1. 1 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

2. 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 Society 11(2): 21. [online] URL: http://www.ecologyandsociety.org/vol11/iss2/art21/

3. 3 Background 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?

4. 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. 5 Common research questions Laboratory experiments models Field experiments models “role games” Statistical analysis Surveys Interviews Evolutionary models Statistical analysis, Surveys Text analysis,.. Educational games

6. 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. 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. 8 Questions for this paper What kind of rules do the form for the different conditions? What makes communication effective?

9. 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. 10 Round 1 (high growth case)

11. 11 Round 2 (high growth case)

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

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

14. 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. 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. 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. 17 TopicAverage number per round per groupKappa score Discussion past rounds (evaluative)4.20.92 Discussion past rounds (procedural)0.80.74 Sanctioning (positive)0.30.74 Sanctioning (negative)2.30.78 Sanctioning (general threats)0.40.70 General strategy (temporal)1.00.75 General strategy (spatial)1.20.66 General strategy (mode)2.20.63 General strategy (general)1.40.84 Specific strategy (time: proposed)0.40.77 Specific strategy (time: discussion)6.90.82 Specific strategy (space: proposed)0.30.70 Specific strategy (space: discussion)7.60.80 Affirmation0.50.66 Experiment (intend)0.70.81 Experiment (procedures)1.80.75 Experiment (software)1.20.78 Experiment (uncertainty)0.10.81 General discussion9.40.75 Off-topic7.40.85

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23. 23 Round 1Round 2Round 3 (individual)(group)(group) Constant202.318***867.138***846.125*** Dummy-mixed growth-63.451***-277.244***-249.226*** Dummy-Low-growth-138.599***-459.712***-528.686*** (Fraction) econ major24.476**201.640**602.62 (Fraction) male18.883**-245.115***1206.602 N882222 F43.20138.25317.720 *** p < 0.01 ** p < 0.05 * P < 0.1

24. 24 Does actions in round 1 affect results on the individual level? Round 1Round 2(individual) Constant202.318***181.917*** Dummy-mixed-growth-63.451***-67.701*** Dummy-Low-growth-138.599***-112.64*** Dummy economics major24.476**27.225* Dummy male18.883**-14.447 Tokens Round 10.03876 N8888 F43.20120.940 *** p < 0.01 ** p < 0.05 * p < 0.1

25. 25 Round 3Round 3Round3 Constant846.125***133.178 254.522 Dummy-mixed-growth-249.226***-8.693 -22.849 Dummy-Low-growth-528.686***-110.721 -256.020 Fraction economics major602.62 -117.75 158.363 Fraction male1206.602 272.401 *266.896 Tokens Round 20.843 **0.756 * Total chat entries1.560 Gini chat contributions-583.393 Past rounds-20.571 Sanctioning3.076 General Strategy-1.671 Specific time-5.939 Specific space1.155 Affirmation44.046 Experiment0.771 General-3.154 Off topic9.823 * N222222 F17.720 13.069 8.466 How does communication affect earnings in round 3?

26. 26 Round 3 Constant0.895 ***0.965 *** Dummy-mixed-growth0.326 ***0.243 Dummy-Low-growth0.386 ***0.309 * Fraction male0.606 ***0.577 ** Total chat entries0.006 * Gini chat contributions-2.978 ***-3.08 ** Past rounds0.003 Sanctioning0.012 General Strategy0.003 Specific time-0.005 Specific space0.010 Affirmation0.095 Experiment0.013 General-0.002 Off topic0.013 N2222 F10.3455.260 How does communication leads to improved performance in round 3?

27. 27 Informal agreements ModeTimeSpace High7/105/103/10 Low3/65/62/6 Mixed1/62/64/6 High growth groups focus on explicit mode Low growth groups focus on time (waiting) Mixed growth on allocating the space

28. 28 Round 3 Constant0.895 ***0.873 *** Dummy-mixed-growth0.326 ***0.258 * Dummy-Low-growth0.386 ***0.311 ** Fraction male0.606 ***0.630 ** Total chat entries0.006 *0.006 * Gini chat contributions-2.978 ***-2.426 ** Mode-0.155 Time0.106 Space0.009 N2222 F10.345 6.313 How does explicit agreements affect the performance in round 3?

29. 29 Spatial concentration SC = 0.25 SC = 1.00

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31. 31 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.