Tag Archives: decision-making

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.

Advertisements

NRC Report on Land Change Modeling

Essential reading for all you land change modelers out there!

The report Advancing Land Change Modeling: Opportunities and Research Requirements was released recently in pre-publication format via the National Academies Press web site: http://www.nap.edu/catalog.php?record_id=18385 Additional report info can be found here as well: http://dels.nas.edu/Report/report/18385. The study committee included several geographers, assessed the current state of land-change modeling, and identified opportunities for future developments in these models.

Urban development, agriculture, and energy production are just a few of the ways that human activities are continually changing and reshaping the Earth’s surface. Land-change models (LCMs) are important tools for understanding and managing present and future landscape conditions, from an individual parcel of land in a city to the vast expanses of forests around the world. A recent explosion in the number and types of land observations, model approaches, and computational infrastructure has ushered in a new generation of land change models capable of informing decision making at a greater level of detail. This National Research Council report, produced at the request of the U.S. Geological Survey and NASA, evaluates the various land-change modeling approaches and their applications, and how they might be improved to better assist science, policy, and decision makers.

Exploring Land-Livelihood Transitions

Figure5_rev (2)Rural livelihoods are changing rapidly with economic globalization and global environmental change, which have direct impacts to environmental and socio-economic suitability. All too often the most vulnerable communities – those with the least resources – face the greatest transitions triggered by changing local and global conditions. Those communities also have livelihoods tied to the land, which may lead to environmental degradation and/or fail to support livelihoods in the future. We must advance our understanding of the causes and consequences of land-livelihood transitions in order to avoid maladapted responses that can lead to a loss of land-livelihood sustainability.

My colleagues and I recently published an article in PLoS ONE that explores these issues with an innovative, generalized agent-based model. Because human decision-making drives land-livelihood transitions, a process-level explanation of adaptive responses is needed to explore the conditions under which land-livelihood transitions emerge. In the short-term, this approach advances the use of agent-based virtual laboratories in sustainability research. In coming generations of this modeling approach, we hope to use model insights to devise effective policy interventions aimed at the decision-making level for supporting sustainability .

Homo economicus is (mostly) dead

Source: Economists.com/blogs/freeexchange.

Source: Economists.com/blogs/freeexchange.

Do I detect a change in the winds of mainstream economics?

A recent article in the Economist gives me hope. It suggests that ideas of non-rational, adaptive, and distributed decision-making – which have been topics of research in agent-based modeling, psychology, neuroscience, anthropology, and behavioral economics for some time – are now starting to seep into the consciousness of mainstream economics.

Describing Daniel McFadden’s recent work titled “The New Science of Pleasure“, the article details how concepts from psychology, such as prospect theory, are casting renewed doubt on the validity of mainstream economics’ hallmark theory of consumer choice. Indeed, mainstream economic theory has come under fire recently in the wake of economic recession stemming from “irrational” financial decisions, which many economists failed to predict or reconcile with their models and theories.

In all fairness, many mainstream economists would readily offer that their models are unrealistic in many ways, and are useful for understanding how economic systems tend towards rationale outcomes in the long-term. True enough. What this article argues, however, is that the assumptions that underlie mainstream economic models and theory can also lead to unrealistic worldviews and policy recommendations.  For example, ‘more choice is good’, but sometimes this can lead to sub-optimal (i.e. not rational) choices because the consumer is overwhelmed with options. From the article, “Explicitly modelling the process of making a choice might prompt economists to take a more ambiguous view of an abundance of choices.”

And this line of reasoning leads to agent-based modeling as a potential tool to understand how choices are made: what psychological elements influence decisions, how those psychological influence vary with individual heterogeneity characteristics, and how decisions are enacted into behavior.

A parting shot from the article: “This is undoubtedly messier than standard economics. So is real life.”

Agent-Based Models in the Real World

thought_process1A recently published News Feature in Proceedings of the National Academy of Sciences by Robert Frederick, titled Agents of Influence, discusses the advancing state of agent-based models (ABMs) and their growing use to inform business and policy decisions. Businesses are employing ABMs to find new efficiencies in complex supply chains, and research efforts to create million-agent models of the economy may soon offer insight into the dynamics of our financial systems and broader economy.

