Standardised and transparent model descriptions for agent-based models

Last month saw initial publication (although officially it is a May publication!) of the paper that came out of the agent-based modelling workshop in which I participated at iEMSs 2014.

Birgit Müller put in some great work to summarise our discussion and bring together the paper which addresses how we describe agent-based models. Standardised and transparent model descriptions for agent-based models: Current status and prospects has several highlights:

  • We describe how agent-based models can be documented with different types of model descriptions.
  • We differentiate eight purposes for which model descriptions are used.
  • We evaluate the different description types on their utility for the different purposes.
  • We conclude that no single description type alone can fulfil all purposes simultaneously.
  • We suggest a minimum standard by combining particular description types.

To present our assessment on how well different purposes are met by alternative description types we produced the figure below. In the figure light grey indicates limited ability, medium grey indicates medium ability and dark grey high ability (an x indicates not applicable). Full details of this assessment are presented in the version of the diagram presented in an online supporting appendix.

Citation and abstract for the paper below. Any questions, or for a reprint, get in touch.

Müller, B., Balbi, S., Buchmann, C.M., de Sousa, L., Dressler, G., Groeneveld, J., Klassert, C.J., Quang Bao Le, Millington, J.D.A., Nolzen, H., Parker, D.C., Polhill, J.G., Schlüter, M., Schulze, J., Schwarz, N., Sun, Z., Taillandier, P. and Weise, H. (2014). Standardised and transparent model descriptions for agent-based models: Current status and prospects. Environmental Modelling & Software, 55, 156-163.

DOI: 10.1016/j.envsoft.2014.01.029

Agent-based models are helpful to investigate complex dynamics in coupled human–natural systems. However, model assessment, model comparison and replication are hampered to a large extent by a lack of transparency and comprehensibility in model descriptions. In this article we address the question of whether an ideal standard for describing models exists. We first suggest a classification for structuring types of model descriptions. Secondly, we differentiate purposes for which model descriptions are important. Thirdly, we review the types of model descriptions and evaluate each on their utility for the purposes. Our evaluation finds that the choice of the appropriate model description type is purpose-dependent and that no single description type alone can fulfil all requirements simultaneously. However, we suggest a minimum standard of model description for good modelling practice, namely the provision of source code and an accessible natural language description, and argue for the development of a common standard.

Agent-based modelling; Domain specific languages; Graphical representations; Model communication; Model comparison; Model development; Model design; Model replication; Standardised protocols

Writing: Landscape Ecology and Land Degradation

July was a busy month of writing. Unfortunately, it wasn’t busy writing on this blog and I failed on my New Year’s resolution to make at least one blog post each calendar month this year.

The writing I was doing was for my contribution to a new Landscape Ecology textbook I’m co-authoring with Dr Rob Francis. I’ve written and contributed to individual chapters for edited books previously (the latest highlighted below), but a whole book is a larger challenge. In particular, it’s been a useful experience thinking about how to structure the presentation of the ideas we want to address, which order they come in, what goes in each chapter, and so forth. I’ve mainly been working on the chapters on scale and disturbance, but have also been thinking about material for the heterogeneity and landscape evolution chapters. I’ve been learning a lot, revisiting old notes (including from my undergraduate lectures with Dr Perry!) and reviewing the content of others’ books. It’s been good thinking about some of the broader issues – such as the shifting-mosaic steady state and diversity-disturbance relationships – as it helps to frame more focused questions and work I’ve been thinking about and doing (including my ongoing research using Mediterranean disturbance-succession simulation modelling). When I get the chance (in amongst other things) I’ll post more here about the progression of the book, it’s aims and how it will fit in with teaching we have planned.

