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Department of Computer Science, Princeton University, United [email protected]://orcid.org/0000-0001-8542-0247Department of Computer Science, Princeton University, United [email protected]://orcid.org/0000-0002-6398-3097 Department of Computer Science, ETH Zurich, [email protected]://orcid.org/0009-0002-7028-2595 \CopyrightBernard Chazelle, Kritkorn Karntikoon, and Jakob Nogler {CCSXML} <ccs2012> <concept> <concept_id>10003752.10010061</concept_id> <concept_desc>Theory of computation Randomness, geometry and discrete structures</concept_desc> <concept_significance>500</concept_significance> </concept> </ccs2012> \ccsdesc[500]Theory of computation Randomness, geometry and discrete structures \fundingThis work was supported in part by NSF grant CCF-2006125. \EventEditorsJohn Q. Open and Joan R. Access \EventNoEds2 \EventLongTitle42nd Conference on Very Important Topics (CVIT 2016) \EventShortTitleCVIT 2016 \EventAcronymCVIT \EventYear2016 \EventDateDecember 24–27, 2016 \EventLocationLittle Whinging, United Kingdom \EventLogo \SeriesVolume42 \ArticleNo23

The Geometry of Cyclical Social Trends

Bernard Chazelle    Kritkorn Karntikoon    Jakob Nogler
Abstract

We investigate the emergence of periodic behavior in opinion dynamics and its underlying geometry. For this, we use a bounded-confidence model with contrarian agents in a convolution social network. This means that agents adapt their opinions by interacting with their neighbors in a time-varying social network. Being contrarian, the agents are kept from reaching consensus. This is the key feature that allows the emergence of cyclical trends. We show that the systems either converge to nonconsensual equilibrium or are attracted to periodic or quasi-periodic orbits. We bound the dimension of the attractors and the period of cyclical trends. We exhibit instances where each orbit is dense and uniformly distributed within its attractor. We also investigate the case of randomly changing social networks.

keywords:
opinion dynamics, Minkowski sums, equidistribution, periodicity

1 Introduction

Much of the work in the area of opinion dynamics has focused on consensus and polarization [3, 14]. Typical questions include: How do agents come to agree or disagree? How do exogenous forces drive them to consensus? How long does it take for opinion formation to settle? Largely left out of the discussion has been the emergence of cyclical trends. Periodic patterns in opinions and preferences is a complex, multifactorial social phenomenon beyond the scope of this work [24]. A question worth examining, however, is whether the process conceals deeper mathematical structure. The purpose of this work is to show that it is, indeed, the case.

This work began with a thought experiment and a computer simulation. The latter revealed highly unexpected behavior, which in turn compelled us to search for an explanation. Our main result is a proof that adding a simple contrarian rule to the classic bounded-confidence model suffices to produce quasi-periodic trajectories. The model is a slight variant of the classic HK framework: a finite collection of agents hold opinions on several topics, which they update at discrete time steps by consulting their neighbors in a (time-varying) social network. The modification is the addition of a simple repulsive force field that keep agents away from tight consensus. The idea is partly inspired by swarming dynamics. For example, birds refrain from flocking too closely. Likewise, near-consensus on a large enough scale tends to induce contrarian reactions among agents [1, 20]. Some political scientists have pointed to contrarianism as one of the reasons for the closeness of some national elections [19, 18].

One of the paradoxical observations we sought to elucidate was why cyclic trends in social networks seem oblivious to the initial opinions of one’s friends: specifically, it is not specific distributions of initial opinions that produce oscillations but, rather, the recurrence of certain symmetries in the networks. We prove that the condition is sufficient (though its necessity is still open). Another mystery was why contrarian opinions tend to orbit toward an attractor whose dimensionality is independent of the number of opinions held by a single agent. These attracting sets are typically Minkowski sums of ellipses. They emerge algorithmically and constitute a natural focus of interest in distributed computational geometry.

Our inquiry builds on the pioneering work of French [16], DeGroot [10], Friedkin & Johnsen [17], and Deffuant et al. [9]. The model we use is a minor modification of the bounded-confidence model model [5, 21]. A Hegselmann-Krause (HK) system consists of nn agents, each one represented by a point in d{\mathbb{R}}^{d}. The dd coordinates for each agent ii represent their current opinions on dd different topics: thus, dd is the dimension of the opinion space. At any (discrete) time, each agent ii moves to the mass center of the agents within a fixed distance rir_{i}, which represents its radius of influence (Fig. 1). This step is repeated ad infinitum. Formally, the agents are positioned at x1(t),,xn(t)dx_{1}(t),\ldots,x_{n}(t)\in{\mathbb{R}}^{d} at time tt and for any t=0,1,2,t=0,1,2,\ldots\,,

xi(t+1)=1|𝒩i(t)|j𝒩i(t)xj(t),with 𝒩i(t)={ 1jn:xi(t)xj(t)2ri}.x_{i}(t+1)=\frac{1}{|\mathcal{N}_{i}(t)|}\sum_{j\in\mathcal{N}_{i}(t)}x_{j}(t)\,,\;\text{with }\mathcal{N}_{i}(t)=\Bigl{\{}\,1\leq j\leq n\,:\,\big{\|}x_{i}(t)-x_{j}(t)\big{\|}_{2}\leq r_{i}\,\Bigr{\}}. (1)
Refer to caption
Figure 1: The evolution of 20,000 random points in an HK system.

Interpreting each 𝒩i(t)\mathcal{N}_{i}(t) as the set of neighbors of agent ii defines the social network GtG_{t} at time tt. In the special case where all the radii of influence are equal (ri=R)(r_{i}=R), convergence into fixed-point clusters occurs within a polynomial number of steps [4, 13, 25]. Computer simulation suggests that the same remains true even when the radii differ but a proof has remained elusive. For cyclical trends to emerge, the social networks require a higher degree of underlying structure. In this work, we assume vertex transitivity (via Cayley graphs), which stipulates that agents cannot be distinguished by their local environment. Before defining the model formally in the next section, we summarize our main findings.

  • Undirected networks always drive the agents to nonconsensual convergence, ie, to fixed points at which they “agree to disagree.” For their behavior to become periodic or quasi-periodic, the social networks need to be directed. We prove that such systems either converge or are attracted to periodic or quasi-periodic orbits. We give precise formulas for the orbits.

  • We investigate the geometry of the attractors (Fig. 2). We bound the rotation number, which indicates the speed at which (quasi)-periodic opinions undergo a full cycle. We exhibit instances where each limiting orbit forms a set that is dense and, in fact, uniformly distributed on its attractor.

  • We explore the case of social networks changing randomly at each step. We prove the surprising result that the dimension of the attractor can decrease because of the randomization. This is a rare case where adding entropy to a system can reduce its dimensionality.

The dynamics of contrarian views has been studied before [1, 11, 15, 19, 18, 20, 26] but, to our knowledge, not for the purpose of explaining cyclical trends. Our mathematical findings can be viewed as a grand generalization of the affine-invariant evolution of planar polygons studied in [6, 8, 12, 22].

Refer to caption
Figure 2: Typical attractors.

2 Contrarian Opinion Dynamics

The social network is a time-dependent Cayley graph over an abelian group. All finite abelian groups are isomorphic to a direct sum of cyclic groups (/n1)(/nm)(\mathbb{Z}/n_{1}\mathbb{Z})\oplus\cdots\oplus(\mathbb{Z}/n_{m}\mathbb{Z}). For notational convenience, we set ni=nn_{i}=n. We regard the toral grid V=(/n)mV=(\mathbb{Z}/n\,\mathbb{Z})^{m} as a vector space, and we write N=|V|=nmN=|V|=n^{m}. Let xv(t)x_{v}(t) be the position of agent vv in d\mathbb{R}^{d} at time tt. We fix xv(0)x_{v}(0) and abbreviate it as xvx_{v}. Choose pp such that 1/N<p<11/N<p<1 and let (Ct)t0(C_{t})_{t\geq 0} be an infinite sequence of subsets of VV. For technical convenience, we assume that each set CtC_{t} spans the vector space VV; hence |Ct|m|C_{t}|\geq m. In the spirit of HK systems, we define the dynamics as follows: for t=0,1,,t=0,1,\ldots,

xv(t+1)=pxv(t)+1p|Ct|wv+Ctxw(t).x_{v}(t+1)=px_{v}(t)+\frac{1-p}{|C_{t}|}\sum_{w\in v+C_{t}}x_{w}(t). (2)

Because of the presence of the “self-confidence” weight pp, we may assume that the convolution set CtC_{t} does not contain the origin 𝟎\mathbf{0}. If we view each xv(t)x_{v}(t) as a row vector in d\mathbb{R}^{d}, the update (2) specifies an NN-by-NN stochastic matrix FCtF_{C_{t}}. Let x(t)x(t) denote the NN-by-dd matrix whose rows are the NN agent positions xv(t)x_{v}(t), for vVv\in V. We have x(t+1)=FCtx(t)x(t+1)=F_{C_{t}}x(t). The matrix FCtF_{C_{t}} may not be symmetric but it is always doubly-stochastic. This means that the mass center 𝟏x(t)/N\mathbf{1}^{\top}x(t)/N is time-invariant. Since the dynamics itself is translation-invariant, we are free to move the mass center to the origin, which we do by assuming 𝟏x=𝟎\mathbf{1}^{\top}x=\mathbf{0}^{\top}, where xx denotes x(0)x(0).

