Group Structure and Information Distribution on the Emergence of Collective Intelligence

References

• Acemoglu D, Ozdaglar A (2011) Opinion dynamics and learning in social networks. Dynamic Dames Appl. 1:3–49.Crossref, Google Scholar
• Albretcht TL, Ropp VA (1984) Communicating about innovation in networks of three U.S. organizations. J. Comm. 34:78–91.Crossref, Google Scholar
• Almaatouq A, Noriega-Campero A, Alotaibi A, Krafft PM, Moussaid M, Pentland A (2020) Adaptive social networks promote the wisdom of crowds. Proc. National Acad. Sci. USA 117(21):11379–11386.Crossref, Google Scholar
• Argote L, Turner ME, Fichman M (1989) To centralize or not to centralize: The effects of uncertainty and threat on group structure and performance. Organ. Behav. Human Decision Processes 43:58–74.Crossref, Google Scholar
• Awal GK, Bharadwaj KK (2014) Team formation in social networks based on collective intelligence: An evolutionary approach. Appl. Intelligence 41:627–648.Crossref, Google Scholar
• Bavelas A (1950) Communication patterns in task-oriented groups. J. Acoustic Soc. Amer. 22(6):725–730.Crossref, Google Scholar
• Becker J, Brackbill D, Centola D (2017) Network dynamics of social influence in the wisdom of crowds. Proc. National Acad. Sci. USA 114(26):E5070–E5076.Crossref, Google Scholar
• Bergh DD, Ketchen DJ, Orlandi I, Heugens PPMAR, Boyd BK (2019) Information asymmetry in management research: Past accomplishments and future opportunities. J. Management 45(1):122–158.Crossref, Google Scholar
• Berktas N, Yaman H (2021) A branch-and-bound algorithm for team formation on social networks. INFORMS J. Comput. 33(3):1162–1176.Link, Google Scholar
• Bonabeau E (2009) Decisions 2.0: The power of collective intelligence. MIT Sloan Management Rev. 50(2):45–52.Google Scholar
• Bordogna C, Albano E (2007) Statistical methods applied to the study of opinion formation models: A brief overview and results of a numerical study of a model based on the social impact theory. J. Phys. Condensed Matter 19(6):065144.Crossref, Google Scholar
• Brodbeck FC, Kerschreiter R, Mojzisch A, Schulz-Hardt S (2007) Group decision making under conditions of distributed knowledge: The information asymmetries model. Acad. Management Rev. 32(3):459–479.Crossref, Google Scholar
• Budescu DV, Chen E (2015) Identifying expertise to extract the wisdom of crowds. Management Sci. 61(2):267–280.Link, Google Scholar
• Carbone G, Giannoccaro I (2015) Model of human collective decision-making in complex environments. Eur. Phys. J. B 88(12):339.Crossref, Google Scholar
• Carvalho V (2016) An overview of applications of proper scoring rules. Decision Anal. 13(4):223–242.Link, Google Scholar
• Castellano C, Fortunato S, Loreto V (2009) Statistical physics of social dynamics. Rev. Modern Phys. 81(2):591.Crossref, Google Scholar
• Csaszar FA, Ostler J (2020) A contingency theory of representational complexity in organizations. Organ. Sci. 31(5):1198–1219.Link, Google Scholar
• Davis-Stober CP, Budescu DV, Broomell SB, Dana J (2015) The composition of optimally wise crowds. Decision Anal. 12(3):130–143.Link, Google Scholar
• De Vincenzo I, Giannoccaro I, Carbone G, Grigolini P (2017) Criticality triggers the emergence of collective intelligence in groups. Phys. Rev. E 96:022309.Crossref, Google Scholar
• Frenken K (2006) A fitness landscape approach to technological complexity, modularity, and vertical disintegration. Structural Change Econom. Dynamics 17:288–305.Crossref, Google Scholar
• Frey V, van de Rijt A (2021) Social influence undermines the wisdom of the crowd in sequential decision making. Management Sci. 67(7):4273–4286.Link, Google Scholar
• Freeman LC (1979) Centrality in social networks I. Conceptual clarification. Soc. Networks 1:215–239.Crossref, Google Scholar
• Gillespie DT (1976) General method for numerically simulating stochastic time evolution of coupled chemical-reactions. J. Comput. Phys. 22(4):403–434.Crossref, Google Scholar
• Glauber RJ (1963) Time‐dependent statistics of the Ising model. J. Math. Phys. 4(2):294–307.Crossref, Google Scholar
• Grover P, Kar AK, Dwivedi YK (2022) Understanding artificial intelligence adoption in operations management: Insights from the review of academic literature and social media discussions. Ann. Oper. Res. 308:177–213.Crossref, Google Scholar
• Gruenfeld DH, Mannix EA, Williams KY, Neale MA (1996) Group composition and decision making: How member familiarity and information distribution affect process and performance. Organ. Behav. Human Decision Processes 67(1):1–15.Crossref, Google Scholar
