Category Archives: Agent-Based Modeling

Theory, methods, and applications of agent-based models.

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!

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Human decision-making in climate system models

GLP_reportOn November 28th, 2011, a workshop in Lake Crackenback, Australia was organized by Prof. Mark Rounsevell, CECS, University of Edinburgh, UK and sponsored by the Global Land Project (GLP) and Australia’s CSIRO. The aim of the workshop was to explore theoretical and modeling approaches for incorporating human decision-making into large-scale climate system models. This theme arose from the recognition that the cumulative effects of local land-use change contribute significantly to global environmental change, and land-use is the result of adaptive decision-making of land-users. In order to understand the linkages between climate systems and land-use, we must integrate decision-level, process-based models (for example, agent-based models) with large-scale climate models.

The perspectives, ideas, and contributions of workshop participants have been synthesized and released as a report from the GLP. A collaborative effort between regional and global climate modelers, land change scientists, and agent-based modelers, this report describes methods for up-scaling local land system models for integration with large-scale climate models.

Although there is much room for improvement in both climate system and agent-based modeling, the integration of these approaches is an important next step for creating realistic climate change scenarios that account for the adaptive responses of land-users.

Introductory ABM Resources

A list of accessible, simple, and interesting ABM papers and resources for introducing new students: http://mass.leeds.ac.uk/2013/02/13/an-excellent-abm-paper/.

Video

Food Systems in China

Food_and_China

In honor of Chinese New Year this weekend, this post features an excellent video from The Perennial Plate that highlights two of my favorite things about China: the countryside and food. When you watch the video, you will see linkages between the Chinese passion for food, a rapidly changing agricultural economy, and underlying cultural stigmas associated with agricultural livelihoods. Interactions between these various elements are having profound impacts on rural livelihoods and land-use in the rapidly changing Chinese economy and culture.

A characteristic pattern of the new China, which struck me during my travels in the countryside and is apparent in this video as well, is the demographic disparity as one travels outside the cities. Older generations remain on the farm, work the land, and care for the young children, while many young adults travel to nearby cities in pursuit of higher wages. Such demographic patterns are reinforced by a long-held stigma against agricultural livelihoods and their association with a peasant’s social status.

Given my interest in sustainable agricultural practices and livelihoods, this video resonated with me personally, as well as reminding me of parallels with the many urban agriculture movements that have become so prevalent around the U.S. This story demonstrates the kind of new cultural and social linkages between urban and rural livelihoods that can be created in China as an adaptation (and perhaps reaction) to an increasingly urban and market-driven economy and society.

Tying into the themes of this blog – agent-based modeling and land-use change – the story told in this video is a reminder of the importance of the cultural and social contexts in which land-use and livelihood decisions are made. In particular, this is a vivid example of how cultural and socio-economic forces can create emergent urban-rural teleconnections that lead to new land-uses and livelihood strategies.

New Paper: Pattern-Oriented Modeling in Multi-Scale ABMs of Land Change

TGIS_screen_captureA particular challenge of investigating the causes of land-use change is the multi-scale nature of factors that influence land-use decisions. In an increasingly globalized world, land-use choices and livelihood strategies are linked to local AND regional to global forces. But attempts to incorporate such multi-scale causation in land change models often run into significant knowledge and data gaps – especially when trying to link incomplete and/or low quality global data to individual agents’ decision-making processes.

Figure4_mainOne way forward, which my co-author Dr. Erle Ellis and I present in this new open-access article in Transactions in GIS, is to use pattern-oriented modeling (Grimm et al., 2005) within an agent-based virtual laboratory to experimentally bound the possible values of uncertain parameters. By targeting characteristic patterns tied to important individual- and landscape-level processes – the selection of which are informed by theory, data, or both – ABMs can be designed and tested to be more realistic despite data limitations. We propose that this experimental method can help overcome significant data gaps, and help land change scientists begin to quantify some global trends in local land change processes.

Comments welcome!

Abstract

Local land-use and -cover changes (LUCCs) are the result of both the decisions and actions of individual land-users, and the larger global and regional economic, political, cultural, and environmental contexts in which land-use systems are embedded. However, the dearth of detailed empirical data and knowledge of the influences of global/regional forces on local land-use decisions is a substantial challenge to formulating multi-scale agent-based models (ABMs) of land change. Pattern-oriented modeling (POM) is a means to cope with such process and parameter uncertainty, and to design process-based land change models despite a lack of detailed process knowledge or empirical data. POM was applied to a simplified agent-based model of LUCC to design and test model relationships linking global market influence to agents’ land-use decisions within an example test site. Results demonstrated that evaluating alternative model parameterizations based on their ability to simultaneously reproduce target patterns led to more realistic land-use outcomes. This framework is promising as an agent-based virtual laboratory to test hypotheses of how and under what conditions driving forces of land change differ from a generalized model representation depending on the particular land-use system and location.

