Abstract
This Chapter informs the reader about how to create and parameterize empirical multiagent models from first principles when fieldwork is difficult or impossible to conduct and data is primarily of qualitative nature. Empirical multiagent models have become ever more popular over the last decade. While informing models using statistical and geospatial data can orient itself on more established techniques and standards, methodological challenges persist in regards to using qualitative data for informing and parameterizing models. Protocols such as ODD are welcome and helpful devices—and hence used in this Chapter—but qualitative data comes with its own peculiarities. The most important of which is, for modeling purposes, that qualitative data tends to inform the logic of agent behavior. The emphasis I thus put on qualitative data to make model design decisions based on evidence and first principles will be reflected by soft adaptations of the ODD protocol. Arguably this may amount to a deeper insight the Chapter is providing: Whereas the usage of such frameworks as ODD increases model reliability, validity is built using qualitative empirical data for informing and parameterizing the agent and model behavior.
The notion of first principle and the argument of this paper are predicated on the conviction that empirical modeling should start with observation and description of the case, including its agents, to be studied (cf., Edmonds and Moss 2004; Moss 2002; Moss and Edmonds 2005).
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Notes
- 1.
- 2.
Sweeping instead is performed to account for varying data sources, creating multiple bodies of knowledge instantiated as models and simulations.
- 3.
In the Anasazi model (Kohler et al. 2005), for example, behavior space was varied exactly because the researchers wanted to create plausible behavioral and environmental hypotheses and explanations for why this Puebloan people society suddenly collapsed. And in policy oriented work behaviors are varied to generate and explore what-if scenarios to inform policy makers (Lempert et al. 2006).
- 4.
Geller et al. (2011b) show that the same also applies to statistical data. Why sweeping the parameter space when the data is known?
- 5.
The following sections describing the model are adapted from Geller and Moss 2008.
- 6.
Alam et al. (2010) describe how to make endorsements an adaptive concept through introducing memory.
- 7.
That experimental work can however be conducted even in very difficult security conditions is shown by Geller et al. 2012.
- 8.
And if I would have to pin it down to a particular case, then 15 would probably fit best.
- 9.
Parts of the modeling philosophy behind our approach, KIDS (Keep It Descriptive Stupid), are described in Edmonds and Moss (2004). It is important to mention our modeling philosophy here, because it defines in parts how we approach parameterization.
- 10.
None of our interviewees disagreed in principle with the representation in Fig. 12.1 in terms of which agent types we chose and how we characterized them as being either powerful or ordinary. We therefore did not deem it necessary to return to the idealtypical representation of qawm and make changes to it.
Figure 12.1 
Idealtypical representation of qawm
- 11.
Nothing changed in principle to our approach as described above with regard to M2, M3, M4a, b and M5.
- 12.
Indeed, this is one of the main reproaches brought against the KIDS approach.
- 13.
See for an important, yet often neglected paper Centola et al. 2007.
- 14.
Note that a rule-based system like JESS makes replication particularly difficult because the sequence of how the model runs is defined by the compiler and explicitly not in the hands of the programmer.
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Geller, A. (2014). Building Empirical Multiagent Models from First Principles When Fieldwork Is Difficult or Impossible. In: Smajgl, A., Barreteau, O. (eds) Empirical Agent-Based Modelling - Challenges and Solutions. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6134-0_12
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