Future Cinema Course

Course Site for Future Cinema 1 and Future Cinema 2: Applied Theory at York University, Canada

Avatars of Story & Story Logic

AVATARS OF STORY
Ryan begins with the Journalist questions What (Chronicle), How ( Memisis )When and Why(Plot)
Every journalist /interviewer knows and uses these to construct stories through answers to the questions within the story/context.
She then looks at the very broad semiotic types of Language (speech acts?) and Images defining their strengths and weaknesses.
Language can represent causality,change, temporality, thought and dialogue- chronicle and plot.
Image can make explicit propositions eg causal relationships, evaluation and judgments. It creates immersive space building cognitive maps
These serve as references as to how to best use the different strengths of each type but not enough detail to address complexity..
She does not delve into the plethora of codes active in theatre, painting and cinema such as gesture, proxemics, facial expression, lighting, point of view, editing and framing.
Propositions and assertions are named as functions.
She analyses real-time commentary (of ball games) and sees the role that interpretation takes in retrospective linking recently described events into a meaningful sequence.
In building Auras in Aurasma we are building single events as islands to be linked. Our script is linear.
It is conceived as a linear network. As a real network where every node can be visited she points out that that is true but the be a story no node can be visited more than once.
For interaction to happen she mandates that there must be a range of choices.
The readers choice at every decision point determines NOT what can happen next BUT the ORDER of presentation of events.
Moreover, and not dealt with, it is the content of the assertion made in a sequence that determines the flow of meaning from one sequence its successor. Its causal link. The logic.
To deal with this we need to look at assertions at a micro level. I always saw this as the model of subject – verb- direct_object clause for example “actor1 did Action with Prop to Actor2 and Reaction1 resulted”
or “Fred hit Don with the Bat and Don collapsed”. Here the second action “collapsed” becomes first action of the next sentence “Don Collapsed and the Paramedic revived him with Ammonia salts”.
So it is the chain of actions which determine the “meaning/theme” of the sequence and the last event which makes the link
So the plot can be a series of events interpreted as a series of statements (standing as indexes of the events) and a statement standing for the whole sequence. This one form of plot graph.

I digress to a web search on State Transitions and find I am on the same turf.
Its an excerpt on Narrative and AI where they compare the results of computer generated conversational responses with those mined from blogs (as sentences / or phrases) ans selected for relevance. The blog mined data was judged more realistic.
Then I am at narrativescience.com a business “story” service
Mines Data
Quill mines your data and creates an appropriate narrative structure to meet the goals of your audience.
Creates Story
Using complex Artificial Intelligence algorithms, Quill extracts and organizes key facts and insights and transforms them into stories, at scale
Delivers Insight
Quill uses data to answer important questions, provide advice and deliver powerful insight in a precise,
clear narrative.

DIGITAL NARRATIVES
Digital narratives are defined as
Simulative NOT representational
Emergent NOT Scripted
Simultaneous NOT Retrospective
Some questions arise here. She frequently states that in practice oversimplification is not useful
Many narratives may mix these modes.
Simulations. Games are simulations. Simulations represent something external to themselves.
Narratives are representaional.
So narratives can contain representations derived from simulations that they contain.

Ryan also refers to another useful model of Jesper Juul, the State Transition Machine which consists of five elements
1. A finite set of states
2. An input Alphabet (Set of possible user actions)
3. A Next State transition function
4. A start State
5. A finish state
So I searched the keywords “narrative” and “algorithm” and “state Transition” the latter which returned examples of code. I now have to find definitions of “state” and “actions” within the State Trasition Machine
So I move to another source. Hermans “Story Logic”
He puts forward frameworks from a number of sources I will not name, mainly dealing with language based story
Story is defined as an ordered set of Propositions
Readers use sequences of propositions to explain a story.
So a reading derives a single abstracted proposition from all the constituent lower order propositions to explain “What the story means” .
So back to the elements that make up propositions.
At the level of the verbclause stories are made up of sequences of delierate, gaol oriented events.
Verbs cover Events, States, Causes, Actions and Motions
Verbs are the semantic core of all clauses
Events are changes of State
Behaviours are comprised of STATES, EVENTS and ACTIONS.
Actions are one type of Event
Entities manifest as Nouns– people, things and places
Statives (state, existants)) have a scope of the total event
Actives(action) have the scope of all the events components and sub-processes.
Causatives (causes) express determinations between two or more existants
Stories don’t just deal with things/existants but causally intertwined sub-processes
Stories prioritise Causes
Now inserting these into the State Transition Machine
The finite set of states – collection of all the states of all the existants.
A Next State Transition Function – Events are changes in states, what causes an event. A causative
The Start State – In terms of story arcs there is an initial problem state
The End State is the Resolution
The Input “Alphabet “ (Set of possible user actions) is all the verb clauses ?

Tue, April 1 2014 » FC2_2014

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