# A Random Walk in 10 Dimensions

Physics in high dimensions is becoming the norm in modern dynamics.  It is not only that string theory operates in ten dimensions (plus one for time), but virtually every complex dynamical system is described and analyzed within state spaces of high dimensionality.  Population dynamics, for instance, may describe hundreds or thousands of different species, each of whose time-varying populations define a separate axis in a high-dimensional space.  Coupled mechanical systems likewise may have hundreds or thousands (or more) of degrees of freedom that are described in high-dimensional phase space

In high-dimensional landscapes, mountain ridges are much more common than mountain peaks. This has profound consequences for the evolution of life, the dynamics of complex systems, and the power of machine learning.

For these reasons, as physics students today are being increasingly exposed to the challenges and problems of high-dimensional dynamics, it is important to build tools they can use to give them an intuitive feeling for the highly unintuitive behavior of systems in high-D.

Within the rapidly-developing field of machine learning, which often deals with landscapes (loss functions or objective functions) in high dimensions that need to be minimized, high dimensions are usually referred to in the negative as “The Curse of Dimensionality”.

Dimensionality might be viewed as a curse for several reasons.  First, it is almost impossible to visualize data in dimensions higher than d = 4 (the fourth dimension can sometimes be visualized using colors or time series).  Second, too many degrees of freedom create too many variables to fit or model, leading to the classic problem of overfitting.  Put simply, there is an absurdly large amount of room in high dimensions.  Third, our intuition about relationships among areas and volumes are highly biased by our low-dimensional 3D experiences, causing us to have serious misconceptions about geometric objects in high-dimensional spaces.  Physical processes occurring in 3D can be over-generalized to give preconceived notions that just don’t hold true in higher dimensions.

Take, for example, the random walk.  It is usually taught starting from a 1-dimensional random walk (flipping a coin) that is then extended to 2D and then to 3D…most textbooks stopping there.  But random walks in high dimensions are the rule rather than the exception in complex systems.  One example that is especially important in this context is the problem of molecular evolution.  Each site on a genome represents an independent degree of freedom, and molecular evolution can be described as a random walk through that space, but the space of all possible genetic mutations is enormous.  Faced with such an astronomically large set of permutations, it is difficult to conceive of how random mutations could possibly create something as complex as, say, ATP synthase which is the basis of all higher bioenergetics.  Fortunately, the answer to this puzzle lies in the physics of random walks in high dimensions.

## Why Ten Dimensions?

This blog presents the physics of random walks in 10 dimensions.  Actually, there is nothing special about 10 dimensions versus 9 or 11 or 20, but it gives a convenient demonstration of high-dimensional physics for several reasons.  First, it is high enough above our 3 dimensions that there is no hope to visualize it effectively, even by using projections, so it forces us to contend with the intrinsic “unvisualizability” of high dimensions.  Second, ten dimensions is just big enough that it behaves roughly like any higher dimension, at least when it comes to random walks.  Third, it is about as big as can be handled with typical memory sizes of computers.  For instance, a ten-dimensional hypercubic lattice with 10 discrete sites along each dimension has 10^10 lattice points (10 Billion or 10 Gigs) which is about the limit of what a typical computer can handle with internal memory.

As a starting point for visualization, let’s begin with the well-known 4D hypercube but extend it to a 4D hyperlattice with three values along each dimension instead of two. The resulting 4D lattice can be displayed in 2D as a network with 3^4 = 81 nodes and 216 links or edges. The result is shown in Fig. 1, represented in two dimensions as a network graph with nodes and edges. Each node has four links with neighbors. Despite the apparent 3D look that this graph has about it, if you look closely you will see the frustration that occurs when trying to link to 4 neighbors, causing many long-distance links.

[See YouTube video for movies showing evolving hyperlattices and random walks in 10D.]

We can also look at a 10D hypercube that has 2^10 = 1024 nodes and 5120 edges, shown in Fig. 2. It is a bit difficult to see the hypercubic symmetry when presented in 2D, but each node has exactly 10 links.

Extending this 10D lattice to 10 positions instead of 2 and trying to visualize it is prohibitive, since the resulting graph in 2D just looks like a mass of overlapping circles. However, our interest extends not just to ten locations per dimension, but to an unlimited number of locations. This is the 10D infinite lattice on which we want to explore the physics of the random walk.

## Diffusion in Ten Dimensions

An unconstrained random walk in 10D is just a minimal extension beyond a simple random walk in 1D. Because each dimension is independent, a single random walker takes a random step along any of the 10 dimensions at each iteration so that motion in any one of the 10 dimensions is just a 1D random walk. Therefore, a simple way to visualize this random walk in 10D is simply to plot the walk against each dimension, as in Fig. 3. There is one chance in ten that the walker will take a positive or negative step along any given dimension at each time point.

An alternate visualization of the 10D random walker is shown in Fig. 4 for the same data as Fig. 3. In this case the displacement is color coded, and each column is a different dimension. Time is on the vertical axis (starting at the top and increasing downward). This type of color map can easily be extended to hundreds of dimensions. Each row is a position vector of the single walker in the 10D space

In the 10D hyperlattice in this section, all lattice sites are accessible at each time point, so there is no constraint preventing the walk from visiting a previously-visited node. There is a possible adjustment that can be made to the walk that prevents it from ever crossing its own path. This is known as a self-avoiding-walk (SAW). In two dimensions, there is a major difference in the geometric and dynamical properties of an ordinary walk and an SAW. However, in dimensions larger than 4, it turns out that there are so many possibilities of where to go (high-dimensional spaces have so much free room) that it is highly unlikely that a random walk will ever cross itself. Therefore, in our 10D hyperlattice we do not need to make the distinction between an ordinary walk and a self-avoiding-walk. However, there are other constraints that can be imposed that mimic how complex systems evolve in time, and these constraints can have important consequences, as we see next.

## Random Walk in a Maximally Rough Landscape

In the infinite hyperlattice of the previous section, all lattice sites are the same and are all equally accessible. However, in the study of complex systems, it is common to assign a value to each node in a high-dimensional lattice. This value can be assigned by a potential function, producing a high-dimensional potential landscape over the lattice geometry. Or the value might be the survival fitness of a species, producing a high-dimensional fitness landscape that governs how species compete and evolve. Or the value might be a loss function (an objective function) in a minimization problem from multivariate analysis or machine learning. In all of these cases, the scalar value on the nodes defines a landscape over which a state point executes a walk. The question then becomes, what are the properties of a landscape in high dimensions, and how does it affect a random walker?

As an example, let’s consider a landscape that is completely random point-to-point. There are no correlations in this landscape, making it maximally rough. Then we require that a random walker takes a walk along iso-potentials in this landscape, never increasing and never decreasing its potential. Beginning with our spatial intuition living in 3D space, we might be concerned that such a walker would quickly get confined in some area of the lanscape. Think of a 2D topo map with countour lines drawn on it — If we start at a certain elevation on a mountain side, then if we must walk along directions that maintain our elevation, we stay on a given contour and eventually come back to our starting point after circling the mountain peak — we are trapped! But this intuition informed by our 3D lives is misleading. What happens in our 10D hyperlattice?

