The HoneyComb Paradigm for Research on Collective Human Behavior.

Collective human behavior such as group movement frequently shows surprising patterns and regularities, such as the emergence of leadership. Recent literature has revealed that these patterns, often visible at the global level of the group, are based on self-organized, individual behaviors that follow several simple local parameters. Understanding the dynamics of human collective behavior can help to improve coordination and leadership in group and crowd scenarios, such as identifying the ideal placement and number of emergency exits in buildings. In this article, we present the experimental paradigm HoneyComb, which can be used to systematically investigate conditions and effects of human collective behavior. This paradigm uses a computer-based multi-user platform, providing a setting that can be shaped and adapted to various types of research questions. Situational conditions (e.g., cost-benefit ratios for specific behavior, monetary incentives and resources, various degrees of uncertainty) can be set by experimenters, depending on the research question. Each participant's motions are recorded by the server as hexagonal coordinates with timestamps at an accuracy of 50 ms and with individual IDs. Thus, a metric can be defined on the playfield, and movement parameters (e.g., distances, velocity, clustering, etc.) of participants can be measured over time. Movement data can in turn be combined with non-computerized data from questionnaires garnered within the same experiment setup. The HoneyComb paradigm is paving the way for new types of human movement experiments. We demonstrate here that these experiments can render results with sufficient internal validity to meaningfully deepen our understanding of human collective behavior.

[1]  Margarete Boos,et al.  Spontaneous flocking in human groups , 2013, Behavioural Processes.

[2]  Charles Yang,et al.  Mechanisms and Constraints in Word Segmentation , 2005 .

[3]  Jens Krause,et al.  Leadership and social information use in human crowds , 2010, Animal Behaviour.

[4]  I. Couzin,et al.  Self-Organization and Collective Behavior in Vertebrates , 2003 .

[5]  Craig W. Reynolds,et al.  A Distributed Behavioral Model , 1987 .

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

[7]  Dirk Helbing,et al.  Collective Information Processing and Pattern Formation in Swarms, Flocks, and Crowds , 2009, Top. Cogn. Sci..

[8]  J. Fischer,et al.  Communication and Cognition in Primate Group Movement , 2011, International Journal of Primatology.

[9]  Dinesh Manocha,et al.  Least-effort trajectories lead to emergent crowd behaviors. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  I. Couzin,et al.  Consensus decision making in human crowds , 2008, Animal Behaviour.

[11]  Vladimir E. Fortov,et al.  STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS Viscosity of a Dusty Plasma Liquid , 2007 .

[12]  I. Couzin,et al.  Inferring the structure and dynamics of interactions in schooling fish , 2011, Proceedings of the National Academy of Sciences.

[13]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1998 .

[14]  Wenzhong Li,et al.  Patterns of Group Movement on a Virtual Playfield: Empirical and Simulation Approaches , 2017 .

[15]  Norman I. Badler,et al.  Controlling individual agents in high-density crowd simulation , 2007, SCA '07.

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

[17]  Andrew J. King,et al.  A rule-of-thumb based on social affiliation explains collective movements in desert baboons , 2011, Animal Behaviour.

[18]  G D Ruxton,et al.  Fish shoal composition: mechanisms and constraints , 2000, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[19]  Larissa Conradt,et al.  Models in animal collective decision-making: information uncertainty and conflicting preferences , 2012, Interface Focus.

[20]  M. Boos,et al.  Competition in human groups—Impact on group cohesion, perceived stress and outcome satisfaction , 2015, Behavioural Processes.

[21]  D. Sumpter.  Collective Animal Behavior , 2010 .

[22]  Bw Spooner.  Planning and design , 1964 .

[23]  Mubbasir Kapadia,et al.  Virtual Sensing and Virtual Reality: How New Technologies Can Boost Research on Crowd Dynamics , 2018, Front. Robot. AI.

[24]  Guy Theraulaz,et al.  Self-Organization in Biological Systems , 2001, Princeton studies in complexity.

[25]  Dirk Helbing,et al.  Self-Organizing Pedestrian Movement , 2001 .

[26]  Demetri Terzopoulos,et al.  Autonomous pedestrians , 2007, Graph. Model..

[27]  Margarete Boos,et al.  Leadership in Moving Human Groups , 2014, PLoS Comput. Biol..