Just this week another book I have been involved with has become available online. Patterns of Land Degradation in Drylands: Understanding Self-Organised Ecogeomorphic Systems is the edited volume that summarises and develops the discussions we had at a workshop in Potsdam, Germany in the summer of 2010. The workshop and writing of the book, led by Eva Mueller, John Wainwright, Tony Parsons and Laura Turnbull, examine processes at the interface of ecology and geomorphology that are associated with land degradation in drylands. I contributed to the book chapters on the current state of the art in studying land degradation in drylands, on resilience, self-organization, complexity and pattern formation, and on pattern-process interrelationships and the role of ecogeomorphology. The book is the first on ecogeomorphology of drylands and contains four case studies from drylands in Europe, Africa, Australia and North America that highlight recent advances in ecogeomorphic research. It’s available online now and will be out in print soon.

Aspiration, Attainment and Success accepted

Back in February last year I wrote a blog post describing my initial work using agent-based modelling to examine spatial patterns of school choice in some of London’s education authorities. Right at the start of this month I presented a summary of the development of that work at the IGU 2013 Conference on Applied GIS and Spatial Modelling (see the slideshare presentation below). And then this week I had a full paper with all the detailed analysis accepted by JASSS – the Journal of Artificial Societies and Social Simulation. Good news!

One of the interesting things we show with the model, which was not readily at the outset of our investigation, is that parent agents with above average but not very high spatial mobility fail to get their child into their preferred school more frequently than other parents – including those with lower mobility. This is partly due to the differing aspirations of parents to move house to ensure they live in appropriate neighbourhoods, given the use of distance (from home to school) to ration places at popular schools. In future, when better informed by individual-level data and used in combination with scenarios of different education policies, our modelling approach will allow us to more rigorously investigate the consequences of education policy for inequalities in access to education.

I’ve pasted the abstract below and because JASSS is freely available online you’ll be able to read the entire paper in a few months when it’s officially published. Any questions before then, just zap me an email.

Millington, J.D.A., Butler, T. and Hamnett, C. (forthcoming) Aspiration, Attainment and Success: An agent-based model of distance-based school allocation Journal of Artificial Societies and Social Simulation

In recent years, UK governments have implemented policies that emphasise the ability of parents to choose which school they wish their child to attend. Inherently spatial school-place allocation rules in many areas have produced a geography of inequality between parents that succeed and fail to get their child into preferred schools based upon where they live. We present an agent-based simulation model developed to investigate the implications of distance-based school-place allocation policies. We show how a simple, abstract model can generate patterns of school popularity, performance and spatial distribution of pupils which are similar to those observed in local education authorities in London, UK. The model represents ‘school’ and ‘parent’ agents. Parental ‘aspiration’ to send their child to the best performing school (as opposed to other criteria) is a primary parent agent attribute in the model. This aspiration attribute is used as a means to constrain the location and movement of parent agents within the modelled environment. Results indicate that these location and movement constraints are needed to generate empirical patterns, and that patterns are generated most closely and consistently when schools agents differ in their ability to increase pupil attainment. Analysis of model output for simulations using these mechanisms shows how parent agents with above-average – but not very high – aspiration fail to get their child a place at their preferred school more frequently than other parent agents. We highlight the kinds of alternative school-place allocation rules and education system policies the model can be used to investigate.

Recursion in society and simulation

This week I visited one of my former PhD advisors, Prof John Wainwright, at Durham University. We’ve been working on a manuscript together for a while now and as it’s stalled recently we thought it time we met up to re-inject some energy into it. The manuscript is a discussion piece about how agent-based modelling (ABM) can contribute to understanding and explanation in geography. We started talking about the idea in Pittsburgh in 2011 at a conference on the Epistemology of Modeling and Simulation. I searched through this blog to see where I’d mentioned the conference and manuscript before, but to my surprise, before this post I hadn’t.

In our discussion of what we can learn through using ABM, John highlighted the work of Kurt Godel and his incompleteness theorems. Not knowing all that much about that stuff I’ve been ploughing my way through Douglas Hofstadter’s tome ‘Godel, Escher and Bach: An Eternal Golden Braid’ – heavy going in places but very interesting. In particular, his discussion of the concept of recursion has taken my notice, as it’s something I’ve been identifying elsewhere.

