frédéric amblard, nils ferrand cemagref lisc 29 march 2000 * thanks to n. jonard for comments

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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

F. Amblard , N. FerrandCemagref LISC

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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


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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)


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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


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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…


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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 ?


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Two related networks…

Institutions

Field


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The decision cycle

Information& Influence

Vote fordelegation

Institutional networkstructuring

Institutionalnegotiation

Applyingregulation

Environment

deci

sions

practices

constraints trust


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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.

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Information & Influence

Back from IMAGES project


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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)


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Decision dynamic

1. Choose randomly an actor

2. Choose one of his accointance using a trust dependent probability law

3. Activate an averaging interaction for one dimension of opinion

(or any other…)

Many times

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Vote for delegation


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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

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Institutional network settling


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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

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Institutional negotiation

Refering to RUG-ICS research


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Exchange model (Stokman & Van Oosten)

Actors discuss various issues simultaneosly cf. political negotiation

Ex : 2 actors & 2 issues P1(O/N) & P2(O/N)

P1 : O

P2 : OP1 : N

P2 : N

P1 : O

P2 : NP1 : O

P2 : N


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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


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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

iejdid

jeiejd O

ss

ssO ##

Global dynamics

acteur1

acteur2

P1P2…P8

P2P7…P1

Liste de problèmes

1.(P1,P8)2.Propose_échange(P1,P8)

3.Évalue (P1,P8)

4.Echange (O/N) => A2 donne engagement

6.ok/pas ok !!!!

5.Evalue les offres par paquet…


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Dialogue entre acteursA1 : (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…

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Implementing decision


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Evaluation of the decision

The decision taken is a set {O*k}

1. Opinion reassessment (applying rule)• « high salience low opinion change» Oik = (1-Sik).(O*k – Oik)

2. Trust reassessment• If the k-delegate (j) won (O*k Oik), Tij trust

strenghtens

• If he lost (O*k Oik), Tij trust lower

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Discussion


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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 !


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What we actually did…

Information& Influence

Vote fordelegation

Institutional networkstructuring

Institutionalnegotiation

Applyingregulation

constraints


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Comments

No environment model Limited social dynamics

– Only delegation and deception– No field actors restructuring– No institutional structure as such


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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


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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

frédéric amblard, nils ferrand cemagref lisc 29 march 2000 * thanks to n. jonard for comments

Documents

decision dynamicchoose

structural change decision

actor i votes

social interactions

peersother networks

influence process

interaction influence

negotiation theory