Tag Archives: AAG

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.

Notes from AAG 2013

AAG2013Last week I attended the 2013 annual meeting of the Association of American Geographers in Los Angeles, CA. In particular, I attended the Land Systems Science Symposium and the Agent-Based and Cellular Automata Model for Geographical Systems sessions. It was great to catch-up with old friends and meet a few new colleagues. Now that the chaos of coming back to work after a week off has passed, I thought this would be a good time to reflect on the happenings of the conference.

Overall, I thought this was a much stronger meeting than last year’s. It was apparent from the Land Systems Science (LSS) Symposium that the field formerly known as “Land Change Science” is beginning to come into its own. The full scope of LSS was on display, as the first two days were dedicated to case studies from different world regions, followed on the last two days by LSS modeling, theory, and applications. I was also impressed by the agent-based modeling (ABM) sessions this year. I sense a real transition in ABM research, as the presentations demonstrated much deeper thinking about the implications of model result, and model building as an art and tool for learning. There also seemed to be a sense that ABM is no longer on the fringe – it’s no longer a new method and the ABM community can now discuss the challenges and weaknesses of ABM more comfortably. It was a sure sign that the ABM method and paradigm are maturing.

Several themes in particular caught my attention over the course of the week:

1. YAAWN – Yet Another Agent-based model … Whatever … Nevermind.

That clever acronym came courtesy of the organizers of the panel on ABMs and land-use change modeling organized by David O’Sullivan and Tom Evans. The general motivation for the title was the observation that the number for place-based, case-specific ABMs has exploded, and as a research community, it is worth asking, “What is the marginal gain from one more case-study ABM?” I, of course, was thrilled to hear such a question, as the drive for more systematic, generalized knowledge motivates my use of agent-based virtual laboratories. The question was posed to the panel, and I particularly liked Dan Brown‘s response. The message was that there will always be a role for case-study ABMs, but it is also necessary to balance the use of empirically detailed models with more abstracted models to build theory. This sentiment was reinforced by Sarah Metcalf, who argued that model hybridity was the next wave of ABM research.

2. Attempts to forge systematic ABM practice and knowledge.

Going along with the general observation of higher quality presentations, the thinking about ABM practice was notably deeper this year. I particularly enjoyed David O’Sullivan‘s presentation of creating a ‘pattern language’ for ABMs and cellular automata. The general idea is to create ‘building block’ models out of generalized processes/structures that facilitate the development of more complicated models. I found this idea analogous to Len Troncale‘s work with isomorphies and systems of systems processes theory. Another important question posed by James Millington was, “When should we use ABMs and how complex do they need to be?” Indeed, this is a fundamental question that should be revisited often.

3. Understanding model outcomes and variability.

Finally, another sign that the ABM field is maturing, there was much discussion about the importance of more thoroughly understanding uncertainty and variability in our models. Chris Bone presented an innovative temporal variant/invariant method for evaluating model performance, which shows much promise for deepening our understanding of the sources of variability within complex systems models.