Spatial simulation of complex adaptive systems

why “agents” only cannot do the job

Arnaud Banos (International mobility support 2016 from InSHS-CNRS)

Globally addicted tocomplex systems

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

Party game

AA BB

Strategy 1

YOUYOUAfter Eric Bonabeau https://hbr.org/2002/03/predicting-the-unpredictable

Party game

AA BB

Strategy 2

YOUYOUAfter Eric Bonabeau https://hbr.org/2002/03/predicting-the-unpredictable

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

?

?

ABM can help a lot!

Fiegel, Banos, Bertelle, 2009

Canned meat

Urban ant-hill

Fosset, Banos, et al. 2016

LEZ ⇒ + 8,3 % exposure to PM10 (average emission rate weighted by living population)

Possible impact of LEZ in Grenoble

Fosset, Banos, et al. 2016

http://www.deviantart.com/

« There is no one best way! »

Herbert SimonNobel Prize Economy 1978

Turing Prize 1975

Spatial simulation of complex adaptive systems

why “agents” only cannot do the job

http://quotesgram.com/agent-smith-quotes-about-purpose/

1- a FEW agents may help revealing:

● Intentionality● Preferences● Constraints and adaptation to constraints● Cooperation● Strategies● And so much more !

SMArtAccess

A = Work

B = Universal Service

C = Commercial Service

D = Home

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 V f

Rules

 

Vi =V f i e-a

n iC i

æ

è ç

ö

ø ÷

Creating cities from scratch

Air pollution

Multi-Agents & Multi-Actors (M2A2S)

M2A2S

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

M2A2S

Objective functions

« Economic »Max #consumers

Max population coverage for US

« Citizen » Min unhappiness

(accessibility, traffic, pollution)

« Public » Min congestion

Max public transportMin air pollution

(concentration and exposure)

« Global »Sustainable city!

Collaborative Game PAMs

Cooperation

2- agents are not always relevant

● Efficiency (computation time)● Scale of the processes

➔Model Coupling

2- agents are not always relevant

● Efficiency (computation time)● Scale of the processes

➔Model Coupling

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)

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 =V f i e-a

n iC i

æ

è ç

ö

ø ÷

https://www.researchgate.net/publication/258397885_Formation_and_Propagation_of_Local_Traffic_Jam

Road traffic modeling

« Ring City »

Banos et al., under press

Hybrid Micro/Macro Model

Road traffic modeling

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

2- agents are not always relevant

● Efficiency (computation time)● Scale of the processes

➔Model Coupling

Epidemic spread

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

Macro VS micro

Banos et al., 2016

Macroscopic approach

SIR macro model

→ Metapopulation model

Node = CityEdge = Flight connection

==> Mobility rate « g »==> Probability of Flowij « mij »

Banos et al., 2016

Model CouplingNode = city ==> SIREdge = flights and passengers (agents)

Banos et al., 2016

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

Containment strategy: quarantine

. Metapopulation model

. MicMac model (100 replications)Banos et al., 2016

Risk culture (not travelling if infected)

. Metapopulation model

. MicMac model (100 replications)Banos et al., 2016

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

Collaboration with Mr Robert

Data-scarce context → be SMART!

Data-scarce context → be SMART!

Topography + Gravity + Active Particles

Data-scarce context → be SMART!

Topography + Gravity + Active Particles+ Dynamic Lanscape

Data-scarce context → be SMART!

Topography + Gravity + Active Particles+ Dynamic Lanscape + Dynamic Rivers

Data-scarce context → be SMART!

Topography + Gravity + Active Particles+ Dynamic Lanscape + Dynamic Rivers + Dynamic Particles/Rivers interactions

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 ?

Incremental complexification of models

● 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