Key individuals and the organisation of labor in ants

Robson, Simon K. A.; Traniello, James F. A.

doi:10.1007/978-3-0348-8739-7_13

Cited by 84 publications

(77 citation statements)

References 38 publications

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“…Elitism was the case where some agents performed a disproportionately large percentage of group labor. These findings were corroborated by similar findings in empirical theoretical biology studies (Chen, 1937b), (Chen, 1937a), (Robson & Traniello, 1999), (Bourke, 1999), (Jeanne, 1986), (Karsai & Wenzel, 1998), (Karsai & Wenzel, 2000). The authors main conclusion was that their response threshold model demonstrated emergent specialization to be function of group size in the given resource allocation task, where group sizes exceeding a critical threshold value contained specialized agents, and group sizes below the critical threshold value contained only unspecialized agents.…”

Section: Division Of Labor As a Function Of Groupsupporting

confidence: 75%

“…Such models typically utilize feedback signals given to agents of the same caste (Kreiger & Billeter, 2000), in order to encourage the emergence of specialization for a specific task. Many variations of these models exist (Bonabeau et al, 1998), (Deneubourg et al, 1987), (Bonabeau et al, 1997), (Robson & Traniello, 1999), (Bonabeau & Theraulaz, 1999), (Theraulaz, Goss, Gervet, & Deneubourg, 1991), (Theraulaz, Gervet, & Semenoff, 1991), (Bonabeau et al, 1996), including those that use evolutionary algorithms (Tarapore, Floreano, & Keller, 2006), (Waibel et al, 2006), and reinforcement learning models , in order to derive threshold values. The goal of such models is typically to optimize global task performance.…”

Section: Division Of Labor Modelsmentioning

confidence: 99%

“…It is obvious that the study of such biologically inspired formalizations of specialization are worthy of future research attention given their applicability to a broad range of optimization tasks including dynamic scheduling and resource allocation. The models of (Theraulaz et al, 1998a), (Campos et al, 2001), (Gautrais et al, 2002), (Bonabeau et al, 1997) were prevalent in that they eloquently demonstrated how behavioral specialization emerged as a result of self-regulating task assignment and accomplishment, for which there exists a large amount of corroborating biological literature and empirical evidence (Chen, 1937b), (Chen, 1937a), (Robson & Traniello, 1999), (Deneubourg et al, 1987), (Theraulaz, Gervet, & Semenoff, 1991), (O'Donnell, 1998).…”

Section: Division Of Labor Models For Collective Resource Distributiomentioning

confidence: 99%

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Emergent Specialization in Biologically Inspired Collective Behavior Systems

Nitschke

Schut

Eiben

2008

Intelligent Complex Adaptive Systems

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Specialization is observable in many complex adaptive systems and is thought by many to be a fundamental mechanism for achieving optimal efficiency within organizations operating within complex adaptive systems. This chapter presents a survey and critique of collective behavior systems designed using biologically inspired principles, where specialization that emerges as a result of system dynamics and is used problem solver or means to increase task performance. The chapter presents an argument for developing design methodologies and principles that facilitate emergent specialization in collective behavior systems. Open problems of current research and future research directions are highlighted for the purpose of encouraging the development of such emergent specialization design methodologies. 2 Emergent Specialization in Biologically Inspired Collective Behavior Systems AbstractSpecialization is observable in many complex adaptive systems and is thought by many to be a fundamental mechanism for achieving optimal efficiency within organizations operating within complex adaptive systems. This chapter presents a survey and critique of collective behavior systems designed using biologically inspired principles, where specialization that emerges as a result of system dynamics and is used problem solver or means to increase task performance. The chapter presents an argument for developing design methodologies and principles that facilitate emergent specialization in collective behavior systems. Open problems of current research and future research directions are highlighted for the purpose of encouraging the development of such emergent specialization design methodologies.

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Section: Division Of Labor As a Function Of Groupsupporting

confidence: 75%

Section: Division Of Labor Modelsmentioning

confidence: 99%

Section: Division Of Labor Models For Collective Resource Distributiomentioning

confidence: 99%

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Emergent Specialization in Biologically Inspired Collective Behavior Systems

Nitschke

Schut

Eiben

2008

Intelligent Complex Adaptive Systems

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“…For example, individuals that are responsible for rapid transmission of beneficial substances, such as food, may also cause accelerated disease spread [24]. Unfortunately, much of our understanding of how keystones operate comes from descriptive studies [25]. We therefore maintain a relatively weak mechanistic understanding of how keystones exert their influence and/or which factors can cause a keystone's impacts to switch from positive to negative.…”

Section: Introductionmentioning

confidence: 99%

The Achilles' heel hypothesis: misinformed keystone individuals impair collective learning and reduce group success

et al. 2016

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Many animal societies rely on highly influential keystone individuals for proper functioning. When information quality is important for group success, such keystone individuals have the potential to diminish group performance if they possess inaccurate information. Here, we test whether information quality (accurate or inaccurate) influences collective outcomes when keystone individuals are the first to acquire it. We trained keystone or generic individuals to attack or avoid novel stimuli and implanted these trained individuals within groups of naive colony-mates. We subsequently tracked how quickly groups learned about their environment in situations that matched (accurate information) or mismatched (inaccurate information) the training of the trained individual. We found that colonies with just one accurately informed individual were quicker to learn to attack a novel prey stimulus than colonies with no informed individuals. However, this effect was no more pronounced when the informed individual was a keystone individual. In contrast, keystones with inaccurate information had larger effects than generic individuals with identical information: groups containing keystones with inaccurate information took longer to learn to attack/avoid prey/predator stimuli and gained less weight than groups harbouring generic individuals with identical information. Our results convey that misinformed keystone individuals can become points of vulnerability for their societies.

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“…'Key individuals', such as catalysts, may enhance the task performance of other workers [17], as demonstrated in honeybees. In honeybees, vibration behaviour performed by certain individuals modulates the search of other nestmates for new nest sites [18], possibly through increasing the interaction rate among nestmates [19].…”

Section: Introductionmentioning

confidence: 99%

The effect of individual variation on the structure and function of interaction networks in harvester ants

Pinter‐Wollman

Wollman

Guetz

et al. 2011

J. R. Soc. Interface.

128

119

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Social insects exhibit coordinated behaviour without central control. Local interactions among individuals determine their behaviour and regulate the activity of the colony. Harvester ants are recruited for outside work, using networks of brief antennal contacts, in the nest chamber closest to the nest exit: the entrance chamber. Here, we combine empirical observations, image analysis and computer simulations to investigate the structure and function of the interaction network in the entrance chamber. Ant interactions were distributed heterogeneously in the chamber, with an interaction hot-spot at the entrance leading further into the nest. The distribution of the total interactions per ant followed a right-skewed distribution, indicating the presence of highly connected individuals. Numbers of ant encounters observed positively correlated with the duration of observation. Individuals varied in interaction frequency, even after accounting for the duration of observation. An ant's interaction frequency was explained by its path shape and location within the entrance chamber. Computer simulations demonstrate that variation among individuals in connectivity accelerates information flow to an extent equivalent to an increase in the total number of interactions. Individual variation in connectivity, arising from variation among ants in location and spatial behaviour, creates interaction centres, which may expedite information flow.

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Key individuals and the organisation of labor in ants

Cited by 84 publications

References 38 publications

Emergent Specialization in Biologically Inspired Collective Behavior Systems

Emergent Specialization in Biologically Inspired Collective Behavior Systems

The Achilles' heel hypothesis: misinformed keystone individuals impair collective learning and reduce group success

The effect of individual variation on the structure and function of interaction networks in harvester ants

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