Group-Level Exploration and Exploitation: A Computer Simulation-Based Analysis

Kunz, Jennifer

doi:10.18564/jasss.1798

Cited by 15 publications

(10 citation statements)

References 43 publications

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“…The literature on knowledge diffusion assumes that that all actors in a network follow the same logic while exchanging knowledge: (a) they seek knowledge when they lack it and (b) they share knowledge if they possess the acquired knowledge (e.g. March, ; Miller et al, ; Kane & Alavi, ; Liao & Wang, ; Kunz, ). Thus, individuals are assumed to be homogeneous in their tendency to share and seek knowledge.…”

Section: Theoretical Developmentmentioning

confidence: 99%

Diffusion of knowledge in social media networks: effects of reputation mechanisms and distribution of knowledge roles

Havakhor¹,

Soror

Sabherwal

2016

Information Systems Journal

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Abstract. Social media platforms serve as important tools for diffusing knowledge within organizations. The factors affecting knowledge diffusion through social media networks (SMNs) need to therefore be better understood. Accordingly, this paper focuses on two SMN-specific characteristics -reputation mechanisms and the distribution of knowledge roles -which are argued to enhance and enable, respectively, the smooth transfer of knowledge in a SMN. To examine their effects, we distinguish between two types of reputation mechanisms -adaptive and objective -and across three distinct knowledge roles in SMNs: seekers, contributors and brokers. We argue that the extent of knowledge diffusion in the SMN depends on the type of mechanism and the relative distribution of these three roles. Using data collected through an agent-based simulation, we find that (a) the distribution of knowledge roles affects knowledge diffusion, with distributions consisted of more brokers outperforming others and (b) objective reputation mechanisms outperform adaptive mechanisms. Furthermore, we find that reputation mechanisms and distribution of knowledge roles interact to influence knowledge diffusion. The study's implications for future research and practice are discussed in the light of its limitations.

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Section: Theoretical Developmentmentioning

confidence: 99%

Diffusion of knowledge in social media networks: effects of reputation mechanisms and distribution of knowledge roles

Havakhor¹,

Soror

Sabherwal

2016

Information Systems Journal

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“…Vessels maximize utility using either: (a) an “explore‐exploit” (EE) algorithm, where each vessel picks a spot to fish randomly in the first period, and then in subsequent periods either picks the most lucrative previous spot (i.e., exploit) or, with some probability, picks a new fishing spot randomly (i.e., explore); or (b) an “explore‐exploit‐imitate” (EEI) algorithm, which is similar to EE except exploiting vessels also have information about the locations and profits of other vessels in the fleet when choosing the best spot (i.e., imitate; see Bailey et al, ; Carrella, ; Carrella et al, for full details). Explore‐exploit models are commonly used in the literature on foraging and environmental management to represent human decision‐making (Berger‐Tal, Nathan, Meron, & Saltz, ; He, Luo, Tan, Wu, & Fan, ; Kunz, ; Roberts & Goldstone, ). Indeed, Carrella et al () compare several possible algorithms in POSEIDON, finding a trade‐off between flexibility and optimization performance, but finding EEI to be among the best on both metrics.…”

Section: Poseidon Modelmentioning

confidence: 99%

Opportunities for agent‐based modelling in human dimensions of fisheries

et al. 2020

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Models of human dimensions of fisheries are important to understanding and predicting how fishing industries respond to changes in marine ecosystems and management institutions. Advances in computation have made it possible to construct agent-based models (ABMs)-which explicitly describe the behaviour of individual people, firms or vessels in order to understand and predict their aggregate behaviours. ABMs are widely used for both academic and applied purposes in many settings including finance, urban planning and the military, but are not yet mainstream in fisheries science and management, despite a growing literature. ABMs are well suited to understanding emergent consequences of fisher interactions, heterogeneity and bounded rationality, especially in complex ecological, social and institutional contexts. For these reasons, we argue that ABMs of human behaviour can contribute significantly to human dimensions of fisheries in three areas: (a) understanding interactions between multiple management institutions; (b) incorporating cognitive and | 571 BURGESS Et al. 7 ABM CHALLENGES 579 7.1 Scope 579 7.2 Calibration and validation 579 7.3 Balancing realism and complexity 578 7.4 Resource and time investment 578 7.5 Documentation and reproducibility 578 8 CONCLUSION: FROM STRATEGIC TO TACTICAL ABMS 581 ACKNOWLEDGEMENTS 581 DATA AVAILABILITY STATEMENT 581 REFERENCES 581 APPENDIX 1 586

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“…The task objective was embodied as a binary character string which consisted of 100 positions c k with k∈{1, …, 100} and c k =1∀ k (Kunz, 2011). At the beginning of each simulation, every team member was randomly assigned a knowledge unit, that is, a string of the same length as the task objective with 0 or 1 in each position, and evaluated for the relevance of expertise according to how closely the string matches the objective.…”

Section: Agent‐based Simulationmentioning

confidence: 99%

The Vital Few: Exploring the Role of Expertise in the Process of Team Creativity

Zhang

Yan

2020

Journal of Creative Behavior

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This study provides insight into the role of expertise in the process of team creativity. Existing process models, that is, the random variation model and the creative synthesis model, either fail to treat team as an independent creative entity or neglect the differentiation in the voice of individual members in team creative process. In contrast, this study recognizes the role of expertise in shaping the process of team creativity and presents an alternative model, the vital few model (experts as “vital few”). An agent‐based simulation was designed to examine the validity of this model. Agent teams generated team creativity both more efficiently and effectively when expertise was employed to reorganize existing knowledge and integrate new knowledge. The results indicate that the vital few model is a more explanatory model compared to the existing models for the process of team creativity. Discussion focuses on contributions of the vital few model, limitations of this study, as well as directions for future research.

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Group-Level Exploration and Exploitation: A Computer Simulation-Based Analysis

Cited by 15 publications

References 43 publications

Diffusion of knowledge in social media networks: effects of reputation mechanisms and distribution of knowledge roles

Diffusion of knowledge in social media networks: effects of reputation mechanisms and distribution of knowledge roles

Opportunities for agent‐based modelling in human dimensions of fisheries

The Vital Few: Exploring the Role of Expertise in the Process of Team Creativity

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