• Herrera-Viedma E, Martínez L, Mata F (2005) A consensus support system model for group decision-making problems with multigranular linguistic preference relations. IEEE Trans. Fuzzy Systems 13(5):644–658.Crossref, Google Scholar
• Introne J, Goggins S (2019) Advice reification, learning, and emergent collective intelligence in online health support communities. Comput. Human Behav. 99:205–218.Crossref, Google Scholar
• Jönsson ML, Hahn U, Olsson EJ (2015) The kind of group you want to belong to: Effects of group structure on group accuracy. Cognition 142:191–204.Crossref, Google Scholar
• Kauffman S, Levin S (1987) Toward a general theory of adaptive walks on rugged landscapes. J. Theoretical Biol. 128(1):11–45.Crossref, Google Scholar
• Kerr NL, Tindale RS (2004) Group performance and decision making. Annu. Rev. Psych. 55:623–655.Crossref, Google Scholar
• Lightle JP, Kagel JH, Arkes HR (2009) Information exchange in group decision making: The hidden profile problem reconsidered. Management Sci. 55(4):568–581.Link, Google Scholar
• Liu JP, Kadziński M, Liao XW, Mao XX (2021) Data-driven preference learning methods for value-driven multiple criteria sorting with interacting criteria. INFORMS J. Comput. 33(2):586–606.Abstract, Google Scholar
• Mann RP, Helbing D (2017) Optimal incentives for collective intelligence. Proc. National Acad. Sci. USA 114(20):5077–5082.Crossref, Google Scholar
• Mason WA, Jones A, Goldstone RL (2008) Propagation of innovations in networked groups. J. Experiment. Psych. General 137(3):422–433.Crossref, Google Scholar
• Massari GF, Giannoccaro I (2021) Investigating the effect of horizontal coopetition on supply chain resilience in complex and turbulent environments. Internat. J. Production Econom. 237:108150.Crossref, Google Scholar
• Massari GF, Giannoccaro I, Carbone G (2019) Are distrust relationships beneficial for group performance? The influence of the scope of distrust on the emergence of collective intelligence. Internat. J. Production Econom. 208:343–355.Crossref, Google Scholar
• Nurmi H (1998) Voting paradoxes and referenda. Soc. Choice Welfare 15(3):333–350.Crossref, Google Scholar
• Palley AB, Soll JB (2019) Extracting the wisdom of crowds when information is shared. Management Sci. 65(5):2291–2309.Abstract, Google Scholar
• Riedl C, Kim YJ, Gupta P, Malone TW, Woolley AW (2021) Quantifying collective intelligence in human groups. Proc. National Acad. Sci. USA 118(21):e2005737118.Crossref, Google Scholar
• Rulke DL, Galaskiewicz J (2000) Distribution of knowledge, group network structure, and group performance. Management Sci. 46(5):612–625.Link, Google Scholar
• Stasser G, Titus W (1985) Pooling of unshared information in group decision making: Biased information sampling during discussion. J. Personality Soc. Psych. 48(6):1467–1478.Crossref, Google Scholar
• Surowiecki J (2004) The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations (Little, Brown, London).Google Scholar
• Tang M, Liao HC (2021) From conventional group decision making to large-scale group decision making: What are the challenges and how to meet them in big data era? A state-of-the-art survey. Omega 100:102141.Crossref, Google Scholar
• Tang M, Liao HC, Xu JP, Streimikiene D, Zheng XS (2020) Adaptive consensus reaching process with hybrid strategies for large-scale group decision making. Eur. J. Oper. Res. 282:957–971.Crossref, Google Scholar
• Varella P, Javidan M, Waldman DA (2012) A model of instrumental networks: The roles of socialized charismatic leadership and group behavior. Organ. Sci. 23(2):582–595.Link, Google Scholar
• Victor P, Cornelis C, de Cock M, Herrera-Viedma E (2011) Practical aggregation operators for gradual trust and distrust. Fuzzy Sets Systems 184:126–147.Crossref, Google Scholar
• Warby SC, Wendt SL, Welinder P, Munk EGS, Carrillo O, Sorensen HBD, Jennum P, et al. (2014) Sleep-spindle detection: Crowdsourcing and evaluating performance of experts, non-experts and automated methods. Nature Methods 11(4):385–392.Crossref, Google Scholar
• Weidlich W (1991) Physics and social science: The approach of synergetics. Phys. Rep. 204(1):1–163.Crossref, Google Scholar
• Woolley AW, Aggarwal I, Malone TW (2015) Collective intelligence and group performance. Current Directions Psych. Sci. 24(6):420–424.Crossref, Google Scholar
• Woolley AW, Chabris CF, Pentland A, Hashmi N, Malone TW (2010) Evidence for a collective intelligence factor in the performance of human groups. Science 330(6004):686–688.Crossref, Google Scholar