Adaptive Traffic and Dynamical Networks: From Plants to People Methods: agents (i.e. decision-making particles) + networks (i.e. connectivity) empirical + simulation + analytics Empirical: Dynamical evolution of nutrient networks in fungal and slime-mold systems AVMs and supply networks in the brain Angiogenesis in cancer tumour growth Virus spreading on networks Supply chains, flow of cases in judicial systems Flow of rumours around FX currency markets Issues: Flow of objects on network: group formation & crowding, congestion Feedback onto network structure: structure vs function What is ‘best’ ? optimal, fault tolerant or just ‘good enough’ centralized vs decentralized competitive vs. cooperative How to control, manage, design ? complexity_splash_2003.asp Mark Fricker, Paul Summers, Pak Ming Hui, Charley Choe, David Smith, Chiu Fan Lee, Tim Jarrett, Sean Gourley, Neil Johnson

N C P C N

Constructing an agent-based fungus.. so function drives structure

Organism ‘develops’ ability to walk/hunt/forage Issues: localization-delocalization, efficient vs. adaptable

...should I try a short-cut through the centre?

If cost c of using central hub is non-linear, we find: Abrupt changes in optimal network structure, which are induced by small changes in c Diverse set of structurally inequivalent networks, which are functionally equivalent

centralized vs decentralized ? Phys. Rev. Lett. 2005

If cost c of using central hub is non-linear, we find: Abrupt changes in optimal network structure, which are induced by small changes in c Diverse set of structurally inequivalent networks, which are functionally equivalent

Efficient, but deadly.... cancer: angiogenesis brain: AVM, aneurysm

updated history at time t S history at time t agent memory m = 2 action +1 action f d b e c a global outcome 0 or 1 f d b e c a strategy space

Evolutionary Minority Game (EMG) Phys. Rev. Lett. ‘99 agent’s strategy/‘gene’ p mutates agent’s performance or ‘wealth’ time distribution of agents 0.5 steady state self-organized segregation into Anti-crowds and Crowds p = probability agent follows common information: past history, news, rumor (right or wrong) p p

coin-toss basic Minority Game (MG) crowding/congestion reduced by - acting ‘dumb’ - choosing second-best - mis-information - heterogeneity in abilities, e.g. m  agent memory m hybrid EMG typical fluctuation size Evolutionary Minority Game (EMG) Phys. Rev. Lett. ‘99

General global resource level L (e.g. # seats) A(t) L N = 100 agents wish to access resource (e.g. attend bar) L ‘freezing’ of evolution A(t) = L A(t)

system’s time evolution Phys. Rev. E (2004) Distribution of duration of extreme large changes in variable-N evolutionary MG -4 ∆ t largest changes/crashes are ‘different’ -7

history at time t S agent memory m = 2 S f d b e c a histories strategies f d b e c a action +1 action history at time t global outcome 0 or 1

random crowd - anticrowd pairs execute uncorrelated random walks sum of variances   walk step-size # of walks typical fluctuation size Minority Game: Each agent has s=2,3,4,.. strategies with memory-length m histories

Hub Capacity L=40 Add in agent decision-making....

K4