Mobility helps problem-solving systems to avoid groupthink

Gomes, Paulo F.; Reia, Sandro M.; Rodrigues, Francisco A.; Fontanari, José F.

doi:10.1103/physreve.99.032301

Cited by 15 publications

(16 citation statements)

References 42 publications

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“…So, we choose the linear size of the square box as M/w. Since the effective area of one site is 1/w, we adopt the linear size 1/ √ w as the spatial scale to measure all distances in our study [39]. In this way, the network properties do not depend on the linear size of the square box and are uniquely defined by the radius r measured in units of 1/ √ w. As any constant value for w works, we set w = 1.0 so the linear size of the square box becomes √ M , and the radius is a dimensionless number.…”

Section: B Random Geometric Graph: Rggmentioning

confidence: 99%

Topological transition in a coupled dynamic in random networks

Gomes,

Fernandes,

Costa

2021

Preprint

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In this work, we study the topological transition in a model proposed by Saeedian et al. (Scientific Reports 2019 9:9726), which considers a coupled dynamics of node and link states, on the network known as random geometric graph (RGG). In that approach, each node has two cultural states and each link has also two states. There are six possible combinations of pairs (two nodes connected by one link) and half of them are categorized as a satisfying combination and the other half are unsatisfying one. The control parameter of the model dictates the probability of link and node updates. The system presents two phases: the absorbing phase is reached when all pairs of nodes become satisfying and, on the other hand, the active phase is present when there are both satisfying and unsatisfying pairs of nodes in the network. We found that, along with the unsatisfying pair density, the assortativity coefficient can also be used as an order parameter of the model. Additionaly, the assortativity coefficient gives an intuitive picture of the features on the topological transition of the network. We also calculated the components and cultural domains to add another view on the topological transition of the network.

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Section: B Random Geometric Graph: Rggmentioning

confidence: 99%

Topological transition in a coupled dynamic in random networks

Gomes,

Fernandes,

Costa

2021

Preprint

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“…Thus, the initial disposition of the agents corresponds to the classic random geometric graph originally introduced to model wireless communication networks [21]. As pointed out, this graph was successfully used as a face-to-face network in the modeling of the dynamics of human interactions [20] as well as in the study of the effects of random motility on cooperative problem-solving systems [22].…”

Section: Modelmentioning

confidence: 99%

Comfort-driven mobility produces spatial fragmentation in Axelrod’s model

2020

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Axelrod's model for the dissemination of culture combines two key ingredients of social dynamics: social influence, through which people become more similar when they interact, and homophily, which is the tendency of individuals to interact preferentially with similar others. In that agentbased model, the agents are fixed to the nodes of a network and are allowed to interact with a predetermined set of peers only, resulting in the frustration of the agents that end up at the boundaries of the cultural domains. Here we modify Axelrod's model by allowing the possibility that the agents move away from their cultural opposites and stay put when near their cultural likes. Hence the social appeal or attractivity between any two agents is proportional to their cultural similarity and determines the odds that those agents will move apart or stay put. We find that the homophilydriven mobility fragments severely the influence network for low initial cultural diversity, resulting in a network composed of a macroscopic number of microscopic components in the thermodynamic limit. For high initial cultural diversity, we find that a macroscopic component coexists with the microscopic ones. The transition between these two fragmentation regimes changes from continuous to discontinuous as the step size increases and disappears altogether at a critical end point, so that for large step sizes the influence network is severely fragmented. Regardless of the fragmentation regime, we find that the absorbing configurations are always multicultural for nonzero step sizes.

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“…Moreover, it also allows for implementing collective adaptation as a process differing from just the aggregation of individual learning dynamics. For example, by autonomously forming and recurrently reorganizing teams, a continuous process of collective adaptation emerges by reconsidering the role of the current members of the team against other potential members with different capabilities [8]. A significant branch of the previous literature concerning team self-organization has implemented auction-based mechanisms for team formation, in which an auction is held and the highest bidders are selected to form the team [16].…”

Section: Introductionmentioning

confidence: 99%

“…Previous research has shown that individual learning is beneficial for task performance, but only up to a threshold [11,13]. Moreover, researchers argue that recurrent team self-organization can be harmful to performance in certain circumstances [8]. This is related to the exploration vs exploitation dilemma, which states that individuals should adequately balance searching for new solutions (i.e., exploration) against building on the solutions already at their disposal (i.e., exploitation) [13].…”

Section: Introductionmentioning

confidence: 99%

Multi-level Adaptation of Distributed Decision-Making Agents in Complex Task Environments

Blanco-Fernández¹,

Leitner²,

Rausch³

2021

Preprint

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To solve complex tasks, individuals often autonomously organize in teams. Examples of complex tasks include disaster relief rescue operations or project development in consulting. The teams that work on such tasks are adaptive at multiple levels: First, by autonomously choosing the individuals that jointly perform a specific task, the team itself adapts to the complex task at hand, whereby the composition of teams might change over time. We refer to this process as selforganization. Second, the members of a team adapt to the complex task environment by learning. There is, however, a lack of extensive research on multi-level adaptation processes that consider self-organization and individual learning as simultaneous processes in the field of management science. We introduce an agentbased model based on the NK-framework to study the effects of simultaneous multi-level adaptation on a team's performance. We implement the multi-level adaptation process by a second-price auction mechanism for self-organization at the team level. Adaptation at the individual level follows an autonomous learning mechanism. Our preliminary results suggest that, depending on the task's complexity, different configurations of individual and collective adaptation can be associated with higher overall task performance. Low complex tasks favour high individual and collective adaptation, while moderate individual and collective adaptation is associated with better performance in case of moderately complex tasks. For highly complex tasks, the results suggest that collective adaptation is harmful to performance.

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Mobility helps problem-solving systems to avoid groupthink

Cited by 15 publications

References 42 publications

Topological transition in a coupled dynamic in random networks

Topological transition in a coupled dynamic in random networks

Comfort-driven mobility produces spatial fragmentation in Axelrod’s model

Multi-level Adaptation of Distributed Decision-Making Agents in Complex Task Environments

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