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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.


Probability of selecting the safe option over a risky option with the same expected quality for a fixed quorum threshold of 100.There are C = 2 options. The safe option has quality vSafe = 2. The riskiness of the risky option is indexed by the potential reward R such that the quality of the risky option is  that is, it has expected quality 2 and variance R − 2.
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Figure 3: Probability of selecting the safe option over a risky option with the same expected quality for a fixed quorum threshold of 100.There are C = 2 options. The safe option has quality vSafe = 2. The riskiness of the risky option is indexed by the potential reward R such that the quality of the risky option is that is, it has expected quality 2 and variance R − 2.

Mentions: We compare the attractiveness of risky and safe options (that is, options with the same expected quality but more or less noise, respectively) in Fig. 3, which shows that (for a fixed quorum threshold of 100) the safe option (with vSafe = 2) is more likely to be selected than a risky option and it is increasingly preferred to even riskier options (that is, as R increases). There is nothing special about the threshold of 100, and the result holds for almost all thresholds (possible exceptions being low thresholds that can be reached by a single draw of the risky option, due to recruitment having discrete increments), as shown in fig. S5. The effect persists with high thresholds because noise in the process of search and recruitment does not inevitably balance out; rather, positive feedback in the process makes it more difficult for the risky option to overcome early indications of low quality. [We prove in the appendix (Proposition 1) that the probability of selecting a safe option with quality vSafe = 1 over a risky option with quality for a quorum threshold of τ = R + 1 is (where B is the Euler beta function), which is an increasing function graphed in fig. S6.] Thus, the decision mechanism exhibits a systematic degree of risk aversion.


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

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

Probability of selecting the safe option over a risky option with the same expected quality for a fixed quorum threshold of 100.There are C = 2 options. The safe option has quality vSafe = 2. The riskiness of the risky option is indexed by the potential reward R such that the quality of the risky option is  that is, it has expected quality 2 and variance R − 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Probability of selecting the safe option over a risky option with the same expected quality for a fixed quorum threshold of 100.There are C = 2 options. The safe option has quality vSafe = 2. The riskiness of the risky option is indexed by the potential reward R such that the quality of the risky option is that is, it has expected quality 2 and variance R − 2.
Mentions: We compare the attractiveness of risky and safe options (that is, options with the same expected quality but more or less noise, respectively) in Fig. 3, which shows that (for a fixed quorum threshold of 100) the safe option (with vSafe = 2) is more likely to be selected than a risky option and it is increasingly preferred to even riskier options (that is, as R increases). There is nothing special about the threshold of 100, and the result holds for almost all thresholds (possible exceptions being low thresholds that can be reached by a single draw of the risky option, due to recruitment having discrete increments), as shown in fig. S5. The effect persists with high thresholds because noise in the process of search and recruitment does not inevitably balance out; rather, positive feedback in the process makes it more difficult for the risky option to overcome early indications of low quality. [We prove in the appendix (Proposition 1) that the probability of selecting a safe option with quality vSafe = 1 over a risky option with quality for a quorum threshold of τ = R + 1 is (where B is the Euler beta function), which is an increasing function graphed in fig. S6.] Thus, the decision mechanism exhibits a systematic degree of risk aversion.

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.