed by Narratives

2 August 2014 - CMN presentation

Giovanni Sileno g.sileno@uva.nl Alexander BoerTom van Engers

Leibniz Center for Law – University of Amsterdam

Towards a Representational Model

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

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

What “stories” are:Not only fictional narrations.. but also:

• personal experiences• journalistic reports • medical cases• legal cases• …

What is a legal case about?witnesses’, experts’ speeches used to justify a certain story reconstruction

• events occurred in a social scenariolawyers’, judges’ speeches used to justify a certain legal interpretation of this story

• legal implications

What is a legal case about?witnesses’, experts’ speeches used to justify a certain story reconstruction

• events occurred in a social scenariolawyers’, judges’ speeches used to justify a certain legal interpretation of the story

• legal implications

What is a legal case about?witnesses’, experts’ speeches used to justify a certain story reconstruction

• events occurred in a social scenariolawyers’, 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

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 • ...

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 !

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 !

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

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

Pierson v PostPierson v Post (1805)

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.

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.

“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, intercepted the fox, killed it and took the animal. [..]

• sequence of events• occurring at certain circumstances

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. [..]

“Shallow” story model

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.

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

“Shallow” story model

• a sequence of events• occurring at certain circumstances+ explicit mechanisms relating events/

circumstances

• What about the implicit mechanisms?

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

Requirements

• bypass natural language issues• we are not targeting narrative

comprehension, but scenario acquisition from different interpreters

• 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

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 timelinesordering as events occur vs how they are told/observed

Three levels of constraints• weak: discourse ordering • medium: relative/absolute time indexing • strong: dependencies (logic or causal)

From “Shallow” to “Deep” story model

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

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

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

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

Agent story scheme

Motive Intent Action ActBex 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

Agent story scheme (Post)

Motive Intent Action Act (Failure)

Motive

Post intends to hunt that fox

Intent

Post sees a fox

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.

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

Agent story scheme (Post)

Motive Intent Action Act (Failure)

• Considering only these events the model is not enough specified.

We need circumstantial conditions

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

Agent story scheme (Post)

Motive Intent Action Act (Failure)+ Motivation

Post is hunting foxesMotivation

Motive

Post intends to hunt that fox

Intent

Post sees a fox

Agent story scheme (Post)

Motive Intent Action Act (Failure)+ Motivation

Post is hunting foxesMotivation

Motive

Post intends to hunt that fox

Intent

Post sees a fox

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

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

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..

Agent story scheme (Pierson)

Motive Intent Action Act (Failure)+ Disposition

Pierson has hunted the fox

Pierson has actually the power to hunt that fox

Action

Act

Pierson is hunting..

Disposition

Agent story scheme (Pierson)

Motive Intent Action Act (Failure)+ Disposition

Pierson has hunted the fox

Pierson has actually the power to hunt that fox

Action

Act

Pierson is hunting..

Disposition

Agent story scheme (Pierson)

Motive Intent Action Act (Failure)+ Disposition

Pierson has hunted the fox

Pierson has actually the power to hunt that fox

Action

Act

Pierson is hunting..

Disposition

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!

Failures

Action failure

Post is hunting the foxPierson hashunted the fox= Post has nothunted the fox

Post failed to hunt the fox

Action

The explicit/implicit expectations (intentions and actions) allow to compute failures

Failures

Pierson hashunted the fox= Post has nothunted 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

Failures

Pierson hashunted the fox= Post has nothunted 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

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

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

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

Reconstructing the puzzle…

Pierson v Post: Story Flow

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

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

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

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

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!

Critical realist framework

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).

Narrative generation

Narrative interpretation

Basic story scheme (events)

Motive Intent Action Act (Failure)

Event characterization with different verbs:

Brutus murdered Caesar. intentBrutus stabbed Caesar. actionBrutus killed Caesar. outcome