Fast-and-Flexible Decision-Making With Modulatory Interactions

Control of networks; network analysis and control; stability of nonlinear systems; Decisions makings; Dynamic interaction; Dynamics models; Making decision; Multi-agent decision making; Multiagent systems (MASs); Network analysis and controls; Opinion dynamics; Stability of nonlinear systems; Control and Systems Engineering; Control and Optimization

Abstract :

[en] Multi-agent systems in biology, society, and engineering are capable of making decisions through the dynamic interaction of their elements. Nonlinearity of the interactions is key for the speed, robustness, and flexibility of multi-agent decision-making. In this work we introduce modulatory, that is, multiplicative, in contrast to additive, interactions in a nonlinear opinion dynamics model of fast-and-flexible decision-making. The original model is nonlinear because network interactions, although additive, are saturated. Modulatory interactions introduce an extra source of nonlinearity that greatly enriches the model's decision-making behavior in a mathematically tractable way. Modulatory interactions are widespread in both biological and social decision-making networks; our model provides new tools to understand the role of these interactions in networked decision-making and to engineer them in artificial systems.

Disciplines :

Engineering, computing & technology: Multidisciplinary, general & others

Author, co-author :

Moreno-Morton, Rodrigo ; Department of Electrical Engineering and Computer Science, University of Liege, Liege, Belgium

Bizyaeva, Anastasia ; Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, United States

Ehrich Leonard, Naomi ; Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, United States

Franci, Alessio ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Brain-Inspired Computing ; Walloon Excellence Research Institute, Wavre, Belgium

Language :

English

Title :

Fast-and-Flexible Decision-Making With Modulatory Interactions

Publication date :

2024

Journal title :

IEEE Control Systems Letters

eISSN :

2475-1456

Publisher :

Institute of Electrical and Electronics Engineers Inc.

Volume :

Pages :

3333 - 3338

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://xplorestaging.ieee.org/ielx8/7782633/10411713/10804645.pdf?arnumber=10804645

Funders :

FPS BOSA - Federal Public Service Policy and Support

Available on ORBi :

