Tag Archives: decision models

The Maturation of Socio-Environmental ABMs: Older and Wiser?

After the avalanche of work that was November and December – and the unintentional hiatus from blogging that created – I have now come up for air and am ready to get back to it. Whether it is a particular point in my career or the fact that we are on the eve of a new year, I’m not sure,  but I’m feeling reflective and thought a post about the state of socio-environmental ABMs seemed appropriate. So, here some of my reflections as I look around the landscape of socio-environmental ABMs.

China_FarmerThe first thing that jumps out at me is the explosion of ABM papers in the realm of socio-environmental research. ABMs have achieved widespread use to answer an equally varied assortment of questions ranging from climate change adaptation, agricultural change, urban development, water management, ecosystem services, and on and on …. Fortunately, there are also a number of comprehensive and specialized reviews that have recently been published to help wade through all of this research. Two papers I found particularly useful are reviews of decision-making models in ABMs (An, 2012) and spatial ABMs in socio-ecological systems (Filatova et al., 2013).

As the title of this posts suggests, there also seems to be a maturation in the development and application of ABMs in the socio-environmental realm. I see this maturation in many forms, but two trends in particular are striking. First, the sophistication of these models and the algorithms they employ is incredible. Many ABMs are becoming increasingly computationally intensive. For example, a push for more rigorous sensitivity analyses is leading some to explore the entire parameter space of ABMs with spatially explicit sensitivity analysis (Ligmann-Zielinska, 2013).  I have also seen an increased implementation of sophisticated computer science techniques, such as cognition-based learning algorithms (Magliocca et al., 2011) and encoding of beliefs (Sun and Müller, 2013).

Second, ABMs have become more realistic. A major trend in the ABM field is developing and marketing ABMs for use with policy analysis. Increased availability of highly detailed data sources has led to an explosion of these application-specific (i.e. ‘case-based’) ABMs. Accompanying the development of realistic models has been an advancement of participatory modeling techniques to actively engage in model design and validation (e.g., Zellner et al., 2012).

Yet, this is the point at which I must pause and ask, “The ABM field is clearly older, but is it wiser?”

Despite the popularity of the approach, ABMers still face significant hurdles to having their work and publications accepted. An interesting survey and companion paper was done by Waldherr and Wiejermans (2013). It describes some of the common critiques that ABM researchers still face when trying to get their work published. While some of the critiques demonstrate a lack of understanding on the part of reviewers, they also illustrate areas where much work remains for ABMers to rigorously test and describe their models in order to answer the critics’ questions.

Finally, the abundance of ‘case-based’ ABMs has led some to ask whether additional case-based ABMs are contributing to the ultimate goal of building coherent theory about the structure, dynamics, and sustainability of socio-environmental systems? Is one more place-based model really advancing the community’s knowledge? How do we move forward with models that are both empirically-grounded and general enough to produce theoretical insights? How do we get to these ‘mid-level’ models? These questions, among others, will be the topic of a panel discussion at the upcoming annual meeting of the Association of American Geographers in Tampa, FL, USA from April 8-12, 2014. I have the privilege of accompanying Steve Manson, Tom Evans, David O’Sullivan, Andrew Crooks, Moira Zellner, Li An, and Sarah Metcalf on the panel – looking forward to the discussion!

The promise of the agent-based approach remains high. Applications of ABMs in the socio-environmental context have matured significantly, but we still have a lot of work to do. I am looking forward to the next phase of ABM development in which multiple approaches, algorithms, and techniques are integrated to advance the reliability and usefulness of our models.

What is an agent-based virtual laboratory?

I have recently had several conversations with colleagues and exchanges with reviewers about the exact meaning of the term ‘agent-based virtual laboratory.’ So, it seems like the perfect time to devote a single post entirely to unpacking this idea.

First, I should acknowledge that many researchers have used agent-based models (ABMs) of differing levels of detail or abstraction to explore the influence of particular processes or parameters on model outcomes. This is certainly a valuable exercise, and in this sense, the virtual laboratory approach is not new. However, these virtual lab efforts have been undertaken with site-specific, case-study ABMs – and this is where the distinction lies.

“All virtual labs can use ABMs, but not all ABMs are virtual labs.”

That is a favorite phrase I have used in multiple presentations on this topic. It is meant to make a clear distinction between ABMs as tools, and virtual labs as an approach. The type of virtual labs I use in my research and write about here are designed from the outset to be generalized modeling environments that can be applied across many different settings and locations. The advantage of this is that one can conduct comparative research – forming hypotheses of how and under what conditions certain processes or factors will be important or not. These hypotheses can be generated systematically across sites and then tested against empirical data. More conventional virtual lab efforts use case-specific ABMs and thus cannot be easily applied to different sites.

Figure4_rev (2)Take, for example, the figure to the right, which shows agricultural intensification patterns generated by agents with generalized decision models in response to alternative environmental conditions. With this framework, the influence of environmental suitability on land-use intensity – and its interactions with other processes, such as increased market influence and population pressure – can be experimentally explored across different sites.

With that said, if one desires to understand a particular context and/or predict possible future scenarios, the case-specific ABM approach is the way to go. There will always be a place for such models. But those models are not the best option for generating generalized knowledge and building theory. For that, a more generalized and transparent modeling framework is needed.

This approach is similar to that of “artificial systems research” that my friend and colleague Len Troncale describes. Quoting from one of his blog entries, a virtual lab approach “enable[s] adding or subtracting different sets of systems processes to see how these alterations effect sustainability of the resulting systems.” Thus, the goal of agent-based virtual laboratories is to explore and form testable hypotheses of how certain factors interact with agents’ decision-making processes to produce emergent system outcomes, and to do so across different land systems to build towards general land system theory.