Obviously, some initial conditions are uninteresting: for example, x=𝟎x=\mathbf{0}. For this reason, we choose xx randomly; specifically, each xvx_{v} is picked iid from the dd-dimensional normal distribution 𝒩(𝟎,1)\mathcal{N}(\mathbf{0},1). In the following, we use the phrase “with high probability,” to refer to an event occurring with probability at least 1ε1-\varepsilon, for any fixed ε>0\varepsilon>0. Once we’ve picked the matrix xx randomly, we place the mass center of the agents at the origin by subtracting its displacement from the origin: xx1N𝟏𝟏xx\leftarrow x-\frac{1}{N}\mathbf{1}\mathbf{1}^{\top}x.

The agents will be attracted to the origin to form a single-point cluster of consensus in the limit. Responding to their contrarian nature, the agents will restore mutual differences by boosting the own opinions. For that reason we consider the scaled dynamics: y(0)=xy(0)=x and, for t0t\geq 0,

y(t+1)=ξtFCty(t),y(t+1)=\xi_{t}F_{C_{t}}y(t), (3)

where ξt\xi_{t} is chosen so that the diameter of the system remains roughly constant. Since scaling leaves the salient topological and geometric properties of the dynamics unchanged, the precise definition of ξt\xi_{t} can vary to fit analytical (or even visual) needs.

2.1 Preliminaries

We define the directed graph GCt=(V,Et)G_{C_{t}}=(V,E_{t}) at time t0t\geq 0, where Et={(v,v+c)|vV,cCt}E_{t}=\bigcup\,\{(v,v+c)\,|\,v\in V,c\in C_{t}\} and Nv={(v,w)|wv+CtV}N_{v}=\{(v,w)\,|\,w\in v+C_{t}\subseteq V\}. For clarity, we drop the subscript tt for the remainder of this section; so we write CC for CtC_{t}.

Lemma 2.1.

The convolution set CC spans the vector space VV if and only if the graph GCG_{C} is strongly connected.

Proof 2.2.

If CC spans VV, then for any pair u,vVu,v\in V, there exist ah/na_{h}\in\mathbb{Z}/n\mathbb{Z}, for each hCh\in C, such that vu=hCahhv-u=\sum_{h\in C}a_{h}h. The right-hand side specifies hah\sum_{h}a_{h} edges (sum taken over \mathbb{N}) that form a path from uu to vv ; therefore GG is strongly connected. Conversely, assuming the latter, there is a path from uu to vv: (w1,w2),,(wk1,wk)(w_{1},w_{2}),\ldots,(w_{k-1},w_{k}), with w1=uw_{1}=u and wk=vw_{k}=v. Thus, vu=iciv-u=\sum_{i}c_{i}, where ci=wiwi1Cc_{i}=w_{i}-w_{i-1}\in C; therefore CC spans VV.

Our assumption about CC implies that each GCG_{C} is strongly connected. The presence of the weight p>0p>0 in (2) ensures that the diagonal of FCF_{C} is positive. Together with the strong connnectivity assumption, this makes the matrix FCF_{C} primitive, meaning that FCk>0F_{C}^{k}>0, for some k>0k>0. By the Perron-Frobenius theorem [27], all the eigenvalues of FCF_{C} lie strictly inside the unit circle in \mathbb{C}, except for the dominant eigenvalue 1, which has multiplicity 11. For any u,vVu,v\in V, we write ψuv=ωu,v\psi_{u}^{v}=\omega^{\langle u,v\rangle}, where ω:=e2πi/n\omega:=e^{2\pi i/n}. We define the vector ψv=(ψuv|uV)\psi^{v}=(\psi_{u}^{v}\,|\,u\in V) and easily verify that {ψv|vV}\{\psi^{v}\,|\,v\in V\} forms an orthogonal eigenbasis for FCF_{C}. The eigenvalue λv\lambda_{v} corresponding to ψv\psi^{v} satisfies

λvψuv=pψuv+1p|C|wu+Cψwv=pψuv+1p|C|(hCψhv)ψuv.\lambda_{v}\psi_{u}^{v}=p\psi_{u}^{v}+\frac{1-p}{|C|}\sum_{w\in u+C}\psi_{w}^{v}=p\psi_{u}^{v}+\frac{1-p}{|C|}\Big{(}\sum_{h\in C}\psi_{h}^{v}\Big{)}\psi_{u}^{v}\,.

We conclude:

Lemma 2.3.

Each vVv\in V corresponds to a distinct eigenvector ψv\psi^{v}, which together form an orthogonal basis for N\mathbb{C}^{N}. The corresponding eigenvalue is given by

λv=p+1p|C|hCωv,h.\lambda_{v}=p+\frac{1-p}{|C|}\sum_{h\in C}\omega^{\langle v,h\rangle}.

We define λ=maxvV{|λv|<1}\lambda=\max_{v\in V}\{|\lambda_{v}|<1\} and denote by W={vV:|λv|=λ}W=\{v\in V:|\lambda_{v}|=\lambda\} the set of subdominant eigenvectors. The argument of λv\lambda_{v} plays a key role in our discussion, so we define θv\theta_{v} such that λv=|λv|ωθv\lambda_{v}=|\lambda_{v}|\omega^{\theta_{v}}, with θv(n/2,n/2]\theta_{v}\in(-n/2,n/2]. By (6), λv0\lambda_{v}\neq 0 for vWv\in W, so θv\theta_{v} is well defined.

2.2 The evolution of opinions

We begin with the case of a fixed convolution set Ct=CC_{t}=C. The initial position of the agents is expressed in eigenspace as x=1NvVψv(ψv)Hxx=\frac{1}{N}\sum_{v\in V}\psi^{v}(\psi^{v})^{\mathrm{H}}x. Let zvz_{v} denote the row vector (ψv)Hx=uVωv,uxu(\psi^{v})^{\mathrm{H}}x=\sum_{u\in V}\omega^{-\langle v,u\rangle}x_{u}. Because (ψv)Hx=𝟏x=𝟎(\psi^{v})^{\mathrm{H}}x=\mathbf{1}^{\top}x=\mathbf{0}^{\top}, for v=𝟎Vv=\mathbf{0}\in V,

x(t)=1NvV{𝟎}λvtψvzv.x(t)=\frac{1}{N}\sum_{v\in V\setminus\{\mathbf{0}\}}\lambda_{v}^{t}\psi^{v}z_{v}. (4)
Lemma 2.4.

With high probability, for all v𝟎v\neq\mathbf{0},

Ω(1/N)=zv2=O(dNlogdN).\Omega\big{(}\sqrt{1/N}\,\big{)}=\|z_{v}\|_{2}=O\big{(}\sqrt{dN\log dN}\,\big{)}.
Proof 2.5.

Let a=(au)uVa=(a_{u})_{u\in V} be the first column of the matrix xx. For each uVu\in V, by the initialization of the system, au=ζuδa_{u}=\zeta_{u}-\delta, where ζu𝒩(0,1)\zeta_{u}\sim\mathcal{N}(0,1) and δ=1N𝟏ζ\delta=\frac{1}{N}\mathbf{1}^{\top}\zeta. Given v𝟎v\neq\mathbf{0}, ψv\psi^{v} is orthogonal to ψ𝟎=𝟏\psi^{\mathbf{0}}=\mathbf{1}; hence (ψv)Ha=(ψv)H(ζδ𝟏)=(ψv)Hζ(\psi^{v})^{\mathrm{H}}a=(\psi^{v})^{\mathrm{H}}(\zeta-\delta\mathbf{1})=(\psi^{v})^{\mathrm{H}}\zeta. Since the random vector ζ\zeta is unbiased and |ωv,u|=1|\omega^{-\langle v,u\rangle}|=1, it follows that var[(ψv)Ha]=uVvarζu=N\hbox{\rm var}\,\big{[}(\psi^{v})^{\mathrm{H}}a\big{]}=\sum_{u\in V}\hbox{\rm var}\,\zeta_{u}=N. Thus, the first coordinate zv,1z_{v,1} of zvz_{v} is of the form a+iba+ib, where aa and bb are sampled (not independently) from 𝒩(0,σ12)\mathcal{N}(0,\sigma_{1}^{2}) and 𝒩(0,σ22)\mathcal{N}(0,\sigma_{2}^{2}), respectively, such that σ12+σ22=N\sigma_{1}^{2}+\sigma_{2}^{2}=N. Thus, |zv,1|δ|z_{v,1}|\leq\delta with probability at most 2δ/πN2\delta/\sqrt{\pi N}. Conversely, by the inequality erfc(z)ez2(z)\leq e^{-z^{2}} for z>0z>0, we find that |zv,1|=O(Nlog(dN/ε))|z_{v,1}|=O(\sqrt{N\log(dN/\varepsilon)}\,), with probability at least 1ε/dN1-\varepsilon/dN, for any 0<ε<10<\varepsilon<1; hence zv2=O(dNlog(dN/ε))\|z_{v}\|_{2}=O(\sqrt{dN\log(dN/\varepsilon)}\,), with probability at least 1ε/N1-\varepsilon/N. Setting δ=επ/4N\delta=\varepsilon\sqrt{\pi/4N} and using a union bound completes the proof.

We upscale the system by setting ξt=1/λ\xi_{t}=1/\lambda; hence y(t+1)=y(t)/λy(t+1)=y(t)/\lambda.

Theorem 2.6.