since 18 June 2025

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Bibliography

A. Bizyaeva, A. Franci, and N. E. Leonard, “Nonlinear opinion dynamics with tunable sensitivity,” IEEE Trans. Autom. Control, vol. 68, no. 3, pp. 1415–1430, Mar. 2023.
N. E. Leonard, A. Bizyaeva, and A. Franci, “Fast and flexible multiagent decision-making,” Annu. Rev. Control, Robot., Auton. Syst., vol. 7, pp. 19–45, Jul. 2024.
A. Bizyaeva, A. Franci, and N. E. Leonard, “Multi-topic belief formation through bifurcations over signed social networks,” 2024, arXiv:2308.02755.
H. R. Wilson and J. D. Cowan, “Excitatory and inhibitory interactions in localized populations of model neurons,” Biophys. J., vol. 12, no. 1, pp. 1–24, 1972.
J. J. Hopfield, “Neurons with graded response have collective computational properties like those of two-state neurons,” Proc. Nat. Acad. Sci., vol. 81, no. 10, pp. 3088–3092, 1984.
N. Le Novere, “Quantitative and logic modelling of molecular and gene networks,” Nat. Rev. Genet., vol. 16, no. 3, pp. 146–158, 2015.
A. V. Hill, “The possible effects of the aggregation of the molecules of hemoglobin on its dissociation curves,” J. Physiol., vol. 40, pp. 4–7, Jan. 1910.
R. Gray, A. Franci, V. Srivastava, and N. E. Leonard, “Multiagent decision-making dynamics inspired by honeybees,” IEEE Trans. Control Netw. Syst., vol. 5, no. 2, pp. 793–806, Jun. 2018.
N. E. Leonard, K. Lipsitz, A. Bizyaeva, A. Franci, and Y. Lelkes, “The nonlinear feedback dynamics of asymmetric political polarization,” Proc. Nat. Acad. Sci., vol. 118, no. 50, 2021, Art. no. e2102149118.
G. Amorim, M. Santos, S. Park, A. Franci, and N. E. Leonard, “Threshold decision-making dynamics adaptive to physical constraints and changing environment,” in Proc. Eur. Control Conf., 2024, pp. 1908–1913.
A. Franci, A. Bizyaeva, S. Park, and N. E. Leonard, “Analysis and control of agreement and disagreement opinion cascades,” Swarm Intell., vol. 15, no. 1, pp. 47–82, 2021.
C. Cathcart, M. Santos, S. Park, and N. Leonard, “Proactive opinion-driven robot navigation around human movers,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), 2023, pp. 4052–4058.
H. Hu, K. Nakamura, K.-C. Hsu, N. E. Leonard, and J. F. Fisac, “Emergent coordination through game-induced nonlinear opinion dynamics,” in Proc. IEEE Conf. Decis. Control (CDC), 2023, pp. 8122–8129.
S. Park, A. Bizyaeva, M. Kawakatsu, A. Franci, and N. E. Leonard, “Tuning cooperative behavior in games with nonlinear opinion dynamics,” IEEE Control Syst. Lett., vol. 6, pp. 2030–2035, 2022.
A. Baptista et al., “Mining higher-order triadic interactions,” 2024, arXiv:2404.14997.
K. Boahen, “Dendrocentric learning for synthetic intelligence,” Nature, vol. 612, no. 7938, pp. 43–50, 2022.
P. R. Murphy, E. Boonstra, and S. Nieuwenhuis, “Global gain modulation generates time-dependent urgency during perceptual choice in humans,” Nat. Commun., vol. 7, no. 1, 2016, Art. no. 13526.
R. A. Silver, “Neuronal arithmetic,” Nat. Rev. Neurosci., vol. 11, no. 7, pp. 474–489, 2010.
K. Krishnamurthy, T. Can, and D. J. Schwab, “Theory of gating in recurrent neural networks,” Phys. Rev. X, vol. 12, no. 1, 2022, Art. no. 11011.
S. C. Iyer et al., “Transcriptional regulation by σ factor phosphorylation in bacteria,” Nat. Microbiol., vol. 5, no. 3, pp. 395–406, Jan. 2020.
K. K. Sinha, S. Bilokapic, Y. Du, D. Malik, and M. Halic, “Histone modifications regulate pioneer transcription factor cooperativity,” Nature, vol. 619, no. 7969, pp. 378–384, Jul. 2023.
M. H. DeGroot, “Reaching a consensus,” J. Amer. Stat. Assoc., vol. 69, no. 345, pp. 118–121, 1974.
A. Bizyaeva, M. O. Arango, Y. Zhou, S. Levin, and N. E. Leonard, “Active risk aversion in SIS epidemics on networks,” in Proc. Amer. Control Conf. (ACC), 2024, pp. 4428–4433.
M. Morrison, J. N. Kutz, and M. Gabbay, “Transitions between peace and systemic war as bifurcations in a signed network dynamical system,” Netw. Sci., vol. 11, no. 3, pp. 458–501, 2023.
T. M. Paine and M. R. Benjamin, “A model for multi-agent autonomy that uses opinion dynamics and multi-objective behavior optimization,” in Proc. IEEE Int. Conf. Robot. Autom. (ICRA), 2024, pp. 8305–8311.
A. Bizyaeva, T. Sorochkin, A. Franci, and N. E. Leonard, “Control of agreement and disagreement cascades with distributed inputs,” in Proc. IEEE Conf. Decis. Control (CDC), 2021, pp. 4994–4999.
M. Golubitsky and D. Schaeffer, Singularities and Groups in Bifurcation Theory, vol. 1, New York, NY, USA: Springer, 1985.
M. Golubitsky and I. Stewart, The Symmetry Perspective: From Equilibrium to Chaos in Phase Space and Physical Space, vol. 200. Basel, Switzerland: Springer 2003.
H. Khalil, Nonlinear Systems, 3rd ed. Hoboken, NJ, USA: Prentice Hall, 2000.
J. Guckenheimer and P. Holmes, Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields, vol. 42. New York, NY, USA: Springer, 2013.
J. Pickard, C. Chen, C. Stansbury, A. Surana, A. M. Bloch, and I. Rajapakse, “Kronecker product of tensors and hypergraphs: Structure and dynamics,” SIAM J. Matrix Anal. Appl., vol. 45, no. 3, pp. 1621–1642, 2024.