the Novel Approaches to Networks of Interacting Autonomes project at the Centre for Policy Modelling - funded under the EPSRC's Novel Computation Initiative

(Spetember 2005 - June 2009)

Summary of NANIA in general:

NANIA is a collaborative project aimed at finding efficient ways to apply computation and rigorous analysis to complex systems in fields such as ecology, earthquakes, epidemics and social science.  It is funded for 4 years as a network of researchers by the EPSRC as part of its Novel Computation Initiative.  The Funded Institutions are the universities of Edinburgh; Manchester; Herriot-Watt; and Manchester Metropolitan (here).

We will develop computer models which represent complex systems and can be validated by comparison with experimental data collected by our collaborators.  These models are based on “autonomes” which represent individuals (animals, species, genes, grains of rock, people) which interact via networks which represent their connections (spatial adjacency, predator-prey relationship, gene in same phonotype, friendship or kin).   The system is the subject to some external driving (food resource, changing environment, applied stress).

The dynamics of these systems typically involves evolution of both the autonomes and the networks until a “steady state” is reached – by which we mean that although the autonomes and network may continue to evolve, certain global features (number of animals, mean connectivity, total use of resource, temperature, plate movement) reach constant values when averages over time.  The nature of these steady states is unknown, as are the general condition which cause them as opposed to a collapse of the system.   Indeed, even defining global properties which encapsulate the complexity of the system without describing every detail is problematic and ambiguous.

NANIA involves a twin track approach.  Specific researchers will make progress in building models of specific systems: food webs, ecologies, earthquakes, gene flow and social organisation.  This work will illuminate those areas, and find important results specific to those fields.  The second, collaborative track will involve pooling our computational expertise to produce efficient and novel ways of solving the problems and analysing our results in the more abstract framework of coarse-graining the complexity to find the common principles which govern the systems.   The NANIA collaboration extends beyond the work funded in this proposal: other work not funded here will contribute to the second track through a series of workshops,  and we adopt an open policy of extending our modelling expertise to other areas where autonome modelling has yet to make an impact.

For more about NANIA in general see the website at:

Summary of NANIA at the CPM:

Social systems are a rich source for abstract models of coordination and control that have more general application.  Where we study conditions that facilitate the spontaneous specialisation of skill and function, and mechanisms for the emergence of groups.  Specialisation of autonomes (individuals) allows all aspects of an environment or problem to be exploited in parallel with the minimum of competition – an attractive paradigm for novel computing, particularly if it can emerge from homogeneous underlying hardware. In ecology the driving force is biological evolution, whilst in social systems the process can be the result of coordination between the individuals.  Specialisation and group formation are most effective when they occur together since they are mutually reinforcing: one  species creates a niche for the other.  The formation of cooperative groups makes it easier for members to develop specialise skills because they can rely upon others in the group to provide for their other needs. 

If benefits do not require individual sacrifice, group formation is stable, but in other cases the possibility of “cheats” taking the benefit without making the sacrifice can make this unstable. Social mechanisms that stabilise groups against “cheats” include: kin-based preference, reputation, tag-based recognition and institutional mechanisms (e.g. contracts).

We will construct of a series of related simulations and mathematical models at several different levels of abstraction based on the high level SDML language.  Individual-based simulations will be constructed based on observed social mechanisms.  The exploration of these will suggest more abstract simulations that focus on possible the key mechanisms involved (e.g. tag-based group formation).  Different "regions" of emergent behaviour in the abstract simulations will be "mapped out", and further mathematical approximations will be drawn up based upon inspection of these behaviour.  Once a reasonable understanding of the seperate mechanisms has been achieved abstract mechanisms will be combined in pairs to explore some of the "synergies" and "competition" between them.


Projects, Workshops and Special Issues arinsing out of the Project

Selected Relevant Publications at the CPM:

During the project

Edmonds, B. (in press) Computational modelling and social theory – the dangers of numerical representation.  In Mollona, E. (ed.) Computational Analysis of Firm Organisations and Strategic Behaviour, Routledge.

Edmonds, B. (2009) The Nature of Noise. In Squazzoni, F. (Ed.) Epistemological Aspects of Computer Simulation in the Social Sciences. LNAI 5466:169-182. (

Galán, J. M., Izquierdo, L. R., Izquierdo, S. S., Santos, J. I., del Olmo, R., López-Paredes, A. and Edmonds, B. (2009). Errors and Artefacts in Agent-Based Modelling. Journal of Artificial Societies and Social Simulation 12(1)1 (

Edmonds, B., Norling, E. and Hales, D. (2009) Towards the Emergence of Social Structure.  Computational and Mathematical Organization Theory, 15(2):78–94. (

Alam, S.J., Edmonds, B. and Meyer, R. (2009) Identifying Structural Changes in Networks Generated from Agent-based Social Simulation Models.  In Ghose, A., Governatori, G. and Sadananda, R. (eds.) Agent Computing and Multi-Agent Systems, 10th Pacific Rim International Conference on Multi-Agents, PRIMA 2007, Bangkok, Thailand, November 21-23, 2007. Revised Papers. Springer LNAI 5044:298-307. (

Norling, E., Powell, C and Edmonds, B. (2008) Cross-Disciplinary Views on Modelling Complex Systems. In.  David, N. & Sichman, J.S. (Eds.) Multi-Agent-Based Simulatin IX, Springer, Lecture Notes in Artificial Intelligence, 5269:183-194.

