CORMAS

1. CORMAS COMMON-POOL RESOURCES MANAGMENT AND MULTI-AGENTS SYSTEMS

2. What are we doing ? • Development of an agent-based simulation platform (CORMAS) dedicated to the field of natural resources management • Test of a companion modelling approach about how to use these types of models • Training courses, knowledge transfers

3. Interactions Resource management and environment Social Dynamics Biological Dynamics

4. A definition of complex systems • Definition: • Complex system : a set of elements interacting among them and with the outside : • Distinction between the whole (the system) and the remainder (environment/outer): reliance to the question • Predominance of interactions : more than the sum of the parts. • Other characteristics: • Descriptions at multiple levels (a minima: elements / system) • Emergence • Structures making

5. Approaches of complex systems • Analytical : element by element (neo-classical economy, plot, individual, etc.) • Holistic or systemic : global behaviour of the system (macro-economy, statistics) • Constructivist : articulation between individual behaviours of the elements (local) and the global behaviour of the system (global). Suitable to study ecosystems and sociosystems • Challenge of complexity : situations far from equilibrium • Intelligibility : intuitive description in terms of objects and agents rather than variables and equations

6. Approaches of complex systems (2) • Example: fish reproduction • Analytical : • Behaviour of one fish • Holistic : • Schaefer model • Constructivist : • Each entity of the system is represented (Molecule, cell, plant, animal, group, etc.). • Reproductive behaviour => evolution of the population • Interactions between entities (Modification, creation, destruction) • Local interactions (reproduction with neighbours) => spatially-explicit model

7. Why consider the individual? • Principle of individual uniqueness • Mathematical models assume that individuals are indifferent and interchangeable (exchange of two individuals randomly chosen in the population) • Weakness of the mixing hypothesis: the differences between individuals drive evolution of systems • Principle of located interactions • An organism is mainly affected by the other organisms and by the environmental conditions in its spatio-temporal neighbourhood

8. Why consider the individual?

9. The essence of complexity Interactions and viewpoints

10. Complexity and resources management

11. Postulates • Individual and collective processes dealing with natural resources management are based on: • Shared representations of interactions between stakeholders and the ecosystem. • Towards modelling to : • Articulate multiple viewpoints ; • Articulate multiple levels (from local to global) ; • Allow retrospective and prospective analyses ; • Be understandable by the stakeholders.

12. ? ? ? ? Modelling and collective dynamics Environment (common-pool resources)

13. ? ? Shared representations of interactions between stakeholders and the ecosystem Environment (common-pool resources)

14. An explicit representation of space • Some elements from CORMAS facilities

15. CORMAS: an agent-based simulation framework • Origin: the aggregation of MA models experiments dedicated to the domain of natural-resources management • Cormas is an agent-based simulation framework • It is oriented towards the building of simulation models • a programming environment. • It provides a framework for developing simulation models of coordination modes between individuals and groups that jointly exploit common resources.

16. CORMAS web site Common-pool Resources and Multi-Agent Systems http://cormas.cirad.fr

17. Agents using or managing resources Spatial objects: points of view DB, GIS Cellular automata Spatialised multi-agent system Environment containing resources, a topographic support

18. Each agent builds its own representation of the environment Spatial grid DB, GIS Cellular automata

19. Spatial hierarchy levels • Various ways of segmenting the space • Relations of composition between spatial entities can define several hierarchical levels • On which entity to associate the processes of the dynamics of the landscape ?

20. Spatial hierarchy levels

21. The basic level • The Cell = the spatial entity element • The grid = a network of automatons

22. Van Neumann Moore The basic level: Regular tessellation

23. The basic level: Regular or Irregular tessellation • From GIS (Raster and Vectorial mode)

24. The basic level: Regular tessellation • From GIS data (Raster mode)

25. The basic level: Vectorial mode

26. The aggregation level • Spatial entities as agents’ viewpoints • Aggregation as reification of POV Example of aggregation with minimum size

27. Forester’sviewpoint Shepherd’sviewpoint Herb Shrub Tree Rock Representation of a Mediterraneanforest

28. Elementary entities Cultivated plots Same hierarchy structure for polygonal spatial entities (vectorial mode)

29. Some dynamics are strongly related to a specific hierarchical level Agricultural dynamics defined at the level of the plots Ecological dynamics defined at the basic level

30. generate produce Shapes Natural processes : Human strategies: of growth, dissemination of pasture, grubbing, clearing spatial vegetation objects influence modify spatial dynamics Spatial object dynamics • Spatial index calculation

