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Aragon_Conviction_Voting
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cleaning up functions

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Andrew Clark 2020-07-13 16:01:55 -04:00
parent 2e85eed486
commit c6eb046373
12 changed files with 113 additions and 273 deletions
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97
Aragon Conviction Voting Model.ipynb
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README.md
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@ -1 +1,46 @@
# Aragon_Conviction_Voting
# Aragon_Conviction_Voting
[Conviction Voting](https://medium.com/giveth/conviction-voting-a-novel-continuous-decision-making-alternative-to-governance-aa746cfb9475) is a novel decision making process where votes express their preference for which proposals they would like to see approved in a continuous rather than discrete way. The longer the community keeps a preference on an individual proposal, the “stronger” the proposal conviction becomes. In the conviction voting model, a graph structure is used to record the the introduction and removal of participants, candidates, proposals, and their outcomes.
## What is cadCAD?
cadCAD (complex adaptive dynamics Computer-Aided Design) is a python based modeling framework for research, validation, and Computer Aided Design of complex systems. Given a model of a complex system, cadCAD can simulate the impact that a set of actions might have on it. This helps users make informed, rigorously tested decisions on how best to modify or interact with the system in order to achieve their goals. cadCAD supports different system modeling approaches and can be easily integrated with common empirical data science workflows. Monte Carlo methods, A/B testing and parameter sweeping features are natively supported and optimized for.
## Reproducibility
In order to reperform this code, we recommend the researcher use the following link to download https://www.anaconda.com/products/individual to download Python 3.7.To install the specific version of cadCAD this repository was built with, run the following code:
pip install cadCAD==0.4.17
Then run cd Aragon_Conviction_Voting to enter the repository. Finally, run jupyter notebook to open a notebook server to run the various notebooks in this repository.
## Simulations
## Background information & concepts addressed
### Systems Thinking
* https://community.cadcad.org/t/introduction-to-systems-thinking/18
* https://community.cadcad.org/t/working-glossary-of-systems-concepts/17
### cadCAD
* https://community.cadcad.org/t/introduction-to-cadcad/15
* https://community.cadcad.org/t/putting-cadcad-in-context/19
* https://github.com/BlockScience/cadCAD/tree/master/tutorials
### Token Engineering
* https://blog.oceanprotocol.com/towards-a-practice-of-token-engineering-b02feeeff7ca
* https://assets.pubpub.org/sy02t720/31581340240758.pdf
### Complex systems
* https://www.frontiersin.org/articles/10.3389/fams.2015.00007/full
* https://epub.wu.ac.at/7433/1/zargham_paruch_shorish.pdf
### Systems Engineering
* http://systems.hitchins.net/systems-engineering/se-monographs/seessence.pdf

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model/economyconfig.py
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@ -10,15 +10,10 @@ from cadCAD.configuration.utils import bound_norm_random, ep_time_step, config_s
from .genesis_states import genesis_states
from .partial_state_update_block import partial_state_update_blocks
params: Dict[str, List[int]] = {
'month': [0,12,36,50,100]
}
sim_config = config_sim({
'N': 1,
'T': range(100), #day
# 'M': #params,
})
seeds = {

