Abstraction, Big Data and Context-Dependency

1. Abstraction, Big Data and Context-Dependency

2. Co-Evolution of Measurement and Theory • Although less celebrated, the history of science shows how measurement and analysis techniques have co-evolved with theories and models of what we observe • Itis a messy process with bits of each happening at different times and orders • Assuming we will get more access to more social data, maybe one thing that could help social science at the present time is more ways to gently abstract/analyse/data-mine this for relevant patterns

3. Abstracting from Data • All modelling/understanding/data-mining/visualisation involves abstracting from data, e.g. what is important and what is not (for your purposes) • The “No Free Lunch” theories from machine learning indicate that no algorithm is always better than any other over all data sets/problems – in other words abstraction has to exploit some knowledge about the specific domain to be effective • However, there are many ways to go about this abstraction and many strategic combinations of these that can be tried

4. Challenges from Big Data (re-expressed in less extreme terms) • Social science is top-heavy (theory-heavy) compared to the amount of measurement/data that happens • Theories of behaviour can (to some extent) be replaced by patterns extracted from suitably detailed data – less use of theory for micro-behaviours, more use of data • Just analysing/picturing what has been going on can be give more insight than qualitative theory or simulation models

5. But… • Rich data – data which records many aspects of an individual at a time – is more useful than temporarily fine but thin data • Data-mining can make progress but is even more productive in collaboration with theory • Mature science connects phenomenological with explanatory models... eventually! • But it certainly gives us many more options and more chance for checking our intuitions

6. New Opportunities from Big Data • Not having to summarise/smooth/abstract from data when we do not need to (just keep the data) • Better validation of models: • at different levels of granularity • over different kinds of outcomes • over time • Checking the consistency/plausibility of micro rules e.g. using pseudo-experiments using subsets derived from big data • As a starting point for suggesting micro/meso level behaviours • Abstracting from data in structurally appropriate ways, e.g.: • ways that preserve enough network structure • first clustering according to possible context, then analysing with respect to these contexts

7. Some Evidence about Context-Dependency • Many aspects of human cognition are known to be highly context-sensitive, including: memory, preferences, language, visual perception, reasoning and emotion • There is a mountain of qualitative research that has documented instances where a specific context is essential to understanding the observed behaviour • Simple observation and introspection tells us that behaviour in different kinds of situation is not only different but decided on in different ways (e.g. in a lecture and a football game)

8. Some Difficulties in Dealing with Context • People use the word “context” for slightly different (but related) things • “Context” is used and preserved as a “bulwark” against (evil) reductionists by some qualitative researchers • Context can be difficult to identify: fuzzy, signalled in subtle, rich ways etc. • Context is recognised and dealt with largely unconsciously

9. Basic Context Heuristic Context-Structured Memory • Rich, automatic, imprecise, messy cognitive context recognition using many inputs (including maybe internal ones) • Crisp, costly, conscious, explicit cognitive processes using material indicated by cognitive context Context Recognition Reasoning/planning/belief revision/etc. etc.

10. Context-Dependency and Randomness Treating context-dependency as noise means you lose information

11. Need for a Meta-Clustering Algorithm Suggests a Context Time of Day ShopLocation Item Type

12. Integrating Aspects of Qualitative Evidence into Formal Models • Identifying kinds of context (those over which we might expect some regularity in terms of shared norms, expectations etc.) might allow suggestions from qualitative evidence to be more easily incorporated into models • For example by providing the repertoire of possible strategies in the context which are decided between • This could enrich agent-based models with more hypotheses for micro-behaviour providing a “menu” for modellers to select from

13. Conclusions • Data-mining approaches might be more effective when co-evolved with hypothesis-driven approaches • Analysing big data with respect to context might allow more patterns to be identified • What the relevant division into context is can be aided using knowledge about micro-processes • It might allow some qualitative observations to be used to help boot-strap this process