[robotics-worldwide] [journals] CfP: Swarm Intelligence (Springer) - Special issue on Collective Decision Making in Living and Artificial Systems

Giovanni Reina a.reina at sheffield.ac.uk
Mon Mar 16 02:51:02 PDT 2020


Special issue of the *Swarm Intelligence* journal (Springer)
*Collective Decision Making*
*in Living and Artificial Systems*

Submission deadline: *August 26, 2020*


Collective decision making refers to the process whereby a group of
individuals process information collectively to reach a common agreement.
Reaching agreement is one of the fundamental cognitive processes upon which
a collective can realize more complex behaviours. The decisions dynamics
and their final outcome are determined by the mechanisms used by
individuals to collect, share, and process information. Collective decision
making is a widespread phenomenon in natural systems that is studied across
taxa and scales, including in humans, group-living animals, and cell
populations, and that has inspired the design of algorithms for
decentralised artificial systems such as robot swarms and wireless sensor
networks.

Examples of collective decisions made by animal groups include choosing a
location where to build the nest, a food patch to feed from, or a common
direction of motion. Human populations are able to reach an agreement on
social norms in the absence of a central coordinating authority or,
similarly, to select one commercial product among equally valuable
alternatives. Collective decisions are also made by populations of cells,
for instance, by collections of neurons interacting with each other to
trigger a coordinated response in the brain. While studies of living
collectives have inspired and continue to inspire the design of artificial
systems, recent technological and theoretical advancements in computer
vision, deep learning, and causal inference are providing novel research
approaches to researchers in the life sciences.

This special issue solicits high-quality scientific contributions on
collective decision making both in natural and artificial systems. We
encourage submissions of research contributions that advance our
theoretical understanding of the field of collective decision making,
report experimental investigations of decision-making mechanisms in living
or artificial collectives, propose innovative solutions to the design of
decentralised decision-making systems, or provide novel perspectives on
natural systems or technological advancements of interest across scientific
boundaries.

Contributions to this special issue on collective decision making may fall
in any of these research areas:
 • Swarm robotics
 • Collective animal behaviour
 • Voting models
 • Cultural evolution
 • Network science
 • Population dynamics
 • Social neuroscience
 • Socio- and Econo-physics
 • Evolutionary game theory
 • Information theory
 • Bounded rationality
 • Wireless sensor networks

*Paper Submission*

All manuscripts must be prepared according to the publication guidelines of
the Swarm Intelligence journal that can be found at the journal website:
https://urldefense.com/v3/__https://www.springer.com/journal/11721__;!!LIr3w8kk_Xxm!_W3TUwj-BpZZGA-SGoQoDyZ1EPKG8d7fAXlJ436yYZZ_WhyKeUCMRl1fMXEVznuECYU_V0fI$ 

Prospective authors are invited to submit their papers using the online
submission system of the journal at https://urldefense.com/v3/__http://www.editorialmanager.com/swrm__;!!LIr3w8kk_Xxm!_W3TUwj-BpZZGA-SGoQoDyZ1EPKG8d7fAXlJ436yYZZ_WhyKeUCMRl1fMXEVznuECfpxRrSp$ 
selecting "*Special Issue on Collective Decision Making*" as the article
type.

*Guest Editors*

 • Dr. Andreagiovanni Reina, University of Sheffield, UK (
andreagiovanni.reina at gmail.com)

 • Dr. Eliseo Ferrante, Vrije Universiteit Amsterdam, NL (e.ferrante at vu.nl)

 • Dr. Gabriele Valentini, Arizona State University, AZ (gvalentini at asu.edu)


-- 
Andreagiovanni Reina, PhD
Research Fellow in Collective Robotics
University of Sheffield, UK
https://urldefense.com/v3/__http://areina.staff.shef.ac.uk__;!!LIr3w8kk_Xxm!_W3TUwj-BpZZGA-SGoQoDyZ1EPKG8d7fAXlJ436yYZZ_WhyKeUCMRl1fMXEVznuECUf5VWY_$ 


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