that follow the cluster estimate ${\ensuremath{\hat{{ {\mathbf g}}}}_T\xspace}$ with a noise characterized by angular diffusion $D_\psi$ and with cell-cell connections modeled as springs of strength $\kappa$ between Delaunay neighbors ([*Methods*]{}). We emphasize that the angular diffusion parameterized by $D_\psi$ is an additional source of noise: as $D_\psi$ increases, cells are less accurate in following the cluster’s estimate of the gradient. These two parameters are systematically varied to study the effects of cluster fluidity on chemotactic accuracy.

Cluster fluidity improves cluster chemotaxis
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Increasing cell-cell adhesion $\kappa$ makes clusters more ordered, moving between fluidlike and crystalline states ([Fig. ]{}\[fig:kappa\]a). As a consequence, rearrangement slows significantly ([Fig. ]{}\[fig:kappa\]c) with $\tau_r \sim \exp(\kappa/2)$ ($C_{rr}(t)$ is single-exponential).

Cluster structure and size change when clusters fluidize ([Fig. ]{}\[fig:kappa\]a), which may in principle affect the shape parameter $\chi$, which also strongly affects the chemotactic accuracy ([Eq. ]{}\[eq:combined\_error\]). However, in our simulations $\chi$ is not significantly dependent on $\kappa$, changing by under 10% ([Fig. ]{}\[fig:kappa\]d). Averaging time $T$ also has only a weak effect on cluster shape and dynamics – changes in $\tau_r$ and $\chi$ when the averaging time $T$ is increased by orders of magnitude are small ([Fig. ]{}\[fig:kappa\]). This is consistent with our assumption decoupling 