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Ant groups optimally amplify the effect of transiently informed individuals.

Gelblum A, Pinkoviezky I, Fonio E, Ghosh A, Gov N, Feinerman O - Nat Commun (2015)

Bottom Line: A downside of behavioural conformism is that it may decrease the group's responsiveness to external information.Our theoretical models predict that the ant-load system can be transitioned through the critical point of this mesoscopic system by varying its size; we present experiments supporting these predictions.Our findings show that efficient group-level processes can arise from transient amplification of individual-based knowledge.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 7610001, Israel.

ABSTRACT
To cooperatively transport a large load, it is important that carriers conform in their efforts and align their forces. A downside of behavioural conformism is that it may decrease the group's responsiveness to external information. Combining experiment and theory, we show how ants optimize collective transport. On the single-ant scale, optimization stems from decision rules that balance individuality and compliance. Macroscopically, these rules poise the system at the transition between random walk and ballistic motion where the collective response to the steering of a single informed ant is maximized. We relate this peak in response to the divergence of susceptibility at a phase transition. Our theoretical models predict that the ant-load system can be transitioned through the critical point of this mesoscopic system by varying its size; we present experiments supporting these predictions. Our findings show that efficient group-level processes can arise from transient amplification of individual-based knowledge.

No MeSH data available.


Related in: MedlinePlus

Group optimality.(a,b) Simulation data of the response of the object to a single attachment of a single-knowledgeable ant as a function of the individuality parameter Find (a) or of the object's radius (b). Insets depict velocity component distributions for small (orange) and large (blue) values of Find as well as its fitted value (pink). Upper left inset depicts trajectories (all starting at the yellow dot, colour coded as before) that take into account the continual arrival of informed ants (scale bars represent 10 cm). The pink dot in b marks the radius of the experimental load. (c,d) Exact solution of the Ising spin model. (c) Normalized (dimensionless) system response as a function of the individuality parameter Find. The blue curve marks the short-term response to a newly attached ant and the red curve the mean-field susceptibility, which diverges at the critical point. (d) Normalized (dimensionless) short-term response to a newly attached ant as a function of the mean number of ants attached to the load (a proxy for load size). Dotted lines mark the critical transition points. (e–g) Experimental verification. (e) Mean absolute curvature of trajectories of objects of different size (total N=90). Synthetic and non-synthetic materials that were used for the small item exhibited similar curvatures (medians: synthetic: 8.84, non-synthetic: 6.53, unpaired two-sample t-test: P=0.6285, N=9) and were therefore pooled together. Thus, the effect presented in the figure is a size effect that cannot be attributed to load substance composition. Inset: mean curvature of simulated tracks of objects of different sizes (calculated on clean board conditions). (f) Time spent at ν>75% transport speed for one (green) versus 2–4 (blue) ants carrying a small load (total N=20). (g) Top: time to negotiate an obstacle (t-test: P<0.01) and (bottom) the maximal backwards displacement (t-test: P<0.0001) towards a successful crossing of a U-shaped block (which required 5-cm backtracking) for two load sizes (total N=11). Scale bar, 10 cm.
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f4: Group optimality.(a,b) Simulation data of the response of the object to a single attachment of a single-knowledgeable ant as a function of the individuality parameter Find (a) or of the object's radius (b). Insets depict velocity component distributions for small (orange) and large (blue) values of Find as well as its fitted value (pink). Upper left inset depicts trajectories (all starting at the yellow dot, colour coded as before) that take into account the continual arrival of informed ants (scale bars represent 10 cm). The pink dot in b marks the radius of the experimental load. (c,d) Exact solution of the Ising spin model. (c) Normalized (dimensionless) system response as a function of the individuality parameter Find. The blue curve marks the short-term response to a newly attached ant and the red curve the mean-field susceptibility, which diverges at the critical point. (d) Normalized (dimensionless) short-term response to a newly attached ant as a function of the mean number of ants attached to the load (a proxy for load size). Dotted lines mark the critical transition points. (e–g) Experimental verification. (e) Mean absolute curvature of trajectories of objects of different size (total N=90). Synthetic and non-synthetic materials that were used for the small item exhibited similar curvatures (medians: synthetic: 8.84, non-synthetic: 6.53, unpaired two-sample t-test: P=0.6285, N=9) and were therefore pooled together. Thus, the effect presented in the figure is a size effect that cannot be attributed to load substance composition. Inset: mean curvature of simulated tracks of objects of different sizes (calculated on clean board conditions). (f) Time spent at ν>75% transport speed for one (green) versus 2–4 (blue) ants carrying a small load (total N=20). (g) Top: time to negotiate an obstacle (t-test: P<0.01) and (bottom) the maximal backwards displacement (t-test: P<0.0001) towards a successful crossing of a U-shaped block (which required 5-cm backtracking) for two load sizes (total N=11). Scale bar, 10 cm.

Mentions: We fix three of the four free model parameters and check for possible optimality in terms of system response as a function of Find. We find that both complete conformity (small values of Find) and strong individualism (large values of Find) reduce the effectiveness of a newly attached ant in steering the load (Fig. 4a). The fitted value of Find lies between these two extremes and this suggests that the ants operate in the transition region between strong and weak conformity, possibly to optimize their responsiveness to a limited influx of information (Fig. 4a, upper left inset). In addition, we find that the working regime of the ants is such that the velocity distribution of the load lies in the transition region between unimodal (tug of war or random walk) and bimodal (persistent motion) behaviours (insets of Fig. 4a).


