Claes Andersson - Model pragmatics

Transcript

1. Chalmers University of Technology University of Technology
2. Chalmers University of Technology Model Pragmatics In the initial literature study, what struck us about the selection of innovation models was this: • The are MANY models out there • They are dispersed across many fields: developmental systems theory, transition research, epistemology, cognitive science, and so on and so forth. • The often cover the same thing but from different perspectives • The complement one another quite well: we were surprised at how seamlessly they could be combined • But there is very little contact between these fields and the authors and users of these models
3. Chalmers University of Technology Workshop ”Innovation, society and complexity: a dynamics of detecting, solving and creating problems” - March 21-23 2012 at the ECLT Many top researchers were attracted to this event, which turned out to be highly stimulating. Synthetic framework where we combine three innovation models, the authors of all were present at the workshop: • Exaptive bootstrapping (David Lane et al) • Generative Entrenchment (William Wimsatt et al) • Multi-Level Perspective (Frank Geels et al) Or really four since the basic ontology of Niche Construction Theory is also important (Kevin Laland).
4. Chalmers University of Technology Innovation is a multi-level phenomenon and different models have covered this on different levels. What we wished to do was to create a multi-level model from various ”cross bearings” on the problem Exaptive bootstrapping cascades, exaptation, bootstrapping Generative entrenchment hierarchy with lock-in stronger at the base and weaker upwards Multi-Level Perspective dynamic formation of emergent ”regimes”, protected ”niches” Why a synthetic model
5. Chalmers University of Technology Illustration Let’s illustrate by reconnecting with the topic of model pragmatics Individual models – e.g. simulations described in a paper. Broader modeling methods, e.g. a family of models by a group Modeling classes, e.g. cellular automata, agent-based etc. Foundational ideas about method and assumption, null-hypotheses etc. More specific, more conscious More general, more ”common sense”