The general concept of recursion involved nesting, like Russian dolls, stories within stories (like in Don Quixote) and images within images:

Computer programmers of take advantage of recursion in their code, calling a given procedure from within that same procedure (hence their love of recursive acronyms like PHP [PHP Hypertext Processor]). An example of how this works is in Saura and Martinez-Millan’s modified random clusters method for generating land cover patterns with given properties. I used this method in the simulation model I developed during my PhD and have re-coded the original algorithm for use in NetLogo [available online here]. In the code (below) the grow-cover_cluster procedure is called from within itself, allowing clusters of pixels to ‘grow themselves’.

However, rather than get into the details of the use of recursion in programming, I want to highlight two other ways in which recursion is important in social activity and its simulation.

The first, is in how society (and social phenomena) has a recursive relationship with the people (and their activities) composing it. For example, Anthony Gidden’s theory of structuration argues that the social structures (i.e., rules and resources) that constrain or prompt individuals’ actions are also ultimately the result of those actions. Hence, there is a duality of structure which is:

“the essential recursiveness of social life, as constituted in social practices: structure is both medium and outcome of reproduction of practices. Structure enters simultaneously into the constitution of the agent and social practices, and ‘exists’ in the generating moments of this constitution”. (p.5 Giddens 1979)

Another example comes from Andrew Sayer in his latest book ‘Why Things Matter to People’ which I’m also progressing through currently. One of Sayer’s arguments is that we humans are “evaluative beings: we don’t just think and interact but evaluate things”. For Sayer, these day-to-day evaluations have a recursive relationship with the broader values that individuals hold, values being ‘sedimented’ valuations, “based on repeated particular experiences and valuations of actions, but [which also tend], recursively, to shape subsequent particular valuations of people and their actions”. (p.26 Sayer 2011)

However, while recursion is often used in computer programming and has been suggested as playing a role in different social processes (like those above), its examination in social simulation and ABM has not been so prominent to date. This was a point made by Paul Thagard at the Pittsburgh epistemology conference. Here, it seems, is an opportunity for those seeking to use simulation methods to better understand social patterns and phenomena. For example, in an ABM how do the interactions between individual agents combine to produce structures which in turn influence future interactions between agents?

Second, it seems to me that there are potentially recursive processes surrounding any single simulation model. For if those we simulate should encounter the model in which they are represented (e.g., through participatory evaluation of the model), and if that encounter influences their future actions, do we not then need to account for such interactions between model and modelee (i.e., the person being modelled) in the model itself? This is a point I raised in the chapter I helped John Wainwright and Dr Mark Mulligan re-write for the second edition of their edited book “Environmental Modelling: Finding Simplicity in Complexity”:

“At the outset of this chapter we highlighted the inherent unpredictability of human behaviour and several of the examples we have presented may have done little to persuade you that current models of decision-making can make accurate forecasts about the future. A major reason for this unpredictability is because socio-economic systems are ‘open’ and have a propensity to structural changes in the very relationships that we hope to model. By open, we mean that the systems have flows of mass, energy, information and values into and out of them that may cause changes in political, economic, social and cultural meanings, processes and states. As a result, the behaviour and relationships of components are open to modification by events and phenomena from outside the system of study. This modification can even apply to us as modellers because of what economist George Soros has termed the ‘human uncertainty principle’ (Soros 2003). Soros draws parallels between his principle and the Heisenberg uncertainty principle in quantum mechanics. However, a more appropriate way to think about this problem might be by considering the distinction Ian Hacking makes between the classification of ‘indifferent’ and ‘interactive’ kinds (Hacking, 1999; also see Hoggart et al., 2002). Indifferent kinds – such as trees, rocks, or fish – are not aware that they are being classified by an observer. In contrast humans are ‘interactive kinds’ because they are aware and can respond to how they are being classified (including how modellers classify different kinds of agent behaviour in their models). Whereas indifferent kinds do not modify their behaviour because of their classification, an interactive kind might. This situation has the potential to invalidate a model of interactive kinds before it has even been used. For example, even if a modeller has correctly classified risk-takers vs. risk avoiders initially, a person in the system being modelled may modify their behaviour (e.g., their evaluation of certain risks) on seeing the results of that behaviour in the model. Although the initial structure of the model was appropriate, the model may potentially later lead to its own invalidity!” (p. 304, Millington et al. 2013)

The new edition was just published this week and will continue to be a great resource for teaching at upper levels (I used the first edition in the Systems Modeling and Simulation course I taught at MSU, for example).

