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Polya's bees: A model of decentralized decision-making.

Golman R, Hagmann D, Miller JH - Sci Adv (2015)

Bottom Line: The proposed common mechanism provides a robust and effective means by which a decentralized system can navigate the speed-accuracy tradeoff and make reasonably good, quick decisions in a variety of environments.This too is adaptive.The model illustrates how natural systems make decentralized decisions, illuminating a mechanism that engineers of social and artificial systems could imitate.

View Article: PubMed Central - PubMed

Affiliation: Department of Social and Decision Sciences, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.

ABSTRACT
How do social systems make decisions with no single individual in control? We observe that a variety of natural systems, including colonies of ants and bees and perhaps even neurons in the human brain, make decentralized decisions using common processes involving information search with positive feedback and consensus choice through quorum sensing. We model this process with an urn scheme that runs until hitting a threshold, and we characterize an inherent tradeoff between the speed and the accuracy of a decision. The proposed common mechanism provides a robust and effective means by which a decentralized system can navigate the speed-accuracy tradeoff and make reasonably good, quick decisions in a variety of environments. Additionally, consensus choice exhibits systemic risk aversion even while individuals are idiosyncratically risk-neutral. This too is adaptive. The model illustrates how natural systems make decentralized decisions, illuminating a mechanism that engineers of social and artificial systems could imitate.

No MeSH data available.


Related in: MedlinePlus

Pareto frontiers of mistake probability and expected waiting time with 2 and 4 options.The optimal choice has quality vc* = 2, whereas the suboptimal choices have quality vc = 1 for all c ≠ c*. As an artifact of specifying recruitment (that is, choice quality) so precisely, there are thresholds for which the decision is both slower and less accurate than for a threshold one unit smaller. The corresponding points on the graph are clearly not on the Pareto frontier, but they are shown for completeness.
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Figure 1: Pareto frontiers of mistake probability and expected waiting time with 2 and 4 options.The optimal choice has quality vc* = 2, whereas the suboptimal choices have quality vc = 1 for all c ≠ c*. As an artifact of specifying recruitment (that is, choice quality) so precisely, there are thresholds for which the decision is both slower and less accurate than for a threshold one unit smaller. The corresponding points on the graph are clearly not on the Pareto frontier, but they are shown for completeness.

Mentions: Increasing the number of possible options C makes for a less accurate decision, but a slightly quicker one as well. (This is shown in the appendix in fig. S1.) More options provide more opportunities for suboptimal options to accumulate a quorum, leading to more mistakes and less decision time. But then, to reach the same level of accuracy, the system needs a higher threshold, and this increases the time required to make the decision (as shown in Fig. 1). Intuitively, more possible options make for a more difficult decision.


Polya's bees: A model of decentralized decision-making.

Golman R, Hagmann D, Miller JH - Sci Adv (2015)

Pareto frontiers of mistake probability and expected waiting time with 2 and 4 options.The optimal choice has quality vc* = 2, whereas the suboptimal choices have quality vc = 1 for all c ≠ c*. As an artifact of specifying recruitment (that is, choice quality) so precisely, there are thresholds for which the decision is both slower and less accurate than for a threshold one unit smaller. The corresponding points on the graph are clearly not on the Pareto frontier, but they are shown for completeness.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4643789&req=5

Figure 1: Pareto frontiers of mistake probability and expected waiting time with 2 and 4 options.The optimal choice has quality vc* = 2, whereas the suboptimal choices have quality vc = 1 for all c ≠ c*. As an artifact of specifying recruitment (that is, choice quality) so precisely, there are thresholds for which the decision is both slower and less accurate than for a threshold one unit smaller. The corresponding points on the graph are clearly not on the Pareto frontier, but they are shown for completeness.
Mentions: Increasing the number of possible options C makes for a less accurate decision, but a slightly quicker one as well. (This is shown in the appendix in fig. S1.) More options provide more opportunities for suboptimal options to accumulate a quorum, leading to more mistakes and less decision time. But then, to reach the same level of accuracy, the system needs a higher threshold, and this increases the time required to make the decision (as shown in Fig. 1). Intuitively, more possible options make for a more difficult decision.

Bottom Line: The proposed common mechanism provides a robust and effective means by which a decentralized system can navigate the speed-accuracy tradeoff and make reasonably good, quick decisions in a variety of environments.This too is adaptive.The model illustrates how natural systems make decentralized decisions, illuminating a mechanism that engineers of social and artificial systems could imitate.

View Article: PubMed Central - PubMed

Affiliation: Department of Social and Decision Sciences, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.

ABSTRACT
How do social systems make decisions with no single individual in control? We observe that a variety of natural systems, including colonies of ants and bees and perhaps even neurons in the human brain, make decentralized decisions using common processes involving information search with positive feedback and consensus choice through quorum sensing. We model this process with an urn scheme that runs until hitting a threshold, and we characterize an inherent tradeoff between the speed and the accuracy of a decision. The proposed common mechanism provides a robust and effective means by which a decentralized system can navigate the speed-accuracy tradeoff and make reasonably good, quick decisions in a variety of environments. Additionally, consensus choice exhibits systemic risk aversion even while individuals are idiosyncratically risk-neutral. This too is adaptive. The model illustrates how natural systems make decentralized decisions, illuminating a mechanism that engineers of social and artificial systems could imitate.

No MeSH data available.


Related in: MedlinePlus