What I like most about this article is that it illustrates how ABMs and complexity thinking are beginning to make their way out of academics and into the real world. A recent example is how Southwest Airlines used ABMs to find more efficient cargo shipping routes, saving the airline millions of dollars. ABMs as virtual laboratories are getting attention, too. The article describes how these models enable decision-makers to explore the consequences of particular business or policy decisions though a range of possible scenarios.

The message is clear: representing heterogeneous, distributed decision-making creates more realistic models, and is enabling researchers, businesses, and policy-makers to navigate complex systems like never before.

Importantly, Frederick does not shy away from the limitations of such models. What is gained in realism by using ABMs often comes at the cost of having to make numerous simplifying assumptions about human behavior. After all, an ABM is only as good as its description of human decision-making processes, which are notoriously unpredictable.

A great closing quote: “Ultimately, … none of these [ABMs] will offer iron-clad predictions, because they have to make simplifying assumptions about human behavior. The true test will be whether those assumptions, and the resulting outputs of the models, convince policymakers to act on their advice.”

Linking management decisions and shoreline dynamics

OBX_erosion

Source: USGS

Shoreline communities along the U.S. Atlantic Coast have a long history of enduring costly and widespread impacts from tropical storms and long-term erosion. Unfortunately, such impacts are likely to worsen with sea-level rise in the future. These impacts are unavoidable – but how we respond to them is up to us. In their new article titled “A coupled physical and economic model of the response of coastal real estate to climate risk,” recently published in Nature Climate Change, Drs. Dylan McNamara and Andrew Keeler address just this aspect of long-term coastline change.

Using coupled agent-based and coastal processes models, they explore the mechanisms underlying shoreline defense decisions in response to long-term sea-level rise and erosion. Those decisions in turn are dependent on property values and individual beliefs of potential impacts. A particularly innovative feature of their model is that collective mitigation actions are determined endogenously through an iterative referendum. Collective action problems become apparent as believers and non-believers of climate risk predictions must decide on community-level adaptation strategies.

The authors find that property owners that disregard predictions of climate change-induced coastal risks tend to be the ones that own property in the riskiest locations, and thus disproportionately receive public disaster assistance funds. In addition, the model is also able to estimate time before abandonment of coastal communities subject to a combination of sea-level rise and erosion.

Many research efforts into climate change adaptation emphasize the physical impacts of climate related hazards. However, this is only one – and arguably the less important – aspect of climate change adaptation. The fate of human-environment systems is largely determined by our decisions of how to respond to changing economic and environmental conditions.

This model gets it right. Explicit consideration of human decision-making, and its underlying motivations, is essential if we are to form realistic expectations of likely future states and formulate successful adaptation strategies.

I look forward to seeing future contributions from these authors!

Decision-Making and Data

Cross_scale_dataThe popularity of agent-based modeling has exploded in the past decade – and for good reason. More and more researchers are recognizing that human decision-making – as messy and unpredictable as it seems – is an incredibly powerful driver of human and natural system dynamics. Agent-based models (ABMs) offer the means to test our understanding of decision-making processes and their consequences with more sophisticated (and often more realistic) simulations. Despite this growing popularity, though, ABMs introduce new and profound challenges in their use, testing, and interpretation.

A question that I am confronted with regularly – and have been thinking a lot about recently – is this: “What are the spatial and temporal scales of human-decision-making relevant to the land change phenomenon I want to study?”

I will likely return to this question in some form or another many times on this blog, but lately I am concerned with the data dimensions of this question. For example, if one wants to model household-driven land use changes in frontier regions (e.g. Parker et al., 2008), then land-use decisions at the household level are probably your likely target. However, what if you want to apply this model over a large region? With lots of agents? Or have it be general enough to apply across many sites in different land-use systems ( a la virtual laboratory!)? Then the relevant level of decision-making becomes murkier, especially when considering the data that will be needed to parameterize and test the model. In this example, you might need to combine household surveys, regional land-use/cover data, census data, and perhaps some global data such as market influence (Verburg et al., 2006).