To make the example easy to analyze, let’s assume that our potential function is restricted to N discrete values. This means that of the 10 neighbors to a given walker site, on average only 10/N are likely to have the same potential value as the given walker site. This constrains the available sites for the walker, and it converts the uniform hyperlattice into a hyperlattice site percolation problem.

Percolation theory is a fascinating topic in statistical physics. There are many deep concepts that come from asking simple questions about how nodes are connected across a network. The most important aspect of percolation theory is the concept of a percolation threshold. Starting with a complete network that is connected end-to-end, start removing nodes at random. For some critical fraction of nodes removed (on average) there will no longer be a single connected cluster that spans the network. This critical fraction is known as the percolation threshold. Above the percolation threshold, a random walker can get from one part of the network to another. Below the percolation threshold, the random walker is confined to a local cluster.

If a hyperlattice has N discrete values for the landscape potential (or height, or contour) and if a random walker can only move to site that has the same value as the walker’s current value (remains on the level set), then only a fraction of the hyperlattice sites are available to the walker, and the question of whether the walker can find a path the spans the hyperlattice becomes simply a question of how the fraction of available sites relates to the percolation threshold.

The percolation threshold for hyperlattices is well known. For reasonably high dimensions, it is given to good accuracy by

where d is the dimension of the hyperlattice. For a 10D hyperlattice the percolation threshold is pc(10) = 0.0568, or about 6%. Therefore, if more than 6% of the sites of the hyperlattice have the same value as the walker’s current site, then the walker is free to roam about the hyperlattice.

If there are N = 5 discrete values for the potential, then 20% of the sites are available, which is above the percolation threshold, and walkers can go as far as they want. This statement holds true no matter what the starting value is. It might be 5, which means the walker is as high on the landscape as they can get. Or it might be 1, which means the walker is as low on the landscape as they can get. Yet even if they are at the top, if the available site fraction is above the percolation threshold, then the walker can stay on the high mountain ridge, spanning the landscape. The same is true if they start at the bottom of a valley. Therefore, mountain ridges are very common, as are deep valleys, yet they allow full mobility about the geography. On the other hand, a so-called mountain peak would be a 5 surrounded by 4’s or lower. The odds for having this happen in 10D are 0.2*(1-0.8^10) = 0.18. Then the total density of mountain peaks, in a 10D hyperlattice with 5 potential values, is only 18%. Therefore, mountain peaks are rare in 10D, while mountain ridges are common. In even higher dimensions, the percolation threshold decreases roughly inversely with the dimensionality, and mountain peaks become extremely rare and play virtually no part in walks about the landscape.

To illustrate this point, Fig. 5 is the same 10D network that is in Fig. 2, but only the nodes sharing the same value are shown for N = 5, which means that only 20% of the nodes are accessible to a walker who stays only on nodes with the same values. There is a “giant cluster” that remains connected, spanning the original network. If the original network is infinite, then the giant cluster is also infinite but contains a finite fraction of the nodes.

The quantitative details of the random walk can change depending on the proximity of the sub-networks (the clusters, the ridges or the level sets) to the percolation threshold. For instance, a random walker in D =10 with N = 5 is shown in Fig. 6. The diffusion is a bit slower than in the unconstrained walk of Figs. 3 and 4. But the ability to wander about the 10D space is retained.

This is then the general important result: In high-dimensional landscapes, mountain ridges are much more common than mountain peaks. This has profound consequences for the evolution of life, the dynamics of complex systems, and the power of machine learning.

## Consequences for Evolution and Machine Learning

When the high-dimensional space is the space of possible mutations on a genome, and when the landscape is a fitness landscape that assigns a survival advantage for one mutation relative to others, then the random walk describes the evolution of a species across generations. The prevalence of ridges, or more generally level sets, in high dimensions has a major consequence for the evolutionary process, because a species can walk along a level set acquiring many possible mutations that have only neutral effects on the survivability of the species. At the same time, the genetic make-up is constantly drifting around in this “neutral network”, allowing the species’ genome to access distant parts of the space. Then, at some point, natural selection may tip the species up a nearby (but rare) peak, and a new equilibrium is attained for the species.

One of the early criticisms of fitness landscapes was the (erroneous) criticism that for a species to move from one fitness peak to another, it would have to go down and cross wide valleys of low fitness to get to another peak. But this was a left-over from thinking in 3D. In high-D, neutral networks are ubiquitous, and a mutation can take a step away from one fitness peak onto one of the neutral networks, which can be sampled by a random walk until the state is near some distant peak. It is no longer necessary to think in terms of high peaks and low valleys of fitness — just random walks. The evolution of extremely complex structures, like ATP synthase, can then be understood as a random walk along networks of nearly-neutral fitness — once our 3D biases are eliminated.

The same arguments hold for many situations in machine learning and especially deep learning. When training a deep neural network, there can be thousands of neural weights that need to be trained through the minimization of a loss function, also known as an objective function. The loss function is the equivalent to a potential, and minimizing the loss function over the thousands of dimensions is the same problem as maximizing the fitness of an evolving species.

At first look, one might think that deep learning is doomed to failure. We have all learned, from the earliest days in calculus, that enough adjustable parameter can fit anything, but the fit is meaningless because it predicts nothing. Deep learning seems to be the worst example of this. How can fitting thousands of adjustable parameters be useful when the dimensionality of the optimization space is orders of magnitude larger than the degrees of freedom of the system being modeled?

The answer comes from the geometry of high dimensions. The prevalence of neutral networks in high dimensions gives lots of chances to escape local minima. In fact, local minima are actually rare in high dimensions, and when they do occur, there is a neutral network nearby onto which they can escape (if the effective temperature of the learning process is set sufficiently high). Therefore, despite the insanely large number of adjustable parameters, general solutions, that are meaningful and predictive, can be found by adding random walks around the objective landscape as a partial strategy in combination with gradient descent.

Given the superficial analogy of deep learning to the human mind, the geometry of random walks in ultra-high dimensions may partially explain our own intelligence and consciousness.

## Biblography

S. Gravilet, Fitness Landscapes and the Origins of Species. Princeton University Press, 2004.

M. Kimura, The Neutral Theory of Molecular Evolution. Cambridge University Press, 1968.

YouTube Vlog on A Random Walk in 10 Dimensions

# Physics in the Age of Contagion. Part 3: Testing and Tracing COVID-19

In the midst of this COVID crisis (and the often botched governmental responses to it), there have been several success stories: Taiwan, South Korea, Australia and New Zealand stand out. What are the secrets to their success? First, is the willingness of the population to accept the seriousness of the pandemic and to act accordingly. Second, is a rapid and coherent (and competent) governmental response. Third, is biotechnology and the physics of ultra-sensitive biomolecule detection.