Let aha_{h} and bhb_{h} be the row vectors whose uu-th coordinates (uVu\in V) are cos(2πh,u/n)\cos(2\pi\langle h,u\rangle/n) and sin(2πh,u/n)\sin(2\pi\langle h,u\rangle/n), respectively. With high probability, for each vVv\in V, the agent vv is attracted to the trajectory of yv(t)y_{v}^{*}(t), where

yv(t)=1NhW(cos2π(tθh+h,v)n,sin2π(tθh+h,v)n)(ahbh)x.y_{v}^{*}(t)=\frac{1}{N}\sum_{h\in W}\left(\cos\frac{2\pi(t\theta_{h}+\langle h,v\rangle)}{n}\,,\,\sin\frac{2\pi(t\theta_{h}+\langle h,v\rangle)}{n}\right)\begin{pmatrix}a_{h}\\ b_{h}\end{pmatrix}x. (5)

Let μ:=max{|λv|/λ<1}\mu:=\max\{|\lambda_{v}|/\lambda<1\} be the third largest (upscaled) eigenvalue, measured in distinct moduli. The error of the approximation decays exponentially fast as a function of μ\mu:

yv(t)yv(t)Fyv(t)F=O(μtN2dlogdN).\frac{\|y_{v}^{*}(t)-y_{v}(t)\|_{F}}{\|y_{v}(t)\|_{F}}=O\big{(}\mu^{t}N^{2}\sqrt{d\log dN}\,\big{)}.
Proof 2.7.

Since the eigenvalues sum up to trFC=pNF_{C}=pN and 1 has multiplicity 1, we have pN1+(N1)λpN\leq 1+(N-1)\lambda; hence, by p>1/Np>1/N,

λpN1N1>0.\lambda\geq\frac{pN-1}{N-1}>0. (6)

Writing μv=λv/λ\mu_{v}=\lambda_{v}/\lambda and μ=max{|μv|<1}\mu=\max\{|\mu_{v}|<1\}, we have |μv|=1|\mu_{v}|=1 for vWv\in W; recall that W={vV:|λv|=λ}W=\{v\in V:|\lambda_{v}|=\lambda\}. By (4), it follows that

y(t)=1NvWμvtψvzv+η(t),y(t)=\frac{1}{N}\sum_{v\in W}\mu_{v}^{t}\psi^{v}z_{v}+\eta(t), (7)

where, by Lemma 2.4, with high probability,

η(t)F\displaystyle{\|\eta(t)\|}_{F} =1NvV(W{𝟎})μvtψvzvF\displaystyle=\Big{\|}\frac{1}{N}\sum_{v\in V\setminus(W\cup\{\mathbf{0}\})}\mu_{v}^{t}\psi^{v}z_{v}\Big{\|}_{F}
1NvV(W{𝟎})μtψv2zv2=O(μtNdlogdN).\displaystyle\leq\frac{1}{N}\sum_{v\in V\setminus(W\cup\{\mathbf{0}\})}\mu^{t}\|\psi^{v}\|_{2}\,\|z_{v}\|_{2}=O\big{(}\mu^{t}N\sqrt{d\log dN}\,\big{)}.

The lower bound of the lemma implies that, for any vWv\in W,

vWμvtψvzvF2\displaystyle\Big{\|}\sum_{v\in W}\mu_{v}^{t}\psi^{v}z_{v}\Big{\|}_{F}^{2} =tr(vWμvtψvzv)H(vWμvtψvzv)=tr{vWzvH(ψv)Hψvzv}\displaystyle=\,\mathrm{tr}\,\Big{(}\sum_{v\in W}\mu_{v}^{t}\psi^{v}z_{v}\Big{)}^{\mathrm{H}}\Big{(}\sum_{v\in W}\mu_{v}^{t}\psi^{v}z_{v}\Big{)}=\,\mathrm{tr}\,\Big{\{}\sum_{v\in W}z_{v}^{\mathrm{H}}(\psi^{v})^{\mathrm{H}}\psi^{v}z_{v}\Big{\}}
=Ntr{vWzvHzv}=NvWzv22Ω(1).\displaystyle=\,N\cdot\mathrm{tr}\,\Big{\{}\sum_{v\in W}z_{v}^{\mathrm{H}}z_{v}\Big{\}}=N\sum_{v\in W}\|z_{v}\|_{2}^{2}\geq\Omega(1).

For large enough t=Ω(log(dN)/log(1/μ))t=\Omega\big{(}\log(dN)/\log(1/\mu)\big{)}, the sum in (7) dominates η(t)\eta(t) with high probability, while the latter decays exponentially fast. Thus the dynamics y(t)y(t) is asymptotically equivalent to y(t)=1NvWμvtψvzvy^{*}(t)=\frac{1}{N}\sum_{v\in W}\mu_{v}^{t}\psi^{v}z_{v}. Recall that λv=|λv|ωθv\lambda_{v}=|\lambda_{v}|\omega^{\theta_{v}}; since, for vWv\in W, μv=λv/λ\mu_{v}=\lambda_{v}/\lambda has modulus 1, it is equal to ωθv\omega^{\theta_{v}}. This implies that yv(t)=1NhWuVωtθh+h,vuxuy_{v}^{*}(t)=\frac{1}{N}\sum_{h\in W}\,\sum_{u\in V}\omega^{t\theta_{h}+\langle h,v-u\rangle}x_{u}. Because yv(t)y_{v}^{*}(t) is real, we can ignore the imaginary part when expanding the expression above, which completes the proof.

2.3 Geometric investigations

The trajectory yv(t)y_{v}^{*}(t) is called the limiting orbit.111The phase space of the dynamical system is dN\mathbb{R}^{dN}, but by abuse of notation we use the word “orbit” to refer the trajectory of a single agent, which lies in d\mathbb{R}^{d}. Theorem 2.6 indicates that, with high probability, every orbit is attracted to its limiting form at an exponential rate, so we may focus on the latter. Given the initial placement xx of the agents, all the limiting orbits lie in the set 𝕊\mathbb{S}, expressed in parametric form by

𝕊=1NhW{(ahx)cosXh+(bhx)sinXh}.\mathbb{S}=\frac{1}{N}\sum_{h\in W}\big{\{}(a_{h}x)\cos X_{h}+(b_{h}x)\sin X_{h}\big{\}}. (8)

Recall that ahxa_{h}x and bhxb_{h}x are row vectors in d\mathbb{R}^{d}. The attractor 𝕊\mathbb{S} is the Minkowski sum of a number of ellipses. We examine the geometric structure 𝕊\mathbb{S} and explain how the limiting orbits embed into it. To do that, we break up the sum (5) into three parts. Given hWh\in W, we know that λh0\lambda_{h}\neq 0 by (6), so there remain the following cases for the subdominant eigenvalues:

  • real λh>0\lambda_{h}>0: the contribution to the sum is cvxc_{v}x, where cvc_{v} is the row vector

    cv:=1NhW:θh=0{ahcos2πh,vn+bhsin2πh,vn}.c_{v}:=\frac{1}{N}\sum_{h\in W:\,\theta_{h}=0}\Big{\{}\,a_{h}\cos\frac{2\pi\langle h,v\rangle}{n}+b_{h}\sin\frac{2\pi\langle h,v\rangle}{n}\,\Big{\}}. (9)
  • real λh<0\lambda_{h}<0: the contribution is (1)tdvx(-1)^{t}d_{v}x, where, likewise, dvd_{v} is the row vector

    dv:=1NhW:θh=n/2{ahcos2πh,vn+bhsin2πh,vn}.d_{v}:=\frac{1}{N}\sum_{h\in W:\,\theta_{h}=n/2}\Big{\{}\,a_{h}\cos\frac{2\pi\langle h,v\rangle}{n}+b_{h}\sin\frac{2\pi\langle h,v\rangle}{n}\,\Big{\}}. (10)
  • nonreal λh\lambda_{h}: we can assume that θh>0\theta_{h}>0 since the conjugate eigenvalue λ¯h=λh\bar{\lambda}_{h}=\lambda_{-h} is also present in WW. The contribution of an eigenvalue is the same as that of its conjugate since ah=aha_{h}=a_{-h} and bh=bhb_{h}=-b_{-h}. So the contribution of a given θ>0\theta>0 is equal to ev,θxe_{v,\theta}x, where

    ev,θ:=2NhW:θh=θ{ahcos2π(tθ+h,v)n+bhsin2π(tθ+h,v)n},e_{v,\theta}:=\frac{2}{N}\sum_{h\in W:\,\theta_{h}=\theta}\left\{\,a_{h}\cos\frac{2\pi(t\theta+\langle h,v\rangle)}{n}+b_{h}\sin\frac{2\pi(t\theta+\langle h,v\rangle)}{n}\,\right\},

    which we can expand as av,θcos2πθtn+bv,θsin2πθtna_{v,\theta}\cos\frac{2\pi\theta t}{n}+b_{v,\theta}\sin\frac{2\pi\theta t}{n}, where222 R(α)=(cosαsinαsinαcosα)R(\alpha)=\begin{pmatrix}\cos\alpha&-\sin\alpha\\ \sin\alpha&\cos\alpha\end{pmatrix}.

    (av,θbv,θ)=2NhW:θh=θR(2πh,vn)(ahbh).\begin{pmatrix}a_{v,\theta}\\ b_{v,\theta}\end{pmatrix}=\frac{2}{N}\sum_{h\in W:\,\theta_{h}=\theta}R\left(\frac{-2\pi\langle h,v\rangle}{n}\right)\begin{pmatrix}a_{h}\\ b_{h}\end{pmatrix}. (11)

Putting all three contributions together, we find

yv(t)=cvx+(1)tdvx+θϑ{av,θcos2πθtn+bv,θsin2πθtn}x,y_{v}^{*}(t)=c_{v}x+(-1)^{t}d_{v}x+\sum_{\theta\in\vartheta}\Big{\{}\,a_{v,\theta}\cos\frac{2\pi\theta t}{n}+b_{v,\theta}\sin\frac{2\pi\theta t}{n}\,\Big{\}}x, (12)

where ϑ\vartheta is the set of distinct θh>0\theta_{h}>0 for hWh\in W and all other quantities are defined in (9, 10, 11). See Figure 3 for an illustration of a doubly-elliptical orbit around its torus-like attractor.