More recently, I discussed these ideas about how models interact with their subjects with Peter McBurney, Professor in Informatics here at KCL. Peter has written a great article entitled ‘What are Models For?’, although it’s somewhat hidden away in the proceedings of a conference. In a similar manner to Epstein, Peter lists the various possible uses for simulation models (other than prediction, which is only one of many) and also discusses two uses in more detail – mensatic and epideictic. The former function relates to how models can bring people around a metaphorical table for discussion (e.g., for identifying and potentially deciding about policy trade-offs). The other, epideictic, relates to how ideas and arguments are presented and leads Peter to argue that by representing real world systems in a simulation model can force people to “engage in structured and rigorous thinking about [their problem] domain”.

John and I will be touching on these ideas about the mensatic and epideictic functions of models in our manuscript. However, beyond this discussion, and of relevance here, Peter discusses meta-models. That is, models of models. The purpose here, and continuing from the passage from my book chapter above, is to produce a model (B) of another model (A) to better understand the relationships between Model A and the real intelligent entities inside the domain that Model A represents:

“As with any model, constructing the meta-model M will allow us to explore “What if?” questions, such as alternative policies regarding the release of information arising from model A to the intelligent entities inside domain X. Indeed, we could even explore the consequences of allowing the entities inside X to have access to our meta-model M.” (p.185, McBurney 2012)

Thus, the models are nested with a hope of better understanding the recursive relationship between models and their subjects. Constructing such meta-models will likely not be trivial, but we’re thinking about it. Hopefully the manuscript John and I are working on will help further these ideas, as does writing blog posts like this.

Selected Reference
McBurney (2012): What are models for? Pages 175-188, in: M. Cossentino, K. Tuyls and G. Weiss (Editors): Post-Proceedings of the Ninth European Workshop on Multi-Agent Systems (EUMAS 2011). Lecture Notes in Computer Science, volume 7541. Berlin, Germany: Springer.

Millington et al. (2013) Representing human activity in environmental modelling In: Wainwright, J. and Mulligan, M. (Eds.) Environmental Modelling: Finding Simplicity in Complexity. (2nd Edition) Wiley, pp. 291-307 [Online] [Wiley]

Errata: Millington et al. (2012)

I just noticed a typo in one of my papers – seems my proof-reading wasn’t up to scratch.

The error is on page 1031 of Millington et al. (2012) in the following passage;

For example, in Group A the two birds with very low initial stress levels (birds 1 and 2 in Fig. 4a with OSL 104 and 103 respectively) had, by chance, chosen one another to influence their stress levels. This caused the stress levels of both these birds to drop rapidly due to their reciprocal influence. In turn, the two other birds in this group with higher initial stress levels (birds 3 and 4 with OSL185 and 207 respectively) had each chosen one of these first two birds (birds 1 and 3 respectively) as the influence on their stress levels. Hence, the stress levels of birds 3 and 4 also dropped quickly leading to early laying, influenced as they were by the rapidly decreasing stress levels of reciprocally-linked birds 1 and 2.

Prize if you can spot it! The line;

…had each chosen one of these first two birds (birds 1 and 3 respectively)…

should read

…had each chosen one of these first two birds (birds 1 and 2 respectively)…

A minor error (‘3’ vs. ‘2’) but this might clear things up for any confused readers out there. Of course, there maybe other things confusing the reader in the paper… if so, just ask!

Millington, J.D.A., O’Sullivan, D., Perry, G.L.W. (2012) Model histories: Narrative explanation in generative simulation modelling Geoforum 43 1025–1034 [link]

Wrapping up 2012

Nearing the end of 2012 and the total number of posts on this blog has been even fewer this year than in 2011. At least I have been tweeting a bit more of late. Here’s a quick round-up of activities and publications since my last post with a look at some of what’s going on in 2013.

