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second policy "check_progress"implemented; moved and renamed most policy/state variable functions over to network_helpers

This commit is contained in:
Andrew Chiw 2020-06-04 16:33:08 +02:00
parent 8f214ed043
commit 71c242104d
2 changed files with 180 additions and 92 deletions
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171
network_helpers.py
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@ -19,7 +19,7 @@ def get_participants(network):
def initial_social_network(network: nx.DiGraph, scale = 1, sigmas=3) -> nx.DiGraph:
participants = get_participants(network)
for i in participants:
for j in participants:
if not(j==i):
@ -32,7 +32,7 @@ def initial_social_network(network: nx.DiGraph, scale = 1, sigmas=3) -> nx.DiGra
def initial_conflict_network(network: nx.DiGraph, rate = .25) -> nx.DiGraph:
proposals = get_proposals(network)
for i in proposals:
for j in proposals:
if not(j==i):
@ -61,53 +61,68 @@ def add_proposals_and_relationships_to_network(n: nx.DiGraph, proposals: int, fu
n.edges[(i, j)]['tokens'] = 0
n.edges[(i, j)]['conviction'] = 0
n.edges[(i, j)]['type'] = 'support'
n = initial_conflict_network(n, rate = .25)
n = initial_social_network(n, scale = 1)
return n
def update_collateral_pool(params, step, sL, s, _input):
commons = s["commons"]
s["collateral_pool"] = commons._collateral_pool
return "collateral_pool", commons._collateral_pool
def update_token_supply(params, step, sL, s, _input):
commons = s["commons"]
s["token_supply"] = commons._token_supply
return "token_supply", commons._token_supply
def update_funding_pool(params, step, sL, s, _input):
commons = s["commons"]
s["funding_pool"] = commons._funding_pool
return "funding_pool", commons._funding_pool
# =========================================================================================================
def gen_new_participant(network, new_participant_tokens):
i = len([node for node in network.nodes])
network.add_node(i)
network.nodes[i]['type']="participant"
s_rv = np.random.rand()
s_rv = np.random.rand()
network.nodes[i]['sentiment'] = s_rv
network.nodes[i]['holdings_vesting']=None
network.nodes[i]['holdings_nonvesting']=TokenBatch(new_participant_tokens, 5, 5)
# Connect this new participant to existing proposals.
for j in get_nodes_by_type(network, 'proposal'):
network.add_edge(i, j)
rv = np.random.rand()
a_rv = 1-4*(1-rv)*rv #polarized distribution
network.edges[(i, j)]['affinity'] = a_rv
network.edges[(i,j)]['tokens'] = a_rv*network.nodes[i]['holdings_nonvesting'].value
network.edges[(i, j)]['conviction'] = 0
network.edges[(i,j)]['type'] = 'support'
return network
def gen_new_proposal(network, funds, supply, trigger_func, scale_factor = 1.0/100):
j = len([node for node in network.nodes])
network.add_node(j)
network.nodes[j]['type']="proposal"
network.nodes[j]['conviction']=0
network.nodes[j]['status']='candidate'
network.nodes[j]['age']=0
rescale = funds*scale_factor
r_rv = gamma.rvs(3,loc=0.001, scale=rescale)
network.nodes[j]['funds_requested'] = r_rv
network.nodes[j]['trigger']= trigger_func(r_rv, funds, supply)
participants = get_nodes_by_type(network, 'participant')
proposing_participant = np.random.choice(participants)
for i in participants:
network.add_edge(i, j)
if i==proposing_participant:
@ -116,11 +131,11 @@ def gen_new_proposal(network, funds, supply, trigger_func, scale_factor = 1.0/10
rv = np.random.rand()
a_rv = 1-4*(1-rv)*rv #polarized distribution
network.edges[(i, j)]['affinity'] = a_rv
network.edges[(i, j)]['conviction'] = 0
network.edges[(i,j)]['tokens'] = 0
network.edges[(i,j)]['type'] = 'support'
return network
def calc_total_funds_requested(network):
@ -135,7 +150,7 @@ def calc_median_affinity(network):
median_affinity = np.median(affinities)
return median_affinity
def driving_process(params, step, sL, s):
def gen_new_participants_proposals_funding_random(params, step, sL, s):
network = s['network']
commons = s['commons']
funds = s['funding_pool']
@ -145,7 +160,7 @@ def driving_process(params, step, sL, s):
arrival_rate = 10/(1+sentiment)
rv1 = np.random.rand()
new_participant = bool(rv1<1/arrival_rate)
if new_participant:
# Below line is quite different from Zargham's original, which gave
# tokens instead. Here we randomly generate each participant's
@ -174,22 +189,22 @@ def driving_process(params, step, sL, s):
scale_factor = 1
#this shouldn't happen but expon is throwing domain errors
if sentiment>.4:
if sentiment>.4:
funds_arrival = expon.rvs(loc = 0, scale = scale_factor )
else:
funds_arrival = 0
