Of Ants and Men: Self-Organized Teams in Human and Insect Organizations

Anderson, Carl W.; McMillan, Elizabeth

doi:10.1207/s15327000em050205

Cited by 14 publications

(15 citation statements)

References 16 publications

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“…Active motor schemas are added and translated into motor activation at the beginning of each (control) time step. 4 Common to all behaviors is a motor schema for collision avoidance.…”

Section: A Navigation Modulementioning

confidence: 99%

“…We conduct two sets of experiments. In the first set we examine three setups (N , D), with a linear relationship between group size N and distance D: (2,30), (4,60), and (8, 120), where distances are expressed in centimeters. For each of the three setups we conduct 10 independent trials.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…Furthermore, a partitioned task is "a task that is split into two or more subtasks that are organized sequentially (Jeanne [41]; reviewed in Ratnieks and Anderson [69]; Anderson and Ratnieks [6])" [3, p. 4]. Anderson and Franks [2], [3], and Anderson and McMillan [4] found that the definition of teamwork, developed primarily from studies of social insects, also applies more Fig. 1.…”

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confidence: 99%

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Teamwork in Self-Organized Robot Colonies

Nouyan

Groß

Bonani

et al. 2009

IEEE Trans. Evol. Computat.

135

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Swarm robotics draws inspiration from decentralized self-organizing biological systems in general and from the collective behavior of social insects in particular. In social insect colonies, many tasks are performed by higher order group or team entities, whose task-solving capacities transcend those of the individual participants. In this paper, we investigate the emergence of such higher order entities. We report on an experimental study in which a team of physical robots performs a foraging task. The robots are "identical" in hardware and control. They make little use of memory and take actions purely on the basis of local information. Our study advances the current state of the art in swarm robotics with respect to the number of real-world robots engaging in teamwork (up to 12 robots in the most challenging experiment). To the best of our knowledge, in this paper we present the first self-organized system of robots that displays a dynamical hierarchy of teamwork (with cooperation also occurring among higher order entities). Our study shows that teamwork requires neither individual recognition nor differences between individuals. This result might also contribute to the ongoing debate on the role of these characteristics in the division of labor in social insects.

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“…Active motor schemas are added and translated into motor activation at the beginning of each (control) time step. 4 Common to all behaviors is a motor schema for collision avoidance.…”

Section: A Navigation Modulementioning

confidence: 99%

Section: A Experimental Setupmentioning

confidence: 99%

mentioning

confidence: 99%

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Teamwork in Self-Organized Robot Colonies

Nouyan

Groß

Bonani

et al. 2009

IEEE Trans. Evol. Computat.

135

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“…The study of complexity in nature, for example the collective animal behaviour of ant colonies and bird-flocking (Maturana and Varela 1987, Anderson and Franks 2001, Sumpter 2005 has generated researcher interest in developing analogies for human learning in the social sciences (Stacey 2001, Anderson and McMillan 2003, Stacey and Griffin 2005. A complexity principle from the biological sciences states that macro-level or global patterns arise from the behaviour of local interacting agents, but this global behaviour cannot be traced back to the behaviour of any individual agent; rather this behaviour emerges (the emergence principle) and is novel (the novelty principle) with respect to the individual agents (De Wolf and Holvoet 2004).…”

Section: From Causative To Emergent Models Of Learning Workmentioning

confidence: 99%

Towards an emergent view of learning work

Johnsson

Boud

2010

International Journal of Lifelong Education

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The purpose of this paper is to challenge models of workplace learning that seek to isolate or manipulate a limited set of features to increase the probability of learning. Such models typically attribute learning (or its absence) to individual engagement, manager expectations or organizational affordances and are therefore at least implicitly causative. In contrast, we discuss the contributions of complexity theory principles such as emergence and novelty that suggest that learning work is more a creative and opportunistic process that emerges from contextualized interactional understandings among actors. Using qualitative case study methods, we discuss the experiences of workers in two organizations asked to 'act up' in their managers' role to ensure work continuity. We believe the differences in how workers take up these opportunities result from a complex combination of situational factors that generate invitational patterns signalled from and by various understandings and interactions among actors doing collective work. Rather than a deficit view of learning that needs fixing, an emergent model of learning work suggests that learning develops as a collective generative endeavour from changing patterns of interactional understandings with others. This re-positioning recognizes that although invitational qualities cannot be deterministically predicted, paying attention to the patterns of cues and signals created from actors interacting together can condition ways of understandings to expand what is possible when work practices also become learning practices.

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“…While authors in the complexity research stream such as Stacey (1996) or Anderson and McMillan (2003) explicitly distinguish between self‐organization and self‐management, in the organizational literature these terms, together with related concepts such as self‐autonomous, self‐regulated or empowered teams, are often used synonymously (Ashmos & Nathan, 2002). Usually, these self‐managed teams have some leeway in decision taking and the definition of goals are explicitly regarded as a form of managerial control with established reporting and reward structures.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

The Dynamics and Functions of Self‐Organization in the Fuzzy Front End: Empirical Evidence from the Austrian Semiconductor Industry

Koch

Leitner²

2008

Creat Innov Manage

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This paper studies the functions, impacts and dynamics of self-organization in the fuzzy front end of innovation. Based on a case study approach, the new product development processes of five Austrian semiconductor companies are analysed. We adopt a complexity science perspective which stresses that self-organization and emergence are key elements of the new product development process. We found that self-organization mechanisms occur in two ways. First, self-organizational activities support formal and top-down managed new product development processes. In this way, they contribute to the acceleration and adaptation of the new product development process and are also a way to overcome bureaucratic structures. Second, we found evidence for the existence of purely emergent bottom-up processes in many cases. In this context, employees intrinsically and without any explicit order or strategy took initiatives to innovate. Such activities run in parallel to or precede formal new product development processes and employees deliberately bypass and even ignore formal processes such as financial incentive systems, suggestion schemes and patenting rules in order to promote their ideas. These activities are often secret until they are mature enough to be presented to the management, when they are then, if evaluated positively, incorporated as official projects in the new product development process.

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Of Ants and Men: Self-Organized Teams in Human and Insect Organizations

Cited by 14 publications

References 16 publications

Teamwork in Self-Organized Robot Colonies

Teamwork in Self-Organized Robot Colonies

Towards an emergent view of learning work

The Dynamics and Functions of Self‐Organization in the Fuzzy Front End: Empirical Evidence from the Austrian Semiconductor Industry

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