Abstract
A key question in the design of engineered competitive systems has been that of the efficiency loss of the associated equilibria. Yet, there is little known in this regard in the context of stochastic dynamic games, particularly in a large population regime. In this paper, we revisit a class of noncooperative games, arising from the synchronization of a large collection of heterogeneous oscillators. In Yin et al. (Proceedings of 2010 American control conference, pp. 1783–1790, 2010), we derived a PDE model for analyzing the associated equilibria in large population regimes through a mean field approximation. Here, we examine the efficiency of the associated mean-field equilibria with respect to a related welfare optimization problem. We construct constrained variational problems both for the noncooperative game and its centralized counterpart and derive the associated nonlinear eigenvalue problems. A relationship between the solutions of these eigenvalue problems is observed and allows for deriving an expression for efficiency loss. By applying bifurcation analysis, a local bound on efficiency loss is derived under an assumption that oscillators share the same frequency. Through numerical case studies, the analytical statements are illustrated in the homogeneous frequency regime; analogous numerical results are provided for the heterogeneous frequency regime.



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This research has been funded by DOE Award DE-SC0003879. A shortened version of this work appeared in the American Control Conference in 2011 [30].
Appendix
Appendix
1.1 A.1 Proof of Lemma 1
Proof
Under Assumption 2, Eq. (11b) can be written as
Integrating both sides of the equation with respect to θ,
where K is a function of ω and is obtained as follows. Integrating both sides of the resulting equation (48) from 0 to 2π with respect to θ again, we obtain
From the assumption that h (thus u) and p are 2π-periodic in θ,
Finally, we get the result (7) by substituting K in (49) back to (48). □
1.2 A.2 Proof of Lemma 2
We consider the functional I[v]=I 1[v]+I 2[v]+I 3[v] where
and derive its first variation. For I 1[v],
For I 2[v],
A straightforward calculation gives
Using (50)–(52), we have obtain the nonlinear problem (15). Finally, (16) is the same constraint as (14).
1.3 A.3 Proof of Lemma 3
Proof
The proof of the first half is same as Lemma 2. It remains to show that \(\lambda^{*}(\omega,a) = \eta_{g}^{*}(\omega,a)\). Multiplying both sides of (15) with \(\frac {R\sigma^{4} v^{*}}{2}\) and integrating from 0 to 2π with respect to θ, we obtain
Because \(\int_{0}^{2\pi} (v^{*})^{2} \,\mathrm{d} \theta= 1\),
where the second equality is obtained through integration by parts of the first term, and the third equality is obtained because v ∗ is periodic function with period 2π. From definition (13), the right-hand side of (53) is \(\eta_{g}^{*}(\omega,a)\). □
1.4 A.4 Proof of Proposition 1
Proof
(i) Let (h,p,η ∗) be a solution to the PDE (11a)–(11c). Then the optimal control is given by \(u^{*} = -\frac{1}{R}\partial_{\theta}h\). We have shown that u ∗ also satisfies (7). Therefore, we obtain
Taking partial derivatives with respect to θ on both sides of (54), we obtain
Let \(v = \sqrt{p}\), then
From the Assumption 2 and Eq. (11a),
Substituting (56) into (57), we obtain the left-hand side (LHS) of (57) as
and the right-hand side (RHS) of (57) as
So Eq. (57) becomes
Multiplying both sides of (58) with \(\frac{2v}{R\sigma^{4}}\), one obtains the nonlinear equation (15). Finally, (16) is just the constraint for density function p=v 2, and (17) is the same as (11c) under Assumption 2.
(ii) First multiplying both sides of (21) with \(\frac{p}{R}\) and do a partial derivative with respect to θ, one obtains
which gives
Since p(θ,t;ω)=v 2(θ−at;ω),
which gives (11b).
Next, substituting p(θ,t;ω)=v 2(θ−at;ω) into (21), one obtains
So
Multiplying both sides of (61) with \(\frac{1}{2R}\), those of (62) with \(-\frac{\sigma^{2}}{2}\) and adding them together, one obtains
Multiplying both sides of (15) with \(\frac{R\sigma^{4}}{2v}\), one obtains
which gives
Substituting (64) into (63), one obtains
where the last equality comes from (60). Rearranging the last equation, one obtains
which gives (11a). Finally, (11c) is obtained from (17) under Assumption 2. □
1.5 A.5 Proof of Lemma 4
Proof
The Euler–Lagrange equation (24) is obtained from considering the first variation of (22)–(23), which can be derived in a fashion similar to that in Lemma 2. Comparing equation (22) with (13), the only difference in the integrand is the first term: the latter is \(\bar{c}v^{2}\) and the former is \(\mathcal{C} [v] v^{2}\). So we derive its first variation here as
Note the integrand of (66) is same as that of (50). Since c •(⋅) is even, (67) can be written as
where (70) is obtained by switching variable θ with ϑ and ω with ω′ in (69), (71) is obtained by rearrangement of (70), and (72) is obtained from definition of \(\mathcal{C}[v]\) in (17). So we obtain
where the integrand is as twice as that in (50), which leads to the difference between (24) and (15).
Multiplying both sides of (24) by \(\frac{\sigma^{4} Rv}{2}\) and integrating from 0 to 2π, we obtain the following:
Taking expectations on both sides, we obtain the result (26). □
1.6 A.6 Proof of Lemma 5
Equation (32) is rewritten as
We substitute the expansion (36) into (73) and the normalization condition ∫v 2 dθ=1, and collect the terms according to different orders of x.
At O(1), we have the steady state solution
At O(x),
Suppose we have the Fourier expansion for the function v 1(θ)
Substitute (76) into (74) to obtain
We collect the terms with respect to e ikθ. When k=0,
When k=1, \((-\sigma^{4}r_{0} - 8\alpha\pi v_{0}^{2}C_{1}^{\bullet}) v_{11} = 0\). If v 11≠0,
When k≥2, \(C_{k}^{\bullet}= 0\),
When k<0, it is similar as k>0. The existence of bifurcation implies v 1≠0, so \(v_{11} = \bar{v}_{1,-1} \neq0\). So we obtain
where |v 11| and ∠v 11 are the amplitude and phase angle, respectively, of v 11.
At O(x 2),
Suppose v 2(θ) also has the Fourier expansion
Substitute (76) and (80) into (79),
Substitute (76) and (80) into (78),

We collect the terms of e ikθ for different values of k. When k=0,
When k=1, −σ 4 r 1 v 11=0,⇒r 1=0. When k=2,
When k>2, v 2k =0. For k<0, it is similar. So we obtain
At O(x 3),
Suppose v 3(θ) has the Fourier expansion
Substitute (76), (80), and (85) into (84),
Substitute (76), (80), and (85) into (84),
We collect the terms of e ikθ for different values of k. When k=0,
When k=1,
In all, we obtain the formula
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Yin, H., Mehta, P.G., Meyn, S.P. et al. On the Efficiency of Equilibria in Mean-Field Oscillator Games. Dyn Games Appl 4, 177–207 (2014). https://doi.org/10.1007/s13235-013-0100-0
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DOI: https://doi.org/10.1007/s13235-013-0100-0