Ant groups optimally amplify the effect of transiently informed individuals.

Gelblum A, Pinkoviezky I, Fonio E, Ghosh A, Gov N, Feinerman O - Nat Commun (2015)

Group optimality.(a,b) Simulation data of the response of the object to a single attachment of a single-knowledgeable ant as a function of the individuality parameter Find (a) or of the object's radius (b). Insets depict velocity component distributions for small (orange) and large (blue) values of Find as well as its fitted value (pink). Upper left inset depicts trajectories (all starting at the yellow dot, colour coded as before) that take into account the continual arrival of informed ants (scale bars represent 10 cm). The pink dot in b marks the radius of the experimental load. (c,d) Exact solution of the Ising spin model. (c) Normalized (dimensionless) system response as a function of the individuality parameter Find. The blue curve marks the short-term response to a newly attached ant and the red curve the mean-field susceptibility, which diverges at the critical point. (d) Normalized (dimensionless) short-term response to a newly attached ant as a function of the mean number of ants attached to the load (a proxy for load size). Dotted lines mark the critical transition points. (e–g) Experimental verification. (e) Mean absolute curvature of trajectories of objects of different size (total N=90). Synthetic and non-synthetic materials that were used for the small item exhibited similar curvatures (medians: synthetic: 8.84, non-synthetic: 6.53, unpaired two-sample t-test: P=0.6285, N=9) and were therefore pooled together. Thus, the effect presented in the figure is a size effect that cannot be attributed to load substance composition. Inset: mean curvature of simulated tracks of objects of different sizes (calculated on clean board conditions). (f) Time spent at ν>75% transport speed for one (green) versus 2–4 (blue) ants carrying a small load (total N=20). (g) Top: time to negotiate an obstacle (t-test: P<0.01) and (bottom) the maximal backwards displacement (t-test: P<0.0001) towards a successful crossing of a U-shaped block (which required 5-cm backtracking) for two load sizes (total N=11). Scale bar, 10 cm.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4525283&req=5

f4: Group optimality.(a,b) Simulation data of the response of the object to a single attachment of a single-knowledgeable ant as a function of the individuality parameter Find (a) or of the object's radius (b). Insets depict velocity component distributions for small (orange) and large (blue) values of Find as well as its fitted value (pink). Upper left inset depicts trajectories (all starting at the yellow dot, colour coded as before) that take into account the continual arrival of informed ants (scale bars represent 10 cm). The pink dot in b marks the radius of the experimental load. (c,d) Exact solution of the Ising spin model. (c) Normalized (dimensionless) system response as a function of the individuality parameter Find. The blue curve marks the short-term response to a newly attached ant and the red curve the mean-field susceptibility, which diverges at the critical point. (d) Normalized (dimensionless) short-term response to a newly attached ant as a function of the mean number of ants attached to the load (a proxy for load size). Dotted lines mark the critical transition points. (e–g) Experimental verification. (e) Mean absolute curvature of trajectories of objects of different size (total N=90). Synthetic and non-synthetic materials that were used for the small item exhibited similar curvatures (medians: synthetic: 8.84, non-synthetic: 6.53, unpaired two-sample t-test: P=0.6285, N=9) and were therefore pooled together. Thus, the effect presented in the figure is a size effect that cannot be attributed to load substance composition. Inset: mean curvature of simulated tracks of objects of different sizes (calculated on clean board conditions). (f) Time spent at ν>75% transport speed for one (green) versus 2–4 (blue) ants carrying a small load (total N=20). (g) Top: time to negotiate an obstacle (t-test: P<0.01) and (bottom) the maximal backwards displacement (t-test: P<0.0001) towards a successful crossing of a U-shaped block (which required 5-cm backtracking) for two load sizes (total N=11). Scale bar, 10 cm.
Mentions: We fix three of the four free model parameters and check for possible optimality in terms of system response as a function of Find. We find that both complete conformity (small values of Find) and strong individualism (large values of Find) reduce the effectiveness of a newly attached ant in steering the load (Fig. 4a). The fitted value of Find lies between these two extremes and this suggests that the ants operate in the transition region between strong and weak conformity, possibly to optimize their responsiveness to a limited influx of information (Fig. 4a, upper left inset). In addition, we find that the working regime of the ants is such that the velocity distribution of the load lies in the transition region between unimodal (tug of war or random walk) and bimodal (persistent motion) behaviours (insets of Fig. 4a).

Bottom Line: A downside of behavioural conformism is that it may decrease the group's responsiveness to external information.Our theoretical models predict that the ant-load system can be transitioned through the critical point of this mesoscopic system by varying its size; we present experiments supporting these predictions.Our findings show that efficient group-level processes can arise from transient amplification of individual-based knowledge.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 7610001, Israel.

ABSTRACT
To cooperatively transport a large load, it is important that carriers conform in their efforts and align their forces. A downside of behavioural conformism is that it may decrease the group's responsiveness to external information. Combining experiment and theory, we show how ants optimize collective transport. On the single-ant scale, optimization stems from decision rules that balance individuality and compliance. Macroscopically, these rules poise the system at the transition between random walk and ballistic motion where the collective response to the steering of a single informed ant is maximized. We relate this peak in response to the divergence of susceptibility at a phase transition. Our theoretical models predict that the ant-load system can be transitioned through the critical point of this mesoscopic system by varying its size; we present experiments supporting these predictions. Our findings show that efficient group-level processes can arise from transient amplification of individual-based knowledge.

No MeSH data available.


Related in: MedlinePlus