We propose a social coherence-based model and simulation framework to study the dynamics of multi-agent organizations. This model rests on the notion of social commitment to represent all the agents’ explicit inter-dependencies including roles and organizational structures. A local coherence-based approach is used that, along with a sanction policy, ensures social control in the system and the emergence of social coherence. We illustrate the model and the simulator with a simple experiment comparing two sanction policies.

1. Towards a Model of Social Coherence
In Multi-Agent Organizations
Erick Martínez
Ivan Kwiatkowski
Philippe Pasquier
{emartinez, pasquier}@sfu.ca

2. Contributions
● Model
● Operational model where agent behaviour is driven by tractable
coherence calculus
● Local coherence-driven agent behaviour drives the dynamics of
multi-agent organizations; from where social coherence emerges
● Local coherence calculus of agents incorporates sanction policies
● Implementation
● Java-based simulation framework for studying the dynamics of
social systems
● Experiments
● We illustrate our model by running some preliminary experiments,
and contrasting two different sanction policies
{emartinez, pasquier}@sfu.ca 2 / 21

3. Social Modelling
Role =〈 Actions , SocCommitmentSchema〉
Ag =〈 Roles Ag , Agenda , Prob Ag action〉
Org =〈 Roles , Agents , f assign : Agents Roles〉
Customer
Tech
Cook Delivery
Pizzeria
(Organization) Pizza Delivery Example
{emartinez, pasquier}@sfu.ca 3 / 21

4. Actions & Exogenous Events
Actions: Exog. Events:
● Performed by agents ● Not necessarily performed
by agents
● Discreet, instant-based,
sequential model of time ● Periodicity > 0, max.
period within which the
● Duration time > 0
event will occur once
〈 orderPizza  , 1〉
x
〈 cleanOven , 5〉
x exog
〈 becomeHungry  , 5〉 x
〈 repairOven  , 30〉
x
〈 makeOvenDirty exog  , 100〉
x
〈 cookPizza  , 7〉
x
〈 deliverPizza , 20〉
x 〈 breakOvenexog   , 200 〉
x
〈 payOrder   , 1〉
x
{emartinez, pasquier}@sfu.ca 4 / 21

5. Relationships Between Actions
Relationships inspired by TÆMS' taxonomy [Hörling et al., 1999]
exog
breakOven  
x disables cookPizza  
x
exog
becomeHungry    x enables orderPizza   
x
makeOvenDirty exog   
x hinders cookPizza   
x
cleanKitchenVent   
x facilitates cookPizza  
x
orderPizza  
x enables cookPizza 
x
cookPizza  
x enables deliverPizza  
x
deliverPizza  
x enables payOrder  
x
cleanOven 
x disables cookPizza 
x
repairOven 
x disables cookPizza 
x
{emartinez, pasquier}@sfu.ca 5 / 21

6. Social (Action) Commitments
● Oriented responsibilities
contracted by debtor
towards creditor
● Dynamics formalized as
a finite state machine
(FSM) [Pasquier et al., 2006]
● Commitments can be
manipulated (state /
transitions)
{emartinez, pasquier}@sfu.ca 6 / 21

7. Social Commitment Schema (SCS)
Role =〈 Actions , SocCommitmentSchema〉
action0  SC debtor , creditor , action1, duration , Sanctions debtor , Sanctions creditor 
orderPizza     SC cook , delivery , cookPizza   , 8, S cok , S delivery 
x x
exog
breakOven    SC tech , cook , repairOven  , 31, S tech , S cook 
x x
makeOvenDirty exog     SC cook , tech , cleanOven  , 6, S cook , S tech 
x x
Role Cook
{emartinez, pasquier}@sfu.ca 7 / 21

8. Pizza Delivery (SCS) Work-flow
exog
becomeHungry    SC customer , cook , orderPizza  , 2, S customer , S cook 
x x
orderPizza   SC cook , delivery , cookPizza  , 8, S cook , S delivery 
x x
cookPizza    SC delivery , customer , deliverPizza  , 21, Sdelivery , S customer 
x x
deliverPizza   SC customer , delivery , payOrder   , 2, S customer , Sdelivery 
x x
breakOven exog    SC tech , cook , repairOven , 31, Stech , Scook 
x x
makeOvenDirty exog     SC cook , tech , cleanOven   , 6, Scook , Stech 
x x
exog
becomeHungry    orderPizza   ...
x x Main work-flow
cookPizza    deliverPizza   ...
x x captured by SCS
payOrder   
x
{emartinez, pasquier}@sfu.ca 8 / 21

9. Instantiated Soc. Commitments (ISC)
orderPizza    SC cook , delivery , cookPizza   , 8, S cook , S delivery 
x x
[t inst , t inst  duration]
ISC Yves : cook , Tom : delivery , deliverPizzaα i , [ 13, 21] , {0, 0, 0}yves , {0}tom 
breakOven exog     SC tech , cook , repairOven  , 31, S tech , S cook 
x x
ISC  Lee : tech, Yves : cook , repairOvenα j , [ 18, 49] , {0, 0, 0}lee , {0}yves 
{emartinez, pasquier}@sfu.ca 9 / 21

10. Social Control Mechanisms
● Sanction-based: positive & negative incentives, decided
a priori, static, centralized enforcement, applied at the
time of violation
● Sanctions are embedded into the life-cycle of social
commitments, e.g.,
Sanctions Creditor
Sanctions Debtor
ISC Yves : cook , Tom : delivery , deliverPizza α i , [13, 21], {0 F , −1 C , −1V } yves , {0C }tom 
F C V C
ISC  Lee : tech , Yves : cook , repairOvenα j , [18, 49] , {1 , −1 , −1 }lee , {−1 } yves 
{emartinez, pasquier}@sfu.ca 10 / 21

