The Physics of U. S. Presidential Elections (why are so many elections so close?)

Well here is another squeaker! The 2020 U. S. presidential election was a dead heat. What is most striking is that half of the past six US presidential elections have been won by less than 1% of the votes cast in certain key battleground states. For instance, in 2000 the election was won in Florida by less than 1/100th of a percent of the total votes cast.

How can so many elections be so close? This question is especially intriguing when one considers the 2020 election, which should have been strongly asymmetric, because one of the two candidates had such serious character flaws. It is also surprising because the country is NOT split 50/50 between urban and rural populations (it’s more like 60/40). And the split of Democrat/Republican is about 33/29 — close, but not as close as the election. So how can the vote be so close so often? Is this a coincidence? Or something fundamental about our political system? The answer lies (partially) in nonlinear dynamics coupled with the libertarian tendencies of American voters.

Rabbits and Sheep

Elections are complex dynamical systems consisting of approximately 140 million degrees of freedom (the voters). Yet US elections are also surprisingly simple. They are dynamical systems with only 2 large political parties, and typically a very small third party.

Voters in a political party are not too different from species in an ecosystem. There are many population dynamics models of things like rabbit and sheep that seek to understand the steady-state solutions when two species vie for the same feedstock (or two parties vie for the same votes). Depending on reproduction rates and competition payoff, one species can often drive the other species to extinction. Yet with fairly small modifications of the model parameters, it is often possible to find a steady-state solution in which both species live in harmony. This is a symbiotic solution to the population dynamics, perhaps because the rabbits help fertilize the grass for the sheep to eat, and the sheep keep away predators for the rabbits.

There are two interesting features to such a symbiotic population-dynamics model. First, because there is a stable steady-state solution, if there is a perturbation of the populations, for instance if the rabbits are culled by the farmer, then the two populations will slowly relax back to the original steady-state solution. For this reason, this solution is called a “stable fixed point”. Deviations away from the steady-state values experience an effective “restoring force” that moves the population values back to the fixed point. The second feature of these models is that the steady state values depend on the parameters of the model. Small changes in the model parameters then cause small changes in the steady-state values. In this sense, this stable fixed point is not fundamental–it depends on the parameters of the model.

Fig. 1 Dynamics of rabbits and sheep competing for the same resource (grass). For these parameters, one species dies off while the other thrives. A slight shift in parameters can turn the central saddle point into a stable fixed point where sheep and rabbits coexist in stead state. ([1] Reprinted from Introduction to Modern Dynamics (Oxford University Press, 2019) pg. 119)

But there are dynamical models which do have a stability that maintains steady values even as the model parameters shift. These models have negative feedback, like many dynamical systems, but if the negative feedback is connected to winner-take-all outcomes of game theory, then a robustly stable fixed point can emerge at precisely the threshold where such a winner would take all.

The Replicator Equation

The replicator equation provides a simple model for competing populations [2]. Despite its simplicity, it can model surprisingly complex behavior. The central equation is a simple growth model

where the growth rate depends on the fitness fa of the a-th species relative to the average fitness φ of all the species. The fitness is given by

where pab is the payoff matrix among the different species (implicit Einstein summation applies). The fitness is frequency dependent through the dependence on xb. The average fitness is

This model has a zero-sum rule that keeps the total population constant. Therefore, a three-species dynamics can be represented on a two-dimensional “simplex” where the three vertices are the pure populations for each of the species. The replicator equation can be applied easily to a three-party system, one simply defines a payoff matrix that is used to define the fitness of a party relative to the others.

The Nonlinear Dynamics of Presidential Elections

Here we will consider the replicator equation with three political parties (Democratic, Republican and Libertarian). Even though the third party is never a serious contender, the extra degree of freedom provided by the third party helps to stabilize the dynamics between the Democrats and the Republicans.

It is already clear that an essentially symbiotic relationship is at play between Democrats and Republicans, because the elections are roughly 50/50. If this were not the case, then a winner-take-all dynamic would drive virtually everyone to one party or the other. Therefore, having 100% Democrats is actually unstable, as is 100% Republicans. When the populations get too far out of balance, they get too monolithic and too inflexible, then defections of members will occur to the other parties to rebalance the system. But this is just a general trend, not something that can explain the nearly perfect 50/50 vote of the 2020 election.

