1 / 30

Frédéric AMBLARD, Nils FERRAND Cemagref LISC 29 March 2000 * Thanks to N. Jonard for comments

Games driven regulation of agents population Application to natural resources dynamics and management policies. Frédéric AMBLARD, Nils FERRAND Cemagref LISC 29 March 2000 * Thanks to N. Jonard for comments. Background applications.

uriel-middleton
Download Presentation

Frédéric AMBLARD, Nils FERRAND Cemagref LISC 29 March 2000 * Thanks to N. Jonard for comments

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Games driven regulation of agents populationApplication to natural resources dynamics and management policies Frédéric AMBLARD, Nils FERRAND Cemagref LISC 29 March 2000 * Thanks to N. Jonard for comments

  2. Background applications • Public policies negotiation support using simulation of their effects on environment and population • Agri-environment (FAIR-IMAGES + Deffuant, Gilbert, Weisbuch) • Landscape dynamics (+ Lifran, Lardon, Antona…) • Water basin management (EVK-FIRMA, others + Moss, Gilbert, Conte, Barreteau, Attonaty, Rio…) • Strong relation with users : stakeholders F. Amblard , N. FerrandCemagref LISC

  3. Our main focus… Social networks & decision • decision = individual utility + social influences • Which social interactions & influences ? • Correlate social structure & decision • Decisions to change social networks ? • Induce structural change  decision ? • « KISS » & decreasing abstraction (Liendenberg) F. Amblard , N. FerrandCemagref LISC

  4. Two (3 ?) levels • (environmental dynamics) • Field actors (farmers, foresters, fishers, etc) : look after individual interest, act directly on the environment, choose practices, discuss, apply regulations • Institutional actors (admin., elected, NGOs) : look after the « common » goods, discuss regulations F. Amblard , N. FerrandCemagref LISC

  5. And two different processes • Protracted (year wise) information and influence process between field actors • Settling new local norms within social groups (cliques)  GERDAL (Darré & co) • We need an interaction & influence theory for deliberating individuals… • Point wise meetings and negotiations between institutions • Reaching an agreement on management policies • We need a negotiation theory for groups… F. Amblard , N. FerrandCemagref LISC

  6. With specific networks • Networks of field actors • Professional networks : « peers » • Other networks : « friends » • Networks of institutional actors • Field representatives ( field) & others • NB : we assume homogeneous hierarchical actors within intitutional groups • Are they related ? F. Amblard , N. FerrandCemagref LISC

  7. Two related networks… Institutions Field F. Amblard , N. FerrandCemagref LISC

  8. The decision cycle Game theory Institutionalnegotiation Institutional networkstructuring decisions Environment Applyingregulation Vote fordelegation practices Multi-agent Information& Influence constraints trust F. Amblard , N. FerrandCemagref LISC

  9. The short story… I am a farmer. I discuss the « way of doing » with others. I make my mind while I am working on fields. I agree with some peers for whom I accept to vote. They represent me in meetings. They try to defend my interest. Some decisions are taken there, which change my constraints. Sometimes I am happy, sometimes not… And it goes on. F. Amblard , N. FerrandCemagref LISC

  10. Information & Influence Back from IMAGES project

  11. Model of field actor • KISS !!! • Opinion = {(oik,sik)}k=1..M(0 <Oik< 1, random init) • Opinion does NOT depend on environmental state (not this time) • Network = {(Aj,{Tik}k)}j « trust structure» • Initialise from reasonable assumptions between O and T (clusters of opinions) F. Amblard , N. FerrandCemagref LISC

  12. Decision dynamic • Choose randomly an actor • Choose one of his accointance using a trust dependent probability law • Activate an averaging interaction for one dimension of opinion (or any other…) Many times F. Amblard , N. FerrandCemagref LISC

  13. Vote for delegation

  14. Who are the opinion leaders ? • Let’s vote issue by issue : • At one time in process, interactions stop • For each issue k : • Each actor i votes • If the issue is important for him : sik > s° • for his relationship j that : • Is sufficiently trustable : Tijk > T° • Minimises opinion distance : |Oik – Ojk| • For each actor, we sum the « received trust » • The R best actors are delegated • R delegates by issue F. Amblard , N. FerrandCemagref LISC

  15. Institutional network settling

  16. The delegate becomes institution • He keeps his opinion • For the issue about which he has been chosen, he gets the average salience of his voters • For the other issues, he keeps his own salience F. Amblard , N. FerrandCemagref LISC

  17. Institutional negotiation Refering to RUG-ICS research

  18. P1 : O P1 : O P2 : O P2 : N  P1 : N P1 : O P2 : N P2 : N Exchange model (Stokman & Van Oosten) • Actors discuss various issues simultaneosly  cf. political negotiation • Ex : 2 actors & 2 issues P1(O/N) & P2(O/N) F. Amblard , N. FerrandCemagref LISC