## Antibody Testing

A virus consists a protein package called a capsid that surrounds polymers of coding RNA. Protein molecules on the capsid are specific to the virus and are the key to testing whether a person has been exposed to the virus. These specific molecules are called antigens, and the body produces antibodies — large biomolecules — that are rapidly evolved by the immune system and released into the blood system to recognize and bind to the antigen. The recognition and binding is highly specific (though not perfect) to the capsid proteins of the virus, so that other types of antibodies (produced to fend off other infections) tend not to bind to it. This specificity enables antibody testing.

In principle, all one needs to do is isolate the COVID-19 antigen, bind it to a surface, and run a sample of a patient’s serum (the part of the blood without the blood cells) over the same surface. If the patient has produced antibodies against the COVID-19, these antibodies will attach to the antigens stuck to the surface. After washing away the rest of the serum, what remains are anti-COVID antibodies attached to the antigens bound to the surface. The next step is to determine whether these antibodies have been bound to the surface or not.

At this stage, there are many possible alternative technologies to detecting the bound antibodies (see section below on the physics of the BioCD for one approach). A conventional detection approach is known as ELISA (Enzyme-linked immunosorbant assay). To detect the bound antibody, a secondary antibody that binds to human antibodies is added to the test well. This secondary antibody contains either a fluorescent molecular tag or an enzyme that causes the color of the well to change (kind of like how a pregnancy test causes a piece of paper to change color). If the COVID antigen binds antibodies from the patient serum, then this second antibody will bind to the first and can be detected by fluorescence or by simple color change.

The technical challenges associated with antibody assays relate to false positives and false negatives. A false positive happens when the serum is too sticky and some antibodies NOT against COVID tend to stick to the surface of the test well. This is called non-specific binding. The secondary antibodies bind to these non-specifically-bound antibodies and a color change reports a detection, when in fact no COVID-specific antibodies were there. This is a false positive — the patient had not been exposed, but the test says they were.

On the other hand, a false negative occurs when the patient serum is possibly too dilute and even though anti-COVID antibodies are present, they don’t bind sufficiently to the test well to be detectable. This is a false negative — the patient had been exposed, but the test says they weren’t. Despite how mature antibody assay technology is, false positives and false negatives are very hard to eliminate. It is fairly common for false rates to be in the range of 5% to 10% even for high-quality immunoassays. The finite accuracy of the tests must be considered when doing community planning for testing and tracking. But the bottom line is that even 90% accuracy on the test can do a lot to stop the spread of the infection. This is because of the geometry of social networks and how important it is to find and isolate the super spreaders.

## Social Networks

The web of any complex set of communities and their interconnections aren’t just random. Whether in interpersonal networks, or networks of cities and states and nations, it’s like the world-wide-web where the most popular webpages get the most links. This is the same phenomenon that makes the rich richer and the poor poorer. It produces a network with a few hubs that have a large fraction of the links. A network model that captures this network topology is known as the Barabasi-Albert model for scale-free networks [1]. A scale-free network tends to have one node that has the most links, then a couple of nodes that have a little fewer links, then several more with even fewer, and so on, until there are a vary large number of nodes with just a single link each.

When it comes to pandemics, this type of network topology is both a curse and a blessing. It is a curse, because if the popular node becomes infected it tends to infect a large fraction of the rest of the network because it is so linked in. But it is a blessing, because if that node can be identified and isolated from the rest of the network, then the chance of the pandemic sweeping across the whole network can be significantly reduced. This is where testing and contact tracing becomes so important. You have to know who is infected and who they are connected with. Only then can you isolate the central nodes of the network and make a dent in the pandemic spread.

An example of a Barabasi-Albert network is shown in Fig. 2 having 128 nodes. Some nodes have many links out (and in) the number of links connecting a node is called the node degree. There are several nodes of very high degree (a degree around 25 in this case) but also very many nodes that have only a single link. It’s the high-degree nodes that matter in a pandemic. If they get infected, then they infect almost the entire network. This scale-free network structure emphasizes the formation of central high-degree nodes. It tends to hold for many social networks, but also can stand for cities across a nation. A city like New York has links all over the country (by flights), while my little town of Lafayette IN might be modeled by a single link to Indianapolis. That same scaling structure is seen across many scales from interactions among nations to interactions among citizens in towns.

In the network of nodes in Fig. 2, each node can be considered as a “compartment” in a multi-compartment SIR model (see my previous blog for the two-compartment SIR model of COVID-19). The infection of each node depends on the SIR dynamics of that node, plus the infections coming in from links other infected nodes. The equations of the dynamics for each node are

where Aab is the adjacency matrix where self-connection is allowed (infection dynamics within a node) and the sums go over all the nodes of the network. In this model, the population of each node is set equal to the degree ka of the node. The spread of the pandemic across the network depends on the links and where the infection begins, but the overall infection is similar to the simple SIR model for a given average network degree

However, if the pandemic starts, but then the highest-degree node (the super spreader) is isolated (by testing and contact tracing), then the saturation of the disease across the network can be decreased in a much greater proportion than simply given by the population of the isolated node. For instance, in the simulation in Fig. 3, a node of degree 20 is removed at 50 days. The fraction of the population that is isolated is only 10%, yet the saturation of the disease across the whole network is decreased by more than a factor of 2.

In a more realistic model with many more nodes, and full testing to allow the infected nodes and their connections to be isolated, the disease can be virtually halted. This is what was achieved in Taiwan and South Korea. The question is why the United States, with its technologically powerful companies and all their capabilities, was so unprepared or unwilling to achieve the same thing.