The Geoforum paper on narrative explanation of simulation modelling is now officially published, as is the first of two Ecological Modelling papers on the Michigan forest modelling work. Citations and abstract for both are below, and are included on my updated publications list. I’ll post more details and info on each in the New Year (promise!). I’ll likely wait to summarise the Michigan paper until the second paper of that couplet is published – hopefully that won’t be too long as it’s now going through the proofs stage.

The proceedings for the iEMSs conference I attended in Leipzig, Germany, this summer are now online. That means that the two papers I presented there are also available. One paper was on the use of social psychology theory for modelling farmer decision-making, and the model I discuss in that paper is available for you to examine. The other paper was a standpoint contribution to a workshop on the place of narrative for explaning decision-making in agent-based models. From that workshop we’re working on a paper to be published in Environmental Modelling and Software about model description methods for agent-based models. More on that next year too hopefully.

In one of my earlier posts this year I talked about agent-based modelling spatial patterns of school choice (I’ll get the images for that post online again soon… maybe). I’ve managed to write up the early stages of that work and have submitted it to JASSS. We’ll see how that goes down. I hope to continue on that work in the new year also, possibly while in New Zealand at the University of Auckland. I’ll be in Auckland visiting and working with George Perry and David O’Sullivan, with whom I published the recent Geoforum paper (highlighted above). On the way to New Zealand I’ll be stopping off in Los Angeles for the Association of American Geographers conference which I haven’t been to previously and which should be interesting.

So that’s it for 2012. A New Year’s resolution for 2013 – post at least once every month on this blog! Especially from Down Under.

Happy Holidays!

Millington, J.D.A., O’Sullivan, D., Perry, G.L.W. (2012) Model histories: Narrative explanation in generative simulation modelling Geoforum 43 1025–1034
The increasing use of computer simulation modelling brings with it epistemological questions about the possibilities and limits of its use for understanding spatio-temporal dynamics of social and environmental systems. These questions include how we learn from simulation models and how we most appropriately explain what we have learnt. Generative simulation modelling provides a framework to investigate how the interactions of individual heterogeneous entities across space and through time produce system-level patterns. This modelling approach includes individual- and agent-based models and is increasingly being applied to study environmental and social systems, and their interactions with one another. Much of the formally presented analysis and interpretation of this type of simulation resorts to statistical summaries of aggregated, system-level patterns. Here, we argue that generative simulation modelling can be recognised as being ‘event-driven’, retaining a history in the patterns produced via simulated events and interactions. Consequently, we explore how a narrative approach might use this simulated history to better explain how patterns are produced as a result of model structure, and we provide an example of this approach using variations of a simulation model of breeding synchrony in bird colonies. This example illustrates not only why observed patterns are produced in this particular case, but also how generative simulation models function more generally. Aggregated summaries of emergent system-level patterns will remain an important component of modellers’ toolkits, but narratives can act as an intermediary between formal descriptions of model structure and these summaries. Using a narrative approach should help generative simulation modellers to better communicate the process by which they learn so that their activities and results can be more widely interpreted. In turn, this will allow non-modellers to foster a fuller appreciation of the function and benefits of generative simulation modelling.