Much has been written on this topic, and as there are many facets to this challenge, there are also many different ways to address it. The various perspectives on this issue seem to consistently fall into categories:

  • How to coordinate different sources of data?
  • What types of data should be used to test the model?
  • What is the focal scale of analysis?

These questions are often raised in the context of using ABMs in combination with a geographic information system (GIS). Crooks and colleagues (2008), Parker (2005), and Brown and colleagues (2005) are good sources to begin answering these questions.

For our purposes here, I’ll ask a simpler question: How does one get these different kinds of data that cover different scales and resolutions to play nicely together?

Coordinating the scale and/or resolution of spatial data in a GIS with those of the decision-making processes in an ABM can be quite tricky. So, as the modeler, you have to make some choices about how closely the model should be “coupled” with the GIS: 1) Spatial data layers can be used simply to initialize an ABM by parameterizing the simulated landscape; 2) GIS and the ABM can be “loosely coupled” such that spatial data files are updated independently and then passed back and forth at designated times; or 3) the ABM can be fully embedded within the GIS (e.g. the new Agent Analyst tool with Repast). The choice of either of these methods will depend on factors such as the research question and the computational expense of passing files between systems. Whichever path one chooses, though, the challenge of coordinating low resolution spatial data with fine-grain social data remains.

Arguably a more important consideration, then, is the level of abstraction within the model. I have recently toyed with this question in a model in which my agents represent aggregations of households in settlements rather than individual households. This, of course, has some conceptual limitations. However, it has important conceptual and practical advantages as well; in particular, the ability to represent a landscape-level entity generally by avoiding some social interactions among households that are notoriously difficult to generalize. This representation is certainly controversial (and probably nonsense to some household livelihoods researchers), but considering the types of data needed to address regional land-use questions, an agent representation that works nicely with global datasets is not entirely a bad thing.

Thus, I will plant this seed: In what situations does a settlement-level agent act as a reasonable representation of land-use decision-making? This is an ongoing topic of research for me, and will undoubtedly lead to subsequent posts. Stay tuned!

References

Brown, D.G., Riolo, R., Robinson, D.T., North, M., and Rand, W. (2005). Spatial process and data models: Toward integration of agent-based models and GIS. Journal of Geographical Systems, 7:25–47.

Crooks, A. T., Castle, C. J. E., and Batty, M. (2008), Key Challenges in Agent-Based Modelling for Geo-spatial Simulation. Computers, Environment and Urban Systems, 32(6): 417-430.

Parker, D.C.  (2005). Integration of geographic information systems and agent-based models of land use: Prospects and challenges. GIS, Spatial Analysis, and Modeling (2005): 403-422.

Verburg PH, Ellis EC, Letourneau A (2011) A global assessment of market accessibility and market influence for global environmental change studies. Environ Res Lett, 6.

About the Agent-Based Virutal Labs (ABVLs) blog

Welcome to the Agent-Based Virtual Labs blog!Landscape_fig

This blog will cover issues relating broadly to the social, economic, and cultural interactions that are  changing the planet’s surface and climate. In particular, these issues will be explored with posts relating to agent-based modeling (ABM), and how ABMs can be used as virtual laboratories to ask questions about peoples’ motivations for observed behaviors that would be impossible to ask any other way. Along the way, topics informing the creation, use, and testing of ABMs will be included, as well as my areas of application of ABVLs such as land-use change, livelihoods in developing countries, and sustainability.

This blog will also be a hub of information for those interested in ABMs or my subject areas of interest. Following the navigation menu will lead you to collections of links for learning and teaching resources, other ABVLs-relevant blogs, and my research topics. Also, at regular intervals, posts will appear that contain an annotated list of links dedicated to topics ‘trending’ in the ABVLs world.

I hope you enjoy and please drop me a line!