The peloton superorganism and protocooperative behavior

Below a critical threshold equivalent to (d) cyclists cooperate (by passing).Above threshold cyclists sustain pace of stronger front rider but cannot pass.When at max speeds pelotons sort so range of max capacity is equivalent to 1-d.Below this range weak cyclists separate from peloton at this max speed.Simulation experiments show a tendency towards predominantly free-riding behavior. A theoretical framework for protocooperative behavior in pelotons (groups of cyclists) is proposed. A threshold between cooperative and free-riding behaviors in pelotons is modeled, together comprising protocooperative behavior (different from protocooperation), hypothesized to emerge in biological systems involving energy savings mechanisms. Further, the tension between intra-group cooperation and inter-group competition is consistent with superorganism properties. Protocooperative behavior parameters: 1. two or more cyclists coupled by drafting benefit; 2. current power output or speed; and 3. maximal sustainable outputs (MSO). Main characteristics: 1. relatively low speed phase in which cyclists naturally pass each other and share highest-cost front position; and 2. free-riding phase in which cyclists maintain speeds of those ahead, but cannot pass. Threshold for protocooperative behavior is equivalent to coefficient of drafting (d), below which cooperative behavior occurs; above which free-riding occurs up to a second threshold when coupled cyclists diverge. Range of cyclists' MSOs in free-riding phase is equivalent to the energy savings benefit of drafting (1-d). When driven to maximal speeds, groups tend to sort such that their MSO ranges equal the free-riding range (1-d).

[1]  H. Ohtsuki,et al.  A simple rule for the evolution of cooperation on graphs and social networks , 2006, Nature.

[2]  J. Steffensen,et al.  Fish swimming in schools save energy regardless of their spatial position , 2014, Behavioral Ecology and Sociobiology.

[3]  Keith Davids,et al.  Sports Teams as Superorganisms , 2012, Sports Medicine.

[4]  M. Nowak Five Rules for the Evolution of Cooperation , 2006, Science.

[5]  T. Roosevelt The New Nationalism , 1989 .

[6]  A. Nevill,et al.  Recovery of power output and muscle metabolites following 30 s of maximal sprint cycling in man. , 1995, The Journal of physiology.

[7]  Hiroki Sayama,et al.  Adaptive long-range migration promotes cooperation under tempting conditions , 2013, ECAL.

[8]  Tim Olds The mathematics of breaking away and chasing in cycling , 1998, European Journal of Applied Physiology and Occupational Physiology.

[9]  Y. Moreno,et al.  Dynamic instability of cooperation due to diverse activity patterns in evolutionary social dilemmas , 2015, 1502.07724.

[10]  H. Weimerskirch,et al.  Energy saving in flight formation , 2001, Nature.

[11]  David Sloan Wilson,et al.  Evolution "for the Good of the Group The process known as group selection was once accepted unthinkingly, then was widely discredited; it's time for a more discriminating assessment , 2008 .

[12]  Wenjian Yu,et al.  Migration as a Mechanism to Promote Cooperation , 2008, Adv. Complex Syst..

[13]  Christopher J. Davidson,et al.  Understanding sprint-cycling performance: the integration of muscle power, resistance, and modeling. , 2007, International journal of sports physiology and performance.

[14]  W. Hamilton,et al.  The evolution of cooperation. , 1984, Science.

[15]  Herbert Gintis,et al.  Game Theory Evolving: A Problem-Centered Introduction to Modeling Strategic Interaction - Second Edition , 2009 .

[16]  Tian Qiu,et al.  Coupled dynamics of mobility and pattern formation in optional public goods games , 2012, ArXiv.

[17]  Chester R. Kyle,et al.  Reduction of Wind Resistance and Power Output of Racing Cyclists and Runners Travelling in Groups , 1979 .

[18]  D. E. Matthews Evolution and the Theory of Games , 1977 .

[19]  J. Carmeliet,et al.  CFD simulations of the aerodynamic drag of two drafting cyclists , 2013 .

[20]  E. Martins-Ratamero Modelling peloton dynamics in competitive cycling : a quantitative approach , 2014 .

[21]  Zhuo Chen,et al.  Evolution of cooperation among mobile agents , 2010, 1006.0772.

[22]  Matjaz Perc,et al.  A deceleration model for bicycle peloton dynamics and group sorting , 2015, Appl. Math. Comput..

[23]  James C Martin,et al.  Validation of a Mathematical Model for Road Cycling Power. , 1998, Journal of applied biomechanics.

[24]  F. Fish Swimming Strategies for Energy Economy , 2010 .

[25]  Yamir Moreno,et al.  Velocity-enhanced cooperation of moving agents playing public goods games. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  Attila Szolnoki,et al.  Evolution of public cooperation on interdependent networks: The impact of biased utility functions , 2012, ArXiv.

[27]  M. Perc,et al.  Collective behavior and the identification of phases in bicycle pelotons , 2014 .

[28]  Tim Olds,et al.  Applications to Cycling , 2001 .

[29]  J. Hagberg,et al.  Energy expenditure during bicycling. , 1990, Journal of applied physiology.

[30]  C. Hemelrijk,et al.  The increased efficiency of fish swimming in a school , 2013, 1307.7282.

[31]  J P Broker,et al.  Racing cyclist power requirements in the 4000-m individual and team pursuits. , 1999, Medicine and science in sports and exercise.

[32]  Elizabeth Bradley,et al.  Cooperation in bike racing - When to work together and when to go it alone , 2011, Complex..

[33]  Keith Davids,et al.  Sports teams as superorganisms: implications of sociobiological models of behaviour for research and practice in team sports performance analysis. , 2012, Sports medicine.

[34]  Attila Szolnoki,et al.  Coevolutionary Games - A Mini Review , 2009, Biosyst..

[35]  Y Moreno,et al.  Effects of mobility in a population of prisoner's dilemma players. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[36]  Grenfell,et al.  Inverse density dependence and the Allee effect. , 1999, Trends in ecology & evolution.

[37]  F. C. Santos,et al.  Scale-free networks provide a unifying framework for the emergence of cooperation. , 2005, Physical review letters.

[38]  H. Reeve,et al.  The emergence of a superorganism through intergroup competition , 2007, Proceedings of the National Academy of Sciences.