  19. Stokman & Van OostenThe exchange condition • « I accept to exchange a position that is less important against one that is more » Calculus of the exchange utility • EU(i,j)(d,e) = Ui(d,e) + Uj(e,d) • Uk(d,e) = Skd - Ske F. Amblard , N. FerrandCemagref LISC

  20. Stokman & Van OostenModel dynamics • Possible exchanges are evaluated • For each exchange, the utility gain is calculated • Exchanges are realized one by one, by decreasing utility order • The exchange rate is • Until stabilizing the model • All actors vote on all the issues F. Amblard , N. FerrandCemagref LISC

  21. 6.ok/pas ok !!!! 2.Propose_échange(P1,P8) 3.Évalue (P1,P8) 1.(P1,P8) 5.Evalue les offres par paquet… 4.Echange (O/N) => A2 donne engagement Global dynamics P2 P7 … P1 P1 P2 … P8 acteur1 acteur2 Liste de problèmes

  22. Dialogue entre acteurs A1 : (à A2) je veux un soutien pour P1... A2 : pour P1 ? En échange d ’un soutien pour P4 alors ! (P4 étant le problème le plus important pour A2) A1 : (pour lequel P4 est un problème plus important que P1 et qui diverge de la position de A2)… Non pas P4… je veux un soutien pour P1 contre pas P4… A2 : un échange de P1 pour P6 alors ? (P6 étant le second problème le plus important) La voix Off : mais comment sais-tu qu ’il est contre toi sur P6 ? A2 : parce que sur chaque problème je connais mes adversaires et que je cherche à les faire passer dans le camp amis… voix Off (à A1) : et comment choisis-tu les agents à qui tu proposes d ’échanger une position ? A1 : parcequ ’il apparaissent deux fois dans mes listes d ’adversaires, il a donc quelquechose à m ’apporter (sa position sur un pb) et j ’ai quelquechose à lui échanger (ma position sur un autre pb), voix Off :(a A2) ta réponse est positive si ton ordre entre les deux pb proposés est différent de celui de A1 alors ? A2 : oui c ’est ça… voix Off (à A1): et comment fais-tu pour choisir un agent plutot qu ’un autre ? A1 : je regarde mes problemes dans l ’ordre de preference inverse et pour chaque probleme je prend les agents dans l ’ordre ou ils sont, ensuite, je prend la liste par le bas et j ’essaye de retrouver cet agent sur un pb moins important, si je ne le trouve pas je passe a l agent suivant, si l ’échange n ’est pas interdit alors je le propose, si il est interdit, j ’essaye de retrouver l ’agent plus loin dans ma liste de problèmes… Voix Off (à A2): je ne comprend pas, échange interdit ??? A2 : si l ’échange qu ’il me propose ne me convient pas (son ordre de préférence est le même que le mien) alors je lui dis que je ne suis pas interessé par cet échange et il ajoute cet échange à sa liste d ’échanges interdits pour moi… F. Amblard , N. FerrandCemagref LISC

  23. Implementing decision

  24. Evaluation of the decision • The decision taken is a set {O*k} • Opinion reassessment (applying rule) • « high salience  low opinion change» • Oik =  (1-Sik).(O*k – Oik) • Trust reassessment • If the k-delegate (j) won (O*k  Oik), Tij trust strenghtens • If he lost (O*k  Oik), Tij trust lower F. Amblard , N. FerrandCemagref LISC

  25. Discussion

  26. Implementation • Influence & information model tested solely within IMAGES (agri-environment) framework • Institutional (Stokman & Van Oosten) model tested under Cormas (99) • Interrelation still to be done… • Keep it tractable ! F. Amblard , N. FerrandCemagref LISC

  27. What we actually did… Institutionalnegotiation Institutional networkstructuring Applyingregulation Vote fordelegation Information& Influence constraints F. Amblard , N. FerrandCemagref LISC

  28. Comments • No environment model • Limited social dynamics • Only delegation and deception • No field actors restructuring • No institutional structure as such F. Amblard , N. FerrandCemagref LISC

  29. Conclusion • A two levels, two time steps model for institutions & field process • Game theory like model for institutions • Mimetic influence model for field actors • Social restructuring and delegation • Very difficult to get data about social nets & influence processes • No minutes of institutional meetings • Using questionnaires F. Amblard , N. FerrandCemagref LISC

  30. Stokman & Van OostenRemarques • Les gains d ’utilité réels peuvent être différents des gains d ’utilité estimés • Si on veut réaliser les conditions de la rationalité parfaite alors on détermine l ’échange de gain d ’utilité maximum, on l ’exécute puis on recommence F. Amblard , N. FerrandCemagref LISC

More Related