Team Dating

Abstract

Online crowds have the potential to do more complex work in teams, rather than as individuals. However, at such a large scale, team formation can be difficult to coordinate. (How) can we rely on the crowd itself to organize into effective teams? Our research explores a strategy for "team dating", a self-organized crowd team formation approach where workers try out and rate different candidate partners. In two online experiments, we find that team dating affects the way that people select partners and how they evaluate them. We use these results to draw useful conclusions for the future of team dating and its implications for collaborative crowdsourcing.

Supply-Demand Matching in Non-Cooperative Social Networks

“…Based on Chamberlain's research, Jiang [3] formally defined the context-aware task allocation problem in group-oriented crowdsourcing, proposed a heuristic context-aware task allocation approach, and proposed a modeling method for natural worker groups in crowdsourcing, including groups with and without leadership. Besides, instead of solving the problem from the requestor's perspective, Lykourentzou [6] explored a "team dating" strategy, which is a self-organized group team formation method. In this method, employees try and evaluate different candidate partners.…”

“…do nothing 11. end for First, judge whether the visited node in Step 5 of algorithm 1 is its own precursor, if it is, do nothing (step 1-2); if not, record ai as the precursor of ax, traverse the precursor set of ax to update ax's optimal precursor in task t (step [3][4][5][6][7][8][9][10][11]. In this process, if the optimal precursor of ax changes to ai, the shortest distance between ax and It is recalculated by calDist(t,ax) (step 8).…”

“…Cnew += c(ax,gj)* q(ax,gj,t2)*(1+η(It2,ax)) agree(It, It2, gj, ai, Contribution) If Nt2 has started to supply, It2 will directly reject the coordination request of It (step 3-4), otherwise, it will try to find a node that can replace ai to supply gj (step [5][6][7][8][9][10][11][12][13][14][15][16][17]. The PreDecide method is used to meet the gj requirements of q(ai,gj,t2) (step 10) (the method will be detailed below) until all requirements are met (step [15][16].…”

“…The demander wants to build a supply network that covers the task demand in both type and quantity. Unlike the previous "team formation problem" [1][2][3][4][5][6][7][8][9][10][11], we had to redesign the solution because of the following characteristics of the supply-demand matching problem:①The same service or product requirement in a task can be segmented to multiple providers to provide collaboratively; ② Different from contributing skills, suppliers of services or products may face capacity caps; ③A single supplier can supply multiple products and services to multiple demanders simultaneously. Besides, due to the selfishness of members in practical application [12] [13], the scheme must be feasible under the constraints of non-cooperation.…”

Reflective agents for personalisation in collaborative games

“…Team formation is an important but much neglected aspect of collaborative games, yet it is a widely-explored topic in non-gaming domains, such as in collaborative filtering and recommendation (Retna Raj and Sasipraba 2015;Ghenname et al 2015;Najafabadi and Mahrin 2016), collaborative design (Xu et al 2010), collaborative crowdsourcing (Lykourentzou et al 2016), and expert collaboration in social networks (Basiri et al 2017). However, in multi-player games, the focus has overwhelmingly been on player matching and more so with a view to finding worthy opponents for players to play against rather than suitable players to play with in a team (Daylamani Zad et al 2012).…”