Millington, J.D.A., Walters, M.B., Matonis, M.S. and Liu, J. (2013) Modelling for forest management synergies and trade-offs: Northern hardwood tree regeneration, timber and deer Ecological Modelling 248 103–112
In many managed forests, tree regeneration density and composition following timber harvest are highly variable. This variability is due to multiple environmental drivers – including browsing by herbivores such as deer, seed availability and physical characteristics of forest gaps and stands – many of which can be influenced by forest management. Identifying management actions that produce regeneration abundance and composition appropriate for the long-term sustainability of multiple forest values (e.g., timber, wildlife) is a difficult task. However, this task can be aided by simulation tools that improve understanding and enable evaluation of synergies and trade-offs between management actions for different resources. We present a forest tree regeneration, growth, and harvest simulation model developed with the express purpose of assisting managers to evaluate the impacts of timber and deer management on tree regeneration and forest dynamics in northern hardwood forests over long time periods under different scenarios. The model couples regeneration and deer density sub-models developed from empirical data with the Ontario variant of the US Forest Service individual-based forest growth model, Forest Vegetation Simulator. Our error analyses show that model output is robust given uncertainty in the sub-models. We investigate scenarios for timber and deer management actions in northern hardwood stands for 200 years. Results indicate that higher levels of mature ironwood (Ostrya virginiana) removal and lower deer densities significantly increase sugar maple (Acer saccharum) regeneration success rates. Furthermore, our results show that although deer densities have an immediate and consistent negative impact on forest regeneration and timber through time, the non-removal of mature ironwood trees has cumulative negative impacts due to feedbacks on competition between ironwood and sugar maple. These results demonstrate the utility of the simulation model to managers for examining long-term impacts, synergies and trade-offs of multiple forest management actions.

Answering forest management questions

Although I’ve been working on new ideas since leaving Michigan and returning to London about a year ago, there’s still lots to do to examining alternative forest management strategies.

Several years ago we set out to develop a simulation model that could be used to investigate the effects of interactions between timber harvest and deer browse disturbances on economic productivity and wildlife habitat. We’ve already published several papers on this work, but just before Christmas we submitted a manuscript to Ecological Modelling entitled ‘Modelling for forest management synergies and trade-offs: Tree regeneration, timber and wildlife’. In the manuscript we report error analyses of the full simulation model (which uses the USFS Forest Vegeation Simulator) and use the model to investigate scenarios of different timber and deer management actions. Our results indicate that greater harvest of commercially low-value ironwood and lower deer densities significantly increase sugar maple regeneration success over the long term.

I expect we’ll also report some of these results at the Fourth Forest Vegetation Simulator (FVS) Conference to be held in April this year in Fort Collins, CO. Our abstract, entitled ‘Investigating combined long-term effects of variable tree regeneration and timber management on forest wildlife and timber production using FVS’, has been accepted for oral presentation. It would be great to be there myself to present the paper and discuss things with other FVS experts, but I’m not sure if that will be possible. If it’s not, Megan Matonis will present as, handily, she’s currently doing her PhD in that neck of the woods at Colorado State University.

In the meantime, Megan and I are in the process of finishing off a different manuscript describing the mesic conifer planting experiment we did in Michigan. In that experiment we planted seedlings of white pine (Pinus strobus), hemlock (Tsuga canadensis), and white spruce (Picea glauca) in northern hardwood stands with variable deer densities and then monitored browse on the seedlings over two years. We found that damage to pine and hemlock seedlings was inversely related to increasing snow depth, and our data suggest a positive relationship between hemlock browse and deer density. These results suggest that hemlock restoration efforts will not be successful without protection from deer. Hopefully we’ll submit the manuscript, possibly to the Northern Journal of Applied Forestry, in the next month or so.

All of this work has been pursued with management in mind, so it was nice this week to receive a call from Bob Doepker, a manager at the Michigan Department of Natural Resources with whom we worked to co-ordinate data collection and establish key research questions. Bob had some questions about the details and implications of our previous findings for deer habitat, tree regeneration and how they should be managed. It was good to catch up, and no doubt our ongoing work will continue to contribute to contemporary management understanding and planning.

Agent-based models – because they’re worth it?

So term is drawing to an end. There’s lots been going on since I last posted here and I’ll write a full update of that over the Christmas break. I’ll just highlight here quickly that the agent-based modelling book I contributed to has now been published.

