"Spatial simulation of complex adaptive systems: why “agents” only cannot do the job" Arnaud Banos, Visiting Professorial Fellow presented a summary of his research as part of the SMART Seminar Series on 23 September 2016. For more information, visit the event page at: http://smart.uow.edu.au/events/UOW221450.html

1. Spatial simulation of complex
adaptive systems
why “agents” only cannot do the job
Arnaud Banos
(International mobility support 2016 from InSHS-CNRS)

2. Globally addicted to
complex systems

3.  A large number of localized interacting entities,
operating within an environment
 These entities, being human or not, act and
are influenced by the local environment and
interaction network they are situated in
 While the behaviour of these entities may be
inspired, guided or limited by upper structures,
they are not directly controlled but operate (at
least partly) on their own, having some “self-
control” over their actions and internal states Non coordinated but interdependant local actions
 Emergence of global structures
Non coordinated but interdependant local actions
 Emergence of global structures
Eric Daudé
Complex Adaptive Spatial Systems

4. Party game
AA BB
Strategy 1
YOUYOU After Eric Bonabeau
https://hbr.org/2002/03/predicting-the-unpredictable

5. Party game
AA BB
Strategy 2
YOUYOU
After Eric Bonabeau
https://hbr.org/2002/03/predicting-the-unpredictable

6. Micro vs Macro
 Difficult to guess macro from
micro in presence of interactions
 SIMULATION
 Difficult to guess micro from
macro  reconstruction
(SIMULATION)
http://www.cartoonstock.com/directory/f/flight_simulator.asp
?
?

7. ABM can help a lot!

8. Fiegel, Banos, Bertelle, 2009
Canned meat

9. Urban ant-hill
Fosset, Banos, et al. 2016

10.  LEZ ⇒ + 8,3 % exposure to
PM10 (average emission rate
weighted by living population)
Possible impact of LEZ in Grenoble
Fosset, Banos, et al. 2016

11. http://www.deviantart.com/

12. « There is no one best way! »
Herbert Simon
Nobel Prize Economy 1978
Turing Prize 1975

13. Spatial simulation of complex
adaptive systems
why “agents” only cannot do the job
http://quotesgram.com/agent-smith-quotes-about-purpose/

14. 1- a FEW agents may help revealing:
●
Intentionality
●
Preferences
●
Constraints and adaptation to constraints
●
Cooperation
●
Strategies
●
And so much more !

15. SMArtAccess
A = Work
B = Universal
Service
C = Commercial
Service
D = Home

16.  D choosen randomly
 A choosen randomly with proba p
 Fixed trip chain: D ⇒ A ⇒ B ⇒ C ⇒ D
 Chain =
 Traffic = Deterministic Single-Regime
speed-Density (Underwood):
min(T = TD,A + TA,B + TB,C + TC,Då ), cc Vf
Rules
Vi = Vfi
e
-a
ni
Ci
æ
è
ç
ö
ø
÷

18. Creating cities from scratch

19. Air pollution

20. Multi-Agents & Multi-Actors (M2
A2
S)

21. M2
A2
S
Cooperative game: no competition but
individual and collective objectives
« Economic »
work places, universal
and commercial services
« Citizen »
home places
« Public »
road network, public
transport, air pollution
restricted areas

22. M2
A2
S
Objective functions
« Economic »
Max #consumers
Max population coverage for US
« Citizen »
Min unhappiness
(accessibility, traffic, pollution)
« Public »
Min congestion
Max public transport
Min air pollution
(concentration and exposure)
« Global »
Sustainable city!

23. Collaborative Game PAMs

24.  Cooperation

25. 2- agents are not always relevant
●
Efficiency (computation time)
●
Scale of the processes
➔
Model Coupling

26. 2- agents are not always relevant
●
Efficiency (computation time)
●
Scale of the processes
➔
Model Coupling

27. Road traffic modeling
VANWAGENINGEN-KESSELSF.,VANLINTH.,VUIKK.,SERGEH.,«Genealogy of traffic
flow models », EURO Journal on Transportation and Logistics, vol. 4, no° 4, p. 445–473,
Springer, 2015
macro
meso
micro
Theoretical developments
Genealogy of trafic models based on the fundamental diagram
Fundamental Diagrams for
Uninterrupted Traffic Flow
(Source: Austroads Guide to Traffic Management
Part 2: Traffic Theory)

