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Almaatouq A, Alsobay M, Yin M, Watts DJ. The Effects of Group Composition and Dynamics on Collective Performance. Top Cogn Sci 2024; 16:302-321. [PMID: 37925669 DOI: 10.1111/tops.12706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/04/2023] [Accepted: 10/24/2023] [Indexed: 11/07/2023]
Abstract
As organizations gravitate to group-based structures, the problem of improving performance through judicious selection of group members has preoccupied scientists and managers alike. However, which individual attributes best predict group performance remains poorly understood. Here, we describe a preregistered experiment in which we simultaneously manipulated four widely studied attributes of group compositions: skill level, skill diversity, social perceptiveness, and cognitive style diversity. We find that while the average skill level of group members, skill diversity, and social perceptiveness are significant predictors of group performance, skill level dominates all other factors combined. Additionally, we explore the relationship between patterns of collaborative behavior and performance outcomes and find that any potential gains in solution quality from additional communication between the group members are outweighed by the overhead time cost, leading to lower overall efficiency. However, groups exhibiting more "turn-taking" behavior are considerably faster and thus more efficient. Finally, contrary to our expectation, we find that group compositional factors (i.e., skill level and social perceptiveness) are not associated with the amount of communication between group members nor turn-taking dynamics.
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Affiliation(s)
| | - Mohammed Alsobay
- Sloan School of Management, Massachusetts Institute of Technology
| | - Ming Yin
- Department of Computer Science, Purdue University
| | - Duncan J Watts
- Department of Computer and Information Science, University of Pennsylvania
- The Annenberg School of Communication, University of Pennsylvania
- Operations, Information, and Decisions Department, University of Pennsylvania
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2
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Smaldino PE, Moser C, Pérez Velilla A, Werling M. Maintaining Transient Diversity Is a General Principle for Improving Collective Problem Solving. Perspect Psychol Sci 2024; 19:454-464. [PMID: 37369100 PMCID: PMC10913329 DOI: 10.1177/17456916231180100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
Humans regularly solve complex problems in cooperative teams. A wide range of mechanisms have been identified that improve the quality of solutions achieved by those teams on reaching consensus. We argue that many of these mechanisms work via increasing the transient diversity of solutions while the group attempts to reach a consensus. These mechanisms can operate at the level of individual psychology (e.g., behavioral inertia), interpersonal communication (e.g., transmission noise), or group structure (e.g., sparse social networks). Transient diversity can be increased by widening the search space of possible solutions or by slowing the diffusion of information and delaying consensus. All of these mechanisms increase the quality of the solution at the cost of increased time to reach it. We review specific mechanisms that facilitate transient diversity and synthesize evidence from both empirical studies and diverse formal models-including multiarmed bandits, NK landscapes, cumulative-innovation models, and evolutionary-transmission models. Apparent exceptions to this principle occur primarily when problems are sufficiently simple that they can be solved by mere trial and error or when the incentives of team members are insufficiently aligned. This work has implications for our understanding of collective intelligence, problem solving, innovation, and cumulative cultural evolution.
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Affiliation(s)
- Paul E. Smaldino
- Department of Cognitive & Information Sciences, University of California, Merced
- Santa Fe Institute, Santa Fe, New Mexico
| | - Cody Moser
- Department of Cognitive & Information Sciences, University of California, Merced
| | | | - Mikkel Werling
- Department of Cognitive & Information Sciences, University of California, Merced
- Interacting Minds Centre, Aarhus University
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3
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Ren R, He J. Network traits driving knowledge evolution in open collaboration systems. PLoS One 2023; 18:e0291097. [PMID: 37963174 PMCID: PMC10645342 DOI: 10.1371/journal.pone.0291097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 08/22/2023] [Indexed: 11/16/2023] Open
Abstract
Network interpretation illuminates our understanding of the dynamic nature of cultural evolution. Guided by cultural evolution theory, this article explores how people collectively develop knowledge through knowledge collaboration network traits. Using network data from 910 artifacts (the WikiProject Aquarium Fishes articles) over 163 weeks, two studies were designed to understand how collaboration network traits drive population and artifact-level knowledge evolution. The first study examines the selection pressure imposed by10 network traits (against 11 content traits) on population-level evolutionary outcomes. While network traits are vital in identifying natural selection pressure, intriguingly, no significant difference was found between network traits and content traits, challenging a recent theory on network-driven evolution. The second study utilizes time series analysis to reveal that three network traits (embeddedness, connectivity, and redundancy) at a prior time predict future artifact development trajectory. This implies that people collectively explore various positions in a potential solution space, suggesting content exploration as a possible explanation of knowledge evolution. In summary, understanding the interplay between network traits and content exploration provides valuable insights into the mechanisms driving knowledge evolution and offers new avenues for future research.
