Modeling crowdsourcing as collective problem solving

Guazzini, Andrea; Vilone, Daniele; Donati, Camillo; Nardi, Annalisa; Levnajić, Zoran

doi:10.1038/srep16557

Cited by 32 publications

(23 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Almost 70% of Gnutella users share no files (Adar and Huberman 2000). The decrease in cooperative behaviour with the increase of group size, has found further confirmation in the simulations conducted by Guazzini and colleagues (Guazzini, Vilone, Donati, Nardi, and Levnajić 2015). When the tasks are not particularly demanding, the authors have observed that very large groups tended to have a minor fitness (i.e., score) compared to smaller groups.…”

Section: Introductionmentioning

confidence: 65%

The selfish vaccine Recipe: A simple mechanism for avoiding free-riding

Guazzini¹,

Duradoni²,

Gronchi³

2016

2016 Winter Simulation Conference (WSC)

Self Cite

View full text Add to dashboard Cite

Social loafing and free riding are common phenomena that may hinder crowdsourcing. The purpose of this work is to identify the minimum conditions that can promote cooperation and group problem solving avoiding free riding and social loafing. We assume two kinds of scenarios (Recipe A, free riders have access to benefits produced by groups and Recipe B, the benefit produced by groups are shared only within the group) and then we investigate the relationship among the tendency to cooperate, group sizes, and difficulty of the task by means of numerical simulations. Results indicate that in the Recipe A world, collective intelligence and crowdsourcing are generally less efficient compared to what observed in the Recipe B world. Indeed, in the latter cooperation appears to be the optimal strategy for the progress of the world. Given the social importance of crowdsourcing, we discuss some useful implications of our results on crowdsourcing projects.

show abstract

Section: Introductionmentioning

confidence: 65%

The selfish vaccine Recipe: A simple mechanism for avoiding free-riding

Guazzini¹,

Duradoni²,

Gronchi³

2016

2016 Winter Simulation Conference (WSC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Generally speaking, there have been many attempts to exploit the properties of human information exchange in order to improve collective decision-making [1][2][3]. By means of social and cognitive-inspired simulations based on the sociophysics approach, in this paper, we employ a numerical simulation framework for crowdsourcing [4] in order to investigate the role of the cost of cooperation and its interaction with other variables (group size, difficulty of the task, the presence of selfish individuals, etc.). We highlight the importance of the cost of cooperation and determine the conditions where higher costs do not hinder the overall performance.…”

Section: Introductionmentioning

confidence: 99%

Simulating the Cost of Cooperation: A Recipe for Collaborative Problem-Solving

et al. 2018

Self Cite

View full text Add to dashboard Cite

Collective problem-solving and decision-making, along with other forms of collaboration online, are central phenomena within ICT. There had been several attempts to create a system able to go beyond the passive accumulation of data. However, those systems often neglect important variables such as group size, the difficulty of the tasks, the tendency to cooperate, and the presence of selfish individuals (free riders). Given the complex relations among those variables, numerical simulations could be the ideal tool to explore such relationships. We take into account the cost of cooperation in collaborative problem solving by employing several simulated scenarios. The role of two parameters was explored: the capacity, the group's capability to solve increasingly challenging tasks coupled with the collective knowledge of a group, and the payoff, an individual's own benefit in terms of new knowledge acquired. The final cooperation rate is only affected by the cost of cooperation in the case of simple tasks and small communities. In contrast, the fitness of the community, the difficulty of the task, and the groups sizes interact in a non-trivial way, hence shedding some light on how to improve crowdsourcing when the cost of cooperation is high.

show abstract

“…In case of a large conflict between groups, on the contrary, the group with more cooperators is subject to win [17]. Thus frequent occurrence of group-level conflicts would explain the survival of cooperation.…”

Section: Introductionmentioning

confidence: 99%

Evolution of Cooperation through Power Law Distributed Conflicts

Kim

2017

Complexity

View full text Add to dashboard Cite

At an individual level, cooperation can be seen as a behaviour that uses personal resource to support others or the groups which one belongs to. In a conflict between two individuals, a selfish person gains an advantage over a cooperative opponent, while in a group-group conflict the group with more cooperators wins. In this work, we develop a population model with continual conflicts at various scales and show cooperation can be sustained even when interpersonal conflicts dominate, as long as the conflict size follows a power law. The power law assumption has been met in several observations from real-world conflicts. Specifically if the population is structured on a scale-free network, both the power law distribution of conflicts and the survival of cooperation can be naturally induced without assuming a homogeneous population or frequent relocation of members. On the scale-free network, even when most people become selfish from continual person-person conflicts, people on the hubs tend to remain unselfish and play a role as “repositories” of cooperation.

show abstract

Modeling crowdsourcing as collective problem solving

Cited by 32 publications

References 46 publications

The selfish vaccine Recipe: A simple mechanism for avoiding free-riding

The selfish vaccine Recipe: A simple mechanism for avoiding free-riding

Simulating the Cost of Cooperation: A Recipe for Collaborative Problem-Solving

Evolution of Cooperation through Power Law Distributed Conflicts

Contact Info

Product

Resources

About