Refer to caption
Figure 3: Two orbits of a single agent around its attractor.

2.3.1 Generic elliptical attraction

We prove that, for almost all values of the self-confidence weight pp, the set WW generates either a single real eigenvalue or two complex conjugate ones. By (12), this shows that almost every limiting orbit is either a single fixed point or a subset of an ellipse in d\mathbb{R}^{d}.

Theorem 2.8.

There exists a set Λ\Lambda of at most (N2)\binom{N}{2} reals such that the set WW is associated with either a single real eigenvalue or two complex conjugate ones, for any p(1/N,1)Λp\in(1/N,1)\setminus\Lambda.

The system is called regular if p(1/N,1)Λp\in(1/N,1)\setminus\Lambda. For such a system, either (i) ϑ={θ}\vartheta=\{\theta\} and cv=dv=𝟎c_{v}=d_{v}=\mathbf{0}, or (ii) ϑ=\vartheta=\emptyset and exactly one of cvc_{v} or dvd_{v} equals 𝟎\mathbf{0}. In other words, by (12), we have three cases depending on the subdominant eigenvalues:

yv(t)={cvx:real positive(1)tdvx:real negative(av,θcos2πθtn+bv,θsin2πθtn)x:conjugate pair.y_{v}^{*}(t)=\begin{cases}\,\,c_{v}x&:\text{\small\emph{real positive}}\\ \,\,(-1)^{t}d_{v}x&:\text{\small\emph{real negative}}\\ \,\,\big{(}\,a_{v,\theta}\cos\frac{2\pi\theta t}{n}+b_{v,\theta}\sin\frac{2\pi\theta t}{n}\,\big{)}x&:\text{\small\emph{conjugate pair}.}\\ \end{cases} (13)
Lemma 2.9.

Consider a triangle abcabc and let e=pc+(1p)ae=pc+(1-p)a and f=pc+(1p)bf=pc+(1-p)b. Let OO be the origin and assume that the segments OeOe and OfOf are of the same length (Fig. 4); then the identity |a|2|b|2=2p1p(ba)c|a|^{2}-|b|^{2}=\frac{2p}{1-p}(b-a)\cdot c holds.

Proof 2.10.

Let d:=12(e+f)d:=\frac{1}{2}(e+f) be the midpoint of efef. The segment OdOd lies on the perpendicular bisector of efef, so it is orthogonal to efef; hence to abab. Thus, d(ba)=0d\cdot(b-a)=0. Since d=12(2pc+(1p)a+(1p)b)d=\frac{1}{2}(2pc+(1-p)a+(1-p)b), the lemma follows from (2pc+(1p)(a+b))(ba)=0(2pc+(1-p)(a+b))\cdot(b-a)=0.

OOeeff\mid\paralleldd\parallel\midccbbaa
Figure 4: A triangle identity.
Proof 2.11 (Proof of Theorem 2.8).

Pick two distinct u,vWu,v\in W. Applying Lemma 2.9 in the complex plane, we set: a=1|C|hCωu,ha=\frac{1}{|C|}\sum_{h\in C}\omega^{\langle u,h\rangle}; b=1|C|hCωv,hb=\frac{1}{|C|}\sum_{h\in C}\omega^{\langle v,h\rangle}; and c=1c=1; thus e=λue=\lambda_{u} and f=λvf=\lambda_{v}, which implies that the segments OeOe and OfOf are of the same length. Abusing notation by treating a,b,ca,b,c as both vectors and complex numbers, we have (ba)c=(ba)(b-a)\cdot c=\Re(b-a); therefore,

(2(ba)+|a|2|b|2)p=|a|2|b|2.\big{(}2\Re(b-a)+|a|^{2}-|b|^{2}\big{)}p=|a|^{2}-|b|^{2}.
  1. 1.

    If 2(ba)+|a|2|b|2=02\Re(b-a)+|a|^{2}-|b|^{2}=0, then |a|=|b||a|=|b|, which in turn implies that (ba)=0\Re(b-a)=0; hence a=b¯a=\bar{b} and λu=λ¯v\lambda_{u}=\bar{\lambda}_{v}.

  2. 2.

    If 2(ba)+|a|2|b|202\Re(b-a)+|a|^{2}-|b|^{2}\neq 0, then pp is unique: p=pu,vp=p_{u,v}.

We form Λ\Lambda by including all of the numbers pu,vp_{u,v}, with u,vWu,v\in W.

In some cases, regularity holds with no need to exclude values of pp:

Theorem 2.12.

If CC forms a basis of VV and nn is prime, then |W|=2m|W|=2m and WW produces exactly two eigenvalues: p+1pm(ω1)p+\frac{1-p}{m}(\omega-1) and its conjugate.

Proof 2.13.

By Lemma 2.3, λv=p+1p|C|hCωv,h\lambda_{v}=p+\frac{1-p}{|C|}\sum_{h\in C}\omega^{\langle v,h\rangle}. Fix nonzero vVv\in V. Because nn is prime and the vectors h1,,hmh_{1},\ldots,h_{m} from CC form a basis over the field /n\mathbb{Z}/n\mathbb{Z}, the mm-by-mm matrix whose ii-th row is hih_{i} is nonsingular. This implies that, in the sum hCωv,h\sum_{h\in C}\omega^{\langle v,h\rangle}, the exponent sequence (1,0,,0)(1,0,\ldots,0) appears for exactly one value vVv\in V and the same is true of (1,0,,0)(-1,0,\ldots,0). This holds true of any one-hot vector with a single ±1\pm 1 at any place and 0 elsewhere. This means that, for 2m2m values of vv, the eigenvalue λv\lambda_{v} is of the form p+1pm(m1+ω)p+\frac{1-p}{m}(m-1+\omega) or its conjugate. Simple examination shows that these are precisely the subdominant eigenvalues.

2.3.2 The case of cycle convolutions

It is useful to consider the case of a single cycle: m=1m=1. For convenience, we momentarily assume that nn is prime and that hCh0(modn)\sum_{h\in C}h\neq 0\pmod{n}; both assumptions will be dropped in subsequent sections.

Lemma 2.14.

Each eigenvalue λv\lambda_{v} is simple.

Proof 2.15.

Because nn is prime, the cyclotomic polynomial for ω\omega is Φ(z)=zn1+zn2++z+1\Phi(z)=z^{n-1}+z^{n-2}+\cdots+z+1. It is the minimal polynomial for ω\omega, which is unique. Note that v,h=vh\langle v,h\rangle=vh, since m=1m=1. Given vVv\in V, we define the polynomial gv(z)=hCzvhg_{v}(z)=\sum_{h\in C}z^{vh} in the quotient ring of rational polynomials [z]/(zn1)\mathbb{Q}[z]/(z^{n}-1). Sorting the summands by degree modulo nn, we have gv(z)=k=0n1qv,kzkg_{v}(z)=\sum_{k=0}^{n-1}q_{v,k}z^{k}, for nonnegative integers qv,kq_{v,k}, where kqv,k=|C|\sum_{k}q_{v,k}=|C|. If λv=λu\lambda_{v}=\lambda_{u}, for some uVu\in V, then, by Lemma 2.3, gv(ω)=gu(ω)g_{v}(\omega)=g_{u}(\omega); hence Φ\Phi divides gvgug_{v}-g_{u}. Because the latter is of degree at most n1n-1, it is either identically zero or equal to Φ\Phi up to a rational factor r0r\neq 0. In the second case,

(qv,n1qu,n1)zn1++(qv,1qu,1)z+qv,0qu,0=rΦ.(q_{v,n-1}-q_{u,n-1})z^{n-1}+\cdots+(q_{v,1}-q_{u,1})z+q_{v,0}-q_{u,0}=r\Phi.

This implies that qv,kqu,k=r0q_{v,k}-q_{u,k}=r\neq 0, for all 0k<n0\leq k<n, which contradicts the fact that kqv,k=kqu,k=|C|\sum_{k}q_{v,k}=\sum_{k}q_{u,k}=|C|; therefore, gv=gug_{v}=g_{u}.

  1. 1.

    If v=0v=0, then gv(z)=|C|g_{v}(z)=|C|; hence gu(z)=|C|g_{u}(z)=|C| and u=0u=0, ie, v=uv=u.

  2. 2.

    If v0v\neq 0, then let Sv={ωvh|hC}S_{v}=\{\omega^{vh}\,|\,h\in C\}. Because /n\mathbb{Z}/n\mathbb{Z} is a field, the |C||C| roots of unity in SvS_{v} are distinct; hence qv,k{0,1}q_{v,k}\in\{0,1\}. It follows that Sv=SuS_{v}=S_{u} and |Sv|=|Su|=|C||S_{v}|=|S_{u}|=|C|; therefore, for some permutation σ\sigma of order |C||C|, we have vh=uσ(h)vh=u\sigma(h), for all hCh\in C. Summing up both sides over hCh\in C gives us vhCh=uhCh(modn)v\sum_{h\in C}h=u\sum_{h\in C}h\pmod{n}; hence v=uv=u, since hCh0(modn)\sum_{h\in C}h\neq 0\pmod{n}.

By (13), the limiting orbit is of the form yv(t)=cvxy_{v}^{*}(t)=c_{v}x or yv(t)=(1)tdvxy_{v}^{*}(t)=(-1)^{t}d_{v}x if the subdominant eigenvalue is real. Otherwise, the orbit of an agent approaches a single ellipse in d\mathbb{R}^{d}: for some θ>0\theta>0, yv(t)=(av,θcos2πθtn+bv,θsin2πθtn)xy_{v}^{*}(t)=\left(\,a_{v,\theta}\cos\frac{2\pi\theta t}{n}+b_{v,\theta}\sin\frac{2\pi\theta t}{n}\,\right)x.

