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Creativity Is More Than a Trait – It’S a Relation

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Creativity Is More Than a Trait – It’S a Relation Creativity is more than a trait – It’s a relation Dissertation zur Erlangung des Doktorgrades an der Fakultät Erziehungswissenschaft, Psychologie und Bewegungswissenschaft, Fachbereich Psychologie der Universität Hamburg vorgelegt von Cara H. Kahl Hamburg, 2012 Tag der Disputation: 10. November 2011 Erstgutachter: Prof. Dr. Erich H. Witte Zweitgutachter: Prof. Dr. Klaus G. Troitzsch Disputationsgutachterin: Prof. Monique Janneck Disputationsgutachter: Prof. Alexander Redlich Vorsitzende des Promotionsprüfungsausschusses: Prof. Sabine Trepte i Evolution produces viable compromises, not perfection. Bernd Baldus. ii Abstract This dissertation begins with an investigation of recent doctoral work on creativ- ity, and it reveals psychologists place the most emphasis on studying individuals and their traits. However, raters of some sort were always part of the studies at hand so the construct appears to emerge from more than just creating individu- als. One of the fewest models accounting for contextual relations in constructing creativity is Csikszentmihalyi’s (1999) systems perspective. It encompasses three interacting subsystems: Individuals producing variations, a field selecting and a do- main retaining them. Its qualitative, longitudinal nature renders it a challenge for empirical experimentation. A methodological alternative capable of operationalising it is agent-based modelling, a form of computer simulation. Two agent-based mod- els were programmed to explore the assumptions made by Csikszentmihalyi (1999), CRESY-I and CRESY-II. Experiments conducted with them reveal the relation be- tween the domain and creating individuals is most influential when variations are not evaluated. As soon as they are, however, the field has the largest effect on variation diversity and creativity. iii Contents Abstract iii List of Figures x List of Tables xv I. Theory 1 1. Contemporary approaches in creativity research 3 1.1. Overview . .3 1.2. Revisiting creativity research . .3 1.3. Classifying creativity research . .4 1.4. Method . .4 1.4.1. Design . .4 1.4.2. Procedure . .5 1.5. Results . .6 1.5.1. Disciplines . .7 1.5.2. Search terms . .7 1.5.3. Comparing samples . .9 1.6. Discussion . 10 2. Creativity is more than a trait: It’s a relation 13 2.1. Introduction . 13 2.2. The Great Trait . 14 2.3. The relation . 16 3. Modeling a social environment to investigate creativity 21 3.1. Defining creativity . 21 3.2. Production . 22 3.3. Evaluation . 24 3.4. The domain . 26 3.5. Integrating perspectives . 28 3.6. Defining a model of creativity . 29 3.6.1. Describing creativity in evolutionary terms . 29 3.6.2. Creators (Variation) . 30 3.6.3. Evaluators (Selection) . 31 3.6.4. Domain (Retention) . 33 3.6.5. Model specifics . 34 3.6.6. Locating creativity in the model . 35 3.6.7. Concluding remarks . 37 v Contents II. Method 39 4. Research style and objectives 41 4.1. Exploration: Creative and systematic science . 41 4.2. Research style . 43 4.3. Research hypotheses vs. research questions . 45 4.4. Research objectives . 47 5. Research method 49 5.1. Agent-based modelling . 49 5.1.1. A primer on agent-based modelling . 49 5.1.2. Verifying agent-based models . 52 5.1.3. Validating agent-based models . 54 5.1.4. Agent-based models: Nothing new to social psychology . 57 5.1.5. Computer models of creativity . 59 5.2. Building agent-based models . 61 5.2.1. Simulation tool . 61 5.2.2. Computational representation . 61 5.2.3. The agent-based models: CRESY-I & CRESY-II . 62 III. Agent-Based Models of Creativity 65 6. Roadmap to CRESY-I 67 6.1. ODD Protocol: Overview . 67 6.1.1. Purpose . 68 6.1.2. Entities, state variables, and scales . 68 6.1.3. Process overview and scheduling . 73 6.2. ODD Protocol: Design concepts . 74 6.2.1. Basic principles . 74 6.2.2. Emergence . 74 6.2.3. Adaptation . 75 6.2.4. Objectives . 76 6.2.5. Learning . 76 6.2.6. Prediction . 76 6.2.7. Sensing . 76 6.2.8. Interaction . 77 6.2.9. Stochasticity . 77 6.2.10. Collectives . 77 6.2.11. Observation . 78 6.3. ODD Protocol: Details . 78 6.3.1. Initialization . 78 6.3.2. Input data . 78 6.3.3. Submodels . 78 vi Contents 6.4. Model verification . 83 6.5. Independent variables . 84 6.6. Dependent variables . 85 6.6.1. hCAll . 85 6.6.2. hX.Y . 87 6.7. Experimental design . 88 7. CRESY-I 91 7.1. Theory . 91 7.1.1. Research questions . 95 7.2. Method . 95 7.3. Data analyses and results . 97 7.3.1. Graphical data analyses . 97 7.3.2. Aggregating dependent variables . 97 7.3.3. Analyses of variances . 102 7.4. Discussion . 109 8. Roadmap to CRESY-II 117 8.1. ODD Protocol: Overview . 117 8.1.1. Purpose . 117 8.1.2. Entities, state variables, and scales . 118 8.1.3. Process overview and scheduling . 124 8.2. ODD Protocol: Design concepts . 124 8.2.1. Basic principles . 124 8.2.2. Emergence . 125 8.2.3. Adaptation . 125 8.2.4. Objectives . 126 8.2.5. Learning . 127 8.2.6. Prediction . 127 8.2.7. Sensing . 127 8.2.8. Interaction . 127 8.2.9. Stochasticity . 128 8.2.10. Collectives . 128 8.2.11. Observation . 129 8.3. ODD Protocol: Details . 129 8.3.1. Initialization . 129 8.3.2. Input data . 129 8.3.3. Submodels . 129 8.4. Model verification . 132 8.5. Independent variables . 134 8.6. Dependent variables . 134 8.6.1. hCAll . 134 8.6.2. hX.Y . 134 8.6.3. Creativity . 134 vii Contents 8.6.4. Reliability . 136 8.7. Experimental design . 136 9. CRESY-II 139 9.1. Theory . 139 9.1.1. Research questions . 144 9.2. Method . 144 9.3. Data analyses and results . 145 9.4. Discussion . 162 IV. Discussion 169 10. Discussion 171 10.1. Summary . 171 10.2. Discovering relations . 173 10.3. From current obstacles to future steps . 175 10.4. Concluding remarks . 179 A. Appendix 181 A.1. Experiment CI-1a . 181 A.1.1. Research questions . 181 A.1.2. Experimental design . 181 A.1.3. Analyses . ..
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