Looking forward to reading and reviewing this. Also, a sure sign that the field of agent-based modeling is maturing.

GIS and Science

AgentAnalyst-frontcover-300dpiAgent Analyst: Agent-Based Modeling in ArcGIS [PDF]

Contributors: Kevin M. Johnston (Editor), Daniel G. Brown, Nicholson Collier, Hamid R. Ekbia, Mary Jo Fraley, Elizabeth R. Groff, Michelle A. Gudorf, Naicong Li, Arika Ligmann-Zielinska, Michael J. North, Derek T. Robinson, and Nathan Strout

Agent Analyst: Agent-Based Modeling in ArcGIS is an introduction to agent-based modeling using an open-source software called Agent Analyst, which is compatible with ArcGIS software. This workbook’s step-by-step exercises, written by agent-based modeling experts, demonstrate how to create agent-based models using points, polygons, rasters, and representative networks. Key topics include creating, manipulating, and scheduling actions and fields. The book shows how to implement basic-to-complex decision making by agents, and demonstrates the code to capture these decisions. Agent Analyst: Agent-Based Modeling in ArcGIS includes exercises, case studies, and code necessary to begin building agent-based models in ArcGIS Desktop 10. You can download Agent Analyst: Agent-Based Modeling in ArcGIS by…

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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.

Friday Features

Welcome to Friday Features! Or something like that … name to be determined. Regardless of the name, here’s the deal: A couple times a month I will post a series of links – a sort of what’s “trending” in the world of agent-based modeling (ABM), land change science, and/or sustainability science. So, without further ado ….

Thought-provoking …

  • A Wired Science post about the legacy of medieval agricultural land-use patterns. An excellent example of how economic rationale manifests itself as striking land-use patterns. Source: Wired Science, Tim De Chant.
  • Another Wired Science post about land-use patterns. Clear evidence Brasíliafor the importance of institutions in shaping land-use patterns and the need to better model institutional agents – a common problem in the ABM world. Source: Wired Science, Betsy Mason.

Interesting academic articles …

CFP: Multi-Agent-Based Simulation 2013

MABS_2013

MABS’13 – The Fourteenth International Workshop on Multi-Agent-Based Simulation Multi-Agent Simulation In conjunction with AAMAS 2013, St. Paul, Minnesota, USA 6th-10th May 2013

Submission deadline: February 3, 2013 For more information: https://sites.google.com/site/mabsworkshop/

Aims and scope. The meeting of researchers from MAS engineering and the social/economic/organizational sciences is extensively recognized for its role in cross-fertilization, and has undoubtedly been an important source of inspiration for the body of knowledge that has been produced in the MAS area. Multi-Agent Based Simulation (MABS) is a vibrant inter-disciplinary area which brings together researchers within the agent-based social simulation community (ABSS) and the Multiagent Systems community (MAS).

The range of technical issues that MABS has dealt with and continues to deal with is diverse and extensive and includes:

Simulation methodologies: – standards for MABS – methodologies and simulation languages for MABS – simulation platforms and tools for MABS – visualization and analytic tools – approaches for large-scale simulations – scalability and robustness in MABS

Simulation of social and economic behavior: – formal and agent models of social behavior – cognitive modeling and social simulation – game theory and simulation – social structure: social networks and simulating organizations – simulating social complexity (e.g. structures and norms, social order, emergence of cooperation and coordinated action, self-organization, the micro-macro link)

Applications: – MABS in environmental modeling – agent-based experimental economics – participative-based simulation – MABS and games

MABS WORKSHOP SERIES The workshop is a continuation of the International Workshop series on Multi-Agent-Based Simulation (MABS). Further details of the previous workshops can be found at http://www.pcs.usp.br/~mabs/.

Important Dates February 3, 2013 – Deadline for paper submission. March 1, 2013 – Acceptance or rejection notification is sent to authors. March 10, 2013 – Deadline for authors sending their revised contribution, according to reviewers’ remarks. May 6-57, 2013 – MABS 2013 workshop takes place.

*This is an abbreviated version. The original CFP can be found at  OpenABM.