2.3.3 Opinion velocities

Assume that the system is regular, so WW is associated with either a single real eigenvalue or two complex conjugate ones. If ϑ=\vartheta=\emptyset, by (12), every agent converges to a fixed point of the attractor 𝕊\mathbb{S} or its limiting orbit has a period of 2. The other case ϑ={θ}\vartheta=\{\theta\} is more interesting. The agent approaches its limiting orbit, which is periodic or quasi-periodic. The rotation number, α:=θ/n\alpha:=\theta/n, is the (average) fraction of a whole rotation covered in a single step. It measures the speed at which the agent cycles around its orbit. We prove a lower bound on that speed.333Its upper bound is 1/21/2.

Theorem 2.16.

The rotation number α\alpha of a regular system satisfies α1pn(12N)N\alpha\geq\frac{1-p}{n}\left(\frac{1}{2N}\right)^{N}.

Proof 2.17.

Of course, this assumes that ϑ\vartheta\neq\emptyset. Fix vVv\in V and let βv=wC(ωv,wωv,w)\beta_{v}=\sum_{w\in C}\big{(}\omega^{\langle v,w\rangle}-\omega^{-\langle v,w\rangle}\bigr{)}; further, assume that βv\beta_{v} is nonzero, hence imaginary. We have βvψuv=guψv\beta_{v}\,\psi_{u}^{v}=g_{u}^{\top}\psi^{v}, where gug_{u} is a vector in {1,0,1}N\{-1,0,1\}^{N}. It follows that βvψv=Aψv\beta_{v}\,\psi^{v}=A\psi^{v}, for an NN-by-NN matrix AA whose nonzero elements are ±1\pm 1 and whose rows are given by gug_{u}^{\top}. Thus, βv\beta_{v} is an imaginary eigenvalue of AA; hence a complex root of the characteristic polynomial det(Aγ𝕀)\det(A-\gamma\mathbb{I}). Let r1r\geq 1 be the rank of AA and let γ1,,γr\gamma_{1},\ldots,\gamma_{r} be its nonzero eigenvalues. Expansion of the determinant gives us a sum of monomials of the form biγlib_{i}\gamma^{l_{i}}, for 1i2NN!1\leq i\leq 2^{N}N!, where bi{1,0,1}b_{i}\in\{-1,0,1\}. The subset of them given by li=Nrl_{i}=N-r add up to the product of the nonzero eigenvalues (times ±γNr\pm\gamma^{N-r}); hence i=1r|γi|1\prod_{i=1}^{r}|\gamma_{i}|\geq 1. Label γ1\gamma_{1} the nonzero eigenvalue of smallest modulus. The sum of the squared moduli of the eigenvalues of a matrix is at most the square of its Frobenius norm; hence no eigenvalue of AA can have a modulus larger than 2N|C|\sqrt{2N|C|} and, therefore

|βv||γ1|=i=1r|γi|i=2r|γi|(12N|C|)r12.|\beta_{v}|\geq|\gamma_{1}|=\frac{\prod_{i=1}^{r}|\gamma_{i}|}{\prod_{i=2}^{r}|\gamma_{i}|}\geq\left(\frac{1}{2N|C|}\right)^{\frac{r-1}{2}}. (14)

Since hWh\in W, it follows from (6) that 0<λ=|λh|<10<\lambda=|\lambda_{h}|<1. Thus,

|θh||sinθh|=|(λh)||λh|1p|C||{wCωh,w}|1p2|C||βh|.|\theta_{h}|\geq|\sin\theta_{h}|=\frac{|\Im(\lambda_{h})|}{|\lambda_{h}|}\geq\frac{1-p}{|C|}\,\Big{|}\Im\Big{\{}\,\sum_{w\in C}\omega^{\langle h,w\rangle}\,\Big{\}}\Big{|}\geq\frac{1-p}{2|C|}\,|\beta_{h}|\,.

With λh\lambda_{h} assumed to be nonreal, Lemma 2.3 implies that so is βh\beta_{h}; hence βh0\beta_{h}\neq 0. Applying (14) completes the proof.

Our next result formalizes the intuitive fact that self-confidence slows down motion. Self-assured agents are reluctant to change opinions.

Theorem 2.18.

The rotation number of a regular system cannot increase with pp.

Proof 2.19.

We must have |ϑ|=1|\vartheta|=1. Let λh\lambda_{h} be (an) eigenvalue corresponding to the unique angle in ϑ\vartheta; recall that 0<θh<n/20<\theta_{h}<n/2. As we replace pp by p>pp^{\prime}>p, we use the prime sign with all relevant quantities post-substitution. Thus, the subdominant eigenvalue for pp^{\prime} associated with ϑ\vartheta^{\prime} is denoted by λv\lambda_{v}^{\prime}; again, we assume that |ϑ|=1|\vartheta^{\prime}|=1. Note that vv might not necessarily be equal to hh; hence the case analysis:

  • v=hv=h: Using the same notation for complex numbers and the points in the plane they represent (Fig. 5), we see that λh\lambda_{h}^{\prime} lies in (the relative interior of) the segment 1λh1\lambda_{h}; hence θh<θh\theta_{h}^{\prime}<\theta_{h}.

  • vhv\neq h: We prove that, as illustrated in Fig. 5, all three conditions |λh|>|λv||\lambda_{h}|>|\lambda_{v}|, |λh|<|λv||\lambda_{h}^{\prime}|<|\lambda_{v}^{\prime}|, and θh<θvn/2\theta_{h}<\theta_{v}^{\prime}\leq n/2, cannot hold at the same time, which will establish the theorem. If we increase qq continuously from pp to pp^{\prime}, θh(q)\theta_{h}(q) decreases continuously. (We use the argument qq to denote the fact that θh\theta_{h} corresponds to the eigenvalue defined with pp replaced by qq.) Since, at the end of that motion, |λh(q)|<|λv(q)||\lambda_{h}(q)|<|\lambda_{v}(q)|, by continuity we have po<pp_{o}<p^{\prime}, where po=min{q>p:|λh(q)|=|λv(q)|}p_{o}=\min\{q>p\,:\,|\lambda_{h}(q)|=|\lambda_{v}(q)|\}. To simplify the notation, we repurpose the use of the prime superscript to refer to pop_{o} (eg, p=pop^{\prime}=p_{o}). So, we now have |λh|=|λv||\lambda_{h}^{\prime}|=|\lambda_{v}^{\prime}| and θh<θv<θvn/2\theta_{h}<\theta_{v}^{\prime}<\theta_{v}\leq n/2. It follows that (i) the point λv\lambda_{v} lies in the pie slice of radius |λh||\lambda_{h}| running counterclockwise from λh\lambda_{h} to |λh|-|\lambda_{h}| on the real axis. Also, because |λh|=|λv||\lambda_{h}^{\prime}|=|\lambda_{v}^{\prime}| and |λh|>|λv||\lambda_{h}|>|\lambda_{v}|, setting c=1c=1 as before in Lemma 2.9 shows that (ii) (λv)>(λh)\Re(\lambda_{v})>\Re(\lambda_{h}).444The keen-eyed observer will notice that in the lemma we must plug in (pop)/(1p)(p_{o}-p)/(1-p) instead of pp. Putting (i, ii) together shows that θhn/4\theta_{h}\geq n/4 (as shown in Fig. 5). Consequently, the slope of the segment λhλv\lambda_{h}\lambda_{v} is negative. Since that segment is parallel to λhλv\lambda_{h}^{\prime}\lambda_{v}^{\prime}, the perpendicular bisector of the latter has positive slope. Since that bisector is above λv\lambda_{v}^{\prime} and (λv)0\Im(\lambda_{v}^{\prime})\geq 0, this implies that 0 and λh\lambda_{h}^{\prime} are on opposite sides of that bisector; hence |λv|<|λh||\lambda_{v}^{\prime}|<|\lambda_{h}^{\prime}|, which is a contradiction.

OO11λh\lambda_{h}θh\theta_{h}λv\lambda_{v}^{\prime}θv\theta_{v}^{\prime}λv\lambda_{v}λh\lambda_{h}^{\prime}\parallel\parallel
Figure 5: Why self-confidence slows down the dynamics: proof by contradiction.

2.4 Equidistributed orbits

The attractor 𝕊\mathbb{S} is the Minkowski sum of a number of ellipses bounded by |W||W|. An agent orbits around an ellipse as it gets attracted to it exponentially fast. In a regular system with ϑ\vartheta\neq\emptyset, its limiting orbit is periodic if the unique angle θh\theta_{h} of ϑ\vartheta is rational; it is quasi-periodic otherwise. In fact, it then forms a dense subset of the ellipse. By (12), this follows from Weyl’s ergodicity principle [23], which states that the set {αt(mod1),|t0}\{\alpha t\pmod{1},|\,t\geq 0\} is uniformly distributed in [0,1)[0,1), for any irrational α\alpha.

Dropping the regularity requirement may produce more exotic dynamics. We exhibit instances where a limiting orbit will not only be dense over the entire attracting set but, in fact, uniformly distributed. In other words, an agent will approach every possible opinion with equal frequency. This will occur when this property holds:555The coordinates of a=(a1,,ak)a=(a_{1},\ldots,a_{k}) are linearly independent over the rationals if 𝟎\mathbf{0} is the only rational vector normal to aa.

