Social decision-making driven by artistic explore–exploit tension

ABSTRACT We studied social decision-making in the rule-based improvisational dance There Might Be Others, where dancers make in-the-moment compositional choices. Rehearsals provided a natural test-bed with communication restricted to non-verbal cues. We observed a key artistic explore–exploit tension in which the dancers switched between exploitation of existing artistic opportunities and riskier exploration of new ones. We investigated how the rules influenced the dynamics using rehearsals together with a model generalized from evolutionary dynamics. We tuned the rules to heighten the tension and modelled nonlinear fitness and feedback dynamics for mutation rate to capture the observed temporal phasing of the dancers' exploration-versus-exploitation. Using bifurcation analysis, we identified key controls of the tension and showed how they could shape the decision-making dynamics of the model much like turning a ‘dial’ in the instructions to the dancers could shape the dance. The investigation became an integral part of the development of the dance.

[1]  K. Arrow,et al.  Social Choice and Individual Values , 1951 .

[2]  Jack Bowden,et al.  Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges. , 2015, Statistical science : a review journal of the Institute of Mathematical Statistics.

[3]  M. Golubitsky,et al.  Singularities and groups in bifurcation theory , 1985 .

[4]  Naomi Ehrich Leonard,et al.  Feedback Controlled Bifurcation of Evolutionary Dynamics with Generalized Fitness , 2018, 2018 Annual American Control Conference (ACC).

[5]  Karen Clemente Playing with Performance: The Element of the Game in Experimental Dance and Theater , 1990 .

[6]  M A Nowak,et al.  Evolution of universal grammar. , 2001, Science.

[7]  Peter Auer,et al.  Finite-time Analysis of the Multiarmed Bandit Problem , 2002, Machine Learning.

[8]  유화자 기독교 사역과 Leadership , 1997 .

[9]  Vaibhav Srivastava,et al.  Multiagent Decision-Making Dynamics Inspired by Honeybees , 2017, IEEE Transactions on Control of Network Systems.

[10]  Naomi Ehrich Leonard,et al.  Decision versus compromise for animal groups in motion , 2011, Proceedings of the National Academy of Sciences.

[11]  Robert Carl,et al.  Terry Riley's In C , 2009 .

[12]  T. L. Lai Andherbertrobbins Asymptotically Efficient Adaptive Allocation Rules , 1985 .

[13]  Reinhard Bürger,et al.  Mathematical properties of mutation-selection models , 1998, Genetica.

[14]  D. Saari Decisions and elections : explaining the unexpected , 2001 .

[15]  Arne Traulsen,et al.  Coevolutionary dynamics in large, but finite populations. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Naomi Ehrich Leonard,et al.  Investigating group behavior in dance: an evolutionary dynamics approach , 2016, 2016 American Control Conference (ACC).

[17]  I. Couzin,et al.  Effective leadership and decision-making in animal groups on the move , 2005, Nature.

[18]  A. Sanfey Social Decision-Making : Insights from Game Theory and Neuroscience , 2022 .

[19]  Angela J. Yu,et al.  Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[20]  Emmanuel Hebey,et al.  Blow-up Theory for Elliptic PDEs in Riemannian Geometry , 2004 .

[21]  J. Deneubourg,et al.  Group movement decisions in capuchin monkeys: The utility of an experimental study and a mathematical model to explore the relationship between individual and collective behaviours , 2007, q-bio/0702023.

[22]  Reza Olfati-Saber,et al.  Consensus and Cooperation in Networked Multi-Agent Systems , 2007, Proceedings of the IEEE.

[23]  Winter A. Mason,et al.  Collaborative learning in networks , 2011, Proceedings of the National Academy of Sciences.

[24]  Daeyeol Lee Game theory and neural basis of social decision making , 2008, Nature Neuroscience.

[25]  Aditya Gopalan,et al.  Stochastic bandits on a social network: Collaborative learning with local information sharing , 2016, ArXiv.

[26]  David Lazer,et al.  The Network Structure of Exploration and Exploitation , 2007 .

[27]  Colin Camerer,et al.  Social neuroeconomics: the neural circuitry of social preferences , 2007, Trends in Cognitive Sciences.

[28]  Roger Guimerà,et al.  Team Assembly Mechanisms Determine Collaboration Network Structure and Team Performance , 2005, Science.

[29]  Naomi Ehrich Leonard,et al.  Hopf Bifurcations and Limit Cycles in Evolutionary Network Dynamics , 2012, SIAM J. Appl. Dyn. Syst..

[30]  T. J. Roper,et al.  Group decision-making in animals , 2003, Nature.

[31]  Aurélien Garivier,et al.  On Bayesian Upper Confidence Bounds for Bandit Problems , 2012, AISTATS.

[32]  Naumaan Nayyar,et al.  Decentralized Learning for Multiplayer Multiarmed Bandits , 2014, IEEE Transactions on Information Theory.

[33]  Naomi Ehrich Leonard,et al.  In the Dance Studio: An Art and Engineering Exploration of Human Flocking , 2018, ArXiv.

[34]  Noa Pinter-Wollman,et al.  Higher-Order Interactions: Understanding the knowledge capacity of social groups using simplicial sets , 2015 .

[35]  T. Seeley,et al.  Group decision making in swarms of honey bees , 1999, Behavioral Ecology and Sociobiology.

[36]  Natalia L. Komarova,et al.  Eavesdropping and language dynamics. , 2010, Journal of theoretical biology.

[37]  M. Nowak Evolutionary Dynamics: Exploring the Equations of Life , 2006 .

[38]  Shirley Dex,et al.  JR 旅客販売総合システム(マルス)における運用及び管理について , 1991 .

[39]  P. Taylor,et al.  Test of optimal sampling by foraging great tits , 1978 .

[40]  W. Marsden I and J , 2012 .

[41]  S. Alexander Reed I n C on I ts O wn T erms : A S tatistical and H istorical V iew , 2011 .

[42]  David Kirsh,et al.  Creative Cognition in Choreography , 2011, ICCC.

[43]  Vaibhav Srivastava,et al.  Distributed cooperative decision-making in multiarmed bandits: Frequentist and Bayesian algorithms , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[44]  D. Helbing,et al.  Leadership, consensus decision making and collective behaviour in humans , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[45]  A. Sen,et al.  Collective Choice and Social Welfare , 2017 .