Agent-Based Models of Geographical Systems, is editied by Alison Heppenstall, Andrew Crooks, Linda See and Mike Batty and presents a comprehensive collection of papers on the background, theory, technical issues and applications of agent-based modelling (ABM) in geographical systems. David O’Sullivan, George Perry, John Wainwright and I put together a paper entitled ‘Agent-based models – because they’re worth it?’ that falls into the ‘Principles and Concepts of Agent-Based Modelling’ section of the book. To give an idea of what the paper is about, here’s the opening paragraph:

“In this chapter we critically examine the usefulness of agent-based models (ABMs) in geography. Such an examination is important be-cause although ABMs offer some advantages when considered purely as faithful representations of their subject matter, agent-based approaches place much greater demands on computational resources, and on the model-builder in their requirements for explicit and well-grounded theories of the drivers of social, economic and cultural activity. Rather than assume that these features ensure that ABMs are self-evidently a good thing – an obviously superior representation in all cases – we take the contrary view, and attempt to identify the circumstances in which the additional effort that taking an agent-based approach requires can be justified. This justification is important as such models are also typically demanding of detailed data both for input parameters and evaluation and so raise other questions about their position within a broader research agenda.”

In the paper we ask:

  • Are modellers agent-based because they should be or because they can be?
  • What are agents? And what do they do?
  • So when do agents make a difference?

To summarise our response to this last question we argue;

“Where agents’ preferences and (spatial) situations differ widely, and where agents’ decisions substantially alter the decision-making con-texts for other agents, there is likely to be a good case for exploring the usefulness of an agent-based approach. This argument focuses attention on three model features: heterogeneity of the decision-making context of agents, the importance of interaction effects, and the overall size and organization of the system.”

Hopefully people will find this, and the rest of the book useful! You can check out the full table of contents here.

O’Sullivan, D., J.D.A. Millington, G.L.W. Perry, J. Wainwright (2012) Agent-based models – because they’re worth it? p.109 – 123 In: Heppenstall, A.J., A.T. Crooks, L.M. See, M. Batty (Eds.) Agent-Based Models of Geographical Systems, Springer. DOI: 10.1007/978-90-481-8927-4_6

Summer 2011 Papers

Since I last posted, THREE of the papers I’ve mentioned here previously have become available online! Here are their details, abstracts are below. Email me if you can’t get hold of them yourself.

Millington, J.D.A., Walters, M.B., Matonis, M.S., Laurent, E.J., Hall, K.R. and Liu, J. (2011) Combined long-term effects of variable tree regeneration and timber management on forest songbirds and timber production Forest Ecology and Management 262 718-729 doi: 10.1016/j.foreco.2011.05.002

Millington, J.D.A. and Perry, G.L.W. (2011) Multi-model inference in biogeography Geography Compass 5(7) 448-530 doi: 10.1111/j.1749-8198.2011.00433.x

Millington, J.D.A., Demeritt, D. and Romero-Calcerrada, R. (2011) Participatory evaluation of agent-based land use models Journal of Land Use Science 6(2-3) 195-210 doi:10.1080/1747423X.2011.558595

Millington, J.D.A. et al. (2011) Combined long-term effects of variable tree regeneration and timber management on forest songbirds and timber production Forest Ecology and Management 262 718-729
The structure of forest stands is an important determinant of habitat use by songbirds, including species of conservation concern. In this paper, we investigate the combined long-term impacts of variable tree regeneration and timber management on stand structure, songbird occupancy probabilities, and timber production in northern hardwood forests. We develop species-specific relationships between bird species occupancy and forest stand structure for canopy-dependent black-throated green warbler (Dendroica virens), eastern wood-pewee (Contopus virens), least flycatcher (Empidonax minimus) and rose-breasted grosbeak (Pheucticus ludovicianus) from field data collected in northern hardwood forests of Michigan’s Upper Peninsula. We integrate these bird-forest structure relationships with a forest simulation model that couples a forest-gap tree regeneration submodel developed from our field data with the US Forest Service Forest Vegetation Simulator (Ontario variant). Our bird occupancy models are better than null models for all species, and indicate species-specific responses to management-related forest structure variables. When simulated over a century, higher overall tree regeneration densities and greater proportions of commercially high value, deer browse-preferred, canopy tree Acer saccharum (sugar maple) than low-value, browse-avoided subcanopy tree Ostrya virginiana (ironwood) ensure conditions allowing larger harvests of merchantable timber and had greater impacts on bird occupancy probability change. Compared to full regeneration, no regeneration over 100 years reduces merchantable timber volumes by up to 25% and drives differences in bird occupancy probability change of up to 30%. We also find that harvest prescriptions can be tailored to affect both timber removal volumes and bird occupancy probability simultaneously, but only when regeneration is adequate. When regeneration is poor (e.g., 25% or less of trees succeed in regenerating), timber harvest prescriptions have a greater relative influence on bird species occupancy probabilities than on the volume of merchantable timber harvested. However, regeneration density and composition, particularly the density of Acer saccharum regenerating, have the greatest long-term effects on canopy bird occupancy probability. Our results imply that forest and wildlife managers need to work together to ensure tree regeneration density and composition are adequate for both timber production and the maintenance of habitat for avian species over the long-term. Where tree regeneration is currently poor (e.g., due to deer herbivory), forest and wildlife managers should pay particularly close attention to the long-term impacts of timber harvest prescriptions on bird species.