## Python Code: NetSIRSF.py

```#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
NetSIRSF.py
Created on Sat May 11 08:56:41 2019
@author: nolte
D. D. Nolte, Introduction to Modern Dynamics: Chaos, Networks, Space and Time, 2nd ed. (Oxford,2019)
"""

# https://www.python-course.eu/networkx.php
# https://networkx.github.io/documentation/stable/tutorial.html
# https://networkx.github.io/documentation/stable/reference/functions.html

import numpy as np
from scipy import integrate
from matplotlib import pyplot as plt
import networkx as nx
import time
from random import random

tstart = time.time()

plt.close('all')

betap = 0.014;
mu = 0.13;

print('beta = ',betap)
print('betap/mu = ',betap/mu)

N = 128      # 50

facoef = 2
k = 1
nodecouple = nx.barabasi_albert_graph(N, k, seed=None)

indhi = 0
deg = 0
for omloop in nodecouple.node:
degtmp = nodecouple.degree(omloop)
if degtmp > deg:
deg = degtmp
indhi = omloop
print('highest degree node = ',indhi)
print('highest degree = ',deg)

plt.figure(1)
colors = [(random(), random(), random()) for _i in range(10)]
nx.draw_circular(nodecouple,node_size=75, node_color=colors)
print(nx.info(nodecouple))

# function: omegout, yout = coupleN(G)
def coupleN(G,tlim):

# function: yd = flow_deriv(x_y)
def flow_deriv(x_y,t0):

N = np.int(x_y.size/2)
yd = np.zeros(shape=(2*N,))
ind = -1
for omloop in G.node:
ind = ind + 1
temp1 = -mu*x_y[ind] + betap*x_y[ind]*x_y[N+ind]
temp2 =  -betap*x_y[ind]*x_y[N+ind]
indx = G.node[cindex]['index']
g = G.node[omloop]['coupling'][cloop]

temp1 = temp1 + g*betap*x_y[indx]*x_y[N+ind]
temp2 = temp2 - g*betap*x_y[indx]*x_y[N+ind]

yd[ind] = temp1
yd[N+ind] = temp2

return yd
# end of function flow_deriv(x_y)
x0 = x_y
t = np.linspace(0,tlim,tlim)      # 600  300
y = integrate.odeint(flow_deriv, x0, t)

return t,y
# end of function: omegout, yout = coupleN(G)

lnk = np.zeros(shape = (N,), dtype=int)
ind = -1
for loop in nodecouple.node:
ind = ind + 1
nodecouple.node[loop]['index'] = ind
lnk[ind] = len(list(nx.neighbors(nodecouple,loop)))

gfac = 0.1

ind = -1
for nodeloop in nodecouple.node:
ind = ind + 1
nodecouple.node[nodeloop]['coupling'] = np.zeros(shape=(lnk[ind],))

x_y = np.zeros(shape=(2*N,))
for loop in nodecouple.node:
x_y[loop]=0
x_y[N+loop]=nodecouple.degree(loop)
#x_y[N+loop]=1
x_y[N-1 ]= 0.01
x_y[2*N-1] = x_y[2*N-1] - 0.01
N0 = np.sum(x_y[N:2*N]) - x_y[indhi] - x_y[N+indhi]
print('N0 = ',N0)

tlim0 = 600
t0,yout0 = coupleN(nodecouple,tlim0)                           # Here is the subfunction call for the flow

plt.figure(2)
plt.yscale('log')
plt.gca().set_ylim(1e-3, 1)
for loop in range(N):
lines1 = plt.plot(t0,yout0[:,loop])
lines2 = plt.plot(t0,yout0[:,N+loop])
lines3 = plt.plot(t0,N0-yout0[:,loop]-yout0[:,N+loop])

plt.setp(lines1, linewidth=0.5)
plt.setp(lines2, linewidth=0.5)
plt.setp(lines3, linewidth=0.5)

Itot = np.sum(yout0[:,0:127],axis = 1) - yout0[:,indhi]
Stot = np.sum(yout0[:,128:255],axis = 1) - yout0[:,N+indhi]
Rtot = N0 - Itot - Stot
plt.figure(3)
#plt.plot(t0,Itot,'r',t0,Stot,'g',t0,Rtot,'b')
plt.plot(t0,Itot/N0,'r',t0,Rtot/N0,'b')
#plt.legend(('Infected','Susceptible','Removed'))
plt.legend(('Infected','Removed'))
plt.hold

# Repeat but innoculate highest-degree node
x_y = np.zeros(shape=(2*N,))
for loop in nodecouple.node:
x_y[loop]=0
x_y[N+loop]=nodecouple.degree(loop)
#x_y[N+loop]=1
x_y[N-1] = 0.01
x_y[2*N-1] = x_y[2*N-1] - 0.01
N0 = np.sum(x_y[N:2*N]) - x_y[indhi] - x_y[N+indhi]

tlim0 = 50
t0,yout0 = coupleN(nodecouple,tlim0)

# remove all edges from highest-degree node
ee = list(nodecouple.edges(indhi))
nodecouple.remove_edges_from(ee)
print(nx.info(nodecouple))

#nodecouple.remove_node(indhi)
lnk = np.zeros(shape = (N,), dtype=int)
ind = -1
for loop in nodecouple.node:
ind = ind + 1
nodecouple.node[loop]['index'] = ind
lnk[ind] = len(list(nx.neighbors(nodecouple,loop)))

ind = -1
x_y = np.zeros(shape=(2*N,))
for nodeloop in nodecouple.node:
ind = ind + 1
nodecouple.node[nodeloop]['coupling'] = np.zeros(shape=(lnk[ind],))
x_y[ind] = yout0[tlim0-1,nodeloop]
x_y[N+ind] = yout0[tlim0-1,N+nodeloop]

tlim1 = 500
t1,yout1 = coupleN(nodecouple,tlim1)

t = np.zeros(shape=(tlim0+tlim1,))
yout = np.zeros(shape=(tlim0+tlim1,2*N))
t[0:tlim0] = t0
t[tlim0:tlim1+tlim0] = tlim0+t1
yout[0:tlim0,:] = yout0
yout[tlim0:tlim1+tlim0,:] = yout1

plt.figure(4)
plt.yscale('log')
plt.gca().set_ylim(1e-3, 1)
for loop in range(N):
lines1 = plt.plot(t,yout[:,loop])
lines2 = plt.plot(t,yout[:,N+loop])
lines3 = plt.plot(t,N0-yout[:,loop]-yout[:,N+loop])

plt.setp(lines1, linewidth=0.5)
plt.setp(lines2, linewidth=0.5)
plt.setp(lines3, linewidth=0.5)

Itot = np.sum(yout[:,0:127],axis = 1) - yout[:,indhi]
Stot = np.sum(yout[:,128:255],axis = 1) - yout[:,N+indhi]
Rtot = N0 - Itot - Stot
plt.figure(3)
#plt.plot(t,Itot,'r',t,Stot,'g',t,Rtot,'b',linestyle='dashed')
plt.plot(t,Itot/N0,'r',t,Rtot/N0,'b',linestyle='dashed')
#plt.legend(('Infected','Susceptible','Removed'))
plt.legend(('Infected','Removed'))
plt.xlabel('Days')
plt.ylabel('Fraction of Sub-Population')
plt.title('Network Dynamics for COVID-19')
plt.show()
plt.hold()

elapsed_time = time.time() - tstart
print('elapsed time = ',format(elapsed_time,'.2f'),'secs')

```

## Caveats and Disclaimers

No effort in the network model was made to fit actual disease statistics. In addition, the network in Figs. 2 and 3 only has 128 nodes, and each node was a “compartment” that had its own SIR dynamics. This is a coarse-graining approach that would need to be significantly improved to try to model an actual network of connections across communities and states. In addition, isolating the super spreader in this model would be like isolating a city rather than an individual, which is not realistic. The value of a heuristic model is to gain a physical intuition about scales and behaviors without being distracted by details of the model.

## Postscript: Physics of the BioCD

Because antibody testing has become such a point of public discussion, it brings to mind a chapter of my own life that was closely related to this topic. About 20 years ago my research group invented and developed an antibody assay called the BioCD [2]. The “CD” stood for “compact disc”, and it was a spinning-disk format that used laser interferometry to perform fast and sensitive measurements of antibodies in blood. We launched a start-up company called QuadraSpec in 2004 to commercialize the technology for large-scale human disease screening.

A conventional compact disc consists of about a billion individual nulling interferometers impressed as pits into plastic. When the read-out laser beam straddles one of the billion pits, it experiences a condition of perfect destructive interferences — a zero. But when it was not shining on a pit it experiences high reflection — a one. So as the laser scans across the surface of the disc as it spins, a series of high and low reflections read off bits of information. Because the disc spins very fast, the data rate is very high, and a billion bits can be read in a matter of minutes.