To create the ultra-stable fixed point at 50/50 requires an additional contribution to the replicator equation. This contribution must create a type of toggle switch that depends on the winner-take-all outcome of the election. If a Democrat wins 51% of the vote, they get 100% of the Oval Office. This extreme outcome then causes a back action on the electorate who is always afraid when one party gets too much power.

Therefore, there must be a shift in the payoff matrix when too many votes are going one way or the other. Because the winner-take-all threshold is at exactly 50% of the vote, this becomes an equilibrium point imposed by the payoff matrix. Deviations in the numbers of voters away from 50% causes a negative feedback that drives the steady-state populations back to 50/50. This means that the payoff matrix becomes a function of the number of voters of one party or the other. In the parlance of nonlinear dynamics, the payoff matrix becomes frequency dependent. This goes one step beyond the original replicator equation where it was the population fitness that was frequency dependent, but not the payoff matrix. Now the payoff matrix also becomes frequency dependent.

The frequency-dependent payoff matrix (in an extremely simple model of the election dynamics) takes on negative feedback between two of the species (here the Democrats and the Republicans). If these are the first and third species, then the payoff matrix becomes

where the feedback coefficient is

and where the population dependences on the off-diagonal terms guarantee that, as soon as one party gains an advantage, there is defection of voters to the other party. This establishes a 50/50 balance that is maintained even when the underlying parameters would predict a strongly asymmetric election.

For instance, look at the dynamics in Fig. 2. For this choice of parameters, the replicator model predicts a 75/25 win for the democrats. However, when the feedback is active, it forces the 50/50 outcome, despite the underlying advantage for the original parameters.

Fig. 2 Comparison of the stabilized election with 50/50 outcome compared to the replicator dynamics without the feedback. For the parameters chosen here, there would be a 75/25 victory of the Democrats over the Republications. However, when the feedback is in play, the votes balance out at 50/50.

There are several interesting features in this model. It may seem that the Libertarians are irrelevant because they never have many voters. But their presence plays a surprisingly important role. The Libertarians tend to stabilize the dynamics so that neither the democrats nor the republicans would get all the votes. Also, there is a saddle point not too far from the pure Libertarian vertex. That Libertarian vertex is an attractor in this model, so under some extreme conditions, this could become a one-party system…maybe not Libertarian in that case, but possibly something more nefarious, of which history can provide many sad examples. It’s a word of caution.

Disclaimers and Caveats

No attempt has been made to actually mode the US electorate. The parameters in the modified replicator equations are chosen purely for illustration purposes. This model illustrates a concept — that feedback in the payoff matrix can create an ultra-stable fixed point that is insensitive to changes in the underlying parameters of the model. This can possibly explain why so many of the US presidential elections are so tight.

Someone interested in doing actual modeling of US elections would need to modify the parameters to match known behavior of the voting registrations and voting records. The model presented here assumes a balanced negative feedback that ensures a 50/50 fixed point. This model is based on the aversion of voters to too much power in one party–an echo of the libertarian tradition in the country. A more sophisticated model would yield the fixed point as a consequence of the dynamics, rather than being a feature assumed in the model. In addition, nonlinearity could be added that would drive the vote off of the 50/50 point when the underlying parameters shift strongly enough. For instance, the 2008 election was not a close one, in part because the strong positive character of one of the candidates galvanized a large fraction of the electorate, driving the dynamics away from the 50/50 balance.


[1] D. D. Nolte, Introduction to Modern Dynamics: Chaos, Networks, Space and Time (Oxford University Press, 2019) 2nd Edition.

[2] Nowak, M. A. (2006). Evolutionary Dynamics: Exploring the Equations of Life. Cambridge, Mass., Harvard University Press.

