Skip to main content
SpringerLink
Log in

Best-of-N Collective Decisions on a Hierarchy

  • Conference paper
  • First Online:
Swarm Intelligence (ANTS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13491))

Included in the following conference series:

  • 832 Accesses

Abstract

The best-of-N problem in collective decision making is complex especially when the number of available alternatives is larger than a few, and no alternative distinctly shines over the others. Additionally, if the quality of the available alternatives is not a priori known and noisy, errors in the quality estimation may lead to the premature selection of sub-optimal alternatives. A typical speed-accuracy trade-off must be faced, which is hardened by the presence of several alternatives to be analyzed in parallel. In this study, we transform a one-shot best-of-N decision problem in a sequence of simpler decisions between a small number of alternatives, by organizing the decision problem in a hierarchy of choices. To this end, we construct an m-ary tree where the leaves represent the available alternatives, and high-level nodes group the low-level ones to present a low-dimension decision problem. Results from multi-agent simulations in both a fully-connected topology and in a spatial decision problem demonstrate that the sequential collective decisions can be parameterized to maximize speed and accuracy against different decision problems. A further improvement relies on an adaptive approach that automatically tunes the system parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    For \(\kappa =1\), decision speed is very similar across different configurations, and the trade-off is dominated by solutions with high r that quickly converge to any option.

References

  1. Albani, D., Hönig, W., Nardi, D., Ayanian, N., Trianni, V.: Hierarchical task assignment and path finding with limited communication for robot swarms. Appl. Sci. 11(7), 3115 (2021)

    Article  Google Scholar 

  2. Albani, D., Manoni, T., Nardi, D., Trianni, V.: Dynamic UAV swarm deployment for non-uniform coverage. In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2018, pp. 523–531 (2018)

    Google Scholar 

  3. Bareiss, D., van den Berg, J.: Generalized reciprocal collision avoidance. Int. J. Robot. Res. 34(12), 1501–1514 (2015)

    Article  Google Scholar 

  4. Bettstetter, C., Hartenstein, H., Pérez-Costa, X.: Stochastic properties of the random waypoint mobility model. Wirel. Netw. 10(5), 555–567 (2004)

    Article  Google Scholar 

  5. Bogacz, R.: Optimal decision-making theories: linking neurobiology with behaviour. Trends Cogn. Sci. 11(3), 118–125 (2007)

    Article  Google Scholar 

  6. Brown, S., Steyvers, M., Wagenmakers, E.J.: Observing evidence accumulation during multi-alternative decisions. J. Math. Psychol. 53(6), 453–462 (2009)

    Article  MathSciNet  Google Scholar 

  7. Busemeyer, J.R., Gluth, S., Rieskamp, J., Turner, B.M.: Cognitive and neural bases of multi-attribute, multi-alternative, value-based decisions. Trends Cogn. Sci. 23(3), 251–263 (2019)

    Article  Google Scholar 

  8. Cassey, T.C., Evens, D.R., Bogacz, R., Marshall, J.A.R., Ludwig, C.J.H.: Adaptive sampling of information in perceptual decision-making. PLoS ONE 8(11), e78993 (2013)

    Google Scholar 

  9. Gluth, S., Spektor, M.S., Rieskamp, J.: Value-based attentional capture affects multi-alternative decision making. eLife 7, e39659 (2018)

    Google Scholar 

  10. Jin, L., Rückin, J., Kiss, S.H., Vidal-Calleja, T., Popović, M.: Adaptive-resolution field mapping using Gaussian process fusion with integral kernels. IEEE Robot. Autom. Lett. 7(3), 7471–7478 (2022)

    Article  Google Scholar 

  11. Kameda, T., Toyokawa, W., Tindale, R.S.: Information aggregation and collective intelligence beyond the wisdom of crowds. Nat. Rev. Psychol. 1, 345–357 (2022)

    Article  Google Scholar 

  12. Kaplan, E.L., Meier, P.: Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53(282), 457–481 (1958)

    Article  MathSciNet  MATH  Google Scholar 

  13. Oddi, F., Cristofaro, A., Trianni, V.: Videos associated to “Best-of-\(N\) collective decisions on a hierarchy” (2022). https://doi.org/10.5281/zenodo.6786939

