Extended Deadline for Abstract Submission to GLP OSM 2014

2014 GLOBAL LAND PROJECT OPEN SCIENCE MEETING

Land transformations: between global challenges and local realities

March 19 – 21, 2014

Berlin, Germany

http://www.glp-osm2014.org

CALL FOR ABSTRACTS – Extended deadline: 31 July 2013

The 2014 Global Land Project Open Science Meeting program will be a combination of plenary sessions involving international figureheads and experts in the field, followed by parallel sessions covering a wide range of topics within the broad themes of the conference. In addition, poster sessions will take place during lunchtimes and early evenings.

We invite you to submit abstracts for presentations and posters for this leading global conference in Land Science. Your contribution should reference one of the conference sessions. The list of sessions is available on: http://www.glp-osm2014.org/conference_sessions.php. In case you do not identify any appropriate session, you may submit to the ‘open session’ that will be structured by the scientific committee.

Abstracts are welcomed in three different formats:

Oral Presentation:

12 minutes + 3 minutes for Q&A

Under conference session category Research Presentation Session

Flash Talk Presentation:

5 minutes based on 3 slides

Under session categories Round-table Discussion Session and Open Session

Poster:
Poster exhibition

Under conference session category Research Presentation Session

Please note that we can only accommodate one oral presentation (plus one flash talk) per attendant. The number of poster presentations is not limited.

More information about the Conference Sessions and Abstract Submissions go to the conference website: http://www.glp-osm2014.org/

Online registration for the conference will open in July 2013.

Complexity in land-livelihood systems

China_FarmerRural livelihoods are inextricably linked to sustainable land-use, and vice versa.

This message seems to be popping-up continuously and forcefully in much of the research articles I’ve been reading lately. And I agree – certainly land-use lies at the heart of the sustainability question, since it is a means of food and income production as well as a main source of impacts to ecosystems. Something I read far less often (still looking if you have suggestions!) is a holistic framework for understanding the complex causes and consequences of land-use and livelihood changes.

The factors driving rural household land-use and livelihood decisions are incredibly complex –  originating and acting both locally and globally, and often creating both rapid and slow changes in incentives and constraints. For example, see this post about both fast and gradual changes occurring in Chinese food systems. Researchers, practitioners, and policy-makers alike are thus left with huge gaps in understanding of how land-use and livelihood changes come about, and you can forget about accurately predicting such changes and how they might influence environmental and/or livelihood sustainability.

Thinking about this challenge led me back to some of my earlier work in complex system science. In particular, I revisited one of my earlier papers about ‘induced coupling‘ – an idea that faster and slower processes sometimes become ‘coupled’ and lead to dramatic systemic changes. So I tried my hand at throwing together a simple version of what this might look like for a coupled land-livelihood system.HCSM_LLS

The red, downward arrows represent ‘entrainment’, or ‘slaving’, of the dynamics of lower-level variables by higher-level variables. The green, upward arrows represent processes of ‘self-organization’, or ‘revolt’, in which the dynamics of lower-level variables influence those of higher-level variables. Dashed arrows represent processes that link variables operating at the same time scales. If you would like to know more about this type of framework, referred to as hierarchical complex systems modeling, I will direct you to work by my friends and colleagues Brad Werner and Dylan McNamara (2007).

Now, the recognition that processes, or ‘drivers’, across multiple scales influence land-use and livelihood decisions is nothing new. However, rarely are temporal scales used as the organizing framework. This viewpoint has the potential to explain why certain drivers have different influences in different contexts due to the relative frequencies of interacting processes.

OK, great … so what? Beyond the potential to advance our fundamental understanding of the causes and consequences of livelihood and land-use changes, such a perspective could help craft policy interventions that address not only short-term needs of rural land-users, but also the effects of long-term challenges to sustainability and well-being.

As always, please feel free to yell at me on twitter @nickmags13 if you disagree, or if you prefer to disagree with me on a more regular basis don’t hesitate to follow this blog or subscribe to the RSS feed or email list. 😉

ABM and GLOBE Project Sessions at the Global Land Project’s 2014 Open Science Meeting

GLP_OSM2014The Global Land Project will hold its second Open Science Meeting (OSM) in Berlin from March 19-21, 2014. This will be a unique opportunity to hear about cutting-edge land and global environmental change research. A list of sessions was recently released here – check it out and see if anything peaks your interest. In particular, I will be co-chairing three sessions related to ABMs, synthesis, and/or GLOBE:

1. Research Session 0126: “Bridging local to global land change studies with the GLOBE online tool” (co-chaired with Erle Ellis)

2. World Cafe Workshop 0075: “From meta-analysis to modeling: understanding local land change globally” (co-chaired with Jasper van Vliet)

3. Short Training Session 0125: “The GLOBE project: evolving new global workflows for land change science” (co-chaired with Erle Ellis and the GLOBE Team)

I attended the first OSM in 2010 (wow, that long ago?!) held at Arizona State University, and it was a great meeting. Session content was exceptional and the meeting was not too big. I highly recommend getting to Berlin next year if you can!

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.”

Used Planet: A Global History

Are we living in the Anthropocene? Published Monday in the Proceedings of the National Academy of Sciences, USA, Erle Ellis and colleagues paint a picture of historical land-use that significantly shaped the Earth’s surface more than 3000 years ago.

See a blog post or media coverage in the New Scientist for more details and to download the paper.

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.

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.

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!

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.