Limits...
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.


Pareto frontiers of mistake probability and expected waiting time with varying choice quality.Left: Varying optimal choice quality. There are C = 2 possible choices, and the quality of the suboptimal choice is v~c* = 1. Right: Varying suboptimal choice quality. There are C = 2 possible choices, and the quality of the optimal choice is vc* = 4.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4643789&req=5

Figure 2: Pareto frontiers of mistake probability and expected waiting time with varying choice quality.Left: Varying optimal choice quality. There are C = 2 possible choices, and the quality of the suboptimal choice is v~c* = 1. Right: Varying suboptimal choice quality. There are C = 2 possible choices, and the quality of the optimal choice is vc* = 4.

Mentions: Increasing the quality of the optimal choice, vc*, makes the decision easier (as shown in the left plot in Fig. 2). (These Pareto frontiers are derived from simulations shown in figs. S2 and S3.) As the quality of the optimal choice increases, the decision can be made faster and with less chance of error. Recruitment becomes more effective, so the agents accumulate at this option more quickly and the system achieves the quorum sooner.


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 varying choice quality.Left: Varying optimal choice quality. There are C = 2 possible choices, and the quality of the suboptimal choice is v~c* = 1. Right: Varying suboptimal choice quality. There are C = 2 possible choices, and the quality of the optimal choice is vc* = 4.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Pareto frontiers of mistake probability and expected waiting time with varying choice quality.Left: Varying optimal choice quality. There are C = 2 possible choices, and the quality of the suboptimal choice is v~c* = 1. Right: Varying suboptimal choice quality. There are C = 2 possible choices, and the quality of the optimal choice is vc* = 4.
Mentions: Increasing the quality of the optimal choice, vc*, makes the decision easier (as shown in the left plot in Fig. 2). (These Pareto frontiers are derived from simulations shown in figs. S2 and S3.) As the quality of the optimal choice increases, the decision can be made faster and with less chance of error. Recruitment becomes more effective, so the agents accumulate at this option more quickly and the system achieves the quorum sooner.

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.