31. Spatial strategies Level 0 strategies Check Level 1 strategies Clear max Brushwood Let Nature works Fire-break forester compact grassland shepherd farmer Landscape tourism’sprofessional Level 3 strategies shepherd naturalist naturalist forester Biodiversity Level 2 strategies Protect grassland Biggest forest

32. Exemple: Pursuit

33. Applications • Standard models • Game of life (Conway) • ECEC: Evolution of Cooperation (Pepper and Smuts) • SPD (Nowakand May) • SugarScape (Axtell and Epstein)

34. Applications • Applied models • [AWARE] : Agent-based Watershed Analyses for Resource and Economic Sustainability in South Africa (Farolfi). • [AutomateVote] : electoral ballot • [Bohol] : Natural Resource Management of the Municipality of Loon in Bohol, Philippines (Campo). • [BrouteLaForêt] : spatial representations and interactions between individuals, space and society (Bonnefoy). • [Burkina] : soil quality indicators in Burkina Faso (Guillobez). • [CatchScape] : River bassins management in north Thaïland (Becu, Perez, Walker) • [Didy] : multiple uses of a forest ecosystem in Madagascar (Abrami). • [Djemiong] : hunting of wild meat in Cameroun (Le Page, Bousquet and Bakam). • [Dricol] : emergence of resource-sharing conventions (Thébaud and Locatelli). • [Echos] : Economic behaviour analysis of the "Stockbreeding wastewater system" actors at the Reunion Island (Farolfi, Bommel).

35. Applications • Applied models • [FauconColombe] : game theory and prey-predator model (Valeix). • [FiliereRaphia] : raphia marketing system in Madagascar (Herimandimby, Randriarijaona, Bousquet and Antona). • [ForPast] : spatial transformations dynamics of sylvopastoral systems (Lardon and Bommel). • [Gemace] : multiple uses of wetlands in Camargue, France (Mathevet). • [JLB] : spatial transformations dynamics of forest systems (Bonnefoy). • [JuMel] : economic exchanges and emerging organizations (Rouchier). • [Kayanza] : firewood in Burundi (Guizol, Ndikumadengue, Bousquet and Antona). • [MagmaS] : exchange of stock-farm effluents in Reunion island (Martin, Piquet, Le Page and Guerrin). • [Markets] : Assessing the performance of different market institutions in West Africa according to communication systems (Galtier). • [Mejan] : pine encroachment of natural ecosystems in Lozère, South of France (Etienne and Le Page). • [Mobe] : regulation of firewood marketing systems in Niger (Martine Antona).

36. Applications • Applied models • [Nong Chok] : Land use change in a peri-urban area, Bangkok Thailand (Anwar and Borne). • [Orizi] : Small irrigation systems under free management (Perez and Becu). • [Pasteur] : sparse resource sharing by herds in sahelian area (Bah and d'Aquino). • [PlotsRental] : plot renting by individual contracts or by centralized auction system (Bousquet and Le Page). • [Potlatch] : economic exchanges and emerging organizations (Rouchier). • [Sabah] : plantation development among small farmers in Malaysia (Guizol). • [SaintGeorges] : pasture and overgrowing brushwood in a village of Lozère, France (Lieurain). • [Samba] : land use in North VietNam (Boissau, Jean-Castella). • [SavaneAgents] : landscape dynamics, agent-based version (Gautier and Bousquet). • [SeaLab] : homing-like reproductive strategies (Le Page). • [Sinuse] : distributed interactions between an underwater table and its users (Feuillette).

37. Applications • Applied models • [SpatioDyn] : spatial dynamics modelling with GIS and MAS (Bonin and Le Page). • [Spiders] : net building by social spiders, a model from Bourjot and Chevrier. • [Stratagènes] : negotiation for phytogenetic resource local management in Madagascar (Aubert and Le Page). • [SylvoPast] : Sylvopastoral management and wildfire prevention in Mediterranean forests (Etienne and LePage). • [WsErosion] : soil erosion risk and agricultural diversification in a Northern-Thailand watershed • [Zambeze] : land-use dynamics in the Zambeze valley

38. Future … • A community of 200 users • Interactive simulation => RPG and Cormas • Distributed interactive simulation • Towards a “Companion” Modelling Approach

39. Models and users • Passive way : • Simulation models are frequently used in a passive way, presenting only the results of experiments performed with the model. • Sensitivity analysis: • People who experience the system dynamics will yield a better understanding of the model. • Interactive simulation : • In a simulation game like Fishbanks (Meadows, 1989) the players make decisions about fishing strategies and the computer computes the fish catches. The players have only limited control over the environment. • A “Companion” Modelling Approach : • Combining ABM and RpG