The idea struck me in late 1999 just before getting on a plane to spend a weekend in New York City: What if each pit were like a test tube, so that instead of reading bits of ones and zeros it could read tiny amounts of protein? Then instead of a billion ones and zeros the disc could read a billion protein concentrations. But nulling interferometers are the least sensitive way to measure something sensitively because it operates at a local minimum in the response curve. The most sensitive way to do interferometry is in the condition of phase quadrature when the signal and reference waves are ninety-degrees out of phase and where the response curve is steepest, as in Fig. 4 . Therefore, the only thing you need to turn a compact disc from reading ones and zeros to proteins is to reduce the height of the pit by half. In practice we used raised ridges of gold instead of pits, but it worked in the same way and was extremely sensitive to the attachment of small amounts of protein.

This first generation BioCD was literally a work of art. It was composed of a radial array of gold strips deposited on a silicon wafer. We were approached in 2004 by an art installation called “Massive Change” that was curated by the Vancouver Art Museum. The art installation travelled to Toronto and then to the Museum of Contemporary Art in Chicago, where we went to see it. Our gold-and-silicon BioCD was on display in a section on art in technology.

The next-gen BioCDs were much simpler, consisting simply of oxide layers on silicon wafers, but they were much more versatile and more sensitive. An optical scan of a printed antibody spot on a BioCD is shown in Fig. 5 The protein height is only about 1 nanometer (the diameter of the spot is 100 microns). Interferometry can measure a change in the height of the spot (caused by binding antibodies from patient serum) by only about 10 picometers averaged over the size of the spot. This exquisite sensitivity enabled us to detect tiny fractions of blood-born antigens and antibodies at the level of only a nanogram per milliliter.

The real estate on a 100 mm diameter disc was sufficient to do 100,000 antibody assays, which would be 256 protein targets across 512 patients on a single BioCD that would take only a few hours to finish reading!

The potential of the BioCD for massively multiplexed protein measurements made it possible to imagine testing a single patient for hundreds of diseases in a matter of hours using only a few drops of blood. Furthermore, by being simple and cheap, the test would allow people to track their health over time to look for emerging health trends.

If this sounds familiar to you, you’re right. That’s exactly what the notorious company Theranos was promising investors 10 years after we first proposed this idea. But here’s the difference: We learned that the tech did not scale. It cost us \$10M to develop a BioCD that could test for just 4 diseases. And it would cost more than an additional \$10M to get it to 8 diseases, because the antibody chemistry is not linear. Each new disease that you try to test creates a combinatorics problem of non-specific binding with all the other antibodies and antigens. To scale the test up to 100 diseases on the single platform using only a few drops of blood would have cost us more than \$1B of R&D expenses — if it was possible at all. So we stopped development at our 4-plex product and sold the technology to a veterinary testing company that uses it today to test for diseases like heart worm and Lymes disease in blood samples from pet animals.

Five years after we walked away from massively multiplexed antibody tests, Theranos proposed the same thing and took in more than \$700M in US investment, but ultimately produced nothing that worked. The saga of Theranos and its charismatic CEO Elizabeth Holmes has been the topic of books and documentaries and movies like “The Inventor: Out for Blood in Silicon Valley” and a rumored big screen movie starring Jennifer Lawrence as Holmes.

The bottom line is that antibody testing is a difficult business, and ramping up rapidly to meet the demands of testing and tracing COVID-19 is going to be challenging. The key is not to demand too much accuracy per test. False positives are bad for the individual, because it lets them go about without immunity and they might get sick, and false negatives are bad, because it locks them in when they could be going about. But if an inexpensive test of only 90% accuracy (a level of accuracy that has already been called “unreliable” in some news reports) can be brought out in massive scale so that virtually everyone can be tested, and tested repeatedly, then the benefit to society would be great. In the scaling networks that tend to characterize human interactions, all it takes is a few high-degree nodes to be isolated to make infection rates plummet.

## References

[1] A. L. Barabasi and R. Albert, “Emergence of scaling in random networks,” Science, vol. 286, no. 5439, pp. 509-512, Oct 15 (1999)

[2] D. D. Nolte, “Review of centrifugal microfluidic and bio-optical disks,” Review Of Scientific Instruments, vol. 80, no. 10, p. 101101, Oct (2009)

[3] D. D. Nolte and F. E. Regnier, “Spinning-Disk Interferometry: The BioCD,” Optics and Photonics News, no. October 2004, pp. 48-53, (2004)

# The Physics of Modern Dynamics (with Python Programs)

It is surprising how much of modern dynamics boils down to an extremely simple formula

This innocuous-looking equation carries such riddles, such surprises, such unintuitive behavior that it can become the object of study for life.  This equation is called a vector flow equation, and it can be used to capture the essential physics of economies, neurons, ecosystems, networks, and even orbits of photons around black holes.  This equation is to modern dynamics what F = ma was to classical mechanics.  It is the starting point for understanding complex systems.

## The Magic of Phase Space

The apparent simplicity of the “flow equation” masks the complexity it contains.  It is a vector equation because each “dimension” is a variable of a complex system.  Many systems of interest may have only a few variables, but ecosystems and economies and social networks may have hundreds or thousands of variables.  Expressed in component format, the flow equation is

where the superscript spans the number of variables.  But even this masks all that can happen with such an equation. Each of the functions fa can be entirely different from each other, and can be any type of function, whether polynomial, rational, algebraic, transcendental or composite, although they must be single-valued.  They are generally nonlinear, and the limitless ways that functions can be nonlinear is where the richness of the flow equation comes from.

The vector flow equation is an ordinary differential equation (ODE) that can be solved for specific trajectories as initial value problems.  A single set of initial conditions defines a unique trajectory.  For instance, the trajectory for a 4-dimensional example is described as the column vector

which is the single-parameter position vector to a point in phase space, also called state space.  The point sweeps through successive configurations as a function of its single parameter—time.  This trajectory is also called an orbit.  In classical mechanics, the focus has tended to be on the behavior of specific orbits that arise from a specific set of initial conditions.  This is the classic “rock thrown from a cliff” problem of introductory physics courses.  However, in modern dynamics, the focus shifts away from individual trajectories to encompass the set of all possible trajectories.

## Why is Modern Dynamics part of Physics?

If finding the solutions to the “x-dot equals f” vector flow equation is all there is to do, then this would just be a math problem—the solution of ODE’s.  There are plenty of gems for mathematicians to look for, and there is an entire of field of study in mathematics called “dynamical systems“, but this would not be “physics”.  Physics as a profession is separate and distinct from mathematics, although the two are sometimes confused.  Physics uses mathematics as its language and as its toolbox, but physics is not mathematics.  Physics is done best when it is done qualitatively—this means with scribbles done on napkins in restaurants or on the back of envelopes while waiting in line. Physics is about recognizing relationships and patterns. Physics is about identifying the limits to scaling properties where the physics changes when scales change. Physics is about the mapping of the simplest possible mathematics onto behavior in the physical world, and recognizing when the simplest possible mathematics is a universal that applies broadly to diverse systems that seem different, but that share the same underlying principles.