Millington, J.D.A. and Perry, G.L.W. (2011) Multi-model inference in biogeography Geography Compass 5(7) 448-530
Multi-model inference (MMI) aims to contribute to the production of scientific knowledge by simultaneously comparing the evidence data provide for multiple hypotheses, each represented as a model. With roots in the method of ‘multiple working hypotheses’, MMI techniques have been advocated as an alternative to null-hypothesis significance testing. In this paper, we review two complementary MMI techniques – model selection and model averaging – and highlight examples of their use by biogeographers. Model selection provides a means to simultaneously compare multiple models to evaluate how well each is supported by data, and potentially to identify the best supported model(s). When model selection indicates no clear ‘best’ model, model averaging is useful to account for parameter uncertainty. Both techniques can be implemented in information-theoretic and Bayesian frameworks and we outline the debate about interpretations of the different approaches. We summarise recommendations for avoiding philosophical and methodological pitfalls, and suggest when each technique is best used. We advocate a pragmatic approach to MMI, one that emphasises the ‘thoughtful, science-based, a priori’ modelling that others have argued is vital to ensure valid scientific inference.

Millington et al. (2011) Participatory evaluation of agent-based land use models Journal of Land Use Science 6(2-3) 195-210
A key issue facing contemporary agent-based land-use models (ABLUMs) is model evaluation. In this article, we outline some of the epistemological problems facing the evaluation of ABLUMs, including the definition of boundaries for modelling open systems. In light of these issues and given the characteristics of ABLUMs, participatory model evaluation by local stakeholders may be a preferable avenue to pursue. We present a case study of participatory model evaluation for an agent-based model designed to examine the impacts of land-use/cover change on wildfire regimes for a region of Spain. Although model output was endorsed by interviewees as credible, several alterations to model structure were suggested. Of broader interest, we found that some interviewees conflated model structure with scenario boundary conditions. If an interactive participatory modelling approach is not possible, an emphasis on ensuring that stakeholders understand the distinction between model structure and scenario boundary conditions will be particularly important.

Bayesian Modelling in Biogeography

Recently I was asked to write a review of the current state-of-the-art of model selection and Bayesian approaches to modelling in biogeography for the Geography Compass journal. The intended audience for the paper will be interested but non-expert, and the paper will “…summarize important research developments in a scholarly way but for a non-specialist audience”. With this in mind, the structure I expect I will aim for will look something like this:

i) Introduction to the general issue of model inference (i.e., “What is the best model to use?”). This section will likely discuss the modelling philosophy espoused by Burnham and Anderson and also highlight some of the criticisms of null-hypothesis testing using p-values. Then I might lead into possible alternatives (to standard p-value testing) such as:

ii) AIC approaches (to find the ‘best approximating model’)

iii) Bayesian approaches (including Bayesian Model Averaging, as I’ve discussed on this blog previously)

iv) Some applied examples (including my deer density modelling for example)

vi) A brief summary

I also expect I will try to offer some practical hint and tips, possibly using boxes with example R code (maybe for the examples in iv). Other published resources I’ll draw on will likely include the excellent books by Ben Bolker and Michael McCarthy. As things progress I may post more, and I’ll be sure to post again when the paper is available to read in full.