Self-governance of a Spatial Explicit Real-time Dynamic Common Resource: A Content Analysis of Communication Patterns

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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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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/a
  rt21/

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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?

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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.

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

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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)

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Questions for this paper

• What kind of rules do the form for the different
  conditions?
• What makes communication effective?

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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)

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Round 1 (high growth case)
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Round 2 (high growth case)
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Tokens in the resource during the rounds
High
Low
Mixed
High-Low
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Average number of tokens collected (blue) and
left over (red) for the 5 rounds
H
L
HL
Mix
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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.

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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 isnt
• 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

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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

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Topic Average number per round per group Kappa score
Discussion past rounds (evaluative) 4.2 0.92
Discussion past rounds (procedural) 0.8 0.74
Sanctioning (positive) 0.3 0.74
Sanctioning (negative) 2.3 0.78
Sanctioning (general threats) 0.4 0.70
General strategy (temporal) 1.0 0.75
General strategy (spatial) 1.2 0.66
General strategy (mode) 2.2 0.63
General strategy (general) 1.4 0.84
Specific strategy (time proposed) 0.4 0.77
Specific strategy (time discussion) 6.9 0.82
Specific strategy (space proposed) 0.3 0.70
Specific strategy (space discussion) 7.6 0.80
Affirmation 0.5 0.66
Experiment (intend) 0.7 0.81
Experiment (procedures) 1.8 0.75
Experiment (software) 1.2 0.78
Experiment (uncertainty) 0.1 0.81
General discussion 9.4 0.75
Off-topic 7.4 0.85
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Round 1 Round 2 Round 3 (individual) (grou
p) (group) Constant 202.318 867.138 846.1
25 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 2
2 F 43.201 38.253 17.720 p lt 0.01 p lt
0.05 P lt 0.1
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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 2
0.940 p lt 0.01 p lt 0.05 p lt 0.1
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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
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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-g
rowth 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 Expe
riment 0.013 General -0.002 Off
topic 0.013 N 22 22 F 10.345 5.260
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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

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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
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Spatial concentration
SC 0.25
SC 1.00
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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.