return funds_arrival
new_participant, new_participant_investment, new_participant_tokens = randomly_gen_new_participant(len(get_participants(network)), sentiment, s['token_supply'], commons)
new_proposal = randomly_gen_new_proposal(calc_total_funds_requested(network), calc_median_affinity(network), funds)
funds_arrival = randomly_gen_new_funding(funds, sentiment)
return({'new_participant':new_participant,
'new_participant_investment':new_participant_investment,
'new_participant_tokens': new_participant_tokens,
'new_proposal':new_proposal,
'new_proposal':new_proposal,
'funds_arrival':funds_arrival})
def add_participants_proposals_to_network(params, step, sL, s, _input):
@ -204,13 +219,13 @@ def add_participants_proposals_to_network(params, step, sL, s, _input):
if new_participant:
network = gen_new_participant(network, _input['new_participant_tokens'])
if new_proposal:
network= gen_new_proposal(network,funds,supply,trigger_func )
#update age of the existing proposals
proposals = get_nodes_by_type(network, 'proposal')
for j in proposals:
network.nodes[j]['age'] = network.nodes[j]['age']+1
if network.nodes[j]['status'] == 'candidate':
@ -218,8 +233,104 @@ def add_participants_proposals_to_network(params, step, sL, s, _input):
network.nodes[j]['trigger'] = trigger_func(requested, funds, supply)
else:
network.nodes[j]['trigger'] = np.nan
key = 'network'
value = network
return (key, value)
return (key, value)
def new_participants_and_new_funds_commons(params, step, sL, s, _input):
commons = s["commons"]
if _input['new_participant']:
tokens, realized_price = commons.deposit(_input['new_participant_investment'])
# print(tokens, realized_price, _input['new_participant_tokens'])
if _input['funds_arrival']:
commons._funding_pool += _input['funds_arrival']
return "commons", commons
# =========================================================================================================
def make_proposals_pass_or_fail_random(params, step, sL, s):
network = s['network']
proposals = get_proposals(network)
completed = []
failed = []
for j in proposals:
if network.nodes[j]['status'] == 'active':
grant_size = network.nodes[j]['funds_requested']
base_completion_rate=params[0]['base_completion_rate']
likelihood = 1.0/(base_completion_rate+np.log(grant_size))
base_failure_rate = params[0]['base_failure_rate']
failure_rate = 1.0/(base_failure_rate+np.log(grant_size))
if np.random.rand() < likelihood:
completed.append(j)
elif np.random.rand() < failure_rate:
failed.append(j)
return({'completed':completed, 'failed':failed})
def get_sentimental(sentiment, force, decay=0):
mu = decay
sentiment = sentiment*(1-mu) + force
if sentiment > 1:
sentiment = 1
return sentiment
def sentiment_decays_wo_completed_proposals(params, step, sL, s, _input):
network = s['network']
proposals = get_proposals(network)
completed = _input['completed']
failed = _input['failed']
grants_outstanding = np.sum([network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='active'])
grants_completed = np.sum([network.nodes[j]['funds_requested'] for j in completed])
grants_failed = np.sum([network.nodes[j]['funds_requested'] for j in failed])
sentiment = s['sentiment']
if grants_outstanding>0:
force = (grants_completed-grants_failed)/grants_outstanding
else:
force=1
mu = params[0]['sentiment_decay']
if (force >=0) and (force <=1):
sentiment = get_sentimental(sentiment, force, mu)
else:
sentiment = get_sentimental(sentiment, 0, mu)
key = 'sentiment'
value = sentiment
return (key, value)
def complete_proposal(params, step, sL, s, _input):
network = s['network']
participants = get_participants(network)
proposals = get_proposals(network)
competitors = get_edges_by_type(network, 'conflict')
completed = _input['completed']
for j in completed:
network.nodes[j]['status']='completed'
for c in proposals:
if (j,c) in competitors:
conflict = network.edges[(j,c)]['conflict']
for i in participants:
network.edges[(i,c)]['affinity'] = network.edges[(i,c)]['affinity'] *(1-conflict)
for i in participants:
force = network.edges[(i,j)]['affinity']
sentiment = network.nodes[i]['sentiment']
network.nodes[i]['sentiment'] = get_sentimental(sentiment, force, decay=0)
failed = _input['failed']
for j in failed:
network.nodes[j]['status']='failed'
for i in participants:
force = -network.edges[(i,j)]['affinity']
sentiment = network.nodes[i]['sentiment']
network.nodes[i]['sentiment'] = get_sentimental(sentiment, force, decay=0)
key = 'network'
value = network
return (key, value)

101
simulation.ipynb
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