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Marco Janssen School of Human Evolution and Social Change,

Self-governance of a Spatial Explicit Real-time Dynamic Common Resource: A Content Analysis of Communication Patterns. Marco Janssen School of Human Evolution and Social Change, Center for the Study of Institutional Diversity. In cooperation with: Allen Lee, Deepali Bhagvat, and Clint Bushman.

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Marco Janssen School of Human Evolution and Social Change,

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  1. Self-governance of a SpatialExplicit Real-time Dynamic Common Resource: A Content Analysis of Communication Patterns Marco Janssen School of Human Evolution and Social Change, Center for the Study of Institutional Diversity In cooperation with: Allen Lee, Deepali Bhagvat, and Clint Bushman

  2. Going back 5 years ago • Agent-based model that try to explain observed patterns in public good data at different scales. Including learning, other-regarding preferences, probablistic choice and signaling. • Janssen, M. A., and T. K. Ahn. 2006. Learning, signaling, and social preferences in public-good games. Ecology and Society11(2): 21. [online] URL: http://www.ecologyandsociety.org/vol11/iss2/art21/

  3. Going beyond Panaceas. The problem of fit between ecological dynamics and institutional arrangements. How do appropriators craft institutions and what helps them to fit it to the ecological context? Background

  4. Towards the inclusion of ecological context of CPR and PG experiments • Traditional experiments uses a very stylized common pool resource/ public good situation. • From case study analysis we find regularities in the institutional arrangements and the ecological context. • A next step in CPR/PG would be to include stylized ecological dynamics.

  5. Commonresearch questions Laboratoryexperiments Field experiments Statistical analysis Surveys Interviews models “role games” Statistical analysis, Surveys Text analysis, .. Educational games models Evolutionary models

  6. Experimental environment • Renewable resource, density dependent regrowth • 4 participants • Text chat between the rounds • Option to reduce tokens of others at the end of each round (at a cost) • Explicit and implicit mode

  7. Design • Each experiment: • Each round is four minutes • Round 0: Practice round (individual) (14x14 cells) • Round 1: Individual round • Round 2: Open access round (28x28 cells) • Round 3-5: chat + open access • Different resource growth experiments: • Low growth (6 groups) • High growth (4 groups) • High / Low growth (6 groups) • Mixed growth (6 groups)

  8. Questions for this paper • What kind of rules do the form for the different conditions? • What makes communication effective?

  9. Demographics • Experiments held in Spring 2007 • Participants randomly invited from undergraduate population of Tempe campus ASU • 88 participants: 59 male, 29 female • Average age: 21.4 years • Show-up fee: $5 • Duration: one hour • Average earnings: $20.80 ($5.48 - $35.86)

  10. Round 1 (high growth case)

  11. Round 2 (high growth case)

  12. Tokens in the resource during the rounds High Low Mixed High-Low

  13. Average number of tokens collected (blue) and left over (red) for the 5 rounds H L HL Mix

  14. Chat • During the 5 minute chat period, the four members of the groups exchange on average 50 messages (stdev 17). This does not vary significantly between rounds of treatments. • Two coders coded the chat text using 20 categories. • Kappa scores of the coded text indicate that the coders are in good agreement.

  15. Chat example • A: we should not take all the tokens right away • A: the more there are the faster they come back • B: ive been shooting for a 50% strategy • A: a good strategy is to switch to the spacebar mode then go for everyother one • B: by taking alternating lines • A: yea last time we ran out with 50 seconds left • A: oooor.... do you guys want to split up the board? • D: i have a feeling this test is about greed so its gonna be hard to decide who is taking tokens too fast and who isn’t • A: yea i know what you mean • D: then at the end you get a chance to pay them back • B: well if we all maintain a quadrant • D: yeah thats not a bad idea • A: yes but we have to all agree • A: no one should go taking other people's quadrant if they run out • B: i volunteer for the SW quadrant • A: i'll take nw • D: ill take ne • C: Just choose one corner • A: ok so everyone agree • A: i'm top left • C: I am taking whatever is left • A: you're bottom right • B: c SE (bottom right) is open • C: ok • A: nice work guys • B: agreed

  16. Chat example – off topic • C: who read the state press today? • B: did • A: nope • D: FALSE • A: majors? • B: bio • D: who ate breakfast today? • A: construction • C: tme • C: \business

  17. Round 1 Round 2 Round 3 (individual) (group) (group) Constant 202.318*** 867.138*** 846.125*** Dummy-mixed growth -63.451*** -277.244*** -249.226*** Dummy-Low-growth -138.599*** -459.712*** -528.686*** (Fraction) econ major 24.476** 201.640** 602.62 (Fraction) male 18.883** -245.115*** 1206.602 N 88 22 22 F 43.201 38.253 17.720 *** p < 0.01 ** p < 0.05 * P < 0.1

  18. Does actions in round 1 affect results on the individual level? Round 1 Round 2 (individual) (individual) Constant 202.318*** 181.917*** Dummy-mixed-growth -63.451*** -67.701*** Dummy-Low-growth -138.599*** -112.64*** Dummy economics major 24.476** 27.225* Dummy male 18.883** -14.447 Tokens Round 1 0.03876 N 88 88 F 43.201 20.940 *** p < 0.01 ** p < 0.05 * p < 0.1

  19. How does communication affect earnings in round 3? Round 3 Round 3 Round3 Constant 846.125*** 133.178 254.522 Dummy-mixed-growth -249.226*** -8.693 -22.849 Dummy-Low-growth -528.686*** -110.721 -256.020 Fraction economics major 602.62 -117.75 158.363 Fraction male 1206.602 272.401 * 266.896 Tokens Round 2 0.843 ** 0.756 * Total chat entries 1.560 Gini chat contributions -583.393 Past rounds -20.571 Sanctioning 3.076 General Strategy -1.671 Specific time -5.939 Specific space 1.155 Affirmation 44.046 Experiment 0.771 General -3.154 Off topic 9.823 * N 22 22 22 F 17.720 13.069 8.466

  20. How does communication leads to improved performance in round 3? Round 3 Round 3 Constant 0.895 *** 0.965 *** Dummy-mixed-growth 0.326 *** 0.243 Dummy-Low-growth 0.386 *** 0.309 * Fraction male 0.606 *** 0.577 ** Total chat entries 0.006 * Gini chat contributions -2.978 *** -3.08 ** Past rounds 0.003 Sanctioning 0.012 General Strategy 0.003 Specific time -0.005 Specific space 0.010 Affirmation 0.095 Experiment 0.013 General -0.002 Off topic 0.013 N 22 22 F 10.345 5.260

  21. Informal agreements • Mode Time Space • High 7/10 5/10 3/10 • Low 3/6 5/6 2/6 • Mixed 1/6 2/6 4/6 • High growth groups focus on explicit mode • Low growth groups focus on time (waiting) • Mixed growth on allocating the space

  22. How does explicit agreements affect the performance in round 3? Round 3 Round 3 Constant 0.895 *** 0.873 *** Dummy-mixed-growth 0.326 *** 0.258 * Dummy-Low-growth 0.386 *** 0.311 ** Fraction male 0.606 *** 0.630 ** Total chat entries 0.006 * 0.006 * Gini chat contributions -2.978 *** -2.426 ** Mode -0.155 Time 0.106 Space 0.009 N 22 22 F 10.345 6.313

  23. Spatial concentration SC = 0.25 SC = 1.00

  24. Conclusions • Participants discuss the timing, location and mode of token collection. • When people contributed more evenly to the chat it increases performance. • More males in the group and more chat also increase performance. • Explicit discussion on rules, nor affirmations or threats affect the results.

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