The effect of model structure in ecosystem models

• Purpose of the project
• Outputs
• Background work
• A summary of the e-mail discussion about model metrics

Project Plan. We have divided the project into a number of subprojects. These subprojects address the main themes which were identified during our background work. In particular they include:

1. Time series analysis; Can nonlinear time series analysis yield a useful metric for model-model and model-data comparison?
2. Network (foodweb) dynamics; Characteristics of networks evolved under different population dynamics equations - Population Dynamics on Networks
3. Group Theory; Formal mapping between 'classical' continuous modelling and Agent Based Modelling;
4. Complex Adaptive Systems; Monitoring and Managing Evolution in CAS applied to sustainable fisheries

Purpose of the project

The task of modeling ecosystems and their interaction with human activities imposes a sort of dilemma to a scientist/practitioner. The model needs to be complex enough to capture and reproduce the dynamics of the interacting subcomponents of the systems and at the same time simple enough to be manageable, comprehensible and capable of running in reasonable time and succinct enough to allow the main results to be communicated to other scientists or resource managers. This compromise is usually obtained via the modeler's accumulated experience and often is not transferable.

In this project we aim to give a Complex System Science perspective of the problem, and see how ideas and tools developed within the CSS community can help modelers in their daily work. Clearly, this problem has many facets and we plan to use our resources to address a number of them. In particular we will address four areas. We will explore:

• how time series analysis can help us in developing methods to assess a model's performance against both real data and results from other synthetic models;
• how network theory can help in the problem of lumping foodwebs by capturing in a compact manner their core structure and inherent dynamics;
• whether mathematical group theory can highlight the relation between 'classic' continuous modeling and agent based modeling , thereby reducing the current gap in their applications;
• and to what extent it is possible to predict the behavior of complex adaptive systems and influence their evolution, thereby opening the door to management actions able to prevent undesirable outcomes.

We expect that our results will be delivered in the form of scientific publications, algorithms and procedures and we encourage applied modelers to interact with us in order to make sure that our work is relevant and quickly disseminated.

Outcomes

Time series analysis. We will deliver on two main topics: the Complexity Map and Measures of Time Series Similarity. The Complexity Map aims to define and measure the complexity of ecological models within a information theoretic framework. Its main strength lies in employing a measure which can be applicable to both models and data. Its purpose is to help defining which model is suitable to study a given data set and, in principle, in what part(s) of the parameter space the analysis needs to focus. The Measures of Time Series Similarity allows us to perform data-to-data, model-to-data and model-to-model comparisons which are crucial to tune and analyse model outputs.

Output:

1. Mapping the complexity of ecological models, Boschetti & Grigg, Ecological Complexity, Under Internal Review.
2. Characterising and comparing model behaviour via nonlinear time series analysis, Grigg & Boschetti, Journal Paper, in preparation.
3. Characterising and comparing the behaviour of ecological models: coping with limits to predictability, Grigg & Boschetti, Book Chapter, in preparation.
4. Prokopenko M, Boschetti F, & Ryan A, An Information-Theoretic Primer On Complexity, Self-Organisation And Emergence, Advances in Complex Systems, submitted.
5. Grigg, Boschetti , Webster, Ecological response in aquatic systems: coping with limits to predictability , Conference paper, Young Water Professionals conference, UNSW, Sydney, 2006.

Group Theory - Foundation of Modelling. The original idea was to study a formal mapping between Individual based discrete models and analytical continuous models. Discussions between Randall Gray and Fabio Boschetti lead to a in depth analysis of the foundation of mathematics and their implication for modelling, with particular emphasis on ecological modelling and its need to extrapolate unexpected (or emergent) behaviours. This is an inherently theoretical work, but its outcome lead to revisit the meaning of modelling and to discuss "the future after the modelling age". We are in contact with a number of theorists in Australia and abroad on this very topic.

Output:

6. Emergence, Novelty and Computability, Boschetti & Gray, Journal Paper, Emergence: Complexity and Organization, Under Internal Review.
7. A Turing test for Emergence, Boschetti & Gray, submitted as book chapter in "Advances in Applied Self-organizing Systems", Springler.
8. The Geometry of Novelty and Diversity, Boschetti, submitted to Biosystems; Special issue on Selection, Self-Organization and Diversity.
9. Complexity of a modelling exercise: a critique of the role of computer simulation in Complex System Science, Boschetti & ??, journal paper, in preparation.
10. Boschetti, Prokopenko, Macreadie, and Grisogono. Defining and detecting emergence in complex networks, Conference paper, In R. Khosla, R. J. Howlett, and L. C. Jain, editors, Knowledge-Based Intelligent Information and Engineering Systems, 9th International Conference, KES 2005, Melbourne, Australia, September 14-16, 2005, Proceedings, Part IV, volume 3684 of Lecture Notes in Computer Science, pages 573-580, 2005.

Complex Adaptive Systems; We have modelled the competition for limited resources in a simplified fishery and shown that optimal catches for a virtual fleet can be obtained while all vessels act competitively and try to maximise their own returns. This is a remarkable result since it contradicts all literature on related problems such as the "Tragedy of the Commons" and the "Minority Game". The work has already been presented at two Conferences to both economists and modellers and received extremely good feedback. An application within the Management Strategy Evaluation approach could represent a natural extension of this work.

Output:

11. Improving resource exploitation via Collective Intelligence by assessing agents' impact on the community outcome, Boschetti, Ecological Economics, Accepted.
12. Strategies for Resource Exploitation, Brede, Boschetti & McDonald, Ecological Economics, submitted.
13. Seven visions of evolution, Batten, Boschetti and Salthe, Journal paper, in preparation.
14. Self-defeating human ecosystems, majority and minority games and collective intelligence, Boschetti & Batten, 2006, Book Chapter, in Understanding Human Behaviour In Complex Adaptive Systems: Building a foundation for promoting adaptive governance, (accepted).
15. Rixon, Robinson & Boschetti, 2006, The Meme as a Design Pattern for Social Learning in Agent-Based Models, submitted to Journal of Artificial Societies and Social Simulation, JASSS.
16. Boschetti, Improved Resource Exploitation by Collective Intelligence, Conference paper, ModSim05, Melbourne, 12-15 December, 2005.
17. Boschetti, Exploring the potential use of Collective Intelligence (COIN) in human communities, Conference Abstract, Econophysics Colloquium, 14-18 November 2005, Research School of Physical Sciences and Engineering Australian National University, Canberra, Australia