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Affiliation(s)
- Ruqin Ren
- Institute of Cultural and Creative Industry, Shanghai Jiao Tong University, Shanghai, China
| | - Jia He
- Institute of Cultural and Creative Industry, Shanghai Jiao Tong University, Shanghai, China
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4
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Fronzetti Colladon A, Grippa F, Broccatelli C, Mauren C, Mckinsey S, Kattan J, Sutton ESJ, Satlin L, Bucuvalas J. Boosting advice and knowledge sharing among healthcare professionals. JKM 2022. [DOI: 10.1108/jkm-06-2022-0499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Purpose
This study aims to investigate the dynamics of knowledge sharing in health care, exploring some of the factors that are more likely to influence the evolution of idea sharing and advice seeking in health care.
Design/methodology/approach
The authors engaged 50 pediatricians representing many subspecialties at a mid-size US children’s hospital using a social network survey to map and measure advice seeking and idea sharing networks. Through the application of Stochastic Actor-Oriented Models, the authors compared the structure of the two networks prior to a leadership program and eight weeks post conclusion.
Findings
The models indicate that health-care professionals carefully and intentionally choose with whom they share ideas and from whom to seek advice. The process is fluid, non-hierarchical and open to changing partners. Significant transitivity effects indicate that the processes of knowledge sharing can be supported by mediation and brokerage.
Originality/value
Hospital administrators can use this method to assess knowledge-sharing dynamics, design and evaluate professional development initiatives and promote new organizational structures that break down communication silos. This work contributes to the literature on knowledge sharing in health care by adopting a social network approach, going beyond the dyadic level and assessing the indirect influence of peers’ relationships on individual networks.
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5
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Reagans RE. Mutual learning in networks: Building theory by piecing together puzzling facts. Research in Organizational Behavior 2022. [DOI: 10.1016/j.riob.2022.100175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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6
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Centola D. The network science of collective intelligence. Trends Cogn Sci 2022; 26:923-941. [PMID: 36180361 DOI: 10.1016/j.tics.2022.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 07/30/2022] [Accepted: 08/18/2022] [Indexed: 01/12/2023]
Abstract
In the last few years, breakthroughs in computational and experimental techniques have produced several key discoveries in the science of networks and human collective intelligence. This review presents the latest scientific findings from two key fields of research: collective problem-solving and the wisdom of the crowd. I demonstrate the core theoretical tensions separating these research traditions and show how recent findings offer a new synthesis for understanding how network dynamics alter collective intelligence, both positively and negatively. I conclude by highlighting current theoretical problems at the forefront of research on networked collective intelligence, as well as vital public policy challenges that require new research efforts.
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Affiliation(s)
- Damon Centola
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104, USA; School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Sociology, University of Pennsylvania, Philadelphia, PA 19104, USA; Network Dynamics Group, University of Pennsylvania, Philadelphia, PA 19104, USA.
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7
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Koçak Ö, Levinthal DA, Puranam P. The Dual Challenge of Search and Coordination for Organizational Adaptation: How Structures of Influence Matter. Organization Science 2022. [DOI: 10.1287/orsc.2022.1601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Organizations increasingly need to adapt to challenges in which search and coordination cannot be decoupled. In response, many have experimented with “agile” and “flat” designs that dismantle traditional forms of hierarchy to harness the distributed knowledge of specialized individuals. Despite the popularity of such practices, there is considerable variation in their implementation as well as conceptual ambiguity about the underlying premise. Does effective rapid experimentation necessarily imply the repudiation of hierarchical structures of influence? We use computational models of multiagent reinforcement learning to study the effectiveness of coordinated search in groups that vary in how they influence each other’s beliefs. We compare the behavior of flat and hierarchical teams with a baseline structure without any influence on beliefs (a “crowd”) when all three are placed in the same task environments. We find that influence on beliefs—whether it is hierarchical or not—makes it less likely that agents stabilize prematurely around their own experiences. However, flat teams can engage in excessive exploration, finding it difficult to converge on good alternatives, whereas hierarchical influence on beliefs reduces simultaneous uncoordinated exploration, introducing a degree of rapid exploitation. As a result, teams that need to achieve agility (i.e., rapid satisfactory results) in environments that require coordinated search may benefit from a hierarchical structure of influence—even when the apex actor has no superior knowledge, foresight, or capacity to control subordinates’ actions.
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Affiliation(s)
- Özgecan Koçak
- Goizueta Business School, Emory University, Atlanta, Georgia 30322
| | - Daniel A. Levinthal
- Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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8
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Abstract
We study the connection between communication network structure and an organization’s collective adaptability to a shifting environment. Research has shown that network centralization—the degree to which communication flows disproportionately through one or more members of the organization rather than being more equally distributed—interferes with collective problem-solving by obstructing the integration of existing ideas, information, and solutions in the network. We hypothesize that the mechanisms responsible for that poor integration of ideas, information, and solutions would nevertheless prove beneficial for problems requiring adaptation to a shifting environment. We conducted a 1,620-subject randomized online laboratory experiment, testing the effect of seven network structures on problem-solving success. To simulate a shifting environment, we designed a murder mystery task and manipulated when each piece of information could be found: early information encouraged an inferior consensus, requiring a collective shift of solution after more information emerged. We find that when the communication network within an organization is more centralized, it achieves the benefits of connectivity (spread of novel better solutions) without the costs (getting stuck on an existing inferior solution). We also find, however, that these benefits of centralization only materialize in networks with two-way flow of information and not when information only flows from the center of the network outward (as can occur in hierarchical structures or digitally mediated communication). We draw on these findings to reconceptualize theory on the impact of centralization—and how it affects conformity pressure (lock-in) and awareness of diverse ideas (learning)—on collective problem-solving that demands adaptation.
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Affiliation(s)
- Ethan S. Bernstein
- Harvard Business School, Organizational Behavior Unit, Boston, Massachusetts 02163
| | - Jesse C. Shore
- Questrom School of Business, Boston University, Boston, Massachusetts 02215
| | - Alice J. Jang
- Pamplin College of Business, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061
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9
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Pittnauer S, Hohnisch M, Ostermaier A, Pfingsten A. Effects of Social Information on Risk Taking and Performance: Understanding Others’ Decisions vs. Comparing Oneself with Others in Short-Term Performance. Organization Science 2021. [DOI: 10.1287/orsc.2021.1507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
When a problem leaves decision makers uncertain as to how to approach it, observing others’ decisions can improve one’s own decisions by promoting more accurate judgments and a better insight into the problem. However, observing others’ decisions may also activate motives that prevent this potential from being realized, for instance, ego concerns that prompt excessive risk taking. Our experimental study investigates how two features of the social environment influence the effect of observing others’ decisions on individual risk taking and performance. We manipulated (1) the psychological distance to others whose decisions could be observed (and thereby the tendency to seek self-enhancing social comparison) and (2) the opportunity for interaction (and thereby for a cumulative effect of any such tendency on decisions over time and for an effect on social information itself). Because the two features covary in real-world settings, we designed two treatments corresponding to the two natural combinations. Both treatments provided participants with two other participants’ period decisions in a multiperiod problem under uncertainty. No new objective information about the problem could be inferred from these decisions. We predicted that participants who observed the decisions of distant others (who had solved the same problem earlier) would perform better than participants in a control sample without any information about others’ decisions and that participants who observed the decisions of proximal others (with whom interaction could arise) would take more risk and perform worse than those who observed distant others’ decisions. The data corroborate our predictions. We discuss implications for organizational learning.
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Affiliation(s)
- Sabine Pittnauer
- Faculty of Industrial Engineering and Management, Technion, Haifa 32000, Israel
| | - Martin Hohnisch
- Faculty of Industrial Engineering and Management, Technion, Haifa 32000, Israel
| | - Andreas Ostermaier
- Department of Business and Management, University of Southern Denmark, 5230 Odense, Denmark
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10
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Bonner BL, Shannahan D, Bain K, Coll K, Meikle NL. The Theory and Measurement of Expertise-Based Problem Solving in Organizational Teams: Revisiting Demonstrability. Organization Science 2021. [DOI: 10.1287/orsc.2021.1481] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The current paper revisits and builds upon task demonstrability, which defines the criteria necessary for groups to choose a correct response if any member prefers that response. We identify boundary conditions of the current conceptualization of task demonstrability with respect to its use in understanding modern organizational teams. Specifically, we argue that, in its current form, task demonstrability is not optimally suited to studying ongoing teams in which member expertise varies and teams work to complete complex multifaceted tasks. To address this issue, we provide a revisited perspective on demonstrability. We specify the nomological network of revisited demonstrability and recast each of its criteria in a form that preserves the original intent of the construct, but has broader applicability, particularly to organizational contexts. We then discuss theoretical implications and managerial applications of the construct. Finally, noting that there is no standard assessment tool for demonstrability (original or revisited), we develop and validate a measure to facilitate future research.
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Affiliation(s)
- Bryan L. Bonner
- David Eccles School of Business, University of Utah, Salt Lake City, Utah 84112
| | - Daniel Shannahan
- School of Business, College of Professional Studies, Northern State University, Aberdeen, South Dakota 57401
| | - Kristin Bain
- Saunders College of Business, Rochester Institute of Technology, Rochester, New York 14623
| | - Kathryn Coll
- David Eccles School of Business, University of Utah, Salt Lake City, Utah 84112
| | - Nathan L. Meikle
- Mendoza College of Business, University of Notre Dame, Notre Dame, Indiana 46556
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11
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Almaatouq A, Alsobay M, Yin M, Watts DJ. Task complexity moderates group synergy. Proc Natl Acad Sci U S A 2021; 118:e2101062118. [PMID: 34479999 DOI: 10.1073/pnas.2101062118] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 07/02/2021] [Indexed: 01/20/2023] Open
Abstract
Scientists and managers alike have been preoccupied with the question of whether and, if so, under what conditions groups of interacting problem solvers outperform autonomous individuals. Here we describe an experiment in which individuals and groups were evaluated on a series of tasks of varying complexity. We find that groups are as fast as the fastest individual and more efficient than the most efficient individual when the task is complex but not when the task is simple. We then precisely quantify synergistic gains and process losses associated with interacting groups, finding that the balance between the two depends on complexity. Our study has the potential to reconcile conflicting findings about group synergy in previous work. Complexity—defined in terms of the number of components and the nature of the interdependencies between them—is clearly a relevant feature of all tasks that groups perform. Yet the role that task complexity plays in determining group performance remains poorly understood, in part because no clear language exists to express complexity in a way that allows for straightforward comparisons across tasks. Here we avoid this analytical difficulty by identifying a class of tasks for which complexity can be varied systematically while keeping all other elements of the task unchanged. We then test the effects of task complexity in a preregistered two-phase experiment in which 1,200 individuals were evaluated on a series of tasks of varying complexity (phase 1) and then randomly assigned to solve similar tasks either in interacting groups or as independent individuals (phase 2). We find that interacting groups are as fast as the fastest individual and more efficient than the most efficient individual for complex tasks but not for simpler ones. Leveraging our highly granular digital data, we define and precisely measure group process losses and synergistic gains and show that the balance between the two switches signs at intermediate values of task complexity. Finally, we find that interacting groups generate more solutions more rapidly and explore the solution space more broadly than independent problem solvers, finding higher-quality solutions than all but the highest-scoring individuals.
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12
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Abstract
In many facets of life, individuals make evaluations that they may update after consulting with others in their networks. But not all individuals have the same positional opportunities for social interaction in a given network or the ability and desire to make use of those opportunities that are available to them. The configuration of a person’s network can also alter how information is spread or interpreted. To complicate matters further, scant research has considered how positions in social networks and the valence of network content interact because of the difficulty of (a) separating the “player” from the position in networks and (b) measuring all germane content in a particular network. This research develops a novel experimental platform that addresses these issues. Participants viewed and evaluated an entrepreneurial video pitch and were then randomly assigned to different networks, and positions within networks, and thus various opportunities for peer influence that were orthogonal to their network history, inclinations, attributes, or capabilities. Furthermore, all the content of social interaction, including its valence, was recorded to test underlying assumptions. Results reveal that those assigned to a position with brokerage opportunities in a network updated their evaluations of the entrepreneurial video considerably more negatively.
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Affiliation(s)
- Jason Greenberg
- Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142
- Center for the Study of Economy and Society, Cornell University, Ithaca, New York 14853
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13
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Tardiff N, Medaglia JD, Bassett DS, Thompson-Schill SL. The modulation of brain network integration and arousal during exploration. Neuroimage 2021; 240:118369. [PMID: 34242784 PMCID: PMC8507424 DOI: 10.1016/j.neuroimage.2021.118369] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 07/01/2021] [Accepted: 07/05/2021] [Indexed: 11/08/2022] Open
Abstract
There is growing interest in how neuromodulators shape brain networks. Recent neuroimaging studies provide evidence that brainstem arousal systems, such as the locus coeruleus-norepinephrine system (LC-NE), influence functional connectivity and brain network topology, suggesting they have a role in flexibly reconfiguring brain networks in order to adapt behavior and cognition to environmental demands. To date, however, the relationship between brainstem arousal systems and functional connectivity has not been assessed within the context of a task with an established relationship between arousal and behavior, with most prior studies relying on incidental variations in arousal or pharmacological manipulation and static brain networks constructed over long periods of time. These factors have likely contributed to a heterogeneity of effects across studies. To address these issues, we took advantage of the association between LC-NE-linked arousal and exploration to probe the relationships between exploratory choice, arousal—as measured indirectly via pupil diameter—and brain network dynamics. Exploration in a bandit task was associated with a shift toward fewer, more weakly connected modules that were more segregated in terms of connectivity and topology but more integrated with respect to the diversity of cognitive systems represented in each module. Functional connectivity strength decreased, and changes in connectivity were correlated with changes in pupil diameter, in line with the hypothesis that brainstem arousal systems influence the dynamic reorganization of brain networks. More broadly, we argue that carefully aligning dynamic network analyses with task designs can increase the temporal resolution at which behaviorally- and cognitively-relevant modulations can be identified, and offer these results as a proof of concept of this approach.
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Affiliation(s)
- Nathan Tardiff
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States.
| | - John D Medaglia
- Department of Psychology, Drexel University, Philadelphia, PA, United States; Department of Neurology, Drexel University, Philadelphia, PA, United States; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Danielle S Bassett
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, United States; Santa Fe Institute, Santa Fe, NM, United States
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14
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Abstract
A crucial challenge for organizations is to pool and aggregate information effectively. Traditionally, organizations have relied on committees and teams, but recently many organizations have explored the use of information markets. In this paper, the authors compared groups and markets in their ability to pool and aggregate information in a hidden-profiles task. In Study 1, groups outperformed markets when there were no conflicts of interest among participants, whereas markets outperformed groups when conflicts of interest were present. Also, participants had more trust in groups to uncover hidden profiles than in markets. Study 2 generalized these findings to a simple prediction task, confirming that people had more trust in groups than in markets. These results were not qualified by conflicts of interest. Drawing on experienced forecasters from Good Judgment Open, Study 3 found that familiarity and experience with markets increased the endorsement and use of markets relative to traditional committees.
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Affiliation(s)
- Boris Maciejovsky
- School of Business, University of California, Riverside, Riverside, California 92521
| | - David V. Budescu
- Department of Psychology, Fordham University, Bronx, New York 10458
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15
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Abstract
Understanding the functions carried out by network subgraphs is important to revealing the organizing principles of diverse complex networks. Here, we study this question in the context of collaborative problem-solving, which is central to a variety of domains from engineering and medicine to economics and social planning. We analyze the frequency of all three- and four-node subgraphs in diverse real problem-solving networks. The results reveal a strong association between a dynamic property of network subgraphs-synchronizability-and the frequency and significance of these subgraphs in problem-solving networks. In particular, we show that highly-synchronizable subgraphs are overrepresented in the networks, while poorly-synchronizable subgraphs are underrepresented, suggesting that dynamical properties affect their prevalence, and thus the global structure of networks. We propose the possibility that selective pressures that favor more synchronizable subgraphs could account for their abundance in problem-solving networks. The empirical results also show that unrelated problem-solving networks display very similar local network structure, implying that network subgraphs could represent organizational routines that enable better coordination and control of problem-solving activities. The findings could also have potential implications in understanding the functionality of network subgraphs in other information-processing networks, including biological and social networks.
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Affiliation(s)
- Dan Braha
- New England Complex Systems Institute, Cambridge, MA, 02139, USA.
- University of Massachusetts Dartmouth, Dartmouth, MA, 02747-2300, USA.
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16
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Brackbill D, Centola D. Impact of network structure on collective learning: An experimental study in a data science competition. PLoS One 2020; 15:e0237978. [PMID: 32886685 PMCID: PMC7473554 DOI: 10.1371/journal.pone.0237978] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 08/06/2020] [Indexed: 11/19/2022] Open
Abstract
Do efficient communication networks accelerate solution discovery? The most prominent theory of organizational design for collective learning maintains that informationally efficient collaboration networks increase a group's ability to find innovative solutions to complex problems. We test this idea against a competing theory that argues that communication networks that are less efficient for information transfer will increase the discovery of novel solutions to complex problems. We conducted a series of experimentally designed Data Science Competitions, in which we manipulated the efficiency of the communication networks among distributed groups of data scientists attempting to find better solutions for complex statistical modeling problems. We present findings from 16 independent competitions, where individuals conduct greedy search and only adopt better solutions. We show that groups with inefficient communication networks consistently discovered better solutions. In every experimental trial, groups with inefficient networks outperformed groups with efficient networks, as measured by both the group's average solution quality and the best solution found by a group member.
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Affiliation(s)
- Devon Brackbill
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Damon Centola
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- School of Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- * E-mail:
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17
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Gomez CJ, Lazer DMJ. Clustering knowledge and dispersing abilities enhances collective problem solving in a network. Nat Commun 2019; 10:5146. [PMID: 31723127 DOI: 10.1038/s41467-019-12650-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Accepted: 09/16/2019] [Indexed: 12/02/2022] Open
Abstract
Diversity tends to generate more and better ideas in social settings, ranging in scale from small-deliberative groups to tech-clusters and cities. Implicit in this research is that there are knowledge-generating benefits from diversity that comes from mixing different individuals, ideas, and perspectives. Here, we utilize agent-based modeling to examine the emergent outcomes resulting from the manipulation of how diversity is distributed and how knowledge is generated within communicative social structures. In the context of problem solving, we focus on cognitive diversity and its two forms: ability and knowledge. For diversity of ability, we find that local diversity (intermixing of different agents) performs best at all time scales. However, for diversity of knowledge, we find that local homogeneity performs best in the long-run, because it maintains global diversity, and thus the knowledge-generating ability of the group, for a longer period. Using agent-based models of a problem-solving task in a network, the authors show that clustering people of similar knowledge maintains solution diversity and increases long run system collective performance. Clustering those with similar abilities, however, lowers solution diversity and performance.
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18
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Abstract
In the information economy, individuals’ work performance is closely associated with their digital communication strategies. This study combines social network and semantic analysis to develop a method to identify top performers based on email communication. By reviewing existing literature, we identified the indicators that quantify email communication into measurable dimensions. To empirically examine the predictive power of the proposed indicators, we collected 2 million email archive of 578 executives in an international service company. Panel regression was employed to derive interpretable association between email indicators and top performance. The results suggest that top performers tend to assume central network positions and have high responsiveness to emails. In email contents, top performers use more positive and complex language, with low emotionality, but rich in influential words that are probably reused by co-workers. To better explore the predictive power of the email indicators, we employed AdaBoost machine learning models, which achieved 83.56% accuracy in identifying top performers. With cluster analysis, we further find three categories of top performers, ‘networkers’ with central network positions, ‘influencers’ with influential ideas and ‘positivists’ with positive sentiments. The findings suggest that top performers have distinctive email communication patterns, laying the foundation for grounding email communication competence in theory. The proposed email analysis method also provides a tool to evaluate the different types of individual communication styles.
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Affiliation(s)
- Qi Wen
- MIT Center for Collective Intelligence, USA; State Key Laboratory of Hydroscience and Engineering, Project Management and Technology Institute, Tsinghua University, China
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19
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Abstract
Many human endeavors—from teams and organizations to crowds and democracies—rely on solving problems collectively. Prior research has shown that when people interact and influence each other while solving complex problems, the average problem-solving performance of the group increases, but the best solution of the group actually decreases in quality. We find that when such influence is intermittent it improves the average while maintaining a high maximum performance. We also show that storing solutions for quick recall is similar to constant social influence. Instead of supporting more transparency, the results imply that technologies and organizations should be redesigned to intermittently isolate people from each other’s work for best collective performance in solving complex problems. People influence each other when they interact to solve problems. Such social influence introduces both benefits (higher average solution quality due to exploitation of existing answers through social learning) and costs (lower maximum solution quality due to a reduction in individual exploration for novel answers) relative to independent problem solving. In contrast to prior work, which has focused on how the presence and network structure of social influence affect performance, here we investigate the effects of time. We show that when social influence is intermittent it provides the benefits of constant social influence without the costs. Human subjects solved the canonical traveling salesperson problem in groups of three, randomized into treatments with constant social influence, intermittent social influence, or no social influence. Groups in the intermittent social-influence treatment found the optimum solution frequently (like groups without influence) but had a high mean performance (like groups with constant influence); they learned from each other, while maintaining a high level of exploration. Solutions improved most on rounds with social influence after a period of separation. We also show that storing subjects’ best solutions so that they could be reloaded and possibly modified in subsequent rounds—a ubiquitous feature of personal productivity software—is similar to constant social influence: It increases mean performance but decreases exploration.
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20
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Andrade Rojas MG, Solis ERR, Zhu JJ. Innovation and network multiplexity: R&D and the concurrent effects of two collaboration networks in an emerging economy. Research Policy 2018. [DOI: 10.1016/j.respol.2018.03.018] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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21
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Abstract
Managing ecosystems is challenging because of the high number of stakeholders, the permeability of man-made political and jurisdictional demarcations in relation to the temporal and spatial extent of biophysical processes, and a limited understanding of complex ecosystem and societal dynamics. Given these conditions, collaborative governance is commonly put forward as the preferred means of addressing environmental problems. Under this paradigm, a deeper understanding of if, when, and how collaboration is effective, and when other means of addressing environmental problems are better suited, is needed. Interdisciplinary research on collaborative networks demonstrates that which actors get involved, with whom they collaborate, and in what ways they are tied to the structures of the ecosystems have profound implications on actors' abilities to address different types of environmental problems.
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Affiliation(s)
- Örjan Bodin
- Stockholm Resilience Centre, Stockholm University, 10691 Stockholm, Sweden.
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22
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Abstract
A longstanding problem in the social, biological, and computational sciences is to determine how groups of distributed individuals can form intelligent collective judgments. Since Galton's discovery of the "wisdom of crowds" [Galton F (1907) Nature 75:450-451], theories of collective intelligence have suggested that the accuracy of group judgments requires individuals to be either independent, with uncorrelated beliefs, or diverse, with negatively correlated beliefs [Page S (2008) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies]. Previous experimental studies have supported this view by arguing that social influence undermines the wisdom of crowds. These results showed that individuals' estimates became more similar when subjects observed each other's beliefs, thereby reducing diversity without a corresponding increase in group accuracy [Lorenz J, Rauhut H, Schweitzer F, Helbing D (2011) Proc Natl Acad Sci USA 108:9020-9025]. By contrast, we show general network conditions under which social influence improves the accuracy of group estimates, even as individual beliefs become more similar. We present theoretical predictions and experimental results showing that, in decentralized communication networks, group estimates become reliably more accurate as a result of information exchange. We further show that the dynamics of group accuracy change with network structure. In centralized networks, where the influence of central individuals dominates the collective estimation process, group estimates become more likely to increase in error.
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23
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Abstract
Experimental research in traditional laboratories comes at a significant logistic and financial cost while drawing data from demographically narrow populations. The growth of online methods of research has resulted in effective means for social psychologists to collect large-scale survey-based data in a cost-effective and timely manner. However, the same advancement has not occurred for social psychologists who rely on experimentation as their primary method of data collection. The aim of this article is to provide an overview of one online laboratory for conducting experiments, Volunteer Science, and report the results of six studies that test canonical behaviors commonly captured in social psychological experiments. Our results show that the online laboratory is capable of performing a variety of studies with large numbers of diverse volunteers. We advocate for the use of the online laboratory as a valid and cost-effective way to perform social psychological experiments with large numbers of diverse subjects.
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Affiliation(s)
- Jason Radford
- University of Chicago, Chicago, IL, USA
- Northeastern University, Boston, MA, USA
| | - Andy Pilny
- University of Kentucky, Lexington, KY, USA
| | | | - Brian Keegan
- University of Colorado, Boulder, Boulder, CO, USA
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24
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Barkoczi D, Galesic M. Social learning strategies modify the effect of network structure on group performance. Nat Commun 2016; 7:13109. [PMID: 27713417 DOI: 10.1038/ncomms13109] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 09/02/2016] [Indexed: 11/30/2022] Open
Abstract
The structure of communication networks is an important determinant of the capacity of teams, organizations and societies to solve policy, business and science problems. Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both well-connected efficient and poorly connected inefficient network structures. Here we argue that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members. We show that efficient networks outperform inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts. However, inefficient networks are superior when individuals follow the best member by copying the group member with the highest payoff. In addition, groups relying on conformity based on a small sample of others excel at complex tasks, while groups following the best member achieve greatest performance for simple tasks. Our findings reconcile contradictory results in the literature and have broad implications for the study of social learning across disciplines. Previous studies have disagreed over whether efficient or inefficient network structures should be more effective in promoting group performance. Here, Barkoczi and Galesic demonstrate that which structure is superior depends on the social learning strategy used by individuals in the network.
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