Bruce Edmonds Centre for Policy Modelling , Manchester Metropolitan University

1. Towards a Context-Sensitive Structure for Behavioural Rules(Context, Scope, Antecedents, and Results) Bruce Edmonds Centre for Policy Modelling,Manchester Metropolitan University

2. Summary of Talk: a view from Cognitive Science Suggest dividing behavioural rules into 4 bits: • Context • Scope • Antecedents • Results • Since this, I argue, seems to align with human cognitive structure • Which are each dealt with and updated in different ways (making their use feasible) • And thus might be a more “natural” structure for human behavioural rules

3. Different Aspects I

4. Different Aspects II Universe of Knowledge Knowledge indicated by current cognitive context Knowledge that is possible to apply given circumstances Cause1 & Cause2…  Result1 & Result2… Cause3 & Cause2…  Result5 & Result2…

5. Bit 1: Context

6. A (simplistic) illustration of context from the point of view of an actor

7. Situational Context • The situation in which an event takes place • This is indefinitely extensive, it could include anything relevant or coincident • The time and place specify it, but relevant details might not be retrievable from this • It is almost universal to abstract to what is relevant about these to a recognised type when communicating about this • Thus the question “What was the context?” often effectively means “What about the situation do I need to know to understand?

8. Cognitive Context (CC) • Many aspects of human cognition are context-dependent, including: memory, visual perception, choice making, reasoning, emotion, and language • The brain somehow deals with situational context effectively, abstracting kinds of situations so relevant information can be easily and preferentially accessed • The relevant correlate of the situational context will be called the cognitive context • It is not known how the brain does this, and probably does this in a rich and complex way that might prevent easy labeling/reification of contexts

9. The Context Heuristic • The kind of situation is recognised in a rich, fuzzy, complex and unconscious manner • Knowledge, habits, norms etc. are learnt forthat kind of situation and are retrieved forit • Reasoning, learning, interaction happens with respect to the recognised kind of situation • Context allows for the world to be dealt with by type of situation, and hence makes reasoning/learning etc. feasible • It is a fallible heuristic with social roots in terms of the coordination of action, norms, habits

10. Some Possible Examples of Cognitive Context? • Greeting someone you do not know • A lecture • An interview • Being Lost • Being Socially Embarrassed • Travelling on a train/bus • Leaving home to go somewhere • Accidently bumping into someone you do not know on the pavement/in the corridor

11. Some Research Responses to Context-Dependency A number of responses: • Only do research within a single context, resisting any generalisation • Only use discursive, natural language approaches where context is implicitly dealt with (but also mostly hidden) • Try to see what (inevitably weaker knowledge) is general across the various contexts in what is being studied

12. Context-Dependency and Randomness Lots of information lost if randomness used to “model” contextual variation

13. However • Although Cognitive Context in General might be hard to identify • Socially Entrenched Contexts are often rather obvious • But one needs to drop the imperative of looking (only) for abstract and general theories for behaviour • Being satisfied with more “mundane” and context-dependent accounts

14. Choice and Update of Cognitive Context • CC is largely learnt from experienced situations in a rich and unconscious way • Occasionally one can realise one has the wrong context if a lot of the detailed knowledge it indicates is simultaneously wrongbut which is the right CC is a matter of recognition from past positive learning • Once CC is learnt it is very difficult to change, but new CC can still be learnt

15. Identifying Context from Narrative Evidence • Apart from socially entrenched contexts (lectures, parties, interviews etc.)… • …the relevant CC is hard to identify from narrative evidence because: • To a large extent, we recognise the right CC for any text unconsciously and easily • The CC are learnt in a rich, “fuzzy” manner over a long period of time by inhabiting them which resists reification • This is one of the prime needs: how to “mark up” the CC behind narrative evidence?

16. Bit 2: Scope

17. About Scope • By “scope” I mean the reasoning as to which knowledge is possible given the circumstances • For example, if all the seats are taken in a lecture, then the norms, habits and patterns as to where one sits do not apply • Reasoning about scope can be complex and is done consciously • However once judgements about scope are made then they tend to be assumed, unless the situation changes critically

18. Scope vs. Cognitive Context • Both scope and cognitive context determine which knowledge is useful for any particular situation that is encountered • However, they play very different roles: • CC is learnt using pattern recognition over a long time, but then is largely a ‘given’, is almost impossible to change when learnt, is quick and automatic and is socially rooted • Scope is largely reasoned afresh each time, taking effort to do so, is possible to re-evaluate but only if needed, and is more individually oriented

19. Identifying and modelling scope • Compared to CC, scope is relatively well studied using formal models of reasoning • e.g. Updating Markoff/state representations of causation, non-monotonic logics, causation in Baysian networks etc. • Scope plays a relatively explicit part in human language, sometimes being explicitly stated, at other times using relatively well understood rules • e.g. conversational implicature • It is often possible to infer participant’s judgements as to scope and possibility, when not explicitly mentioned

20. Bits 3&4: (local) Narrative Steps

21. Encoding Narrative Steps • *If* CC and scope is identified then, I hypothesize, the local narrative structure will be easier to understand, because changing CC and/or scope can do a lot of the “work” in expressing/encoding knowledge • Within CC & scope I suggest a simple basic structure of sets of statements of the form: (on the whole) Z follows/followed from A, B… • A veryspecial case of this is when we say that: A, B… implies Z or that: A, B… causes Z • (I will write A, B…Z), where A, B are the “Antecedents” and Z is the Results

22. About Narrative Steps • These might not be crisp but of the nature More A and B tends to result in more Z • These are often chained in forwards, branching or backwards manner to make an inference or a narrative • (even quite classical) formal logics and annotation systems capture these • Most AI/expert systems encode these, but rarely touch on scope (that is advanced AI) and never on Context

23. Conclusion

24. CSAR as a bridging structure between narrative text and behavioural rules *IF* this structure turns out to be a useful and “natural” encoding of human narrative knowledge/expression then two steps are needed: • Techniques to capture/approximate/guess appropriate Cognitive Contexts and Scope judgments from Narrative Text • AI/Computer science architectures that make the encoding and use of CSAR structured knowledge