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Making sense of a complex world Chris Budd. Much of natural (and human!) behavior appears complex and hard to understand. Rocks underground. Atmosphere and climate. El Nino. Turbulence. Flocking. Geology. Complex designs. Aircraft undercarriage. Photonic crystals. Human behavior.
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Making sense of a complex world Chris Budd
Much of natural (and human!) behavior appears complex and hard to understand Rocks underground
Atmosphere and climate El Nino
Turbulence Flocking Geology
Complex designs Aircraft undercarriage Photonic crystals
Human behavior Stock markets Crowds
What do we mean by a complex system? Many components with individual behavior Nonlinear Coupling between components Many different scales in space and time • The weather.. Air, oceans, sun, CO2 • The earth .. • Disease spread .. People, viruses, pollutants
Human body Stomach Small intestine: 7m x 1.25cm Intestinal wall: Villi and Microvilli
Can scientists, mathematicians and engineers make any sense of complexity? And can we use this knowledge to our advantage?
Traditional view Things are complicated because there are lots of independent things all going on at once
Example: The tides a complicated system which isn’t complex Bombay tides 1872 h(t) t
Kelvin decomposed h(t) into 37 independent periodic functions Kelvin calculated the coefficients using past data andadded them up using an analogue computer
Kelvin’s Tidal predictor US Tidal predictor
In the tides we seecomplicatedbehavior due to alarge numberof independentuncoupledsystems combining their effects The tides are a resultant property of this combination But many examples of complexity in nature are not like this!
The Double Pendulum .. An example ofcomplex behaviorin a simple coupled system Motion can be • Periodicin phase :predictable • Periodicout of phase : predictable • Chaotic :unpredictable
Newton’s laws apply to the double pendulum! Angle of top part Angle of bottom part
Each part of the system is relatively simple, with easy to understand behavior It is the coupling which leads to new complex emergent behavior In this case chaotic motion
Motion of the asteroids is chaotic: will the human race survive?
Emergence .. A property of a complex system which is more than the sum of its parts Emergence arises from the way that the components interact with each other and not just from their individual properties
Emergent properties of complex systems can allow us to make predictions and even to new designs • Emergent Properties Include • Coherent Patterns .. Exotic macroscopic behavior • Scaling laws • Understandable behavior ‘in the large’
Emergent Patterns often arise because of the way that things interact and communicate with each other Flocking BZ reaction Slime mould Can often describe using differential equations
Patterns in rocks Singularity
Microstructure of a real technical ceramic. Al2O3-TiO2 R TiO2 C Al2O3
Frequency Conductivity
The ac conductivity of 255 2D squae networks randomly filled with 512 components 60% 1 k resistors & 40% 1 nF capacitors PERCOLATION DETERMINED DC CONDUCTIVITY POWER LAW EMERGENT PROPERTY Frequency Random percolation Conductivity Emergent scaling law
An emergent scaling law If a is something we can measure b is something that changes They are related by an equation of the form
A very complex example .. The H Bomb r: Radius of fireball E: Energy of the bomb t: Time after the explosion Scaling law G I Taylor
Homogeneous system We see examples of scaling laws in many other complex systems: • The Internet • Networks of friends • Disease • Mechanical systems • Protein and gene interactions • Porous media
This is VERY useful for environmental predictions Scaling law allows us to make calculations at a finer scale than any computational mesh These computations are important in understanding the transport of pollutants underground overlong times
Bringing this all together … forecasting the weather The atmosphere/ocean is a very complex system with many length and time scales
Need to make predictions but … • System has far more degrees of freedom than data • Small scale behavior is very can be chaotic • Small and large scales interact • Lots of random events Turbulence • Computations are hard!
Make use of all of the previous ideas to improve predictability Scaling laws indicate howenergy is transferred from small to large scalesand fromsmall heights to large heightswhich allows us to greatly speed up computations Can fit expected patterns of weather such as depressions and fronts to the sparse data to start and monitor computer weather forecasts allowing for uncertainty Data assimilation Homogenisation Stochastic
Complexity .. May apply to many many other problems Where many things interact with each other • Spread of disease • Customer behavior • Transport networks • Chemical reactions Much still to be discovered!!!
The BICS team: Darryl Almond, Chris Bowen, Nick Britton, Chris Budd, Guler Ergun, Ivan Graham, Giles Hunt, Merilee Hurn, Ilia Kamotski,Vladimir Kamotski, Jan van Lent, Ann Linfield, Nick McCullen, Cathryn Mitchell, Ruth Salway, Rob Scheichl, Hartmut Schwetlick, Valery Smyshlyaev, Chris Williams, Johannes Zimmer