Galileo Unbound

Book Outline Topics

  • Chapter 1: Flight of the Swallows
    • Introduction to motion and trajectories
  • Chapter 2: A New Scientist
    • Galileo’s Biography
  • Chapter 3: Galileo’s Trajectory
    • His study of the science of motion
    • Publication of Two New Sciences
  • Chapter 4: On the Shoulders of Giants
    • Newton’s Principia
    • The Principle of Least Action: Maupertuis, Euler, and Voltaire
    • Lagrange and his new dynamics
  • Chapter 5: Geometry on my Mind
    • Differential geometry of Gauss and Riemann
    • Vector spaces rom Grassmann to Hilbert
    • Fractals: Cantor, Weierstrass, Hausdorff
  • Chapter 6: The Tangled Tale of Phase Space
    • Liouville and Jacobi
    • Entropy and Chaos: Clausius, Boltzmann and Poincare
    • Phase Space: Gibbs and Ehrenfest
  • Chapter 7: The Lens of Gravity
    • Einstein and the warping of light
    • Black Holes: Schwarzschild’s radius
    • Oppenheimer versus Wheeler
    • The Golden Age of General Relativity
  • Chapter 8: On the Quantum Footpath
    • Heisenberg’s matrix mechanics
    • Schrödinger’s wave mechanics
    • Bohr’s complementarity
    • Einstein and entanglement
    • Feynman and the path-integral formulation of quantum
  • Chapter 9: From Butterflies to Hurricanes
    • KAM theory of stability of the solar system
    • Steven Smale’s horseshoe
    • Lorenz’ butterfly: strange attractor
    • Feigenbaum and chaos
  • Chapter 10: Darwin in the Clockworks
    • Charles Darwin and the origin of species
    • Fibonnacci’s bees
    • Economic dynamics
    • Mendel and the landscapes of life
    • Evolutionary dynamics
    • Linus Pauling’s molecular clock and Dawkins meme
  • Chapter 11: The Measure of Life
    • Huygens, von Helmholtz and Rayleigh oscillators
    • Neurodynamics
    • Euler and the Seven Bridges of Königsberg
    • Network theory: Strogatz and Barabasi

In June of 1633 Galileo was found guilty of heresy and sentenced to house arrest for what remained of his life. He was a renaissance Prometheus, bound for giving knowledge to humanity. With little to do, and allowed few visitors, he at last had the uninterrupted time to finish his life’s labor. When Two New Sciences was published in 1638, it contained the seeds of the science of motion that would mature into a grand and abstract vision that permeates all science today. In this way, Galileo was unbound, not by Hercules, but by his own hand as he penned the introduction to his work:

. . . what I consider more important, there have been opened up to this vast and most excellent science, of which my work is merely the beginning, ways and means by which other minds more acute than mine will explore its remote corners.

            Galileo Galilei (1638) Two New Sciences

Galileo Unbound (Oxford University Press, 2018) explores the continuous thread from Galileo’s discovery of the parabolic trajectory to modern dynamics and complex systems. It is a history of expanding dimension and increasing abstraction, until today we speak of entangled quantum particles moving among many worlds, and we envision our lives as trajectories through spaces of thousands of dimensions. Remarkably, common themes persist that predict the evolution of species as readily as the orbits of planets. Galileo laid the foundation upon which Newton built a theory of dynamics that could capture the trajectory of the moon through space using the same physics that controlled the flight of a cannon ball. Late in the nineteenth-century, concepts of motion expanded into multiple dimensions, and in the 20th century geometry became the cause of motion rather than the result when Einstein envisioned the fabric of space-time warped by mass and energy, causing light rays to bend past the Sun. Possibly more radical was Feynman’s dilemma of quantum particles taking all paths at once—setting the stage for the modern fields of quantum field theory and quantum computing. Yet as concepts of motion have evolved, one thing has remained constant—the need to track ever more complex changes and to capture their essence—to find patterns in the chaos as we try to predict and control our world. Today’s ideas of motion go far beyond the parabolic trajectory, but even Galileo might recognize the common thread that winds through all these motions, drawing them together into a unified view that gives us the power to see, at least a little, through the mists shrouding the future.