Assumption 1.

The numbers in ϑ{1}\vartheta\cup\{1\} are linearly independent over the rationals.

We explain this phenomenon next. Order the angles of ϑ\vartheta arbitrarily and define the vector α=(α1,,αs)[0,12]s\alpha=(\alpha_{1},\ldots,\alpha_{s})\in\big{[}0,\frac{1}{2}\big{]}^{s}, where s=|ϑ|s=|\vartheta| and αj=θ/n\alpha_{j}=\theta/n for the jj-th angle θϑ\theta\in\vartheta. We may assume that cv=dv=𝟎c_{v}=d_{v}=\mathbf{0} in (12) since these cases are rotationally trivial. By Assumption 1, 𝟎\mathbf{0} is the only integer vector whose dot product with α\alpha is an integer. We use the standard notation α/=maxksmina|αka|\|\alpha\|_{\,\mathbb{R}/\mathbb{Z}}=\max_{k\leq s}\min_{a\in\mathbb{Z}}|\alpha_{k}-a|. By Kronecker’s approximation theorem [7], for any β[0,1]s\beta\in[0,1]^{s} and any ε>0\varepsilon>0, there exists qq\in\mathbb{Z} such that qαβ/ε\|q\alpha-\beta\|_{\,\mathbb{R}/\mathbb{Z}}\leq\varepsilon. It follows directly that, with high probability, any limiting orbit is dense over the attractor 𝕊\,\mathbb{S}. We prove the stronger result:

Theorem 2.20.

Under Assumption 1, any limiting orbit is uniformly distributed over the attractor 𝕊\,\mathbb{S}.

We mention that, in general, Assumption 1 might be difficult to verify analytically. Empirically, however, density is fairly obvious to ascertain numerically with suitable visual evidence (Fig. 6).

Refer to caption
Figure 6: Two examples where an agent approaches every point on its attractor with equal frequency. In each case, the curve traces the orbit of the agent.

We define the discrepancy D(St)D(S_{t}) of St=(p1,,pt)S_{t}=(p_{1},\ldots,p_{t}), with pisp_{i}\in\mathbb{R}^{s}, as

D(St)=supBJ|A(B;t)tμs(B)|,D(S_{t})=\sup_{B\in J}\Big{|}\,\frac{A(B;t)}{t}-\mu_{s}(B)\,\Big{|},

where μs\mu_{s} is the ss-dimensional Lebesgue measure and A(B;t)=|{i|piB}|A(B;t)=|\{i\,|\,p_{i}\in B\}| and JJ is the set of ss-dimensional boxes of the form i=1s[ai,bi)[0,1]s\prod_{i=1}^{s}\,[a_{i},b_{i})\subset[0,1]^{s}. The infinite sequence SS_{\infty} is said to be uniformly distributed if D(St)D(S_{t}) tends to 0, as tt goes to infinity.

Lemma 2.21.

(Erdős–Turán–Koksma [23], page 116).   For any integer L>0L>0,

D(St)2s23s+1(1L+0<L1r()|1tk=1te2πi,pk|),D(S_{t})\leq 2s^{2}3^{s+1}\Big{(}\,\frac{1}{L}+\sum_{0<\|\ell\|_{\infty}\leq L}\frac{1}{r(\ell)}\,\Big{|}\frac{1}{t}\sum_{k=1}^{t}e^{2\pi i\langle\ell,p_{k}\rangle}\Big{|}\,\Big{)}\,,

where r():=j=1smax{1,|j|}r(\ell):=\prod_{j=1}^{s}\max\{1,|\ell_{j}|\} and =(1,,s)s\ell=(\ell_{1},\ldots,\ell_{s})\in\mathbb{Z}^{s}.

Proof 2.22 (Proof of Theorem 2.20).

We form the sequence p1,,pt[0,1)sp_{1},\ldots,p_{t}\in[0,1)^{s} such that pk=kα(mod1)p_{k}=k\alpha\pmod{1}; where each coordinate of kαk\alpha is replaced by its fractional part. By Lemma 2.21, its box discrepancy satisfies

D(St)2s23s+1(1L+0<L1r()|1tk=1te2πi,kα|),D(S_{t})\leq 2s^{2}3^{s+1}\Big{(}\,\frac{1}{L}+\sum_{0<\|\ell\|_{\infty}\leq L}\frac{1}{r(\ell)}\,\Big{|}\frac{1}{t}\sum_{k=1}^{t}e^{2\pi i\langle\ell,k\alpha\rangle}\Big{|}\,\Big{)}, (15)

By Assumption 1, 𝟎\mathbf{0} is the only integer vector whose dot product with α\alpha is an integer; hence γ:=e2πi,α1\gamma_{\ell}:=e^{2\pi i\langle\ell,\alpha\rangle}\neq 1, for any 𝟎\ell\neq\mathbf{0}. It follows that

|k=1te2πi,kα|=|k=1tγk|=|γγt+11γ|2|1γ|.\Big{|}\sum_{k=1}^{t}e^{2\pi i\langle\ell,k\alpha\rangle}\,\Big{|}=\Big{|}\sum_{k=1}^{t}\gamma_{\ell}^{k}\,\Big{|}=\Big{|}\frac{\gamma_{\ell}-\gamma_{\ell}^{t+1}}{1-\gamma_{\ell}}\Big{|}\leq\frac{2}{|1-\gamma_{\ell}|}\,.

By (15), for any δ>0\delta>0,

D(St)2s23s+1(1L+1t0<L2|1γ|)δ.D(S_{t})\leq 2s^{2}3^{s+1}\Big{(}\,\frac{1}{L}+\frac{1}{t}\sum_{0<\|\ell\|_{\infty}\leq L}\,\frac{2}{|1-\gamma_{\ell}|}\,\Big{)}\leq\delta.

for L=4s23s+1/δL=\lceil 4s^{2}3^{s+1}/\delta\rceil and t(8/δ)s23s+10<L|1γ|1t\geq(8/\delta)s^{2}3^{s+1}\sum_{0<\|\ell\|_{\infty}\leq L}|1-\gamma_{\ell}|^{-1}.

2.5 Examples

We illustrate the range of contrarian opinion dynamics by considering a few specific examples for which calculations are feasible.

2.5.1 Fixed-point attractor

Set m=2m=2 and C={(1,0),(0,1),(1,0),(0,1)}C=\{(1,0),(0,1),(-1,0),(0,-1)\}. By Lemma 2.3, for any v=(v1,v2)Vv=(v_{1},v_{2})\in V,

λv=p+1p2(cos2πv1n+cos2πv2n).\lambda_{v}=p+\frac{1-p}{2}\Big{(}\cos\frac{2\pi v_{1}}{n}+\cos\frac{2\pi v_{2}}{n}\Big{)}.

The eigenvalues are real and λ=maxvV{|λv|<1}=p+12(1p)(1+cos2π/n)\lambda=\max_{v\in V}\{|\lambda_{v}|<1\}=p+\frac{1}{2}(1-p)(1+\cos 2\pi/n). For any hCh\in C, λh=λ\lambda_{h}=\lambda and θh=0\theta_{h}=0; hence CWC\subseteq W. A simple examination shows that, in fact, W=CW=C. By (9, 12), given j[d]j\in[d],666As usual, [d][d] denotes {1,,d}\{1,\ldots,d\}.

yv(t)j=Ajcos2π(v1+αj)n+Bjcos2π(v2+βj)n,y_{v}^{*}(t)_{j}=A_{j}\cos\frac{2\pi(v_{1}+\alpha_{j})}{n}+B_{j}\cos\frac{2\pi(v_{2}+\beta_{j})}{n}\,,

where Aj,Bj,αj,βjA_{j},B_{j},\alpha_{j},\beta_{j} do not depend on vv but only on the initial position xx. This produces a 2D surface in d\mathbb{R}^{d} formed by the Minkowski sum of two ellipses centered at the origin (Fig.7).

Refer to caption
Figure 7: The attractor on which each agent converges to a fixed point.

2.5.2 Periodic and quasi-periodic orbits

Set m=2m=2 and C={(1,0),(0,1)}C=\{(1,0),(0,1)\}. By Lemma 2.3, for any vVv\in V, λv=p+1p2(ωv1+ωv2)\lambda_{v}=p+\frac{1-p}{2}\big{(}\omega^{v_{1}}+\omega^{v_{2}}\big{)}; hence λ=maxvV{|λv|<1}=12|1+p+(1p)ω|\lambda=\max_{v\in V}\{|\lambda_{v}|<1\}=\frac{1}{2}\big{|}1+p+(1-p)\omega\big{|} and W={(1,0),(0,1),(1,0),(0,1)}W=\{(1,0),(0,1),(-1,0),(0,-1)\}. Specifically, λv\lambda_{v} is equal to 12(1+p+(1p)ω)\frac{1}{2}\big{(}1+p+(1-p)\omega\big{)}, for v{(1,0),(0,1)}v\in\{(1,0),(0,1)\}, and to its conjugate, for v{(1,0),(0,1)}v\in\{(-1,0),(0,-1)\}. By (11, 12), we have ϑ={θ}\vartheta=\{\theta\}, where

θ=(n2π)arctan((1p)sin2π/n1+p+(1p)cos2π/n),\theta=\Big{(}\frac{n}{2\pi}\Big{)}\arctan\left(\frac{(1-p)\sin 2\pi/n}{1+p+(1-p)\cos 2\pi/n}\right)\,,

and

yv(t)=(av,θcos2πθtn+bv,θsin2πθtn)x.y_{v}^{*}(t)=\Big{(}\,a_{v,\theta}\cos\frac{2\pi\theta t}{n}+b_{v,\theta}\sin\frac{2\pi\theta t}{n}\,\Big{)}x\,.

