Individual Differences in Social Insect Behavior: Movement and Space Use in Leptothorax allardycei

2019 ◽  
pp. 113-145
Author(s):  
Blaine Cole
Keyword(s):  
Individual Differences ◽  
Social Insect ◽  
Space Use ◽  
Insect Behavior

The Ecology of Individual Differences Empirically Applied to Space-Use and Movement Tactics

2020 ◽  
Vol 196 (1) ◽  
pp. E1-E15 ◽  
Author(s):  
Quinn M. R. Webber ◽  
Michel P. Laforge ◽  
Maegwin Bonar ◽  
Alec L. Robitaille ◽  
Christopher Hart ◽  
...  
Keyword(s):  
Individual Differences ◽  
Space Use

scholarly journals Embedding Swarm Intelligence in Internet of Things Networks: A Social Insect Approach

2020 ◽  
pp. 80-87
Author(s):  
Sergio V Davalos, Ph.D.
Keyword(s):  
Internet Of Things ◽  
Swarm Intelligence ◽  
Social Insect ◽  
Insect Behavior ◽  
Agent Based Modeling ◽  
The Internet ◽  
Evolutionary Adaptation ◽  
Agent Based ◽  
The Internet Of Things

This paper introduces a framework for embedding intelligence in the Internet of Things (IoT) networks. The framework draws upon agent-based modeling, swarm intelligence, social insect behavior, and evolutionary adaptation. The key principles for each of these areas are first discussed. These concepts are then discussed from an IoTs perspective. The resulting capabilities and potential of embedding this type of intelligence are outlined.

scholarly journals Reappraising Social Insect Behavior through Aversive Responsiveness and Learning

PLoS ONE ◽  
2009 ◽  
Vol 4 (1) ◽  
pp. e4197 ◽  
Author(s):  
Edith Roussel ◽  
Julie Carcaud ◽  
Jean-Christophe Sandoz ◽  
Martin Giurfa
Keyword(s):  
Social Insect ◽  
Insect Behavior

scholarly journals Movement, Encounter Rate, and Collective Behavior in Ant Colonies

2020 ◽  
Author(s):  
Deborah M Gordon
Keyword(s):  
Collective Behavior ◽  
Task Allocation ◽  
Social Insect ◽  
Local Density ◽  
Movement Patterns ◽  
Insect Behavior ◽  
Nest Size ◽  
Encounter Rate ◽  
Encounter Rates ◽  
Nest Choice

Abstract Spatial patterns of movement regulate many aspects of social insect behavior, because how workers move around, and how many are there, determines how often they meet and interact. Interactions are usually olfactory; for example, in ants, by means of antennal contact in which one worker assesses the cuticular hydrocarbons of another. Encounter rates may be a simple outcome of local density: a worker experiences more encounters, the more other workers there are around it. This means that encounter rate can be used as a cue for overall density even though no individual can assess global density. Encounter rate as a cue for local density regulates many aspects of social insect behavior, including collective search, task allocation, nest choice, and traffic flow. As colonies grow older and larger, encounter rates change, which leads to changes in task allocation. Nest size affects local density and movement patterns, which influences encounter rate, so that nest size and connectivity influence colony behavior. However, encounter rate is not a simple function of local density when individuals change their movement in response to encounters, thus influencing further encounter rates. Natural selection on the regulation of collective behavior can draw on variation within and among colonies in the relation of movement patterns, encounter rate, and response to encounters.

Swarm Intelligence

1999 ◽  
Author(s):  
Eric Bonabeau ◽  
Marco Dorigo ◽  
Guy Theraulaz
Keyword(s):  
Artificial Intelligence ◽  
Swarm Intelligence ◽  
Social Insects ◽  
Operations Research ◽  
Social Insect ◽  
Multiagent System ◽  
Collective Intelligence ◽  
Insect Behavior ◽  
Changing Environments ◽  
Interacting Agents

Social insects--ants, bees, termites, and wasps--can be viewed as powerful problem-solving systems with sophisticated collective intelligence. Composed of simple interacting agents, this intelligence lies in the networks of interactions among individuals and between individuals and the environment. A fascinating subject, social insects are also a powerful metaphor for artificial intelligence, and the problems they solve--finding food, dividing labor among nestmates, building nests, responding to external challenges--have important counterparts in engineering and computer science. This book provides a detailed look at models of social insect behavior and how to apply these models in the design of complex systems. The book shows how these models replace an emphasis on control, preprogramming, and centralization with designs featuring autonomy, emergence, and distributed functioning. These designs are proving immensely flexible and robust, able to adapt quickly to changing environments and to continue functioning even when individual elements fail. In particular, these designs are an exciting approach to the tremendous growth of complexity in software and information. Swarm Intelligence draws on up-to-date research from biology, neuroscience, artificial intelligence, robotics, operations research, and computer graphics, and each chapter is organized around a particular biological example, which is then used to develop an algorithm, a multiagent system, or a group of robots. The book will be an invaluable resource for a broad range of disciplines.

scholarly journals Personality in social insects: How does worker personality determine colony personality?

2012 ◽  
Vol 58 (4) ◽  
pp. 580-588 ◽  
Author(s):  
Noa Pinter-Wollman
Keyword(s):  
Individual Differences ◽  
Natural Selection ◽  
Social Insects ◽  
Individual Variation ◽  
Social Insect ◽  
External Constraints ◽  
Insect Colonies ◽  
Reproductive Unit ◽  
Shed Light

Abstract Social insect colonies and the workers comprising them, each exhibit consistent individual differences in behavior, also known as ‘personalities’. Because the behavior of social insect colonies emerges from the actions of their workers, individual variation among workers’ personality may be important in determining the variation we observe among colonies. The reproductive unit of social insects, on which natural selection acts, is the colony, not individual workers. Therefore, it is important to understand what mechanisms govern the observed variation among colonies. Here I propose three hypotheses that address how consistent individual differences in the behavior of workers may lead to consistent individual differences in the behavior of colonies: 1. Colonies differ consistently in their average of worker personality; 2. The distribution but not the average of worker personalities varies consistently among colonies; and 3. Colony personality does not emerge from its worker personality composition but from consistent external constraints. I review evidence supporting each of these hypotheses and suggest methods to further investigate them. The study of how colony personality emerges from the personalities of the workers comprising them may shed light on the mechanisms underlying consistent individual differences in the behavior of other animals.

The challenge of accounting for individual differences in folk-economic beliefs

2018 ◽  
Vol 41 ◽  
Author(s):  
Benjamin C. Ruisch ◽  
Rajen A. Anderson ◽  
David A. Pizarro
Keyword(s):  
Individual Differences ◽  
Individual Variation ◽  
Political Ideologies ◽  
Economic Beliefs ◽  
Existing Data

AbstractWe argue that existing data on folk-economic beliefs (FEBs) present challenges to Boyer & Petersen's model. Specifically, the widespread individual variation in endorsement of FEBs casts doubt on the claim that humans are evolutionarily predisposed towards particular economic beliefs. Additionally, the authors' model cannot account for the systematic covariance between certain FEBs, such as those observed in distinct political ideologies.

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