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model/model/conviction_helper_functions.py
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@ -11,15 +11,7 @@ default_rho = 0.02
def trigger_threshold(requested, funds, supply, beta = default_beta, rho = default_rho):
'''
Definition:
Parameters:
Assumptions:
Returns:
Example:
Function that determines threshold for proposals being accepted.
'''
share = requested/funds
if share < beta:
@ -29,16 +21,7 @@ def trigger_threshold(requested, funds, supply, beta = default_beta, rho = defau
def initial_social_network(network, scale = 1, sigmas=3):
'''
Definition:
Function to initialize network x object
Parameters:
Assumptions:
Returns:
Example:
Function to initialize network x social network edges
'''
participants = get_nodes_by_type(network, 'participant')
@ -55,15 +38,7 @@ def initial_social_network(network, scale = 1, sigmas=3):
def initial_conflict_network(network, rate = .25):
'''
Definition:
Function to initialize network x object
Parameters:
Assumptions:
Returns:
Example:
Function to initialize network x conflict edges
'''
proposals = get_nodes_by_type(network, 'proposal')
@ -189,32 +164,14 @@ def get_nodes_by_type(g, node_type_selection):
def get_edges_by_type(g, edge_type_selection):
'''
Definition:
Function to initialize network x object
Parameters:
Assumptions:
Returns:
Example:
Functions to extract edges based on type
'''
return [edge for edge in g.edges if g.edges[edge]['type']== edge_type_selection ]
def conviction_order(network, proposals):
'''
Definition:
Function to initialize network x object
Parameters:
Assumptions:
Returns:
Example:
Function to sort conviction order
'''
ordered = sorted(proposals, key=lambda j:network.nodes[j]['conviction'] , reverse=True)
@ -224,16 +181,6 @@ def conviction_order(network, proposals):
def social_links(network, participant, scale = 1):
'''
Definition:
Function to initialize network x object
Parameters:
Assumptions:
Returns:
Example:
'''
participants = get_nodes_by_type(network, 'participant')
@ -251,16 +198,6 @@ def social_links(network, participant, scale = 1):
def conflict_links(network,proposal ,rate = .25):
'''
Definition:
Function to initialize network x object
Parameters:
Assumptions:
Returns:
Example:
'''
proposals = get_nodes_by_type(network, 'proposal')
@ -277,16 +214,6 @@ def conflict_links(network,proposal ,rate = .25):
def social_affinity_booster(network, proposal, participant):
'''
Definition:
Function to initialize network x object
Parameters:
Assumptions:
Returns:
Example:
'''
participants = get_nodes_by_type(network, 'participant')
@ -313,117 +240,8 @@ def social_affinity_booster(network, proposal, participant):
return np.sum(boosts)
def trigger_sweep(field, trigger_func,xmax=.2,default_alpha=.5):
'''
Definition:
Function to initialize network x object
Parameters:
Assumptions:
Returns:
Example:
'''
if field == 'token_supply':
alpha = default_alpha
share_of_funds = np.arange(.001,xmax,.001)
total_supply = np.arange(0,10**9, 10**6)
demo_data_XY = np.outer(share_of_funds,total_supply)
demo_data_Z0=np.empty(demo_data_XY.shape)
demo_data_Z1=np.empty(demo_data_XY.shape)
demo_data_Z2=np.empty(demo_data_XY.shape)
demo_data_Z3=np.empty(demo_data_XY.shape)
for sof_ind in range(len(share_of_funds)):
sof = share_of_funds[sof_ind]
for ts_ind in range(len(total_supply)):
ts = total_supply[ts_ind]
tc = ts /(1-alpha)
trigger = trigger_func(sof, 1, ts)
demo_data_Z0[sof_ind,ts_ind] = np.log10(trigger)
demo_data_Z1[sof_ind,ts_ind] = trigger
demo_data_Z2[sof_ind,ts_ind] = trigger/tc #share of maximum possible conviction
demo_data_Z3[sof_ind,ts_ind] = np.log10(trigger/tc)
return {'log10_trigger':demo_data_Z0,
'trigger':demo_data_Z1,
'share_of_max_conv': demo_data_Z2,
'log10_share_of_max_conv':demo_data_Z3,
'total_supply':total_supply,
'share_of_funds':share_of_funds}
elif field == 'alpha':
alpha = np.arange(.5,1,.01)
share_of_funds = np.arange(.001,xmax,.001)
total_supply = 10**9
demo_data_XY = np.outer(share_of_funds,alpha)
demo_data_Z4=np.empty(demo_data_XY.shape)
demo_data_Z5=np.empty(demo_data_XY.shape)
demo_data_Z6=np.empty(demo_data_XY.shape)
demo_data_Z7=np.empty(demo_data_XY.shape)
for sof_ind in range(len(share_of_funds)):
sof = share_of_funds[sof_ind]
for a_ind in range(len(alpha)):
ts = total_supply
a = alpha[a_ind]
tc = ts /(1-a)
trigger = trigger_func(sof, 1, ts)
demo_data_Z4[sof_ind,a_ind] = np.log10(trigger)
demo_data_Z5[sof_ind,a_ind] = trigger
demo_data_Z6[sof_ind,a_ind] = trigger/tc #share of maximum possible conviction
demo_data_Z7[sof_ind,a_ind] = np.log10(trigger/tc)
return {'log10_trigger':demo_data_Z4,
'trigger':demo_data_Z5,
'share_of_max_conv': demo_data_Z6,
'log10_share_of_max_conv':demo_data_Z7,
'alpha':alpha,
'share_of_funds':share_of_funds}
else:
return "invalid field"
def trigger_plotter(share_of_funds,Z, color_label,y, ylabel,cmap='jet'):
'''
Definition:
Function to initialize network x object
Parameters:
Assumptions:
Returns:
Example:
'''
dims = (10, 5)
fig, ax = plt.subplots(figsize=dims)
cf = plt.contourf(share_of_funds, y, Z.T, 100, cmap=cmap)
cbar=plt.colorbar(cf)
plt.axis([share_of_funds[0], share_of_funds[-1], y[0], y[-1]])
#ax.set_xscale('log')
plt.ylabel(ylabel)
plt.xlabel('Share of Funds Requested')
plt.title('Trigger Function Map')
cbar.ax.set_ylabel(color_label)
def snap_plot(nets, size_scale = 1/500, ani = False, dims = (20,20), savefigs=False):
'''
Definition:
Function to initialize network x object
Parameters:
Assumptions:
Returns:
Example:
'''
last_net = nets[-1]
@ -556,16 +374,6 @@ def snap_plot(nets, size_scale = 1/500, ani = False, dims = (20,20), savefigs=Fa
def pad(vec, length,fill=True):
'''
Definition:
Function to initialize network x object
Parameters:
Assumptions:
Returns:
Example:
'''
if fill:
@ -581,16 +389,6 @@ def pad(vec, length,fill=True):
def make2D(key, data, fill=False):
'''
Definition:
Function to initialize network x object
Parameters:
Assumptions:
Returns:
Example:
'''
maxL = data[key].apply(len).max()
newkey = 'padded_'+key