So, granted solving ODE’s is not physics, there is still a tremendous amount of good physics that can be done by solving ODE’s. ODE solvers become the modern physicist’s experimental workbench, providing data output from numerical experiments that can test the dependence on parameters in ways that real-world experiments might not be able to access. Physical intuition can be built based on such simulations as the engaged physicist begins to “understand” how the system behaves, able to explain what will happen as the values of parameters are changed.

In the follow sections, three examples of modern dynamics are introduced with a preliminary study, including Python code. These examples are: Galactic dynamics, synchronized networks and ecosystems. Despite their very different natures, their description using dynamical flows share features in common and illustrate the beauty and depth of behavior that can be explored with simple equations.

## Galactic Dynamics

One example of the power and beauty of the vector flow equation and its set of all solutions in phase space is called the Henon-Heiles model of the motion of a star within a galaxy.  Of course, this is a terribly complicated problem that involves tens of billions of stars, but if you average over the gravitational potential of all the other stars, and throw in a couple of conservation laws, the resulting potential can look surprisingly simple.  The motion in the plane of this galactic potential takes two configuration coordinates (x, y) with two associated momenta (px, py) for a total of four dimensions.  The flow equations in four-dimensional phase space are simply

where the terms in the light blue box describe a two-dimensional simple harmonic oscillator (SHO), which is a linear oscillator, modified by the terms in the magenta box that represent the nonlinear galactic potential.  The orbits of this Hamiltonian system are chaotic, and because there is no dissipation in the model, a single orbit will continue forever within certain ranges of phase space governed by energy conservation, but never quite repeating.

### Hamilton4D.py

(Python code on GitHub.)

```#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Hamilton4D.py
Created on Wed Apr 18 06:03:32 2018

@author: nolte

Derived from:
D. D. Nolte, Introduction to Modern Dynamics: Chaos, Networks, Space and Time, 2nd ed. (Oxford,2019)
"""

import numpy as np
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
from scipy import integrate
from matplotlib import pyplot as plt
from matplotlib import cm
import time
import os

plt.close('all')

# model_case 1 = Heiles
# model_case 2 = Crescent
print(' ')
print('Hamilton4D.py')
print('Case: 1 = Heiles')
print('Case: 2 = Crescent')
model_case = int(input('Enter the Model Case (1-2)'))

if model_case == 1:
E = 1       # Heiles: 1, 0.3411   Crescent: 0.05, 1
epsE = 0.3411   # 3411
def flow_deriv(x_y_z_w,tspan):
x, y, z, w = x_y_z_w
a = z
b = w
c = -x - epsE*(2*x*y)
d = -y - epsE*(x**2 - y**2)
return[a,b,c,d]
else:
E = .1       #   Crescent: 0.1, 1
epsE = 1
def flow_deriv(x_y_z_w,tspan):
x, y, z, w = x_y_z_w
a = z
b = w
c = -(epsE*(y-2*x**2)*(-4*x) + x)
d = -(y-epsE*2*x**2)
return[a,b,c,d]

prms = np.sqrt(E)
pmax = np.sqrt(2*E)

# Potential Function
if model_case == 1:
V = np.zeros(shape=(100,100))
for xloop in range(100):
x = -2 + 4*xloop/100
for yloop in range(100):
y = -2 + 4*yloop/100
V[yloop,xloop] = 0.5*x**2 + 0.5*y**2 + epsE*(x**2*y - 0.33333*y**3)
else:
V = np.zeros(shape=(100,100))
for xloop in range(100):
x = -2 + 4*xloop/100
for yloop in range(100):
y = -2 + 4*yloop/100
V[yloop,xloop] = 0.5*x**2 + 0.5*y**2 + epsE*(2*x**4 - 2*x**2*y)

fig = plt.figure(1)
contr = plt.contourf(V,100, cmap=cm.coolwarm, vmin = 0, vmax = 10)
fig.colorbar(contr, shrink=0.5, aspect=5)
fig = plt.show()

repnum = 250
mulnum = 64/repnum

np.random.seed(1)
for reploop  in range(repnum):
px1 = 2*(np.random.random((1))-0.499)*pmax
py1 = np.sign(np.random.random((1))-0.499)*np.real(np.sqrt(2*(E-px1**2/2)))
xp1 = 0
yp1 = 0

x_y_z_w0 = [xp1, yp1, px1, py1]

tspan = np.linspace(1,1000,10000)
x_t = integrate.odeint(flow_deriv, x_y_z_w0, tspan)
siztmp = np.shape(x_t)
siz = siztmp[0]

if reploop % 50 == 0:
plt.figure(2)
lines = plt.plot(x_t[:,0],x_t[:,1])
plt.setp(lines, linewidth=0.5)
plt.show()
time.sleep(0.1)
#os.system("pause")

y1 = x_t[:,0]
y2 = x_t[:,1]
y3 = x_t[:,2]
y4 = x_t[:,3]

py = np.zeros(shape=(2*repnum,))
yvar = np.zeros(shape=(2*repnum,))
cnt = -1
last = y1[1]
for loop in range(2,siz):
if (last < 0)and(y1[loop] > 0):
cnt = cnt+1
del1 = -y1[loop-1]/(y1[loop] - y1[loop-1])
py[cnt] = y4[loop-1] + del1*(y4[loop]-y4[loop-1])
yvar[cnt] = y2[loop-1] + del1*(y2[loop]-y2[loop-1])
last = y1[loop]
else:
last = y1[loop]

plt.figure(3)
lines = plt.plot(yvar,py,'o',ms=1)
plt.show()

if model_case == 1:
plt.savefig('Heiles')
else:
plt.savefig('Crescent')

```

## Networks, Synchronization and Emergence

A central paradigm of nonlinear science is the emergence of patterns and organized behavior from seemingly random interactions among underlying constituents.  Emergent phenomena are among the most awe inspiring topics in science.  Crystals are emergent, forming slowly from solutions of reagents.  Life is emergent, arising out of the chaotic soup of organic molecules on Earth (or on some distant planet).  Intelligence is emergent, and so is consciousness, arising from the interactions among billions of neurons.  Ecosystems are emergent, based on competition and symbiosis among species.  Economies are emergent, based on the transfer of goods and money spanning scales from the local bodega to the global economy.

One of the common underlying properties of emergence is the existence of networks of interactions.  Networks and network science are topics of great current interest driven by the rise of the World Wide Web and social networks.  But networks are ubiquitous and have long been the topic of research into complex and nonlinear systems.  Networks provide a scaffold for understanding many of the emergent systems.  It allows one to think of isolated elements, like molecules or neurons, that interact with many others, like the neighbors in a crystal or distant synaptic connections.

From the point of view of modern dynamics, the state of a node can be a variable or a “dimension” and the interactions among links define the functions of the vector flow equation.  Emergence is then something that “emerges” from the dynamical flow as many elements interact through complex networks to produce simple or emergent patterns.