Fix a coordinate j[d]j\in[d]; we find that

yv(t)j=Ajcos2π(θt+v1+αj)n+Bjcos2π(θt+v2+βj)n,y_{v}^{*}(t)_{j}=A_{j}\cos\frac{2\pi(\theta t+v_{1}+\alpha_{j})}{n}+B_{j}\cos\frac{2\pi(\theta t+v_{2}+\beta_{j})}{n}\,,

for suitable reals Aj,Bj,αj,βjA_{j},B_{j},\alpha_{j},\beta_{j} that depend on the initial position xx but not on vv. This again produces a two-dimensional attracting subset of d\mathbb{R}^{d} formed by the Minkowski sum of two ellipses. In the case of Figure 8, the attractor is a torus pinched along two curves. The main difference from the previous case comes from the limit behavior of the agents. They are not attracted to a fixed point but, rather, to a surface. With high probability, the orbits are asymptotically periodic if θ\theta is rational, and quasi-periodic otherwise. For a case of the former, consider p=0p=0, which gives us

θ=(n2π)arctan(sin2π/n1+cos2π/n)=12;\theta=\Big{(}\frac{n}{2\pi}\Big{)}\arctan\left(\frac{\sin 2\pi/n}{1+\cos 2\pi/n}\right)=\frac{1}{2}\,;

hence periodic orbits.

Refer to caption
Figure 8: A periodic orbit on the left with the full attractor on the right.

2.5.3 Equidistribution over the attractor

Put m=2m=2 and C={(1,0),(0,1),(2,3)}C=\{(1,0),(0,1),(2,3)\}. We set p=1/4p=1/4. For any vVv\in V, we have

λv=p+1p3(ωv1+ωv2+ω2v1+3v2).\lambda_{v}=p+\frac{1-p}{3}\big{(}\omega^{v_{1}}+\omega^{v_{2}}+\omega^{2v_{1}+3v_{2}}\big{)}.

We verified numerically that W={(1,0),(1,1),(1,0),(1,1)}W=\{(1,0),(1,-1),(-1,0),(-1,1)\} and ϑ={θ1,θ2}\vartheta=\{\theta_{1},\theta_{2}\}, where

{θ1=(n2π)arctan(sin2π/n+sin4π/n2+cos2π/n+cos4π/n)θ2=(n2π)arctan(sin2π/n1+3cos2π/n).\begin{cases}\,\,\theta_{1}=\big{(}\frac{n}{2\pi}\big{)}\arctan\left(\frac{\sin 2\pi/n+\sin 4\pi/n}{2+\cos 2\pi/n+\cos 4\pi/n}\right)\\ \,\,\theta_{2}=\big{(}\frac{n}{2\pi}\big{)}\arctan\left(\frac{-\sin 2\pi/n}{1+3\cos 2\pi/n}\right).\end{cases}

By (12),

yv(t)=k=1,2(av,θkcos2πθktn+bv,θksin2πθktn)x.y_{v}^{*}(t)=\sum_{k=1,2}\Big{(}\,a_{v,\theta_{k}}\cos\frac{2\pi\theta_{k}t}{n}+b_{v,\theta_{k}}\sin\frac{2\pi\theta_{k}t}{n}\,\Big{)}x\,.

Computer experimentation points to the linear independence of the numbers 1,θ1,θ21,\theta_{1},\theta_{2} over the rationals. If so, then Assumption 1 from Section 2.4 holds and, by Theorem 2.20, any limiting orbit is uniformly distributed over the attractor 𝕊\,\mathbb{S} (Fig.9).

Refer to caption
Figure 9: A single agent’s orbit is uniformly distributed around its attractor.

3 Dynamic Social Networks

We define a mixed model of contrarian opinion dynamics. Let ={C1,,Cs}\mathcal{M}=\{C_{1},\ldots,C_{s}\} be a set of ss nonempty subsets, each one spanning the vector space VV. At each time step tt, we define the matrix FCF_{C} by choosing, as convolution set CC, a random, uniformly distributed element of \mathcal{M}. As before, we assume that 𝟏x=0\mathbf{1}^{\top}x=0. Let λj,v\lambda_{j,v} be the eigenvalue of FCjF_{C_{j}} associated with vVv\in V. Given an infinite sequence II_{\infty} of indices from [s][s], we denote by It=k1,,ktI_{t}={k_{1},\ldots,k_{t}} be the first tt indices of II_{\infty}, and we write Λv(It)=kItλk,v\Lambda_{v}(I_{t})=\prod_{k\in I_{t}}\lambda_{k,v}. We generalize (4) into

x(t)=1NvV{𝟎}Λv(It)ψvzv,x(t)=\frac{1}{N}\sum_{v\in V\setminus\{\mathbf{0}\}}\Lambda_{v}(I_{t})\,\psi^{v}z_{v}\,, (16)

where zvz_{v} is the row vector uVωv,uxu\sum_{u\in V}\omega^{-\langle v,u\rangle}x_{u}.

3.1 Spectral decomposition

Write λv×=|j=1sλj,v|1/s\lambda_{v}^{\times}=\big{|}\prod_{j=1}^{s}\lambda_{j,v}\big{|}^{1/s} and λ=maxvV{𝟎}λv×\lambda=\max_{v\in V\setminus\{\mathbf{0}\}}\lambda_{v}^{\times}; because all the eigenvalues other than λj,𝟎=1\lambda_{j,\mathbf{0}}=1 lie strictly inside the unit circle, we have λ<1\lambda<1. Without loss of generality, we can assume that λ>0\lambda>0. Indeed, suppose that λ=0\lambda=0; then, for every vV{𝟎}v\in V\setminus\{\mathbf{0}\}, there is j=j(v)j=j(v) such that λj,v=0\lambda_{j,v}=0. This presents us with a “coupon collector’s” scenario: with probability at most N(11/s)tNet/sN(1-1/s)^{t}\leq Ne^{-t/s}, we have Λv(It)0\Lambda_{v}(I_{t})\neq 0 for at least one nonzero vVv\in V. In other words, with high probability, every coordinate of x(t)x(t) in the eigenbasis will vanish after O(slogN)O(s\log N) steps; hence x(t)=0x(t)=0 for all tt large enough. This case is of little interest, so we dismiss it and assume that λ\lambda is positive. We redefine W={vV|λv×=λ}W=\{v\in V\,|\,\lambda_{v}^{\times}=\lambda\}. Let W={vV|λv×<λ}W^{\prime}=\{v\in V\,|\,\lambda_{v}^{\times}<\lambda\}.

Lemma 3.1.

If WW^{\prime} is nonempty, there exists c<1c<1 such that, with high probability, for all tt large enough,

maxwW|Λw(It)|ctminwW|Λw(It)|.\max_{w^{\prime}\in W^{\prime}}|\Lambda_{w^{\prime}}(I_{t})|\leq c^{t}\min_{w\in W}|\Lambda_{w}(I_{t})|.

Note that the high-probability event applies to all times tt larger than a fixed constant. The proof involves the comparison of two multiplicative random walks.

Proof 3.2.

Fix wWw\in W and wWw^{\prime}\in W^{\prime}. We prove that |Λw(It)|ct|Λw(It)||\Lambda_{w^{\prime}}(I_{t})|\leq c^{t}|\Lambda_{w}(I_{t})|. If λw×=0\lambda_{w^{\prime}}^{\times}=0, then λj,w=0\lambda_{j,w^{\prime}}=0, for some jj. With high probability, the sequence ItI_{t} includes the index jj at least once for any tt large enough; hence |Λw(It)|=0|\Lambda_{w^{\prime}}(I_{t})|=0 and the lemma holds. Assume now that λw×>0\lambda_{w^{\prime}}^{\times}>0; for all jj, both of λj,w\lambda_{j,w} and λj,w\lambda_{j,w^{\prime}} are nonzero. Write Sv(It)=logkIt|λk,v|S_{v}(I_{t})=\log\prod_{k\in I_{t}}|\lambda_{k,v}|, for v=w,wv=w,w^{\prime}, and note that Sv(It)=tlogλv×+kItσk,vS_{v}(I_{t})=t\log\lambda_{v}^{\times}+\sum_{k\in I_{t}}\sigma_{k,v}, where σk,v=log|λk,v|logλv×\sigma_{k,v}=\log|\lambda_{k,v}|-\log\lambda_{v}^{\times}. Let σ=maxk,v|σk,v|\sigma=\max_{k,v}|\sigma_{k,v}|. The random variables σk,v\sigma_{k,v} are unbiased and mutually independent in [σ,σ][-\sigma,\sigma]. Classic deviation bounds [2] give us [|kItσk,v|>b]<2eb2/(2tσ2).\mathbb{P}\Big{[}\,\Big{|}\sum_{k\in I_{t}}\sigma_{k,v}\Big{|}>b\,\Big{]}<2e^{-b^{2}/(2t\sigma^{2})}. It follows that |Sv(It)tlogλv×|=O(σtln(tN))\big{|}S_{v}(I_{t})-t\log\lambda_{v}^{\times}\big{|}=O\big{(}\sigma\sqrt{t\ln(tN)}\,\big{)} with probability 1a/(tN)21-a/(tN)^{2}, for an arbitrarily small constant a>0a>0. Since t>01/t2=π2/6\sum_{t>0}1/t^{2}=\pi^{2}/6, it follows that, for arbitrarily small fixed ε>0\varepsilon>0 and all tt large enough, with probability at least 1ε/N21-\varepsilon/N^{2},

log|Λw(It)||Λw(It)|=Sw(It)Sw(It)tlogλw×λw×O(σtlog(tN))t2logλw×λw×,\log\frac{|\Lambda_{w}(I_{t})|}{|\Lambda_{w^{\prime}}(I_{t})|}=S_{w}(I_{t})-S_{w^{\prime}}(I_{t})\geq t\log\frac{\lambda_{w}^{\times}}{\lambda_{w^{\prime}}^{\times}}-O\big{(}\sigma\sqrt{t\log(tN)}\,\big{)}\geq\frac{t}{2}\log\frac{\lambda_{w}^{\times}}{\lambda_{w^{\prime}}^{\times}}\,,

for any given wWw\in W and wWw^{\prime}\in W^{\prime}. Setting c=maxwW,wWλw×/λw×c=\max_{w\in W,w^{\prime}\in W^{\prime}}\sqrt{\lambda_{w^{\prime}}^{\times}/\lambda_{w}^{\times}} and using a union bound completes the proof.