Polhill, J. G. and Edmonds, B. (2007) Open Access for Social Simulation.  Journal of Artificial Societies and Social Simulation, 10(3). ( 

Edmonds, B. (2007) Artificial Science - a Simulation to Study the Social Processes of Science.  In Edmonds, B., Hernandez, C. and Troitzsch, K. G. (eds.) (2007) Social Simulation: Technologies, Advances and New Discoveries.  IGI Publishing.  (

Edmonds, B. (2006) The Emergence of Symbiotic Groups Resulting From Skill-Differentiation and Tags. Journal of Artificial Societies and Social Simulation, 9(1). ( 

Edmonds (2006) How are physical and social spaces related? - cognitive agents as the necessary "glue".  In Billari, F. et al. (eds.) Agent-Based Computational Modelling: Applications in demography, social, economic and environmental sciences. Springer Verlag, 195-214. (

Edmonds, B. Terán, O. and Polhill, G. (2006) To the Outer Limits and Beyond – characterising the envelope of sets of social simulation trajectories. 1st World Congress on Social Simulation (WCSS'06), Kyoto, Japan, August, 2006. (

Norling, E. and Edmonds, B. (2006) Why it is Better to be SLAC than Smart. 1st World Congress on Social Simulation (WCSS'06), Kyoto, Japan, August, 2006.

Edmonds, B. and Norling, E. (2006) Integrating Learning and Inference in Multi-Agent Systems Using Cognitive Context.  7th International Workshop on Multi-Agent-Based Simulation (MABS'06), Japan, July 2006. (

Hales, D. and Edmonds, B. (2005) Applying a socially-inspired technique (tags) to improve cooperation in P2P Networks, IEEE Transactions in Systems, Man and Cybernetics, 35:385-395. (

Edmonds, B. and Hales, D. (2005) Computational Simulation as Theoretical Experiment, Journal of Mathematical Sociology 29(3):209-232. (

Moss, S. and Edmonds, B. (2005) Sociology and Simulation: - Statistical and Qualitative Cross-Validation, American Journal of Sociology, 110(4) 1095-1131. Previous version accessible as (

Edmonds, B. (2005) Simulation and Complexity - how they can relate. In Feldmann, V. and Mühlfeld, K. (eds.) Virtual Worlds of Precision - computer-based simulations in the sciences and social sciences. Lit Verlag 5-32. (

Edmonds, B. (2004) Using Localised ‘Gossip’ to Structure Distributed Learning. CPM Report 04-142, MMU. (  Presented at the AISB 2005 Symposium on Socially Inspired Computing.

Leading up to the project (i.e. those particularly relevant to it)

Edmonds, B. & Bryson, J. (2004) The Insufficiency of Formal Design Methods - the necessity of an experimental approach for the understanding and control of complex MAS. In Jennings, N. R. et al. (eds.) Proceedings of the 3rd Internation Joint Conference on Autonomous Agents & Multi Agent Systems (AAMAS'04), July 19-23, New York, ACM Press,  938-945. (

Edmonds, B. (2004) Using the Experimental Method to Produce Reliable Self-Organised Systems. In Brueckner, S. et al. (eds.) Engineering Self Organising Sytems: Methodologies and Applications, Springer, Lecture Notes in Artificial Intelligence, 3464:84-99. (

Edmonds, B. (2004) Artificial Science – a simulation test-bed for studying the social processes of science. CPM Report 04-138, MMU. (  The 2nd International Conference of the European Social Simulation Association (ESSA 2004), Valadollid, Spain. 16-19th September 2004.

Hales, D. and Edmonds, B. (2003) Can Tags Build Working Systems? - From MABS to ESOA. In Serugendo, G. Di M., et al. (eds.) Engineering Self-Organising Systems. Springer, Lecture Notes in Artificial Intelligence, 2977:186-194. (

Hales, D. and Edmonds, B. (2003)  Evolving Social Rationality for MAS using “Tags”, In Rosenschein, J. S., et al. (eds.) Proceedings of the 2nd International Conference on Autonomous Agents and Multiagent Systems, Melbourne, July 2003 (AAMAS03), ACM Press, 497-503. (

Edmonds, B. (2002) Exploring the Value of Prediction in an Artificial Stock Market. Workshop on Adaptive Behavior in Anticipatory Learning Systems 2002, Edinburgh, Scotland, August, 2002. (ABiALs 2002). Butz V. M., Sigaud, O. and Gérard, P. (eds.) Anticipatory Behavior in Adaptive Learning Systems. Springer, Lecture Notes in Artificial Intelligence, 2684:262-281. (

Edmonds, B. (2002) Learning and Exploiting Context in Agents. Proceedings of the 1st International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), Bologna, Italy, July 2002. ACM Press, 1231-1238. (

Edmonds, B. (2001) Learning Appropriate Contexts. In: Akman, V. et. al (eds.) Modelling and Using Context - CONTEXT 2001, Dundee, July, 2001. Lecture Notes in Artificial Intelligence, 2116:143-155. (

Manchester Complexity Seminars:

There is a loosely connected series of informal seminars in manchester most fridays.  See seperate web page for details:

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