  14. Pavlic, T.P., Hanson, J., Valentini, G., Walker, S.I., Pratt, S.C.: Quorum sensing without deliberation: biological inspiration for externalizing computation to physical spaces in multi-robot systems. Swarm Intell. 15(1–2), 171–203 (2021)

    Article  Google Scholar 

  15. Reina, A., Bose, T., Trianni, V., Marshall, J.A.R.: Psychophysical laws and the superorganism. Sci. Rep. 8(1), 4387–8 (2018)

    Google Scholar 

  16. Reina, A., Marshall, J.A.R., Trianni, V., Bose, T.: Model of the best-of-N nest-site selection process in honeybees. Phys. Rev. E 95(5), 052411–15 (2017)

    Google Scholar 

  17. Reina, A., Valentini, G., Fernández-Oto, C., Dorigo, M., Trianni, V.: A design pattern for decentralised decision making. PLoS ONE 10(10), e0140950–18 (2015)

    Google Scholar 

  18. Ross-Gillespie, A., Kümmerli, R.: Collective decision-making in microbes. Front. Microbiol. 5(54) (2014)

    Google Scholar 

  19. Rubenstein, M., Ahler, C., Hoff, N., Cabrera, A., Nagpal, R.: Kilobot: a low cost robot with scalable operations designed for collective behaviors. Robot. Auton. Syst. 62(7), 966–975 (2014)

    Article  Google Scholar 

  20. Russo, D., Van Roy, B.: An information-theoretic analysis of Thompson Sampling. J. Mach. Learn. Res. 17(1), 2442–2471 (2016)

    MathSciNet  MATH  Google Scholar 

  21. Saha, A., Marshall, J.A.R., Reina, A.: Memory and communication efficient algorithm for decentralized counting of nodes in networks. PLoS ONE 16(11), e0259736 (2021)

    Google Scholar 

  22. Sasaki, T., Pratt, S.C.: The psychology of superorganisms: collective decision making by insect societies. Annu. Rev. Entomol. 63(1), 259–275 (2018)

    Article  Google Scholar 

  23. Sridhar, V.H., et al.: The geometry of decision-making in individuals and collectives. Proc. Natl. Acad. Sci. U.S.A. 118(50), e2102157118 (2021)

    Google Scholar 

  24. Valentini, G., Ferrante, E., Dorigo, M.: The best-of-N problem in robot swarms: formalization, state of the art, and novel perspectives. Front. Robot. AI 4, 1–43 (2017)

    Article  Google Scholar 

Download references

Acknowledgments

Vito Trianni acknowledges partial support from the project TAILOR (H2020-ICT-48 GA: 952215).

Author information

Authors and Affiliations

  1. DIAG, Sapienza University of Rome, Rome, Italy

    Fabio Oddi & Andrea Cristofaro

  2. ISTC, National Research Council, Rome, Italy

    Vito Trianni

Authors
  1. Fabio Oddi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrea Cristofaro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vito Trianni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vito Trianni .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. University of Lübeck, Lübeck, Germany

    Heiko Hamann

  3. University of Manchester, Manchester, UK

    Manuel López-Ibáñez

  4. University of Málaga, Málaga, Spain

    José García-Nieto

  5. Stellenbosch University, Stellenbosch, South Africa

    Andries Engelbrecht

  6. Worcester Polytechnic Institute, Worcester, MA, USA

    Carlo Pinciroli

  7. Université Libre de Bruxelles, Brussels, Belgium

    Volker Strobel

  8. Université Libre de Bruxelles, Brussels, Belgium

    Christian Camacho-Villalón

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Oddi, F., Cristofaro, A., Trianni, V. (2022). Best-of-N Collective Decisions on a Hierarchy. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2022. Lecture Notes in Computer Science, vol 13491. Springer, Cham. https://doi.org/10.1007/978-3-031-20176-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20176-9_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20175-2

  • Online ISBN: 978-3-031-20176-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation