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PhD Thesis - The University of Sydney

PhD Thesis - The University of Sydney

PhD Thesis - The University of

Computational Studies on the Role of Social Learning inthe Formation of Team Mental ModelsA thesis submitted in the fulfilment of therequirements for the degree ofDoctor of PhilosophyVishal SinghDesign LabFaculty of Architecture, Design and PlanningThe University of Sydney2009

  • Page 2 and 3: DECLARATIONI hereby declare that th
  • Page 4 and 5: AbstractThis thesis investigates th
  • Page 6 and 7: 4.1.2 Social learning in team envir
  • Page 8 and 9: 8.1 Review of research objectives .
  • Page 10 and 11: Figure 5.13: Typical interaction be
  • Page 12 and 13: List of Abbreviations and SymbolsAM
  • Page 14 and 15: identify the group to which the tas
  • Page 16 and 17: formation of team mental models and
  • Page 18 and 19: influence the formation of TMM, and
  • Page 20 and 21: 1.1.2 Methodological motivationIn a
  • Page 22 and 23: assumptions) (section 4.1.3, Table
  • Page 24 and 25: familiarity, is greater at higher l
  • Page 26 and 27: Chapter 2BackgroundTeam work and te
  • Page 28 and 29: & Krull, 1988). Since this research
  • Page 30 and 31: Mohrman et al., 1995; Packendorff,
  • Page 32 and 33: the physical boundaries (Leinonen e
  • Page 34 and 35: as a benchmark to assess the accura
  • Page 36 and 37: viewed as a cognitive construct, (K
  • Page 38 and 39: formation and the team performance,
  • Page 40 and 41: & Carley, 2004; Schreiber & Carley,
  • Page 42 and 43: Social agentCarley and Newell (Carl
  • Page 44: vary with the team structure, busyn
  • Page 47 and 48: Busyness levels are expected to inf
  • Page 49 and 50: in levels of TMM formation, with th
  • Page 51 and 52: ordination is required in smaller g
  • Page 53 and 54:

    sub-teams, lower when the team is f

  • Page 55 and 56:

    Social learning should not only all

  • Page 57 and 58:

    Figure 3.13 shows a graph to illust

  • Page 59 and 60:

    TMMformationFlat teamsSocial Clique

  • Page 61 and 62:

    lower in the teams that havepartial

  • Page 63 and 64:

    Chapter 4Conceptual Framework and C

  • Page 65 and 66:

    elating to more than one task group

  • Page 67 and 68:

    2.2.2.2, section 4.1.6.2). This kno

  • Page 69 and 70:

    performer has all the requisite inf

  • Page 71 and 72:

    7. The range of values of the overa

  • Page 73 and 74:

    4.1.6.4 Task allocation and team kn

  • Page 75 and 76:

    equired to evaluate solutions provi

  • Page 77 and 78:

    simulation rounds and the number of

  • Page 79 and 80:

    When the team is initially formed,

  • Page 81 and 82:

    It can be argued that if the agents

  • Page 83 and 84:

    confirms that the solution was acce

  • Page 85 and 86:

    Figure 5.4: Activity diagram for a

  • Page 87 and 88:

    5.4.2 Implementation of TMM for the

  • Page 89 and 90:

    Case 2: Solution rejected by agent

  • Page 91 and 92:

    Allocate task to newActorSet newAct

  • Page 93 and 94:

    solution. The capability range valu

  • Page 95 and 96:

    Figure 5.13: Typical interaction be

  • Page 97 and 98:

    Table 5.3: Types of messages used a

  • Page 99 and 100:

    Based on the task knowledge and the

  • Page 101 and 102:

    Table 5.4: Implementing observation

  • Page 103 and 104:

    For each bid in the shortlistIf ( C

  • Page 105 and 106:

    Figure 5.17: Activity diagram for t

  • Page 107 and 108:

    the source agent allocates the task

  • Page 109 and 110:

    5.10 Computational model as the sim

  • Page 111 and 112:

    Likelihood that an agent is busy in

  • Page 113 and 114:

    6.1.1 Simulation set-up:A routine d

  • Page 115 and 116:

    The simulations with the two types

  • Page 117 and 118:

    Agent’s social learning capabilit

  • Page 119 and 120:

    - - √PI+TO - √PI+IO+TO - √√

  • Page 121 and 122:

    6.2.1.2 Experiments with routine ta

  • Page 123 and 124:

    6.2.2 Simulation resultsThis sectio

  • Page 125 and 126:

    Learning modes and level of TMM for

  • Page 127 and 128:

    50 15.32 0.77 13.70 1.41 5.92 0.406

  • Page 129 and 130:

    In the teams organized as task-base

  • Page 131 and 132:

    has observed the details of the ran

  • Page 133 and 134:

    Std Dev - 10.77 12.25 11.86 12.95 7

  • Page 135 and 136:

    Figure 7.1: Busyness levels and tea

  • Page 137 and 138:

    Figure 7.2(a) and Figure 7.2(b) ill

  • Page 139 and 140:

    Table 7.2 shows the one-way ANOVA r

  • Page 141 and 142:

    7.1.4 Team familiarity, busyness le

  • Page 143 and 144:

    team familiarity, is supported by t

  • Page 145 and 146:

    Figure 7.7(a) and Figure 7.7(b) ill

  • Page 147 and 148:

    Figure 7.9: Team structure and % TM

  • Page 149 and 150:

    % TMM Formation5045403530252015105B

  • Page 151 and 152:

    PI+IO Flat 0.7600 0.7861Thus, TMM f

  • Page 153 and 154:

    Validity of the hypothesis is conti

  • Page 155 and 156:

    However, at lower levels of team fa

  • Page 157 and 158:

    However, the relative difference in

  • Page 159 and 160:

    observations, and (3) learning from

  • Page 161 and 162:

    new employees have developed prior-

  • Page 163 and 164:

    lower when the team is flat, and lo

  • Page 165 and 166:

    as trust and motivation may influen

  • Page 167 and 168:

    is explicitly distributed across th

  • Page 169 and 170:

    context mental models, in addition

  • Page 171 and 172:

    References1. Akgun, A. E., Byrne, J

  • Page 173 and 174:

    49. Cross, N., & Cross, A. C. (1998

  • Page 175 and 176:

    106. Knobe, J. (2006). The concept

  • Page 177 and 178:

    (Eds.), Concept maps: Theory, metho

  • Page 179 and 180:

    GlossaryA-AgentAccuracyAgent mental

  • Page 181 and 182:

    ConspecificCreative taskCritical ta

  • Page 183 and 184:

    observationsidentifies one agent al

  • Page 185 and 186:

    SequentialtasksSimulationController

  • Page 187 and 188:

    team.Computationally, the TMM is re

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