Valoración vigesimal de las rubricas para evaluar aprendizaje colaborativo en el

Given that we want to use simulation modelling to develop theory about energy, which form of simulation modelling should we choose? We consider three types: agent-based simulation (ABS) (Axelrod, 1997a); discrete-event simulation (DES) (Robinson, 2004), and; system dynamics (SD) (Sterman, 2000).

Cross and Parker (2004a; 2004b, chapter 4) identified individuals as having energising or de-energising characteristics when they engage in social interactions. This suggests we must model people - either as specific agents or in aggregate as populations - and we must model them by type - for instance, with a three-way type of “energisers”, “de-energisers” and “neutrals”. Interactions between agents of different type can be handled by ABS, while SD models interdependencies between different populations. But if we wish to represent different cultural attributes as well, things become too complicated for SD. It is possible to model one stock or population for each energising type combined with each combination of cultural attributes, but the numbers of stocks involved mean that macros would be needed to create and modify systems for modestly high settings: 3 * qF stocks, where q is the number of values per attribute, and F the number of attributes. This is before we even consider adding

energy, networks and groups to the model. By way of illustration Appendix A contains a system dynamics version of Axelrod’s Cultural Model (1997a, chapter 7; 1997b) - originally created as an agent-based model - together with some speculation on how energy might be added to it in a manner analogous to what we will do in Chapter 6. We find there that examining the system dynamics model can teach us something about the role of variability from stochastic processes in the agent-based version, but the complexity of the system dynamics model soon makes it an unwieldy tool.

Our desire to model several different concepts in the one model - in ways we have yet to work out - creates problems for DES as well. DES proves useful when one specifically wishes to represent realistic durations and times of occurrence - such as when one wishes to understand waiting times in a queueing system. If energy relates to the rate at which one performs some activity (as in Ryan & Deci’s (2000) concept of intrinsic motivation), this might suggest the representation of timings was important. But rates can be approximated by modelling discrete time steps and when models are more abstract in their timings, or when processes take fixed durations, the apparatus of DES - maintaining and computing lists of events - become an overhead a programmer does not need. Off-the-shelf DES packages such as Witness or Simul8 can handle this for the modeller, but they make harder the programming of interdependencies between the entities or agents - a purpose for which these packages were not designed. When one has only a vague idea of the way to model the concepts one is interested in, rapid development and revision of code is important, and so we rule out DES as well.

Given how many of the social simulation models referred to in Chapter 3 were agent- based, it should come as little surprise that we settle on agent-based simulation modelling for our project. Agents can be heterogeneous - each agent readily acquiring values for attributes different to those of the other agents. Attributes can include cultural traits, rules of behaviour, and constraints due to geographical location and social networks. Although one has to engage in programming computer code much earlier than with commercial DES and SD packages, it is relatively easy to add new attributes to agents, and to add new types of agent as and when one thinks of them. Agent-based simulations model individual interaction events between agents - the so- called micro-sociological level we find in Collins’s Interaction Ritual theory (Collins, 1981; 2004), but are popular for their emergent patterns - perhaps leading to better understanding of meso- and macro-level phenomena (i.e. groups and societies) (Sawyer, 2005, chapter 8). For these reasons, we will adopt the agent-based approach for our own models in Chapter 6.

5.8 Summary

“The trouble with agent-based modelling is that with enough parameters you can prove anything you like.”

(A professor of OR in response to an earlier agent-based modelling paper of ours.)

It should be clear from our lengthy presentation of an Interpretivist paradigm that we do not feel we have to be in the business of “proving” things. “Proving” things with an agent-based model is probably not as easy as introducing extra parameters to fit

statistical models to empirical data. A common characteristic of popular agent-based simulation models is the surprise felt on the part of the modeller at the emergent, macro-level patterns. There is no intention to prove (beyond doubt), but rather to

probe, to explore - to follow one’s curiosity concerning some micro-level interactions.

We are happy to endorse the suggestion that simulation modelling can contribute to theory development in social studies, but we do not limit the ways to those of tests of consistency and sensitivity. The activity of conceptual modelling can facilitate dialogue even when the modelling fails to result in a computer model, and the participants are all data-free theorists. Simulation modelling can assist in the bridging of “structural holes” between theoretical positions, partly unifying existing theories, partly guiding the adaptation of theories. Baker and Quinn implicitly understood the opportunity provided by simulation modelling for developing theories of energy in our sense, as they attempted to form coalitions of various authors plus their own empirical data on energising networks. But we remain unconvinced by their efforts. It is time now to have a go ourselves.