28. Road traffic modeling
Banos et al., under press
« Ring City »
Micro: NaSch (acceleration/deceleration based on front vehicle)
Meso: Underwood
Macro: LWR (Lighthill, Whitham, Richards)
Flow
Traffic conservation
Vi = Vfi
e
-a
ni
Ci
æ
è
ç
ö
ø
÷
https://www.researchgate.net/publication/258397885_
Formation_and_Propagation_of_Local_Traffic_Jam

29. Road traffic modeling
« Ring City »
Banos et al., under press
Hybrid Micro/Macro Model

30. Road traffic modeling
Taillandier, Banos, Corson., Coupling macro and micro models to simulate traffic, in progress

31. 2- agents are not always relevant
●
Efficiency (computation time)
●
Scale of the processes
➔
Model Coupling

32. Epidemic spread
http://www.humanosphere.org/global-health/2013/09/unleashing-big-data-against-disease/

33. Macro VS micro
Banos et al., 2016

34. Macroscopic approach
SIR macro model
→ Metapopulation model
Node = City
Edge = Flight connection
==> Mobility rate « g »
==> Probability of Flowij « mij »
Banos et al., 2016

35. Model CouplingNode = city ==> SIR
Edge = flights and passengers (agents)
Banos et al., 2016

36. Mean Field Approach
If we assume:
- Instantaneous trips
- Complete network
- Constant “g” and “mij”
THEN we can calculate :
- MaxI
- TimeOfMaxI
- Duration
BOTH MODELS ABLE TO REPRODUCE THESE VALUES
Banos et al., 2016

37. Containment strategy: quarantine
. Metapopulation model
. MicMac model (100 replications)Banos et al., 2016

38. Risk culture (not travelling if infected)
. Metapopulation model
. MicMac model (100 replications)
Banos et al., 2016

39. Main advantages
 Simple but not too simple models
“Einstein's razor” > “Occam's razor”
 Deepening understanding → coupling
agents and actors (serious games)
 Coupling processes in space and time
and across scales and levels → coupling
models, formalisms and theories

40. Collaboration with Mr Robert

41. Data-scarce context → be SMART!

42. Data-scarce context → be SMART!
Topography + Gravity + Active Particles

43. Data-scarce context → be SMART!
Topography + Gravity + Active Particles
+ Dynamic Lanscape

44. Data-scarce context → be SMART!
Topography + Gravity + Active Particles
+ Dynamic Lanscape + Dynamic Rivers

45. Data-scarce context → be SMART!
Topography + Gravity + Active Particles
+ Dynamic Lanscape + Dynamic Rivers
+ Dynamic Particles/Rivers interactions

46. Data-scarce context → be SMART!
Flooding scenario in Jakarta
(in red, penalized river sections in term of capacity)
Next:
●
Calibration (sensors, models)
●
validation (Remote sensing, sensors)
●
Data assimilation?
●
Serious game?
●
Complexification ?

47. Incremental complexification of models

48. ●
Fiegel J., BANOS A., Bertelle C., 2009, Modeling and simulation of pedestrian
behaviors in transport areas: the specific case of platform/train exchanges,
ICCSA 2009, 29 june-2 july, Le Havre
●
Fosset P., BANOS A., Beck E., Chardonnel S., Lang C., Marilleau M., Piombini
A., Leysens T., Conesa A., André-Poyaud I., Thèvenin T., 2016, Exploring intra-
urban accessibility and impacts of pollution policies with an agent-based
simulation platform: GaMiroD, Systems, 4(1), 5.
●
BANOS A., 2015, The city, a complex system? The new challenges of urban
modelling, in Lagrée S, Diaz V., A glance at sustainable urban development:
methodological crosscutting and operational approaches, pp. 110-119, AFD, Paris
●
BANOS A., Corson N., Marilleau N., Taillandier P., Multi-scale Traffic
Modelling in NetLogo, in BANOS A., Lang C., Marilleau N. (Dir), Agent-Based
Spatial Simulation with NetLogo: Advanced Users, Wiley, London, To be
published
●
Taillandier P., BANOS A., Corson N., Coupling macro and micro models to
simulate traffic, in progress
●
BANOS A. Corson N., Gaudou B., Laperriere V., Rey S., 2015, The importance
of being hybrid for spatial epidemic models: a multi-scale approach,
Systems,3(4), 309-329
References