Synchronization is a form of emergence that happens when lots of independent oscillators, each vibrating at their own personal frequency, are coupled together to push and pull on each other, entraining all the individual frequencies into one common global oscillation of the entire system.  Synchronization plays an important role in the solar system, explaining why the Moon always shows one face to the Earth, why Saturn’s rings have gaps, and why asteroids are mainly kept away from colliding with the Earth.  Synchronization plays an even more important function in biology where it coordinates the beating of the heart and the functioning of the brain.

One of the most dramatic examples of synchronization is the Kuramoto synchronization phase transition. This occurs when a large set of individual oscillators with differing natural frequencies interact with each other through a weak nonlinear coupling.  For small coupling, all the individual nodes oscillate at their own frequency.  But as the coupling increases, there is a sudden coalescence of all the frequencies into a single common frequency.  This mechanical phase transition, called the Kuramoto transition, has many of the properties of a thermodynamic phase transition, including a solution that utilizes mean field theory.

The simulation of 20 Poncaré phase oscillators with global coupling is shown in Fig. 4 as a function of increasing coupling coefficient g. The original individual frequencies are spread randomly. The oscillators with similar frequencies are the first to synchronize, forming small clumps that then synchronize with other clumps of oscillators, until all oscillators are entrained to a single compromise frequency. The Kuramoto phase transition is not sharp in this case because the value of N = 20 is too small. If the simulation is run for 200 oscillators, there is a sudden transition from unsynchronized to synchronized oscillation at a threshold value of g.

The Kuramoto phase transition is one of the most important fundamental examples of modern dynamics because it illustrates many facets of nonlinear dynamics in a very simple way. It highlights the importance of nonlinearity, the simplification of phase oscillators, the use of mean field theory, the underlying structure of the network, and the example of a mechanical analog to a thermodynamic phase transition. It also has analytical solutions because of its simplicity, while still capturing the intrinsic complexity of nonlinear systems.

### Kuramoto.py

(Python code on GitHub.)

```#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat May 11 08:56:41 2019

@author: nolte

D. D. Nolte, Introduction to Modern Dynamics: Chaos, Networks, Space and Time, 2nd ed. (Oxford,2019)
"""

# https://www.python-course.eu/networkx.php
# https://networkx.github.io/documentation/stable/tutorial.html
# https://networkx.github.io/documentation/stable/reference/functions.html

import numpy as np
from scipy import integrate
from matplotlib import pyplot as plt
import networkx as nx
from UserFunction import linfit
import time

tstart = time.time()

plt.close('all')

Nfac = 25   # 25
N = 50      # 50
width = 0.2

# model_case 1 = complete graph (Kuramoto transition)
# model_case 2 = Erdos-Renyi
model_case = int(input('Input Model Case (1-2)'))
if model_case == 1:
facoef = 0.2
nodecouple = nx.complete_graph(N)
elif model_case == 2:
facoef = 5
nodecouple = nx.erdos_renyi_graph(N,0.1)

# function: omegout, yout = coupleN(G)
def coupleN(G):

# function: yd = flow_deriv(x_y)
def flow_deriv(y,t0):

yp = np.zeros(shape=(N,))
for omloop  in range(N):
temp = omega[omloop]
g = G.node[omloop]['coupling'][cloop]

temp = temp + g*np.sin(y[cindex]-y[omloop])

yp[omloop] = temp

yd = np.zeros(shape=(N,))
for omloop in range(N):
yd[omloop] = yp[omloop]

return yd
# end of function flow_deriv(x_y)

mnomega = 1.0

for nodeloop in range(N):
omega[nodeloop] = G.node[nodeloop]['element']

x_y_z = omega

# Settle-down Solve for the trajectories
tsettle = 100
t = np.linspace(0, tsettle, tsettle)
x_t = integrate.odeint(flow_deriv, x_y_z, t)
x0 = x_t[tsettle-1,0:N]

t = np.linspace(1,1000,1000)
y = integrate.odeint(flow_deriv, x0, t)
siztmp = np.shape(y)
sy = siztmp[0]

# Fit the frequency
m = np.zeros(shape = (N,))
w = np.zeros(shape = (N,))
mtmp = np.zeros(shape=(4,))
btmp = np.zeros(shape=(4,))
for omloop in range(N):

if np.remainder(sy,4) == 0:
mtmp[0],btmp[0] = linfit(t[0:sy//2],y[0:sy//2,omloop]);
mtmp[1],btmp[1] = linfit(t[sy//2+1:sy],y[sy//2+1:sy,omloop]);
mtmp[2],btmp[2] = linfit(t[sy//4+1:3*sy//4],y[sy//4+1:3*sy//4,omloop]);
mtmp[3],btmp[3] = linfit(t,y[:,omloop]);
else:
sytmp = 4*np.floor(sy/4);
mtmp[0],btmp[0] = linfit(t[0:sytmp//2],y[0:sytmp//2,omloop]);
mtmp[1],btmp[1] = linfit(t[sytmp//2+1:sytmp],y[sytmp//2+1:sytmp,omloop]);
mtmp[2],btmp[2] = linfit(t[sytmp//4+1:3*sytmp/4],y[sytmp//4+1:3*sytmp//4,omloop]);
mtmp[3],btmp[3] = linfit(t[0:sytmp],y[0:sytmp,omloop]);

#m[omloop] = np.median(mtmp)
m[omloop] = np.mean(mtmp)

w[omloop] = mnomega + m[omloop]

omegout = m
yout = y

return omegout, yout
# end of function: omegout, yout = coupleN(G)

omega = np.zeros(shape=(N,))
omegatemp = width*(np.random.rand(N)-1)
meanomega = np.mean(omegatemp)
omega = omegatemp - meanomega
sto = np.std(omega)

lnk = np.zeros(shape = (N,), dtype=int)
for loop in range(N):
nodecouple.node[loop]['element'] = omega[loop]
lnk[loop] = np.size(list(nx.neighbors(nodecouple,loop)))

avgdegree = np.mean(lnk)
mnomega = 1

facval = np.zeros(shape = (Nfac,))
yy = np.zeros(shape=(Nfac,N))
xx = np.zeros(shape=(Nfac,))
for facloop in range(Nfac):
print(facloop)

fac = facoef*(16*facloop/(Nfac))*(1/(N-1))*sto/mnomega
for nodeloop in range(N):
nodecouple.node[nodeloop]['coupling'] = np.zeros(shape=(lnk[nodeloop],))

facval[facloop] = fac*avgdegree

omegout, yout = coupleN(nodecouple)                           # Here is the subfunction call for the flow

for omloop in range(N):
yy[facloop,omloop] = omegout[omloop]

xx[facloop] = facval[facloop]

plt.figure(1)
lines = plt.plot(xx,yy)
plt.setp(lines, linewidth=0.5)
plt.show()

elapsed_time = time.time() - tstart
print('elapsed time = ',format(elapsed_time,'.2f'),'secs')

```

## The Web of Life

Ecosystems are among the most complex systems on Earth.  The complex interactions among hundreds or thousands of species may lead to steady homeostasis in some cases, to growth and collapse in other cases, and to oscillations or chaos in yet others.  But the definition of species can be broad and abstract, referring to businesses and markets in economic ecosystems, or to cliches and acquaintances in social ecosystems, among many other examples.  These systems are governed by the laws of evolutionary dynamics that include fitness and survival as well as adaptation.