11. Sanction Policy
● Determines what
sanction gets associated
to what transition
σ SC : T  [−1, 1]
T set of transitions FSM
V CD CC F
σ SC t ={s t=5 ,s t= 2 ,s t =2 ,s t =7 }
{emartinez, pasquier}@sfu.ca 11 / 21

12. Constraint Between ISC
● Constraints over ISC generated automatically from
relationships between actions and time interval
overlapping between ISC
Hard constraints Soft constraints
Disabling (w = 3) Hindering (w = 1)
Overlapping (w = 2.5) Facilitating (w = 1)
Enabling (w = 2)
F C V C
ISC 0 Yves : cook , Tom: delivery , deliverPizzaα i  , [13, 21], {0 , −1 , −1 }yves , {0 }tom 
F C V C
ISC 1  Lee : tech , Yves : cook , repairOvenα j , [18, 49] , {1 , −1 , −1 }lee , {−1 }yves 
repairOven   disables cookPizza  
−
x x  generates neg.constraint C ISC 1, ISC 0 
{emartinez, pasquier}@sfu.ca 12 / 21

13. Time Overlap Constraint (ISC)
● Agent's level of activity: # of accepted ISCs in its
agenda at any given time
● Agents cannot do more than one thing at the time
F C V C
ISC 0 Yves : cook , Tom: delivery , deliverPizzaα i  , [ 13, 21] , {0 , −1 , −1 }yves , {0 }tom 
F C V C
ISC 1 Yves: cook , Liz : delivery , deliverPizza α j , [ 18, 26] , {0 , −1 , −1 }yves , {0 }liz 
ISC 0 « ISC 1 time overlapping constraint
{emartinez, pasquier}@sfu.ca 13 / 21

14. Coherence Degree
● ISCs can have weighted constraints between them
● An agent will do constraint optimization over the network
of ISCs (agenda) it's involved in
● Coherence degree: total weight of satisfied constraints
between ISC in agent's agenda, divided by total weight of
overall constraints
CoherenceDegree  Agenda = ∑ Weight  x , y  / ∑ Weight  x , y
 x , y∈Sat  Agenda  x , y∈Con Agenda
{emartinez, pasquier}@sfu.ca 14 / 21

15. Expected Utility Function
● The expected utility for an agent to attempt to reach state
W' from state W (which only differs by the change of state
of a single ISC x)
G W '  = CoherenceDegree W '  − CoherenceDegreeW  − ResToChange x , T 
where : ResToChange x , T  ≡ − σ SC T  Sanction
Policy
● For now, no probabilities. Decision making is myopic as
agents only consider cancellation penalties
● Utility function can be improved by incorporating
uncertainty. E.g., considering probability of failure,
rewards & penalties
{emartinez, pasquier}@sfu.ca 15 / 21

16. Social Coherence
● In order to maximize the coherence degree of its agenda
(i.e., ISCs) an agent tries to do constraint optimization
● Agent cycle: 1. Calculate CoherenceDegree  Agenda
2. For each active ISC x do
3. Calculate utility of flipping ISC x
4. End For
5. Return ISC x with higher utility gain if any 
● Recursive local search algorithm, no backtracking, worst-case
complexity is polynomial: O(mn2)
● n is the # of ICs
● m is the # of constraints between ICs
{emartinez, pasquier}@sfu.ca 16 / 21

17. SC-JSim Simulator
{emartinez, pasquier}@sfu.ca 17 / 21

18. Experimental Setting
● Pizza delivery organization, with 4 agents:
1 cook, 2 delivery, & 1 technician; plus several customers
● Simulation parameters: periodicity & sanction policy
●
Changed periodicity of event <becomeHungryexog(x), p>, with
p = 80, 40, 20, 10, 5, 2, 1 time steps → increases frequency
of orders
● Two sanction policies:
SPol 0  S debtor = {0 F , 0C , 0V }; S creditor = { 0C }
SPol 1  S debtor = {0 F , −1C , −1V }; S creditor = { −1C }
● Metric: overall % of ISCs fulfilled (efficiency)
{emartinez, pasquier}@sfu.ca 18 / 21

19. Observations
Observation 1. Desirable Observation 2. The Observation 3. Under SPol1
agent behaviour results from efficiency of the organization the organization was more
local coherence degraded from nearly efficient than without any
maximization. Macro-level optimal as frequency of sanctions (i.e., Spol0). This is
social coherence does orders and agent's level of
emerge from local activity was increased. because the sanction policy
coherence maximization. acts as deterrence for easy
cancellations.
{emartinez, pasquier}@sfu.ca 20 / 21

20. Future Work
● Model (extensions):
● Introducing uncertainty reasoning into the coherence
calculus; reasoning about time and actions
● Modelling agents with no knowledge, with partial knowledge,
or with complete/shared knowledge
● Machine learning mechanisms would allow agents to
progressively learn these probabilities
● Experiments:
● Impact of different organizational structures (e.g.,
hierarchies, holarchies, societies, federations)
● Investigate other sanction policies
{emartinez, pasquier}@sfu.ca 20 / 21

21. Acknowledgements
● National Sciences & Engineering Research
Council of Canada (NSERC)
● Marek Hatala (SIAT, SFU)
● Anonymous Reviewers
{emartinez, pasquier}@sfu.ca 21 / 21