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model/model/initialization.py
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@ -6,7 +6,7 @@ import numpy as np
from .conviction_helper_functions import *
# Parameters
#maximum share of funds a proposal can take
# maximum share of funds a proposal can take
beta = .2 #later we should set this to be param so we can sweep it
# tuning param for the trigger function
rho = .001
@ -20,16 +20,13 @@ m= 3 #initial proposals
initial_sentiment = .6
theta =.35
kappa = 6 #bonding curve curvature
alpha = 0.5
sale_price = .1
sensitivity = .75
tmin = 7 #unit days; minimum periods passed before a proposal can pass
min_supp = 50 #number of tokens that must be stake for a proposal to be a candidate
sentiment_decay = .01 #termed mu in the state update function
base_completion_rate = 100
base_failure_rate = 200
# trigger_func = trigger_threshold
tax_rate = .02
initial_funds = 40781.42

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model/model/participants.py
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@ -3,7 +3,6 @@ import numpy as np
from .initialization import *
from .conviction_helper_functions import *
import networkx as nx
# from scipy.stats import expon, gamma
# Phase 2
@ -36,7 +35,7 @@ def check_progress(params, step, sL, s):
# Mechanisms
def complete_proposal(params, step, sL, s, _input):
'''
Book-keeping
Book-keeping of failed and completed proposals. Update network object
'''
network = s['network']

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model/model/proposals.py
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@ -11,6 +11,7 @@ tmin = 7
# Behaviors
def trigger_function(params, step, sL, s):
'''
This policy checks to see if each proposal passes or not.
'''
network = s['network']
funds = s['funds']
@ -53,6 +54,7 @@ def trigger_function(params, step, sL, s):
# Mechanisms
def decrement_funds(params, step, sL, s, _input):
'''
If a proposal passes, funds are decremented by the amount of the proposal
'''
funds = s['funds']
@ -70,6 +72,7 @@ def decrement_funds(params, step, sL, s, _input):
def update_proposals(params, step, sL, s, _input):
'''
If proposal passes, its status is changed in the network object.
'''
network = s['network']

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model/model/system.py
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@ -11,7 +11,10 @@ sentiment = 0.6
# Behaviors
def driving_process(params, step, sL, s):
def driving_process(params, step, sL, s):
'''
Driving process for adding new participants (their funds) and new proposals.
'''
arrival_rate = 10/(1+sentiment)
rv1 = np.random.rand()
new_participant = bool(rv1<1/arrival_rate)
@ -61,6 +64,9 @@ def driving_process(params, step, sL, s):
# Mechanisms
def update_network(params, step, sL, s, _input):
'''
Add new participants and proposals to network object
'''
network = s['network']
funds = s['funds']
@ -93,7 +99,9 @@ def update_network(params, step, sL, s, _input):
return (key, value)
def increment_funds(params, step, sL, s, _input):
'''
Increase funds by the amount of the new particpant's funds.
'''
funds = s['funds']
funds_arrival = _input['funds_arrival']

2
model/run.py
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@ -25,6 +25,7 @@ def run(input_config=configs):
def postprocessing(df):
'''
Function for postprocessing the simulation results to extract key information from the network object.
'''
# Extract information from dataframe
df['conviction'] = df.network.apply(lambda g: np.array([g.nodes[j]['conviction'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate']))
@ -55,6 +56,7 @@ def postprocessing(df):
df['conviction_share_of_trigger_all'] = df.conviction_all/df.triggers_all
# subset to last substep of each simulation
rdf= df[df.substep==df.substep.max()].copy()
return df,rdf