The dimensionality of the dynamical spaces for these systems extends to hundreds or thousands of dimensions—far too complex to visualize when thinking in four dimensions is already challenging.  Yet there are shared principles and common behaviors that emerge even here.  Many of these can be illustrated in a simple three-dimensional system that is represented by a triangular simplex that can be easily visualized, and then generalized back to ultra-high dimensions once they are understood.

A simplex is a closed (N-1)-dimensional geometric figure that describes a zero-sum game (game theory is an integral part of evolutionary dynamics) among N competing species.  For instance, a two-simplex is a triangle that captures the dynamics among three species.  Each vertex of the triangle represents the situation when the entire ecosystem is composed of a single species.  Anywhere inside the triangle represents the situation when all three species are present and interacting.

A classic model of interacting species is the replicator equation. It allows for a fitness-based proliferation and for trade-offs among the individual species. The replicator dynamics equations are shown in Fig. 5.

The population dynamics on the 2D simplex are shown in Fig. 6 for several different pay-off matrices. The matrix values are shown in color and help interpret the trajectories. For instance the simplex on the upper-right shows a fixed point center. This reflects the antisymmetric character of the pay-off matrix around the diagonal. The stable spiral on the lower-left has a nearly asymmetric pay-off matrix, but with unequal off-diagonal magnitudes. The other two cases show central saddle points with stable fixed points on the boundary. A very large variety of behaviors are possible for this very simple system. The Python program is shown in Trirep.py.

### Trirep.py

(Python code on GitHub.)

```#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
trirep.py
Created on Thu May  9 16:23:30 2019

@author: nolte

Derived from:
D. D. Nolte, Introduction to Modern Dynamics: Chaos, Networks, Space and Time, 2nd ed. (Oxford,2019)
"""

import numpy as np
from scipy import integrate
from matplotlib import pyplot as plt

plt.close('all')

def tripartite(x,y,z):

sm = x + y + z
xp = x/sm
yp = y/sm

f = np.sqrt(3)/2

y0 = f*xp
x0 = -0.5*xp - yp + 1;

plt.figure(2)
lines = plt.plot(x0,y0)
plt.setp(lines, linewidth=0.5)
plt.plot([0, 1],[0, 0],'k',linewidth=1)
plt.plot([0, 0.5],[0, f],'k',linewidth=1)
plt.plot([1, 0.5],[0, f],'k',linewidth=1)
plt.show()

def solve_flow(y,tspan):
def flow_deriv(y, t0):
#"""Compute the time-derivative ."""

f = np.zeros(shape=(N,))
for iloop in range(N):
ftemp = 0
for jloop in range(N):
ftemp = ftemp + A[iloop,jloop]*y[jloop]
f[iloop] = ftemp

phitemp = phi0          # Can adjust this from 0 to 1 to stabilize (but Nth population is no longer independent)
for loop in range(N):
phitemp = phitemp + f[loop]*y[loop]
phi = phitemp

yd = np.zeros(shape=(N,))
for loop in range(N-1):
yd[loop] = y[loop]*(f[loop] - phi);

if np.abs(phi0) < 0.01:             # average fitness maintained at zero
yd[N-1] = y[N-1]*(f[N-1]-phi);
else:                                     # non-zero average fitness
ydtemp = 0
for loop in range(N-1):
ydtemp = ydtemp - yd[loop]
yd[N-1] = ydtemp

return yd

# Solve for the trajectories
t = np.linspace(0, tspan, 701)
x_t = integrate.odeint(flow_deriv,y,t)
return t, x_t

# model_case 1 = zero diagonal
# model_case 2 = zero trace
# model_case 3 = asymmetric (zero trace)
print(' ')
print('trirep.py')
print('Case: 1 = antisymm zero diagonal')
print('Case: 2 = antisymm zero trace')
print('Case: 3 = random')
model_case = int(input('Enter the Model Case (1-3)'))

N = 3
asymm = 3      # 1 = zero diag (replicator eqn)   2 = zero trace (autocatylitic model)  3 = random (but zero trace)
phi0 = 0.001            # average fitness (positive number) damps oscillations
T = 100;

if model_case == 1:
Atemp = np.zeros(shape=(N,N))
for yloop in range(N):
for xloop in range(yloop+1,N):
Atemp[yloop,xloop] = 2*(0.5 - np.random.random(1))
Atemp[xloop,yloop] = -Atemp[yloop,xloop]

if model_case == 2:
Atemp = np.zeros(shape=(N,N))
for yloop in range(N):
for xloop in range(yloop+1,N):
Atemp[yloop,xloop] = 2*(0.5 - np.random.random(1))
Atemp[xloop,yloop] = -Atemp[yloop,xloop]
Atemp[yloop,yloop] = 2*(0.5 - np.random.random(1))
tr = np.trace(Atemp)
A = Atemp
for yloop in range(N):
A[yloop,yloop] = Atemp[yloop,yloop] - tr/N

else:
Atemp = np.zeros(shape=(N,N))
for yloop in range(N):
for xloop in range(N):
Atemp[yloop,xloop] = 2*(0.5 - np.random.random(1))

tr = np.trace(Atemp)
A = Atemp
for yloop in range(N):
A[yloop,yloop] = Atemp[yloop,yloop] - tr/N

plt.figure(3)
im = plt.matshow(A,3,cmap=plt.cm.get_cmap('seismic'))  # hsv, seismic, bwr
cbar = im.figure.colorbar(im)

M = 20
delt = 1/M
ep = 0.01;

tempx = np.zeros(shape = (3,))
for xloop in range(M):
tempx[0] = delt*(xloop)+ep;
for yloop in range(M-xloop):
tempx[1] = delt*yloop+ep
tempx[2] = 1 - tempx[0] - tempx[1]

x0 = tempx/np.sum(tempx);          # initial populations

tspan = 70
t, x_t = solve_flow(x0,tspan)

y1 = x_t[:,0]
y2 = x_t[:,1]
y3 = x_t[:,2]

plt.figure(1)
lines = plt.plot(t,y1,t,y2,t,y3)
plt.setp(lines, linewidth=0.5)
plt.show()
plt.ylabel('X Position')
plt.xlabel('Time')

tripartite(y1,y2,y3)
```

## Topics in Modern Dynamics

These three examples are just the tip of the iceberg. The topics in modern dynamics are almost numberless. Any system that changes in time is a potential object of study in modern dynamics. Here is a list of a few topics that spring to mind.

## Bibliography

D. D. Nolte, Introduction to Modern Dynamics: Chaos, Networks, Space and Time, 2nd Ed. (Oxford University Press, 2019) (The physics and the derivations of the equations for the examples in this blog can be found here.)

D. D. Nolte, Galileo Unbound: A Path Across Life, the Universe and Everything (Oxford University Press, 2018) (The historical origins of the examples in this blog can be found here.)