We define the scaled orbit y(t)=x(t)/λty(t)=x(t)/\lambda^{t}. Reprising the argument from Theorem 2.6, we conclude from (16) that, with high probability, the limiting orbit is of the form

y(t)=1NhW(kItλk,hλ)ψhzh=1NhW(kIt|λk,h|λ)ωkItθk,hψhzh,y^{*}(t)=\frac{1}{N}\sum_{h\in W}\Big{(}\prod_{k\in I_{t}}\frac{\lambda_{k,h}}{\lambda}\Big{)}\psi^{h}z_{h}=\frac{1}{N}\sum_{h\in W}\Big{(}\prod_{k\in I_{t}}\frac{|\lambda_{k,h}|}{\lambda}\,\Big{)}\,\omega^{\sum_{k\in I_{t}}\!\theta_{k,h}}\,\psi^{h}z_{h},

where λk,h:=|λk,h|ωθk,h\lambda_{k,h}:=|\lambda_{k,h}|\omega^{\theta_{k,h}}. It follows that

yv(t)=1NhW(kIt|λk,h|λ)ωkItθk,h+h,vuVωh,uxu.y_{v}^{*}(t)=\frac{1}{N}\sum_{h\in W}\Big{(}\prod_{k\in I_{t}}\frac{|\lambda_{k,h}|}{\lambda}\,\Big{)}\,\omega^{\sum_{k\in I_{t}}\!\theta_{k,h}+\langle h,v\rangle}\,\sum_{u\in V}\omega^{-\langle h,u\rangle}x_{u}\,.

If we put Xh=2πn(h,v+kItθk,h)X_{h}=\frac{2\pi}{n}\big{(}\langle h,v\rangle+\sum_{k\in I_{t}}\!\theta_{k,h}\big{)}, then, with aha_{h} and bhb_{h} being the row vectors defined in Theorem 2.6,

yv(t)=1NhW(kIt|λk,h|λ)((ahx)cosXh+(bhx)sinXh).y_{v}^{*}(t)=\frac{1}{N}\sum_{h\in W}\Big{(}\prod_{k\in I_{t}}\frac{|\lambda_{k,h}|}{\lambda}\,\Big{)}\,\Big{(}(a_{h}x)\cos X_{h}+(b_{h}x)\sin X_{h}\Big{)}. (17)

3.2 Surprising attractors

Adding mixing to a model increases the entropy of the system. It is thus to be expected that the attractor of a mixed model should have higher dimensionality than its pure components. The surprise is that this need not be the case. We exhibit instances of contrarian opinion dynamics where mixing decreases the dimension of the attractor. To keep the notation simple, we consider two pure models 1={C1}\mathcal{M}_{1}=\{C_{1}\}, 2={C2}\mathcal{M}_{2}=\{C_{2}\} alongside their mixture 3={C1,C2}\mathcal{M}_{3}=\{C_{1},C_{2}\}.

Theorem 3.3.

For any k[m]k\in[m], there is a choice of C1C_{1} and C2C_{2} such that dim3=k\mathrm{dim}\,\mathcal{M}_{3}=k and dim1=dim2=m\mathrm{dim}\,\mathcal{M}_{1}=\mathrm{dim}\,\mathcal{M}_{2}=m; in other words, the dimension of the mixture’s attractor can be arbitrarily smaller than those of its pure components.

Proof 3.4.

We define C1=(e1,,em)C_{1}=(e_{1},\ldots,e_{m}) and C2=(e1,,ek,2ek+1,,2em)C_{2}=(e_{1},\ldots,e_{k},2e_{k+1},\ldots,2e_{m}), for any k[m]k\in[m], where eie_{i} is the one-hot vector of VV whose ii-th coordinate is 1 and all the others 0. Let WiW_{i} denote the set WW corresponding to the system i\mathcal{M}_{i}. We easily verify that W1=±C1W_{1}=\pm C_{1} and W2=±{e1,,ek,21ek+1,,21em}W_{2}=\pm\big{\{}e_{1},\ldots,e_{k},2^{-1}e_{k+1},\ldots,2^{-1}e_{m}\big{\}}, where 212^{-1} is the inverse of 22 in the field /n\mathbb{Z}/n\mathbb{Z}. A vector vWiv\in W_{i} and its negative contribute to the same ellipse, so we have dim1=dim2=m\mathrm{dim}\,\mathcal{M}_{1}=\mathrm{dim}\,\mathcal{M}_{2}=m. We note that |λk,h|=λ=|1+1pm(ω1)||\lambda_{k,h}|=\lambda=\big{|}1+\frac{1-p}{m}(\omega-1)\big{|}, for hW1W2h\in W_{1}\cup W_{2}; hence λv×=λ\lambda_{v}^{\times}=\lambda for hW1W2h\in W_{1}\cap W_{2} and λv×<λ\lambda_{v}^{\times}<\lambda for all other values of hh. It follows that dim3=k\mathrm{dim}\,\mathcal{M}_{3}=k.

Figure 10 illustrates Theorem 3.3. We have m=2m=2 and n=29n=29. The two convolution sets are C1={(1,0),(0,1)}C_{1}=\{(1,0),(0,1)\} and C2={(1,0),(0,2)}C_{2}=\{(1,0),(0,2)\}. The initial positions are random and identical in all three cases.

Refer to caption
Figure 10: The two attractors of the pure models i\mathcal{M}_{i} (i=1,2i=1,2) on the left, with the lower-dimensional attractor of the mixture on the right.

We can generalize the mixed model by picking C1C_{1} (resp. C2C_{2}) with probability 1q1-q (resp. qq), where 0q10\leq q\leq 1. For this, we redefine λv×(q)=|λ1,v1qλ2,vq|\lambda_{v}^{\times}(q)=\big{|}\lambda_{1,v}^{1-q}\lambda_{2,v}^{q}\big{|} and λ(q)=maxvV{𝟎}λv×(q)\lambda(q)=\max_{v\in V\setminus\{\mathbf{0}\}}\lambda_{v}^{\times}(q).

Theorem 3.5.

There is a choice of C1C_{1} and C2C_{2} such that dim3>dim1=dim2=m\mathrm{dim}\,\mathcal{M}_{3}>\mathrm{dim}\,\mathcal{M}_{1}=\mathrm{dim}\,\mathcal{M}_{2}=m; in other words, the dimension of the mixture’s attractor can be larger than those of its pure components.

Proof 3.6.

Borrowing the notation of the previous proof, we define C1=(e1,,em)C_{1}=(e_{1},\ldots,e_{m}) and C2=(2e1,,2em)C_{2}=(2e_{1},\ldots,2e_{m}) and verify that W1=±C1W_{1}=\pm C_{1} and W2=±{21e1,,21em}W_{2}=\pm\{2^{-1}e_{1},\ldots,2^{-1}e_{m}\big{\}}; hence dim1=dim2=m\mathrm{dim}\,\mathcal{M}_{1}=\mathrm{dim}\,\mathcal{M}_{2}=m. Assuming that n>3n>3, we note that the sets W1W_{1} and W2W_{2} are disjoint. Regarding the mixed system, we have W(q)={vV:λv×(q)=λ(q)}W(q)=\{v\in V\,:\,\lambda_{v}^{\times}(q)=\lambda(q)\}, where W(0)=W1W(0)=W_{1} and W(1)=W2W(1)=W_{2}. Around q=0q=0, we have, for all vW(0)v\in W(0),

λv×(q)=| 1+1pm(ω1)|1q×| 1+1pm(ω21)|q.\lambda_{v}^{\times}(q)=\Big{|}\,1+\frac{1-p}{m}(\omega-1)\,\Big{|}^{1-q}\times\Big{|}\,1+\frac{1-p}{m}(\omega^{2}-1)\,\Big{|}^{q}\,. (18)

Since W(0)W(1)W(0)\neq W(1), by continuity, there are q(0,1)q\in(0,1) and wW(q)W(0)w\in W(q)\setminus W(0) such that λw×(q)\lambda_{w}^{\times}(q) is equal to the right-hand side of (18). This implies that W(q)W(0){w}W(q)\supseteq W(0)\cup\{w\}, which completes the proof.

Figure 11 illustrates the theorem. We have m=2m=2, n=29n=29, and p=0.9p=0.9. The two convolution sets are C1={(1,0),(0,1)}C_{1}=\{(1,0),(0,1)\} and C2={(2,0),(0,2)}C_{2}=\{(2,0),(0,2)\}; the mixture probability is q=0.0306q=0.0306. The initial positions are random and identical in all three cases.

Refer to caption
Figure 11: The two attractors of the pure models i\mathcal{M}_{i} (i=1,2i=1,2) on the left, with the higher-dimensional attractor of the mixture on the right.

References